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Daniel-P-Gonzalez

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# Introduction Since the early days of MRI, a constant drive to higher main magnetic field strengths (B₀) has existed. While the increase in B₀ leads to a superlinear increase in SNR at higher magnetic fields, the wavelengths of the electromagnetic fields at the corresponding Larmor frequencies decrease. At ultra-high field (≥ 7T), the wavelength in tissue is shorter than the diameter of a human torso. Consequently, substantial wave effects can give rise to inhomogenous spin excitations, which can lead to contrast artifacts and complete signal dropouts in the field of view. Not only do these inhomogeneities affect the SNR, they also lead to varying contrast throughout the field of view, severely reducing the clinical utility of the resulting images. To cope with the challenges introduced by the decreased wavelength at 7T and above, many different techniques have been proposed. Examples of these techniques are adiabatic pulses, RF shimming, kT-points, 2D spokes, 3D tailored radiofrequency pulses, Transmit SENSE, and TIAMO. Although there are many differences between these techniques, most of them require a multi-channel transmit system. Moreover, the obtainable transmit power efficiency, specific absorption rate (SAR) efficiency, and pTx speed-up factor improve as the number of independent transmit channels increases. Using 8- and 16-channel transmit systems, an increasing number of clinical feasibility studies at 7T have aimed at regions where imaging is hampered by severe transmit inhomogeneities, for example liver, kidneys, prostate, female pelvis, the hip joints, shoulder, the heart, the spinal cord, and the breasts, or they cover extended regions such as the complete lower extremities. Most of these studies use local transmit arrays to enhance transmit efficiency, and many of these studies report a need for higher transmit power than what was available (generally 8 kW) despite the use of a local array. Such arrays that are placed either directly on or very close to the patient take up significant space in the patient bore of the magnet, reducing the maximum patient size. To get to a more clinical workflow, where a transmit body coil is placed further away from the patient, several stand-off arrays and arrays integrated into the bore have recently been proposed. In this paper we present a 32-channel transmit system add-on for 7T, including modulator system, power amplifiers, and SAR supervision system. The 32-channel body array is integrated into the MRI system by placing it in the gap between the gradient coil and liner of the patient tunnel, similar to body coils at lower field strengths and thereby enabling a workflow very close to conventional clinical MRI. # Methods ## Ethics approval For in-vivo measurements, informed consent was obtained from both participants included in the study. In vivo studies were approved by the institutional review board of the University Clinic Essen. Approval nr. 16 7214BO. ## Original MR system The vendor-provided MR System in this study is a Magnetom 7T with an AS095 gradient coil (Gradient strength 38 mT/m, slew rate 200 mT/m/ms; Siemens Healthineers, Erlangen, Germany). The nominal field of view is 50 cm in all directions, and the system has 32 receivers in total. It is equipped with the “Step 2” 8-channel transmit system, which was not used in this work. ## Overview of system add-on shows a schematic overview of the add-on system. The system only uses 4 input signals for synchronization with the original system, three digital signals and one analog signal. The digital signals are 1 bit for transmit or receive state, 1 bit for the unblank signal for the transmit amplifiers, and 1 bit as a trigger from the sequence to set the modulators to the next pre-programmed state. The analog signal is the RF signal coming from the exciter, which is the signal that would be sent to the power amplifier in the single-channel mode of the original system. The signal from the exciter is split up and fed to the modulators. In the original single-channel system, this signal is directly fed into the vendor power amplifiers that are combined into a single high-power output. The output of the modulators is connected to the power amplifiers in the magnet room. The high-power signals from the amplifiers are fed to the RF coil via directional couplers (DiCos) and transmit/receive (T/R) switches. To be able to switch between reception with local coils or the body array, the receive chain of the system is modified to include RF switches. This is the only change to the vendors transmit or receive path. The RF signals for forward and reflected power are taken from the DiCos and fed into a multiplexing power supervision unit (MUX). This unit feeds a portion of the forward power into the receive chain and sends a logarithmic baseband voltage signal to the operating PC system for amplitude supervision. The operating PC is used to pre-program the modulators with a set of states before a sequence is run. Using the trigger from the sequence, the modulators are successively switched to the next state. The logic controller receives the information about whether the MR system is in transmit or receive mode as well as the unblank signal and distributes the signals across the add-on system. ## Modulators The custom-built modulator units use AD8345 (Analog Devices, Norwood, MA, USA) IQ modulators to modify the exciter signal. The exciter signal is fed into the local oscillator input at a very low power (\<-30 dBm), to ensure that changes in the input are transferred linearly to the output, since in normal operation the input power for the AD8345 is far beyond the 1 dB compression point. The analog input voltages for I and Q are provided by MAX5100 8-bit digital-to- analog converters (DACs) (Maxim Integrated Products, Sunnyvale, CA, USA). These DACs have an input register that allows programming them to a new value before switching them to that new value by a trigger signal. Due to the rise time of the output operational amplifiers of the MAX5100, output settling time is up to 3 μs, depending on the degree of change of the outputs. The modulator system is divided into two half systems with 16 modulators each. Each half system is connected to a 16-bit parallel bus provided by a digital I/O card. On a trigger from the pulse sequence of the MRI system, a new bit pattern is loaded into the input register of the DACs. After all input registers are filled with the new pattern, a trigger is issued to simultaneously move the data from the input register to the DACs, changing the modulator state. To account for delays, the first trigger from the sequence is sent 10 μs before the start of the RF pulse. Adjustment of the data rate of the bus allows to fine tune the delay between the trigger from the MRI pulse sequence and the point in time when the DACs change their output. To account for small offsets in the I and/or Q channel and other systematic errors after calibration, the modulation strategy for pTx is performed as follows. Instead of playing out a rectangular pulse on the exciter and performing the modulation completely with the modulators (“Strategy 1”), the exciter plays out a signal in which each sample is the maximum amplitude across all channels. In this way, a variable dynamic range is achieved for the 8-bit modulators. Errors due to offset in the I and/or Q channel are further decreased by shifting the exciter phase by 180° for each sample, which is accounted for in the phases the modulators apply (“Strategy 2”). Changing the phase each sample by 180° leads to alternatingly adding and subtracting the offset. shows a picture of a modulator unit containing two modulator circuits. The modulators are run at a 100 kHz sampling rate, in accordance with the sampling rate of the system’s gradients. Signals with higher baseband frequency can be generated by the system’s exciter, and these are fed through the modulators, providing extra pulse form control in addition to the modulators. ## High-power amplifiers The custom-built high-power amplifiers are designed to be used close to the magnet in order to minimize cable losses. Each amplifier consists of three amplifier stages with an overall gain of slightly more than 60 dB. The first stage is a MGA-31189-BLKG (Broadcom Inc., San José, USA), the second stage is a MRF6V2010NR1 (NXP Semiconductors N.V., Eindhoven, The Netherlands), and the final amplification stage of each amplifier is a BLF188XR power LDMOS transistor (Ampleon BV, Nijmegen, The Netherlands). With the final stage driven in AB mode, the amplifier has a peak output of 1 kW. Without an unblank signal, the bias voltages of the last two amplifier stages are set to 0 V, and the 50V power supply for the final stage is cut off via a high power MOSFET. This ensures that no power can be transmitted without an unblank signal present. Each amplifier contains a 61 mF capacitor array and a high-power voltage regulator to supply peak power during transmission. DC power supplies for the whole system can continuously deliver 6 kW in total. In case of an emergency shutdown, the capacitor arrays are discharged through load resistors inside the power supplies. shows a picture of an opened power amplifier. shows the position of the two power amplifier racks at the back of the magnet; the second rack is just visible at the right border. To ensure stable behavior independent of reflected power, circulators (JCC0296T0298N20-GIG, JQL Electronics Inc., Rolling Meadows, USA) are connected to the outputs of the amplifiers. These circulators have an insertion loss of 0.15 dB. Since circulators are highly magnetic, the circulators are placed just outside the magnet’s passive shielding, resulting in an extra cable length of 7 m in total (Aircell 7, SSB-Elektronik GmbH, Lippstadt, Germany), which is shorter than the minimum cable length necessary to place the amplifiers outside the magnet room at our site (14 m). The overall attenuation of these cables is 0.78 dB. The air to cool the amplifiers is taken from the room’s air- conditioning input duct by two radial fans in series and delivered via a tubular system over a distance of 15 m. Due to the strong currents on the power supply cables of each amplifier rack, the ground of the DC power supplies is only connected to a common ground directly at the amplifier housing, ensuring that the current of up to 60 A on the feed line is identical in both directions and resulting in no net force on the cable as it crosses the magnetic field. ## Directional couplers The custom-built directional couplers are fabricated in strip-line technology with 1.5-mm-thick FR4 substrate material separating the strips from the top and bottom copper layers. The distance between the conductors is 3.4 mm (center to center) and the width of 0.94 mm was calculated numerically using CST Microwave Studio (CST AG, Darmstadt, Germany). The coupled length is 69 mm, resulting in a coupling of -29 dB. The output is further reduced by 20 dB attenuators for both the forward and reflected output signals. To maximize directivity, the coupled lines are terminated by a variable resistor and a trimmer capacitor, which were fine-tuned after production. The insertion loss is 0.2 dB. The couplers are placed in two boxes with 16 DiCos each on the tables next to the amplifier racks. ## Power supervision The power supervision units get the forward and reflected signals from the directional couplers. A power splitter in the forward path feeds half of the power directly to the receive chain while the other half as well as the reflected signal are each fed into a logarithmic amplifier AD8307 (Analog Devices, Norwood, MA, USA). With the use of the logarithmic amplifiers, the forward and reflected signals are translated into voltage signals proportional to the logarithm of the input power. Since the envelope of the RF power only changes slowly, the result is a low-frequency signal that is multiplexed and sent to two NI5751 digitizer cards (National Instruments, Austin, TX, USA) with 16 analog inputs each that are connected to the operating PC I/O system. Here, the forward power in each channel is sampled every microsecond for amplitude supervision. The software is implemented in a LabView environment (National Instruments, Austin, TX, USA). If the maximum allowed input power is exceeded, the supervision prevents the unblank signal from being conveyed to the amplifiers. ## Receive chain To enable automated switching between local and body coil reception as well as to feed power supervision signals into the system’s receivers, several changes were made to the receive chain. shows the original receive chain of the system as provided by the vendor. It contains a second-stage receive amplifier as well as a switch to allow input of power supervision signals from the vendor-provided 8-channel transmit system. shows the changes made to the receive chain. A custom-built second-stage receive amplifier and power supply for the on-coil pre-amps is used for reception with the body coil. The second-stage amplifier uses an MGA-62563-BLKG (Broadcom Inc., San José, USA), has a gain of 22 dB and a noise figure of 1 dB. The input 1 dB compression point is -3.5 dBm. The power supply for the on-coil pre-amps and the second-stage receive amplifier can be switched off during transmit mode. This helps to protect the pre-amplifiers and reduces spurious signal in the receive path that might affect the power supervision, reducing the demand on the off state-isolation of the following switches. A custom-built 32-channel 2:1 switch (SRS) is used to select between local and body coil reception and another switch (SSS) is used to integrate the signals from the directional couplers for transmit supervision. These switches are identical. Each channel contains three MASWSS0155 GaAs SPDT switches (M/A-COM Technology Solutions Inc., Lowell, MA, USA) to achieve an off-state isolation of better than -55 dB and an insertion loss of better than -0.6 dB. ## Logic controller The logic controller receives the unblank and Tx/Rx signals from the original system. Furthermore, it accepts signals from a digital I/O card from the operating PC. These signals go through a logic circuit and are then distributed to the power amplifiers (unblank signal), to the receive chain (Rx amplifiers on/off, SRS and SSS switching states), and to the T/R switch controller circuitry (transmit/receive state, tune/detune). All logic is implemented in hardware using standard logic gate ICs to reduce the probability of failure as compared to microcontrollers or embedded systems. ## Body coil Since the 7T MR system is equipped with the AS095 gradient coil, there is a space of 34 mm between the bore liner (615 mm diameter) and the gradient coil (683 mm diameter). This space is used to accommodate the integrated body array. The body array is mounted on a polycarbonate frame that consists of two halves forming a cylinder with 22 outer faces. The length of the cylinder is 60 cm and the inside of the polycarbonate frame rests directly on the bore liner. The longitudinal bars stand off from the bore liner so that the coil elements have a distance of about 8 mm from the bore liner. The coil elements are micro strip-line elements with meanders, which were chosen because of their good intrinsic decoupling when lying side by side even in low load conditions. The elements have a length of 25 cm and are arranged in 3 rings: two outer rings of 10 elements and one inner ring of 12 elements. The distance between the ground plane and the strips is 20 mm as was used in previous studies. The ground plane completely encloses the coil. It is made of 0.127-mm-thick RO3010, both sides clad with copper. The copper of both sides is slotted in the longitudinal direction in such a way that the copper strips of the inner and outer layers overlap. In this way, RF currents can flow in the circumferential direction, while eddy currents from the gradient fields are effectively blocked. For mechanical stability the ground plane is glued with epoxy to a 1-mm FR4 sheet on the inner surface. While micro strip-line elements with meanders are well decoupled when lying next to each other, additional decoupling is needed for diagonally adjacent elements and elements placed head-on. shows an excerpt of the schematic of the body array. Decoupling is done using 90° delay lines connected through a very low impedance to ground. These lines provide another path for coupling that reduces overall coupling due to a different phase compared to the parasitic coupling via air and tissue. The two halves of the array are not electrically connected other than the connection of the ground plane. The elements are connected to custom-built, compact T/R switches with a design similar to; the only difference to the published design is that all components were placed on one side of a 81-mm by 42-mm PCB rather than using a folded layout to achieve a flatter profile. An appropriate length of low-loss cable (Aircell 5, SSB-Electronic GmbH, Lippstadt, Germany) transforms the impedance of the pre-amp to ensure pre-amp decoupling. The T/R switches introduce an attenuation of 1.1 dB. The Aircell 5 cables on the bore liner, connecting the T/R switches with the coil elements and the directional couplers, have an overall attenuation of 0.58 dB. Detuning of the elements is performed with dedicated detuning boards containing PIN diodes that produce an RF short circuit of the transmit cable to ground when a forward current is applied. These detuning boards are placed in the transmit chain between the power amplifiers and the T/R switches with an appropriate length of cable to transform the short circuit to an impedance that detunes the elements. ## EM simulations A detailed simulation model of the prototype coil was constructed for validation purposes and safety assessments. The model includes coil elements, the supporting frame, and the MR environment, i.e. RF shield, cryostat, bore liner, and patient table with a length of 200 cm to account for wave propagation. Time-domain simulations were performed in CST Studio Suite 2016 (CST AG, Darmstadt, Germany) using the finite integration technique (FIT). The simulation domain was discretized with a hexahedral grid and 105 million mesh cells. A finer resolution was used inside the coil volume to account for coil details and a coarser mesh was used outside the coil volume. Tuning and decoupling of the coil elements were performed in a circuit co-simulation. All cables between the coil elements and the directional couplers were considered. For coil validation, a homogeneous body-size phantom filled with tissue simulating liquid (*ɛ*_*r**’* = 45, *σ* = 0.55 S m^-1) was simulated in the center of the coil. The phantom is a roughly oval-shaped cylinder with a length of 50 cm, a width of 34 cm, and a height of 20.7 cm; the filling volume is 32 liters. *B*_*1*^*+* maps for excitation of individual channels and CP+ and CP2+ mode were exported for measurement validation. The simulated *B*_*1*^*+* maps were compared to maps acquired with the B1TIAMO technique. For safety assessments, a male and female anatomical body model with a resolution of 2x2x2 mm³ were placed in head-first supine position on the table in the coil center. The normalized electric field distributions were exported and 10g-averaged Q-matrices were calculated for every voxel in the body model. Subsequently, the Q-matrices of both body models were compressed together using the ‘virtual observation point’ (VOP) algorithm with a maximum overestimation of 10% to 420 VOPs. No differentiation was made between the extremities and the rest of the body, so the head and trunk local SAR limit, which is more restrictive, was used for the extremities as well. ## Quality assurance A routinely applied quality assurance (QA) protocol was used to investigate the influence of the add-on system on the performance of the original system. A 1-channel Tx / 32-channel Rx head coil (Nova Medical, Inc., Wilmington, USA) was loaded with a tissue simulating phantom (*ɛ*_*r**’* = 55, *σ* = 0.6 S/m). The QA protocol includes tests to check for the performance of the local RF coil as well as gradient and system stability. The proper function of the RF head coil was verified by *B*_*1*, SNR, and coupling measurements (i.e. noise correlation). Unwanted noise sources and RF spikes were searched for with noise measurements. System stability was measured with high-duty-cycle EPI imaging (TR 1000 ms, TE 30 ms, echo spacing 0.54 ms, BW 2112 Hz/pixel, 64 phase- encoding steps, 16 slices, FOV 220 mm, 3.4x3.4x2 mm³, 3x 250 measurements), and stability parameters such as SNR, Signal-to-Fluctuation-Noise Ratio (SFNR), signal drift, fluctuation, and ghosting were calculated according to the recommendations of the FBRIN consortium. ## Imaging experiments In all imaging experiments the 32-channel body array was used for transmission as well as reception. For phantom measurements a torso phantom filled with tissue simulating polyvinylpyrrolidon solution (*ɛ*_*r**’* = 45, *σ* = 0.55 S m^-1) was used that has the same dimensions as the one used in the simulations. For in-vivo measurements, informed consent was obtained from both participants included in the study. All imaging experiments with the volunteers were performed with an input power not exceeding the maximum allowed input power for the worst-case shim set used within an experiment, with an extra safety margin of a factor of 2. This was done to ensure that even if an erroneous trigger signal would alter the shim setting, the SAR would remain within the allowed limits. *B*_*1* mapping was performed using the B1TIAMO method. Absolute *B*_*1* maps were acquired in a central transversal slice in the torso phantom and compared with simulation data. Furthermore, *B*_*1* mapping was performed in a human volunteer (1.86 m, 80 kg) in an axial slice through the kidneys and a coronal slice through the center of the torso with the CP+ and CP2+ modes. In this case we define the phase for each element via its respective geometrical angle in the xy-plane. Structural imaging was performed in a healthy volunteer of above-average size (1.85 m, 95 kg). A 2D gradient echo sequence with 1.1-mm in-plane resolution and 5-mm slice thickness, a TR of 50 ms, and a TE of 6.1 ms was used to acquire slices with a 50-cm field of view. Data sets from the head to the thighs were acquired in three stations with approximately 10 cm overlap. TIAMO with the CP+ and CP2+ modes was used for transmit homogenization. For the slices in the abdominal region, the volunteer was asked to place the arms above the head. Each image was acquired during a breath hold. The vendor’s gradient distortion correction was used. To demonstrate the selective excitation capability of the system, four different patterns were excited in the torso phantom: the letters “ELH”, the logo of the Erwin L. Hahn Institute for MRI, a checker board, and the logo of the German Cancer Research Center (“DKFZ”). The pulse length was 5.36 ms (536 samples) with a variable density spiral for a 64 x 64 voxel target in 2D spatially selective excitation, implemented in a 3D gradient echo sequence with 128 x 128 in-plane voxels. The sampling rate for the transmit pulse was 100 kHz. # Results Implementation of the add-on system did not have detectable influence on the QA parameters of the system apart from a 1.2 dB reduction in received signal due to the switches integrated in the receive chain. Since these switches are behind the second receive amplifier stage, no change in SNR was detectable. Neither an immediate impact after integration nor a longer-term drift during the following 6 months was found; system users did not report any impairments in image quality that could be correlated with the integration of the add-on system. The amplifiers were successfully tested to play out 10 ms pulses at full peak power. Duty cycle was limited to 10% or smaller to prevent overheating of the amplifiers. shows a comparison of the simulated and measured *B*_*1*^*+* maps in the torso phantom for the CP+ mode (A, C) and the first 16 individual elements of the body array (B, D, E, F). The phase maps are normalized to the phase of the first channel. The maps show good agreement qualitatively and quantitatively in the absolute values (A, B, C, D) as well as in the depicted phase (E, F). The single-channel maps show good agreement even in areas far away from the transmitting elements. The normalized root-mean-square error for the CP+ mode is 16.6%. Further values are shown in. With the shim shown in, a *B*_*1*^*+* amplitude of 13 μT can be reached in the center of the phantom at full peak power. shows an axial cut through the body model at the position of the highest SAR in the CP+ mode, with the SAR distribution as an overlay. The highest SAR in the case of the CP+ mode occurs in the left arm. The values are normalized to 1W total accepted power. shows in-vivo flip angle distributions for the CP+ and CP2+ modes of the body array. shows a coronal structural image including the position of the transverse slice for orientation. show the CP+ mode in transverse and coronal orientation; show the CP2+ mode in the respective orientations. The flip angle is fairly constant in z-direction over a 50 cm field of view. Structural body images are presented in. Due to the capability of both the gradient and RF coil to cover a 50-cm field of view, the head, torso, and thighs of a large volunteer could be imaged in three stations. The images show good uniformity in contrast, and no complete signal dropouts are visible. Both volunteers had undergone many 7T examinations in the past and reported that the examination was very comfortable compared to examinations with local transmit coils. 2D selective excitation examples are shown in. The images show the central slice of the 3D data set for which the pulse calculation was performed. The patterns show sharp edges and only low residual background excitation. # Discussion The results presented here show the feasibility of the presented system. Implementation is straightforward since only very few interfaces are necessary. Only one analog and three digital signals have to be taken from the original MRI system to make the add-on fully functional. In comparison to the development of complete consoles, which might pose more powerful tools, the big advantage is the use of the standard vendor software that clinicians and researchers are accustomed to. For simple RF shimming with the add-on system, the vendor- provided sequences can remain completely unchanged. When selective excitation is used, the sequences only need minimal reprogramming to allow loading of user- provided pulses, greatly simplifying the workflow. Originally the RF amplifiers were placed in the magnet room to reduce the cable length to the coil for three reasons, namely to reduce the cable losses, to use the impedance of the amplifiers for coil decoupling, and to enable a feedback loop for coil decoupling. With the feedback loop included, the amplifiers had an overall gain of about 90 dB. Together with the high in-room RF due to waves traveling out of the bore, this led to unwanted feedback, so the loop had to be disabled. Without the feedback loop, utilizing the amplifiers’ impedance for decoupling led to non-linear behavior depending on the reflected power, which in turn depends on the shim. Therefore, the circulators were included, also increasing the cable length by 7 m. Putting the amplifiers in the same space as the circulators was not possible because of space constraints and cooling constraints. The overall losses between the amplifiers and the inputs of the elements of the body array amount to 2.79 dB. This includes 0.78 dB for the cables to and from the circulators, 0.15 dB for the circulators, 0.20 dB for the directional couplers, 0.58 dB for the cables on the bore liner (Aircell 5) and 1.1 dB for the T/R switches. This leads to approximately 520 W per element at the coil. An improved version of the system that is being installed in Heidelberg now has the amplifiers outside the magnet room, with twice the amplifier peak power and cables with much lower attenuation to account for the increased cable length, leading to approximately 1.5 dB attenuation. This will result in roughly 900 W peak power per coil element. Moving the amplifiers outside the magnet room also facilitates cooling, since active cooling with fans directly at the amplifiers is possible and the duty cycle can therefore be increased. The low transmit efficiency of integrated body arrays is partly offset by the 32 kW total peak power provided by the amplifiers. With the shim shown in, a *B*_*1*^*+* of 13 μT was achieved in the center of the phantom. A dedicated shim for the center of the phantom using a local 8-channel transmit array comprised of the same transmit elements and 8 kW total peak RF power resulted in a maximum achievable *B*_*1*^*+* of 12.4 μT, but covering a smaller field of view in z-direction. A mean peak *B*_*1*^*+* of 20 μT was reported in the prostate over 6 healthy volunteers for a close-fitting 16-channel loop-dipole array as well as 13.2 μT for a 10-element array of fractionated dipoles. Higher amplifier power for the integrated body array presented here would clearly be beneficial to improve transmission. Furthermore, a more efficient design for the body array could lead to further improvements, since the transmit elements in this project were not chosen for their efficiency, but for their good decoupling; dipoles have been shown to be more efficient in integrated designs. Using a more efficient coil design as proposed in and increasing the amplifier peak output power to 2 kW per channel could increase the peak B1+ amplitude to 30 μT in the center of the body. Possible further improvements in addition to switching to dipoles as transmit elements include using T/R switches with reduced attenuation (or even excluding them and only using dedicated receive coils) and using cables with reduced attenuation on the bore liner. Although the hardware of the system is conceived to apply low-power samples of the forward waveforms to the receive chain during transmit, this capability was not used in the current experiments. Instead, more conservative SAR limits based on amplitude-only supervision were utilized. Future work will target real-time monitoring of both the amplitude and phase of the forward signal on all 32 channels, which represents a significant computational challenge but would further relax SAR restrictions. Integrating a body array into the MRI system leaves more space in the bore, allowing for larger patients to be imaged, potentially with slim receive-only arrays, while local transmit arrays tend to take up quite some space in the bore. The large field of view allows imaging of large areas, which enables whole-body imaging with only a few stations. Among other applications, this new coil configuration might be beneficial for contrast agent free time-of-flight imaging of the lower extremities where local coils are known to limit the patient diameter. First structural images show promising homogeneity and image contrast. In this study, the arms were placed outside the field of view because their high signal caused image artifacts due to their proximity to the coil elements. Since reception in this work was done with the body coil, a large SNR boost can be expected from local receive-only coils, also reducing the signal intensity differences between arms and torso. Ultimately, this would allow a workflow at UHF that mimics common practice at lower field strengths like 1.5 and 3 T. Even though the DACs used in the presented systems are comparably slow and need up to 3 μs until the output is settled, the selective excitation examples show that 100 kHz sampling is possible. Imprecisions of the IQ modulators were successfully countered by using the system’s exciter in combination with the IQ modulators to achieve a coupled modulation strategy. Together with the large field of view, the capability for 32-channel selective excitation could potentially be used for reduced field of view imaging of, for example, large parts of the spinal column at UHF, which has already been shown for 16 transmit channels. Since the add-on system does not affect the use of the system in standard configuration, all imaging protocols relying on the single-channel transmit system and the affiliated local Tx/Rx coils can still be used without any change, leading to an overall very versatile system. Future work will have to look more deeply into the capabilities, advantages and drawbacks of a 32-channel transmit system with an integrated body coil compared to standard systems with local transmit coils. # Conclusion We present a 32-channel transmit system add-on that is capable of 100 kHz sampling. Structural imaging with an integrated 32-channel body array is possible and selective excitation is shown. The system does not affect the operation of the original MRI system in single-channel mode. The presented add- on system provides a tool for large field of view imaging and a potentially more clinic-like workflow. The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 291903 MRexcite. The authors thank Kevin Kolpatzeck, Dominik Beyer, Tristan Mathiebe, Jonathan Weine, and Sarah Handtke for help with hardware assembly. [^1]: The authors have declared that no competing interests exist.

# Introduction Inflammatory bowel diseases (IBD), which include ulcerative colitis (UC), are characterised by uncontrolled intestinal inflammation. Colorectal cancer (CRC) is a life-threatening complication of UC, and UC patients are estimated to be at a 2.4 fold increased risk of CRC relative to the general population. The risk of CRC in UC patients increases considerably with duration and extent of active inflammation, diagnosis at a younger age, concomitant primary sclerosing cholangitis (PSC) and family history of CRC. Relative to sporadic CRC, UC associated CRC (UC-CRC) is linked to an earlier age of onset and higher rates of mortality; in one study, 59% of UC-CRC patients had died at 5-years of follow- up. Given the increased risk of UC-CRC, it is necessary to screen UC patients for the development of precancerous lesions (dysplasia), with periodic chromoendoscopies starting 8–10 years after the first appearance of colitis- associated symptoms. During surveillance procedures, targeted biopsies are collected from suspect lesions and assessed by pathologists. The presence of dysplasia is then used to guide and inform the subsequent clinical management of the patient: for low-grade dysplasia (LGD) on flat mucosa it is advisable to reduce the interval between surveillance colonoscopies, in the case of high- grade dysplasia (HGD) colectomy is indicated. UC-associated post-inflammatory polyps (UC-IPs) (pseudopolyps) are raised areas of inflamed mucosa or granulation tissue seen after intestinal mucosal recovery from inflammatory damage. A history of UC-IP may be an indication of the severity of prior inflammation. Current endoscopic surveillance programmes do not consider UC-IPs *per se* important clinically and their recording may well be incomplete. Yet, a correlation between the presence of colonic UC-IPs and increased risk of CRC has been proposed.\[–\] However, it remains to be determined whether the link between UC-IPs and CRC is indirect or whether UC-IPs show any features or changes in gene expression that might be associated with an increased risk of malignancy. The molecular mechanisms that lead to cancer from LGD in UC are poorly understood, and rates of UC-CRC occurrence in patients with diagnosed LGD vary widely between studies 0–54%;\[–\] current estimates are that 19% of patients with a diagnosis of LGD develop HGD or cancer. This variation in outcomes probably reflects the fact that although LGD is treated as a single entity it encompasses a range of underlying molecular changes which cannot be distinguished macroscopically or histologically. Hence there is a significant clinical need for molecular biomarkers to identify those dysplastic lesions that are at high risk of neoplastic progression and malignancy. MicroRNAs (miRNAs) are small non-coding RNAs that inhibit protein translation by binding to complementary sequences in the 3’UTR of target mRNAs. Each miRNA is predicted to bind to multiple potential mRNAs targets and thereby alter signalling pathways within cells. MiRNAs have important roles in development and disease and have attracted particular attention for their role in cancer and IBD, and for their potential use as biomarkers and therapeutic targets. A role for miRNAs in sporadic CRC has been established. However, far less is known about their function in UC-CRC and dysplasia with only two studies reported assessing patients with both UC and CD. The techniques used and the clinical characteristics of the specimens analysed also differ between studies. Moreover, because of the low frequency of IBD-dysplasia the cohorts analysed are relatively small and the result requires independent validation. In order to address the role of miRNAs in UC-dysplasia, we have determined miRNA expression in a cohort of UC patients with and without dysplasia, using samples collected from IBD surveillance centres across the UK. We have also compared miRNAs identified in our study of UC with findings in the published literature on IBD-dysplasia. In addition, we provide the first comprehensive analysis of miRNAs in UC-IPs and compare changes in miRNA profiles between these polyps and UC controls. As miRNAs are also exported in the wider circulation, the potential to utilise miRNAs as non-invasive biomarkers of UC-Dysplasia (UC-DYS) was also explored in a proof-of-principle study. # Materials and methods ## Ethics statement and patient selection Ethics were obtained to recruit patients from multiple hospitals in UK (East London and The City Research Ethics Committee 1, REC 09/H0703/116; UKCRN ID 8099). Inclusion criteria in the study included a diagnosis of UC and enrolment in a surveillance programme. Patients gave written informed consent on forms approved by the Ethics committee and were recruited between 2010 and 2014. The clinical and endoscopic disease activity was recorded for each patient, using the Mayo score. UC phenotype was classified according to Montreal criteria. Further clinical characteristics were also recorded, including age, ethnicity, disease duration, smoking habit, medications, adherence to therapy, family history of colon cancer, concomitant diseases and previous history of dysplasia. ## Collection and selection of samples for microRNA analysis Multiple biopsies were collected from 5 sites throughout the bowel during routine endoscopy for histopathology and miRNA analysis. Biopsies for miRNA analysis were stored in RNAlater at -80°. The local pathologist assessed biopsies taken for histopathology. The resulting pathology reports were used to classify the biopsies taken from the same site and stored in RNAlater into those with and without dysplasia, and with and without evidence of histological inflammation, (neutrophil infiltration of the epithelium). A peripheral blood sample was also collected and centrifuged to obtain serum, and stored at -80°C until further experiments. To reduce possible confounding factors, samples from patients with coexisting primary sclerosing cholangitis, previous history of dysplasia or CRC and colitis limited to the left-sided colon or to the rectum were excluded from this study. Those samples used here were from patients with pancolitis (E3 Montreal). ## Tissue miRNA arrays Biopsies stored in RNA later were homogenised in 700μL Qiazol, and RNA extractions were performed using the miRNAesy Kit (Qiagen, UK) as per the manufacturer’s instructions. The resultant RNA was sent to Exiqon (Denmark) for miRNA profiling using the miRCURY LNA™ microRNA Array (7th Gen). The quantified signals were background corrected and normalized using the quantile normalization method. The normalised data was then log2 transformed prior to statistical analysis. ## Serum miRNA arrays Serum samples were sent to Exiqon (Denmark) and RNA extracted and analysed by qPCR using miRCURY LNATM Universal RT microRNA PCR Human panel I+II. Samples were run in two batches, and each batch contained UC controls and UC-dysplasia samples (batch one: n = 5 and n = 3, respectively; batch two: n = 5 and n = 5, respectively). The data normalisation was performed based on the average of the assays detected in all samples in each batch respectively, and the normalised data log2 transformed. The data for each batch was then median-centred and combined prior to statistical analysis to account for ‘batch’ effects. Finally, miRNAs were filtered to include only miRNAs that were detected above the background threshold in all samples tested. The aim was to ensure that only robustly expressed miRNAs were considered. ## MiRNA qPCR validation A select number of miRNAs were evaluated by qPCR using the miScript primer assays. For qPCR analysis 1000 ng of RNA was reversed transcribed using the miScript II RT Kit (Qiagen) and the miScript HighSpec buffer. The resultant cDNA was then diluted 10-fold prior to qPCR analysis using the miScript miRNA quantification system (Qiagen). Each reaction was performed in duplicate and melt curves performed to ensure a single PCR product was synthesised. The resultant cycle threshold values (Ct’s) were exported into Excel, duplicates were averaged, and data normalised to the geometric mean of RNU6 and SNORD42b, two stably expressed small non-coding RNAs. The normalised expression values were then log2 transformed to equalise variance prior to statistical analysis. ## microRNA in situ hybridization The levels of miR-200b-3p and miR-21-5p were investigated using miRNA *in situ* hybridization (ISH) technology (Exiqon, Denmark) in formalin fixed paraffin embedded (FFPE) tissue taken from the colon of UC patients. All blocks were obtained from the Royal London Pathology archives, and 4 μm sections were cut under RNAse-free conditions. Prior to staining sections were baked at 65°C for 10 mins before deparaffinising in xylene for 10 mins and hydrating through graded alcohols to RNAse-free H₂O. Sections were digested with 10ug/ml proteinase K in PBS at 37°C for 30 minutes, washed in RNAse-free H₂O and dehydrated. Sections were then incubated with Double-DIG (digoxigenin) labelled Locked Nucleic Acid (LNA) ISH probes (60 nM, Exiqon) for miR-200b-3p (619853–360), miR-21-5p (619870–360) or a scrambled negative control (699004–360) at 56°C for 2 hours before a series of stringency washes and staining performed as given previously. A clinical pathologist \[RF\] at the Royal London Hospital confirmed positive staining and reviewed all slides. ## Statistical analyses Unsupervised principal component analysis (PCA) of miRNA levels in UC controls, UC-DYS and UC-IPs was performed to explore natural grouping in the data using Solo software (Eigenvector, USA). The software’s proprietary algorithms were used to exclude miRNAs with excessive missing values and replace any further missing values in the remaining data. The PCA was carried out on the covariance matrix using the log2 transformed array data. Three components were fitted to the model and these were sufficient to separate UC controls from UC dysplasia. To identify individual differentially expressed miRNAs, the array data (both tissue and serum) were analysed using the ‘Significance Analysis of Microarrays’ package in R. Missing values were compensated for using the K-Nearest Neighbours Imputer algorithm, and the number of neighbours set to 6. The log2 transformed data were median-centred, and a two-class unpaired analysis was performed with 1000 permutations. A significance cut-off was set using a delta value of 0.5 and a 2-fold minimum change. Under these stringent cut-offs the estimated false discovery rate was 0.00%. For the qPCR validation analysis a two-way analysis of variance (ANOVA) test was used to explore the relationship between miRNAs in dysplasia and inflammation, each modelled as independent discrete factors. *Post-hoc* analyses were then performed to identify difference between groups with alpha adjusted for multiple testing using the Bonferroni criterion. ## In silico identification of microRNA targets Experimentally validated target genes for candidate miRNAs were obtained using miRTarBase, which contains targets that have been validated by reporter assays, western blot, qPCR, microarray and next-generation sequencing experiments. miRNA-target interactions were also investigated based on the collective information of functional studies of miRNAs in the database. To bioinformatically predict target genes based on the miRNA seed sequence, two predictive algorithms were used, TargetScan as well as miRDB. To investigate the influence of candidate miRNA transcript levels in UC dysplasia, we interrogated the gene signatures obtained by cDNA microarrays in 4 UC patients without dysplasia and 11 UC patients harbouring remote neoplasia. To obtain a list of all differentially expressed genes across the two groups, the microarray datasets were obtained from the GEO repository (accession number GSE37283) and analysed with GEO2R, a web-based application that performs comparisons between groups on the original submitter-supplied microarray datasets. GEO2R uses the GEOquery and limma R packages from the Bioconductor project. For consistency, to identify differentially expressed genes, we applied the following thresholds: adjusted p-value \< 0.05 (based on the Benjamini & Hochberg false discovery rate method) and \|log2(fold-change)\| \> 1. # Results ## Study design and biopsy characteristic To interrogate miRNA profiles in UC-DYS lesions, RNA extracted from biopsies collected during routine surveillance endoscopies with and without evidence of dysplasia was analysed (n = 10 and n = 7, UC controls and UC-DYS, respectively); the clinical characteristics of patients from whom a UC dysplasia sample was taken is given in. For the array, the cohort was predominantly White (6/7), the median age was 73 years, and all patients had a history of pancolitis (E3 disease). Biopsies were taken from sites of dysplasia throughout the colon: 2 from the ascending colon, 2 from the transverse colon, 2 from sigmoid colon and 1 from the rectum. Of these, 6/7 (86%) was classified as LGD and 1/7 had HGD; 3/7 biopsies also had evidence of histological inflammation. RNA isolated from UC-IPs (n = 7) was also analysed. To identify miRNAs associated with UC-DYS that were independent of intestinal location and disease activity, control biopsies were pooled from a number of UC patients with respect to presence or absence of histological inflammation (active and inactive) and the five-biopsy sites (ascending colon, transverse colon, descending colon, caecum, or rectum). This gave a final 10 control samples. RNA isolated from UC-IPs (n = 7) was also analysed to determine whether these possessed similar changes in gene expression to dysplasia specimens. ## Identification of miRNAs associated with dysplasia in ulcerative colitis The expression levels of 1899 miRNAs were assessed by array, of which 1240 (65%) were not detected above the background threshold levels in any of the 24 samples analysed and therefore excluded. One control sample (an active biopsy from the caecum) was further excluded on the basis of a lower call rate than the remaining samples. To explore natural grouping within the data unsupervised principal component analysis (PCA) was performed. For PCA, miRNAs with excessive missing values were excluded (a total of 258), and only the remaining 401 miRNAs were considered. The resultant PCA model demonstrated some separation of UC-DYS from control samples, suggesting that UC-DYS is associated with a shift in miRNA expression profiles. Control samples were separated completely from dysplasia samples within the first three principal components with the sole exception of one sample, an active rectal biopsy. To identify differentially expressed individual miRNAs the array data was analysed using ‘Significance Analysis of Microarrays’ software with an estimated false discovery rate of 0% and a fold change \>2 set as stringent cut-off values for significance. This analysis identified 22 miRNAs that were upregulated in dysplasia and 8 miRNAs that were present at lower levels in UC-DYS lesions. A number of the miRNAs identified had been previously linked to IBD-DYS, including miR-200b-3p, miR-200a-3p, miR-192-5p, and miR-155-5p13. Previously reported associations between miR-21-5p12 were also confirmed; reported difference in miR-31 in IBD-dysplasia could not be fully assessed here as miR-31-5p expression was only detected in 2 of the 24 samples analysed by array. However, it is noteworthy that both biopsies where miR-31-5p levels were detected above the background threshold were dysplastic. ## Changes in miRNA profiles in post inflammatory polyps To determine whether the miRNA profiles of UC-IPs resembled UC-DYS specimens or not, the UC-IP miRNA expression changes were fitted to the PCA model. Interestingly, UC-IPs appeared to form an intermediate group, with some samples clustering with controls and other samples with the UC-DYS specimens. However, relative to controls, in terms of absolute differences in expression, there were only two miRNAs with a fold change\>2 at a FDR of 0.00% in UC-IPs using the SAM software: miR-144-3p was increased in UC-IPs (2.017 fold, q\<0.001) and miR-4795-3p was reduced (0.456 fold, q\<0.001). There was no overlap between miRNAs that marked UC-IPs and those that were associated with UC-DYS. ## qPCR validation of miRNA changes in dysplasia To confirm the findings of the array, qPCR validation was performed for 7 miRNAs upregulated and 1 miRNAs down-regulated in UC-DYS in the array. The validation cohort was expanded in number, but included samples previously analysed by array and therefore does not represent a wholly independent cohort (UC-DYS n = 10 and UC controls n = 16); where multiple biopsies were taken from individual control UC patients they were averaged with respect to disease activity to avoid pseudo- replication of the data. There were no significant differences between UC-DYS and UC controls in our initial analysis (t-test). One potential confounding factor in this analysis is a change in the proportions of UC-DYS samples with active disease analysed in the validation cohort compared to the array cohort: 3/4 in the array cohort vs. 7/11 in the validation cohort. Hence a two-way ANOVA was performed to investigate the relationship between dysplasia, disease activity and miRNAs levels, followed by *post-hoc* comparisons of groups. This demonstrated that for 3 of the miRNAs tested (miR-200b-3p, miR-451a, and miR-27b), there was a significant interaction term for UC-DYS and disease activity. A subsequent *post-hoc* test demonstrated that miR-200b-3p was significantly up-regulated (fold change \>2) in UC-DYS specimens in the absence of histological inflammation relative to inactive controls in line with the results of the array (p = 0.033). However, as disease activity tended to supress miR-200b-3p levels in the UC-DYS group (p = 0.010), dysplasia–associated differences in expression were not seen on a background of histological inflammation. Conversely, and in contrast to the array, a significant reduction in miR-27b was observed in UC-DYS relative to controls on an inactive background (p = 0.016); in the UC-DYS group histological inflammation was associated with higher miR-27b levels (p = 0.015). No significant differences in miR-451a were identified in the *post-hoc* comparisons of the groups once alpha was adjusted for multiple testing. In contrast to previous studies miR-21-5p showed a stronger association with disease activity than with dysplasia. ## miRNA in situ hybridization Associations between miR-200b-3p and UC-DYS detected in the array and validation cohort were further investigated using miRNA ISH techniques on archival FFPE blocks. To determine whether miR-200b-3p was linked to CRC progression in UC patients *in situ* analysis was performed on a series of FFPE specimens from UC controls and from UC patients with confirmed UC-DYS and UC-adenocarcinomas; miR-21 levels were analysed in serial sections of a subset of blocks as a positive control for staining. Representative images are shown in. Discrete miR-200b-3p staining was identified in the epithelial cells of controls, UC-DYS and UC-adenocarcinomas. In general, the strength of staining increased from UC controls through to UC-adenocarcinomas. By comparison, miR-21 was more robustly expressed and localised to the stroma of UC adenocarcinomas. No positive staining was observed in sections incubated with a scrambled negative control probe. ## Identification of miR-200b-3p targets in UC-dysplasia By interrogating the gene signatures from cDNA microarray analysis of UC patients with neoplasia (UCN) compared to UC patients, we found 129 mRNAs that were downregulated, corresponding to 76 genes. Since an increase in miRNA levels would result in mRNA downregulation, we investigated whether the reduced expression of these genes is a result of the miRNA action. Among the 76 down- regulated genes, one gene, *INO80D*, was among the targets of miR-200b-3p obtained by miRTarBase , a database of experimentally confirmed miRNA targets. *INO80D* is a regulatory component of the chromatin remodelling INO80 complex, which is involved in transcriptional regulation, DNA replication and probably DNA repair. We expanded our search to target genes that are predicted by bioinformatics algorithms based on the miRNA seed sequence. Five downregulated genes (*INO80D*, *SHROOM1*, *HMBOX1*, *SLC4A4* and *PLEKHA6*) were among the targets predicted bioinformatically by TargetScan, while six genes (*PPARA*, *SHROOM1*, *SMIM5*, *HMBOX1*, *ANK3*, *KMT2C*) were predicted by miRDB. Among these genes, *KMT2C* is a lysine methyltransferase found to be associated with colorectal cancer as well as acute myeloid leukemia. KMT2C has also been implicated in gastric cancer and could be useful as a marker of prognosis. summarises these results. ## Analysis of serum miRNA profiles in dysplasia and controls MiRNAs are also present in the serum, where there is the potential to exploit them as non-invasive biomarkers of dysplasia. Hence, we sought to determine serum miRNA levels in UC-DYS patients (n = 8) and UC controls (n = 10). Levels of miRNA in the serum were assessed by qPCR array (Exiqon) and the normalised data were analysed using SAM software as per the tissue arrays (with a cut-off of a fold change\>2 set). However, no significantly altered miRNAs in the serum of UC-DYS patients relative to UC controls were identified; with an adjusted cut-off of a fold change\>1.5 there were 4 miRNAs were identified: miR-423-3p and miR-28-5p were present at higher levels, whereas serum levels of miR-150-5p, miR-32-5p were reduced in UC-DYS. However, with a false discovery rate of 1.12 a cut-off of a fold change\> 1.5 is not stringent enough, and these miRNAs are highly likely to represent false positives. # Discussion In this study miRNA profiles associated with dysplastic lesions in UC patients were investigated. Multivariate analysis demonstrated good separation of UC-DYS from UC controls, indicating a shift in miRNA expression profiles in tissue from UC-DYS. However, there was a larger degree of variance between the UC-DYS samples with respect to their miRNA profiles, highlighting the inherent heterogeneity of this group. For future studies it will be important to understand the significance of the profile heterogeneity for UC-DYS progression, as not all LGD will progress to HGD and/or CRC. Whether this heterogeneity in miRNA profiles could be exploited to identify the dysplastic lesion most likely to progress remains to be seen. Encouragingly, from a molecular biomarker perspective, the observed changes in miRNA expression in this study appeared specific to UC-DYS because similar changes were not observed in UC-IPs. In fact, the miRNA profiles of UC-IPs were largely indistinguishable from UC controls, suggesting that these lesions might not have pre-malignant potential that could be linked to miRNAs expression changes. A number of differentially expressed miRNAs in UC-DYS that were identified in this study have been previously linked to IBD-dysplasia, providing independent validation of our results. For example, Olaru and colleagues demonstrated broad increases in the miR-200 family in IBD-dysplasia (both UC and CD patients) relative to chronically inflamed controls, a finding that is in keeping with the up-regulation of miR-200b-3p reported here. While both studies show that increased miR-200b-3p expression is a feature of UC-DYS, it is important to note that in our study, miR-200b-3p was only upregulated in dysplastic lesions of patients in clinical and histological remission, i.e. no inflammatory infiltrates. Inflammation supressed miR-200b-3p and so in patients with ‘active’ disease there was no difference in miR-200b-3p in our validation cohort. Surveillance colonoscopies are already indicated for patients in clinical remission, our data would now indicate that the presence of inflammatory infiltrates in the mucosa should also be considered when assessing the utility as a biomarker. The observation that miR-200b-3p is supressed in patients with evidence of active disease is consistent with reports that show a reduction in miR-200b in inflamed UC colon and reports that the reduced expression of miR-200b in IBD patients is linked to epithelial mesenchymal transition (EMT). The effect of inflammation may be to down-regulate miR-200b-3p in other cell types within the mucosa. As this study focuses predominantly on the analysis of low-grade UC-DYS, it seems likely that the observed up-regulation of the miR-200 family is a very early event in the UC-CRC development pathway. The analysis of miR-200b-3p using miRNA-ISH techniques further supports a functional role for this miRNA in development of UC-CRC as miR-200-3p is localised to intestinal epithelial cells in UC patients consistent with its known role in determining epithelial cell fate. Notably, the strength of miR-200b-3p staining increased at each stage of malignancy, UC-DYS and UC-CRC. In line with the findings of this study, increased expression of miR-200b-3p in colon cancer cells *in vitro* has previously been reported, and high expression of miR-200b-3p observed in sporadic colorectal tumours compared to adjacent mucosa. Previous studies have identified altered mRNA signatures associated with dysplasia in UC that were progressively altered during neoplastic progression. In this study we have utilised this available data set and interrogated it for down-regulated genes with a miR-200b-3p binding site. This list included genes associated with chromatin remodelling and DNA transcription (e.g. INO80D and KMT2C). The functional significance of the suppression of these genes on the progression of UC-DYS is currently unknown. Little is known about the role of the INO80D subunit. However, loss of function mutations in KMT2C, a histone lysine methyltransferase, have been identified in a number of cancers, and colorectal cancer cell lines, pointing towards a tumour suppressor role. This is supported by the fact that KMT2C knockout mouse model that developed ureter epithelial tumors. Correlating miRNA and mRNA expression data sets can give useful insights into the potential action of a miRNA *in vivo* and provide direction for future experimentation that can confirm whether, for example, the suppression of 200b-3p targets in UC-DYS is a direct consequence of the up-regulation of miR-200b-3p. While there was good concordance between our study and the previous studies for IBD-dysplasia and miR-200b-3p, we were unable to fully validate the increase in miR-21-5p in UC-DYS that was observed in the array and has previously been reported by Ludwig and colleges; instead in the validation analysis this miRNA was much more strongly associated with active inflammation as opposed to the presence of dysplasia, which is known to be an independent risk factor for the development of UC DYS and UC CRC. The link between high miR-21-5p may therefore be highly dependent on the patients inflammatory status. Nevertheless, high levels of miR-21 were observed using miRNA ISH techniques in UC-CRC; in these tumours miR-21 was localised to the stroma and not epithelial cells, a finding which contrasts with Ludwig et al, but accords with Hedbäck et al. The conflicting results between studies for miR-21 localisation is likely a results of using different *in situ* hybridization probes, this will need to resolved before a definitive answer on miR-21 localisation can be given. Unfortunately, reported differences in miR-31 in IBD-dysplasia, could not be fully interrogated because of its low expression levels in the samples analysed in this study. However, the only two samples from the array where miR-31 was detected above the background threshold levels were dysplastic, which is consistent with the earlier reported increase in dysplasia in the literature. Our data suggest that up-regulation of miR-31 is not sufficiently consistent across UC-DYS specimens for this miRNA to be a good biomarker candidate of LGD, as the absence of its expression could lead to false negative diagnoses of dysplasia. Attempts to identify a non-invasive serum biomarker of UC-DYS were also made, but no significant differences in serum miRNA expression were observed. This was a novel analysis, and the lack of statistical differences likely reflects the fact that any serum dysplasia signal will be very weak given only a small proportion of cells in the gut are affected, and the serum miRNA pool is derived from all cells in the body. Also, the current study may have been underpowered to detect small differences in miRNA levels in the serum of UC-DYS patients. However, it should be acknowledged that the rarity of the UC-DYS makes large population biomarker studies extremely challenging. Nevertheless, we believe that the data from this study can be used to inform future studies. There are some limitations to our study. The estimated prevalence of LGD in UC is 15 cases per 1000 patients per year in the UK. Therefore significant numbers of patients need to be screened to identify dysplasia specimens for analysis. To address this we recruited from ten NHS trusts with major inflammatory bowel disease surveillance centres across the UK. Despite this, a clear limitation of this study is the sample size, which reflects the low frequency of UC-DYS, and the difficulty in obtaining sufficient specimens from well-phenotyped patients. Independent validation is therefore an important step that requires adequate numbers. It is therefore encouraging that several miRNAs identified here had been previously linked to IBD-dysplasia, e.g. miR-200b-3p. For future studies a standardized miRNA analysis pipeline should be implemented to facilitate direct cross-comparisons between studies, enabling pooling of data and large population studies, as we have recommended in a recent review; each of the studies performed to date have used different miRNA profiling platforms and methods. The data from this study also highlight the need to control for heterogeneity within the UC-DYS group, as potential confounding variables include the size of the field of dysplasia, the extent of inflammation within a lesion, length of disease duration and patient’s medications. Given the relatively advanced age of the UC-dysplasia cohort (median 73-years), there is an increased risk of sporadic CRC and neoplasias, which may not be directly attributed to the underlying presence of UC. These confounders are inherently difficult to control for in small populations, although this is essential if more accurate biomarkers of progression are to be developed, as dysplasia is not a single homogenous group. In summary, CRC is a life-threatening complication of UC, and surveillance programmes use regular colonoscopy to identify early pre-cancerous lesions (dysplasia) and guide patient care. However, we know very little about the molecular changes that occur in these dysplastic lesions and rates of CRC occurrence in patients with confirmed LGD vary widely. A better understanding of the underlying mechanisms of dysplasia may allow for more accurate identification of the lesions that harbour the highest risk of malignancy. In this study we have shown that dysplasia is associated with alterations in the miRNA expression profiles and have demonstrated localisation of this miRNA to epithelial cells in dysplastic lesions and in UC associated CRCs. This study highlights increased expression of miR-200b-3p as a critical component of UC-DYS and a relevant target for therapy. # Supporting information We are indebted to the patients who participated in this study and thank the gastroenterologists (Ailsa Hart, Donagh O’Riordan, Konrad Koss, Matthew Brookes, Stephen Lewis, Tariq Iqbal,) and research nurses and practitioners (Carol Gray, Fiona Stead, Helen Fairlamb, J Rankin, Janet Morse, Judy Sercombe, Keely Lane, Marie Green, Natalie Wheatley, Nicola Lunt, Sue Inniss, Zacharias Tsiamoulos) who contributed samples. Jake Bundy (Imperial College London) and Robert Lowe (Queen Mary’s University London) are thanked for discussion of statistical analysis. ANOVA analysis of variance CRC colorectal rectal cancer CD Crohn’s disease FDR false discovery rate FFPE formalin fixed paraffin embedded HGD high grade dysplasia IBD inflammatory bowel diseases UC-IP post inflammatory polyps ISH *in situ* hybridization LNA locked nucleic acid LGD low grade dysplasia miRNAs microRNAs PCA principal component analysis UC Ulcerative colitis UC-CRC UC associated CRC UC-DYS UC-Dysplasia [^1]: The authors have declared that no completing interests exist. [^2]: **Conceptualization:** AS JOL. **Data curation:** AL CL. **Formal analysis:** AL EG AS. **Funding acquisition:** AS JOL NJ. **Investigation:** AL CF TK KS RJ RF AA NJ. **Methodology:** AL EG JOL AS. **Project administration:** AL CL AS JOL. **Supervision:** AS AA JOL. **Validation:** AL CF AS. **Visualization:** AL EG RF. **Writing – original draft:** AL AS. **Writing – review & editing:** AL CF TK CL KS RJ RF EG AA NJ JOL AS.

# Introduction Chikungunya virus (CHIKV) is an enveloped positive-strand RNA virus belonging to genus *Alphavirus* of the family *Togaviridae*. It is an epidemic viral disease responsible for significant global public health problem mainly in Asian and African continents. Efforts are underway in developing prevention strategy. Recently a VLP based chikungunya vaccine was developed in USA that was found to be protective in primates. Chikungunya infection is generally characterized by fever and joint pains with additional symptoms including chills, vomiting, nausea, headache and rashes. Historical data report the first detection of chikungunya in 1952 in the Makonde Plateau in Africa – where the virus is known to be maintained in the sylvatic cycle of wild primates and mosquitoes such as *Aedes taylori*. Later in 1958 it was detected in urban Asia such as Thailand mainly transmitted by *Aedes aegypti*. In India, where both *Aedes aegypti* and *Aedes albopictus* are known to exist and are widely prevalent during the post monsoon season, CHIKV was first detected in 1963 in West Bengal. It was followed by several epidemics in Chennai, Pondicherry, Vellore, Visakhapatnam, Rajmundry, Kakinada, Nagpur and Barsi between 1964 and 1973. Recently CHIKV resurfaced in India affecting several South Indian states. The outbreak started in 2005 from the coastal regions of Andhra Pradesh and Karnataka. With more than 1.3 million people estimated to be affected CHIKV prevailed across 150 districts of 8 states in India. Despite the number estimated, the actual disease burden was thought to be much higher due to potential underestimation from lack of accurate reporting. CHIKV being an RNA virus is susceptible to high mutation rates which may help the virus to evade the immune response and thus adapt efficiently. However, phylogenetic analysis of E1 gene of CHIKV indicates only three lineages with distinct genotypic and antigenic characteristics i.e. the “Central/East African genotype”, the “Asian genotype” and the “West African genotype”. CHIKV strains with an Asian genotype of E1 gene were reportedly detected during the 1963–73 outbreaks in India, while the more recent outbreaks since 2005 have been caused by the “Central/East African genotype”. Additionally, a mutation at 226 amino- acid (Valine–Alanine) of E1 gene was observed during the recent outbreaks and has been associated with the more efficient replication of CHIKV in *Aedes albopictus*. The high morbidity and loss in daily activity associated with CHIKV infection results in considerable economic loss among the affected nations, specifically India. This emphasizes the need to have a detailed understanding of epidemiology and strain diversity for planning a prevention strategy. Towards this end the present study aimed to evaluate the disease prevalence at various geographical regions and assess the genomic diversity among CHIKV strains infecting the Indian population. # Methods ## Study sites Patients were recruited at three distantly located regions of India (North, West and South) from 1^st June, 2008 through 31^st May, 2009. Patient enrolment was carried out year round at Karnataka Institute of Medical Sciences (KIMS), Hubli, Karnataka (south); Sawai Man Singh Medical College (SMS) Jaipur, Rajasthan (West), and All India Institute of Medical Sciences (AIIMS) New Delhi (north) with AIIMS as the coordinating and testing centre. ## Patients and clinical specimens Patient enrolment involved those with fever ≤7 days duration at the time of their visit to the Out/Inpatient departments at AIIMS (n = 178), KIMS (n = 233) and SMS (n = 129) hospitals and those who were willing to participate in the study (n = 540). The study was approved by each Institution's Ethics Committee (Institutional Ethics Committee, AIIMS; Ethics Committee SMS Medical College, Ethics Committee Karnataka Institute of medical Sciences, Hubli). All patients (or for children parents or guardians) gave written informed consents. Demographic details, clinical data including symptoms and onset and duration of fever were recorded in the specified proforma. Approximately 3 ml of blood was collected from each patient enrolled at the time of admission. Convalescent phase (15–90 days of fever onset) sample could be collected from 30 patients only at KIMS center. Sera were separated from the blood by centrifugation and stored at −70°C in aliquots till further use. Specimens from KIMS and SMS centers were subsequently transferred to AIIMS center within two to three months of collection. Both RTPCR assay and serology (IgM) were employed to test CHIKV in all study specimens at the AIIMS centre. ## Diagnostic RT-PCR Genomic viral RNA from CHIKV infected culture-lysate/patient sera were extracted using QIA Amp Viral RNA minikit (QIAGEN, Germany) according to manufacturer's protocol. The 294b E1 gene fragment of CHIKV was amplified with Qiagen one Step RT-PCR kit as described previously. Briefly, the RNA fragment was reverse transcribed and amplified in a single step and the 294 bp amplified product was analyzed on 2% agarose gel as reported earlier. ## IgM-ELISA All serum specimens were screened for CHIKV specific IgM antibodies by ELISA using a commercial kit (Standard Diagnostics, Inc., Korea) according to manufacturer's recommended procedure. ## Nucleotide sequencing Sequences were determined from the 913 bp E1-amplicon (nt 10246–11158) that had been generated following RT-PCR (n = 15) with forward (5′-TACCCATTCATGTGGGGC-3′) and reverse (5′-TGACTATGTGGTCCTTCGGGGG-3′) primers. Amplification product was gel purified by the Gene-Clean purification method (Q-biogene, Cambridge, UK) and sequenced using a previously described method. Lineage identification and assessment of evolutionary relationships were performed using Bioedit and Mega2 programs as described previously. ## Statistical analysis Statistical analysis was performed using Stata statistical software, version 7.0 (StataCorp LP, College Station, TX, USA). # Results ## Clinical features among suspected chikungunya patients lists patient demographics from a total of 540 cases with fever up to 7days duration regardless of chikungunya virus infection. Among the clinical features recorded, joint pain (62.8%) and headache (63.3%) were most frequently observed while other features included abdominal pain (48.1%), vomiting (43.9%), rash (36.1%), joint swelling (27.8%), cough (27.2%) and restlessness (21.3%). Symptoms such as diarrhea, rhinitis, conjunctival congestion, hepatomegaly, splenomegaly and lymphadenopathy were rarely observed. Symptoms commonly caused by CHIKV i.e. joint pain, joint swelling, abdominal pain and conjuctival congestions were more common among patients at KIMS as compared to SMS and AIIMS. ## Laboratory based diagnostics of Chikungunya viruses CHIKV infection was tested both for the presence of anti-CHIKV IgM antibodies by ELISA and CHIKV RNA by RT-PCR. A total of 137 (25.4%) subjects tested positive for CHIKV through either of the two tests. A solitary case was detected at AIIMS while relatively higher percentages were observed at SMS (16.3%) and KIMS (49.4%). Rate of chikungunya detection was nearly similar between ELISA and RT- PCR. Year round detection of chikungunya was observed at KIMS while at SMS it was rarely detected during summer. Moreover, majority of the CHIKV cases were observed during monsoon and post monsoon seasons both at SMS and KIMS. ## Time kinetics of CHIKV RNA detection by RT-PCR and anti-CHIKV IgM antibodies by ELISA Chikungunya detection was performed by both RT-PCR and IgM-ELISA with detection rates ranging from 4.3%–90% for RT-PCR and 10%–95.6% for ELISA depending on the days of fever onset. This difference in detection rates between RT-PCR and ELISA with respect to days of fever onset was found to be statistically significant through chi square analysis (p\<0.05). RT-PCR assay was more sensitive till first 4 days of fever onset declining after day 4 at which point serology (IgM) was observed to be more effective. Additionally 30 cases from KIMS were assessed for persistence of IgM during convalescence (15–90 days of fever onset). While majority of the chikungunya positive cases (9 of 11) were detected anti-CHIK IgM between 15–90 days post illness, none of the chikungunya negative patients (n = 19) were detected the same (data not shown). ## Association of clinical features with CHIKV laboratory diagnosis Comparison of clinical features between chikungunya positive and negative patients demonstrated rashes, headache, joint pains, joint swelling, abdominal pain, cough and vomiting to be significantly associated with CHIKV confirmed cases (p\<0.05) whereas differences for other symptoms were not found significant. ## Age wise distribution of CHIKV infection among the study population We compared the rate of CHIKV prevalence based on patient age groups 0–5, \>5–18 and \>18 years of age and observed significantly higher detection (p\<0.05) among adult populations (aged \>18 years). An apparent increase in frequency of CHIKV detection with age, from about 16% cases among children to 50% among adults was observed. In addition Chi square analysis revealed significant differences for presence of rashes, headache, joint pain and joint swelling (p\<0.05), between children (\<18 years) and adults (\>18 years). Interestingly, while most of the rashes among adult cases (\>18 years) were of erythrematous type, that of children were maculaopapular type (\<18 years) (data not shown). ## Phylogenetic tree based on E1 gene of CHIKV demonstrates high homology with strains belonging to Central/East African genotype Comparative sequence analysis of chikungunya E1 gene from 15 CHIKV infected patients from KIMS revealed 98.3%–99.9% homology among them, clustering within the Central/East African genotype. Furthermore, all recently detected CHIKV strains worldwide including India clustered in this lineage with 93.4% to 99.9% homology. However, recently detected CHIKV strains from India appeared different from those of previous 1963 and 1973 outbreaks. were similar when the analysis was performed with amino acid sequences deduced from the E1 gene sequences (data not shown). Additionally we detected an amino acid change from lysine to glutamine at residue 132 in E1 gene among 5 of 8 CHIKV infected children, while none among the adults (data not shown). Of note, all these 15 cases were enrolled in the same place and during same period. # Discussion The present multi-centre study confirms and extends the findings of recent reports from India and other parts of world indicating a re-emergence of severe chikungunya disease which is becoming a major public threat in India. This is the first multi-centre study with sites from three distinct geographical locations except the eastern part of the country. The study was carried out with year round enrolment of subjects in comparison to previous CHIKV epidemiological data that was derived mainly from outbreaks,. For the first time both genomic (RT-PCR) and serology (IgM ELISA) based assays were employed throughout the study for laboratory confirmation of chikungunya in a large sample population. However our data on IgM antibody persistence emphasizes the need to analyze paired sample for IgM or IgG test for confirmation of chikungunya infection, particularly for those sampled after day-3 post illness when viremia is low. Amongst the three centres, AIIMS and SMS had a relatively higher male∶female ratio. However this could be attributed to the social bias generally observed against females in the north and west India rather than to disease susceptibility based on gender. Various other studies conducted worldwide have shown inconsistencies on gender bias to disease susceptibility with most studies reporting each gender to be equally susceptible. Discrepancies in the symptoms of Chikungunya among suspected cases across different studies warrant the requirement of a clear and well defined clinical profile of the disease. For example our study detected rashes more frequently among CHIKV cases as compared to other. One reason for this discrepancy could be due to the study design. While earlier studies were retrospective focussing largely on outbreaks, our study was conducted throughout the year and across different sites. Our study also demonstrated a link between chikungunya detection rate and geographical location of study centre. We observed highest percentage of cases at KIMS which is located along the coast in the southern part of the country compared to the other two centres that are landlocked regions with extended periods of dry summer and extreme winter. This is in agreement with findings of other studies those reported the prevalence of chikungunya mostly in the coastal regions including South India and South East Asia. One interesting observation made in the present study was the near absence of chikungunya case at AIIMS site despite reports of dengue prevalence. As both dengue and chikungunya are transmitted by the same vectors, it is not unreasonable to expect similar disease prevalence. However reports of the Delhi State Government showed large number of dengue cases in Delhi during 2008, while only few clinically suspected chikungunya cases were reported. Similarly a study from Delhi detected CHIKV virus in serum samples of patients hospitalized during a dengue outbreak in the year 2006. Since then no confirmed cases of CHIKV infections have been reported from Delhi. One can speculate the reason for the absence of chikungunya to be simply a time factor and that in time CHIKV may make its way from the coastal regions to more inland areas such as Delhi. Hence, this warrants year round surveillance of CHIKV in regions that already have high prevalence of dengue. Beside the geographical location another reason for the lower detection of chikungunya could be due to silent infections that may have gone unnoticed in the past and thus may have primed the immune system against this pathogen. A study from Chennai, nearly a decade prior to the 1964 outbreak, observed high seropositivity among older subjects and they speculated the waning antibodies to CHIKV as a possible reason for the subsequent outbreak. More studies need to be undertaken to prove this hypothesis and to further address the susceptibility of the Delhi populace to chikungunya infection. This is supported by reports of periodic outbreaks of dengue fever in Delhi every 3–4 years. Chikungunya fever has been observed to occur mostly during the monsoon and post monsoon seasons during which time there is high breeding of both *Aedes albopictus* and *Aedes aegypti.* In India the first CHIKV outbreak in 1963 was observed during July to December, coinciding with the monsoon and post monsoon seasons. However, in the present study, CHIKV was detected throughout the year at KIMS centre although with higher rate during the monsoon season, a situation similar to dengue. Our study has confirmed the findings of various other studies that certain symptoms like rashes, headache, joint pain/swelling and abdominal pain are significantly associated with chikungunya infections. Our finding on higher detection rate of chikungunya among adults as compared to children confirms most of those reported worldwide. In addition we found adults more frequently exhibiting symptoms such as rashes, headache, joint pain and joint swelling as compared to children. Interestingly among rashes, erythrematous rash was frequently observed in adults while maculopapular rash was common in children confirming earlier reports in infants. Another interesting observation made in our study was the higher incidence of hepatomegaly among children aged below 5 years. Support for this result can be found in a recent pathogenesis study in mice that demonstrated hepatic involvement during chikungunya infection and showed CHIKV to replicate first in the liver before targeting the muscle and joints. In addition, a recent review reported many atypical symptoms including raised levels of hepatic enzymes associated with chikungunya infection. In addition to assessing clinical and epidemiological implications of chikungunya we tried to identify the molecular composition of the infecting strains. High nucleotide and amino acid homology was observed among the infecting strains which clustered within the Central/East African genotype confirming earlier reports that all recent CHIKV outbreaks in India have been a result of the Central/East African genotype. This is markedly different from the CHIKV strains that caused the outbreak in India during 1963–73 that resulted from the Asian genotype. Our study also confirms earlier reports on the presence of A226V mutation in the E1 gene. It has been suggested that a change in the amino acid at this particular position may provide a selective advantage for CHIKV transmission by mosquitoes by reducing their cholesterol dependence. Our study also observed a lysine-glutamine change at aa 132 in the E1 gene of CHIKV infecting children, an implication that warrants further investigation. In conclusion, to our knowledge this is the first multi-centre study undertaken to determine the epidemiology of chikungunya fever across a wide age group with year round enrolment of large number of patients simultaneously in three different geographical regions of India. Both genomic and serology based assays were employed throughout the study for confirmation of CHIKV. Our study clearly demonstrates high incidence of CHIKV in India particularly in the southern region with moderate prevalence in the western and rare in the northern region. This warrants continuous CHIKV surveillance in India to determine the disease burden for improved healthcare. The authors would like to thank the doctors and nursing staff of AIIMS, KIMS and SMS hospitals for sample collection and the patients and parents of the children for their participation. The authors thank Prof. M. K. Bhan for helpful suggestions and Prof. V.K. Paul, the department chair for general support. [^1]: Conceived and designed the experiments: PR VHR SKK BSS NW. Performed the experiments: PR. Analyzed the data: PR RL MK. Contributed reagents/materials/analysis tools: PR SKK VHR BSS NW. Wrote the paper: PR SS. [^2]: The authors have declared that no competing interests exist.

# Introduction In 1990, the Commission for Health Research and Development (COHRED) reported that less than 10% of global health research funding was spent on health conditions that account for 90% of the global disease burden. This led to an outcry that if it remained the status quo, global inequities in health will persist. COHRED also highlighted the important role health research could play in reducing global health inequities. This has since led to an increase in the number international health research projects in Africa. International health research is research conducted in low and middle income countries (LMICs) with funding from institutions and organisations in high income countries (HICs). Clinical trials have dominated the international health research landscape, however there is currently a growing number of genomics research and biobanking projects in Africa which are funded by institutions and organizations in HICs. Examples include: the African Genome Variation Project; Human Heredity and Health in Africa (H3Africa) Consortium; and the Bridging Biobanking and Biomedical Research across Europe and Africa (B3Africa) initiative. The explanation for the growing interest in genomics research and biobanking in Africa is that it will facilitate cutting-edge health research on African populations; prevent a genomics divide between HICs and Africa; reduce global health inequities or–more skeptically–generate evidence that will be of benefit to genetically homogeneous populations. Despite the potential of international health research to reduce global inequities in health and research, it also has the potential to exploit research participants and African researchers. In the late 1990s, for example, there was a spark of debates on the ethics of international health research in Africa, a number of which highlighted the impact social and economic differences between HICs and LMICs could impact on international health research. The debates also suggested that the asymmetrical nature of these partnerships tend favor research collaborators in HICs while their African counterparts ended up as sample collectors. Contemporary genomics research and biobanking initiatives in Africa are promising to transform the way international health research has typically been structured in Africa. They hope to minimize the possibility of exploitation of African researchers. Their plan is to go beyond the traditional practice of collecting samples in Africa and conducting the scientific analyses outside of the continent, to one that fosters equitable research collaborations. While in theory, the intention to foster equitable research partnerships is laudable, these promises remain largely unchecked. There is a need to go beyond the documented promises and explore whether and how genomics research and biobanking initiatives in Africa are going about realizing these goals, and how successful their efforts are perceived to be by their LMIC partners. In this paper, we document the perceptions and expectations of genomics researchers in Africa, on the benefits of externally funded genomics research and biobanking initiatives in Africa. This study identifies challenges pertaining to equity in research collaborations which HIC partners should consider when they engage in international health research in Africa. # Methods We adopted a qualitative research methodology. Between September 2014 and June 2015, 17 face-to-face semi-structured interviews were conducted with genomics researchers in Africa. Purposive sampling was used to select research participants. Research participants were genomics researchers based in an African research institution and who were directly involved in an international genomics research and biobanking project. Principal investigators and co- principal investigators within the H3Africa Consortium were first invited to be part of the study. We selected this group of participants because, we are part of the consortium and therefore were certain that participants were actively involved in international health research projects. After interviews with these group of researchers, we invited non-H3Africa researchers, involved in other collaborative genomics research or biobanking initiatives in Africa, to be part of the study. The reason was to attain data saturation. H3Africa is an African- led genomics research and biobanking initiative with funding from the National Institute of Health, USA and The Wellcome Trust, United Kingdom. Research ethics clearance was obtained from the Faculty of Health Sciences Health Research Ethics Committee, University of Cape Town (HREC-Ref-618/2014). Written informed consent was obtained from all research participants before the start of the study. Of the 17 interviewees, 14 were part of the H3Africa consortium while 3 were not involved in H3Africa. Interviewees were based in 8 African countries and included biomedical and social scientists. At the time of the interviews, 13 of the 17 researchers were principal investigators or co-principal investigators for a genomics research and/or biobanking project in Africa, while four were research scientists. The researchers were asked questions about their experiences of being part of an international health research consortium; what they perceived to be the benefits and risks; and their recommendations on how international health research should be done in Africa. All interviews were conducted in English and lasted approximately one hour. Audio recorded interviews were transcribed verbatim. Iterative data analysis started immediately after the first few interviews and continued throughout the data collection phase. This allowed for the identification of themes and patterns that were emerging from the data, which were further probed in subsequent interviews. Data collection continued until we reached saturation point where no new patterns or categories were emerging from the interviews. To facilitate analysis and management, we usedNVivo 10, a text-based data analysis software. We performed inductive thematic analysis of the interview transcripts with the objective of identifying, examining and recording patterns of meaning across the dataset. These patterns were identified through a process of data familiarization, data coding and the development and revision of themes and models. NSM did the initial coding for the first few interview transcripts. Following the first round of coding, codes were discussed with JDV. NSM and JDV then separately coded several transcripts to establish validity of the coding scheme and its application. Differences in coding were discussed and the coding scheme was adapted to clarify ambiguities. NSM continued to code the entire dataset and interpreted the coded data. Challenges and evolving data interpretations were discussed regularly with JDV. # Results In the interviews, as genomic researchers shared their perceptions, expectations and recommendations for international health research partnerships in Africa, a number of significant insights emerged. All interviewees were of the view that externally funded genomics research and biobanking initiatives in Africa provided a platform for researchers in Africa to to collaborate, on a large scale, with researchers from different disciplinary backgrounds and from different parts of the world. This, they hoped, will play a critical role in narrowing the existing gap in genomics research capacity between HICs and LMICs and in preventing a possible genomic divide between Africa and HICs. > This opportunity will allow us to bring genomics in Africa to a level > that is comparable to other parts of the world and it could > potentially lead to new therapies and treatment strategies that are > relevant, needed in Africa for which we couldn’t do by using results > from genomics research from other parts of the world either because of > the differences in the inheritability of the different conditions in > Africa compared to other parts of the world or because of the > gene-environment interactions in different parts of Africa and that is > different from what you see in other parts of the world. (R-01) A view that has also been articulated by other researchers in Africa. In one case, An African researcher said that without international health research, researchers in Africa, would be sitting all day in their offices, ‘reading newspapers’ instead of doing research. Although all our interviewees cited the opportunity to collaborate as a benefit, some concurrently stressed that these collaborations carry mutual benefits for all partners, including collaborators in HICs. > This part of the world has not got the necessary equipment to carry > out certain experiments. The way to go is to collaborate. By > collaborating, each individual group brings in particular expertise. > So if we are working on African genomics then the first thing that the > African scientists can bring in is the African genome and then the > other people can come in from other sides and bring in their expertise > and all that. (R-17) Recognition of this mutual benefit is important for international health research partnerships in Africa. International health research has often been portrayed as a form of development aid or altruistic philanthropy, where the receiver is expected to show some gratitude to the giver. This has led to situations whereby HIC collaborators had said to their African collaborators that they were only hired to do the research and not to be involved in key decision making activities. This is problematic in that it highlights international health research as a patronizing and neocolonial activity. Nevertheless, African researchers are increasingly judging these collaborations as being of mutual benefit to all partners, and as a result want their HIC collaborators not to treat them as employees but as partners. For example, some African researchers are now willing to ask their collaborators, upfront, without fear of losing a funding opportunity, “*Do you want me to work with you or to work for you?*”. These are indeed good signs for international health research in Africa because when research collaborations are seen as mutually beneficial to all partners, they are arguably closer to being equitable. It is therefore imperative that HIC partners involved in these collaborations recognize the implications this may have in achieving equitable collaborations when they setup international health research partnerships in Africa. ## Fears of exploitation of African researchers The recognition by African researchers that international health research partnerships are mutually beneficial has however not allayed fears of exploitation of African researchers by their partners in HICs. Despite being generally supportive of African genomics research and biobanking initiatives in Africa, all our interviewees expressed concerns that they may end up being exploited in these collaborations. These fears were primarily shaped by past experiences of exploitation of African scientists. > It could be that African researchers, as it has happened before, are > just being used for collecting materials and that is a very big > potential \[for exploitation\] because if you don’t have capacity to > analyse, make sense out of it, you just collect the material and the > data and send it to people who can analyse and that could be a benefit > not only for publication of an article but longer term benefit of > patents and discoveries… Those are some of the potential risks. (R-07) The fear of exploitation is made worse by uncertainty about sustainability of genomics research and biobanking projects in Africa. One skeptical view explaining the growing interest in African samples is that access to Africa’s rich human genetic diversity makes the continent a choice destination for population genomics studies –though not always to the benefit of African populations, patients or researchers. Interviewees expressed concerns that they were unsure of what will happen to their research projects once the current funding period is over and they no longer have funds to enable them use the samples and data collected in the initial stages of these genomics research and biobanking projects. For example, about two thirds of our interviewees mentioned that whilst their collaborators in HICs have the capacity and resources to continue research on stored samples and the data that were jointly obtained during the funded collaboration, they would not have access to similar research funds. The risk, therefore, is that though initial studies may seek to establish more equitable forms of collaboration, African researchers may still be marginalized in subsequent research projects. > Our capacity just to handle the data first, to analyse them, to handle > the samples, to analyse them or even to have research means in terms > of funding, to take advantage of those samples or data is by far > limited as compared to our international partners that are in the same > project. So what will happen if we design our own research questions > to take advantage of the biobank? Will we still be able to have the > amount of funding as we are having now in \[consortium name\] to > respond to our own question? Otherwise you will see that after > \[consortium name\] the biobanks will benefit more the international > scientists because they will have more resources and we might end up > working in collaboration again with international scientists on their > agenda and not our agenda (R-05) This researcher highlights not only the importance of equity to access to samples, data and funding to support their continued work but also that researchers in Africa want to be able to drive their own research agendas. A theme is further discussed below. The inequitable nature of international health research collaborations have led to calls for funders of international health research to build capacity for health research in Africa as well as for HICs partners to engage with their African collaborators in ways that build mutual trust and respect \[, –\]. In the interviews, we asked the researchers how these fears of exploitation may be assuaged. Their responses can be grouped into two broad categories: Equitable research partnerships and research capacity building. ## Establishing equitable research partnerships The nature of research collaborations between LMIC researchers and HIC researchers is often uneven in terms of access to research funding, research resources and involvement in decision making. These power imbalances have made African research partners to oftentimes remain silent about worrying inequalities. As we described above, a two-thirds majority of our interviewees expressed the desire to be in equitable research partnerships. They also had suggestions for how equitable collaborations may be achieved. This included: setting the “rules” of engagement, involvement of African researchers in decision making and African leadership of international health research projects. ### Setting the “rules” of engagement The first suggestion advanced by our interviewees as one of the major ways of achieving transparent and equitable research partnerships is having “rules of engagement” between collaborators. > I think importantly around developing the rules of engagement, so, > making sure that Africa benefits from the research, not just > \[consortium name\] research but that this kind of work done in > Africa, I mean, setting the pace, that this is how genomics research > should be done in Africa so that Africa benefits. (R-12) Slightly over half of our interviewees who expressed the desire for equitable research partnerships specified that the rules of engagement need to be defined before the start of the collaborations. For these interviewees, it is critical that at an early stage of the collaboration, all partners have an idea of what is expected of each collaborator (both LMIC and HIC partner). Recently, some authors have recorded how collaborators in HICs have dictated what needs to be done in research collaborations and in some cases have said their African collaborators were hired to do research and should implement as requested. The resistance of our interviewees to sign up to this way of collaborating may therefore be evidence that they are fighting what Okeke has termed the ‘little brother effect’ in African biosciences, that is, north-south partnerships that are a mirror of paternalism and colonial hegemony even though the HIC partners may have initially displayed good intentions at the beginning. ### Involvement of African researchers in decision making A second way in which more than half of the interviewees felt equitable partnerships may be achieved was for researchers in Africa to be actively involved in decision making processes in the consortia. As one of the interviewees stated, these collaborations can only be considered fair if they are treated as equal by their collaborators through being involved in decision making activities. > There should also be regulatory procedures that will make sure that > the African scientists are involved and are central to any decision > involving the use of the samples and are actually involved in the > publications and the intellectual property that emanates from such > processes. African scientists should be at the centre of all of this. > So it is not disadvantaging anyone (R-06) Again, these are indications that African researchers do not want to be considered as hired workers but as equal research partners. From the interviews, it is clear that African researchers want to be involved in making decisions on the use of samples collected as part of genomics research and biobanking in Africa. Genomics research and biobanking are generally characterized by the sharing of samples and data and most times the cross-border movement of samples for the purposes of analysis. ### African leadership of genomics research and biobanking in Africa The perceived fears of exploitation has also led to an expressed yearning for an African leadership of international health research consortia in Africa. More than half of the interviewees recommended that African researchers should be at the forefront in the design and conduct of genomics research and biobanking projects in Africa. As one interviewee said, the story of Africans needs to be told by Africans. Interviewees who articulated a preference for African leadership of research projects in Africa, were clear that they wanted to be involved in all phases of the studies including research design and analysis. > First of all, African scientists should be involved in doing this > research. It should not be research that comes from outside Africa, > being implemented by non-Africans researchers in Africa. (R-14) > > Well I think that it \[African ownership\] is a good idea in the sense > that we will not have a stranger telling us our story that is the > positive part of it. (R-16) This perception is similar to what Okwaro and Geissler reported in a study on scientific collaborations in HIV research whereby researchers in an African research institution expressed interest in leading projects in Africa and criticized the use of the term “local PI” as a way of demonstrating local leadership of projects whilst in actual facts ‘local PIs’ are often only used to implement projects designed by northern collaborators. Our interviewees objected to such tokenistic involvement in decision-making and were rather expressing a desire for substantive involvement. The desire for equitable research partnerships does not necessarily mean that African researchers thought all collaborators needed to be equal. In fact all the researchers acknowledged that there were disparities in resources and capacity for genomics research between HIC and LMIC investigators, but indicated that this should not stop them from active and equal participation in network activities as their collaborators in HICs. > We must have a critical mass of people who can talk the language of > genomics, who can, you know, when there is collaboration their > participation is equal. Maybe not in terms of funding but certainly in > terms of intellectual contribution. (R-03) The acknowledgment of these inequalities and how it may impact on the success of international health research in Africa is a step towards achieving equitable international health research partnerships. It is also contrary to what may have been expected a few years back, and shows the willingness and eagerness of African researchers to take up roles that go beyond sample collection. ## Research capacity building Besides building equitable research partnerships as a way of minimizing the possibility of exploitation of African researchers, all our interviewees also said research capacity building will play a big role in minimising exploitation of African researchers and in achieving equitable research partnerships. Some ongoing genomics research and biobanking initiatives in Africa are promising to build research capacity in Africa to ensure that genomics research is done in Africa by Africans. But whilst building health research capacity in LMICs is key to promoting justice in global health research, the central question is which type of capacity building efforts can foster equitable genomics research and biobanking collaborations in Africa. When tour interviewees talked about capacity building for genomics research and biobanking in Africa, it was in three major areas: building of human capacity, infrastructural capacity building and the sustainable access to funding. ### Research capacity building: Training and skill development Training of researchers in LMICs is critical for the success and sustainability of international health research in Africa. Our interviewees confirmed that this was one of the key features of the genomics research and biobanking initiatives they were involved in. Also, researchers who were part of H3Africa or had knowledge of H3Africa activities, identified the training of the next generation of African scientists as one of the main benefits of the collaboration. > In terms of building capacity, training and giving opportunities for > many African students. So that will accelerate capacity within Africa. > (R-05) Training of researchers within these consortia are taking place in institutions in both LMICs and HICs One of our interviewees mentioned that in their project some of the students are being trained out of their home countries and this gives them the opportunity to expand their skills and research networks. > We currently have two trainees from Country Y \[LMIC\] at Country Z > \[HIC\] at the Z college of Medicine, doing their rotation of > laboratories towards their PhDs in genomic sciences and genomic > medicine. We also have three from Country X \[LMIC\] and we are > expecting two more from Country Y \[LMIC\] next year. (R-02) Another form of human capacity building that has been part of the activities of H3Africa and which more than three quarters of the interviewees identified as a major benefit, is postdoctoral skills development, including a focus on transferable skills. These included training in laboratory techniques, statistics, data analysis and grant writing. > Capacity building in our research to help young African researchers, > develop themselves in many fields related to research: statistic, > epidemiology……Through this project, I have developed some skills in > writing grant proposals (R-09) Genomics research and biobanking are relatively new in Africa. Many researchers who now work in this field did not receive formal training in genetics, genomics or associated fields such as bioinformatics. In fact, many of our interviewees, and of the H3Africa researchers more generally, are medical doctors with a strong research record in investigating particular diseases. Whilst being world- experts on these diseases, they do not necessarily have expertise in genomics. Training in key genomic research skills such as for instance those relating to bioinformatics is essential for establishing successful and equitable partnerships in genomics research and biobanking agenda in Africa. The sustainability of such training is a key concern, however. One researcher described that his involvement in H3Africa has provided an opportunity for their research team, together with their collaborators in HICs, to develop degree programs in genomics at African universities as a way of ensuring sustainability. > We are working with the Faculty of Health Sciences in both Country X > \[LMIC\] and Country Y \[LMIC\] to create masters programs, PhD > programs in genomics and bioinformatics. We hope that what will be a > lasting and sustainable benefit to those countries, is by having a > local program in genetics and genomics (R-02) Although just one of our interviewees mentioned this as a sustainable capacity building effort within H3Africa, it is one that will be worth emulating and should be extended to other African countries. Research capacity building strategies aimed at developing undergraduate and postgraduate research programs in African universities and research institutions are vital in sustaining health research in Africa. The third and last form of training and skills development articulated by almost a third of our interviewees was capacity building in research ethics at both the professional and institutional level. This benefit was only mentioned by H3Africa researchers and they were of the opinion that there has been some improvement in research oversight for genomics research and biobanking in Africa as a result of H3Africa activities. At the professional level, it has made African biomedical researchers to think beyond the science of their projects to the ethical issues raised by their research projects. Typically, African researchers have demonstrated a knowledge gap in research ethics and so engaging them in research ethics debates may help improve research oversight in Africa > I can almost be sure that if you visited any study site at the > beginning of H3Africa and visit them now, the way the will be > consenting and engaging with the community will be completely > different, I mean in ethics, they have been challenged to think far > beyond just the compensation for travel, for time and everything … > even our ethics committees have been touched to think far more (R-13) Weak research oversight systems in Africa challenge the execution of health research projects in Africa and make African researchers and research participants more vulnerable to exploitation. Genomics research and biobanking in particular raise unique ethical challenges that may be quite different from other forms of international health research such as clinical trials that ethics committees review more habitually. Furthermore, methods of genomics research and biobanking are relatively new to REC members in Africa and they may not be fully equipped to review such studies. It is therefore important that for international health research projects that will involve sharing of samples and data and that raise complex ethical issues, ethics committee members be appropriately engaged in discussions on genomics research and biobanking. One interviewee mentioned that as a result of meetings organised by H3Africa, research ethics committee members in Africa are becoming familiar with concepts in genomics research and biobanking. In his/her opinion, this had implications in research oversight for genomics research and biobanking in Africa. > Another thing which is already happening that is good is involving the > ethical review committees in giving them additional training in > genomics research because that is a new area for a lot of the IRBs. In > Country X we found that the IRBs had not reviewed this type of > research before and this caused a lot of delay in terms of getting > approvals and feedback from them but things improved after one of the > IRB members attended the last consortium meeting for the special > session for ethical review committees. (R-02) Capacity building in research ethics has impact on regulation of health research in Africa as well as the protection of research participants. Africa has historically been characterised by a history of “parachute” or “postal” research whereby researchers from HICs have come to Africa just for the samples and then “disappear” once samples have been collected. This has led to research ethics committees being overly cautious when reviewing projects and in some cases it has led to tight national regulation for the export of samples. It will appear that genomics and biobanking initiatives in Africa are trying to engage research ethics committees in Africa and to build their capacity to be able to identify ELSIs pertinent to genomics research and biobanking. Infrastructural research capacity building. A second major form of capacity building articulated by all our interviewees is infrastructural capacity building, mainly the setting up of biobanks and the acquisition of laboratory equipment. For these researchers, having a biobank in Africa will be a great resource for biomedical research in Africa. > It \[consortium name\] has really emphasized on building > infrastructure in Africa, giving Africans the tools to solve their > problems.....One big advantage of having a biobank in Africa is that > it really puts all of our resources together. I think that is very > huge for Africa, to begin with. And it is a way of putting our > resources together and giving us the infrastructure to produce high > quality research (R-13) Biobanks are repositories where organized collections of human biological materials, and associated data from large numbers of individuals, are collected, stored and distributed for the purpose of scientific investigations or public health use. Biobanks are therefore an important resource for biomedical research. Also, well curated biobanks in Africa could foster international health research collaborations, south-south collaborations and promote biomedical research in Africa and globally. However, for researchers in Africa to make optimal use of the samples and data stored in the biobanks, they will have to secure the necessary laboratory equipment. One of our interviewees mentioned that their genomics research and biobanking project has given them an opportunity to source for recent and novel laboratory equipment. > In terms of infrastructure, we are building capacity at Q University > and university of Country X \[LMIC\] to do genome sequencing and to > get their sequencers, their Illumina sequencer and so all of those > tools will help in the building of infrastructure in the different > laboratories. (R-02) Besides the modest infrastructural capacity for genomics research and Biobanking in Africa, limited access to technology remains a serious impediment to biomedical research in Africa. It is also a major reason for the export of samples from Africa to HIC, especially when high-throughput technology is required. To minimize export of samples, researchers in Africa will need to acquire up-to date laboratory equipment. As one interviewee explained, African countries must emulate LMICs such as India and China and build their infrastructural capacity for genomics research and biobanking. > We should not just be thinking that anything we want to do, we have to > collect samples and send to Europe, samples to China, samples to > America. We need to build the capacity that we can do work in Africa. > If that is the case then we can produce things that will be valuable > and useable in Africa. And I think that is just what has been done in > several other countries like India. It is very difficult now to take > anything out of India for analysis. (R-14) Export of samples from Africa is a major contributor to exploitation of African researchers. The acquisition of laboratory equipment that will permit research work to be done in Africa will minimize this risk–but only if equipment is maintained. Unfortunately, in many African research institutions, laboratory equipment which are primarily obtained through funds from international health research tend to lie loose with little use once the projects they were obtained for are over. In the unfortunate case of a breakdown, equipment worth thousands of dollars are usually abandoned to the dust for sheer of lack of resources to maintain them. The virtual absence of an infrastructural maintenance culture by research institutions in Africa (both in providing resources and tools required for maintenance) and the impact it has on health research cannot be missed even by a passing observer. ### Access to funding The last and major form of research capacity building is sustainable access to funding for health research. This was mentioned by almost half of the interviewees. For these interviewees, being part of a funded genomics research and biobanking consortium has been a unique opportunity to access and administer funds for large-scale research projects. > For researchers, what it is really doing is, it’s building their > capacity to do research, to do large scale research that they haven’t > done before because it costs a lot of money to do this type of > research. (R-15) Research funding is a major driver of change for biomedical research. A dearth of funding for health research coupled with the lack of research infrastructure (laboratories, biorepositories, databases has held back African scientists from carrying out rigorous health research. The importance of funding health research in Africa has been presented to African governments and while there was a general buy-in by African governments followed by a commitment to allocate a small proportion of their annual national growth domestic product (GDP) to research, there has been little compliance by a vast majority of African governments. Where there has been government commitment, such as in South Africa, tremendous progress has been made in advancing health research. African governments will have to take responsibility and devise research funding schemes that can sustain and foster health research in Africa. One African researcher and a pioneer in research capacity building in Africa has suggested that a Pan- African research funding agency may solve the problem of limited local funding for health research. Organizations like the Alliance for Accelerating Excellence in Science in Africa (<http://aesa.ac.ke/>), a new partnership between the African Academy of Sciences and the New Partnership for Africa’s Development (NEPAD) may play the role of such a Pan-African funding agency especially as it is acting as an agenda setting and funding platform to address Africa’s health and development challenges. However, it is still funded by the Wellcome Trust and may therefore face the crisis of non-sustainability should there be no financial commitment from African governments and the private sector in Africa to support its activities. ## Challenges in building capacity for genomics research and biobanking in Africa While research capacity building could be a major way of achieving fairness in international health research partnerships in Africa, it is hard to sustain research capacity building efforts and to retain human research capital in Africa. Brain drain, for example, is a major impediment to research capacity building efforts in Africa. About a third of our interviewees expressed concerns that although young African scientists are being trained in genomics research and biobanking, the lack of an enabling research environment in Africa could see them migrating to research institutions in HICs. Some interviewees suggested that assurances of a research career in African institutions could curb brain drain. One interviewee explained how their project is working towards overcoming the problem of brain drain > We want these trainees to come back and do research that benefits > their own countries. And so there is an agreement with the host > institutions that there will be faculty positions available for them > when they complete their degrees, so they will be able to go straight > to those faculty positions and to be able to use the infrastructure > that we have been building in the meantime to continue their genomic > research, to apply for additional grants and to nurture their own > students one day. (R-02) Generating a pool of qualified African researchers must be accompanied by a parallel interest in maintaining them in research institutions in Africa. Considering that most research institutions in Africa are government establishments, African governments will have to play a key role in retaining emerging researchers in Africa. And whilst African governments may find it hard to direct funds for research activities, they can at least provide research jobs and research support for emerging African researchers. Equally, whilst the establishment of well-curated biobanks in Africa is one of the greatest benefits of these genomics research and biobanking consortia, maintaining them will be a serious challenge for host institutions. To solve the problem of sustainability a few interviewees again suggested that African governments and the private sector in Africa invest in health research > What I know will be essential will be for African governments to > invest more in biomedical research and development at all levels and > for African governments and the private sector to improve the health > research systems that are currently in place in many African > countries. Those efforts will benefit not just genomic research but > all aspects of health research. (R-01) The quote by the researcher above further highlights the importance of African governments to support health research in Africa as this will enable African researchers to work on local health needs rather than having to rely heavily on external support which could come with the specific research interest and priorities of the funders or collaborators in HICs. # Discussion and conclusion International health research has great potential in fostering health research in Africa. It is also a platform for both LMIC and HIC researchers to share expertise and resources for the purpose of advancing scientific discovery and to access funding for health research. However, power imbalances between collaborators in Africa and HICs may hinder successful research partnerships and has been known to lead to the exploitation of African researchers and research participants. In this study, we document the results of a qualitative study that aimed at exploring African researchers’ perceptions and expectations of the risks and benefits of international health research collaborations, with a particular focus on genomics research and biobanking in Africa. All the interviewees acknowledged that the opportunity to collaborate and access funding is a benefit for African researchers. However, they expressed fears that may be exploited within these collaborations. In their opinion, fears of exploitation may be minimized through setting up equitable research collaborations and building capacity for genomics research and biobanking in Africa. In this paper, we document that despite persisting fears of exploitation of African scientists, all interviewees were of the opinion that the international genomics research and Biobanking consortia in Africa have provided African researchers with a platform to build their capacity to conduct cutting edge genomics research. There is a growing acceptance of the importance of research capacity building and African leadership and ownership of health research in Africa as a means of achieving equitable international health research partnerships. The assumption is that equitable research partnerships would ultimately stand a better chance of building trust between research partners and in fostering health research on African health problems, thereby reducing global health inequities. Extrapolating this trend to genomics research and biobanking in Africa, it is equally important to ensure that international collaborations in African genomics research are fair and equitable–and the rhetoric surrounding these initiatives reflects this trend. Some genomics research and biobanking initiatives in Africa such as the H3Africa Consortium have gone some way in defining some of the aspects of fair and equitable international collaboration in African genomics research and to speculate that H3Africa may be setting a gold standard for how collaborative international health research in Africa should be done to benefit African populations. In this study, we go beyond this documented promise to explore African researchers’ perceptions and expectations of benefits and risks of Africa’s participation in collaborative genomics research and biobanking in Africa. For our interviewees, a key hallmark of equitable collaboration is equip African researchers to make equivalent intellectual contributions to the design and conduct of genomics research through a rigorous process of research capacity building. This may take several forms including training of Masters and PhD students, skills development for postdoctoral scientists, mentorship, peer support through continental networks and infrastructural support. Different forms of research capacity building have been described in the literature and one key component that emerged from the interviews, that goes beyond individual training, is the creation of postgraduate degree programs in genomics in African universities. This approach could foster sustainability of genomics initiatives in Africa. Such degree programs could be designed to meet current needs of African genomics research and need to include training in bioinformatics, genomics medicine, genetic counselling, Bioethics and the social sciences. Equally important in the sustainability of capacity building efforts is the creation of networks that could foster interdisciplinary research in Africa. The H3Africa bioinformatics network (H3ABionet) is an example of a network that provides peer support for bioinformatics research in Africa with nodes in more than 30 different African countries. Such an approach will have to be adopted especially in emerging disciplinary areas that provide support for genomics research and for which there is limited capacity. Examples include genetic counselling, bioethics, sociology and anthropology. Also, because research is a complex activity and process that requires the interplay of individuals, organizations, national and international research systems. Genomics research and biobanking initiatives in Africa will also need to expand their capacity building efforts to include the establishment of centers of excellence for genomics research in Africa, supporting research administration in Africa, strengthening capacity for research-to-policy and promoting public education in genomics in Africa. This will be particularly crucial if these initiatives are to achieve their aim of being an exemplary model for collaborative health research in Africa. A challenge to the H3Africa experience is that it is based on a set of shared, yet often unarticulated, values and principles that seek to promote equity and fairness. Whilst these principles are incorporated into the design of projects and policies of the H3Africa Consortium, they are not necessarily visible to or shared by other initiatives that support genomics research in Africa. A question is how the H3Africa infrastructure could be effective in influencing the design of such initiatives, to ensure that the principles of fairness and equity are also woven into such future endeavors. It is possible that this could be achieved through for instance the African Academy for Sciences (AAS) and its affiliate organization the Alliance for the Acceleration of Excellence in Science in Africa, but only if there is active advocacy on behalf of the African genomics community, including H3Africa researchers, for this to be the case. # Supporting information We are grateful to all researchers who participated as interviewees in this study. The empirical work for this project was supported by an RHDGen MSc Studentship to NSM. RHDGen is an H3Africa collaborative center led by Professor Bongani M Mayosi and funded by the Wellcome Trust (WT099313MA). We thank the two reviewers of the article for their comments and suggestions which helped improved the first version of the manuscript. [^1]: The authors have declared that no competing interests exist.

# Introduction In the past two decades, *Staphylococcus aureus* has emerged one of the most important pathogens causing infections with indwelling medical devices, such as prosthetic joints, prosthetic heart valves, intravascular catheters, and cerebrospinal fluid shunts, which creates an increasing health care problem. For example, prosthetic joint infections occur at a frequency of 1.5–2.5% in primary total hip or total knee arthroplasty with a mortality rate of up to 2.5%. By far the most frequently isolated species from these infections are *Staphylococcus* species, i.e. *S. aureus* (22–39%) and coagulase-negative staphylococci (15–37.5%). The pathogenesis of device-associated infections with staphylococci is mainly characterized by the pathogens ability to colonize the surfaces of the implanted medical device by the formation of a three-dimensional structure of microorganisms embedded in a thick extracellular matrix composed of polysaccharides, proteins, extracellular DNA, and host factors, known as biofilm. Microorganisms within a biofilm are protected against antimicrobial chemotherapy as well as against the immune system of the host. Biofilm formation occurs in a two-step process. The first step involves the adherence of the bacteria to artificial surfaces that can occur either directly or via host factors acting as bridging molecules, such as the extracellular matrix and plasma proteins fibrinogen (Fg) and fibronectin (Fn) or platelets. In the second step, the bacteria proliferate and accumulate into a biofilm requiring intercellular adhesion. Direct *S. aureus* adherence to the unmodified artificial surface may be mediated by the major autolysin Atl, which is highly homologous to the *S. epidermidis* autolysin/adhesin AtlE shown to be involved in the attachment to polymer surfaces. *S. aureus* host-factor binding proteins that typically belong to the family of <u>m</u>icrobial <u>s</u>urface <u>c</u>omponents <u>r</u>ecognizing <u>a</u>dhesive <u>m</u>atrix <u>m</u>olecules (MSCRAMM) are involved in binding to host factor-coated foreign material, among them Fn-binding proteins (FnBpA, FnBpB, Ebh), Fg-binding proteins (ClfA, ClfB), collagen-binding protein (Cna), bone-sialoprotein-binding protein (Bbp), and von Willebrand factor (vWf)-binding protein A (Spa). Staphylococcal biofilm accumulation is mediated by polysaccharide as well as protein factors. The intercellular polysaccharide adhesin (PIA), a β-1,6-N-acetylglucosaminoglycan, is produced by the gene products encoded by the *icaADBC* operon that was first identified in *S. epidermidis* and is also present in *S. aureus*. Surface proteins conferring biofilm accumulation include the accumulation-associated protein (Aap) from *S. epidermidis*, and the homologous *S. aureus* surface protein G (SasG). In *S. aureus*, another protein, the biofilm-associated protein (Bap), is involved in biofilm accumulation. However, so far the *bap* gene has not been found in any *S. aureus* isolate of human origin, but has only been identified within bovine mastitis isolates. A recent study demonstrated that all 18 *S. aureus* isolates from prosthetic joint infections carry the *icaADBC* operon, produce PIA, and are biofilm- positive. Surprisingly, the biofilms of all 18 *S. aureus* isolates could be almost completely eradicated by the treatment with dispersin B (DspB), an enzyme with specific β-1,6-hexosaminidase activity as well as by the treatment with trypsin suggesting that both, proteinaceous adhesins and PIA contribute to biofilm formation in these *S. aureus* isolates. This was in contrast to *S. epidermidis* isolates from prosthetic joint infections. Only 62% of the 52 *S. epidermidis* isolates carry the *icaADBC* operon and *S. epidermidis* biofilms produced by *icaADBC*-positive strains were disintegrated by DspB, but not by proteases. Furthermore, biofilms produced by *icaADBC*-negative strains were disintegrated by proteases, but not by DspB. Thus, different mechanisms seem to be involved in biofilm formation in clinical *S. aureus-* and *S. epidermidis-*associated prosthetic joint infection isolates. More specifically, in *S. aureus*-associated prosthetic joint infections, polysaccharide and protein factors seem to act synergistically in biofilm formation. Only 33% of the analyzed *S. aureus* strains carry the *sasG* gene and none of them carry the *bap* gene, indicating the existence of further, not yet identified surface proteins involved in biofilm accumulation of *S. aureus*. Upon a search for LPXTG-motif containing surface-anchored proteins encoded by the *S. aureus* Col genome (<http://www.tigr.org>), we chose to study the so far uncharacterized SasC. We heterologously expressed *sasC* from *S. aureus* Col and from the clinical *S. aureus* isolate 4074 in *Staphylococcus carnosus* under the control of a xylose-inducible promoter. *S. carnosus* expressing *sasC* formed huge cell aggregates indicative of intercellular adhesion, which were disintegrated by protease treatment. Upon plasmid-encoded expression of *sasC*, *S. carnosus* as well as *S. aureus* not only formed huge cell aggregates, but also formed a much more pronounced biofilm in microtiter plates as well as in glass tubes than the respective wild-type strains. The domain conferring cell aggregation and biofilm formation was localized to the N-terminal domain of SasC. In conclusion, we identified SasC as a novel *S. aureus* factor involved in intercellular adhesion and biofilm accumulation. # Results ## Identification and cloning of the *sasC* gene of *S. aureus* As a candidate gene conferring biofilm formation in *S. aureus*, we amplified a DNA fragment containing the *sasC* gene including the ribosome binding site by polymerase chain reaction (PCR) from *S. aureus* 4074 and *S. aureus* Col genomic DNA using the primers CHsasCfor and CHsasCrev yielding 6577 bp DNA fragments. The DNA fragments were cloned into the *Bam*HI and *Sma*I sites of the vector pCX19 in *S. carnosus*, creating plasmids pSasC4074 or pSasCCol. ## Nucleotide sequence of *sasC* and amino acid sequence analysis of the deduced protein The nucleotide sequence of the cloned *sasC* gene from *S. aureus* 4074 was determined on both strands. *sasC* consists of 6558 nucleotides and encodes a deduced protein of 2186 amino acids (aa) with a predicted molecular mass of 237.9 kDa. The ATG start codon is preceded by a putative ribosome binding site at a distance of 8 bp. Putative −10 (TATATT, nucleotides −61 to −56) and −35 (TAAACA, nucleotides −80 to −75) promotor sequences were deduced from homologous DNA sequences from strain *S. aureus* Col. A putative ρ-independent terminator consisting of two stem-loops is located downstream of the TAA stop codon. The deduced SasC sequence contains a putative signal peptide in the first 37 aa that contains an YSIRK motif, which seems to play a role in signal peptide processing. The predicted *sasC* gene product is composed of 25.1% hydrophobic, 12.1% basic, and 13.2% acidic aa. The theoretical pI value of SasC is 5.08. The deduced aa sequence of SasC of strain 4074 shares 97% identical aa with homologous proteins from strains *S. aureus* MW2 and MSSA476, 96% identical aa with strains USA300, COL, Newman, NCTC8325 (accession number: Q2FXH4), and N315 and 89% identical aa with strain MRSA252. Besides, SasC shares 31% identical aa with Mrp and FmtB proteins from strain Col that have been implicated in methicillin-resistance. Sequence comparison of the deduced SasC sequence with known protein sequences in databases revealed a domain structure of SasC. The central portion of SasC contains a domain, which is similar to the motif <u>f</u>ound <u>i</u>n <u>v</u>arious <u>ar</u>chitectures (FIVAR; 54 aa, starting at N-590 and ending at D-643). The FIVAR domain is followed by 17 direct repeated sequences of 72 aa each separated by a stretch of 5 aa, which are homologous to a <u>d</u>omain of <u>u</u>nknown <u>f</u>unction (DUF1542) also found in other cell surface proteins. The first DUF1542 repeat starts at Q-671 and the last ends at I-1974. The DUF1542 repeats share between 18 and 47% identical aa. The FIVAR motif and the DUF1542 domain are also present within Mrp and FmtB (see above) as well as within the cell surface protein Ebh from *S. aureus* and the homologous Embp from *S. epidermidis*. ## SasC mediates strong cell aggregation in *S. carnosus* After overnight growth in tryptic soy (TS) broth supplemented with 1% xylose, *S. carnosus* expressing *sasC* formed huge cell clusters that were visible macroscopically and microscopically. In contrast, the strains did not form cell clusters without induction by xylose. The cell clusters were dissolved upon treatment with trypsin or proteinase K. Disruption of cell clusters by trypsin was concentration-dependent. ## Characterization of *S. carnosus* and *S. aureus* expressing *sasC* or *sasC*-subclones In order to dissect the functional domains within SasC, we constructed subclones of *sasC* expressing either the N-terminal domain including the FIVAR motif (subclone 1) or 8 of the 17 DUF1542 repeats (subclone 2) in *S. carnosus* yielding *S. carnosus* (pSasCsub1) or *S. carnosus* (pSasCsub2), respectively. An inverse PCR with the plasmid pSasC4074 as a template was carried out for the construction of subclone 1 and subclone 2 by using the primers CHsasCSub1rev/CHsasCSub1for and CHsasCSub2rev/CHsasCSub2for, respectively. The resulting PCR fragments were ligated and subsequently, *S. carnosus* was transformed with the ligation mixtures. Correct clones were verified by PCR and DNA sequencing. However, DNA sequence analysis of subclone 1 revealed a 33-bp deletion in the C-terminal region so that aa T-654 is fused to N-1987 rather than to D-1976. To functionally characterize *sasC* also in the *S. aureus* background, we transformed *S. aureus* 4074 and *S. aureus* SH1000, which is a 8325-4 derivative reconstituted for its *rsbU* mutation, with the plasmids pSasC4074, pSasCsub1, or pSasCsub2 leading to overexpression of *sasC* and its subfragments in these strains. To verify the production of the whole SasC or the truncated SasC proteins, cell lysates of the strains were prepared and analyzed by SDS-PAGE. *S. carnosus* and the *S. aureus* strains expressing *sasC* revealed an additional protein band corresponding to the size of SasC of 238 kDa. The strains expressing subfragments 1 or 2 revealed additional protein bands at 93 or 96 kDa, respectively. The strains expressing *sasC* or the subfragments 1 or 2 were further characterized. ## (i) Cell aggregation In contrast to the wild-type strains, *S. carnosus* (pSasC4074), *S. aureus* 4074 (pSasC4074) and *S. aureus* SH1000 (pSasC4074) as well as *S. carnosus* (pSasCsub1) and *S. aureus* SH1000 (pSasCsub1) formed huge cell aggregates that were visible macroscopically in cultures grown overnight on TS agar supplemented with 1% xylose and resuspended in PBS on glass slides. The cell aggregates formed by *S. aureus* 4074 (pSasCsub1) were somewhat smaller in size (comparable to those mediated by the *icaADBC* operon encoding the production of the polysaccharide intercellular adhesin, PIA –13) corresponding to a lower expression level of the fusion protein. In contrast, the parent strains or strains expressing the subfragment 2 did not form visible cell aggregates. Thus, *sasC* and its subfragment 1 confer cell aggregation. ## (ii) Biofilm formation on polystyrene and glass Biofilm formation on polystyrene was determined in the quantitative biofilm assay. Whilst *S. carnosus* (pCX19) revealed a biofilm-negative phenotype (*A*490 value: 0.11), *S. carnosus* (pSasC4074) and *S. carnosus* (pSasCSub1) showed an enhanced biofilm formation corresponding to an *A*490 value of 0.43 and 0.4. In contrast, biofilm formation of strain *S. carnosus* (pSasCsub2) is comparable to that of the wild type (*A*490 value: 0.05). In *S. aureus*, the expression of *sasC* or the subfragment 1 also led to a markedly increased biofilm formation as shown for strain SH1000 \[*A*490 value of 1.5 or 1.4 for *S. aureus* SH1000 (pSasC4074) or *S. aureus* SH1000 (pSasCsub1), respectively, versus *A*490 value of 0.7 for *S. aureus* SH1000\]. As observed with *S. carnosus*, the biofilm formation of *S. aureus* SH1000 (pSasCsub2) is comparable with that of its wild type (*A*490 value: 0.8). The results for *S. aureus* 4074 are similar (not shown). The stronger capacity for biofilm formation mediated by *sasC* or the subfragment 1 also could be observed on a glass surface with *S. carnosus* as well as with *S. aureus* SH1000 and *S. aureus* 4074 (not shown) and was comparable with that of *S. carnosus* (pCN27) producing PIA. Thus, *sasC* and its subfragment 1 mediate biofilm formation on polystyrene and on glass. ## (iii) Initial attachment to polystyrene and to glass Bacterial biofilm formation results from initial attachment of bacterial cells to a surface and subsequent accumulation into multilayered cell clusters, which requires intercellular adhesion visible as cell aggregation. To determine, whether *sasC* not only mediates cell aggregation, but also initial attachment, we analyzed the capacity of the respective strains for attachment to a polystyrene or a glass surface. Initial attachment of *S. carnosus* (pCX19) to polystyrene was very low indicated by a low number of attached bacteria. *S. carnosus* strains expressing *sasC* or its subfragments showed higher initial attachment with *S. carnosus* (pSasCsub1) yielding the highest numbers of attached bacteria. Essentially the same was observed with *S. aureus* SH1000 strains albeit at a higher level of attachment and with *S. aureus* SH1000 (pSasCsub2) showing numbers of attached bacteria that were comparable with the wild type. Initial attachment of the strains to a glass surface generally was higher and similar with *S. carnosus* and *S. aureus* SH1000 wild-type strains and the same strains expressing *sasC* or the subfragment 2. In contrast, both strains expressing subfragment 1 showed a slightly lower number of attached bacterial cells. Thus, expression of *sasC* slightly increased attachment to a polystyrene surface, but did not increase attachment to glass. ## (iv) Binding to Fg, vWF, thrombospondin-1 (TSP-1), and platelets The ability of *S. aureus* to bind to extracellular matrix and plasma proteins and to host cells determines its capacity for tissue colonization. The potential of *sasC* to mediate binding to the extracellular matrix and plasma proteins Fg, TSP-1, and vWf as well as to platelets was analyzed by flow cytometry. While *S. aureus* 4074 bound to Fg, TSP-1, and vWf as well as to activated platelets in a dose-dependent fashion, *S. carnosus, S. carnosus* (pSasC4074), and *S. carnosus* (pSasCsub2) did not. Thus, *sasC* does not mediate binding to these extracellular matrix proteins or to activated platelets. ## Biofilm formation of a *sasC* transposon (Tn)*917* insertion mutant (SMH2035) To further support the role for SasC in biofilm formation, we analyzed the capacity of a *sasC* Tn*917* insertion mutant (SMH2035) for biofilm formation in comparison to its wild-type strain *S. aureus* SH1000. Under all conditions tested, the *sasC* mutant strain showed reduced biofilm formation in microtiter plates, i.e. the addition of 1% xylose \[*A*490 value of 0,4 for SMH2035 versus *A*490 value of 0,7 for SH1000\], 0.25% glucose \[*A*490 value of 1,6 for SMH2035 versus *A*490 value of 1,8 for SH1000\] or no additional carbohydrate source in the biofilm assay \[*A*490 value of 0,5 for SMH2035 versus *A*490 value of 1,0 for SH1000\]. The biofilm-forming capacity of both strains was most pronounced upon addition of 0.25% glucose, which is known to induce the production of PIA in *S. epidermidis* and probably leads to enhanced PIA production also in *S. aureus*, which might partially obscure the function of SasC. The presence of 1% xylose seems to slightly reduce biofilm formation. So far, the reason for this is unknown. ## Expression and purification of the 6 x Histidine (His)-DUF1542 fusion protein in *Escherichia coli* For expression of the *sasC* portion encoding 8 of the 17 DUF1542-repeats in *E. coli*, the PCR-amplified fragments were cloned into the expression vector pQE30Xa. One representative clone expressing the DUF1542 repeats (6 x His- DUF1542) contained the plasmid pHis-DUF1542. Subsequently, the 6 x His-DUF1542 fusion protein was purified from *E. coli* (pHis-DUF1542) via its His-tag using Ni-NTA affinity chromatography under native conditions. SDS-PAGE of the affinity-purified fusion proteins revealed an approximately 70-kDa protein for the 6 x His-DUF1542 fusion protein. A protein with the same size was present in cell lysates of an induced culture of *E. coli* (pHis-DUF1542) and absent from cell lysates of an induced culture of *E. coli* (pQE30) (not shown) and of a non-induced culture of *E. coli* (pHis-DUF1542). ## Surface location of SasC To detect the surface location of SasC, the antiserum against the 6 x His- DUF1542 fusion protein that was raised in rabbits was used in immunofluorescence microscopy. The anti-His-DUF1542 antiserum strongly reacted with cells of *S. carnosus* (pSasC4074) and *S. aureus* 4074 (pSasC4074) (not shown) indicating the cell surface location of SasC. With *S. carnosus* (pSasC4074), no immunofluorescence was detected with the preimmune serum (not shown) and with *S. carnosus* (pCX19), no immunofluorescence was detected with the anti-His- DUF1542 antiserum. However, there was some immunofluorescence detectable with the preimmune serum and strain *S. aureus* 4074 (not shown), which may be due to the IgG-binding and surface-associated proteins A and Eap. ## Characterization of *sasC* expression in *S. aureus* strains To analyze the production of SasC in *S. aureus*, we performed Western blot analysis using the anti-His-DUF1542 antiserum. In cultures of *S. aureus* 4074, Col, and SH1000, a faint band corresponding to SasC was detected in lysostaphin lysates of cultures after 10, 16, 24, and 48 h of growth (not shown). A strong production of SasC was observed in control strains *S. aureus* 4074 (pSasC4074) and *S. carnosus* (pSasC4074), while no protein isolated from *S. carnosus* (pCX19) reacted with the anti-His-DUF1542 antiserum (not shown). SasC did not react with the preimmune serum (not shown). ## Prevalence of *sasC* To determine the prevalence of the *sasC* gene among clinical *S. aureus* isolates, we performed PCR analysis using primers CHsasC1for and CHsasC1rev. An approximate 500 bp fragment encoding a portion of the N-terminal SasC domain was found to be present in 97% (66/68) of the clinical *S. aureus* strains that were tested (not shown). This indicates a very high prevalence of the *sasC* gene among clinical *S. aureus* isolates. # Discussion The most frequently isolated bacteria from implant-associated infections are *S. aureus* and coagulase-negative staphylococci causing significant morbidity and mortality. The pathogenicity of these infections is characterized by the pathogens pronounced ability to form biofilms. To date, several *S. aureus* genes have been implicated in biofilm formation, among them *atl*, *dltA*, and the *icaADBC* gene cluster. *S. aureus* surface proteins reported to be involved in biofilm formation include SasG, the Fn- and Fg-binding proteins FnBPA and FnBPB , and the biofilm-associated protein Bap. SasG is homologous to the accumulation-associated protein Aap, which mediates biofilm accumulation in *S. epidermidis*, and the plasmin-sensitive surface protein Pls, which so far has not been implicated in biofilm formation. Furthermore, extracellular genomic DNA (eDNA) has been established as another important component of *S. aureus* biofilms. eDNA is released from the bacteria by cell lysis and may implicate an additional role for the major *S. aureus* autolysin Atl in biofilm development besides its function in initial attachment. However, a recent study indicated the existence of further, yet unidentified surface proteins contributing to *S. aureus* biofilm formation. As a potential candidate, we identified SasC in the *S. aureus* genome and expressed its gene heterologously in *S. carnosus* as well as in the *S. aureus* strains SH1000 and 4074 under a xylose-inducible promotor. All strains expressing *sasC* showed a high-level production of a protein with a molecular size of approximately 240 kDa corresponding to SasC. Expression of *sasC* led to strong cell cluster formation, intercellular adhesion, and biofilm formation. Furthermore, a *sasC* Tn*917* insertion mutant (SMH2035) showed a reduced capability for biofilm formation in comparison to its wild type. SasG and Aap promote biofilm formation via their B-repeats. Each B-repeat also known as G5 domain consists of 128 aa and is present 7 and 5 times in SasG and Aap, respectively. Recently, the G5 domains were found to be zinc-dependent adhesion modules and a “zinc zipper” mechanism was suggested for G5 domain-based intercellular adhesion in SasG- or Aap-mediated biofilm accumulation. SasC also contains a repeat region with 17 repeats of 72 aa being similar to the DUF1542 domain (see). The SasC repeats do not share sequence similarities with the B-repeats of SasG and Aap and are not involved in biofilm formation. Instead in SasC, the domain conferring cell aggregation and biofilm formation could be localized to the N-terminal domain by subcloning experiments. Furthermore, SasG and Aap must undergo proteolytic cleavage to become active, while SasC-mediated biofilm formation does not depend on proteolytic cleavage: the formation of SasC-mediated biofilms was unchanged in the presence of the protease inhibitor α2-macroglobulin (data not shown) that blocked SasG and Aap- mediated biofilm formation,. Moreover, *S. carnosus* expressing *aap* only formed a detectable biofilm after treatment with 2 µg/ml of trypsin leading to the production of a truncated version of Aap or when an N-terminally truncated version of *aap* was cloned in *S. carnosus*. In contrast, no truncation of SasC was involved in SasC-mediated biofilm formation. Thus, the mechanism of SasC- mediated biofilm accumulation clearly differs from that mediated by SasG/Aap. Because of the lack of sequence similarities, the SasC-mediated mechanism probably is also distinct from that mediated by Bap. Recently, a role for FnBPA and FnBPB in biofilm accumulation, which is independent of their Fn- and Fg-binding activities was reported. Like with SasC, the domain conferring biofilm accumulation is located within the N-terminal domain (A domain), which also includes the domain for Fg-binding. However, biofilm formation seems to be independent of Fg-binding, because a single aa exchange within that region abolished Fg-binding, but did not influence the capability for biofilm formation. Because the FnBPs and SasC do not share significant sequence similarities within their N-terminal domains, the mechanism of biofilm accumulation between the two also seems to differ. The N-terminal SasC domain shows significant homology to the N-terminal domains of Mrp and FmtB, both of which have been reported to be involved in methicillin resistance. However, the mechanism of Mrp and FmtB conferring methicillin resistance is not completely clear yet and may be indirect. A function of these proteins in biofilm formation has not been proposed so far. The mechanism of SasC-mediated biofilm accumulation is not known yet and may involve either protein-protein interactions conferred by the N-terminal SasC domain or protein-carbohydrate interactions conferred by the FIVAR motif. The FIVAR motif is located at the C-terminus of the N-terminal domain and has been proposed to have a sugar-binding function. Further analyses are necessary to elucidate these possibilities. SasC not only mediates cell cluster formation and intercellular adhesion, but also slightly increases the attachment of the cells to polystyrene. Initial attachment to a polystyrene surface depends on cell surface hydrophobicity. With 25.1% hydrophobic aa, the percentage of hydrophobic aa of SasC is comparable to that of AtlE (26.4%) previously found to mediate initial attachment to polystyrene in *S. epidermidis*. Remarkably, the higher level of initial attachment to polystyrene observed with *S. carnosus* and *S. aureus* producing subclone 1 correlated with a higher percentage of hydrophobic aa (28.1%) and a lower percentage of charged aa (23.7%: 12.3% basic and 11.4% acidic aa) of subclone 1 in comparison to subclone 2, which contains 26.6% hydrophobic aa and 30.1% charged aa (14.7% basic and 15.4% acidic aa). Although the *S. aureus* wild-type strains Col, SH1000, and 4074 all harbour the *sasC* gene, we were not able to detect SasC in lysostaphin lysates by SDS-PAGE, but only by Western immunoblot analysis indicating very low *sasC* expression *in vitro*. However, the identification of SasC as an *in vivo*-expressed antigen during infection delineates its potential clinical significance. Thus, *sasC* expression may be induced *in vivo*. Several genes are involved in the regulation of *S. aureus* biofilm formation, such as *agr*, *sarA*, *sigB*, *rbf*, *tcaR* , *arlRS*, and *alsSD*. Further experiments are necessary to characterize the expression of *sasC*. Homologous *sasC* genes were found in the eight sequenced *S. aureus* genomes analyzed and the sequence similarities of the SasC gene products ranged from 89% to 97%. Consistently, we found a very high prevalence of the *sasC* gene among clinical *S. aureus* strains delineating the potential importance of SasC in colonization and infection. In conclusion, we identified and characterized a novel *S. aureus* surface protein, SasC, involved in cell aggregation and biofilm formation, which may play an important role in colonization during infection with this important pathogen. # Materials and Methods ## Ethics Statement This study was conducted according to the principles expressed in the Declaration of Helsinki. The study was approved by the local ethics committee (Ethikkommission der Aertzekammer Westfalen-Lippe und der Medizinische Fakultaet der WWU Muenster) (reference number: Sitzung 19.05.1999). All volunteers provided written informed consent for the collection of samples and subsequent analysis. ## Bacterial strains, plasmids, and media The *sasC* gene was cloned from the clinical strain *S. aureus* 4074 and from *S. aureus* Col. *S. carnosus* TM300, *S. aureus* 4074, and *S. aureus* SH1000 were used as cloning hosts. The *sasC* Tn*917* insertion mutant SMH2035 was isolated by sequencing Tn insertion junctions from a Tn*917* mutant library of *S. aureus* SH1000, using direct sequencing from isolated genomic DNA. One of the mutants gave a junction sequence, which mapped to an insertion point at 1812698 in the *S. aureus* N315 genome. This corresponded to inactivation of SasC at aa 934. *E. coli* TG1 was used to construct the plasmid for the production of the 6 x His-DUF1542 fusion protein and its purification. For the expression of *sasC* in staphylococci, the vector pCX19, a derivative of the xylose-inducible expression vector pCX15 and for the production and purification of the 6 x His-DUF1542 fusion protein, the Qia*express* vector pQE30Xa (Qiagen) was used. To determine the prevalence of *sasC*, a 489 bp internal *sasC* fragment was amplified from genomic DNA of 68 clinical *S. aureus* isolates obtained from patients at the University Hospital of Münster (Münster, Germany). The staphylococci were routinely cultivated in TS broth (TSB). *E. coli* strains were grown in Luria-Bertani (LB) medium. TS and LB agar contained 1.4% agar. TSB and TS agar were supplemented with 1% xylose to induce *sasC* expression. Selection for resistance to antibiotics in *E. coli* was performed with 100 µg/ml ampicillin and in staphylococci with 10 µg/ml chloramphenicol or 5 µg/ml erythromycin, when appropriate. Wild-type strains not harboring a plasmid were grown in the presence of 0.07% ethanol in the respective assays, when compared to the strains harboring a plasmid to rule out an effect of the ethanol. Because the *sasC* Tn*917* insertion mutant SMH2035 is stable, no antibiotic or ethanol was included in the assays comparing the SH1000 strain and its *sasC* mutant. Bovine serum albumin (BSA) and α-thrombin (bovine) were purchased from Sigma. Human Fg was purchased from Enzyme Research Labs. The monoclonal antibody against human CD42a (GP IX) (conjugated with phycoerythrin \[PE\]) was delivered by Exalpha via NatuTec. Syto 13 for labeling of staphylococcal cells was purchased from Molecular Probes via Mobitec. ## DNA manipulations, transformation, PCR, DNA sequencing, websites, and Pfam accession numbers DNA manipulations and transformation of *E. coli* were performed according to standard procedures. *S. carnosus* and *S. aureus* strains were transformed with plasmid DNA by protoplast transformation. Plasmid DNA was isolated using the Qiagen Plasmid Kit and chromosomal DNA was isolated using the QIAamp DNA Blood Mini Kit according to the instructions of the manufacturer (Qiagen). PCR was carried out with the PCR Supermix High Fidelity (Invitrogen) or with the Expand Long Template PCR System (Roche, Mannheim, Germany) in accordance with the protocol of the supplier. The primers were synthesized by MWG-Biotech (Ebersberg, Germany). The DNA sequence of both strands of the *sasC* gene of the clinical isolate *S. aureus* 4074 was determined by MWG-Biotech using a LI-COR DNA sequencer. The DNA and deduced protein sequences were analyzed using the program “JustBio” at <http://www.justbio.com>. The protein sequences were compared with those of known proteins using the programs BLASTP and FASTA. The alignments were done using the program ClustalW at the European Bioinformatics Institute (EBI, Cambridge, UK). The Pfam accession numbers are: PF04650 for the YSIRK_signal; PF07554 for the FIVAR motif; PF07564 for the DUF1542 domain; PF00746 for the Gram_pos_anchor (LPXTG_anchor) available at: <http://pfam.sanger.ac.uk/>. The signal peptide of SasC was predicted by using the program “SignalP” at <http://www.cbs.dtu.dk/services/SignalP/>). ## Biofilm formation assay For quantification of the biofilm-forming capacity, a test for biofilm production was performed essentially as described previously. Briefly, strains were grown in TSB supplemented with 1% xylose for 24 h at 37°C in 96-well polystyrene microtiter plates (cell star; Greiner, Frickenhausen, Germany), the wells were washed with phosphate-buffered saline (PBS) and adherent biofilms were stained with 0.1% safranin (Serva). In some experiments, instead of 1% xylose, 0.25% glucose or no additional carbohydrate source was added. Absorbance was measured with a Micro-ELISA-Autoreader at 492 nm. Strains were tested at least in triplicate. Determination of biofilm formation on a glass surface was carried out essentially in the same way, except that glass tubes were used instead of microtiter plates and 5 ml of TSB were inoculated instead of 200 µl. ## Initial adherence to a polystyrene or a glass surface Initial cell attachment was tested as described previously. Briefly, diluted cell suspensions of bacteria in PBS were incubated for 30 min in polystyrene Petri dishes (Sarstedt) at 37°C or on glass slides and after a washing procedure, attached bacteria were evaluated by phase-contrast microscopy. ## Construction and purification of the 6 x His-DUF1542 fusion protein and anti-His-DUF1542 antiserum For the construction of the His-tagged fusion protein, the primers CHsasCDUFfor and CHsasCDUFrev were used to amplify 8 of the DUF1542-repeats of *sasC* from genomic DNA of *S. aureus* Col, introducing a *Bam*HI-site at the 3′ end. The PCR-amplified fragment was cloned into the vector pQE30Xa, so that the gene fragment is in frame with the His-codons. One representative clone expressing the DUF1542-repeats was designated *E. coli* (pHis-DUF1542). The 6 x His-DUF1542 fusion protein was purified under native conditions via its His-tag using Ni-NTA affinity chromatography (Ni-NTA Spin Kit; Qiagen) according to the protocol of the suppliers. The yield of the 6 x His-DUF1542 fusion protein was in the range of 150 µg per 10 ml culture volume as determined by the Coomassie Plus™ Protein Assay (PIERCE, Rockford, IL, USA distributed by Perbio Science, Bonn, Germany). The purified 6 x His-DUF1542 fusion protein was used to immunize rabbits by Eurogentec (Belgium) according to their standard immunization program. ## Protein isolation, SDS-PAGE, and Western blot analysis Staphylococcal surface proteins covalently linked to the peptidoglycan were prepared from cultures that were grown overnight in TSB broth at 37°C by lysostaphin treatment. For this, the staphylococcal cells were harvested by centrifugation, washed and then, the cell pellet was resuspended in 20 ml Tris- buffered saline (TBS) pH 7,4 per g bacteria. After adding 500 µg lysostaphin (Ambi Products LLC, Lawrence, NY, USA; distributed by WAK Chemie, Steinbach, Germany) and 100 µg DNAse (Sigma) per g bacteria, the suspension was incubated at 37°C with shaking. Afterwards, the cell debris was removed by centrifugation (45 min, 13.000 rpm, 4°C). Lysostaphin lysates were stored at −20°C. Crude cell lysates of *E. coli* (pHis-DUF1542) were prepared from non-induced and induced (addition of 1 mM Isopropyl-β-D-thiogalactoside \[IPTG\] and continued growth of 4 h) cultures by harvesting the cells, resuspending the cell pellet in sample buffer, and heating the suspension for 5 min at 95°C. Additionally, as a negative control, a crude cell lysate was prepared from an induced culture of *E. coli* (pQE30). After centrifugation, 7 µl of the cell lysates from *E. coli*, 9 µl of the staphylococcal cell lysates, or 2 µl of the purified protein (containing 1.5 µg) were subjected to SDS-PAGE (10% separation gel and 4.5% stacking gel). Proteins were stained with Coomassie brilliant blue R250 (0.1%). For Western immunoblot analysis, staphylococcal surface proteins, crude cell lysates, or purified proteins were prepared and separated by SDS-PAGE as described above and transferred to a nitrocellulose membrane (Schleicher and Schuell, Dassel, Germany). The membranes were then blocked in Tris-buffered saline (TBS)/3% BSA (overnight) and washed three times with TBS/0.5% Tween 20 (TBST). Afterwards, the nitrocellulose membranes were incubated for 2 h with the anti-His-DUF1542 antiserum diluted 1∶2000 in TBST/3% BSA. As a negative control, incubation was performed in TBST/3% BSA with (1∶2000) or without preimmune serum. The reaction of proteins with specific antibodies was detected by incubation (1 h) with anti-rabbit immunoglobulin G (IgG)/alkaline phosphatase (AP) conjugate (Dako GmbH, Hamburg, Germany) diluted 1∶5000 in TBST/3% BSA and subsequent color reaction. ## Purification and fluorescence-labelling of Fg, thrombospondin-1 (TSP-1), or von Willebrand factor (vWF) Labelling of Fg was performed as described previously. TSP-1 in an adhesive conformation was purified from freshly isolated human platelets as described before. For labelling TSP-1 with fluorescein-isothiocyanate (FITC) (Calbiochem; La Jolla, CA, USA), FITC was added to TBS/2 mM CaCl₂ containing TSP-1 in a molar ratio of 600∶1 and incubated for 24 h at 4°C. Unbound label was removed using a Sephadex G-25 PD-10 column equilibrated with TBS. The concentration of FITC-labeled TSP-1 was determined by using the Pierce BCA protein assay (Pierce Europe B.V., BA oud-Beijerland, The Netherlands) according to the manufacturers instructions. vWf was purified and labeled according to Hartleib *et al*. ## Flow cytometric analysis of Fg-FITC, TSP-1-FITC, or vWF-FITC binding to staphylococci Measurement of the binding of Fg-FITC, TSP-1-FITC, or vWf-FITC to staphylococcal cells was analyzed as described before for the binding of vWf-FITC to *S. aureus*. Briefly, bacteria from an overnight culture (120,000 cells/µl) were incubated with Fg-FITC, TSP-1-FITC, or vWf-FITC (final concentrations 0, 50, or 100 µg/ml) in TBS/2 mM CaCl₂ for 10 min at room temperature. After washing and sonication, bacteria (5,000 cells/determination) were analyzed in a flow cytometer (Becton Dickinson, FACSCalibur flow cytometer, Heidelberg, Germany) using an excitation wave length of 488 nm at the FACSCalibur standard configuration with a 530 nm bandpass filter. Data were obtained from fluorescence channels in a logarithmic mode. ## Preparation of platelets Blood was taken from healthy adult volunteers who had not taken any medication affecting platelet function for at least 2 weeks before the study. Platelet-rich plasma (PRP) was prepared from anticoagulated blood by centrifugation and the platelets were gel-filtered on a Sephadex Cl-2B column. To inhibit fibrin polymerisation, experiments were performed in the presence of the peptide GPRP (1.25 mM) as described previously. The platelets were labeled by incubation with a monoclonal anti-CD42a (GP IX) antibody conjugated with PE at saturated concentrations for 30 min. ## Preparation of bacteria and flow cytometric measurement of *Staphylococcus*-platelet associate formation *Staphylococcus*-platelet associate formation was measured essentially as described before. Briefly, bacteria grown over night were washed in TBS, briefly sonicated, and diluted with TBS/2 mM CaCl₂ to 250,000 bacteria/µl. Bacteria were labeled with the fluorescent dye Syto 13 (emission similar to FITC) at a concentration of 2 µM for 10 min, washed in TBS and briefly sonicated again. Platelets were activated with α-thrombin at the given concentrations for 4 min and subsequently, labeled bacteria were added. Bacteria and platelets (10∶1) were coincubated for 15 min at room temperature and conjugate formation was measured immediately thereafter in a flow cytometer. Associates were identified by double labelling with Syto 13 (FL-1, “FITC like” signal) and anti CD42a-PE (FL-2, PE signal), and given as the rate of bacteria-positive platelets. Given are the mean values of three independent experiments. ## Immunofluorescence microscopy The detection of SasC by immunofluorescence microscopy was performed essentially as described before. Briefly, cultures were grown aerobically in TSB for 16 h at 37°C. After washing, aliquots (30 µl) were applied to glass slides. The slides were air-dried and the bacteria were fixed by heat. The fixed cells were incubated with anti-His-DUF1542 antiserum or preimmune serum diluted 1∶500 in PBS for 2 h at 37°C in a humid chamber, washed 4 times with PBS, and then incubated with FITC-conjugated anti-rabbit F(ab′)₂ fragment diluted 1∶500 for 1 h at 37°C in a humid chamber. The slides were washed twice with PBS and twice with double-distilled water. Then, the slides were dried, covered with fluorescent mounting medium (Dako, Hamburg, Germany), and viewed with a fluorescence microscope (Zeiss, Oberkochen, Germany). ## Nucleotide sequence accession number The EMBL/GenBank/DDBJ accession number of the *sasC* DNA sequence of strain 4074 is FM202067. We thank S. Weber and M. Rauth for excellent technical assistance. Sequence data for identification and cloning the *sasC* gene was obtained from the Institute for Genomic Research website at <http://www.tigr.org>. [^1]: Conceived and designed the experiments: SF BK GP CH. Performed the experiments: KS MJ SMH NH AB AS CH. Analyzed the data: KS MJ SMH AB AS BK CH. Contributed reagents/materials/analysis tools: CN. Wrote the paper: CH. [^2]: The authors have declared that no competing interests exist.

# Introduction The discovery of the process of transformation was key to the development of the field of molecular genetics. The first evidence that genetic information could be introduced into a cell came in 1928 when Frederick Griffith discovered that a “transforming factor” could make a harmless strain of *Streptococcus pneumoniae* virulent after being exposed to a heat-killed virulent strain, giving rise to the term transformation. It was not until 1944 that Avery and colleagues used transformation to prove that this factor was DNA. The era of eukaryotic molecular genetics began over thirty years later when Hinnen and colleagues employed transformation in brewer’s yeast to integrate a plasmid into the *Saccharomyces cerevisiae* genome. Beggs subsequently demonstrated that *S*. *cerevisiae* could maintain a plasmid carrying the 2μ origin of replication without the need for integration. These discoveries established *S*. *cerevisiae* as the premier eukaryotic model for molecular genetics. Transformation protocols were subsequently developed for *Neurospora crassa* and *Aspergillus nidulans*, and over the following decades, the development of transformation protocols made many previously intractable species easier to study. *Cryptococcus neoformans* is one such species. Found worldwide in association with bird guano, *C*. *neoformans* primarily causes disease in immunocompromised individuals, disseminating *via* the lungs to cause life-threatening meningoencephalitis; it is classified as an AIDS-defining illness. In developed countries, the mortality rate is as high as 20%, but in developing countries where there is limited availability of treatment, infection can result in close to 100% mortality. While transformation of *C*. *neoformans via* electroporation was achieved over 25 years ago, the technique was not widely adopted due to its extremely low homologous integration efficiency and the instability of transformants. It was not until the development of a biolistic protocol in 1993 that molecular genetic manipulation in this organism became routine. Although biolistic technology is now widely employed, creating gene deletions in *C*. *neoformans* can still be difficult due to the poor reproducibility of the biolistic technique and low levels of integration *via* homologous recombination; the majority of transformants are either ectopic integrants or unstable. Upon introduction of genetic material into a cell *via* transformation there are, broadly, four possible fates. First, the exogenous DNA may be maintained extrachromosomally in the form of a plasmid or minichromosome, provided this is possible in the host species and the DNA sequence is appropriate. Second, the foreign DNA may simply be degraded by the host machinery. Third, the exogenous DNA may integrate into the genome in a targeted manner *via* homologous recombination, and lastly, the exogenous DNA may integrate at a random site in the genome. These two mechanisms of integration into the genome occur by very different mechanisms. Homologous recombination occurs through crossing over where DNA sequences are exchanged between two similar molecules of DNA; this method is the basis for creating targeted gene deletions. While creating gene deletions *via* homologous recombination occurs readily in species such as *S*. *cerevisiae*, it occurs at a lower frequency in many organisms, including *C*. *neoformans*. These species instead tend to predominantly employ non-homologous end joining (NHEJ) and integrate the transformed DNA into a random location in the genome. In eukaryotes, NHEJ begins with the DNA-dependent protein kinase heterodimeric regulatory factor Ku70-Ku80 forming a link between two broken DNA ends and acting to structurally support, align and protect them from further degradation. In humans, the Ku70-Ku80 heterodimer recruits the catalytic subunit DNA- PK_cs, which is thought to induce conformational changes that allow end-processing enzymes to access the DNA ends; in *S*. *cerevisiae*, a complex consisting of Mre11, Rad50 and Xrs2 is thought to perform this function instead. In both species the process of NHEJ is completed when the DNA ligase IV-Xrcc4 complex ligates the broken ends of the DNA back together \[, –\]. Homologous recombination and NHEJ compete directly against one another to incorporate foreign DNA into the genome. Unfortunately for molecular geneticists, in most species NHEJ usually triumphs over homologous recombination, frustrating researcher’s attempts to make precise genetic modifications. One solution to this problem is to enhance homologous recombination by creating mutants defective in NHEJ. Deletion of the *ku* genes in *N*. *crassa*, *Kluyveromyces lactis*, and *A*. *nidulans* have all resulted in increased gene deletion success, with targeted integration rates exceeding 90%. Ku deletion mutants have also been generated in *C*. *neoformans*, with mutants exhibiting almost 100% homologous recombination following biolistic transformation. Lin *et al*. have also shown that the use of a *ku80Δ* mutant strain increases the rate of homologous integration when using electroporation up to 75%, making this previously superseded technique a viable alternative to biolistic transformation provided the recipient strain is a *ku* mutant. Unfortunately, using Ku deletion mutants to ensure targeted integration subsequently requires sexual crosses (both time consuming and technically difficult) with a wild-type partner to restore NHEJ because loss of the Ku heterodimer alters virulence. Expression of *ku80* is increased during infection in a human host, and a *ku80Δ* mutant is less successful in a competition model of murine infection. Consequently, while useful, Ku deletion strains have not been adopted for widespread use by the *C*. *neoformans* community. Here we describe an alternate strategy to enhance homologous integration in *C*. *neoformans* by transient inhibition of NHEJ with the aid of chemical inhibitors. Using a range of candidate drugs that have been shown to inhibit NHEJ in mammalian cell lines, we have successfully identified compounds that enable rates of homologous integration consistently greater than 50%. The efficacy of these compounds in *C*. *neoformans* provides an attractive tool for the community as they provide the ability to increase homologous integration in a strain-independent fashion, making molecular genetic manipulations easier to achieve. # Methods ## Strains and growth conditions *C*. *neoformans* var. *grubii* type strain H99 was stored in 15% glycerol at -80°C until use, at which point it was maintained on YPD at 4°C for a maximum of two weeks; this strain was used for all transformations. *C*. *neoformans* was cultured in liquid (2% bacto-peptone, 1% yeast extract, and 2% glucose (Sigma, USA)) or solid (2% agar added) YPD media at 30°C. Biolistic transformations were performed on solid YPD media supplemented with 1 M sorbitol (Sigma, USA), and transformants were selected on solid YPD medium containing 100 μg/mL G418 (Sigma, USA). Adenine auxotrophy was detected on YNB media (0.45% yeast nitrogen base w/o amino acids and ammonium sulfate, 2% glucose, 0.5% ammonium sulfate, 2% agar (Sigma, USA)), and melanin production on L-DOPA media (Sigma, USA). ## Checkerboard assays The inhibitors NU7026, NU7441, AG14361 (Selleckchem, USA), mirin (Life Chemicals, Canada), SCR7 (DSK Biopharma Products, USA), W7 hydrochloride (TCI UK Fine Chemicals, UK), vanillin, chlorpromazine, as well as the DNA damaging agents phleomycin, hydroxyurea and 6-mercaptopurine riboside (Sigma, USA) were stored at -20°C until use. Stock solutions and serial two-fold dilutions of each drug were prepared immediately prior to MIC testing according to the recommendations of CLSI (CLSI M27-A2) modified for *C*. *neoformans*. For the Fractional Inhibitory Concentration (FIC) assays, inhibitors were serially diluted along the abscissa of a 96 well plate, while the DNA damaging agents were diluted along the ordinate. Plates were incubated at 35°C and visually scored at 24 and 48 hr. All MIC and FIC assays were performed in duplicate. MIC was defined by the lowest concentration of a specific drug that inhibits all visual growth, and was used to determine a ΣFIC value for each combination set. The ΣFIC was calculated as follows: ΣFIC = FIC A + FIC B, where FIC A equals the MIC of the DNA damaging agent in the combination divided by the MIC of the DNA damaging agent alone, and FIC B equals the MIC of the inhibitor in the combination divided by the MIC of the inhibitor alone. The combination was considered strongly synergistic when the ΣFIC value is \<0.5, weakly synergistic when ΣFIC is 0.6 to 1.0, additive when ΣFIC is 1.0 to 2.0 and antagonistic when the ΣFIC is \>2. ## Gene deletion construct generation Primers used in this study are listed in ; all PCR was performed using Phusion DNA Polymerase (New England Biolabs, USA). A deletion construct for the *ADE2* gene was generated as per Arras *et al*.. Briefly, the construct was prepared using overlap PCR, employing primers UQ1439 and UQ1442 to join the *ADE2* 5’ region (primers UQ1439 and UQ1440), the G418 resistance marker *NEO* (UQ234 and UQ235) and the *ADE2* 3’ region (UQ1441 and UQ1442). For *LAC1*, primers UQ3686 and UQ3691 were employed to join the *LAC1* 5’ region (primers UQ3686 and UQ3687), the G418 resistance marker (UQ3688 and UQ3689) and the *LAC1* 3’ region (UQ3690 and UQ3691); H99 genomic DNA was used as the template for *ADE2* and *LAC1*, and the plasmid pJAF1 for *NEO*. ## Transformations Transformations were performed using a procedure modified from Tofaletti *et al*. and Davidson *et al*. On the day of transformation, fresh YPD agar plates supplemented with 1 M sorbitol and the tested inhibitor were prepared and dried. A 50 mL *C*. *neoformans* culture grown at 30°C for 16 hr was pelleted by centrifugation, washed in dH₂O, centrifuged and the pellet resuspended in 500 μL dH₂O. Approximately 10⁷ cells were plated onto the inhibitor media, dried in a biosafety cabinet, then incubated for 4 hr at 30°C. For transformation, 0.25 g of 0.6 μm Gold Microcarriers (Bio-Rad No. 165–2262) were resuspended in 2 mL 100% ethanol. Transforming DNA samples were prepared by mixing 10 μL Gold Microcarriers (Bio-Rad, USA), 10 μL of construct DNA (1 μg/μL), 10 μL 2.5 M CaCl₂ (Sigma, USA) and 2 μl fresh 1 M spermidine (Sigma, USA), then incubated for 5 min at 20°C. Following centrifugation and washing in 500 μL 100% ethanol, the sample was pelleted and resuspended in 5 μL 100% ethanol. For each transformation a Macrocarrier disk (No. 165–2335, Bio-Rad, USA) and a Stopping Screen (No. 165–2336, Bio-Rad, USA) were sterilized in 100% ethanol and dried in a Petri dish. Transforming DNA was pipetted onto the center of the disk, dried then inserted into the Macrocarrier Holder of a Bio-Rad Biolistic PDS-1000/He Particle Delivery System (No. 165–2257, Bio-Rad, USA). A 1,350 psi Rupture Disk (No. 165–2330, Bio-Rad, USA) sterilized in 70% isopropanol was inserted into the Rupture Disk Retaining Cap. The sterilized Stopping Screen was inserted into the Microcarrier Launch Assembly, the Macrocarrier Holder attached with the Macrocarrier Cover Lid, and placed in position one in the PDS-1000/He chamber. Transformation was performed with the *C*. *neoformans* plate in position three, bombarded at 1,350 psi under a vacuum of 27 in.Hg. After recovery at 30°C for 4 hr, 2.5 mL dH₂O was added to each plate, the cells suspended with a 1 mL pipette tip, and spread over five YPD plates containing 100 μg/mL G418. Transformants appeared after 3–5 days incubated at 30°C. ## Mutant verification Correct *ade2Δ* integrants were initially identified *via* their pink coloration on YPD media and adenine auxotrophy on YNB media. Representative pink transformants were selected for Southern blot analyses to validate correct integration of the *ADE2* deletion cassette using the UQ1439 and UQ1442 PCR product as a probe. Correct *lac1Δ* integrants were identified by their lack of melanization on L-DOPA media, with random transformants verified by Southern blot using the UQ3686 and UQ3691 PCR product as a probe. All transformations were performed in triplicate and only transformations that resulted in 10 or more colonies were included in subsequent analyses. To determine significance of correct integration rates between conditions, two-tailed *t*-tests were completed in GraphPad Prism Version 6.0c (GraphPad Software, USA). *P*-values of \<0.05 were considered statistically significant. # Results ## Identification of potential inhibitors of *C*. *neoformans* NHEJ Given the success in increasing homologous integration in many fungal species by disrupting NHEJ *via* deletion of genes encoding the Ku proteins, we hypothesized that it might be possible to achieve similar results by instead transiently inhibiting key proteins involved in NHEJ repair. Through literature searches we were able to identify seven candidate chemical compounds that are known to inhibit different aspects of mammalian NHEJ, are cost effective, and are readily available on the market. Therapeutic inhibition of the mammalian DNA-dependent protein kinase heterodimeric regulatory factor (the Ku heterodimer) can be achieved through depletion of its cofactor inositol hexakisphosphate (InsP₆) *via* the calmodulin antagonists N-(6-aminohexyl)-5-chloro-1-naphthalenesulfonamide (W7) and 2-chloro-10-(3-dimethylaminopropyl)phenothiazine hydrochloride (chlorpromazine). Likewise, inhibition of the catalytic subunit of the mammalian DNA-dependent protein kinase catalytic subunit (DNA-PK_cs) is also possible; the *Vanilla planifolia*-derived phenolic aldehyde vanillin, the benzochromenone 2-(morpholin-4-yl)-benzo\[h\]chomen-4-one (Nu7026) and the aryl- substituted chromenone 8-dibenzothiophen-4-yl-2-morpholin-4-yl-chromen-4-one (Nu7441) all inhibit mammalian DNA-PK, and have been investigated as anticancer agents. Z-5-(4-hydroxybenzylidene)-2-imino-1,3-thiazolidin-4-one (mirin) is a known inhibitor of Mre11, and 5,6-bis(((E)-benzylidene)amino)-2-thioxo-2,3-dihydropyrimidin-4(1H)-one (SCR7) targets DNA ligase IV. In addition to these known NHEJ inhibitors, we also chose to study a compound not directly associated with NHEJ—the poly(ADP-ribose) polymerase-1 (PARP-1) inhibitor 1-(4-((dimethylamino)methyl)phenyl)-8,9-dihydro-2,7,9a- triazabenzo\[cd\]azulen-6(7H)-one (AG14361), a tricyclic benzimidazole that inhibits single strand DNA repair. As mammalian cells lacking PARP-1 have been shown to have an increased frequency of homologous recombination, we reasoned that treatment with an inhibitor of this enzyme may also lead to an increase in homologous recombination in *C*. *neoformans*. To our knowledge, none of these eight drugs have ever been tested against *C*. *neoformans*. ## Growth inhibition of *C*. *neoformans* by inhibitors of mammalian NHEJ It has previously been demonstrated that the eight chosen inhibitors abolish growth in mammalian cell lines at high concentrations, however it is unknown if they exhibit toxicity against *C*. *neoformans*. To determine the minimum inhibitory concentration (MIC) for each compound in *C*. *neoformans*, we based our initial concentration ranges on MIC values identified in previous studies against mammalian cell lines. Initial tests revealed that growth of *C*. *neoformans* var. *grubii* type strain H99, the strain in which the vast majority of molecular genetics is performed in this species, is unaffected at the concentrations toxic to mammalian cell culture. Following testing with increasing concentrations, growth inhibition was eventually observed at concentrations substantially higher than that tolerated by mammalian cells. ## Synergy between inhibitors and DNA damaging agents While our MIC assays showed that the candidate NHEJ inhibitors were toxic to *C*. *neoformans* in sufficient concentrations, it did not prove that the observed effects were mediated *via* DNA repair-mediated processes. To provide this evidence we investigated their synergy with known DNA damaging agents; if the observed toxicity was due to inhibition of DNA repair, treatment with the inhibitors should increase sensitivity to DNA damaging agents. The growth of strain H99 was tested in a checkerboard assay, with increasing concentrations of each DNA damaging agent tested in combination with increasing concentrations of each inhibitor to determine the factorial inhibition concentration (FIC). The FIC index is based on the Loewe additive zero- interaction theory, which states that a drug cannot interact with itself and therefore a drug combination will always be additive with an index of 1. If the index fluctuates lower indicating synergy it is because more drug would be required in order to produce the same affect of the drugs alone, and the reverse also. As the DNA damaging agents are designed to induce double strand DNA breaks and the NHEJ proteins should be involved in the repair of these breaks, we hypothesized that if a synergistic effect was observed the tested compounds were potentially inhibiting *C*. *neoformans* NHEJ. As DNA damaging agents vary in their effectiveness and method of action, we selected three for our FIC assays. Phleomycin attracts hydrogen atoms from deoxyribose, producing apurinic and apyrimidinic sites that enable single strand breaks, which can in turn become double strand breaks. Hydroxyurea depletes cells of dNTPs *via* inhibition of ribonucleotide reductase, resulting in stalled replication forks that can collapse into double strand breaks. 6-mercaptopurine is converted into thioinosinic acid, which perturbs purine metabolism and results in double strand breaks. As a starting point for FICs, MIC values were determined for the DNA damaging agents: 10 μg/mL for phleomycin, 1500 μg/mL for mercaptopurine, and 1500 μg/mL for hydroxyurea. An FIC is considered synergistic when the ΣFIC is \<0.5 and weakly synergistic when ΣFIC is \>0.5 to \<1.0. All of the inhibitors tested showed a weak synergistic effect against at least one of the DNA damaging agents tested. Nu7026, SCR7 and chlorpromazine exhibited synergy with all three DNA damaging agents, and AG14361 with two. Vanillin, Nu7441, mirin and W7 all showed a weak synergistic effect with only one DNA damaging agent. These results were promising, given the *C*. *neoformans ku80Δ* mutant also exhibits slight sensitivity to phleomycin. ## Treatment with W7, chlorpromazine, NU7026, mirin and AG14361 enhance frequency of deletion of *ADE2* Given that all eight tested inhibitors exhibited synergy with DNA damaging agents, all were trialed in biolistic transformation by including the inhibitor into the YPD + sorbitol plates used at the beginning of the protocol; the strain to be transformed is incubated on this media for four hours prior to transformation and recovers for four hours after. The fungus is therefore only exposed to the inhibitor for eight hours. Adding inhibitor in varying concentrations at this point would ensure disruption of NHEJ at the time of transformed DNA entry and integration. After recovery, the *C*. *neoformans* was transferred to G418 selective media lacking inhibitor, enabling reestablishment of NHEJ. Four concentrations per inhibitor were trialed, all less than the MIC. In some instances, much lower concentrations were needed; for example, after no colonies would grow on high concentrations of NU7026, concentrations were subsequently dropped to 5, 2.5 and 1.125 μg/mL. The first gene ever deleted *via* biolistic transformation in *C*. *neoformans* was the phosphoribosylaminoimidazole carboxylase-encoding *ADE2* gene, loss of function mutants of which are easily identified due to their pink colonies and adenine auxotrophy. We employed the same locus in our initial studies as we had data indicating it was unusually easily deleted, with \~35% of deletion construct transformants of the *ade2Δ* genotype. Inclusion of either W7 or chlorpromazine, inhibitors of mammalian calmodulin proposed to inhibit NHEJ through depletion of Ku cofactor InsP₆, resulted in a significant increase in the deletion of *ADE2*. Treatment with W7 at either 5 or 12.5 μg/mL was particularly effective, resulting in deletion frequencies equivalent to those reported for a *ku80Δ* mutant. A statistically supported increase in *ADE2* deletion was also observed, but to a lesser extent, for one of the three inhibitors of mammalian DNA-PKcs (Nu7026), the Mre11 inhibitor mirin and the PARP-1 inhibitor AG14361. Importantly, none of the strains obtained exhibited changes in growth rate or morphology. Furthermore, while we observed an increase in the proportion of correct integrants, there was no gross change in the total number of colonies obtained from each transformation event. ## Four of the inhibitors also showed an increase when using *LAC1* as a target Our success in enhancing deletion of the *ADE2* gene was tempered by one significant fact; it is known that the rate of homologous recombination differs from locus to locus, and that *ADE2* is particularly easy to delete. To address concerns of locus specificity, we focused on the five most promising inhibitors—W7, chlorpromazine, NU7026, mirin and AG14361 –in biolistic transformations to delete the laccase-encoding *LAC1* gene Each of the inhibitors were this time tested only at the optimal concentration observed in the previous experiment. Without addition of any inhibitor, our control frequency for *LAC1* deletion was only \~10%; much lower than for *ADE2* and more in line with typical deletion frequencies observed in the field. Once again W7 and chlorpromazine performed as the best inhibitors, along with Nu7026. In contrast to the *ADE2* experiment, inhibitor treatment during *LAC1* deletion resulted in approximately half of all transformants exhibiting the deletion genotype, a 5-fold increase in frequency when compared to the no inhibitor control. The failure of mirin to yield a statistically significant result in the *LAC1* experiment could be due to the typically large fluctuations in the number of transformants observed during biolistic transformation. # Discussion Despite the clinical importance of *C*. *neoformans*, there have been few improvements over the last decade in the molecular tools available to characterize this species. The current methodologies have been invaluable in elucidating the function of many genes, however these techniques have their limitations and new advances are required to facilitate everyday experiments, particularly for large-scale genomic studies. A good example of this is the recent publication of the sequence of *C*. *neoformans* var. *grubii* type strain H99, which represents one of the most complete eukaryotic genomes to date, due in no small part to the extensive transcriptomics performed to enable its annotation. Of the 6967 predicted protein coding genes, 45.5% (3170) are listed as hypothetical proteins. The functions of the 1197 miscRNAs also identified during transcriptome sequencing are likewise unknown. To enable a deeper understanding of these genes, as well as those with an annotation based purely on bioinformatic homology, a more robust procedure for performing molecular genetic modifications is essential. Efforts to enhance the biolistic transformation procedure through disruption of NHEJ have had some success. The use of a *ku80Δ* strain can increase rates of recombination to nearly 100%, but recent publications have revealed that the Ku proteins may be playing a role during infection of the mammalian host. Liu and colleagues demonstrated that a *ku80Δ* mutant has diminished virulence in murine infection competition experiments, and Chen *et al*. discovered transcription of *KU80* is higher in cells isolated from the cerebrospinal fluid of an infected human than from cells grown on YPD. Given these observations, it would be unwise to employ strains carrying the *ku80Δ* mutation during *in vivo* studies since it may influence the outcome of infection. Consequently, the *ku80Δ* mutant has seen little use by the community since its development a decade ago. However the concept underlying the use of *kuΔ* mutants still has merit. By employing discoveries made in the field of mammalian NHEJ, we have been able to emulate the higher homologous recombination rates of the *ku80Δ* mutant without the experimental consequences of permanently compromising NHEJ at the genetic level. Of the eight inhibitors of mammalian NHEJ that we tested, four influenced the rate of homologous recombination for multiple loci. Notably, some of the best results observed correlated with employing the inhibitor at a concentration much lower than the MIC, and much lower than the concentration that confers increased sensitivity to DNA damaging agents. For example, W7 enhanced the rate of generation of a gene deletion at only 5 μg/mL, substantially lower than the concentration at which it displayed weak synergy with the DNA damaging activity of hydroxyurea. Importantly, the use of transient drug treatment to enhance homologous integration efficiency is also highly cost effective. The best performing inhibitor, W7, is inexpensive and used at such low concentrations that it only costs around \$USD 0.26 per plate; a negligible expense that significantly increases the chance of gaining a correct gene deletion on the first attempt, saving both time and reagents on repeated efforts to perform a simple genetic modification. We now employ W7 in all transformations in our laboratory, routinely achieving correct integration rates exceeding 50%. In this work we demonstrated a new, easily applied methodology that will expedite acquisition of precise genetic alterations in *C*. *neoformans* that has distinct advantages over current protocols. We have successfully employed multiple inhibitors to reproducibly enhance the deletion rate at multiple loci. Based on this success, we anticipate that the use of these inhibitors will not only become widespread in the *Cryptococcus* community, but may also find use in other fungal species as well. More importantly, they will greatly facilitate the ongoing efforts designed to elucidate the function of all genes in the *C*. *neoformans* genome, enabling a much deeper understanding of the process of pathogenesis in this clinically important species. # Supporting Information [^1]: The authors have declared that no competing interests exist. [^2]: **Conceptualization:** SDMA JAF. **Formal analysis:** SDMA. **Funding acquisition:** JAF. **Investigation:** SDMA. **Methodology:** SDMA. **Supervision:** JAF. **Writing – original draft:** SDMA JAF. **Writing – review & editing:** SDMA JAF.

# Introduction *Coxiella burnetii* is the Gram negative bacteria that causes the multifaceted human disease Q fever, an ever-present global public health threat. Endemic and hyperendemic regions experience regular cases of Q fever. Sporadic outbreaks also transpire, the largest being in The Netherlands between 2007 and 2010, which emphasized both the serious health and economic impact of Q fever. In particular, the more insidious chronic Q fever can cause significant morbidity and potentially death. Current therapeutics can be problematic due to contra- indications or long courses so there is considerable need for novel drugs to treat Q fever effectively. Recent stable isotope labelling studies demonstrated ¹³C-label incorporation into lactate by *C*. *burnetii*, despite the apparent lack of a known lactate synthesis pathway within the *C*. *burnetii* genome. Lactate production can be beneficial to bacteria by several means. It can be the end product of efficient metabolism pathways, play a role in redox and energy homeostasis, be metabolized an alternative carbon source, or modify host immune response. The ability to synthesize lactate has been linked to virulence in disparate species such as *Bacillus cereus*, *Staphylococcus aureus*, *Neisseria gonorrhoeae*, and lactic acid bacteria *Enterococcus faecalis*, *Streptococcus pyogenes*, *Streptococcus mutans* and *Streptococcus pneumoniae*. Furthermore, inhibitors of a unique lactate synthesis enzyme of *Cryptosporidium parvum* reduced replication and pathogenicity. As yet, the method and the essentiality of lactate synthesis to *C*. *burnetii* replication has not been investigated. Should unusual enzyme(s) responsible for lactate production in *C*. *burnetii* be determined and found to play an important role in the organism’s metabolism and pathogenicity, they may represent useful future anti-*Coxiella* targets. Lactate dehydrogenases (LDHs) are widely distributed within prokaryotic and eukaryotic organisms, but no LDH is annotated on the *C*. *burnetii* genome. LDHs produce lactate from pyruvate and regenerate NADH from NAD⁺ in the process. An LDH is also absent from genome annotations of the close *C*. *burnetii* relative *Legionella pneumophila* and some strains of fellow acidophile *Helicobacter pylori*. LDHs share a common ancestor with malate dehydrogenase enzymes (MDHs) within the same superfamily of dehydrogenases. Their highly similar structures, containing many conserved residues, can result in substrate flexibility. Some can use multiple substrates in their native state, others can be manipulated to switch substrate preferences with very few residue substitutions. This feature is more widespread in LDHs than MDHs. The *C*. *burnetii* gene *cbu1241*, annotated as a putative malate dehydrogenase, has previously been shortlisted as a potential virulence factor that is also expected to play a vital metabolic role and thus provides a promising anti-*Coxiella* drug target. Similarly, malic enzymes (MEs) and malolactic enzymes (MLEs) are closely related enzymes capable of catalyzing multiple reactions. MEs act to decarboxylate malate to pyruvate, whereas MLEs decarboxylate malate to lactate without reduction of an essential NAD⁺ cofactor. MEs are broadly distributed throughout eukaryotes and prokaryotes, with the exception of most lactic acid bacteria, whereas malolactic enzymes (MLEs) are yet to be found outside lactic acid bacteria. Substrate flexibility has been demonstrated for numerous members of both MEs and MLEs, with the MLE of *Oenococcus oeni* capable of particularly diverse catalytic activity, possessing MDH, LDH and ME functions in addition to MLE function. In this study, we analyzed bioinformatic information regarding two *C*. *burnetii* genes *cbu1241*, annotated as a putative malate dehydrogenase, and *cbu0823*, annotated as a putative NAD⁺-dependent malic enzyme, in relation to likely substrate preferences. We then characterized the biosynthetic capabilities of recombinant CBU1241 and CBU0823 *in vitro* using both spectrophotometric absorbance measurements and gas chromatography-mass spectrometry (GC-MS) analysis, with a particular focus on the ability of these enzymes to synthesize lactate. Furthermore, we investigated the involvement of *cbu0823* in lactate biosynthesis *in vivo* utilising stable isotope labelling techniques and a previously constructed *cbu0823* transposon mutant. This approach also provided additional information as to the broader effect of a non-functional malic enzyme on the central carbon metabolism of *C*. *burnetii*. Finally, we examined the necessity of *cbu0823* for efficient replication of *C*. *burnetii* both axenically and intracellularly. # Materials and methods ## Cell strains and culture conditions *C*. *burnetii* Nine Mile RSA439 (phase II, clone 4) referred to as NMII RSA439 and strains derived from this parent were axenically cultured in liquid acidified citrate cysteine medium 2 (ACCM-2) at 37°C in 2.5% O₂ and 5% CO₂. Antibiotic selection for *C*. *burnetii* transposon mutants was accomplished using chloramphenicol (3μg/ml) with additional kanamycin (350μg/ml) for complemented mutant strains. All plasmid construction was carried out in *Escherichia coli* DH5α and recombinant protein expression in *E*. *coli* JM109. *E*. *coli* strains were cultured in LB broth at 37°C with agitation unless otherwise specified, adding ampicillin (100 μg/ml), chloramphenicol (25μg/ml), or kanamycin (50 μg/ml) as required for plasmid selection. THP-1 human monocytic cells (ATCC TIB-202), were propagated in RPMI + GlutaMAX medium (Gibco, California, USA) supplemented with 5–10% fetal calf serum at 37°C in 5% CO₂. ## Bioinformatics Protein sequences were retrieved from the NCBI Protein Database within the Geneious Prime software. Sequence alignments were generated using Clustal Omega 1.2.2 within Geneious Prime on default settings, with the alignment order set on “Group sequences by similarity”. Residues were compared to known key residues and motifs. ## Protein expression Full-length *cbu0823* was amplified using PCR with gene-specific oligonucleotides from purified *C*. *burnetii* genomic DNA. The gene was cloned into pQE-30 to produce recombinant CBU0823 with an N-terminus 6x-His tag. *cbu1241* was amplified with gene-specific oligonucleotides and cloned into pGEX-4-T1 to produce an N-terminus glutathione S-transferase (GST)-fusion protein. A commercial wine making strain of *Oenococcus oeni* was purchased for PCR template as a freeze-dried preparation (Vitilac-F, Martin Vialatte, Magenta, France). The full-length malolactic enzyme was amplified using gene-specific oligonucleotides then cloned into pQE-30. Cloned genes and affinity tags were confirmed with Sanger sequencing for all plasmids. Proteins were expressed by culturing *E*. *coli* containing the relevant protein expression plasmid construct, the empty pQE-30 or pGEX-4-T1 plasmids (negative control) in 500 ml LB broth. At OD₆₀₀ 0.6–0.8, protein expression was induced using 0.5 mM Isopropyl β-d-1-thiogalactopyranoside (IPTG) for 16 hours with agitation at 19°C. Whole cell lysate was then analyzed for protein expression using SDS-PAGE in TGX Stain-Free protein gels (BioRad, California, USA) under denaturing conditions and immunoblotting using primary mouse anti-His IgG (Invitrogen, California, USA) and secondary sheep anti-mouse IgG (GE Healthcare, Illinois, USA) for His-tagged proteins or with anti-GST-HRP conjugate (GE Lifesciences, Illinois, USA) for GST-tagged proteins. The purification procedure used was dependent on affinity tag and protein characteristics. Pellets containing 6xHis-CBU0823 and empty pQE-30 were resuspended in 10 ml 50 mM NaH₂PO₄/300 mM NaCl/10 mM imidazole pH 8.0 lysis buffer containing 1 mg/ml lysozyme, then incubated on ice for 30 minutes. 1 mM phenylmethylsulfonyl fluoride (PMSF) was added prior to overnight incubation at 4°C. Cells were sonicated and 1% Triton X-100 added before centrifugation. The resultant supernatant was dialyzed against the same lysis buffer overnight at 4°C, before being filtered and loaded onto 1 ml NTA-Ni agarose beads (QIAGEN, Hilden, Germany). Column washing with 50 mM NaH₂PO₄/300 mM NaCl/20 mM imidazole pH 8.0 and elution with 50 mM NaH₂PO₄/300 mM NaCl/250 mM imidazole pH 8.0 was performed as per manufacturer’s recommendations, except elution volume was increased to 12 ml. All elutions were pooled and buffer exchanged into phosphate-buffered saline (PBS), then concentrated to a final volume of 5 ml using Amicon Ultra-15 UltraCel-30K MWCO Centrifugal Filter (Millipore, Massachusetts, USA) before storage at 4°C. 6xHis-*O*. *oeni* MLE (denoted 6xHis-oMLE) purification was carried out as above except buffers were altered to accommodate preferences of a previously described 6xHis-oMLE. oMLE lysis buffer contained 100 mM HEPES/100 mM KCl/10 mM pH 6.5, wash buffer 100 mM HEPES/100 mM KCl/20 mM imidazole pH 6.5, and elution buffer 100 mM HEPES/100 mM KCl/250 mM imidazole pH 5.9. Elutions were buffer exchanged into oMLE storage buffer containing 100 mM HEPES/0.1 m MnCl₂ pH 6.0. Bacterial pellets containing GST-1241 and GST alone were resuspended in a PBS buffer pH 7.4 and cell lysis performed as above. The resultant supernatants were syringe filtered prior to loading onto Glutathione Sepharose 4B beads (GE Healthcare, Illinois, USA). PBS pH 7.4 was used as column wash before eluting with 50 mM Tris/10 mM reduced glutathione (Sigma, Massachusetts, USA) pH 8.0. GST-1241 and GST were buffer exchanged into PBS pH 7.4 as above, with the exception of an Amicon Ultra-15 UltraCel-10K MWCO Centrifugal Filter (Millipore, Massachusetts, USA) for the smaller GST protein. Purified protein was visualized on TGX Stain-Free gels and immunoblotted for His-tagged or GST-tagged proteins as above to confirm the presence of the correct protein tags on purified proteins. Purified protein was quantified using Qubit^™ Protein Assay Kit with the Qubit^™ 3.0 Fluorometer (Invitrogen, California, USA) as per manufacturer’s instructions. ## Spectrophotometric enzyme activity assays NADP⁺ and NADPH were sourced from Roche (Basel, Switzerland) and all other reagents were purchased from Sigma (Massachusetts, USA). Reactions were prepared in triplicate in a single tube and the respective protein added immediately prior to dispensing single reactions of 200 μl into 96-well plates. This set of reactions was repeated three times in total for each measurement point, with the mean of each set used to represent the experiment in data analysis. Assays were repeated a minimum of 3 times. Immediately after dispensing, measurement of light absorbance at 340 nm, with software pathlength correction on, was performed on either Synergy H1 Hybrid multi-mode reader (BioTek, Vermont, USA) or FLUOstar microplate reader (BMG Labtech, Ortenberg, Germany). Measurements were taken every minute for 10 measurements. Within this paper, the standard MDH forward reaction was 2 mM oxaloacetate (OAA) and 0.5 mM NADH in PBS pH 7.4. The standard LDH forward reaction was considered to be 2 mM pyruvate and 0.5 mM NADH in PBS pH 7.4. This can also detect ME reverse activity. The standard ME forward reaction for this paper contained 3 mM malate, 2.5 mM NAD⁺ and 1 mM MnCl₂ in PBS pH 7.4. This can also detect MDH enzyme reverse activity. To assess the necessity of the metal cation cofactor for 6xHis-CBU0823 activity, selected reactions were repeated with or without 1 mM MnCl₂. Suitability of other 2+ metal ions for 6xHis-CBU0823 cofactor function was tested in the standard ME reaction by replacing MnCl₂ with 1 mM of CaCl₂, CuCl₂, MgCl₂, ZnSO₄, FeSO₄, or NiSO₄. To examine cofactor preference, NAD⁺ was replaced with NADP⁺ in the standard ME assay and NADH with NADPH in the standard MDH assay. Standard reactions contained 2 μg GST-CBU1241, 2 μg GST (negative control), 20 μg 6xHis-CBU0823, 28.8 μg 6xHis-oMLE (equivalent to amount used in), or equivalent volume as 6xHis-oMLE of pQE-30 negative control. Commercially prepared enzymes were used as positive controls, namely 0.013 units MDH (from pig heart mitochondria, Roche, Basel, Switzerland) and 0.1 units LDH (from beef heart, Sigma, Massachusetts, USA). To verify 6xHis-oMLE protein activity, oMLE assays were performed at 45°C with oMLE storage buffer replacing PBS and altered substrate concentrations to reproduce the previous characterisation of this enzyme. The effect of pH on enzyme activity was determined relative to pH 7.4 by adjusting the pH of the PBS buffer before mixing of assay reactions through the range pH 4 to 11 for 1 μg GST-CBU1241 and pH 5 to 11 for 2 μg 6xHis-CBU0823 per well. Enzyme kinetic properties for GST-CBU1241 were assessed using 0.1 μg GST-CBU1241 per well. Kinetics for OAA were assessed by increasing OAA concentration from 0.1 mM to 5 mM while maintaining NADH at 0.5 mM. Kinetics for NADH were assessed by increasing NADH concentration from 0.05 mM to 1.5 mM while maintaining OAA at 0.6 mM. Enzyme kinetic properties for 6xHis-CBU0823 were assessed using 2 μg 6xHis- CBU0823 per well. Kinetics for malate were assessed by increasing malate concentration from 0.5 mM to 10 mM in reactions while maintaining NAD⁺ at 2.5 mM and MnCl₂ at 1 mM. Kinetics for NAD⁺ were assessed by increasing NAD⁺ concentration from 0.1 mM to 5 mM while maintaining malate at 3 mM and MnCl₂ at 1 mM. Kinetics for MnCl₂ were assessed by increasing MnCl₂ concentration from 0.01 mM to 10 mM while maintaining malate at 3 mM and NAD⁺ at 2.5 mM. Michaelis constant (K_m) and maximal reaction velocity within the system (V_max) were calculated for GST-CBU1241 for OAA and 6xHis- CBU0823 for malate, NAD⁺ and MnCl₂ using substrate concentrations below inhibitory levels on a Lineweaver-Burke plot using nonlinear regression (straight line) within GraphPad Prism 9.0 on default settings. This equates to above 1.5 mM^-1 OAA for GST-CBU1241, and above 0.3 mM^-1 malate, above 1.25 mM^-1 NADH and above 1 mM^-1 MnCl₂ for 6xHis-CBU0823. Rate constant (k_cat) was calculated from V_max. Substrate inhibitor constant (K_Si) was calculated using equations from. Briefly, using the calculated V_max, K_Si was represented by the slope when v/(V_max−v) was plotted against 1/\[S\] (reciprocal of the quotient velocity plot). For GST-CBU1241, this slope was calculated for 1/\[OAA\] between 0.5 and 4 mM^-1. For 6xHis-CBU0823, the slope was calculated for 1/\[malate\] between 0 and 0.4 mM^-1, for 1/\[NADH\] between 0 and 1.25 mM^-1, and for 1/\[MnCl₂\] between 0 and 1 mM^-1. ## Enzyme activity assay with GC-MS product detection 200 μl reactions containing 3 mM malate, 5 mM NAD⁺ and 1 mM MnCl₂ and either 54 μg 6xHis-CBU0823, 28.8 μg 6xHis-oMLE, 3 μl of negative control purification (equivalent volume as 6xHis-CBU0823) or 3 μl PBS of blank control were prepared, with six replicates of each. After 20 minutes, the reaction was stopped by adding 100 μl of the reaction to 300 μl 100% methanol, then centrifugation used to remove the protein. Internal standards of 1 nmol ¹³C₆-sorbitol and 10 nmol ¹³C₅,¹⁵N-labelled valine were added to a 40 μl aliquot of the previous supernatant before storage at -80°C. For GC-MS analysis, 5 μl of this aliquot was dried completely in glass vial inserts within a rotational vacuum concentrator (Concentrator *Plus*, Eppendorf, Hamburg, Germany), including a final drying step of 30 μl 100% methanol. The samples were derivatized with methoxyamine (Sigma, Massachusetts, USA) and N-bistrimethylsilyltrifluoroacetamide (BSTFA) containing 1% trimethylchlorosilane (TMCS; Thermo Scientific, Massachusetts, USA) before metabolites were analyzed on the Agilent 6545 series quadrupole mass spectrometer (Agilent Technologies, California, USA), using a protocol described previously. Metabolite identification was performed by comparing molecular mass and retention time to authentic standards using MassHunter software (Agilent Technologies, California, USA). Results for each replicate were normalized to the valine internal standard value. ## *C*. *burnetii* transposon mutant complementation *C*. *burnetii cbu0823* transposon (Tn) mutant from a previously generated transposon mutant library was clonally isolated on semi-solid ACCM-2 agarose containing chloramphenicol. PCR screening using transposon- and gene-specific oligonucleotide pairings confirmed the transposon insertion within the *0823*::Tn mutant 832 basepairs downstream of the start codon, previously documented in. To create the plasmid for genetic complementation of the *0823*::Tn mutant, full length *cbu0823* was amplified using gene-specific oligonucleotides, then cloned into the *C*. *burnetii* complementation vector pJB-kan:3xFLAG-MCS. Sequence and FLAG tag was confirmed by Sanger sequencing. The complemented *0823*::Tn mutant strain was generated by introducing the pJB- kan:3xFLAG-*cbu0823* plasmid into the *0823*::Tn mutant as described previously. Recovered bacteria were plated onto ACCM-2 agarose with chloramphenicol and kanamycin. After 7 days of incubation, colonies were picked into 1 ml ACCM-2 with chloramphenicol and kanamycin selection in 24-well plates. Wells containing visible growth were harvested and whole cell lysate screened for 3xFLAG-CBU0823 expression by immunoblotting with primary anti-FLAG antibody (Sigma, Massachusetts, USA) and secondary anti-mouse IgG (GE Healthcare, Illinois, USA). ## Stable isotope labelling analysis The method used in Hauslein et al. was chosen to investigate lactate biosynthesis in *C*. *burnetii* as it produced the highest stable isotope label incorporation into lactate of published works. Five replicates of 20 ml ACCM-2 containing 5 mM ¹³C-U-glucose (Sigma, Massachusetts, USA), each inoculated with 2 x 10⁶ GE/ml of the relevant *C*. *burnetii* strain, were incubated for 7 days as previously described,then processed for metabolite extraction in methanol:water:chloroform 3:1:1 v/v as previously described. Preparation of glass inserts, derivatization and GC-MS analysis was performed as for the enzyme activity GC-MS assay, except the whole aqueous phase sample was dried down. DExSI software was used for metabolite identification by comparison with an in-house Metabolomics Australia library of authentic standards for molecular masses and retention times. The peak integrations for all relevant mass isotopologues were combined for every detected metabolite and corrected to natural background isotopic abundance to give fractional labelling. The fractional labelling was then normalized to the initial ¹³C-glucose level within each replicate. Isotopologues were graphed using DExSI applying the inbuilt natural isotopic abundance correction for 0% unlabelled biomass. Detected metabolites were mapped to known *C*. *burnetii* metabolic pathways. ## Intracellular replication assays in THP-1 cells THP-1 cells were seeded at 5 x 10⁵ cells per well into 24-well plates, with or without sterile glass coverslips, differentiated into macrophage-like cells by treating with 10 nM phorbol 12-myristate 13-acetate (PMA) and incubated for 3 days. 7-day cultures of *C*. *burnetii* strains were pelleted and resuspended in PBS. Genome equivalents were quantified using a quantitative qPCR that targets *ompA* as described previously, using oligonucleotides listed in. Each well was infected with a multiplicity of infection (MOI) of 25 with *C*. *burnetii* resuspended in 500 μl RPMI + 5% FCS for 4 hours at 37°C 5% CO₂. Each strain was prepared in triplicate per replicate and six independent biological replicates were performed. After infection, all wells were washed with PBS, then, except for day 0, 500 μl RPMI + 5% FCS was added per well for incubation at 37°C 5% CO₂. Bacteria were harvested on days 0, 1, 3, 5, and 7. With the exception of day 0, the media from each well was collected and pelleted for 15 minutes at 13 200 x g. Attached cells were lyzed with nuclease-free water for 20 minutes. The lyzed cells were scraped from the well and added to the pellet from the media, before repeat centrifugation. The resulting pellet was resuspended in 100 μl nuclease- free water and *C*. *burnetii* genome equivalents (GE) determined by the *ompA* qPCR. Fold change was calculated for each time point relative to day 0. Immunofluorescence microscopy slides were prepared on day 3 post-infection. The media was removed from wells containing coverslips and cells fixed in 4% paraformaldehyde before being permeabilized with 0.05% saponin + 2% BSA in PBS for 1 hour, then washed three times with PBS. Primary antibodies against *C*. *burnetii* (Roy Laboratory, Yale University, Connecticut, USA) and LAMP-1 (Developmental Studies Hybridoma Bank, Iowa, USA) at 1:10,000 and 1:500 respectively were applied for one hour, before further washing with PBS. Secondary antibodies anti-mouse AlexaFluor 488 and anti-rabbit AlexaFluor 568 (Thermo Fisher Scientific, Massachusetts, USA) were, both at 1:3,000, were applied for one hour and the first PBS wash that followed contained 1:10,000 4′,6-diamidino-2-phenylindole (DAPI) (Invitrogen, California, USA). Dako Fluorescent Mounting Media (Agilent Technologies, California, USA) was used to mount coverslips to glass slides. Images were obtained using Leica 780 and 700 confocal microscopes (Biological Optical Microscopy Platform, University of Melbourne, Melbourne, Australia) and processed in ImageJ. Vacuole size quantification was completed in ImageJ, by measuring vacuole area in μm² as seen on the anti-LAMP channel of at least 50 vacuoles per replicate. Four of the six biological replicates had sufficient quality immunofluorescence images to allow for vacuole quantification. ## Statistical analysis All graphs were prepared using Prism 9.0 (GraphPad, California, USA). Unpaired two-tailed *t*-tests were performed in Prism 9.0 to compare the means of each enzyme activity in the GC-MS detection activity assay, for each metabolite in the stable isotope labelling experiment, as well as average vacuole area. Ordinary one-way ANOVA was performed in conjunction with Tukey’s multiple comparisons test in Prism 9.0 to examine differences in cofactor preference of 6xHis-CBU0823. Statistical differences in the intracellular replication assays between strains at each time point were calculated in Excel using unpaired two- tailed Student’s *t*-tests. A threshold significance of *p \<* 0.05 was used in all analyses. # Results ## The CBU1241 amino acid sequence is consistent with other malate dehydrogenases and CBU1241 possesses malate dehydrogenase function *in vitro* Clustal Omega alignment of select MDH and LDH protein sequences revealed that CBU1241 and the putative MDH from *Legionella pneumophila* str. Philadelphia 1 shared the greatest identity at 63%. CBU1241 was more similar to the putative *Thermus thermophilus* MDH (56% identity) and the pig cytoplasmic MDH isozyme (46% identity) than to *E*. *coli* MDH (24% identity). This subgrouping of *T*. *thermophilus* with eukaryotic cytoplasm MDHs, as well as *Mycobacterium* species, has been noted previously and *C*. *burnetii* and *L*. *pneumophila* MDHs appear to lie within the same subgroup. The presence of a Glu at position 42 and an Arg at position 92 predicts that CBU1241 should have MDH function. Other residues typical of the LDH and MDH superfamily involved in substrate binding (Arg98, Asn131 and Arg162) and proton transfer during catalytic action (Asp159 and His187) are conserved in CBU1241. No residues uncharacteristic of an MDH were identified in CBU1241. To assess the *in vitro* catalytic activities of CBU1241 in enzyme activity assays, recombinant GST-CBU1241 was expressed and purified. When GST-CBU1241 was used in the MDH assay containing OAA and NADH, oxidation of NADH was observed as decreasing absorbance at 340 nm. GST alone did not produce a measurable absorbance change, suggesting that CBU1241 exhibited its predicted MDH function *in vitro*. The positive control, a commercially produced MDH, produced robust absorbance change in the same assay conditions. To evaluate lactate production *in vitro*, GST-CBU1241 was used in the LDH assay containing pyruvate and NADH. No change in absorbance was detected with either GST-CBU1241 or GST. By contrast, the commercially produced LDH showed activity in standard assay conditions. GST-CBU1241 MDH activity was maximal at pH 9.0, showing a preference for alkaline conditions. Enzyme kinetics showed that GST-CBU1241 activity was maximal between 0.3 and 0.6 mM OAA and substrate inhibition was observed above this concentration. For OAA, K_m was calculated as 0.11 mM (95% CI 0.07–0.17), V_max as 63.61 μmol/min per mg (95% CI 54.61–76.22), k_cat as 65.20 s^-1 (95% CI 55.98–78.13) and K_i was calculated as 1.25 mM (95% CI -0.85–3.39). Lineweaver-Burke and reciprocal of the quotient velocity plots are included in the supplementary information. Enzyme activity more closely resembled classical Michaelis-Menten kinetics with increasing NADH concentrations. The calculated K_m for NADH was 0.12 mM (95% CI 0.06–0.22), V_max 49.13 μmol/min per mg (95% CI 40.68–59.45), and k_cat 50.36 s^-1 (95% CI 41.70–60.94). These values fall amongst those reported for other bacterial MDH proteins. Together, this *in vitro* assessment demonstrated that GST-CBU1241 acts as an MDH, having no LDH activity, with a preference for alkaline conditions. ## CBU0823 shares many key residues with other bacterial malic enzymes and functioned as a malic enzyme and malate dehydrogenase *in vitro* Clustal Omega alignment of ME and MLE protein sequences showed CBU0823 was most similar to one of the putative NAD⁺-dependent MEs of *L*. *pneumophila* Q5ZRB1 with 55% identity, which was expected given their close phylogeny. CBU0823 also showed high identity with MLEs, 42% with the multifunctional *O*. *oeni* MLE and 41% with the MLE from *Lactobacillus casei*, permitting that CBU0823 could possess MLE function. CBU0823 contained some key residue motifs not predicted by its NAD⁺-dependent ME annotation. The presence of Gly162 in CBU0823 is more consistent with MLEs, as bacterial MEs usually contain an Arg, however, yeast MEs also contain a Gly at the equivalent residue. Moreover, Val117 in the malate binding site of CBU0823 differed from both MLEs (Ile) and bacterial MEs (Cys), although all residues are hydrophobic. In CBU0823, the GXGXXG motif beginning at Gly307 is consistent with other prokaryotic ME sequences and corresponds to the observed NAD⁺ binding site in resolved ME crystal structures. Additional evidence for an NAD⁺-preference by CBU0823 can be found where the strictly conserved Asp341 and Gly344 is not surrounded by the motif of NADP⁺-preferring enzymes (Ser342, Lys343 and Arg351). Residues involved in catalytic activity (Tyr109, Lys180 and Asp274) and residues known to bind the metal cation cofactor (Glu251, Asp252, Asp275) were strictly conserved in CBU0823, reflecting their crucial functions. To assess the *in vitro* catalytic activities of CBU0823, recombinant 6xHis- CBU0823 was expressed and purified. When the 6xHis-CBU0823 recombinant protein was added to the ME assay containing malate, NAD⁺ and MnCl₂, NAD⁺ reduction was observed as increasing absorbance at 340 nm, indicating 6xHis-CBU0823 exhibited the annotated ME function *in vitro*. The pQE-30 negative control produced no absorbance change in this ME assay. Akin to GST-CBU1241, there was no change in absorbance when 6xHis-CBU0823 was used in the LDH standard assay, suggesting no lactate production. Interestingly, 6xHis-CBU0823 was capable of oxidizing NADH in the presence of OAA in the standard MDH reaction, indicating it possessed both ME and MDH activity. 6xHis-CBU0823 preferred increasingly alkaline conditions and activity was not detected in acidic conditions. The ME activity of recombinant 6xHis-CBU0823 exhibited a strong preference for NAD⁺, as activity with NADP⁺ cofactor was significantly reduced to 8.23±0.24% to that with NAD⁺. This is consistent with other enzymes from the same enzyme class. Removal of the Mn²⁺ cofactor also significantly reduced ME activity of 6xHis-CBU0823 to 20.14±13.38%. Fe²⁺ effectively replaced Mn²⁺ as cofactor. All other tested metals provided significantly reduced activity, with Zn²⁺ the next most proficient cofactor replacement with only 29.98±12.68% activity. Michaelis-Menten plots revealed substrate inhibition also occurred with 6xHis- CBU0823. Maximum activity was between 2 and 3 mM malate, at 0.8 mM NADH, and at 1 mM MnCl₂. For malate, K_m was calculated as 0.40 mM (95% CI 0.01–1.25), V_max as 1.06 μmol/min per mg (95% CI 0.78–1.67), k_cat as 1.12 s^-1 (95% CI 0.82–1.77) and K_i was calculated as 10.86 mM (95% CI -7.31–29.05). These values fall amongst those reported for other bacterial MDH proteins. Lineweaver-Burk and reciprocal of the quotient velocity plots are included in the supplementary information. For NADH, K_m was calculated as 0.31 mM (95% CI 0.03-undefined), V_max as 2.06 μmol/min per mg (95% CI 0.78-undefined), k_cat as 2.18 s^-1 (95% CI 0.83-undefined) and K_i was calculated as 6.30 mM (95% CI 0.27–12.33). Lineweaver-Burk and reciprocal of the quotient velocity plots are included in the supplementary information. For MnCl₂, K_m was calculated as 0.06 mM (95% CI 0.04–0.09), V_max as 1.34 μmol/min per mg (95% CI 1.07–1.90), k_cat as 1.45 s^-1 (95% CI 1.13–2.01) and K_i was calculated as 6.82 mM (95% CI -5.15–18.79). Lineweaver-Burk and reciprocal of the quotient velocity plots are included in the supplementary information. 6xHis-oMLE did not perform well in the standard assay conditions. For example, in the ME standard reaction, 6xHis-oMLE did not have detectable activity. To confirm the purified protein could behave similarly to the previously published study, reactions were repeated with substrate concentrations as previously published at 45°C using oMLE storage buffer as diluent. 6xHis-oMLE activity was improved in these conditions. Notably, the observed reduction of OAA indicating MDH activity was not detected in the previous characterisation and represents yet more substrate flexibility of this versatile enzyme. These *in vitro* data demonstrated that CBU0823 acts preferentially as an ME, converting malate to pyruvate. It is also capable of MDH activity but not of MLE or LDH activity. It has a strong preference for alkaline conditions and NAD⁺/NADH and Mn²⁺ as cofactors, though it will tolerate some substitutions. ## CBU0823 does not exhibit malolactic enzyme activity *in vitro* The GC-MS analysis of *in vitro* activity indicated that 6xHis-oMLE could generate amounts of lactate and pyruvate significantly above baseline, corroborating the previously published work showing oMLE has ME and MLE activity. 6xHis-CBU0823 similarly produced pyruvate significantly above baseline supporting that malic enzyme activity was demonstrated in, but did not produce lactate above baseline levels , suggesting it cannot act as an MLE. Comparatively 6xHis-CBU0823 generated significantly less pyruvate than 6xHis- oMLE, particularly as double the amount of 6xHis-CBU0823 enzyme was used. This suggests that despite the ME activity of CBU0823 being more efficient than its MDH activity, it is still less active than the malic enzyme activity of the multipurpose enzyme oMLE in these assay conditions. The instability of OAA precludes its detection by GC-MS, therefore we were unable to confirm whether CBU0823 could produce OAA by means of a reverse malate dehydrogenase activity. ## Loss of *cbu0823* causes no detectable change in ¹³C-incorporation into lactate In order to evaluate the *in vivo* capacity of CBU0823 to synthesize lactate in *C*. *burnetii*, we wanted to compare stable isotope label enrichment in lactate in the *cbu0823* transposon insertion mutant to that of the wildtype strain. Following confirmation of the transposon insertion in the *0823*::Tn mutant, it was genetically complemented with pJB-kan:3xFLAG-*cbu0823* plasmid to provide constitutively expressed 3xFLAG-CBU0823. Expression of a correct sized product was confirmed on a western blot probed for FLAG tag. The *C*. *burnetii* strains were cultured for 7 days in ACCM-2 containing \[¹³C\]glucose before harvest and analysis, conditions in which *C*. *burnetii* can incorporate labelling into lactate. In wildtype, label enrichment into lactate was 14±4.6%, compared to 9.3±4.0% in the *0823*::Tn mutant and 11.2±3.8% in the *0823*::Tn pFLAG-CBU0823 complemented mutant. These ¹³C-incorporation levels were not statistically significantly different (*p* \> 0.05), suggesting CBU0823 is not involved in lactate biosynthesis. The wildtype label enrichment in lactate detected in this study is similar to the previously published study, despite differing label enrichment calculation methodologies. In the small amount of labelled lactate detected, all strains showed an isotopologue pattern of mostly M+2, with a small amount of M+1, once natural isotopic abundance correction was applied, suggesting the precursor might be a TCA cycle intermediate. In contrast, Hauslein et al. observed a predominantly M+3, more suggestive of a pyruvate or glycolysis intermediate precursor. ## Loss of *cbu0823* alters central carbon metabolism of *C*. *burnetii* The stable isotope labelling studies examining lactate biosynthesis advantageously provided information about the wider consequences of disabling *cbu0823* to *C*. *burnetii* central carbon metabolism. After 7 days incubation with \[¹³C\]glucose, the fraction labelled was significantly reduced in lower glycolysis intermediates within *0823*::Tn mutant compared to wildtype. For example, glycerate 3-phosphate within the *0823*::Tn mutant contained only 60.7±2.1% ¹³C-incorporation compared to 95.0±8.0% in wildtype and 87.6±13.5% in the *0823*::Tn pFLAG-CBU0823 complemented mutant. This suggests that overall the glycolytic pathway is less active in the *0823*::Tn mutant compared to wildtype, at least in its utilization of glucose. The significant reduction in ¹³C-incorporation from \[¹³C\]glucose was also observed in TCA cycle intermediates in the absence of CBU0823. All metabolites contained significantly lower label enrichment in the *0823*::Tn mutant than in wildtype, except for succinate. For instance, label incorporation into malate, the substrate for CBU0823 and the product of CBU1241 in the *in vitro* assays, was 34.2±3.2% in wildtype yet only 15.4±1.3% in the *0823*::Tn mutant and 20.6±3.3% in the *0823*::Tn pFLAG-CBU0823 complemented mutant strain. Additionally, TCA cycle derivates aspartate and glutamate had lower label inclusion levels, suggesting the decreased fraction labelling continued into these derivative biosynthesis pathways as well. The M+2 isotopologue was most prevalent through detected TCA cycle intermediates, supporting previous findings of carbon entering the TCA cycle via a fully labelled acetyl-CoA intermediate. It is worth noting that the inconsistent pattern on label enrichment before and after citrate/isocitrate is likely due to citrate/isocitrate enrichment dilution by unlabelled citrate, present in large amounts in *C*. *burnetii* axenic media and remaining despite multiple washes, and is not related to any unusual TCA cycle direction within *C*. *burnetii*. The negative label enrichment in serine detected is physiologically impossible and probably detection of noise rather than true ¹³C-incorporation levels. The differences observed between wildtype and the *0823*::Tn pFLAG-CBU0823 complemented mutant strain are likely explained by the non-physiological, constitutive expression of pFLAG-CBU0823. Taken as a whole, these data suggest the loss of CBU0823, a malic enzyme, within *C*. *burnetii* led to significant reductions in carbon flux from glucose through glycolysis and within the TCA cycle as well. This is potentially due to an inability to maintain adequate metabolic intermediates in the absence of the malic enzyme, particularly malate and acetyl coenzyme A (acetyl CoA), thus preventing metabolic processes from continuing as normal. ## *cbu0823* is not required for efficient *C*. *burnetii* replication Finally, we investigated the impact what impact the metabolic alterations seen in the *0823*::Tn mutant might have on *C*. *burnetii* replication inside host cells. The fold change in genome equivalents of *C*. *burnetii* strains was measured within THP-1 macrophage-like cells infected with an MOI of 25 over 7 days by qPCR. No significant differences were observed in replication between strains over 7 days (*p* \> 0.05). The strains were visibly indistinguishable on representative immunofluorescent images taken on day 3 post-infection and no significant differences in vacuole size were observed. # Discussion It is yet to be determined how *C*. *burnetii* accomplishes lactate synthesis, though it can reproducibly incorporate stable isotope label into lactate in multiple studies. This study has demonstrated that CBU1241 and CBU0823 are unlikely to be responsible for the lactate production observed. Neither CBU0823 nor CBU1241 exhibited any LDH activity in *in vitro* enzyme activity assays and CBU0823 did not produce lactate from an MLE reaction. *In vivo* assessment indicated the *0823*::Tn mutant was as able to incorporate ¹³C label into lactate in an equivalent manner to wildtype. Taken together, these results indicate that the metabolic pathway by which *C*. *burnetii* synthesizes lactate is still to be established. The characterization in this study of CBU1241 revealed robust OAA reduction. *in vitro* CBU1241 is clearly affected by substrate inhibition, with activity diminishing above 0.6 mM OAA. 0.6 mM is a lower concentration than for many other published bacterial MDHs, though it is comparable to pig heart mitochondrial MDH and higher than some, such as *T*. *flavus* MDH. Due to its instability, accurate intracellular OAA values are currently not available. Together with the lack of knowledge regarding the MDH malate reduction reaction, the level of substrate inhibition CBU1241 experiences *in vivo* remains unknown. The variation of two key residues within CBU0823 from other ME suggested the enzyme could possess the substrate flexibility characteristic of malic enzymes (MEs). In this study, the *in vitro* experiments demonstrated recombinant CBU0823 lacked any detectable LDH or MLE function but did possess both ME and MDH activity. Nonetheless, the simple *in vitro* assessment of CBU0823, and CBU1241, function may not fully represent enzyme capabilities *in vivo*. The reduced incorporation of label into metabolites across central carbon metabolism in the *0823*::Tn mutant cultures appears similar to the rerouting of carbon away from glycolysis and the TCA cycle noted in ME mutant strains of *Sinorhizobium meliloti*, a soil dwelling symbiont bacteria associated with alfalfa roots. These metabolism modifications may be attributable to the loss of the cataplerotic reaction catalyzed by MEs, removing malate from the TCA cycle, and the resultant anaplerotic reaction of pyruvate conversion to acetyl CoA for entry into the TCA cycle. Instead, acetyl CoA replenishment from TCA cycle intermediates would require the anaplerotic reaction of OAA to phosphoenolpyruvate (PEP) by PEP carboxykinase (PckA). In other words, TCA cycle function in the ME mutants likely would rely on the slowest reaction in the cycle, the MDH enzyme, to produce sufficient OAA to both remain in the TCA cycle and to exit the TCA cycle for replenishment of acetyl CoA. From this data, we cannot determine how the loss of the secondary MDH function of CBU0823 in the *0823*::Tn mutant contributed to TCA cycle dysfunction. The replication of the *0823*::Tn mutant was not lower than wildtype intracellularly. The *S*. *meliloti* ME mutants with comparable metabolome changes did exhibit slowed replication, in contrast to *E*. *coli* ME mutants where simultaneous removal of the alternate pyruvate producing PckA pathway was required to affect replication. The MDH-PckA pathway may provide sufficient OAA and PEP to maintain typical *C*. *burnetii* replication, especially as the normal replication rate of *C*. *burnetii* is comparatively slow. Furthermore, reduction in TCA cycle activity reduces oxidative stress, an issue for *C*. *burnetii* within its replicative niche, and the reduced TCA activity may be of benefit to the *0823*::Tn mutant. Moreover, this study concentrates on glucose utilization whereas *C*. *burnetii* has a metabolism capable of utilizing amino acids, thus the additional energy required by the *0823*::Tn mutant for replication could be provided by carbon sources other than glucose. This study has demonstrated the *C*. *burnetii* gene *cbu1241* encodes an enzyme with *in vitro* MDH function and *cbu0823* encodes an enzyme with both ME and MDH function *in vitro*, albeit a less efficient MDH than *cbu1241*. Neither enzyme demonstrated the capacity to produce lactate *in vitro*, and *in vitro* labelling studies suggested that CBU1241 was not responsible for lactate production *in vivo*. Although not required for efficient replication in axenic culture or within host cells, *cbu0823*, most likely through its malic enzyme function, is required for normal glycolysis and TCA cycle function in *C*. *burnetii*. Future work examining lactate production by *C*. *burnetii* may include large scale screening studies of mutant libraries and/or sophisticated bioinformatic analysis, in order to identify new potential candidate enzymes. # Supporting information Technical support was provided by Chen Ai Khoo, Newton Laboratory, Peter Doherty Institute, University of Melbourne. Confocal imaging was performed at the Biological Optical Microscopy Platform, University of Melbourne ([www.microscopy.unimelb.edu.au](https://www.microscopy.unimelb.edu.au)). [^1]: The authors have declared that no competing interests exist.

# Introduction Persons with severe mental illness (SMI) including schizophrenia, bipolar disorder and major depression are approximately twice as likely to have a stroke. Increased stroke risk among those with SMI has been attributed to unhealthy lifestyle choices like tobacco use, substance abuse, low physical exercise, poor nutrition, and morbid obesity. Use of atypical antipsychotics, such as olanzapine and clozapine, also account for significant weight gain and may contribute to risk for stroke. Additionally, functional impairments associated with SMI place them among the most vulnerable of social groups where they encounter poverty, chronic unemployment, substandard housing, poorer access to quality health care, disrupted social relations, and delayed preventative care, which exacerbate overall risk for poor health outcomes. Consequently, individuals with SMI experience about 25 years of reduced life expectancy. Little is known about stroke survivors with SMI regarding their patterns of post-stroke hospitalization. Unplanned hospitalizations among stroke survivors are costly, may reflect suboptimal patient outcomes, and are often related to other co-morbid conditions. However, there is conflicting information on whether persons with SMI are at increased risk for non-psychiatric hospitalization after an initial stroke. For instance, a recent systematic review of hospitalization among stroke survivors does not mention SMI as a contributing factor in any reviewed studies. Yet, previous research suggests that stroke survivors with SMI may be particularly vulnerable to poor stroke outcomes requiring hospitalization because they have high readmission rates across a spectrum of other health conditions, even after adjusting for other comorbidities and lifestyle factors. Factors that have been related to post-stroke hospitalizations among stroke survivors include age, race, education, insurance coverage, history of physical comorbidity prior to stroke, hospitalizations prior to stroke, hypertension, coronary artery disease, diabetes, and residential proximity to hospital. The current study compares the risk of non-psychiatric hospitalizations for veteran stroke survivors with and without SMI and examines patterns of hospitalization in Veterans Health Administration (VHA) hospitals over a five- year period. Veteran stroke survivors, whether or not they have severe mental illnesses, are vulnerable and have relatively poor health status. For example, the health status of veterans is consistently found to be poorer in comparison to the rest of the United States with increased rates of obesity, diabetes, homelessness, and substance abuse. Individuals with SMI in the general population who have never had a stroke have higher non-psychiatric hospitalizations even after adjusting for other co-morbidities and lifestyle factors. Yet, there is a paucity of research on the influence of SMI on non- psychiatric hospitalizations among veteran stroke survivors receiving care in the VHA system. It is notable that in a relatively recent systematic review of predictors of hospital admissions after stroke that not a single reviewed study considered SMI as either a primary independent variable or as a covariate related to post-stroke hospitalizations. The aim of this study is to begin to fill the gap in the literature on the role of SMI in post-stroke non-psychiatric hospitalizations. # Materials and methods ## Hypothesis We conducted a five-year retrospective cohort study of veterans identified through the U.S. Department of Veterans Integrated Service Network Five as having an index stroke in 2003 and receiving their care in the VHA system. Our hypothesis was that stroke survivors with SMI would have increased risk of non-psychiatric hospitalizations over five-years compared to stroke survivors without SMI after adjustment for inpatient stroke treatment variation, age, race, marital status, education, income, health insurance coverage above VHA benefits, proximity of patient’s home to a VHA hospital, physical co-morbid conditions, prior pre-stork hospitalizations, mental co-morbid conditions and history of stroke risk factors of hypertension, diabetes, and peripheral vascular disease. The rationale for this hypothesis was that individuals with SMI have myriad lifestyle factors, health care access difficulties, and medication side effects, which increase their risk of physical diseases resulting in greater non-psychiatric hospitalization rates. ## Defining stroke, non-psychiatric hospitalizations and SMI The International Classification of Diseases, Ninth Revision (ICD-9) was used to identify ischemic and hemorrhagic stroke participants. ICD-9 diagnosis codes have been extensively used for stroke outcomes research and have been validated with high sensitivity (86%), specificity (95%), and positive predictive value (86–92%). The following ICD-9 CM diagnostic codes were used to identify stroke patients from either inpatient or outpatient encounters: 433.x1, 434 (excluding 434.x0), or 436. Outpatient records were important to consider because many VHA medical centers contract with their affiliated medical school hospital to provide specialty care services like acute stroke care. Thus, a majority of acute stroke care is not provided in the VHA facility. Once a patient has been stabilized they may be transferred to the VHA medical center or to some other level of rehab or nursing care. Thus, an outpatient claim may be the first instance that the stroke event gets captured within the claims system. Outpatient claims were used to capture those patients whose index stroke hospitalization occurred in a non-VA facility. Additionally, ICD-9 code 436 is not routinely used to identify stroke, however, after consulting with stroke specialists at the VHA, it was recommended for inclusion because it was a code commonly used to capture strokes in patients for whom they personally did not provide care. Moreover, based on previous work in identifying neurologic cohorts from VHA data, the stroke diagnostic codes must occur as the first or second diagnosis indicating a primary diagnosis. The VHA captures up to ten diagnosis codes per encounter with codes listed in descending order of their impact on that episode of care. This method identified a total of 2,299 veterans. Nearly 80% of those identified based on an inpatient encounter had diagnosis codes of 431–434, whereas 89% of those identified based on an outpatient encounter had only a ICD-9 436 diagnostic code. Thus, ICD-9 code 436 was used when there was unambiguous evidence of stroke but that index event (acute care inpatient encounter) was not managed in the VHA facility. Finally, we then looked back three years for the presence of a stroke diagnosis to identify only veterans with an index stroke hospitalization in 2003 yielding 523 such cases who comprise the study group. Non-psychiatric hospitalizations were defined as inpatient admissions for any cause except treatment for psychiatric or substance abuse disorders from the VHA Inpatient data file. Consistent with previous research, non-psychiatric hospitalizations were considered within the following time intervals from index stroke discharge: within 30 days, 31 to 90 days, 91 to 364 days, and within year-long intervals thereafter up to five years. All admissions within 30-days post-stroke discharge were due to stroke-related complications. Causes of hospitalization were determined by examining the principle ICD-9 diagnosis code for each hospitalization. ICD-9 codes were further categorized into eleven discrete groupings, including: neurologic, cardiovascular, orthopedic, infections, kidney disorders, endocrine disorders, digestive disorders, cancer, blood disorders, respiratory disorders, or miscellaneous. SMI was defined in our sample by ICD-9 codes for schizophrenia (295.00–295.05; 295.10–295.15; 295.20–295.25; 295.30–295.35; 295.40–295.45; 295.50–295.55; 295.60–295.65; 295.70–295.75; 295.80–295.85; and 295.90–295.95), bipolar disorder (296.00–296.06; 296.10–296.16; 296.40–296.46; 296.50–296.56; 296.60–296.66; 296.70; 296.80–296.82; 296.89–296.90; and 296.81), and major depression (296.20–296.26 and 296.30–296.36). ICD-9 codes 297, 298, and 300 were also initially included in our determination of SMI, however, none of these codes were present in the administrative data over the two-year look-back period. Likewise, A patient was categorized as having SMI if at least one instance of an ICD-9 code occurred in the two-year look-back period in either inpatient or outpatient diagnostic fields. Randomly selected medical charts were reviewed (n = 20) to validate agreement with this classification algorithm. In this review, every patient categorized as having SMI was found to have had more than three previous encounters with the VHA related to SMI. ## Study covariates defines the covariates, their central tendency, and coding scheme. Demographic covariates included age (18–44, 45–64, 65–74, or \>75 years), marital status (married or not married), annual income (in 2002 dollars), health insurance coverage (dual beneficiary or only VHA benefits), and proximity of patient’s home to a VA hospital (in miles). Race was coded dichotomously (Caucasian or non-Caucasian) because 97.8% of non-whites were African American. Sex was excluded as a covariate because only 3% of the sample were women. History of physical comorbidity was measured as a composite score using the Elixhauser Index, which is a method of categorizing 30 comorbidities of patients based on the ICD-9 diagnosis codes found in administrative data. History of mental health comorbidity was measured using ICD-9 codes for post-traumatic stress disorder (309.81), acute depression (311; 308; 309.0; 309.1; and 313.1) and substance abuse (291; 292, 303; 304; 305; 535.3; 571.1; 648.3; and 790.3). History of stroke risk factors was measured by presence of ICD-9 codes for hypertension (401.x; 402.x; 403.x; and 405.x), diabetes (250.0x–250.4x; 250.7x; and 250.9x), and peripheral vascular disease (440.x; 441.x; 443.1; 443.9; 447.1; 557.1; 557.9; and V43.4) calculated for the year prior to stroke. Counts of 1-year pre- stroke non-psychiatric hospitalizations may be predictive of future hospitalizations and were considered as a covariate. Length of hospital stay at index stroke was measured in days. Quality of stroke treatment was measured using three of ten indicators identified by the Joint Commission as evidence for appropriate stroke care. These indicators included: appropriate assessment for rehabilitation (yes or no), appropriately discharged on a statin (yes or no), and appropriately discharged on antithrombotic medication (yes or no). SAS algorithms (Appendix 1) were written to identify ICD-9 diagnosis, procedure, and inpatient pharmacy administration codes indicative of the Joint Commission stroke processes of care. ## Statistical methods Unadjusted bivariate analyses were used to describe the differences between stroke survivors with SMI compared to those without SMI for all covariates and hospitalizations over time. Bonferroni corrected significance was used to minimize the chances of making a Type I error across multiple comparisons with a p\< 0.0030 needed for statistical significance. Hospitalizations in the VHA dataset were skewed to the right with substantial zero admissions at each time interval and had distributions with long right tails. Deviance and Kolmogorov- Smirnov tests for goodness-of-fit were calculated for negative binomial, zero inflated negative binomnial, ordered probit and logit, and Poisson distribution models for each time period examined. The data best fit a Poisson distribution model for every time period except for between 4 and 5 years post-stroke, where a negative binomial model had preferable deviance and Kolmogorov-Smirnov scores. However, the Poisson model for between 4 and 5 years post-stroke had a nearly equal mean (1.48) and variance (1.63). Therefore, for consistency Poisson regression was used at every time period with log-linked functions and corrections for overdispersion to test the hypothesis that SMI is associated with non-psychiatric hospitalizationsAdditionally, negative binomial regression model for the time period between 4 and 5 years post-stroke was calculated yielding equivalent results as the Poisson regression. Confounding between the variable SMI and other independent variables was assessed by adding and then removing each independent variable to a Poisson model. Independent variables that altered the parameter estimate for SMI by greater than ±15% were considered to be confounding and were removed from final models. All statistical calculations were performed using SAS version 9.3. The University of Maryland, Baltimore institutional review board approved the study. # Results The study population consisted of 523 patients with an index stroke in fiscal year 2003, 100 with SMI comorbidity and 423 with no SMI comorbidity. summarizes the demographic, comorbid, and treatment differences between those with and without SMI. Patients with SMI comorbidity were significantly more likely to be younger and without a marital partner, only insured by the VHA, have more comorbid diagnoses, have more pre-stroke non-psychiatric hospitalizations and peripheral vascular disease. Patients with SMI did not significantly differ from those without SMI by race, proximity to hospital, income, history of hypertension or diabetes, hospital length of stay, or by any of the quality of stroke care indicators. Causes of non-psychiatric hospitalizations were examined in the year preceding stroke and after being discharged from inpatient care for index stroke. In the year prior to stroke, patients with SMI had significantly (p\<0.05) more admissions related to cardiovascular (SMI 46.3% vs. No SMI 31.6%) and infectious (SMI 17.0% vs. No SMI 8.7%) causes, and fewer admissions related to orthopedic (SMI 1.4% vs. No SMI 10.1%) causes. Patients with SMI had significantly (p\<0.05) more hospitalizations related to cardiovascular (SMI 42.5% vs. No SMI 31.2%) causes and significantly fewer hospitalizations related to recurrent stroke (SMI 1.5% vs. No SMI 6.7%) during the first year post-stroke. Patients with SMI, who were re-hospitalized with a stroke complication within 30 days after being discharged for index stroke had significantly more cardiovascular complications (SMI 42.8% vs. No SMI 32.4%), but fewer hospitalizations related to neurological causes (SMI 18.4% vs. 32.4%). During the period 31 to 90 days post-stroke SMI patients had significantly more hospitalizations related to cardiovascular (SMI 40.0% vs. No SMI 25.5%) and infectious (SMI 20.0% vs. No SMI 9.9%) causes. Patients with SMI had significantly more cardiovascular causes (SMI 37.0% vs. No SMI 26.5%) of hospitalization during the period of 1 to \<2 years post-stroke. There were no significant differences between patients with and without SMI regarding causes of hospitalization in any time period greater than two years. summarizes the differences in hospitalizations for patients with and without SMI at various time intervals from index stroke. Mean pre-stroke non-psychiatric hospitalizations were higher (p = 0.0004) among patients with SMI (1.47 ± 0.51) compared to those without SMI (1.00 ± 1.33) in our sample. Mean non-psychiatric hospitalizations continued to be significantly higher among patients with SMI compared to patients without SMI at all the examined time intervals during the first year after stroke, and for the entire first year considered cumulatively. In time intervals 1 to \<2 years, 2 to \<3 years, and 3 to \<4 years post-stroke there was no statistical difference between mean non-psychiatric hospitalizations among patients with and without SMI. However, by the fourth year after stroke, patients with SMI have significantly fewer non-psychiatric hospitalizations compared to those without SMI. Tables and summarizes the results of the multiple Poisson regression by transforming beta coefficients from the regression into relative risk calculations, which allow for interpretation and testing of the hypothesis that SMI is associated with increased non-psychiatric hospitalizations, after adjustment by covariates. PTSD exhibited a confounding relationship with SMI during the time intervals of 91 to 364 days and 3 to \< 4 years post-stroke. History of depression was also confounding with SMI during the time intervals of ≤ 30 days, 91 to 364 days, and 1 to \< 2 years post-stroke. No other independent variables exhibited a confounding relationship with SMI. After adjusting for covariates, there are no longer significant differences in the risk for non- psychiatric hospitalizations between patients with and without SMI at any time period after stroke. While history of SMI was not associated with post-stroke non-psychiatric hospitalizations after adjustment, a number of other independent variables were signficantly associated with non-psychiatric hospitalizations. Patients discharged on antithrombotic medications had a lower risk for re-admission (RR =.62; 95% CI:.50–.74) for a stroke-related complication within 30-days of index stroke discharge compared to patients not discharged on antithrombotics. During the time period 91 to 364 days post-stroke discharge minority patients (RR = 0.66; 95% CI:.46–.91) had signficantly lower risk for non-psychiatric hospitalizations compared to white patients, while patients with higher Elixhauser co-morbidity index scores (RR = 1.21; 95% CI: 1.12–1.33) were at greater risk for non-psychiatric hospitalizations. Additionally, during the time period 91 to 364 days the longer the initial stroke hospital length stay the greater risk for non-psychiatric hospitalizations (RR = 1.19; 95% CI: 1.07–1.30). During the time period 1 to \< 2 years post-stroke patients with higher Elixhauser co-morbidity index scores (RR = 1.13; 95% CI: 1.02–1.24) and patients with more hospitalizations in the year preceding index stoke (RR = 1.06; 95% CI: 1.01–1.11) were at signficantly greater risk for non-psychiatric hospitalizations, while patients with a history of substance abuse (RR = 0.50; 95% CI: 0.30–0.81) had lower risk for non-psychiatric hsopitalization. Additionally, patients discharged on statin medications (RR =.63; 95% CI:.45–.88\] also had lower risk of non-psychiatric hospitalizaiton. During the time period 2 to \< 3 years post-stroke only patients with higer Elixhauser co- morbidity index (RR = 1.11; 95% CI: 1.03–1.26) were at signficantly increased risk for non-psychiatric hospitalization. During the time period 3 to \< 4 years post-stroke patients with higher Elixhauser co-morbidity index scores (RR = 1.18; 95% CI: 1.04–1.35) were at significantly greater risk for non-psychiatric hospitalizations, while patients with at history of depression (RR =.43; 95% CI:.20–.92) and a history of substance abuse (RR =.49; 95% CI:.25–.98) were at lower risk for non-psychiatric hospitalizations. Lastly, during the time period of 4 to \< 5 years post-stroke, patients with higher Elixhauser co-morbidity index scores (RR = 1.16; 95% CI: 1.02–1.36) and patients with more hospitalizations in the year preceding index stroke (RR = 1.09; 95% CI: 1.01–1.17) were at signficantly greater risk for non-psychiatric hospitalizations. Tables and present results using an approach of fitting a full model and using data reduction for collinear variables to test the hypothesis that SMI is associated with non-psychiatric hospitalizations. As a final measure to ensure accurate interpretation of results, variables except for SMI were removed from the model if α \>.5 to achieve greater parsimony. For instance, in the time period of \<30 days only the variables history of SMI, race, history of PTSD, marital status, income, distance from VA to home, antithrombotic at discharge, and assessed for rehab were retained in the model, while ten other variables were excluded because they had α \>.5. This procedure was repeated for each of the time periods yielding no meaningfully divergent results from a full model presented in Tables and. The same variables retained statistical significance at each time period with similar relative risks for non-psychiatric hospitalization. None of the parsimonious models demonstrated history of SMI was significantly associated with increased risk of non-psychiatric hospitalization after controlling for covariates. # Discussion The aim of this study was to determine the relationship between SMI and non-SMI and the risk for non-psychiatric hospitalization in the five years after index stroke. In unadjusted descriptive analyses, patients with a history of SMI had significantly higher mean non-psychiatric hospitalizations in the year prior to index stroke and over the course of the entire first year post-stroke compared to patients with no SMI comorbidity. In Poisson regression analysis after adjusting for a broad set of patient characteristics, differences in non- psychiatric hospitalizations between the two groups were no longer significant. Patient characteristics included in the regression analysis were age, marital status, income, insurance status, proximity to hospital, physical co-morbidity, acute mental health history, hospital length of stay at index stroke, and quality of inpatient stroke treatment. This analysis highlights that patients with SMI are medically complex with multiple risk factors for hospitalization, including significantly higher levels of physical co-morbidity. Patients with this type of profile, notwithstanding mental illness, are likely to have increased non-psychiatric hospitalizations. Because individuals with SMI have extensive co-morbidity, tend to delay care, engage in risky health behaviors, and do not effectively adhere to prescribed treatment, their physical health and hospitalization rates are influenced by their mental health status. Our findings differ with several studies examining the association between hospital admissions and mental illness in the general population. Saravay and colleagues found that those with post-stroke depression averaged twice as many readmissions and spent twice as many days re-hospitalized over a 4-year period. Similarly, Borckardt and colleagues found that psychiatrically involved outpatients had higher average readmissions (mean = 1.6) compared to non- psychiatric outpatients (mean = 1.34) over one year. However, both studies only report unadjusted readmissions and do not utilize regression techniques to account for the effects of other patient characteristics on hospitalizations. In our sample, 43.3% of veterans had health insurance coverage with Medicare or Medicaid or both. Patients with SMI were significantly more likely (70%) to have only VHA benefits compared to those without SMI (54%). Those veterans with Medicare or Medicaid are likely to have received a portion of their care outside of the VHA system. Since we did not have access to data describing non-VHA care we are unable to account for the full spectrum of care delivered to stroke patients in our sample. However, it is more likely that stroke survivors with SMI received more of their care in the VHA system compared to those without SMI. Nevertheless, insurance status was not a significant predictor of hospitalizations in our sample. It is possible that the results obtained by the regression analysis reflect the success of longstanding effort to integrate physical and mental health care in the VHA. Hankin and colleagues indicate that the VHA health care system has been intentionally designed to effectively manage patients with mental illness. In the mid-1990s, the VHA health care system launched an extensive reengineering process with the intent of creating greater integration of primary care with mental health services. The VHA’s design process recognized and attempted to correct system-level factors that contribute to poor health outcomes such as poor access to care, and ubiquitous segregation of medical and mental healthcare leading to disjointed, uncoordinated, and poor quality care. Several subsequent studies evaluating the effectiveness of the VHAs care integration have emerged over the last decade, all of which demonstrate substantial improvement in mental health quality of care indicators. In the general population (non-VHA), suboptimal management of comorbidities is widely reported among patients with SMI, as are unfavorable disparities in morbidity and mortality. In VHA systems of care, individuals with SMI die 13.8 years earlier than those without SMI, contrasted with non-VHA systems of care where individuals with SMI die 25 years earlier. In a comparison of the quality of medical care in VHA and non-VHA settings between 1990 and 2009, the care delivered in VHA showed greater adherence to accepted processes of care, greater rates of evidence-based pharmaceutical therapy, greater mental and primary care provider co-location, greater collaboration on diagnostic and treatment plans, improved quality monitoring and better health outcomes. These findings suggest that the VHA’s model for integrated medical and mental health care reduces disparities in years of lost life and could partially explain the findings of our study. Future research should examine the impact of the integrated nature of VHA health care among patients with SMI to determine the extent to which coordinated care confers benefits to this population, and whether the VHA model could provide lessons to improve care for the SMI in health care reform. It is notable that in the time period of within 30-days post-stroke, both SMI and non-SMI patients who were discharged on antithrombotic medications had significantly lower risk for stroke-related readmission. Blood clots complicate recovery from stroke and antithrombotic medications reduce the formation of such clots. Our findings validate the use of antithrombotic agents to reduce unnecessary future morbidity, which may have relevance and implications in the current landscape of health care reform. Additionally, patients with higher Elixhauser comorbidity index scores were at greater risk of non-psychiatric hospitalization after 91 days post-stroke, which is consistent with the results of several studies. Our finding validates previous work indicating that physical comorbidity increases risk for hospitalizations after surviving a stroke. There are several limitations of our study that may have implications on the interpretation and generalizability of results. First, caution should be used when generalizing results beyond the VHA because of the unique sociodemographic and health profiles of the veteran population and the integrated nature of mental health care within the VHA. Second, the study utilized administrative data that, as we have noted, has limitations. Third, as mentioned previously, our data set does not contain health care utilization outside of the VHA system. Since veterans without SMI were more likely to have Medicare, Medicaid or other insurances, they would be more likely than the SMI to have utilized health services that remain unmeasured in this study. Further, since the VHA does not provide emergency department services for acute stroke, patients being transported to the hospital in response to a 911 call will be taken to a non-VHA hospital. We assume that veterans without health insurance, and particularly those with SMI, who require hospitalization will be transferred as soon as possible to the VHA. Consequently, examining non-VHA care might yield different results regarding the relationship between SMI and non-psychiatric hospitalizations. Fourth, several additional relevant variables were not available for our analysis. For instance, there were no direct measures of stroke severity based on clinical measures, rather we relied on index stroke hospital length of stay as a proxy for severity. Data on lifestyle factors such as obesity, alcohol use, smoking, and physical activity were also not available. Additionally, antipsychotic medication usage was not considered in regression models and may have provided valuable insight into the results. These factors should be assessed in future studies. Strengths of the study are 1) the VHA system is one of the largest integrated and standardized health systems in the United States and regular audits conducted by clinical specialists are in place to cross-check diagnosis codes with more robust patient chart review. Moreover, research from as early as 1998 has demonstrated adequate reliability for demographic variables (kappa = 0.92), diagnosis variables (kappa = 0.39–1.0) and cohort identification in administrative data in the VHA system. 2) This longitudinal VHA database provides a good opportunity to examine post stroke utilization for the SMI, in part because a large percentage of this population has no other source of health coverage. # Conclusion The severely mentally ill present a challenging and costly patient population for health systems. Although previous studies have examined hospitalizations among stroke survivors, limited data are available on the burden of SMI on post- stroke non-psychiatric hospitalizations. Unadjusted results highlight differences between the SMI and non-SMI stroke survivors, with the SMI experiencing more non-psychiatric hospitalizations both prior to and up to one year after their initial stroke. After adjusting for numerous patient characteristics, we found that patients with SMI, who were receiving services in the VHA system, were not at higher risk for non-psychiatric hospitalization during any time period after their index stroke for up to five years, when compared to patients without SMI. These findings suggest that the integrated nature of VHA healthcare for patients with SMI may confer benefits to this high risk population through coordination between mental and physical health care. Because integrated services for the SMI are considered one of the strengths of the VHA, our findings are provocative, suggesting that the VHA model may be useful to other health systems as the country is challenged by an aging population living with SMI, stroke, and other chronic conditions. Additionally, the adjusted difference demonstrating that individuals discharged on antithrombotic medications were at lower risk of re-admission within 30 days highlight the importance of this quality indicator for effective stroke management, irrespective of mental illness. # Supporting information [^1]: The authors have declared that no competing interests exist. [^2]: ‡ These authors also contributed equally to this work.

# Introduction Facial expressions are critical elements in nonverbal communication, which allow observers to infer the internal state of others and - in case of negative states - to be alarmed of impeding danger or be prepared for empathic behavior. The question is how specific this warning signal can be. Is the observer informed about an impeding threat in general by suggesting that somebody is experiencing a negative affective state or can facial expressions point to the specific type of threat? Clearly, facial expressions can only be specifically perceived if they are sufficiently distinct. Pain and disgust are suitable models to address this question because of their minimal expressive difference, which allow for very critical testing. Both states are elicited by harmful or potentially harmful stimuli (that can be threatening to our physical integrity) and are characterized by strong feelings of unpleasantness and both states result in defensive behavior. However, despite of these similarities, the respective threat to the body and the resulting subjective experiences are fundamentally different. Are facial expressions during the experience of pain and disgust specific enough to capture the distinctness of these two negative affective states? When looking at previous findings, there is on the one hand evidence that would suggest high distinctness of facial expressions of pain and disgust whereas on the other hand some findings favor the assumption of expressive overlap between the two facial expressions. Evidence for the latter can be found in research that focused on the facial muscle movements elicited during pain and disgust. Using the Facial Action Coding System (FACS) – which is considered to be the gold standard in facial expression research – it has been shown that the experience of disgust elicits contraction of the eyebrows (Action Unit (AU) 4), nose wrinkling (AU9), upper lip raise (AU10), jaw drop (AU25/26/27), raising of the chin (AU17) and narrowing the eyes (AU6/7). Amongst these facial movements, the activity of the musculus levator labii superior (which leads to the upper lip raise and nose wrinkling) seems to be the most central one whereas for the other movements there is no complete consensus between studies. Interestingly, pain similarly elicits contraction of the eyebrows (AU4), nose wrinkling (AU9), upper lip raise (AU10) and narrowing the eyes (AU6/7). In addition, closing the eyes for more than 0.5 seconds (AU43) has also been shown to occur in the context of pain. Thus, there seems to be a great overlap between the facial movements that are elicited during pain and disgust, which might challenge the idea of distinctness between the two facial expressions. In line with this, observers often confuse facial expressions of pain with those of disgust which might not be surprising given the similarity in facial ovements involved in the two expressions. However, a certain degree of expressive overlap or confusion of both states by observers would not necessarily preclude sufficient distinctness of facial expressions to act as specific warning signals. Especially since observers can distinguish facial expressions of pain and disgust clearly above chance level, there is some evidence for sufficient distinctness of these two facial expressions. Furthermore, the overlap might be in fact less than it seems to be due to the characteristics of the material used for disgust induction. Disgust has often been induced using pictures and films that also contain pain-related content, i.e. individuals with injuries or pictures of ripped off limbs, mutilations, etc.. Hence, it is difficult to decide whether facial expressions elicited by these confounded stimuli are specific for disgust or are also representative for pain. So far, the overlap in facial expressions between pain and disgust has not yet been systematically studied. The study would be worthwhile just because of the proximity of the two facial expressions and the risk of overlap. If the facial expressions are distinct in these two cases they are in other cases too. No study has yet investigated these two facial expressions in one sample; which is the only way to test whether these two facial expressions are – despite similarities – still distinct in the same person (intra-individual comparison). Thus, the aim of the present study was to investigate whether individuals facially encode the specific emotional quality of pain and disgust (leading to distinct facial expressions) or whether they simply encode the similar negative valence and arousal of both states (leading to similar facial expressions). We assessed facial and subjective responses to pain (induced by heat stimuli) and to disgust (induced by pictures) in one sample and used disgust stimuli with and without pain-related content to be able to compare facial expressions elicited by “pure” disgust and by disgust due to pain-confounded contents. Based on previous findings, we hypothesized that pain and disgust elicit a very similar set of single facial muscle movements. However, given that observers can differentiate between pain and disgust expressions above chance level, we expected that the distinctness between the two facial expressions might rely on different combinations of these single facial movements. # Methods ## Participants Sixty healthy volunteers (30 males and 30 females, mean age 22.9 ± 4.3), were recruited via advertisements posted in the university buildings of the University of Bamberg. Exclusion criteria were current experience of acute or chronic pain, psychological illnesses (especially any kind of anxiety disorders) and physical illnesses. None of the participants had taken analgesics, psychotropic medication or alcohol the day before testing. ### Ethics statement All participants provided written informed consent and received monetary compensation (25 €) or course credits for their participation. The study (including the consent procedure) was approved by the ethics committee of the University of Bamberg. ## Materials and procedure ### General protocol The study was composed of two experimental blocks: a pain induction block and a disgust induction block. The order of blocks was balanced across participants, with half of the participants starting with the pain induction block, whereas the other half started with the disgust induction block. In the pain block participants received heat stimulation of non-painful and painful heat intensities. In the disgust block participants viewed pictures of different emotional content: neutral (as fillers), positive (to counteract an unspecific lowering of mood), disgust due to pain-related content (e.g. mutilation) and “pure” disgust pictures (e.g. bodily excrements). In both the pain and the disgust blocks participants were seated in front of a 19-inch computer screen positioned 50 cm in front of them. Each target stimulus (heat or picture stimuli) was preceded by a fixation cross (duration 1s - 3s) to orient eye gaze to the center of the screen and to minimize eye and head movements for later off-line analyses of facial display. The fixation cross was followed by 5 s of heat stimulation (pain block) or by 5 s of picture presentation (disgust block), respectively. After stimulus offset participants had to rate their experiences on separate scales. To familiarize participants with the rating procedures, two familiarization trials were conducted at the beginning of each block. During the whole session, which lasted almost 2 hours, participants sat upright in a comfortable chair. ### Pain block Following a previous protocol that has been shown to successfully elicit facial expressions of pain, pain was induced by use of a Peltier-based, computerized thermal stimulator (Medoc TSA-2001; Medoc Ltd, Ramat Yishai, Israel) with a 3 × 3 cm² contact probe attached to the outer part of the left lower leg (midpoint between ankle and knee). To ensure that painful stimuli were indeed perceived as being painful without exceeding individuals pain tolerance, we adjusted stimulation temperature to the individual pain threshold. Thus, heat pain thresholds were determined first, using the method of adjustment. Participants were asked to adjust pain threshold starting from 38 °C, using heating and cooling buttons (rate of change: 0.5 °C/s), until they obtained a level which was felt as barely painful. This procedure was repeated in 4 trials, the first trial was a familiarization trial. The threshold estimate was the average of the last 3 trials. Thereafter, the “pain block” started. Here, 10 painful stimuli (pain threshold +3°C) and 10 non-painful heat stimuli (pain threshold -3°C) were applied in randomized order. The non-painful heat stimuli were applied to have a neutral reference for facial expression analyses. The painful intensity of +3°C above threshold was chosen since this intensity elicits a painful sensation of mild to moderate intensity and thus, the arousal and valence of this painful intensity should be comparable to the disgust pictures we selected. The temperature increased (rate of rise: 4°C/s) from baseline (38°C) to these pre-set temperatures, was kept constant for 5 s (plateau phase) and returned to baseline. Facial as well as subjective responses (ratings) to each stimulus were assessed. ### Disgust block Color pictures of emotional content (800x600 format) were presented for 5 seconds on the computer screen in a randomized order. Pictures were mostly selected from the International Affective Picture System. (Those IAPS pictures, listed by their identification numbers, are as follows: neutral – 219, 1731, 2745.1, 5551, 5720, 5800, 5900,7009, 7041, 7052; pain-disgust – 3101, 3150, 3261, 3400, 7361, 8230, 9405; disgust – 9301, 9320; happy – 1340, 1440, 1441, 1710, 1750, 2091, 2165, 2501, 4625, 7325). Since the IAPS does not include a sufficient number of disgust pictures, 12 pictures were selected from the internet: 3 pain-disgust pictures (content: decubitus, open fracture, suppurated sore) and 8 “pure” disgust pictures (content: moldy toast, rotten teeth, excrements, a woman vomiting, a man vomiting, snot, vomit in a toilet, infected toenail, spitting person). We made sure that the pictures taken from the internet matched the IAPS pictures with regard to arousal and valence and thus, asked 40 individuals in a pilot study to rate the IAPS and non-IAPS pictures. No difference was found between non-IAPS and IAPS pictures (non-IAPS pictures: valence: 3.4 (SD 1.2), arousal: 5.7 (1.3); IAPS pictures: valence: 3.2 (SD 1.1), arousal: 5.9 (SD 1.2); all p-values\>0.05). We also included neutral pictures from the IAPS to have a neutral reference for facial expression analyses (as we did with the non-painful heat intensities in the pain block). The picture set also included pictures with happy content, which were only presented to avoid a lowering of mood by only showing negative pictures, but were excluded from further analysis. ## Assessment of facial responses Faces of the participants were continuously videotaped throughout both testing blocks using a camcorder (JVC GZ-MG30) mounted on top of the computer screen. Subjects were instructed not to talk during the experimental blocks. A LED behind the subject, visible to the camera, but not to the participant, was lighted concurrently with the (thermal stimuli or pictures) to mark the on- and offset of the stimulation. Facial expressions were coded from the video recordings using the Facial Action Coding System, which is based on anatomical analysis of facial movements and distinguishes 44 different Action Units (AUs) produced by single muscles or combinations of muscles. Two FACS coders identified frequencies and intensities (5-point scale) of all Action Units that occurred during the stimulation (inter- rater reliability =.90 tested in a sub-set (15 %) of the video segments). To segment videos and to enter the FACS codes, we used the Observer Video-Pro (Noldus Information Technology). Time segments of 5 s, beginning just after stimulus onset (in case of the heat stimuli, stimulus onset was defined as start of the 5 second plateau phase), were selected for scoring (the onset of a facial action had to lie in this time window in order to be scored). In total, 20 video segments for thermal stimulation (10 painful and 10 non-painful) and 30 segments of picture presentation (10 neutral, 10 pain-disgust and 10 “pure” disgust) were analyzed per subject. As has been done in preceding studies (especially on facial responses to pain) we combined those AUs that represent similar facial movements (AU1/2, AU6/7, AU9/10 and AU25/26/27). ## Self-Report After stimulus offset, participants were asked to rate their sensations on four separate scales. Two of these scales were Visual Analogue Scales (VAS; which appeared simultaneously on the screen) which assessed pain and disgust intensity, respectively. Participants were asked to rate both intensities by moving a cursor on the 100 mm VAS scales with the endpoints “no pain” and “extremely strong pain” or “no disgust” and “extremely strong disgust”, respectively. The cursor appeared in random positions to avoid response tendencies due pre-selection of scale ranges. Participants had 16 s to provide both VAS ratings. Following the VAS ratings, participants were asked to rate the valence and arousal of the pain and disgust stimuli using Self Assessment Manikins (SAM) scales that appeared on the computer screen (rating was done by mouse click on the manikins or spaces in-between, resulting into 9 categories). Both SAM scales appeared simultaneously on the screen and participants had 12 seconds to provide their ratings. To familiarize subjects, two practice trials were conducted in each block. ## Statistical Analyses ### Ratings of pain and disgust (VAS, SAM) To investigate whether we succeeded in inducing painful experiences in the pain block and disgust experiences in the disgust block, respectively, we compared VAS ratings between the different types of stimuli (painful heat vs. pain- disgust pictures vs. “pure” disgust pictures) using a multivariate analysis of variance (VAS pain and VAS disgust) with repeated measurement. To investigate whether the different types of stimuli elicited comparable levels of valence and arousal ratings, we again used a multivariate analysis of variance (SAM valence and SAM arousal) with repeated measurement. If a MANOVA revealed significant effects, post-hoc T-Tests (bonferroni-corrected) were conducted for single comparisons. ## Facial responses To investigate whether facial responses elicited during pain and disgust induction differ from each other we used a two-step approach. In a <u>first step</u>, we analyzed which individual facial actions are elicited during pain and disgust (step 1a) and whether the strength with which these individual facial actions are displayed (frequency and intensity values) differs between the affective states (step 1b). However, given that facial expressions are not only characterized by single facial actions but more importantly by the specific combination of facial actions, we compared in a <u>second step</u> the occurrence of facial action combination during pain and disgust induction. ### Step 1a: Which individual facial actions are displayed during pain and disgust experiences, respectively? First, we wanted to analyze which single Action Units (AUs) are relevant for pain, pain-disgust and “pure disgust” expressions, respectively. For that purpose, we calculated which AUs occurred with a frequency of at least 5% in the segments recorded (this was done separately for pain, pain-disgust and “pure” disgust stimuli; see for details). This critical level of 5 % was derived from earlier studies. Furthermore, in order for an AU to be classified as being relevant for the respective state, it also had to occur more often during the respective state compared to neutral conditions (neutral pictures or non-painful intensities, respectively). To determine this, effect sizes (Cohen’s *d*) for frequency differences in AUs between pain and non-painful heat, between “pure” disgust and neutral pictures as well as between pain-disgust and neutral pictures were computed. This procedure has been proven to be successful in former studies \[20,24.25\] to identify AUs that are relevant for specific types of affective states. AUs which showed an effect size d ≥.05 (medium effect) were selected as pain- or disgust-relevant facial responses (these AUs are displayed bold). ### Step 1b: Are these individual facial actions that encode pain and disgust displayed with similar strength during pain and disgust induction? In a next step, we analyzed whether those single facial actions that are used to encode pain as well as pain-disgust and “pure” disgust are displayed with similar strength during pain and disgust induction. The strength of each single AU was computed by forming product terms (multiplying the frequency and intensity value of each individual AU). These product terms were then entered into a multivariate analysis of variance with repeated measurement. If the MANOVA revealed significant effects, post-hoc T-Tests (bonferroni-corrected) were conducted for single comparisons. ### Step 2: Which combinations of single facial actions occur during pain and disgust induction? In order to compare combinations of facial actions between pain, pain-disgust and “pure disgust” expression, we focused on those AUs that proved to be relevant for all three affective states (see results of step 1a). These were the AUs 4, 6/7 and 9/10, allowing for combinations of 3 elements at maximum. We assessed in which combinations these selected AUs were displayed during pain and during disgust induction and then used chi-square analyses to compare the frequency of facial action combinations between pain, pain-disgust and “pure” disgust expressions. Statistical analyses were run by means of the statistic software SPSS 21.0. Findings were considered to be statistically significant at p \< 0.05. # Results ## Ratings of pain and disgust (VAS, SAM) VAS: The MANOVA revealed that VAS intensity ratings differed significantly between the different types of stimuli (painful heat vs. pain-disgust pictures vs. “pure” disgust pictures) (F(4,236)=234.00; p\<0.001). As the univariate results showed, these differences were significant for the VAS pain ratings (F(2,118)=331,86; p\<0.001) as well as for the VAS disgust ratings (F(2,118)=146.47; p\<0.001). As expected, pain stimuli were rated to be more painful compared to the disgust pictures (both pain-disgust and “pure” disgust; p-values of the post-hoc comparisons: p\<0.001; see also). Moreover, the pain- disgust pictures were also rated to be more painful compare to the “pure” disgust pictures (p-value of the post-hoc analysis: p\<0.001). With regard to the VAS disgust ratings, participants rated the disgust pictures (both pain- disgust and “pure” disgust) to be more disgusting compared to the pain stimuli (p-values of the post-hoc comparisons: p\<0.001; see also). VAS disgust ratings of the two picture categories (pain-disgust and “pure” disgust) did not differ significantly (p-value of the post-hoc analysis: p=0.151). SAM: The MANOVA revealed that the different types of stimuli (painful heat vs. pain-disgust pictures vs. “pure” disgust pictures) elicited significant differences in the SAM ratings (F(4,236)=7.33; p\<0.001). As univarate results revealed, both valence (F(2,118)=10.30, p\<0.001) as well as arousal ratings (F(2,118)=12.40; p\<0.001) changed significantly depending on the types of stimuli. However, as post-hoc comparisons revealed, these differences were only due to pain-disgust pictures being rated to be more arousing and more negative in valence than the pain and “pure” disgust stimuli (all p-values \<0.05); whereas pain stimuli and “pure” disgust pictures elicited comparable levels of arousal (p=0.562) and valence ratings (p=0.989) (see also). *In summary*, our pain and disgust inductions produced similar levels of negative valence and arousal because even though the pain-disgust pictures elicited higher levels of valence and arousal compared to pain and “pure” disgust stimuli, the mean difference in descriptive values between the three types of stimuli was always lower than 1-scale-point on the SAM scales. In contrast, pain and disgust inductions produced clear differences in pain and disgust VAS ratings and thus, participants appeared to be completely aware of the particular origin of the affective state. Accordingly, we produced conditions that will allow us determining whether facial responses only indicate general valence and arousal of affective states or whether they reflect the specific type of affective state. ## Facial expression of pain and disgust ### Step 1a: Which single facial actions are displayed during pain and disgust experiences, respectively? lists all those AUs that occurred above a critical occurrence level of 5% during pain, pain-disgust and “pure” disgust induction, respectively. As can be seen, pain induction elicited more single facial actions compared to the disgust induction procedures. However, when focusing on those facial actions that proved to be relevant for each of the three types of affective states (medium effect size for the difference to the respective control conditions) there is a great overlap between pain and disgust. As can be seen in, the facial actions “brow lowering” (AU4), “orbit tightening” (AU6/7) and “levator contraction” (AU9/10) encoded pain as well as the two types of disgust experiences. The only difference between affective states was that pain experience was additionally accompanied by “smiles” (AU12) and “mouth opening” (AU25/26/27) whereas “pure” disgust also led to “brow raising” (AU1/2). Thus, pain and disgust experiences were accompanied by mostly the same facial actions (see also were examples of facial expressions are given). ### Step 1b: Are these single facial actions that encode pain and disgust displayed with similar strength during pain and disgust experiences? The MANOVA revealed that those single facial actions that encode both pain and disgust are displayed with different strength during pain and disgust induction (F(6,234)=9.39; p\<0.001). As the univariate findings showed, AU4 (F(2,118)=19.63; p\<0.001) as well as AU6/7 (F(2,118)=9.11; p\<0.001) were displayed with different strength depending on the type of affective state, whereas AU9/10 did not differ between affective states (F(2,118)=0.95; p=0.390). When conducting post-hoc analyses for single comparisons we found that AU 4 was displayed less strongly in response to pain induction compared to the two categories of disgust pictures (both p-values \<0.010, see also). Moreover, AU 4 was displayed more strongly in response to the pain-disgust compared to the “pure” disgust pictures (p\<0.001). With regard to AU 6/7, this facial action was displayed most strongly in response to pain induction compared to disgust induction (pain-disgust and “pure” disgust pictures; both p-values \<0.050, see also) and did not differ between pain-disgust and “pure” disgust (p=0.686) (see also were examples of facial expressions are given). Given the small but nevertheless significant differences in arousal ratings between affective states (with pain-disgust pictures being rated as being more arousing compared to the other two affective states), we wanted to ensure that our findings on facial responses are indeed not affected by these differences in arousal. To control for this, we divided the group of subjects into those who rated the pain-disgust pictures as more arousing than the other two affective states and those who rated them as equally arousing (median split of the averaged difference scores). We found that the findings on facial responses (as displayed) were not affected by differences in arousal ratings between affective states, given that the same findings were obtained in those individuals who rated the affective states as being differently arousing and in those individuals who rated the affective states as being equally arousing. ### Step 2: Which combinations of single facial actions occur during pain and disgust induction? As can be seen in, we found that in more than half of the segments (both in the pain and in the disgust block) facial expressions were only composed of one single facial action. Especially lowering of the brow (AU4) and orbit tightening (AU6) were often displayed alone (indicated by being combined with “∅”). The most frequent facial action combination was the lowering of the brow together with orbit tightening (combination AU4, AU6/7) both during pain and disgust induction. Despite these similarities, chi-square tests (goodness of fit) revealed significant differences in facial action combinations between pain and “pure” disgust (χ²(6)=20.64; p=0.002) as well as between pain and pain-disgust (χ²(6)=22.75; p\<0.001) induction. In contrast, the two disgust categories (“pure” and pain-disgust) elicited a similar distribution of facial action combinations (χ²(6)=5.72; p=0.46). The standardized residuals (stand. res.) - which indicate the importance of each cell to the chi- square value - revealed that brow lowering (AU4) by itself (stand. res. 3.1/3.1), levator contraction (AU9/10) by itself (stand. res. 2.5/1.3) and the combination of these two facial actions (stand. res. 1.6/2.3) occurred markedly more frequently during disgust (“pure” disgust/pain-disgust) compared to pain induction. In contrast, orbit tightening (AU6/7) by itself (stand. res. 3.3/5.7) and the combination of orbit tightening and brow lowering (AU4) (stand. res. 2.8 for the difference to “pure” disgust) occurred markedly more frequent during pain compared to “pure” disgust/pain-disgust induction. *In summary*, pain and disgust experiences seem to elicit the same single facial actions. However, the strength, with which these single facial actions are displayed during pain and disgust differs significantly, even when controlling for differences in arousal ratings. Moreover, when considering combinations of single facial actions, we also found clear differences between pain and disgust. Whereas brow lowering (AU4), levator contraction (AU9/10) and the combination of these two facial responses are more pronounced during disgust, pain seems to be encoded by a more pronounced orbit tightening (AU6/7) (often in combination with brow lowering (AU4)). # Discussion The aim of our study was to investigate whether facial displays occurring during the experience of pain and of disgust overlap substantially and indistinguishably or whether they are distinct enough to communicate different states. Pain and disgust were selected for this comparison because both are elicited by actual or potential harm to the body, are characterized by strong feelings of unpleasantness, result into defensive behavior and facial expressions of both states are often confused by observers. Facial responses to pain and disgust are ideal models to test the distinctiveness of facial expressions because of their expressive similarity, which guarantees - in case of proven differences in facial encoding - differential encoding also for most of the other emotional states. It has to be kept in mind that the strong similarity between the two states exists, although the subjective experience clearly reflects the specific type of threat and allows for differential self- reports. Our main findings were that nearly the same single facial actions were elicited during the experience of pain and disgust. However, these facial actions were displayed with different strength and were differently combined depending on whether pain or disgust was experienced. We will discuss these findings in more detail below. As stated above, we found that nearly the same single facial muscle movements were elicited during the experience of pain and disgust. More precisely, the contraction of the eyebrows (orbicularis oculi muscle; AU 6/7), contraction of muscles surrounding the eyes (corrugator muscle; AU4) and lifting the upper lip (levator muscle; AU9/10) were observed both during pain and disgust induction. This overlap in facial actions is well in line with previous findings which have also reported similar facial actions in response to pain and disgust induction (although this is the first study to directly compare facial responses to pain and disgust in one sample). Given that we induced both pain and disgust in one sample we were also able to directly compare the strength to which each of these facial actions was displayed during pain and disgust experiences. (For note, the ratings of valence and arousal were very similar during disgust and pain.). We found that although contraction of the muscles surrounding the eyes (AU6/7) occurred both during pain and disgust, this facial action was more relevant for pain than it was for disgust (see also examples displayed). This is in line with previous assumptions that the orbicularis oculi activation is the most prominent facial response to pain. In contrast, the contraction of the eyebrows occurred more strongly during disgust compared to pain induction. Moreover, facial expressions of pain and disgust could be even better differentiated when considering how these single facial actions were combined. The most frequent facial response that can be observed when an individual is experiencing pain seems to be the contraction of the muscles surrounding the eyes (AU6/7), either by itself or in combination with contraction of the eyebrows (AU4), which accounted for nearly 2/3 of all facial responses to pain in the present study. Facial expressions of disgust, on the other hand, seem to be more variable than pain expressions. Most often contraction of the eyebrows (AU4) is displayed by itself. In contrast to pain, disgust is also more often encoded by raising the upper lip/wrinkling the nose (AU9/10) by itself as well as by the combination of eyebrow contraction and upper lip raise (see also for examples of facial expressions). Consequently, although the same facial actions are used to facially encode pain and disgust, these facial actions are differently combined and displayed with different strength during pain and disgust experiences, thus, suggesting that the facial displays can indeed signal categorically distinct states although similar muscles are involved. The literature on decoding of affective states by observers has introduced two perspectives a discrete-category and a dimensional view, which are worthwhile being considered here; although our experiment dealt with the encoding of facial displays by senders and not with the decoding by receivers. According to the dimensional view facial expressions mainly convey values on the dimensions of valence and arousal. Given that pain and disgust elicit very similar valence and arousal ratings one would expect – based on the dimensional view – also very similar facial responses to pain and disgust. In accordance with this expectation (namely that facial responses mainly convey valence and arousal information), we indeed found a great overlap of single facial actions that encode both pain and disgust. In other words, the type of single facial actions being displayed might convey mainly information on valence and arousal to the observer. However, we also found that despite the great overlap of single facial actions, facial responses to pain and disgust are also able to signal categorically discrete states. This is in line with the discrete-category view hypothesized that reading the facial expression of emotions results into clearly differential classifications because each emotion has its specific facial readout. Our data support such a hypothesis with regard to the affective states pain and disgust. When we took into consideration not only the type of single facial actions but the strength to which they are displayed as well as how they are combined, facial expressions of pain and disgust were clearly distinct. Thus, the types of single facial actions being elicited seem to convey information on valence and arousal (dimensional view) whereas the strength and the combination in which they are displayed seem to form the basis for communicating differentially emotional states, allowing for their identification by an observer (discrete-category view). Given that the differential classification of disgust and pain is amongst the most difficult ones, our data also corroborate a more general conclusion. We used two different picture categories to induce disgust, namely pictures showing pain-related content (e.g. mutilation) as well as pictures without any pain-related content (e.g. body waste products) to investigate whether the previously reported overlap between facial responses to pain and disgust might be simply due to facial expressions of disgust having been elicited by pain- confounded stimuli. We found no clear differences between facial expressions in response to pain-disgust and “pure” disgust pictures. Moreover, facial responses to the pain-disgust pictures were not more similar to pain than the “pure” disgust responses. Thus, our data clearly suggest, that this confound of disgust with pain-related content is not responsible for the great overlap in facial actions elicited during the experience of pain and disgust. As a limitation of the present study, it has to be mentioned that the methods used to induce pain and disgust were quite different. Whereas for the pain induction procedure, a physical stimulus was used to directly induce the affective state “pain”, we induced the affective state “disgust” by use of picture stimuli, which are only icons of the actual threat to the body. The reason for using pictures to induce disgust was that we wanted to include both “pure” disgust and pain-confounded disgust stimuli, which cannot be all made available as physical stimuli. As explained above, we did this to control - for the first time - for this confound of pain and disgust contents in some of the IAPS designated disgust pictures. In summary, investigating facial and subjective responses to pain and disgust induction in one sample of participants revealed that the single facial actions elicited during the two states are rather similar than different. This similarity is paralleled by the similarity of the valence and arousal ratings. However, when considering the strength with which each of these single facial actions are displayed and how these single facial actions are combined during the experience of pain and disgust, significant differences occurred. Consequently, facial expressions of pain and disgust seem distinct enough to also encode the specific type of the threat to the body. Thus, facial expressions seem to be able to signal both, the general valence as well as arousal and the specific threat to the body. This implies that the differential decoding of these two states by an observer is possible without additional verbal or contextual information. This is of special interest for clinical practice, given that raising awareness in observers about these distinct differences could help to improve the detection of pain in patients who are not able to provide a self-report of pain (e.g. patients with dementia). Moreover, in future studies the question should be answered how the decoding of the facial activity during pain and disgust is affected when systematically varying the strength and combination of single facial actions (e.g. by using animated avatar facial expressions). The findings of these studies may confirm that the features we found in the present study are indeed crucial for the identification of pain and disgust by observers. [^1]: The authors have declared that no competing interests exist. [^2]: Conceived and designed the experiments: MK SL. Performed the experiments: JP SH. Analyzed the data: MK JP. Contributed reagents/materials/analysis tools: MK. Wrote the manuscript: MK JP SH SL.

# Introduction Accurate partners or research followers are imperative in scientific research. Mining deeper author relationships in the academic network involving various significance is achievable, which can help scholars establish potential cooperative or reference relationships. The research visual field can also be expanded, and the research content can be deepened. The establishment of a citation relationship among scholars is mainly based on the correlation of their research contents. If this relationship is deeply mined, potential partners could be found. Given that the citation data were preserved completely and accurately in document database, the processes and results of relationship mining would be feasible and reliable. As a mature quantitative research method in bibliometrics and scientometrics, citation analysis is extensively used in scientific evaluation, scholarly communications, academic behavior analysis, and information retrieval. Author citation analysis mainly includes three types: author co-citation (AC), author bibliographic coupling (ABC), and author direct citation (ADC), which is collectively called “tripartite citation analysis” in this study. For example, in a field, both papers of Authors A and B were cited by the same paper; thus, A and B have a co-citation relationship marked as AC (A, B). Authors C and D both cite the same paper in their respective articles; C and D thus have a bibliographic-coupling relationship marked as ABC (C, D). In addition, Author D cites a paper written by A in his bibliography, or vice versa; thus, D and A have a direct-citation or cross-citation relationship marked as ADC (A, D). On mining author relationship in scholarly networks based on tripartite citation analysis, two key questions should be addressed. 1. Which bibliographic-coupled or co-cited authors did not collaborate yet or do not cite each other regularly? If we called these relationships as potential communication relationship (PCR), and the latter two as actual communication relationship (ACR), could the discovery and usage of PCR contribute to the achievement of the ACR? Furthermore, how is the quantitative relation of PCR and ACR? This concern is the first point to be investigated in this study. 2. In view of the similarities or diversity among tripartite citation relationships at the author level, how can tripartite relationships be synthetically used in discovering deeper author relationships serving for broader scholarly communication and relevant recommendations? According to these primary relationships, deducing the integrated relationships between Authors A and C, or B and D, even B and C, the association strength in these potential relationships is the second point to be answered in this study. # Related studies ## Separate study of tripartite citation analysis AC analysis is the most commonly used method for the empirical analysis of disciplinary paradigm, and has been frequently studied and improved. Some AC analyses have been conducted since Small introduced document co-citation analysis and White and Griffith developed AC analysis. Bibliographic coupling was proposed as early as 1963. However, author coupling relationship has not gained considerable attention until it was formally proposed and empirically studied by Zhao and Strotmann; the authors named this method ABC analysis, which can be used to complement AC analysis in comprehensively viewing the intellectual structure by mapping the research activities of active authors for a realistic picture of the current state of research in a field. In comparison with co-citation and bibliographic coupling, direct citation (sometimes also called inter-citation or cross-citation) is a direct citation relationship without a third-party paper. Paper-level direct citation has been used in different scenarios, such as research front detection, domanial historiography mapping, and publication classification. Boyack and Klavans found that bibliographic coupling slightly outperforms co-citation analysis and that direct citation is the least accurate science mapping. Shibata et al. revealed that direct citation could detect large and newly emerging clusters earlier, indicating that the research front detection exhibited the best performance, whereas co-citation showed the worst. Numerous studies have focused on journal direct citation; several key research achievements have shown that journal direct citation can reveal the academic influence of journals, as well as the theme evolution and field division of periodicals. Direct citation can also be used at the macrolevel, such as citation between subject categories, to build the global map of science. Wang et al. extensively studied ADC analysis, which can be used to determine author relationship from another angle and reveal the knowledge communication and disciplinary structure in scientometrics. This process was then named as ADC analysis by Yang and Wang. ## Comparative study of tripartite citation analysis The three types of citation analysis methods can reveal author relationships in a field in various ways. Some studies have focused on the comparative analysis of these methods, even comparing them with other author co-occurrence network analysis methods, such as co-authorship (CA), word-based author coupling (WAC), and journal-based author coupling (JAC). Related studies are especially represented by Lu, Yan, and Qiu et al. Lu and Wolfram conducted a comparative study of word-based, topic-based, and author co-citation approaches to measure author research relatedness. Findings show that two word-based approaches produced similar outcomes, except in the case in which two authors were frequent co-authors for the majority of their articles, and that topic-based approach produced the most distinctive map. Yan and Ding explored the similarities among six types of scholarly networks (bibliographic-coupling, direct citation, co- citation, topical, co-authorship, and co-word networks) aggregated at institution level; they also detected high similarity between co-citation and direct citation networks. Moreover, the authors recommended the use of hybrid or heterogeneous networks to study research interaction and scholarly communications. Qiu and Dong constructed five types of author co-occurrence networks in the field of information library sciences, such as CA, WAC, JAC, AC, and ABC. In their research, the capabilities of different types of author co- occurrence relationships in revealing scientific structure are compared through hierarchical clustering and correlation analysis by quadratic assignment procedure (QAP) test. ABC analysis also exhibited a significant advantage in revealing discipline structure and presented the highest correlation with other networks. The idea of combining different author co-occurrence networks in scholarly communication and intellectual structure analysis was also proposed. ## Combined study of tripartite citation analysis The combination of these tripartite citation analysis methods (including AC, ABC, and ADC) has been extensively studied. Small proposed a method for effectively combining them; however, only few researchers have adopted this combined linkage technique at a large scale. Persson and Gómez-Núñez et al. have attempted to combine these citation measures in a normalized manner to weigh existing direct citation relationships between articles or journals. According to Persson’s research, direct citations weighted with shared references (bibliographic coupling) and co-citations at the article level could be better applied to domain intellectual structure detection. In addition, citation-based measure calculation and integration (involving co-citation, bibliographic coupling, and cross citation) at the journal level was also proposed and proven in the application of refining the journal classification, improving journal ranking, and further updating the subject classification structure proposed by Gómez-Núñez et al. At the author level, Wang proposed a comprehensive and comparative approach by combining CA, AC, ABC, ADC, and author keyword coupling (AKC), supplemented by social network analysis (SNA), to evaluate the academic impact of the core authors in the field of scientometrics. Existing studies are focused on intellectual structure detection and optimization according to tripartite citation analysis. The assessment of the author scholarly impact by combining various citation analysis is also paid attention in few studies. ## Mining author relationship in scholarly networks Practical research on the discovery of potential author relationships in communication networks by tripartite citation analysis is limited. Currently, approaches for identifying potential collaboration mainly involve machine- learning techniques, link-prediction techniques, and SNA. Zhang and Yu proposed supervised machine-learning approaches to predict research collaborations by the semantic features in the field of biomedicine and author network topological features, including co-authorship network connectivity, research profile similarity, collective productivity, and seniority. Chen and Fang developed a latent collaboration index model for evaluating the collaboration probability among patent assignees by incorporating two network-related factors (i.e., degree and network distance) and complementary factors (i.e., assignees types, geographical distances, and topic similarities). Guns and Rousseau introduced a method for predicting or recommending high-potential future collaborations based on a combination of link prediction and machine-learning techniques. Daud et al. used discriminative and generative machine-learning techniques for predicting the emerging scholars in a co-author network based on three classes of features (i.e., author, venues, and co-authorship). These studies have focused on the use of combined relationships of direct citation and co-authorship in scholarly networks without considering other relation networks in discovering potential collaboration. # Data and methodology ## Basic data Scientometrics is an international journal, launched in 1978. The journal covers all aspects of scientometrics and published 46.31% of scientometrics research paper of the world. Given the Scientometrics Journal as the representative communication channel in the field of scientometrics, the characteristic trends and patterns of the past decades in scientometric research become evident. Bibliographic data from the journal of Scientometrics have formed the main data object in some of recent empirical studies focusing on mapping the intellectual structure or detecting social network community in the field of scientometrics. Therefore, this study also employed bibliographic data that cover all types of documents published in Scientometrics in 1978–2011 and 2011–2015 as representative experimental data object in the field of scientometrics. All data were retrieved from Web of Science (WOS). Data retrieval in the first period was completed in the middle of 2011; the data will be used in deduction and mining. Meanwhile, data retrieval in the second period was completed in the middle of 2015; the data will be used in verifying the results obtained by the first sample. The first retrieval recalled a total of 2,989 documents, of which 2,982 include author information, 2,815 include references, and 2,812 include both the author information and references. The most prominent contributors are pioneers in most research studies. For example, when evaluating author influence levels, only pioneer authors were considered, which was done by most studies only considered (such as, in uncovering knowledge communication, and in revealing implicit relationship) because the cited references only contain the first listed author of the cited document in the database of WOS. Moreover, a complex contribution allocation problem existed when considering all authors in relation analysis. This problem has not been fully solved, which is beyond the scope of this research topic. Thus, only the first authors of each paper were considered in the current study. In counting only the first author in the citation data, the results include 35,796 citations, 16,057 cited authors, and 1,484 citing authors (as first signature identity in the publications). Each author’s name is identified by his surname and first initial only. The second dataset covers 1,318 documents in total, all of which include the author information and 1,308 include references (involving 27,083 valid citations). ## Methodologies Thus far, a uniform standard for identifying core authors in scientometrics has not been developed. Lotka and Price identified excellent scientists according to the number of their published papers during the study on scientists’ productivity and activity patterns. Garfield treated those authors with high- cited frequency from SCI as excellent scientists. Some scholars also adopted different approaches to evaluate core authors in information science; however, they all considered both the number of published papers and the cited frequency. Therefore, the present study identified 94 authors who have published 5 or more papers and received 10 or more citations as core authors from the first dataset. AC, ABC, and ADC analyses are used in discovering author relationships with co- citation, bibliographic coupling, and direct citation in scientometrics, respectively. CA and AKC analyses were also complementarily used to discover or verify author relationships in this study. AKC analysis was introduced by Liu et al. and was formally proposed by Liu and Zhang. This method was re-introduced and compared with CA and ABC analyses by Qiu and Wang, Liu and Wang, Song and Wu, and Yang et al.. The AKC analysis is supposed to expand the keyword co- occurrence relationship at the author level; it can also be used to establish author relationships through the keyword coupling strength of authors’ oeuvres. The oeuvres can be used to discover PCRs among authors bound by the same research themes and then describe the knowledge structure of a field or discipline. The networks of CA and AC were directly constructed by their co-occurrence relationships in the same records. The network of ADC comprised two-way direct citing network between author pairs (i.e., symmetrized by summing the two directional citation values as the total correlation score). The citing and cited links should be equally treated as the direct relationship between author pairs. Thus, the summing symmetrization was selected instead of the lowest or highest value method or even an asymmetrical matrix. However, the symmetrizing processcould be improved by involving the total number of citations and references of authors’ publications to eliminate the effect of the absolute value. Although the original value can reflect an actual situation, the direct citation frequencies must be normalized. However, such normalization could only be done in another study, because researchers have not reached a consensus on which measure is most appropriate for normalization purposes. For ABC and AKC, basic matrixes, including authors\*cited reference matrix and authors\*keywords matrix, were initially generated and then transformed into ABC and AKC networks via formulas, selecting the minimum method to calculate the coupling strength as suggested by Ma. All of the original co-occurrence matrixes including AC, ADC, ABC, CA and AKC are supplied in the Supporting Information (– Tables corresponding to the period of “Before 2011”; – Tables corresponding to the period of “After 2011”.) Co-occurrence analysis and deductive reasoning methods are used in mining deeper and more potential author relationships based on the original tripartite citation analysis. VBA program can process all types of citation analysis data. The final results of author relationship mining will be visualized by the Network Workbench Tool software with the analysis of MST-PathFinder Network Scaling. The use of PathFinder can simplify the network and highlight its important structural features and core associated nodes. This method was used in this study to highlight the visualization of the network and improve map readability. The QAP is a unique method of measuring relationships in relational data. It compares the value of various corresponding elements in two (or more) squares and gives the Pearson correlation coefficient between two matrixes by comparing the corresponding grid values in each square. A non-parametric test is performed on the coefficients based on the replacement of the matrix data. A comparison on proximity results in this study was conducted using QAP, and the statistics process (including centrality measurement) was annotated in the documentation of Ucinet software. # Study process ## Discovery of PCRs In this study, five original relation matrixes (including CA, AC, ABC, ADC, and AKC) were first developed. These five matrixes were compared by QAP, and the result was saved and marked as QAP1. Excluding ACR (including CA and ADC matrixes) from PCR (including AC and ABC matrixes), the AC′ and ABC′ matrixes could be obtained. The AC′, ABC′, and AKC matrixes were re-compared, and the result was marked as QAP2. Then, a comparison between QAP1 and QAP2 was performed. AKC is based on the similarity of research themes (can be called “inherent connection”), whereas AC and ABC are based on citation relationships (can be called “exterior connection”). When the inherent and exterior connections are highly consistent with each other, the PCR is assumed to convert into ACR. To test this assumption, the results obtained by PCR from the ACR relationships were compared with the AKC matrix (2011–2015) and ACR matrix (2011–2015). ## Deep relationship mining between author pairs In this study, the tripartite citation analysis could be applied in deep relationship mining at the author level. To make these relationships comparable, original relation matrixes should be normalized. The normalization method was based on Salton’s cosine similarity measures, which results in similarity values ranging between 0 and 1. The following five steps (some of the steps are shown) aid in determining author relationship mining based on tripartite citation analysis, such as “A–C,” “B–D,” and “B–C,” which has been discussed earlier. These steps could also be regarded as the algorithm in relation mining. The implication of each variable A, B, C, and D refers to the author of the matrix, L; Q refers to the relationship between the authors in the adjacency list O; and P refers to the relationship between the authors in the adjacency matrix. 1. *First step: Obtaining the fundamental citation relationship with strength (\>0) among the core authors from the original matrixes*. Tripartite adjacency matrixes were transformed into the corresponding adjacency list: AC list {L_1i, Q_1i} versus matrix {O_1i, P_1j}; relational degree X_i (i stands for the ID of the author pair) in the list can replace X_ij (i/j stand for different authors in the matrix). ABC list {L_2i, Q_2i} versus matrix {O_2i, P_2j}, and relational degree Y_i versus Y_ij. ADC list {L_3i, Q_3i} versus matrix {O_3i, P_3j}, and relational degree Z_i versus Z_ij. We used the adjacency list for the calculation. 2. *Second step: Filtering no-explicit-relationship author pairs*. The no-relationship author pairs (X_i = 0, Y_i = 0, Z_i = 0, and no cooperation) were filtered as {O_4i, P_4j} in the adjacency matrix and {L_4i, Q_4i} in the adjacency list, which formed the basic object in the subsequent analysis. 3. *Third step: Mining the relationship of A–C from {L_1i, Q_1i} {L_3i, Q_3i} {L_4i, Q_4i}*. {L_4i, Q_4i} was remarked as {A_k, C_k} (k stands for the number of author pairs) to find the D_k with the relations {A_k–D_k, C_k–D_k}. Looking for the synchronous relations with strength between A_k and D_k, and C_k and D_k from {L_1i, Q_1i} {L_3i, Q_3i}, and matching the author pairs in {A_k, C_k}, the pseudocode is as follows: If one author in the pair of {A_k, C_k} = one author in a pair of {L_1i, Q_1i}, and another one in the pair of {A_k, C_k} = one author in a pair of {L_3i, Q_3i}, and another one in the pair of {L_1i, Q_1i} = another one in the pair of {L_3i, Q_3i} then the “one author in the pair of {A_k, C_k}” (so as the “one author in a pair of {L_1i, Q_1i}”) as C_α, the “one author in a pair of {L_3i, Q_3i}” (so as the “another one in the pair of {A_k, C_k}”) as A_α, and the “another one in the pair of {L_1i, Q_1i}” (so as the “another one in the pair of {L_3i, Q_3i}”) as D_α are marked. Finally, A_α and C_α could be connected according to D_α, and the final relationship strength of A_α and C_α would be the top value in all of the correlation scores (respectively equaling to the products of Y_k and Z_k). 4. *Fourth step: Mining the relationship of B–D from {L_2i, Q_2i} {L_3i, Q_3i} {L_4i, Q_4i}*. {L_4i, Q_4i} was remarked as {B_k, D_k} (k stands for the number of author pairs) to find the A_k with the relations {A_k–D_k, A_k–B_k}. The synchronous relationship with strength between A_k and D_k, and A_k and B_k were searched from {L_2i, Q_2i} {L_3i, Q_3i}, and the author pairs in {B_k, D_k} were matched. This process is similar to the process of A–C. Thus, the pseudocode was omitted. Finally, connected author pairs {B_β, D_β} with relationship strength X_k multiplied by Z_k could be acquired according to their co-connection with A_k. 5. *Fifth step: Mining the relationship of B–C from {L_1i, Q_1i} {L_2i, Q_2i} {L_3i, Q_3i} {L_4i, Q_4i}*. The remaining (no relationship such as A–C and B–D) of {L_4i, Q_4i} were remarked as {B_k, C_k} (k stands for the number of author pairs) to find the A_k and D_k with the relationship {A_k–D_k, A_k–B_k, and C_k–D_k}. The synchronous relationship with strength between A_k and D_k, A_k and B_k, and C_k and D_k were searched from {L_1i, Q_1i} {L_2i, Q_2i} {L_3i, Q_3i}, and the author pairs in {B_k, C_k} were matched. The pseudocodes are as follows: If one author in the pair of {B_k, C_k} = one author in a pair of {L_2i, Q_2i}, and another one in the pair of {B_k, C_k} = one author in a pair of {L_1i, Q_1i}, and another one in the pair of {L_2i, Q_2i} = one author in the pair of {L_3i, Q_3i}, and another one in the pair of {L_1i, Q_1i} = another one in the pair of {L_3i, Q_3i} then marking the “one author in the pair of {B_k, C_k}” (so as the “one author in a pair of {L_2i, Q_2i}”) as B_χ, “another one in the pair of {B_k, C_k}” (so as “the one author in a pair of {L_1i, Q_1i}”) as C_χ, one author in the pair of {L_3i, Q_3i} (so as the “another one in the pair of {L_2i, Q_2i}”) as A_χ, and another one in the pair of {L_1i, Q_1i} (so as the “another one in the pair of {L_3i, Q_3i}) as D_χ Finally, B_χ and C_χ could be connected according to A_χ and D_χ, and the final relationship strength of B_χ and C_χ would be the top value in all of the correlation scores (respectively equaling to the products of X_k, Y_k, and Z_k). Thus far, all relationships among author pairs in {L_4i, Q_4i} had been established. According to the above algorithm, potential relationships among no-direct- relationship core author set could be generated by the VBA program and Access databases. Finally, the comparison of new relationships and direct correlations (including CA, AC, ABC, ADC, and AKC) in 2011–2015 would be performed to identify the effectiveness of citation mining applied in the detection or promotion of more potential communications. # Results and discussion ## Analysis results of AC, ABC, and ADC According to the tripartite citation analysis of AC, ABC, and ADC, we obtained three original relation matrixes and the corresponding normalized matrixes. These tripartite matrixes and the AKC matrix could be visualized by the Network Workbench Tool (Figs –). The core nodes in each network are different, as shown in Figs. In the AC network, “(Moed HF, Narin F, Vlachy J)–Garfield E–Braun T–Schubert A–Glanzel W–Egghe L–Rousseau R–Thelwall M” are core associated scholars, all of whom form the main path in the network. In the ABC network, the main associated scholars include “Schubert A–Glanzel W–Meyer M–Leydesdorff L,” in which new core nodes, such as Garg KC, Bar-ilan J, Guan JC, and Zitt M, also emerge. In the ADC network, Leydesdorff L becomes the superior core node, and the associated path of “Abramo G–Glanzel W–Leydesdorff L–Bornmann L–Garfield E” becomes the main path. In the AKC network, “Schubert A–Rousseau R–Vinkler P–Glanzel W–Leydesdorff L” becomes the major associate scholars, and the key associations of Thelwall M, Guan JC, Zitt M, and Glanzel W are also reflected. These scholars are also at the heart of correlation formation among other scholars. Generally, the main connected path of “Garfield E–Schubert A–Glanzel W–Leydesdorff L” is more consistent in these four networks. However, the difference is also distinct, such that Garfield E is the main supporter for most core paths in the AC network, while the main supporter in the ABC and ADC networks are Glanzel W and Leydesdorff L, respectively. In view of the ADC revealing a more direct relationship, Leydesdorff L is more likely to be the builder of the potential connection. As shown in Figs, author relevance was preliminarily identified by different citation methods. For example, *Leydesdorff L* has the strongest correlation with other authors in the direct citation networks, whereas the core correlation of the three types of indirect network (PCR) is relatively low, and some are even weak in the cooperative correlation. Meanwhile, *Narin F* presents the highest correlation degree in the AC network, and the correlation degree is comparatively lower in other networks. *Bornmann L* and *Sooryamoorthy R* are strongly correlated with the ADC and AKC networks, respectively. However, their correlations are low in other networks. In terms of partnership, these two authors do not establish any cooperative relationship with other co-authors. *Glanzel W* has the core link status and high centrality degree in all networks, followed by *Schubert A* and *Braun T*. In addition, *Bonitz M*, *Nagpaul PS*, *Mccain KW*, *Eto H*, *Stefaniak B*, *Krauskopf M*, *Wagner-Dobler R*, and *Macias-Chapula CA* have established indirect relationship in AC/ABC/AKC network. However, no direct relationship is observed in CA/ADC. Ucinet software was used to calculate the centrality measurement of the five types of network. The top ten authors are shown in. In, ND is the abbreviation of NrmDegree, which represents the normalization of degree. ## Results of PCR discovery In this comparative study, AKC analysis was applied to produce the AKC matrix, in which the implementation process is similar with that of the ABC analysis (i.e., the authors are correlated with one another by indexing the same keywords). presents the result of the QAP correlation test of CA, AKC, AC, ABC, and ADC matrixes. The results show that the correlation between the CA and ABC matrixes is the strongest, followed by the AC and ADC matrixes, and the AC and ABC matrixes. These findings indicate that the current study and the topic structure revealed by these three pairs are perhaps the most similar, or can be mutually complementary. In addition, the ABC matrix generally has the highest degree of correlations compared with all other relationship matrixes, which to some extent shows that the application of the ABC analysis can more accurately reveal the scientific structure of the disciplines. One of the possible reasons for using this analysis method is to divide into research groups and discover the subject structure by numerous scholars. This result supports the findings of Yan and Qiu, both of which revealed that ABC nearly has the highest similarity with other networks at the author level. Furthermore, the correlation coefficient of AKC and ABC matrixes is at a middle level, which indicates that AKC and ABC analyses share similarities to a certain extent. This finding is consistent with the conclusion of Yang. Excluding ACR connections (including CA and ADC) from PCR (including AC, ABC, and AKC), the matrixes of AC′, ABC′, and AKC′ were obtained. The QAP correlation test for the new three matrixes was performed, with results shown in. The two groups of correlation strengths in successive QAP results were compared (marked in color red). The comparison shows that the relation degrees among AC′, ABC′, and AKC′ could also maintain significant correlations, especially AKC′ and ABC′, which share higher relevancy than the original matrixes. This condition indicates that these authors connected by PCR are likely to produce academic exchanges based on similar themes. The three new matrixes with ACR connections from 2011 to 2015 were further compared in. According to the QAP analysis, the correlation coefficient between the new ACR (after 2011) and the old PCR (before 2011) is 0.225 (p\<0.001). This result can also sustain the assumption about applying PCR in the detection of new academic exchanges. We converted the new relationships into author pairs and analyzed them with Pearson correlation. We found that the three previous relationships showed a more apparent correlation with the new PCR and ACR. Among those relationships, ABC has the strongest correlation and the highest predictability. In addition, the previous and new PCRs are consistent, and the correlation between the new PCR and ACR is significant. Meanwhile, ABC also reflects the highest correlation, followed by AC. Further analysis of the author pairs before and after 2011 demonstrates that several author pairs have strong PCR correlations, such as *Bordons M*–*Glanzel W*, *Katz JS*–*Leydesdorff L*, and *Braun T*–*Rousseau R*. In addition, the new ACR correlations appeared after 2011, which suggests that the PCR relationship promotes the occurrence of the ACR relationship. The new main ACR author pairs are listed in. ## Results of author relationship mining According to various analyses of AC, ABC, ADC, and CA, we found that *Glanzel W* had direct relationship with others, whereas most of the core authors have not related to all of the other authors. Following the five steps described above, new relationships between Authors A and C, B and D, and B and C were discovered, with respective author relation pairs: 1,793, 1,916, and 10. Subsequently, the final results among A–C, B–D, and B–C were acquired and visualized by the Network Workbench Tool, as shown in Figs. It is needed to point out that although the networks generated by PathFinder are sparse (in this network, only core node and connections are preserved, in order to concentrate on crucial points), the identification of the implicit associated author pairs is based on all the network data. Therefore, the relational mining process is still valid in this finite data set. As shown in Figs, *Pinto M*, *Lee YG*, *Prathap G*, and *Breimer LH* et al. had few direct relationships with others, and even rarer relationship among them. In particular, *Breimer LH*, whose research focused on interdisciplinary fields, such as medical and information sciences, had no co-citation relation with the core authors in the field of scientometrics, and few direct-citation and bibliographic-coupling relations with others. However, in the relation mining of “A–C” by joining ABC with ADC, indirect relationships between *Breimer LH* and 77 authors were established, and *Breimer LH* became a core node in the A–C network to replace the independent node in the AC network and peripheral node in other networks. Meanwhile, *Prathap G*, who was occupied with interdisciplinary research between material and information sciences, established indirect relationships with approximately 60 authors. *Pinto M*, an emerging scholar in scientometrics (with 5 papers and 10 citations in the dataset), has not yet formed a certain influence in the field. Few direct relations are observed between *Breimer LH* and elder statesmen in the field. However, he was also supposed to be the core node in A–C and B–D networks. The first direct relationship mining among *Breimer LH*, *Inhaber H*, *Lee YG*, *Sengupta IN*, *Vaughan L*, and *Pinto M* was not fully achieved. Thus, the mining of “B–C” joining ABC, ADC, and AC were conducted with the newly discovered direct relationships. As shown in the results presented in, the core link status of *Breimer LH* was re-verified, and his research can be considered gaining more attention from colleagues and that more communication and linkages are established over time because of him. To verify the existence of author relationship mining based on tripartite citation analysis proposed in this study, correlation analysis between the mining results and author direct relationship status (such as co-author and co- citation) was recently performed to reveal the predictive and practical value of the mining method and results. New AC, ABC, ADC, AKC, and CA matrixes from 2011 to 2015 have been investigated; and five matrixes, including CA, AC, ABC, ADC (symmetrized), and AKC were developed. Four author pairs could be identified according to the comparison of data mining before 2011(A–C and B–D), as well as evident relationship after 2011 (AKC″, CA″, AC″, ADC″, and ABC″), which are shown in. Although no co-authorship exists among these author pairs, the other direct relationships, such as AKC, ADC, and ABC, are still evident, especially *Leydesdorff L* and *Prathap G*. On the normalization process, given the presence of large amounts of 0 module caused by less amount of data within a limited time, another type of standardized method was selected. The AC matrix was considered as an example; the co-citation frequency between Authors A and B is x, the total frequency of A co-cited with all authors is m, the total frequency of B co-cited with all authors is n, and the correlation strength between Authors A and B is x/m+x/n. This analogy indicates that standardized matrixes of CA, AC, ABC, and ADC were obtained. Finally, a comprehensive correlation (CC) matrix was developed by adding four types of correlation values. Pearson correlation test was performed among author pairs of A–C, B–D, CC, and AKC, which correspond to two types of data sets, namely, A–C and B–D. The results are shown in, and the CC matrix was visualized using the Network Workbench Tool. Network Scaling MST-PathFinder was performed to show the network clearly. Therefore, the previously revealed correlations are not fully displayed. As shown in, certain degrees of positive correlation are observed among A–C, AKC, and CC, and B−D and AKC, which could indicate that the indirect relationships mentioned above would turn into direct relationships to some extent in the near future. Scholars in the field have consciously or unconsciously paid close attention to or cite links (including co-cited, coupled, and citing) with other scholars who shared indirect relationships instead of the direct relationships with the former authors, and even produced substantial cooperation among one other. Notably, the correlation between the AKC and CC matrixes is relatively significant. This result can be compared with previous results shown in Tables and, in which AKC is also significantly correlated with others, even though the related values are comparatively lower (except for ABC). Therefore, AKC analysis may help in revealing the evolution of the existing relationships. Meanwhile, the relationship mining method proposed in this study could aid in revealing unknown relationships that complement with AKC analysis or other methods, such as topic analysis. # Conclusions Various relationships exist in academic networks, such as CA, AC, ABC, ADC, and AKC. In a given field, the intensity and the associated attributes among scholars may exhibit significant differences in terms of the different network correlations. Some scholars showed strong ACR correlations, while some had key positions in PCR association. In this study, we compared the five types of matrixes by QAP and found that ABC has the nearly highest similarity with other networks. This finding can demonstrate the superiority of ABC analysis in revealing an academic community and its scientific structure. Furthermore, the correlation coefficient of ABC and AKC is higher than the coefficients among AKC and others, indicating that AKC and ABC can be complementarily applied in potential communication mining. By comparing the relationship of ACR and PCR, a particular phenomenon was observed, in which only PCR existed among scholars without ACR in the field of scientometrics, such as among *Bonitz M*, *Nagpaul PS*, *Mccain KW*, and *Eto H*. By analyzing PCR with the ACR correlations excluded (i.e., including AC′, ABC′, and AKC′) by QAP, we found that the relationship degrees among AC′, ABC′, and AKC′ can also maintain significant correlations, especially AKC′ and ABC′, which share higher relevancy than the original matrixes. This result indicates that these authors connected by PCR are likely to produce more academic exchanges and scientific innovations because of similar themes rather than social attribute association (e.g., teacher–student or co-worker relationships). By conducting Pearson correlation analysis, the case study confirmed that a significant correlation existed between the PCR that appeared before 2011 and the new ACR″ and PCR″, which appeared after 2011. Furthermore, continuity existed between PCR′ and PCR″, and the associated relationship between ACR″ and PCR″ were also significant. Particularly, ABC′, ABC″, and other relations have been highly correlated, which indicated that ABC analysis shows a good application potentiality in relationship prediction and discovery to some extent and may reflect actual communication. On the basis of the algorithm design and the empirical analysis, the deduction from the analysis results from AC, ABC, and ADC to the potential author relationships mining is probable and practicable. For example, the relationship between *Leydesdorff L* and *Prathap G* revealed by A–C/B–D in the case study achieved a high degree of correlation in the practice after 2011 (including AC, ADC, and ABC, which did not exist before 2011). The author correlation between *Breimer LH* and *Vaughan L* obtained by two-time mining was also consistent with the new correlation in 2011, which once again confirmed the validity and the potential value of the proposed method for revealing author relationships. The results presented above revealed that the indirect relationships among interdisciplinary scholars or novice researchers can be mined by the method combined with tripartite citation analysis, which helps with specific scientific cooperation and broader communication. In comparison with the direct relationship presented recently by Pearson correlation, the author mining method proposed in this study helps in revealing unknown relationships and could complement with AKC analysis or other methods, such as topic analysis. These methods could be applied in discovering research fellows, exploring potential partners, as well as tracking scholars with related research and their research direction. In conclusion, this study attempted to discover PCRs. Through the correlations between the measurements, the proposed method could be used to explain that the establishment of co-citation or coupling relation may promote the production of the actual communications. This finding suggests that these two relations could identify potential collaboration partners for both individuals and teams. The proposed author relationship mining method based on tripartite citation analysis could also be an effective method for discovering future relationships among scholars and promoting scientific communication and innovation development. In addition, we recognized the existence of limitations in the dataset of “core authors” by selecting only the first author as citation data and defining the threshold of the publication number and citation frequency. We performed such step despite the fact that only the first cited authors tallied in the database of WOS and regardless of the difficulty of a specific time window for obtaining a sufficient linking signal (e.g., the data in the first period of 1978–2011, which was acquired in 2012, is difficult to be regained at present). However, this paper was an attempt to propose an idea and process in author relationship mining in the context of five types of scholarly networks; thus, the collection of core authors targeted by this research was supposed to be useful in the application of relationship mining method. Nevertheless, we are faced with the data limitation, thus the need to present a more credible empirical study with a sizable sample and enhance the practicality and effectiveness in PCR discovery by tripartite citation analysis. Finally, as an attempt, the proposed method should also be applied in various fields. However, the method was tested only in the field of scientometrics due to computational complexity, the amount of data obtained, and so on. In addition, some of the studies exhibit positive results, which are applicable only in the field of scientometrics. In the context of scientometrics, the results are easier to explain and rigorously confirmed. In further research work, the proposed method should be applied in other fields to further confirm its effectivity and rationality. # Supporting information We wish to thank the two anonymous referees for their important insightful comments and suggestions. [^1]: The authors have declared that no competing interests exist.

# Introduction There is abundant evidence worldwide on lifelong human contamination from mixtures of environmental chemicals as persistent organic pollutants (POPs); yet, the vast majority of studies report each pollutant individually, with little attention to concentrations of mixtures in individual persons or social groups. Thus, the complex features of such internal, body contamination remain unsatisfactorily characterized. Biomonitoring surveys, for instance, do not integrate the number of compounds detected per person and the concentration of each compound. Possible health effects of POPs include a variety of developmental, metabolic, neurodegenerative, and neoplastic disorders. Reasonable concerns exist about such effects at low concentrations; such issues can be integrated with the fact that it is common for humans to have mixtures of POPs at low and high concentrations. Approaches to these issues include ‘Environment-Wide Association Studies’ (EWAS), and analyses of concentrations of POPs combined, using estimates of total body burden, or different sums of concentrations. Efforts to improve exposure assessment must continue: not only to advance etiologic studies and risk assessment, but also to foster knowledge on the characteristics of human chemical contamination itself. Such knowledge is a recognized right of citizens in democratic societies; it is also essential to evaluate the impacts of health, industrial, and related policies. Indeed, the sources and pathways of exposure to pollutants are socioeconomic and cultural. Thus, strong relationships exist between concentrations of individual POPs and social factors, including income, education, and race / ethnicity. Unfortunately, such relationships have seldom been analyzed integrating several compounds and their concentrations. Recently, a set of indicators that integrate the number of compounds detected per person and their corresponding concentrations was proposed, including the number of compounds detected at high concentrations. The analyses were based on the general population of Catalonia, Spain. Because studies in the U.S. on combinations of POPs and other chemicals raised relevant questions about the levels and effects of such mixtures, and because of the relatively large size of the U.S. population, we aimed at applying the methodology to the U.S. general population. Therefore, the objectives of the present study were to analyze the number of POPs detected per person at high concentrations (nPhc) in the U.S. National Health and Nutrition Examination Survey (NHANES), and to analyze the associations between such indicator and main socioeconomic factors. Our main hypotheses were that most of the U.S. population would have POPs at low and high concentrations, and that sociodemographic factors (such as age, gender, body mass index (BMI), parity, or income) that are often related with POP concentrations when each compound is analyzed individually would continue to show similar relationships when the POPs are jointly analyzed. # Materials and Methods ## Data Conducted by the Centers for Disease Control and Prevention’s (CDC) National Center for Health Statistics (NCHS), the National Health and Nutrition Examination Survey (NHANES) collects nationally representative environmental biomonitoring data from about 5,000 annual participants in each two-year cycle. NHANES is a publicly available data set, and all participants provide written informed consent, consistent with approval by the NCHS Institutional Review Board. Ethical approval for use of NHANES data is not required as it is anonymized. We examined data from NHANES laboratory and demographic files corresponding to 2003–2004, which is the last period with valid updated individual data for compounds considered in the present study. Except for perfluorinated compounds (PFCs), in NHANES 2005–2006 and 2007–2008 serum concentrations of POPs were measured using weighted pooled-samples, and no data for POPs have been published for NHANES 2009–2010 and 2011–2012. Therefore, it is not possible to calculate the number of POPs detected per person at high concentrations in more recent periods. In each NHANES, most chemicals or their metabolites were measured in serum samples from random subsamples of about 2,500 participants aged 12 years and older. The chemicals’ concentrations were analyzed by CDC’s Environmental Health Laboratory using mass spectrometry and related methods. Data for 91 POPs were analyzed, including: 13 organochlorine compounds (OCs) and their respective metabolites; 10 polybrominated diphenyl ethers (PBDEs); the polybrominated biphenyl (PBB) 153; 29 non-dioxin-like polychlorinated biphenyls (PCBs); 3 dioxin-like coplanar PCBs; 6 dioxin-like mono-ortho-substituted PCBs; 10 polychlorinated dibenzofurans (PCDFs); 7 polychlorinated dibenzo-p-dioxins (PCDDs); and 12 PFCs. Thus, serum concentrations of lipophilic chemicals (e.g., dioxins and PCBs) are presented per gram of total lipid (better reflecting the amount stored in body fat); results of analyses per whole weight of serum were similar and are not presented. Concentrations of PFCs, non-lipophilic POPs, are shown per liter of serum. Limits of detection (LOD) for whole weight POP concentrations were different for each serum sample of each person, while LODs for lipid-adjusted concentrations were the same values for all samples and individuals (values ranged from 3.8 pg/g of lipid and 7.8 ng/g of lipid). Finally, LODs for PFCs ranged from 0.1 to 1.0 μg/L. We considered important covariates as age, sex, race/ethnicity (non-Hispanic white, henceforth ‘White’; Mexican American; non-Hispanic black, henceforth ‘Black’; other Hispanic; and other), education (categorized to less than high school diploma, high school diploma, and greater than high school diploma), and body mass index (BMI) in kg/m². To estimate the participants' income we used the family’s total income divided by the family size-specific poverty threshold income ratio (PIR), with two categories: “Low” income (PIR \< 2), and “High” income (PIR ≥ 2). In women, we also considered the number of pregnancies resulting in live births, and the number of children breastfed ≥1 month (henceforth, ‘breastfeeding’). ## Statistical analyses The present study included 4,739 participants ≥20 years old (for all adults 85 years and older, age was coded at 85 years to reduce the risk of disclosure). They came from three subsamples. There were no significant differences between the 1,610, 1,585 and 1,544 participants of each subsample in a broad range of sociodemographic variables (including sex, race/ethnicity, educational level, PIR, BMI, number of pregnancies or breastfeeding). We imputed the unmeasured POP values by the median serum concentration of each POP according to age, sex, race/ethnicity, PIR, BMI, and, in women, number of pregnancies. In 79 POPs the imputation was performed using concentrations adjusted by lipids, and in 12 PFCs, in μg/L. We calculated the total toxic equivalency (TEQ) for 26 POPs: 3 dioxin-like coplanar PCBs, 6 dioxin-like mono-*ortho*-substituted PCBs, 10 PCDFs, and 7 PCDDs. To compare POP concentrations in the present study and pooled concentrations in NHANES 2005–2006 and 2007–2008 we computed concentrations of POPs by sex, race/ethnicity and age groups. We also compared PFC serum concentrations in the present study and concentrations in NHANES of 2005–2006, 2007–2008, 2009–2010 and 2011–2012. Descriptive values for POP concentrations imputed are summarized in, sorted from the highest to the lowest percentage of detection. Based on previous work by Porta et al. (2012), we calculated the number of POPs detected in each person at high concentrations (nPhc) as follows: for each subject we added the number of POPs whose serum concentrations were equal to or greater than a selected cutoff point. To be conservative, in the main analyses we included only 37 POPs that had been detected (each) in \>85% of the study subjects (henceforth called the most prevalent POPs). Such 37 POPs were: 2 OCs, 3 PBDEs, PBB 153, 23 non-dioxin-like PCBs, 3 dioxin-like PCBs, one PCDD \[1,2,3,4,6,7,8-Heptachlorodibenzo-*p*-dioxin (HpCDD)\], and 4 PFCs. Ancillary analyses included 50 compounds detected in \>50% of subjects. Finally, other analyses included all 91 POPs, with quartiles, quintiles and deciles defined after the imputation of concentrations. As usual, serum concentrations of POPs did not follow a normal distribution, and the increment of concentrations in the highest percentiles was very strong (e.g., for *p*,*p’*-DDE the increment of concentrations between P75 and P90 was of 2.14 times, and between P90 and the maximum it was 14.5 times; for PCB 153 the corresponding figures were 53% and 12.36 times, respectively). We defined ‘high concentrations’ using compound- and population-specific percentiles, based on actual POP distributions, as cutoff points. In the main statistical analyses the cutoff point used was percentile 90 (P90), the upper decile. Univariate statistics were computed as customary. The highest correlations were observed between PCB congeners 170 and 180, 138 and 153, 146 and 153 (all Spearman’s *ρ* \>0.982 and *p’s* \<0.001). Fisher’s exact test for homogeneity was applied to assess the relationship between two categorical variables. For comparisons between continuous variables ANOVA, Kruskal-Wallis, and Mann- Whitney’s *U* tests were used. When a tendency was observed, Mantel–Haenszel’s χ² test and Jonckheere-Terpstra test for linear trend were used. To estimate the magnitude of associations between the socioeconomic factors and the number of most prevalent POPs with concentrations in the upper decile, multivariate-adjusted odds ratios (ORs) and their corresponding 95% confidence intervals (CI) were calculated by unconditional logistic regression with progressive degrees of adjustment. The main effects of all predictors were independently explored in the base models, and final models were adjusted for age, gender, BMI, race/ethnicity and poverty income, in accordance with the nature of the variables and the study objectives. The number of POPs with concentrations in the upper decile was tested in different regression models using 3 different categorizations (all dichotomous): ≥1 POP (vs. no Phc), ≥6 POPs (vs. \<6 POPs) and ≥10 POPs (vs. \<10 POPs). Categorical ordinal variables were analyzed for a linear dose–response relation through the multivariate analogue of Mantel’s extension test; when a linear trend was not apparent, the probability test was used. Analyses were conducted using SPSS version 18 (SPSS, Armonk, NY, USA, 2009). # Results Over 67% of the 4,739 participants (73.8% of men and 61.1% of women) had one or more of the 37 most prevalent POPs at concentrations equal to or greater than the 90th percentile (≥P90), while 38.0% had ≥3 POPs, and over 13% had ≥10 POPs each in such top decile. Over 37% of subjects had ≥10 compounds each at concentrations in the top quartile (≥P75). The number of POPs detected per person ranged between 23 and 74, with an average of 49.7. Over 57% of participants had ≥50 POPs detected. In over 45% of participants who had only one POP at high concentrations (Phc) (≥P90), this chemical was an OC, a PBDE or PBB 153. By contrast, among subjects with numerous Phc, the majority of such compounds were PCBs. For instance, when the nPhc was ≥3, more than 40% of these compounds were PCBs and HpCDD. The median age of participants with ≥10 POPs at high concentrations (Phc) was 70 years, while for participants with \<10 Phc it was 45 years, and for participants without any Phc, 39 years. Over 11% of Whites, 2.2% of Mexican Americans, and 29.2% of Blacks had ≥10 Phc (*p* \<0.001). Subjects with ≥10 Phc had a slightly lower median BMI than subjects with \<10 Phc (26.7 Kg/m² and 27.6 Kg/m², respectively, *p* for trend = 0.004). Women with ≥10 Phc had a higher number of pregnancies resulting in live births than women with \<10 Phc (age-unadjusted medians: 3.0 and 2.0, respectively, *p* for trend \<0.001). There were significant differences in the nPhc by sex, age, BMI, race/ethnicity, educational level, PIR, and, in women, by number of pregnancies, and breastfeeding. Multivariate analyses adjusted by age, gender and BMI showed that, as compared to Whites, Blacks had an odds ratio (OR) = 10.1 of having ≥10 Phc, whilst for Mexican Americans the OR was 0.2 (both *p’s* \<0.001). When further adjusted by poverty income the OR for Blacks decreased to 9.2, and for Mexican Americans to 0.18 (both *p’s* \<0.001). Differences between Blacks and Whites were larger in the older age groups / birth cohorts, and null in the younger ones (*p* for interaction \<0.001). For PIR ≤2, or “Low” income (vs. PIR\>2 or “High” income) the OR of having ≥10 Phc was 1.13 (*p* \>0.05) when the model was adjusted by age, gender and BMI, and 1.24 (*p* = 0.045) when further adjusted by race/ethnicity. The OR for obesity (vs. normal weight) was 0.74 (*p* for trend = 0.015) in the model adjusted by age and gender, and, when further adjusting by race/ethnicity and poverty income, it was 0.58 (*p* for trend \<0.001). In women, pregnancy halved the probability of having ≥10 Phc when adjusting by age and body mass index (OR = 0.48, *p* = 0.048). In models assessing the relationship between sociodemographic factors and the probability of having ≥1 POPs at concentrations ≥P90 (vs. not having POPs with concentrations ≥P90), adjusted by age, gender and BMI, the OR for Blacks (vs. Whites) was 1.10 (*p* = 0.266) and for Mexican Americans, 0.74 (*p* \<0.001). When further adjusted by race/ethnicity, the OR for PIR≤2 (vs. PIR\>2) was 1.17 (*p* = 0.026); and for obesity (vs. normal weight), 0.73 (*p* for trend \<0.001 for BMI). The corresponding figures for the probability of having ≥6 POPs at concentrations ≥P90 were 3.37 for Blacks and 0.51 for Mexican Americans (both *p* \<0.001), 1.24 for PIR≤2 (*p* \<0.01), and 0.91 for obesity (vs. normal weight) (*p* for trend = 0.353). Because of the influence of PCBs in the previous results, we also analyzed associations among sociodemographic factors and the likelihood of having ≥1 of 6 POPs other than PCBs (i.e., OCs, PBDEs, and PBB 153 detected ≥85% of subjects) at high concentrations. Contrary to what was observed when all compound families were considered, for Blacks (vs. Whites) the OR of having ≥1 of such POPs was 0.76, and for Mexican Americans, 1.41, adjusting by age, gender and BMI (both *p’s* \<0.01). The corresponding OR for PIR≤2 was 1.19 (*p* = 0.011), and for obesity, 0.82 (*p* for trend = 0.013). When further adjusting by race/ethnicity, the OR for PIR≤2 was 1.15 (*p* = 0.038), and for obesity, 0.80 (*p* for trend = 0.006). The geometric mean (GM) of nPhc doubled when the cutoff P75 was used instead of P90. For the cutoff P75 the percentage of subjects with ≥10 Phc was 37.5 for POPs detected in ≥85% of participants, and 45.2 for POPs detected in ≥50% of participants. However, the percentage of subjects without any Phc decreased slightly when the number of POPs included in the analyses increased. In the 1,183 participants with the highest total TEQ concentrations (≥P75 of the distribution of total TEQ concentrations \[i.e., ≥26.68 pg WHO-TEQ/g of lipid\]), the percentage of subjects with ≥10 Phc was about twice the corresponding figure observed when all 4,739 participants were considered. Over 90% of the 1,183 subjects had ≥1 POPs a) not included in TEQ calculations, and b) with concentrations ≥P90. Over 30% had ≥10 such POPs, and almost 7% had ≥20 such POPs. Spearman’s *ρ* coefficient between the total TEQ concentration and nPhc (considering the 24 POPs not included in TEQ calculations, and the P90 in all participants for high concentrations) was 0.475 (*p* \<0.001). Over 43% of participants had TEQ concentrations ≥21 pg WHO-TEQ/g of lipid, a biomonitoring equivalent value. Taking into account health-based guidelines for other compounds, less than 1% of participants had concentrations of hexachlorobenzene ≥47 ng/g of lipid, and concentrations for the sum of *p*,*p’*-DDT and *p*,*p’*-DDE ≥5,000 ng/g of lipid. Two subjects had concentrations of BDE 99 ≥520 ng/g of lipid; 2 participants (aged \<40 years) had concentrations ≥700 ng/g of lipid for the sum of 35 PCBs (without dioxin- like coplanar PCBs), and 6 participants (aged ≥40 years) had concentrations ≥1,800 ng/g of lipid. 10% of participants had concentrations for the sum of PCBs 138, 153 and 180 ≥216 ng/g of lipid. Only 4 participants had concentrations for the sum of these three PCBs ≥900 ng/g of lipid. We also compared the concentrations of POPs detected in ≥85% of the participants in the present study (2003–2004), and their respective pooled concentrations for the NHANES periods 2005–2006 and 2007–2008. The concentrations of some POPs in the present study were only slightly higher than in subsequent periods; they were not higher or not statistically significant in the case of *p*,*p’*-DDE, PBB 153 and some PBDEs compounds. For 3 PFCs, concentrations in 2003–2004 were similar to concentrations in 2005–2010, and slightly higher than concentrations in 2011–2012. # Discussion More than half of the study population had concentrations in the top decile of ≥1 of the most commonly detected POPs, 38% had ≥3, and over 13% had ≥10 POPs each in their respective top decile. Findings are thus partly in contrast with the notion that human POP concentrations are low in the vast majority of the population: such view holds only when each individual compound is looked at separately, but not when the individual human is of concern. Median age of participants with ≥10 of most prevalent POPs at high concentrations was 70 years, while median age of participants without any Phc was 39 years. This could be due to biological aging effects or to birth cohort effects. Furthermore, the median age of participants without any Phc was near the median age of participants with 1 or 2 Phc. There were also significant differences in the nPhc by gender, race/ethnicity, educational level, PIR, BMI, parity, and breastfeeding. These results are in accordance with our main hypotheses (most of the U.S. population had POPs at low and high concentrations; sociodemographic factors related with each POP concentration showed similar relationships for the joint analysis of POPs). Race/ethnicity was the sociodemographic factor most associated with a higher nPhc: Blacks had 9 times a greater chance of having ≥10 Phc than Whites, and Mexican Americans over 4 times a lower chance. The nPhc indicator not only shows that Blacks have higher body concentrations of POPs than Whites (or Mexican Americans lower concentrations), but it also quantifies how many POPs are in a specific high concentration range. The NHANES questionnaires had a large number of sociodemographic items; in this study, we used the sociodemographic factors that were available and related with body concentrations of POPs. Results of unconditional logistic regression models for ≥1 Phc, ≥6 Phc, and ≥10 Phc (vs. no Phc, \<6 Phc, and \<10 Phc, respectively) in the subsample without imputations and PCBs, PCDDs/Fs analyzed by the sociodemographic factors (to assess the possible biases of imputations), were similar to results of models with imputations, except in some models for gender, which was not statistically significant, although ORs were similar. Most studies found an inverse association between PCB levels in blood and BMI, as in the present analyses for all participants and 37 POPs. Also rarely if ever noted before: high percentages of subjects with TEQ ≥P75 (≥26.68 pg WHO-TEQ/g of lipid) had numerous POPs not included in TEQ calculations, at high concentrations. Findings suggest that studies using TEQ measures could be even more relevant if they additionally assessed subgroups with high nPhc. Results do not imply that nPhc and related exposure indicators are preferable to other indicators to evaluate associations between POP mixtures and clinical outcomes; nPhc indicators just provide a different and complementary approach to indicators such as the sum of concentrations of PCBs, or the sum of orders of POPs. Our goal was not to evaluate whether individuals have increased health risks due to multiple compounds at high concentrations, nor to assess the role of modes and mechanisms of action, but to propose a new and useful approach for exposure assessment. However, severe adverse health effects have been reported for concentrations similar to or lower than P90 in the present study; e.g., in an NHANES study the OR of having diabetes for a concentration of ≥60.2 ng/g lipid of PCB 153 was 5.9 (95% CI = 3.0–11.9); in the present study the P90 for PCB 153 was 79.8 ng/g lipid. P90 of concentrations of individual PCBs in NHANES is as high or higher than in other countries with population-based surveys as Canada and Australia. For *p*,*p’*-DDE and *β*-hexachlorocyclohexane (*β*-HCH), it is also as high or higher than in Canada, Australia and Germany. In this study, over 43% of participants had TEQ concentrations ≥21 pg WHO-TEQ/g of lipid, which is the biomonitoring equivalent value published for dioxin TEQ, a health-risk based screening guideline. Also, in the present study 10% of participants had concentrations for the sum of PCBs 138, 153 and 180 equal to or greater than the Human Biomonitoring level-I (HBM-I), which is 3 μg/L plasma or, when accounting for lipids, 216 ng/g lipid for the present study. HBM-I is a health-related exposure limit recommended for PCBs by the German Human Biomonitoring Commission. For compounds considered in the present study, other biomonitoring equivalents values are only available for hexachlorobenzene, the arithmetic sum of *p*,*p’*-DDT and *p*,*p’*-DDE, the sum of 35 PCBs, and BDE 99; for these compounds very few subjects had concentrations above the corresponding biomonitoring equivalents in this study. To our knowledge, no current health- related limit values are available for the rest of PBDEs or for PFCs. Although there are regulations and guidelines for other pollutants (e.g. lead, mercury, cadmium and other metals) and for POPs in air, soil, water and food (e.g., tolerable daily intakes), there are hardly any other guidelines for human POP concentrations to define levels of concern than the ones mentioned above. Beyond findings on concentrations of individual compounds, the indicators illuminate a crucial–and usually overlooked–feature of human contamination by POPs: the frequency of mixtures of POPs at high concentrations. The approach could naturally be developed to integrate other pollutants of concern. Importantly, 2003–2004 is the last period of NHANES in which the individual concentration of each compound is available for each individual subject. In 2005–2006 and 2007–2008 serum concentrations of POPs (except PFCs) were measured in weighted pooled-samples (not in individual samples); no data were published for 2009–2010 and 2011–2012. Therefore, data to calculate the number of POPs detected per person at high concentrations in more recent periods are not available. Concentrations of most POPs in 2003–2004 were only slightly higher than in more recent periods (Tables in S5 File). Virtually all major contemporary health effects of POPs will be influenced by concentrations experienced by human cohorts during several decades, not just by recent exposures. Furthermore, the nPhc can be fruitfully applied to analyze data from many periods and settings. Different POPs were analyzed in participants of the three NHANES 2003–2004 subsamples; even in two of the three subsamples all selected POPs were not analyzed in all participants. Each sample, however, is valid; and it is efficient not to analyze all POPs in all participants. For PFCs, the LODs were constant for each sample analyzed. For the other 55 compounds, the LOD for the whole weight concentrations was different for each serum sample of each person. When PCDD/Fs, PCBs, OCs, PBDEs, and PBB 153 concentrations were measured in serum lipid, LOD calculations were performed using the chemical concentration expressed per amount of lipid, and the LOD concentration expressed per amount of lipid was the highest LOD among all the individual samples analyzed. LODs for lipid adjusted concentrations were highest compared to the LODs for the whole weight concentrations, and rates of detection were lower, than for whole weight concentrations; as a consequence, lipid adjusted results are more conservative (e.g., because there were less compounds detected in ≥85% of participants). In the present study, some associations between nPhc and sociodemographic factors are quite influenced by the predominance of PCBs and HpCDD at high concentrations among subjects with ≥3 nPhc. The cutoff point for nPhc should be chosen with this issue in mind, while also avoiding a too high nPhc (e.g., because of lower detection rates of some POPs). Serum concentrations of POPs do not follow a normal distribution. Values for P90 can be much higher than the P75 (e.g., for *p*,*p’*-DDE the P90 value was 2.14 times greater than P75, and for PCB 153 it was 53%). Such differences between highest concentrations minimize a possible misclassification of concentrations in ≥P90 or \<P90 due to laboratory measurement errors. The minimum percentage of participants with concentrations in the top decile of ≥1 POPs will be 10%, but such percentage will not necessarily, linearly, or indefinitely increase (nor approach 100%) as the number of compounds considered increases: the percentage of participants with concentrations of ≥1 POPs in the top decile is only partly positively influenced by the number of compounds considered; it is also inversely influenced by the magnitude of the correlations between the pairs of compounds, being highest when POPs are completely uncorrelated (for details see Suppl. Material of Porta et al., 2012). Therefore, the nPhc follows a distribution that is influenced by all the correlations between the pairs of compounds, and results may not be due to chance. Figure 1 of Supplemental Material of Porta et al., 2012 shows different values for ≥1 by the number of POPs considered, and the values when the POPs were completely uncorrelated. For ≥10 POPs at high concentrations (rather than ≥1 POP) this situation is even more restrictive; when we focused on ≥10 POPs at high concentrations, it was statistically possible for the minimum percentage of participants with ≥10 POPs at high concentrations to be 0% (i.e., it was not statistically inevitable for that percentage to be 10%), since it is possible that highly-correlated sets of POPs comprise 9 or less POPs. Furthermore, such minimum percentage also depends on the number of POPs analyzed, the number of POPs in the top decile, and the number of participants included. # Conclusion In summary, more than 13% of the US population may have ≥10 POPs each at concentrations in the top decile. This finding is not to be expected just on statistical grounds. High percentages of subjects with TEQ ≥P75 have numerous POPs not included in TEQ calculations, at high concentrations. The nPhc is related to race/ethnicity, age, and BMI. It is also likely to be related to other relevant social, environmental, and individual factors. The study findings foster knowledge on previously unknown characteristics of human chemical contamination in the US population. Such knowledge is a right of citizens, and could also be considered when evaluating the impacts of relevant public and private policies. # Supporting Information The authors gratefully acknowledge technical and scientific assistance provided by Natàlia Pallarès, David J. MacFarlane, Manuel Pastor, Yolanda Rovira and Ferran Sanz. *β*-HCH *β*-hexachlorocyclohexane BMI body mass index CDC Centers for Disease Control and Prevention CI confidence interval GM geometric mean HBM Human Biomonitoring LOD limit of detection NHANES National Health and Nutrition Examination Survey nPhc number of POPs detected per person at high concentrations PBBs polybrominated biphenyls PBDEs polybrominated diphenyl ethers PCBs polychlorinated biphenyls PCDDs polychlorinated dibenzo-*p*-dioxins PCDFs polychlorinated dibenzofurans PFCs perfluorinated compounds Phc POPs at high concentrations PIR family’s total income divided by the family size-specific poverty threshold income POP persistent organic pollutant OCs organochlorine compounds OR odds ratio TEQ total toxic equivalency [^1]: The authors have declared that no competing interests exist. [^2]: **Conceived and designed the experiments:** JP DHL MP. **Analyzed the data:** JP TL. **Wrote the paper:** JP MG MP.

# Introduction T2 ribonucleases are conserved nucleases found in all branches of life. In eukaryotic cells, T2 ribonucleases affect a variety of processes including the regulation of self-incompatibility by S-RNases in plants –, modulation of host immune cell responses by viral and schistosome T2 enzymes, and neurological development and tumor progression in humans. In these contexts, T2 ribonucleases can have both catalytic and catalytic-independent functions (reviewed). For example, the effects of RNASET2 in humans on tumor progression appear to be independent of its catalytic activity. In contrast, the catalytic activity of the RNASET2 ortholog in *Saccharomyces cerevisiae*, Rny1, is required for cleavage of tRNA and rRNA molecules. Analysis of Rny1 in yeast suggests it also has properties analogous to those seen for other RNASET2 orthologs. For example, during oxidative stress, Rny1 is required for the production of tRNA and rRNA fragments. Similarly, expressing human RNASET2 rescues tRNA cleavage in yeast strains lacking Rny1, and zebrafish neurons deficient for RNASET2 and plants deficient for RNS2 accumulate rRNA. In addition, Rny1 is a glycosylated protein that can localize to vacuoles. Glycosylation and acidic nuclease activity are typical of T2 ribonucleases, but whether glycosylation is required for activity is unknown. Finally, Rny1 affects cellular growth and sensitivity to stress independently of its nuclease activity. This non-catalytic lethality resembles the ability of RNASET2 to suppress ovarian tumor establishment in its catalytically inactive form, and parallels the capacity of certain catalytically mutant pestiviral T2 ribonucleases to elicit host immune cell depletion. Since Rny1 has a signal sequence and has been reported to primarily be a secreted or vacuolar localized protein, an unresolved issue is how Rny1 and its RNA substrates interact in the cell. Since an Rny1-GFP fusion protein shows reduced vacuolar signal during stress, one possibility is that Rny1 enters the cytoplasm to engage RNAs for cleavage during stress. Precedent for this model comes from experiments showing that a predominant mitochondrial nuclease, Nuc1, exits mitochondria during stress to modulate nuclear and possibly cytoplasmic RNA degradation, and in mammalian cells, lysosomal cathepsins can be released to the cytosol during some responses leading to cell death (reviewed). Alternatively, or in a second mechanism, cytoplasmic RNA might enter the vacuole, where the T2 enzyme has been shown to localize during stress. Recently, Haud *et al.* presented evidence for the accumulation of rRNA within lysosomes with loss of RNASET2 in zebrafish neurons. Thus, an unresolved issue is how compartmentation of Rny1 affects its function and access to RNA substrates. Cleavage of tRNA is not unique to yeast and is conserved in eukaryotes as a response to specific stresses, producing tRNA cleavage products mapping primarily to the anticodon loop,. In mammalian cells, these fragments inhibit translation and localize to stress granules, which are cytoplasmic untranslating mRNPs that can aggregate during stress (reviewed). Coupled with the fact that rRNA fragments accumulate during stress conditions that induce tRNA cleavage, these data suggest the possible regulation of translation complexes and associated translating RNAs in a stress-specific manner by ribonucleases such as Rny1, and loss-of-function of these enzymes might impinge on cellular survival during stresses. Interestingly, the human RNASET2 has been reported to localize to P-bodies although the significance of this localization remains to be determined. To begin to understand how Rny1 functions in both catalytic and catalytic- independent manners we have analyzed the regions of Rny1 for their functional importance. We demonstrate that catalytic-independent inhibition of growth is a combinatorial property of the protein and is affected by a fungal-specific C-terminal extension, the conserved catalytic core, and the presence of a signal peptide. Catalytic functions of Rny1 are independent of the C-terminal extension, are affected by many mutations in the catalytic core, and also require a signal peptide. Biochemical flotation assays reveal that in *rny1*Δ cells, some tRNA molecules associate with membranes suggesting that cleavage of tRNAs by Rny1 can involve either tRNA association with, or uptake into, membrane compartments. # Results ## Domain Organization of Rny1 Our general strategy was to make mutations in specific parts of Rny1 and examine their effects on catalytic and catalytic-independent functions. In this light, Rny1 possesses three domains defined in its initial characterization. At its N-terminus it encodes a signal peptide (amino acids \#1–18), presumably for insertion into the ER during translation. In the central region of the protein is a conserved RNaseT2 catalytic module (amino acids \# 19–293). Within this region are critical amino acids known to be required for activity of RnaseT2 enzymes and for the nuclease activity of Rny1. For example, substitution of two catalytic histidine residues with phenylalanine (H87F and H160F) produces a catalytically inactive Rny1, referred to as rny1-ci. Finally, the C-terminal region of Rny1 is a domain that is conserved in fungal species and is not seen in other eukaryotes. To analyze the function of the conserved catalytic core, we desired to identify potential surface loops of amino acids that would not directly affect the folding of the protein, but might affect substrate interaction, or possibly protein-protein interactions. To do this, we took advantage of the high resolution structures of other T2 ribonucleases to predict possible loops for mutagenesis. Using Swiss Model (<http://swissmodel.expasy.org/>) and the known three-dimensional structure for the fungal T2 ribonuclease, ACTIBIND, we generated a predicted structure for Rny1 based on its 39% homology to this enzyme. In addition, using COBALT (<http://www.ncbi.nlm.nih.gov/tools/cobalt/cobalt.cgi>), we aligned Rny1 to ACTIBIND and another fungal T2 ribonuclease, Rh, of known structure. This information revealed the position of eleven loops available for surface interactions, two of which (L4 and L7) could involve RNA binding based on previously published alignments of Rh to T2 ribonucleases with known nucleotide- bound structures. In 10 of these loops, we generated mutants by replacing all loop residues with alanine, generating mutations in catalytically active and/or inactive backgrounds. Contributions of these loops were examined in assays for growth-inhibition in wild-type and/or catalytically inactive backgrounds, and mutations in wild-type backgrounds were assayed for the ability to cleave tRNA. To analyze how either point mutations, or deletions to specific regions of Rny1 affect its function we used two assays. First, to assess the effect of a mutation on non-catalytic inhibition of growth, we examined how mutations impacted growth inhibition when Rny1 is over-expressed from the *GAL* promoter. These experiments were done in a *hir2*Δ background, which we had seen can increase the growth inhibitory effects of Rny1 over-expression (data not shown). Second, to determine the effect of a mutation on nucleolytic function, we examined how a given Rny1 variant could restore tRNA fragment production to an *rny1*Δ strain. ## Analysis of Domains We examined if the signal peptide, the catalytic core or the C-terminal extension was necessary or sufficient to inhibit cell growth when over- expressed. Moreover, to avoid any complications due to Rny1 nuclease activity, deletions of these regions were made in the context of the rny1-ci mutation. This led to the following key observations. First, we observed that deletion of the signal sequence reduced toxicity. Second, we observed that deletion of either the central conserved core, or the C- terminal extension reduced, but did not abolish, toxicity. These effects are not due to loss of protein expression since these variants were all expressed. We interpret these observations to argue that nuclease-independent toxicity is a combinatorial property of both the central conserved core and the C-terminal extensions and requires the protein to contain a signal sequence. We also examined the effects of these deletions on tRNA cleavage when Rny1 is over-expressed. We observed that both the signal peptide and the central RNaseT2 domain were required for efficient tRNA fragment production, and their deletions resemble the phenotype of the rny1-ci allele. In contrast, the C-terminal extension is not required (ΔCTD lane). The ability to express proteins from the mutant constructs containing catalytic sequences was not lost. We conclude that in addition to the catalytic core domain, a signal sequence is required for cleavage of RNA substrates by Rny1. One possible interpretation of our results is that glycosylation might be important for Rny1’s functions. We analyzed Rny1-GFP fusion proteins where the GFP is either fused to the C-terminus of the protein or was inserted immediately after the signal peptide. We observed that fusion of GFP to the C-terminal end of the protein (Rny1-GFP) still allowed inhibition of cell growth when over- expressed (data not shown), was able to restore tRNA fragment production in a *rny1*Δ strain, and was glycosylated as judged by a reduction in molecular weight when treated with the endoglycosidase, PNGaseF. In contrast, the fusion with GFP inserted just after the signal peptide failed to inhibit growth when over-expressed (data not shown), failed to restore tRNA fragment production to an *rny1*Δ strain, and was not glycosylated. These observations are consistent with the requirement for a signal peptide for function and support a model whereby Rny1 activity requires insertion of the nascent peptide into the ER and possibly glycosylation. ## Analysis of Mutations in the Catalytic Core Domain We also analyzed how mutations in the loop regions of the core domain of Rny1 affected its function. We observed that none of the mutations in the loop regions reduced the ability of Rny1 to inhibit growth when over-expressed either in the active Rny1 or rny1-ci background. This is consistent with the observations above that growth inhibition is a combinatorial property of the whole protein. We also observed that mutations within loop 2, 3, 6, or 7 inhibited tRNA cleavage, while loop 10 was dispensable for this activity even though mutant proteins were all expressed at similar levels. A surprising observation was that mutations in loop 4, which are near a predicted RNA binding site alter the predominant cleavage product. This is surprising since RNAseT2 enzymes are thought to be generally non-specific in their cleavage sites and to cleave tRNA predominantly in the anticodon loop since this is the most exposed part of the tRNA. One possibility is that the mutations in the L4 loop alter the positioning of the tRNA in the active site to preferentially lead to cleave at other sites in the tRNA. Taken together, we suggest that specific loop regions in the catalytic core are required for Rny1’s catalytic activity and can play a role in determining the specific site of RNA cleavage. ## Rny1 can affect tRNA Cleavage in a Vacuole or Vacuole-like Compartment An unresolved issue is how Rny1 is exposed to its RNA substrates during stress. One possibility, suggested by the loss of Rny1-GFP from vacuoles during stress, is that Rny1 is released to the cytoplasm and then can cleave various RNAs. Alternatively, or possibly in addition, RNAs might be transported into the vacuole by an autophagy-related process. One prediction of this latter model is that in the absence of Rny1, RNAs would be transported into vacuole or vacuole- like compartments, but they would not be degraded due to Rny1’s absence. Accordingly, increased accumulation of specific RNAs should be observed within biochemical fractions containing vacuoles. To test this possibility, we floated cell lysates from early stationary phase cells (where tRNAs are being cleaved by Rny1) on Ficoll step gradients (process diagrammed). In this experiment, we compared *rny1*Δ strains either expressing Rny1 on a functional low-copy plasmid or an empty vector. RNA and proteins were prepared from equal volumes of each fraction and resolved by urea-acrylamide electrophoresis and SDS-PAGE, respectively. As expected for membrane compartments, we observed that ER (Dpm1) and mitochondrial markers (Porin) floated in the 8 and 8–12% fractions (although some remained in the input pellet). In contrast, we observed that the vacuolar marker Cpy1 was distributed across the gradient suggesting the presence of a diversity of vacuoles with different densities. Consistent with this interpretation, we observed that intact vacuoles were present in each fraction as judged by staining with a vacuole specific dye (MDY-64) and examination of the fractions on a microscope. Given this, we conclude that vacuoles in yeast have a range of densities, distribute across the gradient, and the lightest fractions have the purest vacuole fractions due to the absence of other membrane-bound compartments. Examination of tRNAs in the wild-type strain revealed that the tRNA cleavage products were distributed in the 8, 8–12, and 15% fractions. The presence of these fragments in the 8 and 8–12% fractions suggests that they are either associated with, or within, a membrane compartment. The absence of these fragments from the lightest fractions suggests that if they are produced by Rny1 action within vacuoles, those vacuoles are of higher density. Interestingly, in the *rny1*Δ strain, we reproducibly observed an increased level of the full- length tRNA in the lightest fractions. This suggests that the full-length tRNA can associate with light vacuoles, and is either fully degraded by Rny1 in that context, or such vacuoles mature to higher densities in wild -type strains. Thus, although we cannot rigorously determine the nature of the tRNA-membrane interactions, these observations demonstrate that both full-length and fragmented tRNAs can associate with membrane-bound compartments. # Discussion Our results reveal that multiple features of Rny1 contribute to its function in growth inhibition. Deleting either the signal sequence, the T2 conserved region, or the fungal C-terminal extension partially alleviated the growth defects with Rny1 over-expression. These effects could not be attributed to a loss of protein expression since mutant proteins were expressed similarly to the wild-type protein. In contrast, Rny1’s nuclease activity maps to specific loops within the conserved T2 region. Loss of the T2 region, but not the C-terminal extension, inhibited Rny1’s ability to cleave tRNA. Within this region, we identified specific loops required for cleavage of tRNA (, loops 2, 3, 4, 6, and 7), two of which (loop 4 and loop 7) align to loops predicted to be involved in nucleotide binding. Targeting to membrane compartments is important for Rny1’s cleavage of tRNA. Rny1’s signal peptide, presumably inserted into the ER during translation, is required for cleavage of tRNA. Several possibilities could be envisioned to explain the role of the signal peptide in regulation of Rny1’s functions. One possibility is that in the absence of ER targeting, loss of glycosylation disrupts interactions required for function. This is supported by our evidence that GFP-Rny1, which fails to be glycosylated, also fails to cleave tRNA and partially rescues growth inhibition (data not shown). Another possibility is that the signal peptide directs Rny1’s vacuolar targeting, and this localization could enable processing of the Rny1 zymogen that renders it active. Lastly, it is possible that loss of the signal peptide could render an expressed but structurally impaired protein. In this case, our evidence further strengthens a model for a veritable catalytic-independent function for Rny1, requiring structural integrity, since a protein lacking the signal peptide partially alleviates growth inhibition. Our work raises the possibility that some tRNAs are taken up into vacuoles, or associate with vacuoles, for degradation by Rny1. Our analysis of vacuoles on Ficoll gradients revealed that specific fractions are enriched for CPY, a vacuolar protein, but not mitochondrial or ER proteins (fractions 2 and 4, lower panels). These same vacuolar fractions clearly displayed tRNA in an *rny1*Δ strain (, fractions 2 and 4, upper panels), and expression of a functional form of Rny1 nearly eliminated detection of tRNA in these fractions (fractions 2 and 4, upper panels). The fragments of tRNA cleavage accumulated in denser fractions, containing CPY and other organellar markers. Our microscopic analysis of Ficoll fractions from these gradients revealed that intact vacuoles partition throughout the gradient (data not shown) consistent with the observation that CPY signal is found throughout the gradient. We speculate that tRNA fragments might associate with a denser form of vacuoles, perhaps associated with additional proteins recruited in an Rny1-dependent manner, which are not easily resolved from ER and mitochondrial markers. In the absence of Rny1, undigested tRNA might associate with lighter vacuoles more easily resolved from other cellular organelles. Our evidence suggests that Rny1 might participate in a new form of tRNA ribophagy. To our knowledge, this is first example of tRNAs being targeted for vacuolar association or uptake. Prior work has demonstrated that rRNA accumulates within the lysosomes of neurons deficient for RNASET2 and that ribosomal proteins traffic to vacuoles during autophagic conditions. While tRNA cleavage does not require autophagy proteins, our work supports a model whereby tRNAs are turned over at, or within, vacuoles in a T2 ribonuclease-dependent manner during nutrient-limiting conditions, perhaps utilizing novel targeting mechanisms that do not include normal autophagy. It is still possible that Rny1 can exit vacuoles to cleave RNA within the cytosol, in addition to acting as a nuclease within vacuoles. In previous work, our lab observed decreased vacuolar signal for the Rny1-GFP fusion protein during oxidative stress, supporting a model for translocation of Rny1 to the cytosol to contact RNA substrates. This translocation resembles that observed for the predominant mitochondrial nuclease, Nuc1, which exits mitochondria during oxidative stress to modulate nuclear and possibly cytoplasmic RNA degradation. Likewise, in mammalian cells, lysosomal cathepsins can enter the cytosol during oxidative stress and other stresses cumulating in cell death (reviewed in). Hence, we speculate that in our studies of stationary phase conditions, Rny1 acts within vacuoles, and oxidative stress could trigger Rny1’s translocation similarly to Nuc1 and lysosomal cathepsins. Our work provides two important implications for general T2 ribonuclease functions. First, our studies suggest that glycosylation and membrane targeting of T2 ribonucleases could be important for nuclease and toxic functions and could regulate self-recognition and immune cell interactions. In the case of RNASET2, whose catalytic mutants inhibit tumorigenesis by recruiting competent immune cells to tumor sites, plasma membrane trafficking (evidenced) and glycosylation of the T2 protein might enable immune surveillance at the cell surface. Second, our studies suggest that tRNA, in addition to rRNA, might accumulate within acidic organelles of cells deficient for T2 ribonuclease function during stress. Since ribosomal proteins are also taken up into vacuoles and degraded during similar cellular stress, it is possible that translation complex proteins and RNA are turned over at vacuoles to control translation, through vacuolar protease and T2 ribonuclease functions. Thus, in diseases arising from RNASET2 deficiency, it is possible that regulation of protein synthesis is aberrant and drives pathogenesis. # Materials and Methods ## Yeast Strains and Growth Conditions Yeast strains and plasmids used in this study are described in. Cells were grown at 30°C in all experiments. For experiments over-expressing Rny1, pRP1584 and pRP1587 were used with pRP861 as a vector control. Cells were grown in selective synthetic media containing 2% sucrose to saturation, and these cultures were pelleted, aspirated, and diluted (OD600 = 0.1) in selective synthetic media with 2% galactose as the sole carbon source and grown to early midlog (OD600 = 0.3–0.5). For experiments to analyze tRNA fragment cleavage, cells were grown to saturation in media specified in figure legends (either selective synthetic media or yeast extract/peptone media both containing 2% dextrose), then diluted and grown to early midlog. Early midlog times were recorded, and cells were grown three days from this time to represent stationary phase growth. For frog ponding, yeast strains were patched to selective media plates, diluted to OD600 = 0.1 in selective media containing 2% sucrose in the first column of a 96-well plate, then diluted by 10-fold across into four additional columns. These columns were directly plated without additional growth. ## Plasmids and Plasmid Construction Plasmids used in this study are listed in. The *GAL RNY1 2µ* plasmid (pRP1584) and its counterpart plasmid containing mutations to produce catalytically inactive Rny1 (pRP1587) were the templates utilized to generate *cis* mutants by PCR using primers for site-directed mutagenesis. ## RNA Analyses Total RNA was prepared from liquid nitrogen flash frozen pellets. For all RNA analyses excepting that of Ficoll fractions, a hot acid phenol preparation was used. All steps prior to acid phenol addition were performed at 4°C. Samples were suspended in TNE buffer (50 mM Tris-Cl pH7.4, 100 mM NaCl, 10 mM EDTA), lysed with beads (two one-minute high speed vortexes interrupted by a one-minute incubation on ice to prevent overheating), then vortexed with SDS added to 1% and an equivalent volume of acid phenol chloroform. Vortexing was repeated, then samples were heated to 65°C for seven minutes, followed by additional vortexing. After acid phenol chloroform extraction, an additional acid phenol chloroform extraction, and one chloroform extraction, RNA was precipitated, washed, dried, and resuspended in deionized formamide. For Ficoll flotation assays, equal amounts of RNA were prepared (400 µl) using TriZol LS reagent (Invitrogen, Grand Island, NY, USA) following the manufacturer’s protocol, and pellets were resuspended in deionized formamide. Equal amounts of RNA (20 µg) as determined by A260, or equal amounts of RNA prepared from equal volumes (for analyses of floating RNA on Ficoll gradients), were resolved on 10% acrylamide, 47% urea, 1XTBE gels next to HinfI-digested, alkaline phosphatase treated, γ-³²P-5′ end-labelled PhiX174 markers. Electrophoretic transfer to positively charged nylon membranes was performed in 0.5XTBE buffer. Blots were UV cross-linked twice, prewashed once at 65°C, using 0.1%SDS 0.1XSSC, then prehybridized in 6XSSC 0.1%SDS 10X Denhardt’s at 42°C. Hybridization with γ-³²P-5′ end-labelled probes was performed in the prehybridization buffer. Blots were washed with 6XSSC 0.1%SDS and placed against phosphor screens to expose, and screens were scanned into a Typhoon scanner (GE Healthcare, Piscataway, NJ, USA) and quantitated using ImageQuant software. ## Protein Analyses Lysates were prepared from harvested pellets of cells lysed using 5 M urea, boiled, then vortexed in glass beads for 5 minutes. A solution of 125 mM Tris-Cl pH6.8, 2% SDS was added at 2.5x the volume of 5 M urea used, and this was vortexed into the mixture, then samples were boiled a second time. Collected lysate was clarified by spinning at 16000 RCF, and the supernatant was harvested for analysis. Cleared lysates, or proteins analyzed from Ficoll fractions, were suspended in protein loading buffer (0.05 M Tris pH6.5, 1%SDS, 0.01% bromophenol blue, 10% glycerol), boiled, and run on a 10% Tris-SDS acrylamide gel next to SeeBlue Plus2 protein molecular weight standards (Invitrogen, Grand Island, NY, USA). Gels were transferred to nitrocellulose and probed using standard Western blotting protocols. Antibodies used were supplied by Invitrogen Molecular Probes, Grand Island, NY, USA (CPY, Dpm1, Porin), Novagen, Madison, WI, USA (His tag detection of Rny1), and Covance, Princeton, NJ, USA (GFP) all used with an anti-mouse secondary coupled to HRP (Sigma, St. Louis, MO, USA, \#A4416). Signals were revealed using Pierce (Rockford, IL, USA) SuperSignal West Dura and exposing the blots to film and developing in a film processor (Konica, Mahwah, NJ, USA). Films were scanned into.tif format using an HP Scanjet Pro flatbed scanner (Hewlett Packard, Palo Alto, CA, USA), and images were analyzed using a cross-reactive band as a loading control. ## PNGase F Digests Wild-type strains expressing Rny1-GFP or GFP-Rny1 were harvested at midlog after continuous growth in synthetic selective media containing galactose for expression. Total lysates were prepared and quantitated for proteins by Bio-Rad (Hercules, CA, USA) assay. Reactions utilized NEB reagents (Ipswich, MA, USA). Two reactions, each containing 20 µg of proteins, were prepared for each sample, diluting into glycoprotein denaturing buffer. Digests were first denatured (10′ at 100°C), then G7 reaction buffer, NP40, PNGase F or ddH20 were added according to the NEB protocol. Reactions were incubated at 37°C for 1 hour, then protein loading buffer was added, and samples were resolved by SDS-PAGE and analyzed by Western blot (performed as indicated in Protein analysis) probing for GFP (Covance Princeton, NJ, USA). ## Preparation of Vacuoles by Ficoll Flotation Gradients Vacuoles were floated on Ficoll gradients using a modified version of the protocol described here. For our experiments, we diluted 40 ml saturated starter cultures growing in synthetic media supplemented with complete amino acids and 2% dextrose (complete+dex) to 0.1 OD600/ml in 300 ml of fresh complete+dex. Cells were grown in 1L flasks shaking at 30°C, and the time of early midlog (0.3–0.4 OD600/ml) was noted. Twenty-four hours from this point, cells were harvested for analysis, and the OD600/ml was noted. Equal amounts of OD600 units (631) were harvested and processed for each sample. Cell walls were rendered susceptible to spheroplasting by resuspending in 30 ml of DTT solution (detailed) and incubating at 30°C for ten minutes, then harvested cells were spheroplasted using 18 ml of spheroplasting buffer containing 15,000 units of lyticase (Sigma) incubated at 30°C for 35′–45′. The A800 readings in water were observed and noted to determine optimal times for harvesting spheroplasts (10-fold reduction in A800), but all samples were harvested at the same time to avoid differences in processing. After harvesting spheroplasts, dextran removal of plasma membranes was performed as described suspending in 15% Ficoll and adding 480 µl of 0.4 mg/ml DEAE Dextran (Sigma, St. Louis, MO, USA). In our experiments, we applied 3 ml of our lysate to the bottom of a pre-chilled SW40 tube (#331374 Beckman tubes), and we layered 2.5 ml of each of the following solutions in the following order on top: 12%, 8%, 4%, and 0%. Gradients were ultracentrifuged for 90 minutes at 110,000*xg* using a pre-chilled SW40 swinging bucket rotor. All steps following spheroplasting were performed in a 4°C room, and all tubes were prechilled. Following ultracentrifugation, 1 ml fractions were collected from the top and placed in protease inhibitors, using the 20xpic stock diluted to 1x for RNA fractions. From each fraction, an equivalent volume was removed to another tube for protein analysis, and this tube was pre-loaded with chilled complete EDTA-free protease inhibitors (Roche, Basel, Switzerland) to yield 1X, prepared from a 7X stock in PS buffer. All of the samples, except for aliquots reserved on ice in a 4°C room for analysis of vacuoles and ribonuclease protection assays, were flash frozen in liquid nitrogen and stored at −80°C. Separate tubes for protein analysis and for RNA analysis ensured that thawing was only performed once (in a 4°C room) to prepare RNA or resolve proteins on gels. Purified vacuoles were examined using MDY-64 (Invitrogen Molecular Probes, Grand Island, NY, USA) label and found to be intact using the GFP filter on a Delta Vision microscope. We thank Dr. Pamela A. Silver for providing the *GAL 2 µ* plasmid used in our studies (pRP861), Dr. Greg Odorizzi for valuable discussions relating to our vacuolar analyses, Dr. Carolyn Decker for aiding in the completion of experiments, and all members of the Parker lab for their valuable feedback in the preparation of this manuscript. [^1]: Conceived and designed the experiments: NL RP. Performed the experiments: NL. Analyzed the data: NL RP. Contributed reagents/materials/analysis tools: RP. Wrote the paper: NL RP. [^2]: The authors have declared that no competing interests exist.

# Introduction Since the discovery of toll-like receptors (TLRs), we have come to appreciate their crucial role in the induction of adaptive immunity against pathogens. In response to microbes, the engagement of pathogen associated molecular patterns by TLRs initiates a complex process of antigen presenting cell (APC) maturation and pro-inflammatory cytokine production. These intricately coordinated cellular processes are instrumental to the functional differentiation of pathogen- specific T cells. Although it is suggested that stimulation via different TLRs is sufficient to promote distinct immune responses, very little is known about how this program is set by mature DCs. This issue is particularly difficult to dissect *in vivo*, since it's not possible to directly compare stepwise events that occur upon bacterial versus viral infections. Previous studies have shown that the maturation of DCs is a key event that promotes autoimmunity in a variety of models. Evidence indicates that, TLR3 and TLR7 stimulation are critical for the activation and recruitment of autoreactive CD8⁺ T cells and subsequent destruction of pancreatic islet β cells. Activation of APCs via TLR4 or TLR9 can also disrupt self-tolerance and result in the induction of EAE. Likewise, TLR3, TLR4 and TLR9 appear to play a critical role in the development of autoimmune myocarditis. While infection is a well recognized trigger of autoimmunity, recent studies suggest that endogenous TLR ligands expand TLR signaling capacity and may therefore play a role in autoimmune disorders including those arising from sterile inflammation. Stimulation of different PRR has the potential to induce a particular cocktail of pro-inflammatory cytokines. A given cytokine milieu may then direct the initiation of distinct adaptive immune responses. Hirschfeld et al. along with subsequent studies shed light on the cytokine specificity of TLR2 and TLR4 and its impact on T helper cell differentiation. Following TLR4 engagement by lipopolysaccharide (LPS), murine macrophages produce large amounts of tumor necrosis factor alpha (TNFα), interleukin (IL)-1β, IL-12 and IP-10 which selectively induced a Th1 response. In contrast, peptidoglycan (PGN) engagement of TLR2 induces moderate production of TNFα and IL-1β, without IL-12 and IP-10, biasing toward a Th2 response. Evidence suggests that differential adaptor engagement and downstream signaling by different TLRs can contribute to the production of a given cytokine milieu. For instance, TLR3 and TLR4 can signal via TRIF and activate IRF3 dependent production of IFNβ. TLR sensors of nucleic acids, TLR3, TLR7, TLR8 and TLR9, have the ability of producing both IFNα and IFNβ via IRF3 and IRF7 activation. In addition to differential adaptor engagement and downstream signaling, qualitative parameters of the TLR ligand interaction itself may contribute to the induction of certain cytokines, as in the case of TLR9 signaling where distinct endosomal trafficking of A type but not B type CpG result in IRF-7 and IFNα production. One key cytokine that is produced upon DC maturation is the pro-inflammatory cytokine IL-12. IL-12, a heterodimeric cytokine consisting of the IL-12p35 and IL-12p40 subunits, is readily produced by stimuli such as double stranded RNA, LPS, flagellin, single stranded RNA and bacterial DNA. Although evidence suggests that IL-12 plays an important role and has been referred to as signal 3, after the TCR induced signal 1 and costimulatory signal 2. The role of IL-12 in the induction of adaptive immune responses remain controversial. In addition to playing a significant role in the induction of Th1 responses, IL-12 also the functional maturation of cytotoxic T lymphocytes (CTLs) by augmenting proliferation, survival and generation of effector molecules such as perforin and granzymes. Studies have also identified IL-12's role in modulating lymphocyte trafficking by modulating P-SGL1 expression. Given IL-12's involvement in promoting multiple facets of T cell immunity, it is believed to be a key mediator of autoimmunity and has been widely used as a marker for DC activation in cancer immunotherapy,. Indeed, studies in several murine experimental models and clinical settings also point to IL-12 playing an important role in the development organ specific autoimmunity. Analysis in autoimmune prone murine strains demonstrated that predisposition to autoimmunity may be attributed to enhanced IL-12 production by APCs due to alterations in the induction of NF-κB. In an autoimmune myocarditis model, IL-12 facilitates the differentiation of pathogenic CD8 T cell effectors. In the non-obese diabetic (NOD) model, the administration of IL-12 or ablation of IL-12 mediated Stat4 signaling can markedly accelerate or completely prevent the onset of diabetes respectively. Furthermore, blocking IL-12 in patients with active Crohn disease, which is commonly associated with increased production of IL-12 by APCs, can induce stable remission. IL-12's impact on the development of organ specific autoimmunity is however not decidedly pathogenic across all experimental settings. In some models, IL-12 appears non-pathogenic or even protective. Complicating the interpretation of these data is the discovery of the anti-inflammatory effects of closely related cytokine IL-35, which has redundant usage of the IL-12p35 subunit. One potential contributing factor to IL-12's divergent role in the pathogenesis of autoimmune disorders may be related to the different molecular mechanisms that are necessary to initiate different autoimmune diseases. It is possible that in certain models of autoimmunity, the induction of disease depends on certain cytokines induced by different PRR ligands. The current study investigated whether different APC maturation stimuli influenced the requirement for IL-12 in the induction of a CD8 T cell mediated autoimmune response *in vivo*. In this report, we used the previously characterized RIP-gp transgenic model, where the lymphocytic choriomeningitis virus glycoprotein (LCMV-GP) is expressed in the pancreatic islet β cells. In this model, T cells specific for the LCMV-GP remain ignorant towards the LCMV-GP expressed in the pancreas under the steady state. Upon activation, these autoreactive CD8⁺ T cells infiltrate the pancreatic islets and mediate the destruction of the GP+ β-cells, resulting in diabetes. Recent work has shown an alternate way to activate endogenous GP-specific T cells using mature bone marrow derived dendritic cells (BMDCs). We have compared the ability of Poly I∶C and LPS to mature DCs since TLR3 acts via the adapter TRIF, while TLR4 uses both TRIF and MyD88. By maturing WT or IL-12 deficient BMDCs with Poly I∶C or LPS and pulsing mature DCs with GP peptides, the impact of TLR3 or TLR4 stimuli on the requirement for IL-12 in breaking tolerance and inducing autoimmunity can be evaluated. Unexpectedly our studies showed that autoimmunity induced with Poly I∶C matured DCs is IL-12 independent, whereas IL-12 was essential for the induction of autoimmunity using LPS matured DCs. Our analysis also showed that Poly I∶C stimulated DCs produce high amounts of IFNα, compared with LPS stimulated DCs. Accordingly, IFNα given together with LPS stimulated DCs was able to induce autoimmunity in the absence of IL-12. Therefore, IFNα can overcome the requirement for IL-12 in the induction of T cell mediated pathology elicited by LPS-matured DCs. These studies clearly demonstrate that different TLR matured DCs require different cytokines to elicit a functional immunopathological adaptive immune response. These observations have important implications for controlling tissue specific autoimmunity and anti-tumor immunity. # Results ## TLR induced DC maturation programs the requirement for IL-12 in the induction of autoimmunity We first explored whether the p40 subunit of IL-12 plays an obligatory role in the induction of autoimmunity in our model. RIP-gp/p40−/− mice were generated and infected with LCMV and blood glucose was monitored for 3 weeks. Approximately 8 to 10 days after LCMV infection, all LCMV-infected RIP-gp/p40−/− mice were diabetic. There was no significant difference in the kinetics or incidence of disease in comparison with LCMV-infected RIP-gp/p40+/+ mice. Therefore, the p40 subunit of IL-12 does not appear to play an essential role in virus induced autoimmunity. We further evaluated the role of the p40 subunit using an alternate method to induce autoimmunity. BMDCs were matured using Poly I∶C or LPS, then pulsed with both class I and class II LCMV-GP peptides (gp33, gp276, and gp61 peptides) and given i.v. to RIP-gp transgenic mice. Poly I∶C matured p40−/− BMDCs were as effective as wild type BMDCs in inducing diabetes. However, the treatment of RIP-gp/p40−/− mice with LPS matured p40−/−BMDCs showed a reduced incidence of diabetes, in contrast to the treatment of RIP-gp/p40+/+ mice with LPS-matured p40+/+ BMDCs. The induction of diabetes using this DC based model is dependent upon CD8 T cells (Dissanayake D. unpublished). Therefore, the absolute requirement for the p40 subunit of IL-12 is dependent upon the stimuli that triggered DC maturation. Since activated B cells, macrophages, and neutrophils express p40 it remains possible that the requirement for p40 in the induction diabetes may reflect a defect in B cell, macrophage, or neutrophil functions. To determine whether the ability of the DC alone to produce p40 could influence diabetes induction, we treated RIP-gp/p40+/+ mice with LPS matured p40−/− BMDCs or conversely treated RIP-gp/p40−/− mice with LPS matured p40+/+ BMDCs. RIP-gp/p40−/− mice treated with LPS matured p40+/+ BMDCs developed diabetes, but not vice versa clearly demonstrating that p40 production is required by LPS matured DCs to induce CD8 mediated immune pathology. Studies have shown that the IL-12p40 subunit can also heterodimerize with the p19 subunit to form IL-23. IL-23 plays an important pathogenic role in several autoimmune models via the induction and maintenance of Th17 cells. To determine whether the lack of diabetes induction can be attributed to the lack of IL-23, we treated RIP-gp mice with LPS matured p35−/− BMDCs or p19−/− BMDCs. Fifty percent of RIP-gp mice treated with LPS matured p19−/− BMDCs developed diabetes, unlike the mice given LPS matured p35−/− BMDCs, indicating that the production of IL-12 by DCs is critically important for diabetes induction. Together, our data demonstrate that the different pathogen-related stimuli define the requirement for IL-12 in the induction of autoimmunity. Specifically, IL-12 production by DCs plays a critical role in diabetes induction when DCs are matured with LPS, but not with Poly I∶C treatment or LCMV infection. ## Reduced T cell infiltration in mice treated with LPS matured DCs To gain insights into the mechanism through which LPS matured DCs have a limited capacity to induce diabetes, the pancreas was evaluated by immunohistochemistry. Five days after RIP-GP mice were given WT or p40−/− LPS matured DCs, the pancreas was taken and stained for CD8 T cell infiltration. RIP-GP mice that were given LPS treated p40−/− DCs showed reduced CD8+ T cell inflammation in the islets which correlated with a reduced incidence of diabetes. A series of experiments were done to evaluate why there may be reduced CD8 inflammation. ## Induction of T cell responses in the absence of p40 IL-12 has previously been described to be important for T cell survival and the induction of effector functions. Therefore, we examined the impact of IL-12 on LCMV-GP specific T cell responses. CFSE labeled P14 T cells specific for gp33 peptide in the context of H-2^b were stimulated with gp33 peptide pulsed, LPS or Poly I∶C matured p40+/+ and p40−/− BMDCs for 3 days *in vitro*. In the absence of IL-12 secretion by DCs, antigen-specific T cell proliferation and upregulation of the activation marker CD44 was unaffected. Furthermore, the lack of IL-12 did not enhance T cell apoptosis since the proportion of CD8 T cells that were CFSE-7AAD+ were comparable between co-cultures with p40+/+ and p40−/− BMDCs under all stimulation conditions. To determine whether T cell functional maturation was impaired in the absence of IL-12, we evaluated cytotoxic activity in DC primed mice. C57BL/6 mice were treated with LPS or Poly I∶C stimulated p40+/+ and p40−/− BMDCs pulsed with gp33 peptide. gp33 specific cytolytic activity was assessed *in vivo* on day 5 by quantifying the ratio of gp33 peptide pulsed targets to AV peptide pulsed control target cells remaining in the spleen of primed C57BL/6 mice. C57BL/6 mice given Poly I∶C stimulated p40−/− BMDCs showed gp33-specific cytolytic activity comparable to C57BL/6 mice treated with Poly I∶C stimulated p40+/+ BMDCs. Interestingly, C57BL/6 mice given LPS stimulated p40−/− BMDCs had higher gp33 specific cytolytic activity compared with C57BL/6 mice treated with LPS stimulated p40+/+ BMDCs. Therefore changes in CD8+ T cell activation, function or survival was unlikely to account for differences observed in the infiltration of the pancreas of mice treated with LPS matured p40−/− DCs. ## Evaluating DC cytokine production in the absence of p40 Studies have shown that the induction of pro-inflammatory mediators by DCs plays a critical role in promoting the maturation and homing of T cells. To determine whether the induction of autoimmunity by LPS or Poly I∶C matured DCs was linked to the production of different cytokines, we compared the cytokine production from p40+/+ and p40−/− BMDC's. 12 hours after stimulation with LPS, p40+/+ BMDCs exhibit robust production of IL-12. Although Poly I∶C induced IL-12 production, the response is clearly less than that observed after LPS stimulation. Notably, the induction of intracellular levels of TNFα was similar upon LPS versus Poly I∶C stimulation. Therefore IL-12 is differentially induced in LPS matured DCs compared with Poly I∶C maturation, and IL-12 was essential for the induction of autoimmunity in our model after LPS -induced DC maturation. Cytokine production was also evaluated in the supernatant from these cultures using a cytometric bead assay. p40+/+ and p40−/− BMDCs stimulated with LPS or Poly I∶C showed an approximate 10,000 fold and 1,000 fold increase in the production of IL-6 compared to unstimulated control cultures respectively, but revealed no significant difference in the production of IL-6 in the absence of p40. P40+/+ and p40−/− BMDCs stimulated with LPS and Poly I∶C induced an approximate 200 fold and 20 fold increase in the production of TNFα respectively. In the absence of IL-12, LPS stimulated p40−/− BMDCs had a slight reduction in TNFα production detectable by the cytometric bead assay that was not observed by intracellular cytokine staining (T-test p\<0.05). No difference in TNFα production was observed between poly I∶C stimulated WT and p40−/− DCs. We also examined the production of the anti-inflammatory cytokine IL-10 and chemotactic factor MCP-1 (data not shown) but did not find a significance difference in production with either stimulation conditions in the presence or absence of IL-12. Similar to observations by others, there appears to be a qualitative difference in the ability of LPS and Poly I∶C to induce IL-10 production, as only LPS stimulation induced significant IL-10 production. Although these studies were done to examine whether the absence of p40 would have a negative impact on cytokine production, we found that the intrinsic ability of LPS versus Poly I∶C to stimulate cytokines had more influence on the levels of cytokines that were produced, than the absence of p40. ## IFNα can promote autoimmunity in the absence of IL-12 Since the absence of p40 did not result in a profound reduction in cytokine secretion by BMDCs, we decided to evaluate whether different levels of cytokines were made upon LPS versus Poly I∶C stimulation of BMDCs. It is possible that cytokines that are produced by DCs after Poly I∶C stimulation, are able to bypass the requirement for IL-12. One significant qualitative difference noted in the literature between TLR4 and TLR3 stimulation is the robust induction of IFNα by TLR3. Considering IFNα has been described to negatively regulate IL-12 production and our findings that diabetes induction is independent of IL-12 when autoreactive T cells are activated when IFNα is significantly represented in the cytokine milieu (with LCMV infection or Poly I∶C stimulation), the possibility arises that IFNα induction is critical and may overcome the requirement for IL-12 in LPS matured DCs. Indeed, in characterizing the IFNα response in our model, we observed robust production of IFNα, 12 hours after stimulating BMDCs with Poly I∶C but not LPS. However, we found no evidence of reciprocal co- regulation of IFNα by IL-12 as IFNα production was not significantly enhanced in the absence of IL-12. Therefore, it is clear that Poly I∶C stimulation leads to high levels of IFNα production that is independent of p40 in our culture conditions, and thus IFNα may have a critical role in promoting autoimmunity in this model. To investigate whether IFNα can overcome the requirement for IL-12 in LPS matured DCs, RIP-gp mice were given LPS matured p40−/− DCs with various combinations of IFNα. IFNα was included in BMDC cultures to promote DC maturation and/or administered *in vivo* 3 days after DC treatment, to evaluate the impact on the induction of diabetes. The following combinations were tested: LPS matured p40−/−DCs, LPS plus IFNα matured p40−/−DCs, LPS matured p40−/−DCs with IFNα treatment 3 days later, or LPS plus IFNα matured p40−/−DCs with IFNα treatment 3 days later. As in previous experiments, RIP-gp/p40+/+ hosts given LPS matured p40−/−DCs did not lead to the induction of autoimmunity. Although nearly a quarter of RIP-gp/p40+/+ hosts receiving LPS plus IFNα matured p40−/−DCs became diabetic, the effect of IFNα on diabetes induction had the most impact when RIP-gp/p40+/+ mice were given IFNα 3 days after treatment with LPS matured or LPS plus IFNα matured p40−/−DC. Treatment of RIP/p40+/+ mice with IFNα alone was not sufficient to induce diabetes. These results suggest that IFNα is a critical cytokine that can circumvent the requirement for IL-12 and therefore is a key mediator of autoimmune pathology in the absence of IL-12. Furthermore, our results suggest that the effects of IFNα's may be mediated by modifying the host's T cells or target organ in addition to directly enhancing the immunostimulatory activities of IL-12 deficient DCs. IFNα has previously been described to enhance the effector functions and survival of CD8 T cells. To assess whether IFNα circumvents the LPS specific requirement for IL-12 by enhancing host's T cell responses, we examined the *in vivo* CTL activity of p40+/+ C57BL/6 primed with LPS plus IFNα stimulated peptide pulsed p40−/− BMDCs or LPS stimulated p40−/− BMDCs followed by the administration of exogenous IFNα to the host 3 days later. However, we did not observe a change in gp33 specific CTL activity in either treatment condition (data not shown). Furthermore, in lethally irradiated RIP-gp mice reconstituted with IFNαR−/− bone marrow, treatment with LPS stimulated p40−/− BMDCs followed by the administration of IFNα 3 days later induced autoimmunity with similar kinetics and incidence of disease compared to RIP-GP/p40+/+ mice reconstituted with IFNαR+/+ bone marrow. These data indicate that exogenous IFNα does not act directly on host T cells or on secondary events involving DC populations (such as cross priming) and acts by promoting inflammation. To determine whether IFNα plays a role in promoting an inflammatory response in the absence of IL-12, RIP-gp hosts were given LPS matured p40+/+ BMDCs or LPS matured p40−/− BMDCs and CD8+ T cell infiltration in the pancreas was examined five days post-treatment by immunohistochemistry. In the absence of IL-12, RIP- gp mice treated with LPS matured p40−/− BMDCs have clearly reduced CD8 T cell infiltration in the pancreatic islets. The CD8 T cell infiltration was both antigen specific and dependent on the activation of BMDCs as infiltration was not observed in C57BL/6 mice treated with LPS matured p40+/+ BMDCs (no peptide) or in RIP-gp mice given immature peptide pulsed p40−/− or p40+/+ BMDCs. Furthermore, when RIP-gp mice were treated with exogenous IFNα 3 days after injection with LPS matured p40−/− BMDCs, autoreactive CD8 T cell infiltration was enhanced and comparable to that observed in RIP-gp mice injected with LPS matured p40+/+ BMDCs. Quantitative analysis of the degree of infiltration showed that that percent of islets with heavy infiltration is similar to mice immunized with WT BMDCs. Together, our results indicate that IFNα can compensate for IL-12's important role in diabetes induced by LPS matured DCs by promoting CD8+ T cell infiltration. # Discussion With this model, we can evaluate the impact of different TLR signals on the induction of adaptive immunity *in vivo*, using the same population of DCs and the same defined antigens. We have uncovered novel insights for how different TLR maturation signals influence the induction of autoimmunity. We demonstrated that the transfer of TLR3 or TLR4 matured DCs presenting self-antigens promotes CD8 T cell mediated autoimmune responses and overt organ specific autoimmunity *in vivo*. Our findings add to the growing body of evidence implicating a wide breadth of TLRs in the pathogenesis of autoimmune disorders and support the rationale behind targeting TLRs to promote tumor specific immune responses. Our investigation also revealed previously unappreciated dynamics between TLR signaling and their pro-inflammatory mediators in the pathogenesis of autoimmunity. Specifically, our findings highlight the crucial influence of the type of stimuli used to promote DC maturation and the differential requirement for IL-12 in the pathogenesis of autoimmunity *in vivo*. ## Importance of IL-12 in the induction of autoimmunity is dependent upon TLR stimulation Within the context of the dialogue between APCs and naïve T cells, along with antigen and co-stimulation, IL-12 produced by APCs is thought to play a crucial instructive role in determining the cellular fate of naïve T cells and has thus been coined the term “Signal 3”. In contrast to this work, our study has shown IL-12 surprisingly plays little role in mediating the proliferation, activation or survival of CD8 T cells. Furthermore, IL-12 appears to have minimal impact on the functional differentiation of CTLs *in vivo*. Our findings suggest a rather surprisingly minimal physiological role for IL-12 in these facets of CD8 T cell functional maturation *in vivo*. In spite of the normal induction of CTLs after stimulation *in vivo* with p40 deficient DCs, TLR4 mediated induction of autoimmune diabetes is completely abrogated in the absence of IL-12. An examination of the severity of inflammation in the pancreatic β islets cells indicated that IL-12 is an important component of the inflammatory milieu induced by TLR4 signaling, and promotes CD8 T cell inflammation. Previous studies have found IL-12 to be a critical pathogenic factor in promoting local inflammation and diabetes. In addition, during the priming stage of naïve T cells, IL-12 may play a direct or indirect role, i.e. via regulating the expression of co-stimulatory molecules, in modulating the expression of homing and trafficking molecules by CD8 T cells. As our experiments are done in p40 sufficient RIP-gp hosts, our findings suggest DC-produced IL-12 plays an important role in programming CD8 T cell trafficking during early encounter with cognate antigen bearing DCs. In stark contrast to the importance of IL-12 in TLR4 mediated DC maturation and subsequent induction of diabetes, IL-12 was not required in TLR3 mediated DC induction of autoimmunity. IL-12 was not an essential component of the cytokine milieu induced after TLR3- induced DC maturation and was not required to facilitate CD8 T cell proliferation, survival, functional maturation and homing to target tissues. The emerging paradigm from studies with infectious agents suggests that IL-12 plays a pivotal role in promoting T cell responses to bacterial and parasitic infections, while it is not required in some viral infections. Our model provides a direct way to evaluate the differences in bacterial induced DC maturation (LPS) versus virus induced DC maturation (Poly I∶C). It is not possible to directly compare the induction of immunity by various pathogens because they may have different inherent ways to mature the DC *in vivo*. Furthermore, these pathogens will present a multitude of antigens that are presented in different contexts and different doses. Using our model, we can directly compare the *in vivo* consequence of LPS versus Poly I∶C mature DCs using the same antigens. Our data directly demonstrate that IL-12 is required for the induction of autoimmunity using LPS matured DCs but not Poly I∶C matured DCs, and support the hypothesis that IL-12 is required for bacterial but not viral infections. ## Predominant role for IFNα in promoting tissue specific T cell responses In order to explain why TLR 3 was not dependent upon IL-12 producing DCs to induce CD8+ T cell mediated pathology, we hypothesized that other components of the inflammatory milieu induced by TLR3 stimulation may functionally compensate for IL-12. Although we found no conclusive evidence of compensatory upregulation of proinflammatory mediators such as TNFα, IL-6 and MCP-1, or downregulation of anti-inflammatory cytokines such as IL-10, we detected a clear quantitative difference in IFNα production between TLR3 and TLR4 stimulated BMDCs. Previous studies have suggested that there may be functional redundancy between IL-12 and IFNα/β signaling in facilitating T cell responses against some viral infections. Other studies have shown that IFNα but not IL-12 p40 subunit is critical for the induction of CD4+ Th1 responses after systemic administration of Poly I∶C. Using this model, our results indicate that in the absence of IL-12, IFNα is a critical pathogenic mediator of autoimmunity *in vivo*. In contrast with previous studies which have shown IFNα directly enhances T cell survival and effector functions, IFNα does not act directly on T cells to mediate CD8 T cell mediated autoimmune responses in our model. Neither did we find evidence of enhanced CTL activity in mice treated with exogenous IFNα. In SLE patients, the heightened level of IFNα in their sera is thought to contribute to the breakdown of peripheral tolerance via the induction of DC differentiation. In contrast, studies have shown that DC treatment with proinflammatory cytokines are not sufficient to promote functional T helper immunity, while other studies have shown that Poly I∶C treatment can promote autoimmunity, only when islet cells are engineered to express CD80. Our results suggest IL-12 and IFNα both act to promote CD8 T cell trafficking and local inflammation to the target organ. This is supported by previous observations of IFNα's critical role in autoimmunity by promoting inflammation and upregulating MHC class I molecules on the target tissues. It is likely that IFNα influences immunity in multiple ways in different models because of the variety of ways it has been shown to impact the immune response. Although we have not examined the relative requirements for IFNα for the induction of diabetes in this model by performing the reciprocal experiments with IFNα deficient DCs matured in different activation contexts, studies suggest that IFNα is likely a required factor in the context of TLR activation associated with viral infections. However, the absolute requirement for IFNα production by DCs matured with different TLR stimuli has yet to be elucidated in our model. ## Perspectives In the classical paradigm of DC-T cell interaction, DCs exist in 2 basic functional states, immature and mature. Naïve T cells that encounter antigen on immature DCs become tolerized while those that encounter antigen on mature DCs become activated. However, current evidence extends this notion and suggests that DCs exist in ‘states’ other than simply immature or mature and that these states can be modified by other factors or in some cases other subsets of cells. One would predict that maturation with different pattern recognition motifs should induce a different type of DC and a corresponding pathogen related immune response. Our findings begin to unravel some of these intricacies, and using the same peptides and same DC subset, show that LPS matured DCs are functionally different from Poly I∶C matured DCs in their ability to induce autoimmunity *in vivo*. Furthermore, our studies suggest that IL-12 may not be an appropriate surrogate marker for functionally mature DCs and the relevance of using IL-12 to extrapolate immunogenicity of DC vaccines in tumor immunotherapy should be reconsidered. Furthermore, therapeutic targeting of IL-12 for treatment of autoimmunity should also take into consideration the potential importance of IFNα in disease progression. # Materials and Methods ## Ethics Statement This study was carried out in strict accordance with the recommendations made by the Canadian Council for Animal Care. The protocol was approved by the Animal Care Committee at the PMH/OCI institute, protocol number 929. ## Mice and diabetes monitoring All mice used in the experiments are on the C57BL/6 background. Wild type C57BL/6, p35−/− and p40−/− mice were purchased from the Jackson laboratory. RIP- gp and P14 TCR transgenic mice, were maintained in our animal facility according to institutional guidelines. P19−/− and IFNαR−/− mice were kind gifts from N. Ghilardi and D. Pinschewer. Genotyping for all mice were performed by PCR. Diabetes induction was monitored by blood glucose measurements prior to treatment and then 2–3 times per week after treatment. Blood glucose levels were measured using Accu-Chek III glucometers and Chemstrips (Roche). ## LCMV infection LCMV WE strain was originally obtained from F. Lehmann-Grube and grown on L929 cells and titrated as previously described. RIP-gp/p40+/+ and RIP-gp/p40−/− mice were infected with 3000 PFU of LCMV WE and monitored for alterations in blood glucose. ## BMDC culture Bone marrow was extracted from the femur and tibia passed through a screen and washed in cold media. 2×10⁶ bone marrow cells were resuspended in 10 ml of DC media (RPMI, 10% LPS free FCS, β-mercaptoethanol, L-glutamine, 40 ng/ml GM-CSF(PeproTech)) and cultured in 10 cm petri dishes at 37°C. On day 3, 10 ml of DC media was added to the culture. On day 6 and 8, 10 ml of culture was removed, resuspended in 10 ml of fresh DC media and added back to the culture. On day 9, BMDCs were resuspended in DC media⁻ (ex GM-CSF) at 2×10⁶cell/ml, re-plated in 24 well plates and stimulated with LPS (100 ng/ml), Poly I∶C (100 µg/ml), and/or IFNα (1000 u/ml). On day 10, BMDCs were pulsed for 2 hours with gp33 peptide alone or gp33, gp276 and gp61 peptides (10⁻⁶ M) respectively, and washed prior to use in *in vitro* proliferation assays or intravenous infusions into treated mice. For intravenous infusions, 2×10⁶ BMDCs prepared in 200 µl of sterile HBSS were given to each treated mouse. For *in vivo* treatment, mice were infused with 10000 u of IFNα. ## Flow cytometric analyses BMDCs, cell cultures and single cell suspensions from spleens were stained with antibodies specific for CD11c, CD11b, CD80, CD86, MHCI CD8, CD44, TNF and IL-12p40/p70 flow-cytometry antibodies (BD and eBioscience). Flow cytometry data was acquired on FACSCanto (BD) and anaylzed with Flowjo (Tree Star). ## In vitro proliferation assay Single cell suspensions were prepared from the spleen of P14 transgenic mice and CD8⁺ T cells were purified using CD8 negative sort kit (Miltenyi Biotech). Purified CD8⁺ T cells were then labeled with CFSE (5-\[and-6\]-carboxyfluorescein diacetate, succinimidyl ester). 1×10⁵ CFSE-labeled CD8⁺ T cells were co-cultured with 2×10⁴ gp33 peptide pulsed BMDCs in 200 µl of complete IMDM in 96 well round-bottom plates and incubated for 3 days in a 37°C incubator. ## CFSE (5-\[and-6\]-carboxyfluorescein diacetate, succinimidyl ester) labeling After washing single cell suspensions in serum-free RPMI 1640 (GIBCO BRL), cells were resuspended in serum-free media at 10⁷ cells per 200 µl containing 10 µM CFSE (Molecular Probes). After incubation for 15 minutes in 37°C incubator, cells were washed in RPMI 1640 containing 10% FCS (Sigma- Aldrich). ## In vivo CTL assay RIP-gp mice were injected intravenously with 2×10⁶ gp33, gp276 and gp61 peptide-pulsed, LPS/PolyI∶C stimulated BMDCs. 5 days after the initial treatment, single cell suspensions from spleens of C57BL/6 mice were pulsed with gp33 or AV peptide (10⁻⁶ M) and labeled with CFSE at 1.5 µM and 20 µM respectively. 2×10⁷ each of gp33-pulsed and AV-pulsed CFSE-labeled splenocytes were injected intravenously into each treated RIP-gp mouse. 4 hours later, these mice were sacrificed and single cell suspensions from their spleens were analyzed. The percent specific lysis is calculated by dividing the number of CFSE^Hi cells by the number of CFSE^Lo cells. ## Intracellular cytokine staining Intracellular cytokine staining was performed using the BD Cytofix/Cytoperm kit as per manufacturer's instructions. ## Cytometric bead array analysis Media from BMDC cultures were collected after overnight stimulation with LPS or Poly I∶C. The CBA analysis was performed using the BD CBA assay as per manufacturer's instructions. ## Bone marrow chimeras Bone marrow was extracted from IFNαR+/+ and IFNαR−/− donor mice previously treated with CD4 and CD8 depleting antibodies. 4×10⁶ bone marrow cells were transferred intravenously into irradiated (9 Grays) sex-matched RIP- gp recipients, and recipients were treated with BMDCs for analysis 6–13 weeks after reconstitution. ## IFNα assay Media from BMDC cultures were collected after a 12-hour stimulation with LPS or Poly I∶C and assayed with mouse IFN Alpha ELISA kit from PBL Interferon Source, as per manufacturer's instruction. ## Immunohistochemistry Immunohistochemistry were performed as previously described. We would like to thank N. Ghilardi and D. Pinschewer for providing mice, members of the Ohashi lab and our families. [^1]: Conceived and designed the experiments: AL PO. Performed the experiments: AL DD SD AE. Analyzed the data: AL PO. Contributed reagents/materials/analysis tools: DD SD. Wrote the paper: AL PO. [^2]: The authors have declared that no competing interests exist.