Murine neutrophils present Class II restricted antigen

Neutrophils were originally described as short lived, terminally differentiated phagocytes that contribute only to the innate immune response. Recent evidence of neutrophil cytokine production and expression of numerous cell surface proteins has suggested that neutrophils are likely to influence adaptive responses and may satisfy the criteria of antigen presenting cells. Under certain inflammatory conditions human neutrophils express major histocompatibilty complex (MHC) Class II and the costimulatory molecules CD80 and CD86. We have employed a murine T cell hybridoma with a transgenic T cell receptor specific for ovalbumin peptide 323–339 (OVA323–339), and a green fluorescent reporter of T cell receptor ligation, to directly investigate neutrophil-T cell interactions. These cells provide an ideal model system, allowing precise identification of antigen specificity and a clear readout of T cell activation. Additionally, whilst murine neutrophils have previously been shown to stimulate MHC Class I-dependent CD8+ T cell activation, CD4+ T cells stimulation via MHC Class II-expressing neutrophils has not been investigated. We addressed this by isolating murine neutrophils, loading with OVA323–339 and co-culturing with T cells specific for the OVA323–339/MHC Class II complex, and this resulted in T cell activation, as determined by expression of the green-fluorescent protein reporter. Antigen-pulsed neutrophils were also able to prime naïve OVA-specific CD4+ T cells in a contact-dependent manner, as shown by proliferation and cytokine production. Activation of lymphocytes was not due to contaminating macrophages. These studies demonstrate that murine neutrophils present MHC Class II-restricted peptides and induce T cell proliferation, confirming findings in human neutrophils, and demonstrate a novel pro inflammatory effect of murine neutrophils.
1
Introduction
The range of functions ascribed to neutrophils has expanded significantly, from terminally differentiated, short-lived phagocytes that are solely involved in innate immune responses to cells that are now believed to influence the development of adaptive immune responses. Emerging evidence indicates that neutrophils may communicate with T cells through direct cell contact via major histocompatibility complex (MHC) Class I, Class II [1,2], and co-stimulatory molecules [3,4]. Additionally, a neutrophil-derived cytokine milieu [5] may have significant impact on the adaptive response to infection, for example via neutrophil-derived IL-12 [6,7].
There is considerable evidence to suggest that neutrophils satisfy the criteria of antigen presenting cells, with some capacity to process antigen. Human neutrophils express MHC Class II, either following in vitro activation via CD11b [8,9], with IFNγ, and GM-CSF [10,11] or following in vivo activation in RA synovial fluid [4,12], in patients with active Wegener's Granulomatosis [13], in patients receiving GM-CSF [14], and in persistent localized Staphylococcus aureus infections [15]. In addition to MHC Class II expression, stimulation of normal human PMN with IFNγ, or via CD11b induces CD80 and CD86 expression [8,11]. Human neutrophil presentation of superantigens is documented [2]. However, there are conflicting reports of neutrophils’ capacity to process and present tetanus toxoid on MHC Class II and activate T cells [1,2]. A capacity for extracellular antigen processing had been suggested as neutrophil gelatinase may cleave collagen to a Class II restricted immunodominant epitope [16].
Harding et al. demonstrated murine neutrophil Class I restricted antigen presentation [17] and additionally show that neutrophils processed phagocytosed bacteria via an alternate MHC Class I antigen-processing pathway, and such neutrophils may ‘regurgitate’ processed peptide into the extracellular space and this peptide may then bind MHC Class I on neighbouring cells (e.g. macrophages) for presentation to CD8 cells [17].
Human studies of antigen-specific neutrophil-T cell interactions are confounded by low frequency of antigen-specific T cells in peripheral blood, variation of MHC allele expression, which therefore impedes ideal peptide antigen identification. To overcome these complications, we have employed a murine model system. Moreover, despite the important role of the neutrophil in the pathogenesis of numerous inflammatory diseases, many of which are modelled in rodents, there is a relative paucity of information on murine Class II restricted antigen presentation. We, therefore, sought to investigate whether murine neutrophils present antigens via MHC Class II.
2
Materials and methods
2.1
Murine peritoneal neutrophil and macrophage separation
Peritoneal exudate cells (PEC) were obtained as previously described [18]. Briefly, 6–8-week-old BALB/c mice (Harlan Olac, Bicester, UK) received 1.5 ml i.p. Brewers thioglycollate medium (Difco, East Molesey, UK) and 4 h later animals were sacrificed and cells harvested by peritoneal lavage. PEC were washed once then resuspended in 8 ml PBS, and layered over 3 ml Histopaque® 1083 (Sigma, Poole, UK), then centrifuged at 700 × g for 30 min at 20 °C. The enriched population of neutrophils contained within the cell pellet was washed twice in RPMI (Invitrogen, Paisley, UK) then resuspended at 2 × 106 ml−1 in medium supplemented with 2 mM l-glutamine, 100 IU penicillin, 100 μg/ml streptomycin (all Invitrogen) and 10% foetal calf serum (Harlan). Peritoneal macrophages were obtained by adhering PEC to a polystyrene flask for 3–4 h after which remaining adherent cells were incubated with ice cold PBS (without calcium or magnesium) at 4 °C for 10 min. Cells were harvested with a cell scraper, then washed in RPMI and resuspended at 2 × 106 ml−1.
2.2
Murine lymph node preparation
DO11.10 mice, with CD4+ T cells specific for the OVA323–339 peptide in the context of the MHC Class II molecule I-Ad
[19] were obtained originally from N. Lycke, University of Göteborg, Sweden. This T cell receptor is recognized by the KJ1.26 clonotypic antibody. Single cell suspensions of lymph nodes from DO11.10 mice were obtained by passing tissues through 100 μm Nitex membrane (Cadish, London, UK) using a 5 ml syringe plunger. To enrich for lymphocytes in lymph node cell suspensions, cells were resuspended at 4 × 106 ml−1 in complete RPMI, and cultured in 150 cm3 polystyrene flasks (Iwaki, Japan), for 2 h after which non-adherent cells were removed. Cells were washed three times in RPMI and resuspended at the desired cell concentration in complete RPMI with 25 mM HEPES.
2.3
Assessment of cell proliferation and cytokine production
Cells were cultured under conditions indicated in figure legends. Where indicated, transwells (0.2 μm membrane, Fisher Scientific, UK) separated cell populations. Cell proliferation was assessed by incorporation of [3H]thymidine (0.5 μCi/well) for the last 18 h of culture. Cells were harvested using a Betaplate 96-well harvester (Wallac Oy, Turku, Finland) and [3H]thymidine incorporation measured on a Betaplate liquid scintillation counter (Wallac). Concentration of cytokines in supernatants were estimated by ELISA. Paired antibodies (Pharmingen, Oxford, UK) were used in accordance with the manufacturer's recommendations. All assays were optimised and validated prior to use. Maxisorp 96-well plates (Nunc) were coated with capture antibody in 0.1 M NaHCO3 overnight at 4 °C. Plates were blocked for 2 h; samples and standards added for 2 h, followed by biotinylated secondary antibody for 1 h, then ExtrAvidin peroxidase (Sigma) conjugate for 45 min. Plates were developed with TMB (3,3′,5,5′-tetra-methylbenzidine, KPL, Gaithersburg, USA), and then read at 630 nm with a Dynex MRX II plate reader and analysed using Dynex Revelation 3.2 software (Dynex, Middlesex, UK). The levels of cytokine in supernatants were calculated by comparison with recombinant cytokine standards (R&D Systems, Abingdon, UK).
2.4
Hybridoma cells
The DO11.10-GFP hybridoma (kind gift of David Underhill, Department of Immunology, University of Washington, Seattle, Washington [20]) cells were grown in medium containing a 0.5 mg/ml geneticin. Co-cultures were established in which neutrophils or macrophages were incubated with 10 μg/ml OVA323–339 peptide and DO11.10-GFP hybridoma cells, in the ratio indicated in the figure legends. Cells were cultured for 14 h, washed, and then analysed by flow cytometry.
2.5
Flow cytometry
Aliquots of 1 × 106 cells in 12 mm × 75 mm polystyrene tubes (Falcon BD, Oxford, UK) were resuspended in 100 μl FACS buffer (PBS, 2% FCS and 0.05% NaN3) containing Fc Block (Becton Dickenson, Oxford, UK) as well as appropriate antibody or isotype control. Neutrophils were identified with anti GR-1 (RB6-8C5, Caltag), macrophages with anti F4/80 (C1:A3-1, Caltag), and MHC Class II assessed (2G9, BD Biosciences), and T cells identified using anti CD4+ (RM4-5, BD) and a clonotypic antibody specific for the OVA323–339 TCR (KJ1.26). After washing, samples were analyzed using a FACSCalibur flow cytometer equipped with a 488 nm argon laser and a 635 nm red diode laser and analyzed using CellQuest software (both BD).
2.6
Statistical analysis
Statistical analysis was performed on data as indicated in the figure legends, using Minitab software.
3
Results
3.1
Purification of murine neutrophils
Neutrophils were purified from thioglycollate-induced peritoneal exudate cells (PEC), as there is a relative paucity of neutrophils in murine peripheral blood (data not shown). PEC were harvested 4 h post-thioglycollate administration, previously shown to be the peak of neutrophil recruitment, and after which an increasing proportion of macrophages are recruited [18]. Approximately 65% of thioglycollate-induced PEC were identified as neutrophils on the basis of GR1 staining (data not shown). PEC were further enhanced for neutrophils with a Ficoll gradient purification as previously described [18]. Following purification, samples were routinely 85% GR1 positive (Fig. 1a). Importantly, samples contained few macrophages (<4%) as determined by F4/80 staining (Fig. 1b). Cytospins of neutrophil preparations were prepared and demonstrated characteristic multilobed nuclei in >85% or cells (data not shown). Macrophage samples were 50–60% F4/80 positive cells (data not shown). Murine neutrophils, expressing GR-1, were found to express MHC Class II (Fig. 1c), albeit at low levels relative to F4/80 positive macrophages (Fig. 1d).
3.2
Murine neutrophils are able to present peptide antigen
The murine T cell hybridoma, DO11.10-GFP, is activated by OVA323–339 peptide in the context of I-Ad, without any requirement for co-stimulation. The hybridoma does not respond to peptide alone (Fig. 2, ‘background’). Neutrophils or macrophages alone had no effect on GFP expression by DO11.10-GFP. However, neutrophils or macrophages loaded with OVA323–339 stimulated significant GFP expression by hybridoma cells when cultured at a 1:1 ratio. A dilution of macrophages was performed to estimate the contribution of contaminating macrophages to the results observed with neutrophil populations, demonstrating that at a ratio of 1 macrophage to 10 DO11.10-GFP cells, there was significantly less expression of GFP protein compared to that induced by neutrophils at a 1:1 ratio (Fig. 2). At a ratio of 1:100, there was no consistent GFP expression detected (data not shown). As there were <5% F4/80 positive macrophages contaminating the neutrophil samples, these titration experiments indicated that only a small proportion of the T cell hybridoma activation observed in the ‘neutrophil presentation’ assays could be attributable to macrophage contamination.
3.3
Neutrophils can drive naïve T cell proliferation and cytokine production
To investigate neutrophils antigen presentation in a co-stimulation dependent system, and the ability of neutrophils not only to present but also to prime T cells, neutrophils were co-cultured with primary T cells. Lymph nodes were obtained from DO11.10 transgenic BALB/c mice. Single cell suspensions of lymph nodes were allowed to adhere to plastic for 3 h to remove the majority of adherent antigen presenting cells, after which the population was routinely 70% CD3 positive (data not shown). The presence of remaining endogenous APCs in the lymph node facilitated a strong response to OVA323–339 peptide alone (Fig. 3a, c and e). Therefore, neutrophils and macrophages were pulsed with OVA323–339 for 2 h then washed three times to remove unbound peptide prior to the addition of transgenic T cells. Cells were co-cultured for 72 h at the ratios indicated in the figure legends. Neutrophils pulsed with OVA323–339-induced proliferation (Fig. 3b), IL-2 (Fig. 3d) and IFNγ (Fig. 3f) production. Low levels of IL-5 were detected in some experiments (data not shown). The neutrophil-induced responses were less than those evoked by macrophages at the same cell ratio. A titration of macrophages was performed to give an estimate of the contribution of contaminating cells, demonstrating that only a small proportion of neutrophil-induced T cell responses could be attributable to contaminating macrophages.
3.4
Cell contact dependence of neutrophil T cell interactions
To determine the relative contribution of cell contact dependent mechanisms and soluble factors in neutrophil-T cell interactions, cells were separated by 0.2 μm membranes placed between the cell populations, allowing diffusion of soluble proteins but preventing cell contact. The responding OVA323–339-specific T cells were not a 100% pure T cell population and contained some APCs, demonstrated by the lymph node response to OVA323–339 alone (Fig. 4a and b ‘OVA’). Thus, the transwells permitted investigation of whether lymph node-derived APCs were presenting soluble proteins derived from dead or degraded neutrophils. Bone marrow-derived dendritic cells (DC) were used as a positive control. Free OVA323–339 or ConA crossed the transwell membrane and stimulated cell proliferation and cytokine production (Fig. 4a). However, when antigen pulsed neutrophils were separated from the lymph node cells by the transwell, antigen-specific proliferation and cytokine production was reduced (Fig. 4a and b). Importantly, antigen pulsed cell cultures did not release sufficient free antigen to stimulate a response from the OVA323–339-specific T cells below (Fig. 4a). Thus, the antigen-specific T cell proliferation and cytokine production induced by neutrophil antigen presentation is cell contact dependent.
4
Discussion
Data presented here demonstrate that murine neutrophils are indeed capable of directly presenting antigen on Class II MHC and activating T cells independently of other APCs. The DO11.10-GFP hybridoma cells respond exclusively in a Class II restricted manner, and our data demonstrate that neutrophils, rather than contaminating cells, were mediating this response.
At comparable cell ratios, neutrophil-mediated T cell activation was lower than that mediated by macrophages, commensurate with reduced expression of MHC Class II by neutrophils. Although neutrophils have been reported to express co-stimulatory molecules, we detected only low levels of CD40 and were unable to consistently detect CD80 or CD86 in our neutrophil preparations (in contrast to the F4/80 positive macrophages which expressed CD40, CD80 and CD86; data not shown). Thus it is possible that: co-stimulatory molecule expression below the limits of detection of methods used here is sufficient for inducing a response, that T cells are responding in a co-stimulation independent manner, or that co-stimulation has occurred via alternative molecules.
Neutrophil antigen processing capacity was investigated using whole ovalbumin protein compared to OVA323–339. These studies indicated that these neutrophils did not process the ovalbumin effectively, demonstrated by a lack of response by DO11.10-GFP hybridoma (data not shown). An MHC Class I processing pathway within neutrophils was elegantly defined with E. coli transfected to express a MHC Class I restricted epitope of OVA. This demonstrated that neutrophils are capable of non-classical antigen processing for presentation on MHC Class I [17]. Although neutrophil enzyme mediated degradation of collagen generates a Class II epitopes (14), there is no current evidence of any Class II processing pathway. It has been proposed that as macrophage phagosomes are adequate for Class II antigen processing [21], therefore a parallel system may exist in neutrophils. A definitive investigation of murine neutrophil MHC Class II antigen processing awaits transfection of bacteria with OVA323–339, thus elucidating the effects on antigen processing of concomitant neutrophil activation by bacteria.
There is increasing evidence documenting the potential for neutrophils to acquire macrophage [22] or dendritic cell (3) characteristics, often in response to in vitro conditions. We document in vitro antigen presentation by in vivo activated neutrophils. It will be of interest to delineate whether such neutrophil behaviour can be observed strictly in vivo. Physiologically, the location of such neutrophil-T cell interactions is likely to be restricted to sites of chronic or established inflammation, for example persistent bacterial infection [15]. Neutrophils have been shown to degrade proteins to release antigens recognized by auto reactive T cells [16], and such antigens may then be transported to the draining LN by migrating DCs. Due to the neutrophils’ unique proteolytic activities, this may represent a mechanism of presentation of novel or cryptic antigens. Furthermore, there is evidence that neutrophils may passively acquire membrane proteins, including MHC, from other cells [23]. In our GFP-hybridoma studies (Fig. 2), this seems unlikely as the neutrophils were harvested from the peritoneum before there was significant in vivo macrophage migration, and immediately purified in vitro. However, this phenomenon may have contributed to the neutrophil mediated T cell activation in co-culture (Fig. 3).
There are overall fewer studies of murine neutrophils compared to human neutrophils, which is likely to be a reflection of the ease of acquiring human neutrophils that constitute 70% of circulating leucocytes, compared to limited quantity of murine peripheral blood available and the relative paucity of neutrophils, contributing only 20% of circulating leukocytes in rodents. These studies were only possible through use of rodent cells, as the TCR transgenic T cells absolutely control antigen specificity of response, which was central to this investigation of neutrophil-T cell interactions. However, the significantly different proportion of neutrophils in murine and human peripheral blood raises questions as to whether murine and human neutrophils perform parallel functions as is assumed. Our data document a novel, Class II restricted antigen presenting function of murine neutrophils, confirming findings in other human systems, and expanding our understanding of the role of the neutrophil in the adaptive immune response.


Evaluation of limited sampling strategies for tacrolimus

Objective In literature, a great diversity of limited sampling strategies (LSS) have been recommended for tacrolimus monitoring, however proper validation of these strategies to accurately predict the area under the time concentration curve (AUC0–12) is limited. The aim of this study was to determine whether these LSS might be useful for AUC prediction of other patient populations.
Introduction
The calcineurin-inhibitor tacrolimus, used widely after organ transplantation, has a narrow therapeutic index and highly variable pharmacokinetic characteristics. Close monitoring of the drug concentration is required to achieve an optimum efficiency by minimizing the risk of subtherapeutic and toxic blood concentrations. Efficacy and side effects of tacrolimus are highly correlated with the area under the time concentration curve (AUC0–12) [1]. Elevated tacrolimus concentrations may lead to severe side effects such as nephrotoxicity, neurotoxicity and hyperglycaemia [2–4], while subtherapeutic tacrolimus concentrations increase the risk of transplant rejection enormously [5–7].
The most exact way to monitor the total tacrolimus exposure is by creating 12-h pharmacokinetic profiles, which implies that the tacrolimus concentration should be measured at at least six different time points. The AUC0–12 can then be calculated according to the trapezoidal rule using the tacrolimus concentrations measured at different time points. Since recording a complete 12-h pharmacokinetic profile for every patient is not feasible in clinical practice, traditionally many transplant centres have used tacrolimus trough (C0) concentrations to estimate the tacrolimus exposure. Although tacrolimus C0 concentrations are generally considered to be a good indication of the total systemic drug exposure [1, 8], its usefulness in differentiating graft rejection episodes from nephrotoxicity has been questioned [6, 9–11]. Recently, the correlation between individual tacrolimus concentrations and AUC0–12 has been studied in kidney [12–18], liver [19], heart [20, 21] and lung [22] transplant recipients. In these studies, a poor association was found between the tacrolimus C0 concentrations and the AUC0–12, while tacrolimus concentrations measured at other time points showed much better correlations with the AUC0–12. Additionally, strategies have been developed that included a limited number of sampling time points within a short time post-dose, the so-called limited sampling strategies (LSS). Several two- and three-time-point sampling strategies showed a high correlation with the AUC0–12 in published studies and were able to predict the AUC0–12 more accurately than the C0 concentration alone [12, 15–18, 20, 22].
Based on the number of published studies regarding LSS for tacrolimus, there seems to be a growing interest for non-C0 concentration measurements as an indicator of between-patient variability and as a guide for dose adjustments. Most of these studies recommend different LSS, but these strategies have not been validated with an independent population. Ting et al. [23] recently reported that validation of the different LSS with an independent transplant population is an absolute prerequisite. The question was whether the LSS described in literature could be used in other centres with different populations. Predictive value of LSS from literature was evaluated using our own renal post-transplant group of 37 patients with known AUCs. Also the predictive value of trough levels (C0 and C12) determined in our own population was investigated.
Materials and methods
Patient population
In total, 37 Caucasian renal transplant recipients for whom a complete 12-h time tacrolimus concentration curve had been determined in a past clinical trial were included in this study (Table 1). The transplant recipients underwent a renal transplantation at least 1 year ago. Patients taking medication known to interact with tacrolimus, who suffered from gastro-intestinal or liver disease, pre-transplantation diabetes mellitus or other disorders that could have altered the absorption of tacrolimus were excluded from this study as illustrated in Table 1.
Table 1Demographic characteristics of renal-transplant recipientsDemographic characteristicsPatients (n = 37)Gender (male/female)24/13Age (years, mean ± SD)51.3 ± 10.9Length (cm, mean ± SD)174 ± 8.4Weight (kg, mean ± SD)77.4 ± 13.5Body mass index (kg/m2, mean ± SD)25.6 ± 3.42Primary kidney disease (n) Glomerulonephritis1 Chronic pyelonephritis2 IgA nephropathy4 Hypertensive nephropathy7 Diabetes mellitus nephropathy0 Polycystic kidney disease8 Unknown4 Other11Transplantation number (n) First30 Second6 Third or more1Tacrolimus mono therapy (n)29Tacrolimus dose (mg kg−1 day−1, mean ± SD)0.054 ± 0.029C0 (ng/mL, mean ± SD)6.59 ± 1.39AUC0–12 (ng×h/mL, mean ± SD)122.5 ± 31.1Cmax (ng/mL, mean ± SD)20.9 ± 6.5Tmax (h, mean ± SD)1.24 ± 0.43Use of azothioprine/MMF/rapamycine/steroids3/4/0/2Time since transplantation (days, mean and range)1,542 (453–4,128)Haemoglobin (mmol/L, ref. M: 8.2–11.0, F: 7.3–9.7)8.52 ± 0.83Haematocrit fraction (ref. M: 0.41–0.52, F: 0.36–0.48)0.41 ± 0.04ALAT (units/L, ref. M: <45, F: <35)24 ± 13ASAT (units/L, ref. M: <35, F: <30)17 ± 10Serum albumin (g/L, ref. 34–45)37.0 ± 3.84Serum creatinine (μmol/L, ref. M: 71–110, F: 53–97)128 ± 29Creatinine clearance (Cockcroft-Gault; mL/min, ref. 90–140)58.4 ± 26.6Ref. are the reference values applied in the Clinical Chemistry and Haematology Laboratory of the University Hospital in Maastricht. M Male, F female, MMF mycophenolate mofetil
Prior to the blood sample collection, there was no tacrolimus dose change for at least 1 week. After overnight fasting, the blood samples were collected immediately before (C0) and 0.5 (C0.5), 1 (C1), 2 (C2), 3 (C3), 4 (C4), 5 (C5), 7.5 (C7.5) and 12 (C12) h after the morning tacrolimus administration. Patients were not allowed to take food until 1 h after ingesting the tacrolimus dose and were advised to avoid grapefruit intake to prevent alterations in the tacrolimus metabolism. Demographic as well as clinical data were determined at the time of recording the 12-h time tacrolimus concentration curve. The study was performed in accordance with the Declaration of Helsinki and its amendments. The protocol was approved by the local Medical Ethics Committee and written informed consent for participation in this study was obtained from all patients.
Determination of tacrolimus concentrations
The tacrolimus blood concentrations were determined in ethylene diamine tetra-acetic acid (EDTA) whole blood, using a method based on high-pressure liquid chromatography tandem mass spectrometry (HPLC-MS/MS). The assay is linear from 1 to 300 μg/l. Intra-assay precision and accuracy were 3.4, 2.2, and 3.0% and 102, 94 and 94% respectively at 3.04, 6.23 and 13.0 μg/l (n = 6). Inter-assay precision and accuracy were 8.2, 5.2, and 4.6% and 102, 94 and 93% (n = 9) respectively. Lower limit of quantification was 1.0 μg/l. The laboratory participates in the International Tacrolimus Proficiency Testing Scheme.
Limited sampling strategies investigated
In our opinion, a suitable limited sampling strategy for tacrolimus should consist of two or three time concentration points within a short time post-dose (≤4 h) including a trough level. We selected 24 LLS from the literature [12, 15, 16, 18, 20, 22]. Eighteen of these strategies were based on regression analysis [12, 16, 18, 20, 22], and six other strategies were based on Bayesian fitting [15]. Also strategies based on the tacrolimus C0 and C12 concentrations were developed for our own renal transplant patient population and compared with above-mentioned LSS.
Pharmacokinetics and statistical analysis
The area under the tacrolimus time concentration curve (AUC0–12) was calculated from the time versus tacrolimus concentration plot using the linear trapezoidal rule in MWPharm 3.50 (Mediware, Groningen, The Netherlands). The predicted AUC0–12 (AUCpred), calculated with the 24 different LSS, were validated by determining the predictive performance as described by Sheiner and Beal [24]. The percentage of the prediction error (PE) and the percentage of the absolute prediction error (APE) are parameters often used for validation in LSS [12, 14–16, 18, 20, 22]. Given the high pharmacokinetic variability, an APE of less than 15% was considered clinically acceptable [16, 25, 26].
Prediction bias was measured as a percentage of the prediction error [PE (%)] using the following formula:
Prediction precision was measured as a percentage of the APE using the following formula:
The variance in the strength of association between the AUCpred and the AUCactual was reflected by the linear regression coefficient of multiple determination (R2). All values are expressed as mean ± SD. All statistical analyses were performed with use of SPSS 12.0 software for windows (Chicago, IL, USA).
Results
Evaluation of predictive performances of the limited sampling strategies
Table 2 shows an overview of the studies describing the LSS evaluated in the present study. The regression equations and the R2 found by the investigators for the evaluated LSS are summarised in Table 3. Table 4 describes the R2, which represents the association between AUCpred and AUCactual and the calculated PE and APE of the 24 evaluated LSS for our 37 pharmacokinetic profiles. Thirteen of the 18 LSS (72%) based on regression analysis had a predictivity of >90%. Additionally, all except three of the LSS examined gave a better prediction of the complete AUC0–12 in comparison with LSS based on a trough concentration C0 and C12 (mean 62%). Predictivity of all six LSS based on Bayesian fitting was <90% (mean 66.8%). Additionally, Fig. 1 illustrates an overview of the performances of the 26 LSS evaluated in our well-characterized population of renal transplant recipients.
Table 2Overview of the characteristics of transplant patients included in the studies that described limited sampling strategiesStudyTransplanted organNumber of patientsaNumber of AUC0–12 curves for validation (Ib/NIc)Analytical methoddTime since transplantationeInclusion criteriafWong et al. [16]Kidney180/18Imx II2.5 years1,2Aumente Rubio et al. [20]Heart220/25Imx<1 year–Pisitkun et al. [18]Kidney150/15Imx II8.7 months1,2,3Armendariz et al. [12]Kidney2213/14ImxUnknown–Scholten et al. [15]Kidney4364/20ImxDiffersg2Ragette et al. [22]Lung150/31Imx7.3 months–aNumber of transplant patients used in the included study for both developing and validating the limited sampling strategies.bNumber of AUC0–12 used for developing the limited sampling strategies.cNumber of independent (I) and dependent (NI) AUC0–12 used in the study to validate the created limited sampling strategies.dThe analytical method used to determine the whole blood tacrolimus concentration.eThe mean time after transplantation.fThe inclusion criteria used for the transplant patients in the different studies. 1 Tacrolimus administrated when patients were in the fasting state, 2 patients selected for using no interfering medication with tacrolimus, 3 patients selected with a normal liver function test.gTwenty-two pharmacokinetic profiles were obtained within 2 weeks after transplantation, and 42 pharmacokinetic profiles were obtained between 6 and 52 weeks after transplantation.Table 3Overview of limited sampling strategies and their reported coefficients of correlation (R2) with the complete tacrolimus AUC0–12EquationTime pointsRegression equationsR2Ref1.C00.542.C120.793.aC0, C2, C40.93[16]4.aC2, C40.93[16]5.aC0, C2, C40.97[20]6.C0, C40.95[20]7.C0, C1, C20.93[18]8.aC0, C1, C40.97[18]9.aC0, C2, C40.97[18]10.aC1, C2, C40.96[18]11.C0, C10.91[18]12.aC0, C40.95[18]13.a C1, C40.95[18]14.aC2, C40.96[18]15.a,bC0, C1, C40.97[18]16.a,bC0, C2, C40.96[18]17.C0, C1, C40.97[12]18.C0, C1, C3Bayesian estimation of the actual AUC0–120.97[15]19.C0, C2, C3Bayesian estimation of the actual AUC0–120.96[15]20.C0, C2, C4Bayesian estimation of the actual AUC0–120.97[15]21.C0, C2Bayesian estimation of the actual AUC0–120.94[15]22.C0, C3Bayesian estimation of the actual AUC0–120.96[15]23.C0, C4Bayesian estimation of the actual AUC0–120.95[15]24.aC0, C2, C40.98[22]25.C0, C40.96[22]26.aC2, C40.94[22]Limited sample strategies derived from the linear trapezoidal rule and the complete 12-h AUC.aLimited sampling strategies that are able to predict 90% of complete AUC0–12 of the renal transplant recipients within the absolute prediction error (APE) of 15%.bLimited sample strategies derived from the linear trapezoidal rule and the actual AUC0–12.Table 4Evaluation of predictive performance of limited sampling strategies to estimate the complete AUC0–12 in the 37 renal transplant recipientsEquationTime pointsR2Mean PE (%)Mean APE (%)≤15%a23.bC0, C40.760−14.9 ± 13.8 (−46.0–33.2)17.9 ± 9.43 (1.12–46.0)13 (35%)22.bC0, C30.779−11.5 ± 14.0 (−41.9 to 33.1)15.7 ± 8.83 (2.0–41.9)21 (57%)1.C00.5362.11 ± 14.8 (−27.1 to 24.4)12.3 ± 8.22 (0.7–27.1)22 (59%)11.C0, C10.7036.58 ± 14.8 (−26.5 to 43.7)12.6 ± 10.1 (0.1–43.7)24 (65%)2.C120.809.56 ± 11.6 (−12.7 to 29.9)12.0 ± 8.97 (0.3–29.9)24 (65%)19.bC0, C2, C30.502−4.44 ± 17.4 (−45.3 to 50.6)13.7 ± 11.4 (0.4–50.6)25 (68%)20.bC0, C2, C40.537−5.11 ± 16.3 (−43.1 to 50.3)12.9 ± 10.4 (0.2–50.3)28 (76%)18.bC0, C1, C30.5259.95 ± 19.4 (−29.7 to 88.8)13.1 ± 17.4 (0.4–88.8)30 (81%)25.C0, C40.911−7.83 ± 6.36 (−21.3 to 2.4)8.08 ± 6.02 (0.1–21.3)30 (81%)7.C0, C1, C20.8692.35 ± 9.96 (−17.2 to 27.3)8.03 ± 6.22 (0.0–27.3)31 (84%)6.C0, C40.896−5.97 ± 6.71 (−20.1 to 4.7)6.63 ± 6.04 (0.6–20.1)31 (84%)21.bC0, C20.802−3.69 ± 10.2 (−19.6 to 18.6)9.10 ± 5.67 (0.4–19.6)31 (84%)17.C0, C1, C40.9435.91 ± 7.06 (−8.8 to 26.3)7.02 ± 5.93 (0.2–26.3)33 (89%)15.cC0, C1, C40.9345.00 ± 7.28 (−9.8 to 25.8)6.81 ± 5.57 (0.2–25.8)34 (92%)14.C2, C40.9642.28 ± 6.58 (−17.1 to 16.1)5.45 ± 4.24 (0.7–17.1)35 (95%)24.C0, C2, C40.941−4.81 ± 5.26 (−17.3 to 2.8)5.32 ± 4.73 (0.1–17.3)35 (95%)13.C1, C40.9736.30 ± 4.84 (−5.9 to 17.8)6.68 ± 4.28 (0.3–17.8)36 (97%)8.C0, C1, C40.9673.37 ± 5.21 (−5.2 to 17.7)4.87 ± 3.80 (0.2–17.7)36 (97%)9.C0, C2, C40.9620.10 ± 6.37 (−16.7 to 14.7)4.71 ± 4.22 (0.3–16.7)36 (97%)26.C2, C40.9593.38 ± 5.24 (−7.6 to 15.5)5.20 ± 3.37 (0.0–15.5)36 (97%)10.C1, C2, C40.9763.07 ± 5.40 (−14.9 to 13.2)4.99 ± 3.64 (0.1–14.9)37 (100%)16.cC0, C2, C40.953−1.58 ± 5.29 (−14.9 to 10.1)4.00 ± 3.75 (0.0–14.9)37 (100%)12.C0, C40.9303.55 ± 6.30 (−9.8 to 14.3)6.29 ± 3.46 (0.1–14.3)37 (100%)4.C2, C40.963−1.66 ± 4.99 (−12.0 to 14.3)4.13 ± 3.20 (0.2–14.3)37 (100%)5.C0, C2, C40.9591.33 ± 5.24 (−11.8 to 14.0)4.22 ± 3.32 (0.5–14.0)37 (100%)3.C0, C2, C40.965−0.20 ± 4.79 (−10.4 to 13.7)3.64 ± 3.06 (0.2–13.7)37 (100%)aNumber and percentage of calculated AUC0–12 with a prediction error within 15%.bBayesian estimation of the actual AUC0–12.cLimited sample strategies derived from the linear trapezoidal rule and the actual AUC0–12.Fig. 1An overview of the predictive performances of the limited sampling strategies published using 37 different pharmacokinetic profiles recorded in our renal transplant recipients. The number of limited sampling strategies categorized into the percentage of AUC0–12 within a prediction error of <15% is plotted on the x-axis, the number of limited sampling strategies is plotted on the y-axis
Discussion
Our results confirm the results of several other studies [12, 14–16, 18, 20, 22] that trough concentrations C0 and C12 have a lower predictive value for the complete 12-h AUC than almost all other studied LSS. The predictivity of LSS based on Bayesian estimation of the AUCactual was lower than the LSS based on regression analysis. Therefore a trough level and one or two time points in the early phase (≤4 h) post-dose seem not to be sufficient for a Bayesian estimation strategy to fit correctly most of the AUC0–12 and thus predict the complete AUC0–12 reliably. The differences in variability and shape between the curves of post-transplant recipients combined with just two or three sample points might have been caused by the large differences found between the AUCpred calculated according to the Bayesian estimation strategy and the complete AUCactual.
In contrast to most studies that describe LSS for tacrolimus in literature, we used an HPLC-MS/MS assay to determine the tacrolimus concentration. Because there seems to be a fixed difference of about 15% between the immunoassay and the HPLC-MS/MS, the prediction will change proportionally, and the predictivity of the LSS will be the same. Also potential interfering drug-drug interactions will have an equal influence on the different tacrolimus concentrations, which consequently has no effect on the predictivity of the different LSS.
Ting et al. [23] recently suggested that LSS should only be applied on transplant patient populations that are comparable with the transplant patient population that was used to develop the LSS. However, the renal transplant patient group examined in the present study was not exactly comparable with the transplant patient populations in which the equations for the LSS were developed. For example Aumente Rubio et al. [20] and Ragette et al. [22] used heart and lung transplant recipients respectively to develop and validate their LSS. Despite the fact that the LSS were developed with the pharmacokinetic profiles of patients who underwent a different kind of transplantation, Eqs. (5), (24) and (26) were able to predict at least 90% of the AUC0–12 within an APE of 15%, which suggests that these LSS are more robust than expected by Ting et al. [23]. Even though LSS gave a better reflection of the tacrolimus exposure, they are currently not often applied by clinical transplant practioners, possibly for logistical and financial reasons.
In conclusion, after validating several LSS from the literature, the present study indicates that all but three LSS gave a better prediction of the complete AUC0–12 than the trough concentrations C0 or C12. Moreover, LSS could produce satisfactory predictions for AUC0–12 recorded in an independent renal transplant patient population, although further evaluation of their reliability is necessary.

limited sampling strategy
tacrolimus
auc0–12
trough level


Functional analysis of genes involved in the biosynthesis of isoprene in Bacillus subtilis

In comparison to other bacteria Bacillus subtilis emits the volatile compound isoprene in high concentrations. Isoprene is the smallest representative of the natural product group of terpenoids. A search in the genome of B. subtilis resulted in a set of genes with yet unknown function, but putatively involved in the methylerythritol phosphate (MEP) pathway to isoprene. Further identification of these genes would give the possibility to engineer B. subtilis as a host cell for the production of terpenoids like the valuable plant-produced drugs artemisinin and paclitaxel. Conditional knock-out strains of putative genes were analyzed for the amount of isoprene emitted. Differences in isoprene emission were used to identify the function of the enzymes and of the corresponding selected genes in the MEP pathway. We give proof on a biochemical level that several of these selected genes from this species are involved in isoprene biosynthesis. This opens the possibilities to investigate the physiological function of isoprene emission and to increase the endogenous flux to the terpenoid precursors, isopentenyl diphosphate and dimethylallyl diphosphate, for the heterologous production of more complex terpenoids in B. subtilis.
Introduction
Isoprene (2-methyl-1,3-butadiene; I) or actually isopentenyl diphosphate (IDP; II) is the general precursor of all terpenoids, that represent a very diverse class of natural products. Two evolutionary distinct routes occur in nature for the biosynthesis of IDP. The spread of the two pathways is well investigated for organisms with a sequenced genome (Lange et al. 2000; Boucher and Doolittle 2000; Rohdich et al. 2001). In eukaryotes and archaea IDP and its isomer dimethylallyl diphosphate (DMADP; III) are formed via the mevalonate pathway (reviewed by Kuzuyama 2002). This pathway is well studied and for many organisms the enzymes are characterized and the encoding genes identified. More recently, another pathway to IDP was discovered in some eubacteria and in plastids of higher plants, that proceeds via the intermediate methylerythritol phosphate (MEP) (Rohmer et al. 1993; Rohmer 1999).
Most Gram-negative bacteria, including Escherichia coli, use the MEP pathway and for E. coli this is now well documented. For Gram-positive bacteria the situation is less clear. Gram-positive cocci have been reported to only possess genes for the mevalonate pathway (Wilding et al. 2000). In Streptomyces aeriouvifer both pathways are found (Seto et al. 1996). In Bacillus subtilis, being regarded as the prototype for Gram-positives, only homologues of the genes for the MEP pathway are present (Wagner et al. 2000).
For E. coli the complete MEP pathway has been elucidated and the genes involved have been identified and the corresponding enzymes described (reviewed by Eisenreich et al. 2004). The pathway exists of seven subsequent enzymatic steps (Fig. 1). The first reaction of the MEP pathway is catalyzed by a transketolase (DXS) and concerns the condensation of pyruvate (IV) and d-glyceraldehyde-3-phosphate (V) to 1-deoxy-d-xylulose 5-phosphate (VI) (Sprenger et al. 1997; Lois et al. 1998). 1-deoxy-d-xylulose is a branching intermediate in the biosynthesis of isoprenoids, thiamine (vitamin B1) (White 1978; David et al. 1981, 1982), and pyridoxol (vitamin B6), respectively (Hill et al. 1989). The second step is catalyzed by the IspC protein, initiating a rearrangement in the carbon skeleton followed by a reduction in the keto function of (VI), and finally delivering 2C-methyl-d-erythritol 4-phosphate (Takahashi et al. 1998). Five other subsequently acting enzymes are needed for the synthesis of IDP, including a phosphocytidyl transferase (IspD), a kinase (IspE), a cyclophosphate synthase (IspF), and two reductases (IspG, IspH) (Fig. 1).
Fig. 1Methylerythritol phosphate pathway of isoprenoids [Dxs (1), 1 deoxy-d-xylulose 5-phosphate synthase; IspC (2), 2C-methyl-d-erythritol 4-phosphate synthase; IspD (3), 4-diphosphocytidyl-2C-methyl-d-erythritol 4-phosphate synthase; IspE (4), 4-diphosphocytidyl-2C-methyl-d-erythritol kinase; IspF (5), 2C-methyl-d-erythritol 2,4-diphosphate synthase; IspG (6), 2C-methyl-d-erythritol 2,4-cyclodiphosphate; IspH (7), 1-hydroxy-2-methyl-butenyl 4-diphosphate reductase; Idi (8), isopentyl diphosphate isomerase; IspA (9), farnesyl diphosphate synthase]. The putative genes are mentioned in italics for every biosynthetic step, including the essential gene YpgA, which is indicated with an *. Numbering of compounds and biosynthetic steps refers to the text
Bacteria use the precursors IDP and DMADP for the synthesis of several compounds including the side chains of ubiquinone or menaquinone. Next to that, several bacterial species use these precursors to synthesize isoprene, that is emitted to their environment (Kuzma et al. 1995). B. subtilis emits isoprene in high levels compared to other bacterial species as has been described for B. subtilis 6051, B. subtilis 23059, and B. subtilis 23856 (Kuzma et al. 1995). Using a bioreactor system, the emission of isoprene by B. subtilis 6051 was found to occur in three phases during the growth curve (Wagner et al. 1999). The phases correspond with respectively, glucose metabolism and secretion of acetoin, catabolism of acetoin, and the early stage of sporulation. The general laboratory strain B. subtilis 168 showed another pattern of isoprene emission, lacking phases 2 and 3 (Fall and Copley 2000). The function of the emitted isoprene has been postulated as being a signal molecule in the natural environment of the microorganism. Another possible explanation for the emitted isoprene is the efflux as an overflow metabolite in the bacterial pathway to isoprenoid structures (Fall and Copley 2000). The uptake of isoprene by microorganisms present in soil samples has been described as a sink of atmospheric isoprene (Cleveland and Yavitt 1998). However, there is no full evidence supporting one of the hypotheses mentioned. Isoprene originates from DMADP. The conversion of DMADP in isoprene is known to be an enzymatic process in the poplar tree from which a gene has been isolated and characterized (Miller et al. 2001; Schnitzler et al. 2005). Attempts to prove the enzymatic conversion to isoprene in B. subtilis suggested the involvement of an enzymatic step. Enzymatic activity has been partially purified, but the enzyme turned out to be very labile (Sivy et al. 2002). Until now the encoding gene could not be identified. A search for a homologue protein of the isoprene synthase from poplar tree in the genome of the B. subtilis did not yield a candidate. Based on homology with known genes, mostly from E. coli, candidate genes for the other enzymatic steps in the MEP pathway in B. subtilis have been suggested. Identification of the genes involved in this pathway should shed some light on the function of isoprene synthesis in Bacillus physiology. Due to the essentiality of the products downstream of the isoprenoid biosynthesis, like menaquinone, it is not remarkable that the candidate genes, except for the isomerase, have been shown to be essential genes for the survival of the bacterial cell (Kobayashi et al. 2003). In the construction of the BFAN collection these knock-out strains were not viable and therefore conditional mutants have been made using a pMUTIN3-vector. The expression in these strains can be regulated by the isopropyl-beta-d-thiogalactopyranoside (IPTG) inducible Pspac promoter (Yansura and Henner 1984; Vagner et al. 1998). Using this mutant system, expression levels can be regulated to a minimum for survival by decreasing the amounts of IPTG. In that way, the function of the genes can be investigated. Identification of the involvement of certain genes in the isoprenoid biosynthetic pathway, may create possibilities to redirect the metabolic flux toward IDP. The possibilities of pathway engineering have shown their strength already in other microorganisms, like E. coli and Saccharomyces cerevisiae. The metabolic flux was dependent mainly on the deoxyxylulose 5-phosphate synthase (DXS), the isopentenyl diphosphate isomerase (Idi), and the prenyltransferase delivering farnesyl diphosphate (IspA) (Harker and Bramley 1999; Kim and Keasling 2001; Kajiwara et al. 1997; Martin et al. 2003). By up-regulation of the MEP pathway, B. subtilis can be developed as an interesting host organism for the production of complex terpenoid compounds, such as the valuable drugs artemisinin and paclitaxel. In the present study we describe the functional analysis of genes in B. subtilis putatively encoding enzymes involved in the biosynthesis of the terpenoid precursor isopentenyl diphosphate. We used quantitative isoprene emission of different well controllable knock-out Bacillus strains in their environment to determine the function of the tested genes.
Materials and methods
Bacterial strains and media
Bacterial strains used are listed in Table 1. B. subtilis strains 6051, 23059 and 23856 were obtained from the American Type Culture Collection (ATCC) (Rockville, USA). B. subtilis mutant strains were obtained through a chromosomal integration of pMUTIN3 derived plasmids (Vagner et al. 1998). Experiments were performed using Luria-Bertani (LB)-medium containing 1% bacto-tryptone, 0.5% yeast extract, and 0.5% NaCl (all purchased from Duchefa, Zwijndrecht, the Netherlands). If required, medium was supplemented with erythromycin (2 μg/ml; Duchefa, the Netherlands) or the specified concentration of isopropyl-β-d-thiogalactopyranoside (IPTG; Duchefa, the Netherlands). For growing on plates the medium was solidified with 1.5% agar.
Table 1Putative genes for the MEP pathway in the genome of B. subtilis and the percentage of identities with known proteins from E. coli, the essentiality of each gene, the corresponding BFAN mutant strain (explained in detail in the text, with numbering of the respective enzymatic steps)EnzymeE. coliB. subtilisEssential geneMutant strainDxs (1)dxsdxs/yqiE (43%)+168IyqiEIspC/Dxr (2)dxryluB (43%)+168IyluBIspD (3)ygbPyacM (36%)+168IyacMIspE (4)ychByabH (27%)+168IyabHIspF (5)ygbByacN (56%)+168IyacNIspG (6)gcpEyqfY (46%)+168IyqfYIspH (7)lytByqfP (35%)+168IyqfPIdi Iidi––Idi II (8)–ypgA (39%)a–168ypgAIpsA/FPPS (9)ispAyqiD (43%)+168IyqiDaHomologue of Streptomyces protein (Kaneda et al. 2001)
Detection of bacterial isoprene emission
A single colony of the different B. subtilis strains was transferred from a plate to 10 ml Luria-Bertani (LB) medium (if required supplemented with 2 μg/ml erythromycin and 100 μM IPTG) and grown over night (37°C; 300 rpm). Before the inoculation (1:100) of fresh LB medium containing different specified concentrations of IPTG, cells were gently washed three times with fresh LB-medium without IPTG by resuspending and centrifugating.
Isoprene accumulation was measured on-line by sampling every 15 min, during a period of nine hours, 15 ml of the air above 50 ml bacterial culture, growing (37°C; 300 rpm) in a 500 ml Erlenmeyer air tight flask (CBN, the Netherlands), and transferring into a gas-chromatography system suitable for the sensitive detection of isoprene (Syntech Spectras GC955 series 601, Synspec BV, the Netherlands) (Loreto and Delfine 2000). The air was pumped through a Tenax GA trap, desorbed at 180°C and transferred to an AT 5 column under a flow of 2.5 ml/min nitrogen (3.7 bars; quality 5). The temperature program used was 3 min at 50°C followed by an increase in temperature to 70°C at 5 min; kept at this temperature until 12 min and than lowered to 50°C again. The isoprene present was detected by photo ionization at 10.6 eV. The gas chromatograph was calibrated using the dynamic gas dilution principle with several concentrations of gaseous isoprene using liquid isoprene (Sigma, USA) diluted in methanol and evaporated with a gas dilutor (MK5, MCZ Umwelttechnik, Germany). During the isoprene detection the growth of the bacterial culture was determined by measuring the optical density at 600 nm (OD600 nm) every hour.
Results
Isoprene emission in wild-type Bacillus strains
The emission of isoprene from B. subtilis wild type strains 168, 6051, 23059, and 23856 was investigated. For all four strains isoprene accumulated in the logarithmic phase of growth, leading to a high increase in isoprene in the flask. The absolute maximum of wild type strains varied between 400 and 700 μg/m3. Starting from the late logarithmic phase to the beginning of the stationary phase the amount of isoprene in the flask decreased slowly.
Figure 2 shows the accumulation of isoprene during the growth of the wild type strains 168 and 6051. B. subtilis 6051 reached higher levels of isoprene, but this is probably not caused by a higher production rate rather than the amount of cells present, as reflected by the optical density of these strains. Corrected for the amount of cells, determined as the optical density at 600 nm, B. subtilis 168 accumulated at maximum 148 μg/ m3 / OD600 nm compared to 166 μg/ m3 / OD600 nm for B. subtilis 6051. The other two wild type strains tested, 23059 and 23856, showed a similar accumulation of isoprene as the strains described before. Corrected for the amount of cells, 23059 produced 192 μg/ m3 / OD600 nm and 23856 produced 112 μg/ m3 / OD600 nm isoprene at maximum accumulation (Table 2).
Fig. 2Isoprene emitted (solid lines) by wild type strains B. subtilis 168 (•) and B. subtilis 6051 (○) during growth (dashed lines). Zero time (t = 0) indicates the transition point between the exponential and the post-exponential growth phasesTable 2Maximum concentration of isoprene accumulated after emission by the different wild type Bacillus subtilis strains corrected for the amount of cells (OD600 nm)Bacillus subtilis strainMaximal isoprene accumulation (μg/m3/OD600 nm)16814960511662305919223856111
Mutant strains
To find the optimal conditions in which expression of the genes can be limited without killing the cells, each conditional mutant strain was subjected to IPTG depletion by growing the strains on agar plates containing different concentrations of IPTG varying from 0 to 1 mM. Decreasing the concentration of IPTG caused growth inhibition after overnight incubation resulting in smaller and finally no colonies (on plates without IPTG) at 37°C. Table 3 shows the minimal concentration of IPTG at which still some growth was observed for the different conditional mutant strains. This concentration of IPTG was used in our experiments and defined as the condition causing limited induction. Most mutant strains indeed showed growth inhibition at lower concentration of IPTG. The mutant strain168IyqfY, the mutant for the putative gene encoding a homologue of the IspG protein (step 6) did not show any dependency on IPTG on plates (varying IPTG concentrations from 0 to 1 mM). Plates without IPTG incubated overnight resulted in the growth of normal colonies and the liquid cultures obtained growth curves comparable with wild type B. subtilis 168, independent of the concentration of IPTG. For some of the other mutant strains there was some growth of the bacterial cultures without IPTG as well, although severely impaired.
Table 3Maximum amount of isoprene accumulated after emission by the different mutant Bacillus strains, corrected for the amount of cells (OD600 nm)Bacillus strainLimited expression (μM IPTG)Maximal isoprene accumulation (μg/m3/OD600 nm)Relative decreaseLimited inductionFull inductionFull repression168IyqiE (1)1081192715.2168IyluB (2)131167135.4168IyacM (3)504113427.9168IyabH (4)252916075.6168IyacN (5)1014119208.8168IyqfY (6)–n.d.194147–168IyqfP (7)113130179.8168ypgA (8)–––103–168IyqiD (9)101221661391.4The relative decrease is calculated as the maximal amount of isoprene at full induction devided by the maximal amount of isoprene at limited induction (dashes indicate that measurement did not apply here; explained in detail in the text, with numbering of the respective enzymatic steps).n.d., not determined
For all conditional knock out strains isoprene emission was measured at the concentration of IPTG with limited expression in comparison to 1 mM (full induction) and no IPTG (full repression).
Figure 3 represents the data for strain 168IyqiE, the conditional mutant strain for the first step in the biosynthetic pathway. Full induction of the dxs gene resulted in a normal growth curve of the bacterial culture and in an isoprene accumulation profile comparable to the profile of the wild type stain B. subtilis 168. Depletion of IPTG inhibited cell growth. The maximal concentration of isoprene accumulated in the flask and corrected for the amount of cells was 15 fold lower for the culture supplemented with 10 μM IPTG (8 μg/ m3 / OD600 nm) than for the culture with 1 mM IPTG (119 μg/ m3 / OD600 nm).
Fig. 3Isoprene emitted (solid lines) by the B. subtilis mutant 168IyqiE during growth (dashed lines) in the presence of 1 mM IPTG (▾), 10 μM IPTG (○), and no IPTG (•). Zero time (t = 0) indicates the transition point between the exponential and the post-exponential growth phases
Table 3 shows the maximum concentration of isoprene accumulated in the flask calculated on the amount of cells present at that time point for all tested mutant strains. Mutant strains for putative genes yluB, yacM, yabH, yacN, and yqfP encoding enzymes for respectively, steps 2, 3, 4, 5, and 7 showed significant differences in isoprene emission, varying from a 5–28 fold decrease at limited expression levels in comparison to full induction.
The knock-out strain of ypgA, putatively encoding an isopentenyl diphosphate isomerase (step 8), is the only knock-out of a nonessential gene. The mutant strain showed a normal growth curve and did not show a significant difference in the emission of isoprene in comparison to the wild type strain B. subtilis 168.
We also tested the function of yqiD, encoding a homologue for farnesyl diphosphate synthase (FDP; 7), synthase IspA (step 9) of E. coli. Depletion of IPTG to 10 μM did not result in a significant change in the isoprene emission by the mutant strain, while it did show to be dependent on IPTG for growth.
Discussion
To identify genes involved in the biosynthesis of the highly volatile compound isoprene, an efficient online detection system for this terpenoid (precursor) in the air above bacterial cultures was developed. Since the B. subtilis strains 6051, 23059, and 23856 were known to emit isoprene (Kuzma et al. 1995), we used these strains to set up the detection system for B. subtilis 168 for which the genome has been sequenced (Kunst et al. 1997) and functional knock outs have been made. All four strains emitted isoprene in our experiment. The differences in the amounts of isoprene emitted corresponded with the levels as described before, where B. subtilis 6051 and B. subtilis 23059 emitted higher amounts than B. subtilis 23856 (Kuzma et al. 1995). Since B. subtilis 168 showed emission of isoprene, the use of the experimental set up to investigate the isoprene synthesis by the mutant strains was justified. From the profile of isoprene emission with a maximum at the transition of the growth curve from the logarithmic phase to the stationary phase, it was concluded that from this time point on maximum isoprene concentrations had been reached and that amounts sampled from the culture every 15 min exceeded the production of new isoprene. We could not detect isoprene production in three phases using standard incubation in a shaking flask as has been described for B. subtilis 6051 grown in a bioreactor.
The conditional knock out strain of the yqiE encoding DXS, the transketolase responsible for the first step in the biosynthesis of isoprenoids, appeared to be highly dependent on IPTG. The dramatic decrease in isoprene production at low concentration of IPTG supported the role of this gene in the biosynthetic pathway of isoprene. yqiE has been identified as dxs encoding 1-d-deoxyxylulose-5-phosphate in B. subtilis before (Harker and Bramley 1999; Hecht et al. 2001). However, the confirming results for this mutant strain proved that the experimental set up of our work was valid to investigate the function of the other putative genes. Apparently, the isoprene emission is severely decreased when genes in the pathway are expressed at lower level than in the wild type strain.
Growth at the end of the incubation after several hours, as shown for many of the liquid cultures under IPTG depletion or even without suppletion of IPTG, may be explained by the occurrence of reversions in the mutant strain (Zanen et al. 2006). Another explanation could be the presence of a not fully repressed promoter in the complementation strain. It has been reported that in the used vector pMUTIN3 the Pspac promoter can give some low expression even in the absence of IPTG (Vagner et al. 1998; Petit et al. 1998). For genes showing low levels of expression in the wild type strain it is easy to get a pseudo wild type level of expression in the mutant strain. This may be in particular the case for highly efficient enzymes in the biosynthetic pathway.
For the putative genes yluB, yacM, yabH, yacN, and yqfP, encoding homologues of the enzymatic steps 2, 3, 4, 5 and 7, the isoprene accumulation of the conditional knock outs by depletion of IPTG clearly proved the involvement of these genes in the biosynthetic pathway isoprene. B. subtilis 168IyqfY (step 6) did not depend on IPTG and therefore nothing can be concluded from the data obtained with this mutant strain other than that this enzyme may be highly efficient leading to complementation already obtained at the low expression levels caused by leaking activity of the Pspac promoter (Zanen et al. 2006).
The nonessential gene ypgA encodes a homologue protein for an isopentenyl diphosphate isomerase (Idi) (step 8). The B. subtilis gene ypgA encodes for a so-called isopentenyl diphosphate isomerase type II protein (Takagi et al. 2004), while the genome of E. coli harbours a gene for a different Idi type I enzyme (Hahn et al. 1999). Interestingly, all archaea contain the Idi type II and all eukaryotes contain Idi type I. In genomes of eubacteria both are found, but there are also genomes without any of the described genes (Boucher and Doolittle 2000). In contrast to all other genes investigated in this study, the gene ypgA encoding for the IdiII was shown to be a nonessential gene in B. subtilis (Takagi et al. 2004). We also observed that the knock-out mutant of the bacterial strain is viable and produces isoprene. The idiI gene of E. coli has also been shown to be nonessential. Deletion mutants of this gene were viable on minimal medium (Hahn et al. 1999). Our findings, that the isoprene emission is not influenced by the knock out of the ypgA gene, can be explained by the enzymatic mechanism of the IspH protein, step 7, before the isomerase. IDP and DMADP can be synthesized independently by the catalytic action of IspH. A hypothetical mechanism for this reaction has been described (Eisenreich et al. 2004). This supports the nonessential character of the gene. The presence of isoprene proves that the isomer DMADP is still synthesized in the mutant and that the cells do not depend on the isomerase for the production of DMADP from IDP only. The isomerase functions in the balance of IDP and DMADP as a salvage protein (Eisenreich et al. 2004).
The first enzymatic biosynthetic step downstream the formation of the precursors IDP and DMADP is catalyzed by IspA, farnesyl diphosphate synthase (step 9). Therefore the putative gene is not involved in the biosynthesis of isoprene itself. The observation that isoprene is still emitted while the expression of the gene was depleted is easily explained by its function in the pathway downstream of IDP biosynthesis. Although some accumulation of the precursors IDP and DMADP could be expected by blocking yqiD, no significant differences in the flux toward the isoprene emission were detected. Apparently the efflux of isoprene is not influenced by the changes in FDP synthesis downstream in the isoprenoid pathway. This can be regarded as a contradiction to the hypothesis that isoprene emission functions as an efflux of an overflow metabolite.
In this study the involvement of several B. subtilis genes in the MEP pathway to isoprene was established by studying isoprene emission of mutants. Five genes, essential for viability, yluB, yacM, yabH, yacN, and yqfP, were shown to be essential for the isoprene production as well. Where these genes were at first instance depicted based on homology of the encoding proteins, the results of this study proved the functional involvement of the genes in the biosynthesis of isoprenoids on a biochemical level. A knockout of the sixth candidate gene, yqfY, did not yield a reduction in isoprene emission, nor a growth retardation, which might be explained by a not fully repressed Pspac promoter present in vector pMUTIN3. Knock-outs of the two other genes investigated in this study, showing no growth retardation as well as no reduction in isoprene emission, behaved in accordance with expectation.
Although it remains unknown why isoprene is emitted by bacteria, the knowledge about the pathway to isoprene can be used for further investigations towards a better understanding of the metabolic flux to IDP and its physiological function. Next to that the IDP pool can be optimized by metabolic pathway engineering, creating a B. subtilis strain as an efficient Gram-positive host for the heterologous production of terpenoids, like the valuable plant-derived pharmaceuticals artemisinin and paclitaxel. The potential of using heterologous production organisms for the supply of terpenoids is already under investigation for E. coli and for several yeast strains (Martin et al. 2003; Lindahl et al. 2006; Ro et al. 2006; Dejong et al. 2006). For the purpose of metabolic pathway engineering it is interesting to search for a gene encoding isoprene synthase in B. subtilis as well. Evidence for an enzymatic bioconversion and the identification of this specific enzyme could give the possibility to block the efflux of isoprene thereby increasing the amount of isoprenoid available for the synthesis of terpenoids.

isoprene
bacillus subtilis
terpenoid
mep pathway
methylerythritol phosphate pathway
gas chromatography


Left ventricular dyssynchrony assessed by two three-dimensional imaging modalities: phase analysis of gated myocardial perfusion SPECT and tri-plane tissue Doppler imaging

Purpose To compare left ventricular (LV) dyssynchrony assessment by phase analysis from gated myocardial perfusion SPECT (GMPS) with LV dyssynchrony assessment by tri-plane tissue Doppler imaging (TDI). Baseline LV dyssynchrony assessed with standard deviation (SD) of time-to-peak systolic velocity of 12 LV segments (Ts-SD) with TDI has proven to be a powerful predictor of response to CRT. Information on LV dyssynchrony can also be provided by GMPS with phase analysis of regional LV maximal count changes throughout the cardiac cycle.
Introduction
Cardiac resynchronization therapy (CRT) has become an established therapy for patients with end-stage heart failure [1]. However, up to 30% of the patients do not respond to CRT when selection is based on the traditional clinical and electrocardiographic criteria [New York Heart Association (NYHA) functional class III or IV, left ventricular ejection fraction (LVEF) <35%, and QRS duration >120 ms] [2]. In the search for better selection criteria for CRT, it has been shown that patients with LV dyssynchrony have a higher likelihood of a positive response to CRT [3–8]. Over the past few years, the assessment of LV dyssynchrony has been studied extensively with echocardiography.
The most frequently used technique is tissue Doppler imaging (TDI), which permits evaluation of timing of regional myocardial velocity and comparison of different regions yields information on LV dyssynchrony.
Bax et al. [3] have used a four-segment model to assess LV dyssynchrony (four basal LV segments: anterior, lateral, inferior and septal) and reported that a delay ≥ 65 ms among the four segments was a strong predictor of response to CRT. Other authors extended the analysis by evaluating the activation pattern of the entire LV. This method, originally described by Yu and co-workers [6–8], uses the standard deviation (SD) of electromechanical activation times based on a 12-segment model of LV (Ts-SD). The authors investigated 56 patients with heart failure who underwent CRT implantation and demonstrated that a cut-off value of 34.4 ms for Ts-SD could predict reverse LV remodeling after CRT with a sensitivity of 87% and a specificity of 81% [7]. However, to calculate this parameter of LV dyssynchrony, three apical views need to be acquired separately. A 3-dimensional (3D) TDI imaging modality (tri-plane TDI) has become available to overcome this limitation. Tri-plane TDI allows simultaneous acquisition of all LV segments during the same heartbeat rendering the technique more precise than the 2-dimensional (2D) TDI equivalent. Another recent development is the use of gated myocardial perfusion single photon emission computed tomography (GMPS) for assessment of LV dyssynchrony [9, 10]. A count-based method has been developed to extract phase information from the regional LV count changes throughout the cardiac cycle, and this phase information is related to the time interval when a region, in the 3D LV wall starts to contract (phase analysis). This technique has recently been implemented in the Emory Cardiac Toolbox as a diagnostic tool for assessment of LV mechanical dyssynchrony [9].
To further validate the use of GMPS with phase analysis for the assessment of LV dyssynchrony, a direct comparison with tri-plane TDI was performed in a cohort of heart failure patients.
Materials and methods
Patients and study protocol
Forty consecutive patients with heart failure, referred for evaluation of potential eligibility for CRT, were included in this study. Traditional selection criteria for CRT were applied, including moderate-to-severe drug refractory heart failure (NYHA class III or IV), depressed LVEF (<35%) and prolonged QRS duration (>120 ms). Patients with a recent myocardial infarction (<3 months) or decompensated heart failure were excluded. Evaluation of the clinical status included assessment of NYHA functional class, quality-of-life score (using the Minnesota quality-of-life questionnaire) and exercise capacity (using the 6-min walk test). Extensive echocardiographic analysis, using 3D transthoracic echocardiography, was performed to assess LV dyssynchrony. Resting GMPS with technetium-99m tetrofosmin was clinically performed to exclude extensive ischemia and/or viability [11, 12], in order to refer patients to revascularization if indicated. Thereafter, all scans were subjected to phase analysis to evaluate LV dyssynchrony. The results of the phase analysis with GMPS to assess LV dyssynchrony were subsequently compared with LV dyssynchrony derived from tri-plane TDI.
Echocardiography
3D data set acquisition
All patients underwent transthoracic echocardiography performed with a commercially available echocardiographic platform (VIVID 7, GE Vingmed Ultrasound, Horten, Norway) and equipped with a 3V-probe for 3D acquisition.
Patients were scanned in left lateral decubitus position, from the apical window in tri-plane modus, acquiring simultaneously the apical four-, two- and three-chamber views. Care was taken to visualize the true LV apex. Color-coded TDI was applied to the tri-plane view to assess longitudinal myocardial regional function. Gain settings, filters and pulse repetition frequency were adjusted to optimize color saturation. Sector size and depth were optimized for the highest possible frame rate. At least two consecutive beats were recorded from each view, and the images were digitally stored for off-line analysis (EchoPac, GE Vingmed Ultrasound, Horten, Norway). The echocardiographic examination and the off-line analysis were performed by the same experienced echocardiographist, blinded to the GMPS and clinical data.
3D analysis of LV dyssynchrony
During post-processing, the tri-plane TDI dataset was used to analyze myocardial velocity curves as previously described with 2D TDI echocardiography [6–8]. Sample volumes were placed in the basal and mid segments of the septal, lateral, inferior, anterior, posterior and anteroseptal LV walls to calculate the time from the beginning of the QRS complex to peak systolic velocity (Ts), using the same heart beat (Fig. 1). Subsequently, the standard deviation of Ts (Ts-SD) of the 12 myocardial segments was calculated and used as a parameter of LV dyssynchrony [6–8]. A cut-off value of Ts-SD ≥ 33 ms was used to define the presence of substantial LV dyssynchrony.
Fig. 1Example of the myocardial velocity curves that can be derived by positioning the sample volume in any LV segment of the tri-plane dataset. This patient has substantial LV dyssynchrony: the postero-lateral and anterior walls (orange, light blue and red curves) are activated later than the septal and inferior walls (yellow and green curves); standard deviation of 12 LV segments' Ts is 57.5 ms
Gated myocardial perfusion SPECT
Resting GMPS with technetium-99m tetrofosmin (500 MBq, injected at rest) was performed using a triple head SPECT camera system (GCA 9300/HG, Toshiba Corp.) equipped with low energy high-resolution collimators. Around the 140-KeV energy peak of technetium-99m tetrofosmin, a 20% window was used. A total of 90 projections (step and shoot mode, 35 s per projection, imaging time 23 min) were obtained over a 360-degrees circular orbit. GMPS acquisition involved 16 frames per cardiac cycle. Data were reconstructed by filtered backprojection and then re-oriented to yield gated short-axis images. Data reconstruction was performed over 360 degrees. These images were then submitted to the Emory Cardiac Toolbox for phase analysis [9]. GMPS images were analyzed at the department of radiology of Emory University School of Medicine by J.C. and E.V.G., who were blinded to the echocardiographic and clinical data. A phase distribution was extracted from a GMPS study, representing the regional onset of mechanical contraction of the LV. It can be displayed in a polar map or in 3D and used to generate a phase histogram. An example of a patient without LV dyssynchrony is shown in Fig. 2a, whereas an example of a patient with substantial LV dyssynchrony as assessed with phase analysis by GMPS is shown in Fig. 2b. The following four quantitative indices were obtained from the phase analysis of all patients: (1) histogram bandwidth, which includes 95% of the elements of the phase distribution; (2) phase SD, which is the SD of the phase distribution; (3) phase histogram skewness, which indicates the symmetry of the histogram; (4) histogram kurtosis, which indicates the degree to which the histogram is peaked [9]. In a healthy individual the LV contracts in a coordinated manner, and the phase distribution is nearly uniform with a highly peaked distribution of the histograms. As the LV contraction becomes dyssynchronous, the phase SD and histogram bandwidth are expected to increase (Fig. 2b).
Fig. 2a Example of a patient without LV dyssynchrony on GMPS. The non-normalized (upper panel) and normalized (lower panel) phase distributions are nearly uniform and the corresponding phase histograms are highly-peaked, narrow distributions. b Example of a patient (same as in Fig. 1) with LV dyssynchrony on GMPS. The non-normalized (upper panel) and normalized (lower panel) phase distributions show significant non-uniformity and the corresponding phase histograms have widespread distributions
Statistical analysis
Continuous data are presented as mean ± SD; dichotomous data are presented as numbers and percentages. Comparison of data was performed using the unpaired Student's t test or χ2 test when appropriate. Pearson’s correlation analysis was performed to evaluate the relation between histogram bandwidth, phase SD, histogram skewness and histogram kurtosis by the phase analysis of GMPS and LV dyssynchrony (Ts-SD) by tri-plane TDI. A p-value <0.05 was considered to be statistically significant. A statistical software program SPSS 12.0 (SPSS Inc, Chicago, Il) was used for statistical analysis.
Results
Study population
A total of 40 consecutive patients with heart failure were included in this study [29 (72%) men; mean age 66±10 years]. Baseline characteristics are summarized in Table 1. Patients had severe LV dysfunction (mean LVEF 26 ±7%), with extensive LV dilatation (mean LV end-diastolic volume 210±48 ml). LV dyssynchrony assessment with tri-plane TDI demonstrated a mean value of Ts-SD of 34±15 ms.
Table 1Baseline characteristics of the study population (n=40)Age (years)66±10Gender (M/F) 29/11NYHA class 3.0±0.46-MWT (m)282±126QoL score38±17QRS duration (ms)147±33Etiology, n (%) Ischemic25 (62) Idiopathic 15 (38)LVEF (%)26±7LVEDV (ml)210±48LVESV (ml)157±43Medication, n (%) ACE Inhibitors34 (85) β-blockers28 (70) Diuretics and/or spironolactone36 (90)LVEDV = left ventricular end-diastolic volume; LVEF = left ventricular ejection fraction; LVESV = left ventricular end-systolic volume; 6-MWT = 6-min walk test; QoL = quality of life score; Ts-SD = standard deviation of time to peak systolic velocity of 12 LV segments
Histogram bandwidth
The mean histogram bandwidth was 130°±69° (range 41° to 260°); the normal value is 38.7°±11.8° for men and 30.6°±9.6° for women [9]. A good correlation between histogram bandwidth and LV dyssynchrony, assessed with tri-plane TDI, was observed (r=0.77, p<0.0001, Fig. 3).
Fig. 3Correlation between histogram bandwidth assessed with GMPS and LV dyssynchrony assessed with tri-plane TDI (Ts-SD)
Phase SD
The mean phase SD was 40.2°±18.8° (range 14.5° to 77.9°), whereas the normal value is 14.2°±5.1° for men and 11.8°±5.2° for women [9]. Pearson’s correlation showed a good correlation between phase SD and Ts-SD (r=0.74, p<0.0001, Fig. 4).
Fig. 4Phase SD assessed with GMPS versus LV dyssynchrony assessed with tri-plane TDI (Ts-SD)
Histogram skewness
The mean histogram skewness was 2.50±0.81 (range 1.16 to 5.49), whereas the normal value is 4.19±0.68 for men and 4.60±0.72 for women [9]. A poor correlation with LV dyssynchrony assessed with tri-plane TDI was demonstrated by Pearson’s correlation (r=-0.30, p=0.06).
Histogram kurtosis
The mean histogram kurtosis was 7.38±6.23 (range 1.08 to 37.42), whereas the normal value is 19.72±7.68 for men and 23.21±8.16 for women [9]. No correlation between histogram kurtosis and Ts-SD was shown by Pearson’s correlation (r=−0.14, p=0.38).
Substantial versus no substantial LV dyssynchrony
Of the 40 patients included, 20 (50%) had a substantial LV dyssynchrony (defined as Ts-SD  ≥ 33 ms) [7, 8]. No significant differences in baseline characteristics were noted between the two patient groups (Table 2). Histogram bandwidth and phase SD were significantly higher in patients with Ts-SD  ≥ 33 ms compared to patients with Ts-SD <33 ms: 186°±52° versus 74°±24° (p<0.0001) and 55.3°±13.6° versus 25.1°±7.6° (p<0.0001), respectively (Fig. 5a and b). In addition, histogram skewness was significantly lower in patients with Ts-SD  ≥ 33 ms than in patients with Ts-SD <33 ms (2.23±0.93 versus 2.78±0.55, p=0.03). No significant difference between the two groups was observed for histogram kurtosis (6.36±7.94 versus 8.4±3.79, p=NS) (Fig. 5c and d). An example of a patient with substantial LV dyssynchrony on both tri-plane TDI as well as on phase analysis with GMPS is provided in Figs. 1 and 2b.
Table 2Baseline characteristics of patients with substantial (≥33 ms) and no substantial LV dyssynchrony (<33 ms) assessed by SD of time-to-peak systolic velocity of 12 LV segments (Ts-SD) Ts-SD ≥33 ms (n=20)Ts-SD <33 ms (n=20)p-valueAge (years)67±1066±9NSGender (M/F)14/615/5NSQRS duration (ms)145±31149±36NSNYHA class 3.0±0.43.0±0.4NSEtiology, n (%) Ischemic 14 (70)11 (55)NS Idiopathic6 (30)9 (45)NSLVEF (%)25±726±7NSLVEDV (ml)209±47207±50NSLVESV (ml)159±46153±42NSHistogram bandwidth (°)186±5274±24<0.0001Phase SD (°)55.3±13.625.1±7.6<0.0001Histogram skewness2.23±0.932.78±0.550.03Histogram kurtosis6.36±7.948.40±3.79NSFor abbreviations see Table 1.Fig. 5a Patients with substantial versus no substantial LV dyssynchrony (Ts-SD), using a cut-off value ≥33 ms. Histogram bandwidth is significantly higher in patients with substantial LV dyssynchrony. b Patients with substantial versus no substantial LV dyssynchrony (Ts-SD), using a cut-off value ≥33 ms. Phase SD is significantly higher in patients with substantial LV dyssynchrony. c Patients with substantial versus no substantial LV dyssynchrony (Ts-SD), using a cut-off value ≥33 ms. Histogram skewness is significantly lower in patients with substantial LV dyssynchrony. d Patients with substantial versus no substantial LV dyssynchrony (Ts-SD), using a cut-off value ≥33 ms. No significant difference in histogram kurtosis was demonstrated between patients without and patients with substantial LV dyssynchrony as assessed with tri-plane echocardiography
Ischemic versus idiopathic dilated cardiomyopathy
In total, 25 patients (62%) had an ischemic cardiomyopathy and 15 patients (38%) had an idiopathic dilated cardiomyopathy. No differences in echocardiographic variables, including LV dyssynchrony assessed by tri-plane TDI, or GMPS phase analysis indices were found between ischemic and non-ischemic patients. No significant differences in correlation coefficients were observed between patients with ischemic cardiomyopathy and patients with idiopathic dilated cardiomyopathy for the four different GMPS variables (Table 3).
Table 3Correlation coefficients of LV dyssynchrony, assessed with tri-plane TDI, versus the different LV dyssynchrony parameters, derived from phase analysis of GMPS (idiopathic dilated cardiomyopathy versus ischemic cardiomyopathy) Ischemic cardiomyopathy (n=25)Idiopathic dilated cardiomyopathy (n=15)p-valueHistogram bandwidthr=0.79r=0.78NSPhase SDr=0.77r=0.75NSHistogram skewnessr=−0.22r=−0.48NSHistogram kurtosisr=−0.07r=−0.35NS
Discussion
The main findings of the present study can be summarized as follows:
(1) LV dyssynchrony assessment with phase analysis on GMPS is feasible in heart failure patients screened for eligibility for CRT implantation. The phase analysis variables histogram bandwidth and phase SD show good correlation with LV dyssynchrony as measured with tri-plane TDI (Ts-SD), suggesting that GMPS phase analysis can be used to quantify LV dyssynchrony. (2) Patients with substantial LV dyssynchrony on tri-plane TDI have significantly higher values of histogram bandwidth and phase SD derived from phase analysis with GMPS (histogram bandwidth: 186°±52° versus 74°±24°, p<0.0001) and phase SD: 55.3°±13.6° versus 25.1°±7.6° (p<0.0001).
CRT is now considered an important therapeutic option in the treatment of patients with end-stage heart failure [1]. Although encouraging beneficial effects have been reported, including improvement in LVEF and reduction in heart failure symptoms, it has also been demonstrated that up to 30% of patients do not have a favorable response to CRT. The presence of LV dyssynchrony is considered a key issue in the identification of potential responders to CRT [3–6]. Echocardiography and in particular TDI have been extensively used to assess LV dyssynchrony. Several parameters derived from myocardial velocity curves of different LV segments have been proposed to quantify LV dyssynchrony and predict response to CRT [3–8]. Yu and coworkers described a model (derived from 2D TDI data) to quantify global LV dyssynchrony using Ts-SD of 12 LV segments [6–8]. The authors reported a cut-off value >32.6 ms to distinguish between responders and non-responders [8] and a cut-off value >34.4 ms as the most powerful predictor of LV reverse remodeling [7]. In this study LV dyssynchrony was evaluated by Ts-SD from tri-plane TDI and compared with LV dyssynchrony assessment with phase analysis by GMPS. The tri-plane TDI technique avoids the problem of heart rate variability since simultaneous acquisition of the three apical views is possible; in the present study a cut-off value  ≥ 33 ms was used to define substantial LV dyssynchrony on tri-plane TDI.
Information on cardiac dyssynchrony can also be derived from nuclear imaging. Kerwin et al. [13] evaluated interventricular dyssynchrony in 13 heart failure patients using multi-gated equilibrium blood pool scintigraphy and phase analysis. Patients were imaged during biventricular pacing and in sinus rhythm. During biventricular pacing, a significant improvement in interventricular synchrony was observed. Moreover, an improvement in LVEF was reported for all 13 patients during biventricular pacing (17.2±7.9% to 22.5±8.3%, p<0.0001), and the increase in LVEF correlated significantly with the improvement in interventricular synchrony (r=0.86; p<0.001). Fauchier et al. [14] used equilibrium radionuclide angiography and phase analysis to evaluate the prognostic value of interventricular and intraventricular dyssynchrony in 103 patients with idiopathic dilated cardiomyopathy. Phase images were generated from the scintigraphic data, and mean phase angles and SDs were calculated for the right and left ventricle. During a follow-up period of 27±23 months, 18 major cardiac events occurred. Multivariate analysis revealed that LV intraventricular dyssynchrony and elevated pulmonary capillary wedge pressure were the only independent predictors of cardiac events.
GMPS is currently used in patients with heart failure to exclude ischemia and/or viability before referring for CRT implantation and to assess total scar burden in patients with ischemic cardiomyopathy [11, 12]. Indeed, various studies have shown that the presence of severe resting perfusion defects, as assessed with GMPS, limits the response to CRT [15–17]. Recently, Chen et al. [9] developed with GMPS a count-based method to extract phase information from the regional LV count changes throughout the cardiac cycle. The phase information is related to the regional onset of mechanical contraction of the LV (phase analysis) and provides information on the synchronicity of the LV contraction. The authors assessed in 90 normal individuals the normal range for four quantitative indices that can be used as markers of LV dyssynchrony (histogram bandwidth, phase SD, histogram skewness and histogram kurtosis) [9]. Recently, these four GMPS indices have been compared with LV dyssynchrony assessment by TDI in 75 patients with severe heart failure. It was shown that among the four quantitative indices of phase analysis, the variables histogram bandwidth and phase SD correlated best with LV dyssynchrony as assessed by TDI [10]. The current study involved a different patient subset and provides further support that phase analysis with GMPS can be useful in the evaluation of LV dyssynchrony.
The value of GMPS to assess LV dyssynchrony has been analyzed, and the indices derived from phase analysis were compared with Ts-SD measured by tri-plane TDI. It was shown that histogram bandwidth and phase SD correlated well with Ts-SD as evaluated by tri-plane TDI (histogram bandwidth: r=0.77 and phase SD: r=0.74). In addition, significantly higher values for histogram bandwidth and phase SD were observed in patients with substantial LV dyssynchrony on tri-plane TDI compared with patients without substantial LV dyssynchrony on tri-plane TDI.
Limitations
In the present study, follow-up data after CRT implantation were not available. Therefore, the value of phase analysis to predict response to CRT could not be determined. Furthermore, GMPS may be less suitable for repeated analysis during the follow-up after CRT implantation, due to the radiation burden of the technique.
Conclusions
The results of this study confirm the feasibility to evaluate LV dyssynchrony with phase analysis by GMPS and its applicability in the clinical setting. In particular, histogram bandwidth and phase SD showed a good correlation with LV dyssynchrony measured with Ts-SD as assessed with tri-plane TDI. Future prospective studies in larger patient populations with follow-up after CRT implantation are needed to elucidate the potential role of GMPS with phase analysis for prediction of response to CRT.

lv dyssynchrony
tdi
heart failure
nuclear imaging
gated spect