Sensory and cognitive mechanisms of change detection in the context of speech

The aim of this study was to dissociate the contributions of memory-based (cognitive) and adaptation-based (sensory) mechanisms underlying deviance detection in the context of natural speech. Twenty healthy right-handed native speakers of English participated in an event-related design scan in which natural speech stimuli, /de:/ (“deh”) and /deI/ (“day”); (/te:/ (“teh”) and /teI/ (“tay”) served as standards and deviants within functional magnetic resonance imaging event-related “oddball” paradigm designed to elicit the mismatch negativity component. Thus, “oddball” blocks could involve either a word deviant (“day”) resulting in a “word advantage” effect, or a non-word deviant (“deh” or “tay”). We utilized an experimental protocol controlling for refractoriness similar to that used previously when deviance detection was studied in the context of tones. Results showed that the cognitive and sensory mechanisms of deviance detection were located in the anterior and posterior auditory cortices, respectively, as was previously found in the context of tones. The cognitive effect, that was most robust for the word deviant, diminished in the “oddball” condition. In addition, the results indicated that the lexical status of the speech stimulus interacts with acoustic factors exerting a top-down modulation of the extent to which novel sounds gain access to the subject’s awareness through memory-based processes. Thus, the more salient the deviant stimulus is the more likely it is to be released from the effects of adaptation exerted by the posterior auditory cortex.
Introduction
The mismatch negativity (MMN) component of the event-related-potentials (ERPs) is assumed to reflect the operation of a pre-attentive memory-based comparison mechanism (e.g., Näätänen et al. 2005). For a wide range of stimuli, there is evidence that the MMN is elicited by a cognitive mechanism based on auditory sensory memory that compares between the incoming deviant stimulus and the standard template (Näätänen and Alho 1997; Näätänen et al. 2001).
The contrasting view is that attention switch to novel sounds is based on the transient frequency-specific adaptation of posterior auditory-cortex feature-detector neurons (Desimone 1992; Näätänen 1990, 1992; Ulanovsky et al. 2003; Jääskeläinen et al. 2004). Specifically, it has been suggested that MMN arises because of selective adaptation of the N1 response by preceding standard stimuli (sensory component) leading to its attenuation. This attenuation is interpreted in terms of neural refractoriness (Näätänen 1992). Thus, this account of MMN elicitation does not rely on memory representations and is sensorial in nature.
Previous research has shown that the MMN is sensitive to acoustic as well as to phonetic attributes of phonemes (e.g., Tampas et al. 2005). Furthermore, it was demonstrated that the MMN reflects the activation of memory networks for language sounds and spoken words (e.g., Pulvermüller et al. 2003; Shtyrov et al. 2005; Pulvermüller and Shtyrov 2006), the latter referred to as the “word advantage” effect (Pettigrew et al. 2004).
The design of the current functional magnetic resonance imaging (fMRI) study was a combination of modified protocols utilized in previous studies (i.e., Pettigrew et al. 2004; Opitz et al. 2005) that enabled to disentangle cognitive and sensory contributions to change detection (Opitz et al. 2005; Maess et al. 2007) when the deviant stimulus could be either a word (“day”) resulting in the “word advantage” effect, or a non-word deviant (“deh” or “tay”).
We hypothesized that the sensory component will be located bilaterally in regions posterior to Heschl’s gyrus (HG), including the posterior superior temporal gyrus (STG), regardless of the identity of the deviant stimulus (“deh”, “day” or “tay”). This component relies on frequency-specific neurons in the auditory cortex responsible for the repetition-related decrement of N1 and its counterpart, the N1m (Romani et al. 1982; Jääskelainen et al. 2004; Opitz et al. 2005). Moreover, it was shown that the region posterior to HG is broadly tuned with respect to phonetic features (Ahveninen et al. 2006).
In contrast, it was shown that areas anterior to HG process sound-identity cues such as speech (Binder et al. 2000; Obleser et al. 2007; Ahveninen et al. 2006) and pitch (Warren and Griffiths 2003). Therefore, we assumed that the cognitive component which involves the representation of the current auditory event (Schröger 1997) will differentiate between the deviant word (“day”) and the non-words (“teh” and “tay”, i.e., the “word advantage” effect) and will be located anterior to HG.
Methods
Subjects
Twenty right-handed adult healthy subjects, native speakers of English, 8 women and 12 men, participated in the study. Subjects ranged in age from 23 to 28. All subjects gave informed consent to participate in the study.
Experimental protocol
The procedure is a modification of the one used by Opitz et al. (2005). Each subject participated in 12 functional imaging runs. The speech stimuli were grouped into two types of blocks “oddball” and “control”. In the first type of block, the “oddball” block, deviant exemplars occurred quasi-randomly embedded within standard stimuli so that the frequency of occurrence throughout the block was balanced among exemplars. The interval between two successive deviants varied quasi-randomly with gaps of 4, 6, 8, 10 or 12 standards with the constraint that gap size was balanced throughout the block. In the “Oddball” blocks deviants appeared 42 times out of a total of 354 stimuli (probability of occurrence = 12%).
In the “control” blocks the same physical deviants and standards as in the “oddball” blocks (deviant-counterparts and standard-counterparts, respectively), occurred quasi-randomly, while deviants were constrained by the same spacing rule mentioned above. However, each of the “control” blocks contained eight different equiprobable stimuli, including the deviant and standard counterparts. Thus, in the control runs the seven stimuli beside the deviant-counterpart served as “filler” or contextual stimuli which were added to the sequence so that the deviant-counterpart will appear at the same probability as any other stimulus in the sequence. Each of the stimuli in the “control” block (a total of eight different stimuli) repeated 42 times and appeared with equal probability which was identical to that of the deviants in the “oddball” block (12%). There were three blocks of each type (“oddball”/“control”). Each block was repeated twice. In total, 12 blocks of approximately 6 min each were randomly presented for each subject within a session.
Three non-words, /de:/ “deh”; /te:/ “teh”; and /teI/ “tay”, and one word, /deI/ “day”, were recorded from a male native English speaker in a sound-proof chamber. These stimuli comprised the following standard-deviant pairs that resulted in the three “oddball” runs: (1) deh (standard)–day (deviant); (2) day–deh and (3) teh–tay. Thus, “deh” and “day” swapped their roles as standard and deviant in the second “oddball” block, whereas the third block controlled for the acoustic change associated with the transition from a monophthong (/e:/) to a diphthong (/eI/) occurring in the first standard-deviant pair. Additional four stimuli, /pe:/ “peh”; /peI/ “pay”; /be:/ “beh”; and /beI/ “bay”, together with the previous four mentioned above were embedded within three control runs, each containing a deviant-counterpart , either “day”, “deh” or “tay” appearing with the same probability and obeying the same spacing rule as in the oddball blocks.
Three exemplars for each stimulus (e.g., deh1, deh2, deh3) were selected (out of a pool of 24 recordings per stimulus) on the basis of acoustic similarity (see Table 1 for the values of the lowest three formants). The parameters that were used to choose similar exemplars for each stimulus included the shape of the spectrogram at the voice onset, vowel durations, pitch and formant values (Hz) of the first three formants at the beginning (ca. 84 ms) and end (ca. 168 ms) of the /e/ segment (ca. 252 ms duration) of each stimulus. The stimuli were truncated to 280 ms and normalized to the same loudness level by using Adobe Audition 1.5 trial version software package. Spectral analysis of the stimuli was conducted by PRAAT software version 4.3.19 (http://www.praat.org).
Table 1Pitch and frequencies of the main speech stimuli (in Hz)StimulusF0F1 onset of /e/F1 end of /e/MeanF2 onset of /e/F2 end of /e/MeanF3 onset of /e/F3 end of /e/Meandeh11015546346031,7141,6671,7022,5482,6172,636deh2995396406241,7261,5931,6582,5762,5832,636deh31015396306141,7301,6001,6662,5852,5902,635day11044774064112,0762,2302,1422,6932,7062,703day21024924124191,9832,1372,0892,6452,6692,668day31034974624512,0182,0632,0512,6842,7032,685tay11015815275371,8581,9111,9422,5812,4952,641tay21026105666001,7871,8871,9062,6712,6352,670tay31036005896221,7901,8391,9152,6602,6222,681teh11036366237091,7091,6621,7472,5872,6252,695teh21017836267071,7501,6501,7352,5842,5252,639teh31018576157471,8051,6081,7482,5632,5312,655F0 = pitch. F1, F2 and F3 indicate the frequencies (Hz) of the first, second and third formants, respectively. F1, F2 and F3 frequencies are given for the beginning and end of the /e/ segment common to the four main stimuli of the study. The mean frequency across the length of the common /e/ section is also indicated
The reason for using three tokens for each consonant-vowel stimulus was to control as much as possible for acoustic factors, other than those inherent in the structure of the stimulus, which could confound the elicitation of a deviant response. Using three different exemplars for each consonant-vowel (CV) stimulus diminished the likelihood of a contingency developing between a specific deviant-standard pair because of an uncontrolled acoustic facet associated with either the deviant or the standard.
As could be seen in Table 1 the frequency parameters across homogeneous exemplars (i.e., the three representatives of the same CV) were very similar to each other. In addition, the F1/F2 ratio in “teh” and “tay” is similar to the F1/F2 ratio in “deh” and “day”, respectively. Thus, the comparison between the responses to “teh” and “tay” could serve as a suitable control for the latter pair (Jacobsen et al. 2004).
The speech stimuli appeared randomly within each block and their occurrence was balanced throughout the block for standards as well as deviants. Stimuli were presented with an SOA (stimulus onset asynchrony) of 1 s at 95 dB SPL via headphones to subjects with ear-plugs (see Table 2 for an example of a “control” and “oddball” blocks in case of the “deh”–“day” contrast). Stimulus presentation was carried out by E-Prime 1.1 (1.1.4.1) (Psychology Software Tools http://www.pstnet.com).
Table 2“Oddball” and “control” sequences for the “deh” (standard)-“day” (deviant) pairOddballdeh1(A) deh1 deh3 deh2 day1(B) deh1 deh2 deh1 deh3 deh1 deh1 deh3 day3…Controlbeh1 deh3(C) tay2 teh3 bay2 bay3 peh2 deh2 day2(D) peh3 bay1 teh3 pay1 day2….A–D indicate the stimuli contrasted to evaluate: the deviance effect (B vs. A), the cognitive effect (B vs. D) and the sensory effect (A vs. C). The numbers attached to the stimuli indicate different exemplars of the same CV stimulus. Each contrast was computed across all exemplars of a specific CV. In the “oddball” sequence “day1” serves as a deviant and “deh1” functions as a standard. In the “control” condition “deh3” is a standard control counterpart and “day2” serves as a deviant control counterpart
Contrasting “oddball” deviants and standards with the corresponding stimuli in “control” runs (deviant and standard counterparts) allowed disentangling the two mechanisms of deviance detection, namely, the sensory one based on refractoriness (Näätänen and Picton 1987) from the cognitive one based on memory-based processes (Näätänen 1990; Näätänen and Winkler 1999).
The subjects were instructed to count every stimulus, press a button when they reached 100 and then reset their count and restart counting from zero again. This task was chosen to control for a possible contamination by N2b and P3b ERP-components which are elicited when deviants are being discriminated from standards (Donchin et al. 1997; Opitz et al. 2005).
Behavioral assessment of discrimination between stimuli
Thirteen subjects (out of the 20 participating in the study) rated the stimuli in a behavioral session performed outside of the magnet. The behavioral assessment was carried out in a different session. Stimuli were presented simultaneously with recorded MR scanner noise. The stimuli and the scanner noise were both presented in 62 dB SPL which was a convenient hearing level for both the stimuli and the noise presented together.
Subjects were presented with “triplets” comprised of stimuli containing /e:/ and /eI/. In each block, 15 triplets were presented separated by a 2 s interval of silence allowing the subject to respond. The stimuli were the same as those presented in the fMRI experiment. There were several patterns of triplet presentation in each block, as follows. There were three triplets in which “day” was presented first followed by two “deh”-s; three triplets in which “day” was presented last and preceded by two “deh”-s; three triplets where “deh” was presented first followed by two “day”-s and three triplets were “deh” was last and preceded by two “day”-s. In addition, there were three triplets in which “day”, “deh” and “tay” each appeared in the middle position, respectively, while the neighboring stimuli (in the first and third positions) where either the /eI/ or /e:/ counterparts (i.e., “deh” “day” “deh”; “day” “deh” “day” and “teh “tay” “teh”).
Overall, four blocks of 15 triplets each were presented to the subjects. The order of triplets was randomized within each block. The three different exemplars representing each CV stimulus were balanced across the four blocks. The subjects were required to indicate the outlier in each triplet by pressing the key (either “1”,”2” or “3” on the keyboard) that corresponded to the position of the outlier in the triplet. The outliers were assigned mainly to the extreme positions (1 or 3) in the triplet to simulate more closely the “oddball” design in which the deviant is surrounded by repetitive standards. The trials with the outlier appearing in the middle position were introduced to minimize the probability of guessing the identity of the third stimulus in the triplet after hearing the first two which were non-identical. Thus, subjects could guess the identity of the outlier with more confidence only after hearing two identical stimuli in a row.
Data acquisition parameters
Data was collected on a 3T Siemens Trio scanner. Each study began with two localizers: a three-plane localizer and a multiple-slice sagittal localizer. These were followed by the acquisition of twenty-five 6 mm T1-weighted axial slices (TR = 300 ms, TE = 2.47 ms, flip angle = 60°, FOV = 220 mm, 256 acquisition matrix). For each subject, 12 functional imaging scans were collected with slices in the same locations as the anatomical T1-weighted data. Functional images were recorded using a gradient-echo EPI sequence (TR = 1,550 ms, TE = 30 ms, flip angle = 80°, FOV = 220 mm, 64 acquisition matrix). Each functional run involved the acquisition of 245 volumes with twenty-five 6-mm axial slices. Images were converted to analyze format and the first ten volumes of each functional series were removed to account for the approach to steady-state magnetization, leaving 235 volumes for analysis.
Image analysis
Preprocessing
First, using sinc interpolation, the data from each slice were adjusted for slice acquisition time and then all data were motion corrected using SPM99 for six rigid body motions (displacement in the x, y, z direction and rotation: for pitch, roll, yaw). Flags were set for de-correlation and masking so a pixel was set to zero for every time point if it moved outside the volume. Functional image data were motion corrected by realigning the time sequence imaging to the first image in the middle run of the sequence using SPM99.
Individual subject data (responses to the deviants, standards and control stimuli) were analyzed using a General Linear Model on each voxel in the entire brain volume (Rajeevan et al. 2007). The data were normalized to a signal measure of 100 and were spatially smoothed with a 8.08 mm Gaussian kernel to account for variations in the location of activation across participants. The output maps are normalized beta-maps which are in the acquired space (3.44 mm × 3.44 mm × 6 mm).
To take these data into a common reference space, three registrations were calculated within the Yale BioImage Suite software package (http://www.bioimagesuite.org/, Papademetris et al. 2006) using the intensity-only component of the method reported in Papademetris et al. (2004). The first registration performs a linear registration between the individual subject raw functional image and that subject’s 2D anatomical image. The 2D anatomical image was then linearally registered to the individual’s 3D anatomical image. The 3D differs from the 2D in that it has a 1 × 1 × 1 mm resolution whereas the 2D z-dimension is set by slice-thickness and its x–y dimensions are set by voxel size. Finally, a non-linear registration is computed between the individual 3D anatomical image and a reference 3D image. The reference brain used was the Colin27 Brain (Holmes et al. 1998) commonly applied in SPM and other software packages. All three registrations were applied sequentially to the individual normalized beta-maps to bring all data into the common reference space.
Statistical analyses
We used a two-stage random-effects model to analyze the data. In the first stage, statistical maps were calculated in the comparison of interest for each subject as described above. In the second stage, across subject analyses were conducted and the distribution of the individual subjects’ statistics were tested for significance.
Within subject analyses
For each of the three deviants (“deh”, “day” and “tay”) the following six types of maps were calculated: (1) Deviant maps computed from oddball runs extracting the response to low frequency stimuli (2) Standard maps computed from oddball runs extracting the response to high frequency stimuli (3) Deviant control maps computed from control runs extracting the response to the same physical stimuli that served as deviants in the oddball runs (deviant-counterparts) (4) Standard control maps computed from control runs extracting the response to the same physical stimuli that served as standards in the oddball runs (standard-counterparts). The contrasts extracting each deviant-counterpart and standard-counterpart stimuli were carried out against the same baseline that formed part of the “filler” stimuli, i.e., “beh”, “peh”, “bay” and “pay”. The stimuli that served as deviants and standards (i.e., “deh”, “day”, “teh”, “tay”) were taken out of the baseline so that their extraction could be conducted against a common baseline. (5) A Word map computed from control runs contrasting the response to the meaningful word (“day”) with the non-words that formed part of the “filler” stimuli, i.e., “deh”, “beh”, “peh”. (6) A Non-word map computed from control runs contrasting the response to the non-word (“tay”) with the non-words that formed part of the “filler” stimuli (i.e., “deh”, “beh”, “peh”.) The non-word “tay” was excluded from the baseline, since it was the only non-word with a diphthong (/eI/), and to avoid contrasting it with itself.
Across subject analyses
Composite maps. Two types of composites were created: three composites of deviant maps, one for each deviant (“deh”, “day”, “tay”) and three composites of the deviant-control maps, one composite for each control run that included either “deh”, “day” or “tay” as a deviant-counterpart, respectively.
Paired t tests. The contrast between deviants and their respective counterparts isolates the cognitive component of deviance detection. For this contrast, a paired t test was calculated between the Deviant map and the Deviant control map. The contrast between standards and their respective counterparts accounts for the sensory component based on refractoriness. For this contrast, a paired t test was calculated between the Standard map and the Standard control map.
Statistical images were corrected for multiple comparisons over the whole brain using a magnitude threshold derived from Monte–Carlo simulation that takes into account the number of contiguous activated voxels (Forman et al. 1995). Individual voxel thresholds were set at P < 0.001 for both the composites and paired t test maps. Data were corrected for multiple comparisons by spatial extent of contiguous supra-threshold individual voxels (experiment-wise P < 0.001 for a cluster). In a Monte–Carlo simulation within the AFNI software package, using a smoothing kernel of 8.08 mm, a connection radius of 7.72 mm on 3.48 mm × 3.48 mm × 6 mm voxels, it was determined that an activation volume of 1,278 μl satisfied the P < 0.001 threshold.
Regions of interest analyses. The different regions of interest (ROIs) were identified on the basis of the results obtained by the paired t test maps prior to cluster-size correction (P < 0.001) reflecting the cognitive and sensory mechanisms of change-detection. Then, each participant’s model estimate of the percent signal change for each region of activation, averaged across voxels within the region was calculated for each of the six composite maps (three deviant maps and three deviant-control maps). The ROIs identified were analyzed in a 3 (ROI: anterior auditory cortex, posterior auditory cortex, superior/middle frontal gyrus) × 2 (task-type: “oddball”/“control”) × 3 (deviant: “deh”, “day”, “tay”) × 2 (laterality: left/right) repeated measures ANOVA with subjects (n = 20) as a random factor.
ROI analysis for the Word/Non-word maps. To further investigate the “word effect”, each participant’s model estimate of the percent signal change for each region of activation, averaged across voxels within the region was calculated for the Word map and Non-word map. Both types of maps were calculated from control blocks. Specifically, to create the Word map “day” was contrasted with a baseline comprised of “filler” or contextual stimuli that were non-words (i.e., “beh”, “peh”, “deh”, “the”). To create the Non-Word map “tay” was contrasted with the same baseline. The delta blood-oxygen-level-dependent (BOLD) measures were subjected to a 2 (Laterality: left/right) × 3 (ROI: anterior auditory cortex, posterior auditory cortex, superior/middle frontal gyrus) × 2 [Lexical Status: word (“day”) vs. non-word (“tay”)] repeated measures ANOVA with subjects (n = 20) as a random factor.
Results
Behavioral results
There were no differences in response accuracy scores, computed across the three possible positions of the outlier, among the three CVs. The response accuracy for “day” was 95.88% (±5.01), for “tay” 96.70 (±5.15) and for “deh” 95.33 (±4.22) (the number in brackets denotes the standard deviation). A paired t test that was run on the individual response accuracy data for each CV confirmed that there was no difference in accuracy levels between CVs (in all three possible comparisons P > 0.1). We have also examined the response accuracy scores for the first and third positions in the triplet for each of the CVs. The response accuracy scores ranged between 98 and 99% among the three different CV stimuli as well as between the first and third positions (since there were only four triplets per block in which the outlier appeared in the middle, the middle position was excluded from the latter calculation).
Table 3 displays the reaction time data for each deviant and for each of the three possible positions of the outlier within a triplet. As expected, it could be seen that the reaction time for the third position in the triplet was the shortest. To evaluate this trend statistically a two-way ANOVA was run with Stimulus (“day”, “tay”, “deh”) and Position (first, second, third) as factors and with Greenhouse–Geisser adjustment to account for sphericity. Only the Position factor was significant [F(2,38) = 28.70, P < 0.001]. Paired comparisons (Bonferroni corrected, P < 0.05) confirmed that reaction time associated with stimuli in the first and second position in the triplet was prolonged relative to that associated with the third stimulus in the triplet. Taken together, there is dissociation between response accuracy and reaction time data. While no differences in response accuracy were found, reaction time was shorter when the outlier CV stimulus was in the third position in the triplet.
Table 3Reaction time (ms) in the behavioral triplet testStimulusPosition123day787.58 ± 132.07907.01 ± 240.71638.55 ± 215.98tay858.59 ± 180.23961.06 ± 187.15650.98 ± 212.58deh864.03 ± 164.90847.17 ± 158.27628.33 ± 137.63Reaction time is given for each of the three positions in the triplet and for each stimulus that served as a deviant in the “oddball” blocks. ±Standard deviation
This confirms our prediction that reaction time will be shorter in case of an outlier presented in the third position following two identical stimuli. However, the fact that the main effect of Stimulus as well as the interaction between Stimulus and Position did not reach significance confirms that the stimuli were equally discriminable in the context of the “oddball” paradigms used in this experiment.
Composite maps
Deviance effect
The spread of activation associated with “tay” deviant was very similar to that in response to “day” (Fig. 1). To elucidate the positions and extents of the activated brain regions associated with the similar responses to the deviants “day” and “tay”, images in the coronal and sagittal planes were included (Fig. 1, bottom panels).
Fig. 1Deviant maps: brain regions in response to deviant stimuli embedded within the “oddball” paradigms. BOLD contrasts were superimposed on a reference anatomical image (Holmes et al. 1998). Upper panels: axial sections displaying responses to the deviants “deh” (left), “day” (middle) and “tay” (right). Bottom panels: coronal (two left panels) and sagittal (two right panels) sections of the responses to the deviants “day” and “tay”. Axial sections range from z = −6 to 46 (by increments of 4 mm). Coronal sections range from y = −1 to 50 (by increments of 5 mm). Sagittal sections range from x = −57 to 59 (by increments of 8 mm). Display follows radiological convention (left side of the brain is displayed on the right side of the screen)
The size of the right STG region activated in the word condition (i.e., “day”), was similar to that activated in the non-word condition (i.e., “tay”) (Table 4). In contrast, the size of the left STG region activated in the word condition was almost twofold larger than that activated by the “tay” condition. In both hemispheres, the brain regions activated in the “day” and the “tay” conditions were larger than those observed for the “deh” condition. Furthermore, the “day”, “tay” and “deh” stimuli appear to activate different regions of the STG. The “day” stimulus activated parts of the posterior STG (i.e., BA 22/42), whereas the “tay” and “deh” stimuli activated large parts of the superior and middle temporal cortices (i.e., BA 22/22) (Table 4).
Table 4Brain regions activated in Deviant maps (Fig. 1)StimulusVolume size (mm3)Mean t valueTalairach coordinates (mm)SideIdentified regionBAxyzday10,8034.7256−224RTSTG22/42day11,3494.73−59−216LTSTG22/42deh3,5624.9155−224RTSTG21/22deh1,5544.4−62−234LTSTG22tay9,1074.6856−182RTSTG21/22tay6,0344.97−57−183LTSTG21/22Talairach coordinates (Talairach and Tournoux 1988) are given for the center of mass. The mean t value was computed across the voxels within an anatomical region. Maps were thresholded at P < 0.001, corrected for multiple comparisonsSTG superior temporal gyrus, RT right, LT left, BA Brodmann area(s)
Similar brain regions to those implicated in the deviance response were also found in the study of Opitz et al. (2005) in the context of tones. Specifically, in that study the deviance effect also implicated HG (primary auditory cortex) and the superior temporal plane (Talairach coordinates of peak location: −49, −14, 9; 53, −21, 10, respectively).
Deviant control maps
While activity extended from the STG across the lateral sulcus to the central sulcus in response to “day”, “deh” had a similar but more focal response in the vicinity of the primary auditory cortex. To elucidate the positions and extents of the activated brain regions associated with the more similar responses to the deviants “day” and “deh”, images in the coronal and sagittal planes were included (Fig. 2, bottom panels). In response to “tay” activity in the STG did not reach significance and activation was only observed in the left precentral gyrus (Fig. 2, Table 5).
Fig. 2Deviant control maps: brain regions in response to deviant-counterparts stimuli embedded within the “control” paradigms. Upper panels: axial sections displaying responses to the deviant controls “deh” (left), “day” (middle) and “tay” (right). Bottom panels: coronal (two left panels) and sagittal (two right panels) sections of the responses to the deviant controls “day” and “deh”. Additional display details as in Fig. 1Table 5Brain regions activated in Deviant control maps (Fig. 2)StimulusVolume size (mm3)Mean t valueTalairach coordinates (mm)SideIdentified regionBAxyzday21,5944.9353−1615RTPoCG/PrCG STG40/43/6day27,9194.93−52−1816LTPoCG/PrCG40/43/6day4,600−4.39−244830LTSFrG9day2,860−4.2−9−80−14LTLT OCCP18/19deh4,3904.6853−219RTSTG42deh5,0214.4−51−216LTSTG42tay3,4014.6−49−838LTPrCG6PoCG postcentral gyrus, PrCG precentral gyrus, SFrG superior frontal gyrus, STG superior temporal gyrus, RT right, LT left, OCCP occipital pole, BA Brodmann area(s). Other details as in Table 4
The size of STG activation was the most extensive over the left STG for deviant “day” in comparison to “tay” where it did not reach statistical significance, and “deh” where it was more than five times smaller (Fig. 2, Table 5). In addition, in response to “day” a negative differential BOLD was evident in the left superior frontal gyrus and left occipital pole (Fig. 2, Table 5).
Paired t test maps
Cognitive effect
The cognitive effect was derived by contrasting the Deviant maps with the corresponding Deviant control maps. Only the contrast map for “day” showed activations at the significance level used throughout this study (P < 0.001, corrected for multiple comparisons), reflecting the “word-advantage” effect. The negative differential BOLD associated with the cognitive effect implicated bilaterally the region extending from the postcentral gyrus to HG including parts of the Sylvian fissure, lateral sulcus and insula (Table 6, Fig. 3). The location of this region is proximal to the one obtained by Opitz et al. (2005) that was associated with the cognitive mechanism and located bilaterally in a non-primary auditory area within the lateral temporal lobe in the anterior rim of HG (Talairach coordinates of peak location: −42, −13, 6 and 49, −12, 7).
Table 6Brain regions implicated in the cognitive effect (Fig. 3)StimulusVolume size (mm3)Mean t valueTalairach coordinates (mm)SideIdentified regionBAxyzday4,179−4.3741−1616LTLS/INSa40/43day3,802−4.5−50−2019RTLS/INSa40/43LS lateral sulcus, INS insulaaRegion chosen for ROI analysis. Other details as in Table 4Fig. 3Brain regions implicated in the cognitive effect, showing less activation in the Deviant map than in the Deviant control map for “day”. BOLD contrasts were superimposed on a reference anatomical image (Holmes et al. 1998). Axial sections range from z = −6 to 46 (by increments of 4 mm)
Refractoriness effect
By looking at the t test images associated with refractoriness (Fig. 4) a negative differential BOLD could be observed posterior to HG in response to each of the standard-deviant pairs (Table 7). In case of the refractoriness effect when “deh” served as the standard activity also extended to the superior temporal sulcus (Fig. 4, left panel). Opitz et al. (2005) have also found activations in a proximal brain region in the lateral aspect of the posterior rim of HG bilaterally that was associated with the sensory mechanism of change detection (Talairach coordinates of peak location: −51, −18, 7 and 53, −19, 4).
Fig. 4Brain regions implicated in the sensory (refractoriness) effect, showing less activation in Standard maps than in Standard control maps. Responses for the contrasts with “deh” (left) “day” (middle) and “teh” are shown. BOLD contrasts were superimposed on a reference anatomical image (Holmes et al. 1998). Axial sections range from z = −6 to 46 (by increments of 4 mm)Table 7Brain regions implicated in the sensory (refractoriness) effect (Fig. 4)StimulusVolume size (mm3)Mean t valueTalairach coordinates (mm)SideIdentified regionBAxyzday2,777−4.2759−187RTSTGa22/42day4,473−4.39−59−204LTSTGa22/42deh7724.08274330RTS/MFrGa8/9deh2,5104.29−334330LTS/MFrGa8/9deh3,1894.3131442MedialACC32deh2,726−4.4157−263RTSTG22deh3,030−4.39−57−183LTSTG22tay2,048−4.3559−226RTSTG22/42tay2,395−4.53−53−174LTSTG22STG superior temporal gyrus, S/MFrG superior/middle frontal gyrus, ACC anterior cingulate cortexaRegion chosen for ROI analysis; other details as in Table 4
From here on, we will refer to the region posterior to HG (Fig. 4, Table 7) as posterior auditory cortex. This region was defined functionally as showing a refractoriness effect in the current study. The region anterior to HG extending from the postcentral gyrus to HG (Fig. 3, Table 6) will be referred to as anterior auditory cortex. This region was defined functionally as showing a cognitive effect in the current study. This terminology is based on the one used by Opitz et al. (2005) that described the regions implicated in the sensory and cognitive effects as residing in the vicinity of the posterior and anterior rim of HG, respectively.
In addition, when the sensory component was extracted for “deh” standard (Fig. 4, left panel and Table 7) positive differential BOLD was observed in the superior/middle frontal gyrus as well as in the anterior cingulate cortex (ACC). Thus, the positive differential BOLD observed in the ACC and in the frontal gyrus is a direct result of contrasting “deh” with a baseline of “day” deviants to create the map reflecting the sensory component for “deh” standard.
ROI analysis
Six ROIs were chosen to be included in the ROI analysis. These ROIs were defined on the basis of previous findings and the data obtained in the current study. We have chosen the ROIs which were directly linked to the mechanisms associated with change detection, namely, the cognitive and sensory effects, or those that might be related to both change-detection and to the processing of linguistic stimuli. This is described in more detail in the section “Discussion”.
The following ROIs were identified on the basis of the paired t test maps. From the paired t test map reflecting the cognitive effect (Fig. 3) the anterior auditory cortex was chosen bilaterally. From the paired t test map reflecting the refractoriness effect when “day” served as a standard the posterior auditory cortex was chosen bilaterally (Fig. 4, middle panel). From the paired t test map reflecting the sensory component when “deh” served as the standard the superior/middle frontal gyrus was chosen bilaterally (Fig. 4, left panel, Table 7). Note that all the ROIs were defined based on the t-maps prior to cluster-size correction (P < 0.001). All except the right superior/middle frontal gyrus were apparent in the t test maps after cluster-size correction.
Figure 5 displays the average delta BOLD for each condition (“oddball”/“control”) and hemisphere (left/right) as a function of deviant stimulus and ROI. From Fig. 5 it can be seen that the largest differences in delta BOLD between conditions (“oddball”/”control”) are evident over the left hemisphere, mainly the anterior auditory cortex and superior frontal gyrus. These were larger for “day” than “deh” and smallest for “tay”. The latter was associated with diminished levels of delta BOLD within the posterior auditory cortex in the control condition.
Fig. 5Average delta BOLD for each condition (“oddball”/“control”) and hemisphere (left/right) as a function of ROI and deviant stimulus (“deh”, “day” or “tay”). Anter_Aud anterior auditory cortex, Post_Aud posterior auditory cortex, Sup/Mid_Frontal superior/middle frontal gyrus
The ROI data was subjected to a four-way ANOVA with Laterality (Left, Right), ROI (anterior auditory cortex, posterior auditory cortex, superior/middle frontal gyrus), Deviant (“deh”, “day”, “tay”) and Task type (Control, Oddball) as independent factors, with an appropriate adjustment for sphericity (Greenhouse–Geisser correction). A main effect of ROI was found [F(2,38) = 49.17, P < 0.001], as well as a Laterality × ROI [F(2,38) = 5.39, P < 0.05], Deviant × ROI [F(4,76) = 4.23, P < 0.05], Task Type × ROI [F(2,38) = 7.25, P < 0.01], and Deviant × Task Type × ROI [F(4,76) = 4.60, P < 0.01] interactions. The Laterality × ROI interaction was due to enhanced delta BOLD over the left hemisphere associated with the anterior auditory cortex (P < 0.05, Bonferroni corrected).
The Deviant × Task Type × ROI interaction is depicted in Fig. 6. A set of paired comparisons (Bonferroni corrected, P < 0.05) were conducted separately within each task-type (“oddball”/”control”). In the “oddball” condition the comparisons were conducted among the three deviants for the posterior auditory cortex only, where a gradient of delta BOLD could be observed as a function of deviant type. In the “control” condition comparisons were performed among the three deviants for each of the ROIs. In the “oddball” condition a significantly larger delta BOLD was found in response to “tay” than to “deh” in the posterior auditory cortex. In the “control” condition delta BOLD to the word “day” in the anterior and posterior auditory cortices was significantly larger than that associated with the non-word “tay”.
Fig. 6ROI × stimulus ("deh", "day","tay") × condition (“oddball”/“control”) interaction. The interaction was obtained by calculating the individual percent signal change in each ROI (across hemispheres) for each of the six composite maps (three Deviant maps and three Deviant-control maps). See text for the results of the a priori tests. Abbreviations as in Fig. 5
In a separate set of paired comparisons (Bonferroni corrected, P < 0.05) the averages of delta BOLD between conditions (“oddball”/”control”) were compared for each of the three deviant stimuli and each of the ROIs (across hemispheres). A larger delta BOLD was found for the response to “day” in the “control” condition relative to the “oddball” condition implicating the anterior auditory cortex.
ROI analysis for the Word and Non-word maps
To further investigate the “word effect”, each participant’s model estimate of the percent signal change for each region of activation, was calculated separately for the Word map and Non-word map (Fig. 7, Table 8). The ROI data was subjected to a three-way ANOVA with Laterality (left, right), ROI (anterior auditory cortex, posterior auditory cortex, superior/middle frontal gyrus) and Lexical Status [word (“day”), non-word (“tay”)] as independent factors, with an appropriate adjustment for sphericity (Greenhouse–Geisser correction). A main effect of ROI was found [F(2,38) = 21.03, P < 0.001] as well as a Lexical Status × ROI interaction [F(2,38) = 6.05, P < 0.01] (Fig. 8). Paired comparisons (Bonferroni corrected, P < 0.05) found a significant difference between “day” and “tay” only within the anterior auditory cortex.
Fig. 7Word and Non-Word maps. Brain regions in response to “day” deviant-counterpart (Word map) and “tay” deviant-counterpart (Non-word map). Stimuli were contrasted against a baseline comprised of the non-word “fillers” (i.e., “peh”, “beh”, “deh”, “teh”). Left panel: response to the Word-map. Right panel: response to the Non-word map. BOLD contrasts were superimposed on a reference anatomical image (Holmes et al. 1998). Axial sections range from z = −6 to 46 (by increments of 4 mm)Table 8Brain regions activated in Word and Non-word maps in response to “day” and “tay”, respectively (Figs. 7, 8)StimulusVolume size (mm3)Mean t valueTalairach coordinates (mm)SideIdentified regionBAxyzday (Word-map)12,5794.6355−1614RTPoCG/PrCG STG40/43/6day (Word-map)14,2604.65−53−1813LTSTG42tay (Non-Word-map)4,8464.92−52−834LTPrCG4PoCG postcentral gyrus, PrCG precentral gyrus, STG superior temporal gyrus, other details as in Table 4Fig. 8ROI × Lexical Status interaction. The interaction was obtained by calculating the individual percent signal change in each ROI for the Word and Non-word maps. Abbreviations as in Fig. 5
The distribution of percent signal change across subjects
To learn more about the consistency and extent of activation of specific brain regions, data were analyzed for individual subjects. Specifically, the percent signal change of individual subjects was computed for each of the six ROIs chosen for the group level analysis as well as for additional two brain regions that might be implicated in speech perception, that is, the left occipital pole and the left precentral gyrus. The results showed that the majority of subjects within each statistical test showed the same pattern of activity (see Table 9).
Table 9The distribution of percent signal change across subjectsBrain regionSideMap typeNo. of subj. displaying increases (+)/decreases (−) (n = 20)Mean ± SD % signal changeLS/INSRTCognitive (“day”), Fig. 317(−)−0.09 ± 0.07LS/INSLTCognitive (“day”), Fig. 318(−)−0.09 ± 0.10STGRTRefractoriness (“day”), Fig. 418(−)−0.15 ± 0.17STGLTRefractoriness (“day”), Fig. 417(−)−0.16 ± 0.14S/MFrGRTRefractoriness (“deh”), Fig. 413(+)0.06 ± 0.12S/MFrGLTRefractoriness (“deh”), Fig. 413(+)0.07 ± 0.10PrCGLTDeviant control (“tay”), Fig. 219(+)0.13 ± 0.13OCCPLTDeviant control (“day”), Fig. 218(−)−0.24 ± 0.20SD standard deviation. For each region and hemisphere the number of subjects presenting increases or decreases in percent signal change is indicated as well as the mean and SD across subjects. The stimulus in brackets denotes the specific map from which the brain region was selected. For the regions associated with the t test maps of the cognitive and refractoriness effects, data are reported for the difference percent signal change between the contrasted conditions in the t testLS lateral sulcus, INS insula, STG superior temporal gyrus, S/MFrG superior/middle frontal gyrus, PrCG precentral gyrus, OCCP occipital pole, RT right, LT left, No. of subj number of subjects, n total number of subjects that participated in the study
Discussion
Main findings
Overall, the results of the current study confirmed our hypotheses. Specifically, the sensory mechanism was located in the posterior auditory cortex (Fig. 4, Table 7) and the cognitive mechanism in the anterior auditory cortex (Fig. 3, Table 6). These locations match, respectively, those found in the context of tones (Opitz et al. 2005). Moreover, as expected, the cognitive effect was statistically significant only when the deviant was a word (i.e., “day”).
The similarity between the brain regions activated by speech in this study and the regions activated by tones (Opitz et al. 2005) could not be explained by the analysis of the physical sound features. First, the contribution of the acoustic component to the cognitive mechanism in this study was removed by contrasting deviants with their physical identical control counterparts. Second, our data suggest that the salience of the sounds, either tones or speech stimuli, accounts for the activation of the anterior auditory cortex (Jääskeläinen et al. 2004; Ahveninen et al. 2006) (see also ‘Support for the existence of “what” and “where” auditory streams’). Thus, the acoustic component could only account for the similar activation observed in the posterior HG. This is also in line with previous findings that link dorsal temporal brain areas including HG and planum temporale with the analysis of auditory features of complex sounds (Binder et al. 1996; Wessinger et al. 2001; Hall et al. 2002, 2003; Seifritz et al. 2002).
Activation loci in superior and middle temporal areas that were found to be activated in the Deviant (Fig. 1) and Deviant control maps (Fig. 2) had also been earlier reported to contribute to lexical and semantic processing (Price 2000; Salmelin et al. 2000; Scott and Johnsrude 2003; Hickok and Poeppel 2004). Similarly, the left dominance for processing intelligible speech that was found in our study in the “oddball” condition match previous results that link the left posterior STG to higher level linguistic processes (Narain et al. 2003).
Support for the existence of “what” and “where” auditory streams
The results of this study are in agreement with the segregation of the auditory system into “what” and “where” processing streams (Jääskeläinen et al. 2004; Ahveninen et al. 2006) associated with the analysis of auditory object content and location features that reside in areas anterior and posterior to primary auditory cortex, respectively (Rauschecker and Tian 2000). It was suggested (Jääskeläinen et al. 2004) that the “where” system is responsible both for fast analysis of sound location which is important for attentional orienting, and for detecting the degree of sound novelty, which affects its degree of distractibility. Specifically, the degree to which unattended novel sounds distracted visual forced-choice task performance coincided with the extent that the posterior N1 response was released from inhibition. On the other hand, the anterior N1 response was associated with the processing of fine object features (Jääskeläinen et al. 2004). Similarly, the results in our study indicate that the deviant word was released from inhibition in the “control” condition because of its lexical status and that this “word advantage effect” was located anterior to HG. This is in agreement with the finding that neurons in the anterior auditory cortex are more sharply tuned to phonetic features of sounds (Ahveninen et al. 2006).
The effect of speech perception
Despite the general similarity to the results obtained in the context of tones, some of the results are specific to speech perception, as follows. In the “oddball” condition the brain regions activated in response to the deviants “day” and “tay” were larger than those observed for the “deh” deviant (Deviant maps, Fig. 1). In addition, in the control condition the precentral gyrus was activated in response to both “day” and “tay” (Fig. 2). These similarities between “day” and “tay” may reflect an extra processing effort associated with the diphthong vowel /eI/ shared by “day” and “tay” (Sonty et al. 2003; Bohland and Guenther 2006). However, the fact that the similarity between the responses to “day” and “tay” diminished in the control condition (Fig. 2) may indicate that the /eI/ diphthong associated with deviant “day” was more salient among repetitive “deh” standards (“oddball” condition) than among the variable filler stimuli (“control” condition) (Nordby et al. 1994; Sabri and Campbell 2000).
The more similar responses between “day” and “deh” in the control condition (Fig. 2) may be explained by the shared /e:/ monophthong that might have elicited an expectation to hear a meaningful word (“day”) while presented with “deh”. This expectation could account for the more extensive response in the STG relative to “tay” (Fig. 2). The finding that activation in the left anterior auditory cortex dissociates the “oddball” and “control” conditions strengthens this interpretation (Scott et al. 2006) as well as the observed increase in the percent signal change in that region associated with both “deh” and “day” (Fig. 5, “Left” panels). Thus, in the “control” condition (Fig. 2), an interaction between lexical and acoustic features affected the results.
There were additional findings indicating left hemisphere dominance, as follows. The size of STG activation was the most extensive over the left STG for “day” in comparison to “tay” where it did not reach statistical significance and “deh” where it was more than five times smaller (Deviant control maps, Fig. 2, Table 5). Furthermore, the “day” control map revealed negative differential BOLD in the left superior frontal gyrus as well as in the left occipital pole (Fig. 2, Table 5). Left hemisphere dominance was also evident in the middle/superior frontal gyrus in the sensory mechanism map for “deh” (Fig. 4, Table 7). In addition, the four-way ANOVA (Fig. 5) has shown.
This pattern of left hemisphere dominance is in agreement with a number of imaging and clinical studies (for a review see Tervaniemi and Hugdahl 2003). The left middle/superior frontal gyrus activations are in line with previous findings implicating these brain regions with tasks engaging phonological working memory (Paulesu et al. 1993; Burton et al. 2000; Siok et al. 2003; LoCasto et al. 2004) and with the suggestion that a significant portion of active frontal areas is recruited for extracting acoustic information and maintaining it in memory (LoCasto et al. 2004). The negative differential activation evident in the left occipital pole associated with the “day” control map (Fig. 2) may indicate that meaningful words are more likely to elicit activity in visual processing regions (Billingsley-Marshall et al. 2007).
It is noteworthy that the sensory mechanism map for “deh” revealed a positive differential BOLD in the ACC (Fig. 4, Table 7). The ACC is implicated in initiating or inhibiting action and is considered to be part of a larger network that includes medial/lateral frontal, prefrontal and temporal regions (Wang et al. 2005; Dias et al. 2006; Gold et al. 2006). Hence, it is possible that in our study inhibitory activations occurred in the ACC (as well as in the superior frontal gyrus) in response to “day” since it was a meaningful stimulus that interfered with the main counting task (see also Rinne et al. 2005). Since standard “deh” was contrasted with deviant “day” to create the “deh” standard map, the positive differential BOLD located at the ACC and middle/superior frontal gyrus (Fig. 4, “deh”) might have been caused by a reduced activation associated with the response to deviant “day”.
A possible effect of the lexical status in the “oddball condition
It appears that the lexical status of the deviant stimulus affected its processing not only in the “control” condition but also in the “oddball” condition in which the effects of adaptation caused by the repeating standards were more pronounced. Specifically, the “day” stimulus activated parts of the posterior STG (i.e., BA 22/42), whereas the “tay” and “deh” stimuli activated large parts of the superior and middle temporal cortices (i.e., BA 21/22) (Table 4). However, the size of the left STG region activated by “day” deviant was almost twofold larger than that activated by “tay” deviant (Deviant maps, Fig. 2, Table 4). This is in agreement with the assumption that the left posterior STG is the focus of a multi-modal network associated with language comprehension (Aboitiz and Garcia 1997; Narain et al. 2003). More strongly left-lateralized posterior superior-temporal activation is associated with analysis of speech sounds for mapping onto higher levels of language processing (e.g., syllable, word) (Price et al. 1992; Zatorre et al. 1996). Thus, although the word advantage effect was not salient in the “oddball” condition, the different spread of activation between “day” and the other non-words may indicate differential processing based on the lexical status of the deviant stimulus.
Summary
Taken together, the results of the current study corroborate the existence of two independent mechanisms contributing to the change-detection response (Opitz et al. 2005; Hoshiyama et al. 2007; Maess et al. 2007): a sensory mechanism reflected by different refractory states of those subpopulations activated by the standard and the deviant and a cognitive mechanism which relies on auditory sensory memory representations which gives rise to the word advantage effect. Thus, on the one hand, our results support the view that the MMN represents a change-detection mechanism functionally and spatially distinct from an afferent input population (N1 generators) (Näätänen et al. 2005). On the other hand, our results indicate that a release from the inhibitory effects of adaptation is a prerequisite for the full realization of the significance of the deviant stimulus. This is in line with the adaptation hypothesis that assumes that the posterior auditory cortex gates novel sounds to awareness (Jääskeläinen et al. 2004).
Conclusion
Our findings serve to unify the two opposing views suggested by Jääskeläinen et al. (2004) and Näätänen et al. (2005). Specifically, the gate to awareness for auditory deviation (Jääskeläinen et al. 2004; Näätänen et al. 2005) relies on adaptation that modulates the extent to which novel sounds will be accessible to memory-based processes. In case of the present study, the sensory component serves to modulate the salience of the speech stimulus by the degree to which it will be accessible to cortical memory traces for speech sounds (Pulvermüller and Shtyrov 2006). Furthermore, the lexical status of the speech stimulus interacts with acoustic factors exerting a top-down effect on the novelty value of the auditory object that affects, in turn, its degree of accessibility to the cognitive component.

functional magnetic resonance imaging (fmri)


Lovastatin sensitized human glioblastoma cells to TRAIL-induced apoptosis

Synergy study with chemotherapeutic agents is a common in vitro strategy in the search for effective cancer therapy. For non-chemotherapeutic agents, efficacious synergistic effects are uncommon. Here, we have examined two non-chemotherapeutic agents for synergistic effects: lovastatin and Tumor Necrosis Factor (TNF)-related apoptosis-inducing ligand (TRAIL) for synergistic effects; on three human malignant glioblastoma cell lines, M059K, M59J, and A172. Cells treated with lovastatin plus TRAIL for 48 h showed 50% apoptotic cell death, whereas TRAIL alone (1,000 ng/ml) did not, suggesting that lovastatin sensitized the glioblastoma cells to TRAIL attack. Cell cycle analysis indicated that lovastatin increased G0–G1 arrest in these cells. Annexin V study demonstrated that apoptosis was the predominant mode of cell death. We conclude that the combination of lovastatin and TRAIL enhances apoptosis synergistically. Moreover, lovastatin sensitized glioblastoma cells to TRAIL, suggesting a new strategy to treat glioblastoma.
Introduction
Glioblastomas are the most common intracranial brain tumors. Its prognosis is usually poor, with survival times of less than 15 months from first diagnosis [1]. Surgical resection and chemotherapy are common treatments [2]. Despite recent advances in the understanding of the molecular mechanism of tumourogenesis, the outcome of malignant glioma remains poor [3]. Thus, new effective forms of therapy are needed.
The Tumor Necrosis Factor-Related Apoptosis-Inducing Ligand (TRAIL) [4], a member of the TNF superfamily, can bind with death receptors, DR4 and DR5 [5, 6] and induces apoptosis in a wide range of cancer cells without harming normal cells. The specific property of TRAIL has attracted many researchers to look for new treatments by combining it with chemotherapeutic agents such as phenoxazine derivatives [7], doxorubicin and cisplatin [8]. Such combinations have shown synergistic effects on different types of cancer cells in vitro.
Lovastatin, a 3-hydroxy-3-methlyglutaryl CoA (HMG CoA) reductase inhibitor is a commonly used cholesterol-lowering agent for prevention of atherosclerotic cardiovascular diseases [9, 10]. Lovastatin blocks the mevalonate pathway and reduces the formation of the downstream products, cholesterol, geranylgeranyl proteins, farnesylated [11]. Recently both in vitro and in vitro studies have found that lovastatin has antiproliferative, proapoptotic and anti-invasive properties in a wide range of cancer cell types [12]. Lovastatin is known to have an apoptotic effect on tumor cells and its combination with chemotherapeutics and cytokines often exert a synergistic effect against tumor growth [13–15]. The mechanism that leads to lovastatin-induced apoptosis is not yet clear but the main event is thought to be associated with the alteration of mitochondrial stress, which releases cytochrome C, activates pro-caspase cascade and finally leads to apoptotic cell death.
Escape from apoptotic regulation is one of the major characteristics of cancer [16, 17], and many successful anti-cancer agents induce apoptosis by damaging DNA. Unfortunately such agents may also severely affect normal cells. Given the fact that both lovastatin and TRAIL are non-chemotherapeutic agents and capable of inducing apoptosis in different types of cancer cells, it is important to determine whether the combination of these two agents would produce synergistic effects that may be lighten for a novel therapeutic application in gliomas.
We therefore hypothesized that the combination of TRAIL and lovastatin, neither of which alone has noxious effects on healthy cells, could generate a regime that was effective in killing cancer cells but caused minimal insult to normal healthy cells. In this study we report the effects of TRAIL in combination with a non-chemotherapeutic drug, lovastatin, on glioblastoma cells.
Materials and methods
Reagents
2-Methyl-1,2,3,7,8,8a-hexahdro-3,7-dimethyl-8-[2-(tetrahydro-4-hydroxy-6-oxo-2H-pyran-2-yl)ethyl]-1-naphthalenyl ester butanoic acid (Lovastatin), DL-Mevalonic acid lactone, and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) were purchased from Sigma (St Louis, MO). Lovastatin was dissolved in DMSO for stock and adjusted to final concentrations using complete medium or serum free medium. Soluble Human TRAIL (Apo2L) was affinity purified from lysates of bacteria transformed with pET plasmid containing TRAIL [18]. Cellular DNA fragmentation ELISA kit (Roche, Mannheim, Germany), RNeasy kit, DNA extraction kit (Qiagen, Germany) and RT-PCR kit (Promega, Madison, WI) were used. Three primary antibodies used were as follows: rabbit polyclonal antibody to DR4 (Chemicon International, 1:1,000 dilution), rabbit polyclonal antibody to DR5 (Cell Signaling Technology, 1:1,000 dilution), and rabbit polyclonal antibody to β-tubulin (Santa Cruz Biotechnology, 1:1,000 dilution). Goat anti-rabbit secondary antibody was obtained from Santa Cruz Biotechnology.
Cell culture
Three human glioblastoma cell lines, A172, M059J, and M059K were purchased from American Type Culture Collection (Rockville, MD). The glioblastoma cells were kept in Dulbecco’s modified Eagle’s medium (DMEM/F12) (GIBCO BRL, Grand Island, NY) with or without 10% fetal bovine serum, 1% penicillin and streptomycin at 37°C under 5% CO2. Media were changed every 3 days.
Measurement of cell viability
We measured cell viability using the MTT assay. MTT is a water-soluble tetrazolium salt that is metabolized by viable cells to a colored, water-insoluble formazan salt. Thus the salt allows cell viability measurements. In short, 1 × 104 cells were cultured in the serum free DMEM/F12 medium in the presence of lovastatin with or without TRAIL in a 96 well microtiter plate for designated time periods. The medium was aspirated and 100 μl MTT (0.5 mg/ml in PBS) were added to each well and the cells incubated for 3 h. After MTT medium was aspirated, the cells were solubilized in 200 μl DMSO. The optical density of each sample at 570 nm (reference 630 nm) was measured using a microplate reader. The optical density of the media was proportional to the degree of viable cells.
Cell cycle analysis
Propidium iodide (PI) staining and flow cytometry were used to determine the stage of the cell cycle. Cells were treated with 20 μM lovastatin for 48 h at 37°C and the control cells were treated with normal medium (DMSO) or serum free medium (DMSO). 2 × 106 treated cells were washed with 5 ml PBS and then were trypsinized at 37°C for 5–10 min. Cells were spun down and washed with 5 ml PBS. Finally cells were resuspended in 500 μl PBS and fixed with 4.5 ml 70% Ethanol with gentle vortexing. Cells were allowed to sit in −20°C for overnight. Fixed cells were spun down and washed with 5 ml PBS, and then cells were resuspended in 500 μl PI (2 μg/ml)/Triton X-100 (0.1% v/v) staining solution with RNase A (200 μg/ml) in dark and analyzed by a flow cytometer. The staining solution was purchased from Chemicon (Temecula, CA).
Apoptosis assay
Apoptotic cells were determined by two methods, Annexin-V and PI stained cells by flow cytometry and DNA fragmentation assay. During apoptosis, translocation of phosphatidylserine from inner membrane to outer membrane is a common phenomenon. Cells were stained with Annexin V for analysis of phosphoserine inversion, which was considered to be a sensitive marker of apoptosis. Using an Annexin V-FITC apoptosis detection kit (Molecular Probe Inc, Eugene, OR), the levels of binding of Annexin V and staining with PI were measured for the detection of early and late apoptosis respectively. All of the procedures were preformed under manufacturer’s guidelines. Cells were treated with lovastatin or/and TRAIL for 48 h and then stained with Annexin-V and PI. Viable cells were recognized as negative for both Annexin-V and PI; early apoptotic events were positive for Annexin-V but negative for PI staining. Late apoptotic events were positive to both Annexin V and PI. Necrotic cells were positive for PI staining only.
DNA fragmentation determination, was carried out with the ELISA assay kit. After cells (1 × 104) were treated with lovastatin and/or TRAIL for 48 h, DNA fragmentation was detected using 96 wells microplate reader.
RT-PCR
Total RNA was isolated using Qiagen RNeasy extraction kit and performed according to the manufacturer’s protocol. Total RNA (5 μg) was reversely transcribed using Promega RT-PCR kit and thermal program was set at 42°C for 15 min and 95°C for 5 min. PCR reaction was performed using the following primers, which have previously been tested successfully: TRAIL-R1, 5′-CTG AGC AAC GCA GAC TCG CTG TCC AC-3′ and 5′-TCC AAG GAC ACG GCA GAG CCT GTG CCA T-3′; TRAIL-R2, 5′-GCC TCA TGG ACA ATG AGA TAA AGG TGG CT-3′ and 5′-CCA AAT CTC AAA GTA CGC ACA AAC GG-3′; TRAIL-R3, 5′-GAA GAA TTT GGT GCC AAT GCC ACT G-3′ and 5′-CTC TTG GAC TTG GCT GGG AGA TGT G-3′; TRAIL-R4, 5′-CTT TTC CGG CGG CGT TCA TGT CCT TC-3′ and 5′-GTT TCT TCC AGG CTG CTT CCC TTT GTA G-3′. The thermal program was set up as one cycle at 94°C for 5 min, 30 cycles at 94°C for 1 min, 55°C for 1 min, 72°C for 2 min, and one cycle at 72°C for 5 min. PCR products were resolved and visualized on a 2% agarose gel stained with ethidium bromide.
Western blot analysis
Western blot was performed according to previous description [19, 20]. Briefly, after 2 × 106 cells were treated for 48 h, total protein was isolated and reacted with the relevant antibodies. The probed proteins were visualized using the enhanced chemiluminescence Western blotting detection system (ECL Western Blotting Detection, Amersham Biosciences).
Statistics
The statistical significance was analyzed using one-way ANOVA analysis and Student’s t-test. All statistical work was carried out using the SPSS software for Windows (Release 11.0.1, Chicago, IL). Differences were considered to be significant when P < 0.05.
Results
Cell viability measured by MTT assay
We first examined the anti-proliferation effect of TRAIL on three glioblastoma cell lines. M059K and M059J glioblastoma cells were resistant to TRAIL-induced cell death and remained 100% cell viable following treatment in both normal and serum free conditions (Fig. 1a, b). Only A172 showed minor cell death after TRAIL treatment (22%, Fig. 1a).
Fig. 1Anti-proliferation effects on glioblastoma cell lines A172, M059K, and M059J. MTT assays were preformed after 48 h incubation time treated with 0, 500, 1,000 ng/ml TRAIL in normal medium (a) or serum free medium (b). A172 subjected to minor TRAIL-induced cell death while M059J and M059K cells were resistant to TRAIL. Same trend of data were obtained in both normal medium and serum free condition. Experiment set were repeated at least three times with triplicate wells for each condition (mean ± SD)
Synergistic effects by TRAIL and lovastatin were then examined. A172, M059J, and M059K glioblastoma cells were incubated in 1, 5, 20, and 40 μM lovastatin alone or together with 500 ng/ml TRAIL for 48 h. In A172 glioblastoma cells, 5, 20, and 40 μM lovastatin with 500 ng/ml TRAIL promoted significant cell death when compared with lovastatin control (Fig. 2a). 5, 20 and 40 μM lovastatin alone induced 50%, 60% and 76% of cell death respectively, however in the presence of 500 ng/ml TRAIL, lovastatin induced 78%, 94%, and 92% of cell death respectively. 1, 5, 20, and 40 μM lovastatin with 500 ng/ml TRAIL also synergistically promoted cell death in M059J and M059K. In M059J cells, 1, 5, 20 and 40 μM lovastatin alone caused 40%, 55%, 40% and 60% cell death whereas in the presence of 500 ng/ml TRAIL, lovastatin induced 83%, 94%, 95% and 95% cell death respectively (Fig. 2b). There were 26%, 51%, 58%, and 71% cell death induced by 1, 5, 20, and 40 μM lovastatin alone in M059K, in the presence of 500 ng/ml TRAIL, lovastatin at all concentration tested caused about 98% of cell death (Fig. 2c). Collectively the combination of TRAIL and lovastatin was much more effective than lovastatin alone in the induction of cell death in all three glioblastoma cells tested and the result indicated that there was a synergistic effect when TRAIL and lovastatin were used together.
Fig. 2Synergistic anti-proliferation effects on glioblastoma cell lines A172, M059J, and M059K. Glioblastoma cells were treated with 0, 5, 20 and 40 μM lovastatin alone or 0 μM lovastatin plus 500 ng/ml TRAIL, 5 μM lovastatin plus 500 ng/ml TRAIL, 20 μM lovastatin plus 500 ng/ml TRAIL and 40 μM lovastatin plus 500 ng/ml TRAIL for 48 h. Percentage of viable glioblastoma cells showed synergistic anti-proliferation effects by combined the two agents. In A172 glioblastoma cells (a), 500 ng/ml TRAIL with 5, 20 and 40 μM lovastatin induced cell death significantly when compared with lovastatin only groups. In M059J (b) and M059K (c), 500 ng/ml TRAIL with 1, 5, 20 and 40 μM lovastatin induced cell death significantly when compared with lovastatin only groups. ANOVA were used for statistics analysis and *P < 0.05, **P < 0.01. Experimental set were repeated for at least three times with triplicate wells for each condition (mean ± SD)
Cell cycle determination by PI staining
Propidium iodide staining and flow cytometry were used to determine the degree of cell synchronization by 20 μM lovastatin. Cells were incubated with normal serum medium, serum free medium and 20 μM lovastatin with normal serum medium for 48 h. PI-stained cells were analyzed using flow cytometry to quantify cells in certain cell cycle stages. A significant increase in cell population at G0/G1 phase was observed when the cells were treated with 20 μM lovastatin indicating that lovastatin was able to arrest the cells at G0/G1 stage. G0/G1 cell population was also increased by serum free medium conditions (positive control) and the increase reached a significant level except in M059J cells (Fig. 3).
Fig. 3Lovastatin-induced glioblastoma cells arrested in G0–G1 Phase. Propidium Iodide staining for cell cycle analysis were performed after glioblastoma cells were treated with normal medium without lovastatin, serum free medium without lovastatin and 20 μM lovastatin for 48 h. Serum free condition was used as a positive control which is commonly known to induce cell arrest in G0–G1 phase. Serum free condition and lovastatin increased G0–G1 cell arrest in all glioblastoma cells and reached significant level (except serum free condition in M059J). ANOVA were used for statistics analysis and *P < 0.05, **P < 0.01. Experimental set were repeated for at least three times with triplicate wells for each condition (mean ± SD)
Apoptosis is the major mode of cell death
In order to determine the mode of cell death induced by lovastatin and TRAIL, Annexin V and PI staining was employed for this purpose. Cells were incubated with 5 or 20 μM of lovastatin in the presence or absence of 500 ng/ml TRAIL for 48 h. The percentage of cell death of glioblastoma cells treated with both lovastatin and TRAIL was significantly higher than that with either agent alone (Fig. 4a, b and c). A172 glioblastoma cells were vulnerable to TRAIL induced apoptosis (Fig. 4a) but significantly more apoptotic cells were observed following treatment with both TRAIL and lovastatin (P < 0.005) (Fig. 4a). TRAIL was able to induce apoptosis in 50% of A172 glioblastoma cells. However, TRAIL in combination with 5 and 20 μM could induce apoptosis in approximately 67% and 74% cells respectively, indicating a synergistic effect occurred. Similarly, a significant synergistic effect was also observed in M059K and M059J cells (P < 0.005) (Fig. 4b, c), with up to nearly a 10-fold increase in apoptotic cells. 5 μM lovastatin only induced apoptosis in 5.3% and 2.3% M059J and M059K respectively, which were not different from the control (without any treatment). 5 μM lovastatin plus 500 ng/ml TRAIL dramatically increased apoptotic cells to 47.8% and 61.4% in M059J and M059K respectively. M059J cells, which lack of DNA-dependent protein kinase expression [21], were less vulnerable to the two agents, implying that DNA-dependent protein kinase may play a role in apoptosis induced by TRAIL and lovastatin.
Fig. 4The synergistic apoptotic effects were quantified by flow cytometry using Annexin V and PI staining. Glioblastoma cells were treated with DMSO (Control), 5 μM lovastatin, 20 μM lovastatin, 500 ng/ml TRAIL, 5 μM lovastatin plus 500 ng/ml TRAIL and 20 μM lovastatin plus 500 ng/ml TRAIL for 48 h. Then cells were stained with Annexin V and PI to determine percentage of apoptotic cell death using flow cytometry. Synergistic apoptotic effects were observed in three glioblastoma cell lines A172 (a), M059J (b) and M059K (c), reached significant level. The portion of apoptotic cell death was indicated in low-right quarter of the flow-cytometry scatter plot and the trend of apoptotic cells between groups. ANOVA were used for statistics analysis and *P < 0.05, **P < 0.01 (compared to control group), +P < 0.05, ++P < 0.01 (compared to TRAIL only group). Experimental set were repeated for at least three times (mean ± SD)
DNA fragmentation in glioblastoma cells
DNA fragmentation is one of the hallmarks when cells undergo apoptosis. We performed ELISA assay to confirm DNA fragmentation was induced in the cells treated with both lovastatin and TRAIL. A synergistic effect on DNA fragmentation by both agents was demonstrated in all three cell lines tested (Fig. 5a, b and c). The level of DNA fragmentation was increased in treated A172 glioblastoma cells, but it was only statistically significant in the cells treated with TRAIL plus 20 μM lovastatin. In M059J and M059K cells, however, there was a dramatic elevation of DNA fragmentation in a dose-dependent manner when both of the agents were applied. This observation supports the argument that the cell death induced by the two agents was apoptotic and that both agents were able to function in a synergistic manner.
Fig. 5DNA fragmentation was detected in combination of lovastatin and TRAIL. Same treatment in apoptotic cell staining was preformed for DNA fragmentation ELISA detection. Significant level of DNA fragmentation was detected in all glioblastoma cells when combined with lovastatin and TRAIL. ANOVA were used for statistics analysis and *P < 0.05, **P < 0.01 (compared to control group), +P < 0.05, ++P < 0.01 (compared to TRAIL only group). Experimental set were repeated for at least three times (mean ± SD)
The expression of TRAIL receptors in glioblastoma cells
Resistance to cell death induced by TRAIL may be adapted by an altered level of TRAIL receptors. TRAIL-R3 (DcR1) and TRAIL-R4 (DcR2) are known to attenuate TRAIL-induced apoptosis whereas TRAIL-R1 (DR4) and TRAIL-R2 (DR5) promote TRAIL-induced apoptosis [5]. The effect of lovastatin treatment on the TRAIL receptors was not determined. RT-PCR was performed to investigate the expression profiles on three glioblastoma cell lines tested. Cells were treated with normal serum medium, serum free medium, serum medium with 5 or 20 μM lovastatin for 48 h. TRAIL-R3 and R4 were not detected in all conditions and cell types tested. TRAIL-R1 was only detected on M059J cells with serum free medium or lovastatin (Fig. 6a). TRAIL-R2 was expressed on all cell types of cells with or without lovastatin and its level was not significantly different between lovastatin-treated cells and the controls. Therefore, the expression of TRAIL-R2 did not contribute to the lovastatin-induced cell death in glioblastoma cells tested.
Fig. 6The expression of TRAIL receptor mRNA in glioblastoma cells. The cells were treated with normal serum medium, serum free medium, 5 and 20 μM lovastatin with normal serum medium. At the end of the treatment, RNA was isolated for the detection of TRAIL-R1 (DR4) (a) and TRAIL-R2 (DR5) (b) by RT-PCR
After we tested the mRNA expression of TRAIL receptors on glioblastoma cells, the further investigation was performed to determine the active protein expression of TRAIL-R1 and matured TRAIL-R2 by Western blot. The result showed that the protein expression profiles of TRAIL-R1 and TRAIL-R2 were found in three glioblastoma cells with or without lovastatin, suggesting the protein of these two receptors was underwent post-translational modifications. The significantly differences were only found on the TRAIL-R1 expression of M059K cells with 20 μM lovastatin (P = 0.05) and the expression of TRAIL-R2 in A172 cells with lovastatin (P < 0.01) (Fig. 7).
Fig. 7The expression of TRAIL-R1 (DR4) and TRAIL-R2 (DR5) proteins in glioblastoma cells. The cells were treated with normal serum medium, serum free medium, 5 and 20 μM lovastatin with normal serum medium for 48 h. At the end of the treatment, proteins were isolated for the detection of TRAIL-R1 (a, b) and TRAIL-R2 (c, d) by Western blot. Representative Western blots were shown (a, c). The target bands were scanned and normalized to β-tubulin. The index of densities was calculated (b, d). *P = 0.05, **P < 0.01
Discussion
Malignant glioblastoma is one of the major causes of brain tumors morbidity. Aggressive infiltration in the CNS ultimately leads to death in nearly all cases [2]. Malignant glioblastoma carries with aberrant biological and biochemical properties including several activating mutations that can lead to chemotherapeutics resistance [22]. Targeting apoptotic signaling machinery is thought to be a promising alternative for glioblastoma treatment [23]. The preliminary data presented in this report indicate that lovastatin, a blood cholesterol lowering medicine, sensitizes glioblastoma cells to TRAIL-mediated apoptosis. Earlier reports show statin-induced cell death through a mitochondrial-mediate pathway (intrinsic pathway) that is closely related to the Bcl-2 family protein Bid and activation of caspase 8, 9 and 3 [24–27]. However, the mechanism for statin activation of caspase 8 remains unknown because caspase 8 is normally activated by receptor-mediated signals, such as Fas ligand and TRAIL [5].
Lovastatin has been found to enhance TRAIL-induced cytotoxicity in a synergistic manner in glioblastoma cells. We demonstrated a synergistic effect produced by the combination of lovastatin and TRAIL on glioblastoma cells. Lovastatin was found to sensitize the cells to cell death induced by TRAIL. The mode of cell death induced by both agents in combination was apoptosis, as demonstrated by two different methods, Annexin V and PI staining and DNA fragmentation assay. We also demonstrated that two of the glioblastoma cell lines tested were resistant to TRAIL induced apoptosis. From this we inferred that lovastatin not only sensitized these glioblastoma cells through its effects on the TRAIL receptor pathway but also triggered an unknown mechanism: Lovastatin served as a cytostatic agent and turned on an unknown mechanism to support TRAIL-induced apoptosis in these glioblastoma cells. It has been shown that colon and lung tumor cells arrested in G0–G1 stages are vulnerable to TRAIL-induced cell death [28]. However, our G0–G1 arrested glioblastoma cell lines remained resistant to TRAIL-induced cell death in the serum free control. Our finding suggests that a combination of TRAIL and lovastatin together may form a new treatment for glioblastoma multiforme.
The mechanism by which lovastatin sensitizes glioblastoma cells to TRAIL induced-apoptosis remains unknown. In human glioblastoma, lovastatin has been shown to induce or to enhance apoptosis by altering a number of apoptotic molecules. For example, it can induce apoptosis or downregulate cell proliferation by targeting Ras in primary cultured human glioblastoma cells [29] and increase pro-apoptotic Bim in U87 and U251 glioblastoma cells [30]. Lovastatin has also been shown to downregulate RhoA and increase iNOS in T98G and A172 glioblastoma cells [31]. Additionally, Lovastatin may induce apoptosis by increasing p21 and the apoptosis induced can be prevented by the overexpression of Bcl-2 [32], suggesting a mitochondrial-related apoptosis. It is noted that this study by Schmidt et al. fails to document that lovastatin can enhance death receptor (CD95)-mediated apoptosis in glioblastoma cells LN-18, LN-229, LN-308 and T98G [32]. The result from our study indicate that lovastatin in combination with TRAIL can synergistically induce apoptosis in A172, M059K and M059J glioblastoma cells. The apoptosis induced is associated with G0–G1 arrest but not with pro-apoptotic Bid (data not shown). It is well known that TRAIL-induced apoptosis takes place via a death receptor-mediated pathway [5, 33].
Our study showed that TRAIL-R1 mRNA expressed in M059J but was hardly detected in the other two cell lines. However, TRAIL-R1 protein was detectable in all three cell lines, suggesting that the TRAIL-R1 was modified by a post-translational mechanism in the cells tested. The level of TRAIL-R1 protein was higher in M059K cells treated with 20 μM of lovastatin. However, such an elevation of TRAIL-R1 is unlikely to be responsible for apoptosis induced by lovastatin because the apoptotic rate between M059K was not different from the other cell lines. TRAIL-R2 can be detected at both protein and RNA levels. The level of TRAIL-2 mRNA was not different in the cells treated with lovastatin but its protein level was much higher in A178 cells treated with either 5 or 10 mM lovastatin, the result of which indicated that TRAIL-R2 was modified by a post-translational mechanism in A172 cells. It is noted that the base level of TRAIL-2 in A172 cells does not differ from other two cell lines tested. Therefore, it is unlikely that TRAIL-2 could count for the relatively higher sensitivity of A172 cells to TRAIL stimulation than the other two cell lines. All three cell lines underwent a similar level of cell death when they were treated by lovastatin. Therefore, the contribution of lovastatin-induced TRAIL-2 protein to the cell death in A172 cells seems to be minimal. Such results appear to be in line with a study using mevastatin, a similar HMG-CoA reductase inhibitor. Mevastatin can significantly induce apoptosis of myeloma cells in a pathway independent of death receptors including TRAIL-R2 [34]. Nevertheless, further quantitative tests are needed in glioblastoma cells to verify the result obtained.
Apoptosis induced by lovastatin is generally considered to be via the mitochondrial-mediated pathway [30; 32]. It is possible for both pathways to talk to each other to amplify apoptotic signals and this is indeed a case for TRAIL, since TRAIL is capable of inducing either mitochondrial-independent or -dependent apoptotic pathways in some types of cells [17, 33]. The Bid is a molecule functions as a bridge that links death receptor- and mitochondrial-mediated pathways. Therefore, without involvement of Bid, apoptosis induced by TRAIL and lovastatin in combination in the present study seem to be separately mediated by these two pathways. However, considering the fact that two out of three glioblastoma cells tested are insensitive to TRAIL treatment but they become responsive in the presence of lovastatin, it can be hypothesized that lovastatin treatment may remove an unknown inhibitory factor(s) that overcomes the TRAIL-mediated pathway or that lovastatin may “wake up” an activator(s) that normally remains in a resting condition. One of inhibitory factors known to involve TRAIL-mediated pathways is its decoys, TRAIL-R3 and TRAIL-R4 [5, 33]. However, it can be seem from our study that this inhibitory factor(s), if any, should not be the decoys TRAIL-R3 and TRAIL-R4 since both are not detectable in the cells tested. One possible explanation for this synergistic effect is the activation of caspase 8, an initiator caspase in death receptor-mediated pathways. Lovastatin has been shown to enhance caspase 8 activity [35]. It is possible that caspase-8 is the activator factors waken up by lovastatin. Whatever it is, the mechanism accounting for the synergistic effect of TRAIL and lovastatin against glioblastoma cells in the present study is complicated and remains to be uncovered.
Mevalonate is a critical component of a complex biochemical pathway and its products are vital for a variety of important cellular functions including cell signaling, protein synthesis, and cell cycle regulation [11]. Little is known about the molecular events leading to apoptosis of cancer cells due to lovastatin exposure. It is likely that apoptosis is abrogated by mevalonate and GGPP and is partially reversed by FPP [12].
Our results also reinforced the conclusion of our previous study, that DNA-dependent protein kinase (DNA-PK) plays an important role in cell apoptosis. M059J cells that lack of DNA-PK activity are resistant not only to total cell death but also apoptosis [19, 20]. Experts disagree over the functions of DNA-PK in cell regulation. DNA-PK has been reported to promote cell death by interacting with telomeres, whereas other reports suggest that DNA-PK protects cells from cell death via caspase-independent or p53 independent pathways. The reason for this paradoxical finding remains unknown.
This study demonstrated a synergistic interaction between lovastatin and TRAIL, but the mechanisms of action by which lovastatin sensitized glioblastoma cells remains unknown. Our results are in agreement with the concept of combined cancer therapeutic action via both intrinsic and extrinsic apoptotic cell death pathways. This combination of non-chemotherapeutic agents, TRAIL and lovastatin, may offer a potential regime for glioblastoma treatment.

tumor necrosis factor (tnf)-related apoptosis-inducing ligand (trail)


TbARF1 influences lysosomal function but not endocytosis in procyclic stage Trypanosoma brucei

The ADP ribosylation factors (Arfs) are a highly conserved subfamily of the Ras small GTPases with crucial roles in vesicle budding and membrane trafficking. Unlike in other eukaryotes, the orthologue of Arf1 in the host bloodstream form of Trypanosoma brucei is essential for the maintenance of endocytosis. In contrast, as shown in this study, knockdown of TbARF1 by RNA interference has no effect on fluid-phase endocytosis in the insect stage of the parasite. The protein remains essential for the viability of these procyclic cells but the major effect of TbARF1-depletion is enlargement of the lysosome. Our data indicate that protein trafficking and lysosomal function are differentially regulated by multiple factors, including TbARF1, during progression through the T. brucei lifecycle.
1
Introduction
The secretory system plays a critical role in the efficient sorting and transport of a range of products to the surface of all eukaryotic cells. The small GTPase Arf1 is a vital component of this system, localizing to the Golgi apparatus in yeast and mammalian systems where it has highly conserved functions in vesicular assembly and the activation of phospholipid-modifying enzymes [1]. An exception is the orthologue of Arf1 (TbARF1) that has been recently characterized functionally in the bloodstream form (BSF) of the kinetoplastid parasite Trypanosoma brucei
[2], a cell type which supports extremely rapid rates of internalisation and recycling of its major outer membrane protein, variant surface glycoprotein (VSG; [3]). Although TbARF1 localizes at or proximal to the Golgi apparatus in these parasites, the primary effect of TbARF1 knockdown is severe inhibition of the endocytic system, leading to cell death. In contrast, overexpression of a constitutively active GTP-bound form of TbARF1 inhibits protein trafficking from the Golgi apparatus to the lysosome [2].
Here we show that the vital role of TbARF1 in endocytosis is not conserved throughout the T. brucei lifecycle. In the more sedentary insect procyclic form (PCF) of the parasite, ARF1 is required for cell viability but depletion of the protein by RNA interference (RNAi) has no effect on the uptake of dextran by fluid-phase endocytosis. Instead, the lysosome becomes enlarged, although degradation of the protein p67 within this organelle is not significantly impaired.
2
Materials and methods
2.1
Disruption of TbARF1 expression by RNAi
The plasmid p2T7ARF1 [2] contains a non-conserved region of the T. brucei open reading frame (residues 101–225) between two opposing T7 promoters under the control of tetracycline repressors. Mid-log phase parasites of T. brucei procyclic cell line 29–13 [4] were electroporated with 10 μg of NotI-digested p2T7ARF1 using methods described previously [5] to produce the stable cell line 29-13/p2T7ARF1. Expression of ARF1-specific dsRNA was induced by incubating parasites in 100 ng/ml tetracycline. Expression of TbARF1 was monitored by quantitative PCR, using SYBR Green Mastermix (Applied Biosystems). Total RNA was extracted from parasites using TRIzol Reagent (Invitrogen), treated with DNase I (Ambion) and reverse-transcribed using Omniscript RT (Qiagen). A 66 bp fragment of TbARF1 was amplified using SYBR Green Mastermix (Applied Biosystems) on a Prism7000 (Applied Biosystems) and compared to levels of a constitutively expressed control, α-tubulin. Oligonucleotides for amplification were: TbARF1 RTF1, 5′-GGCTTCCGCTTTCAAATCC-3′, TbARF1 RTR1, 5′-CATCAAGGCCGACCATAAGAA-3′, TbαTub RTF1, 5′-CGTGAGGCTATCTGCATCCA-3′ and TbαTub RTR1, 5′-CCCAGCAGGCGTTACCAA-3′.
2.2
Microscopy
Indirect immunofluorescence assays on parasites were performed as described [6]. Primary antibodies were gifts as follows: rabbit anti-TbGRASP from Graham Warren (Department of Cell Biology, Yale University School of Medicine, New Haven, CT, USA), mouse anti-p67 from Jay Bangs (Department of Medical Microbiology and Immunology, Madison, WI, USA). Primary antibodies were detected using Alexa-Fluor 488 or 633-conjugated secondary antibodies (Invitrogen). Samples were visualized by confocal microscopy using a Zeiss LSM 510 meta with a Plan-Apochromat 63x/1.4 Oil DIC I objective lens and images acquired using LSM 510 version 3.2 software (Zeiss).
For transmission electron microscopy, cells were sequentially treated in 1% (w/v) glutaraldehyde for 1 h, 1% (w/v) tannic acid for 10 min, 0.5% (w/v) osmium tetroxide for 45 min (all in 100 mM phosphate buffer), then in 1% (w/v) aqueous uranyl acetate for 1 h. After dehydration in an acetone series, cells were embedded in Spurrs resin. Sections were cut on a Leica Ultracut, stained with saturated uranyl acetate in 50% ethanol and Reynolds lead citrate, and viewed with a Tecnai 12 BioTwin (FEI) at 120 kV. Images were acquired with a SIS MegaView III digital camera.
2.3
Trafficking assays
To monitor the uptake of dextran, cells were resuspended at 5 × 107/ml in 1 mg/ml Alexa-Fluor 488-conjugated dextran (Invitrogen) in SDM-79 medium and incubated for 20 min at 26 °C. For microscopy, parasites were then fixed in 4% paraformaldehyde and co-stained with DAPI, as described previously [7]. For FACS analysis, cells were fixed as above, then resuspended in PBS at a density of 5 × 107/ml. Samples were then divided into two equal aliquots and anti-488 antibody (1:100, Invitrogen) added to one aliquot for 30 min at RT, before washing in PBS. Fluorescence was measured on a Dako Cytomation CyAn flow cytometer using the FL1 detector and results analyzed with Summit v4.1 software.
ER-to-lysosome trafficking and subsequent degradation of the protein p67 was analyzed by metabolic labelling of cells and immunoprecipitation as described previously [8].
3
Results
3.1
TbARF1 is essential for viability in T. brucei procyclic cells
We studied the functions of TbARF1 in PCF cells by knocking down expression using tetracycline-inducible RNAi. Detection of phenotypic effects arising from TbARF1 knockdown in these cells was considerably delayed in comparison to BSF parasites [2] in which cell death was evident by 24 h post-induction. This probably reflects a lower dependence on rapid protein trafficking in the PCF cell, as observed previously [9]. There was no significant decrease in PCF cell division until 90 h post-induction, when the cells started to become rounded and less motile, followed by cell death from 96 h (Fig. 1A, B). Quantitative PCR showed a significant loss of TbARF1-specific RNA by 16 h post-induction (Fig. 1C). Induced cells had no significant differences in nucleus and kinetoplast configurations between uninduced and induced cells over a 120 h time course (data not shown), indicating no major defects in the regulation of cell division.
3.2
Loss of ARF1 has no effect on fluid-phase endocytosis
The effects on fluid-phase endocytosis in these TbARF1-depleted procyclic cells were determined by analyzing the uptake of fluorescently labelled dextran. Analysis by microscopy showed internalization of dextran in both uninduced parasites and cells induced with tetracycline for 96 h (data not shown). We also used a novel FACS-based method to distinguish between external binding and internalization of dextran in these cells (Fig. 2A). Cells were incubated in the presence of Alexa Fluor 488-conjugated dextran, before fixing and incubation in anti-488 antibody which quenches the fluorescence associated with externally bound dextran. No significance differences were observed between uninduced and induced cells, either in total dextran fluorescence or internalized dextran alone (i.e. following antibody treatment) (Fig. 2A).
3.3
Loss of ARF1 causes an enlargement of the lysosome
Immunofluorescence assays using the lysosomal marker, p67, showed that TbARF1-depleted procyclic parasites have an enlargement of the lysosome (Fig. 2B). Densitometric analysis of 20 cells per experimental group revealed that p67 staining in cells induced for 96 h covered an area approximately three times as large as in uninduced cells. No differences were seen in the size or position of the Golgi apparatus, as indicated by localization of the GRASP protein (Fig. 2B). The main defect visualized by transmission electron microscopy was the presence of a single electron-dense rosette-like structure near the flagellar pocket in a subset (30%) of cells by 72 h post-induction, before detection of a growth defect in these cells. This structure (Fig. 2C–E) is composed of concentric layers of membrane and resembles that of membranous cytoplasmic bodies (MCBs) found in mammalian cells. These are lysosomal in origin and typically found in sphingolipidoses, a group of lysosomal storage diseases characterized by deficiencies in the lysosomal enzymes required for sphingolipid degradation [10]. Similar structures of mitochondrial origin have also been observed in T. brucei following depletion of the glycosomal PEX11-like protein GIM5B [11]. However, we found no obvious differences in mitochondrial structure in procyclic cells following the induction of TbARF1 RNAi, either by electron microscopy or fluorescent staining with Mitotracker (Invitrogen, data not shown). We therefore conclude that the structure detected in Fig. 2 is lysosomal in origin, although proof of this designation requires immuno-electron microscopy with a lysosomal probe.
3.4
Effects of ARF1 depletion on lysosomal trafficking and degradation of p67
The trafficking and degradation of p67 was analyzed by pulse-chase metabolic labelling of cells, followed by immunoprecipitation, as described previously [5,8]. In contrast to this process in BSF parasites, ER- synthesized p67 is not further modified in the Golgi in PCF cells, but proteolytic cleavage in the lysosome results in the generation of four glycosylated fragments: gp75, gp42, gp32 and gp28 [8]. In Fig. 2F, proteolytic fragments resulting from lysosomal degradation accumulated in uninduced and induced cells over the same time course, indicating that both trafficking of p67 and its subsequent degradation were unimpaired in cells depleted of TbARF1 protein (Fig. 2F).
4
Discussion
The observations presented here and described previously [2] show that TbARF1 is essential for viability throughout the T. brucei lifecycle. However, the downstream effects of modulating expression of this protein differ between the two major life cycle stages. In the highly active bloodstream form, TbARF1 is required for both receptor-mediated and fluid-phase endocytosis, and depletion of the protein by RNAi causes a severe defect in these mechanisms, rapidly followed by cell death [2]. In contrast, procyclic cells significantly downregulate the expression of proteins associated with receptor-mediated endocytosis, including clathrin [12], and fluid-phase endocytosis is unaffected by the knockdown of TbARF1 expression. Rather than affecting the endocytic system, the absence of TbARF1 causes enlargement of the lysosome, indicating an accumulation of undegraded and/or mistargeted material.
An interesting finding is that lysosomal targeting of p67 is not disrupted by knockdown of TbARF1 in either of the two life cycle stages but is inhibited by an increase in activated ARF1 protein in BSF parasites [2]. Lysosomal targeting in trypanosomes is not yet fully understood but must differ from higher eukaryotes in several respects. In mammals, most soluble lysosomal enzymes, including acid hydrolases, are sorted using mannose-6-phosphate signals [13] but there is no evidence to suggest that this mechanism is conserved in lower eukaryotes such as the kinetoplastids [8]. An alternative to the mannose-6 receptor, sortilin, has also been implicated in hydrolase sorting [13] but orthologues of this protein are not encoded by the T. brucei genome. The remaining class of identified signals, either tyrosine or dileucine-based motifs, are able to bind to GGA and AP adapter proteins, which then trigger the packaging of cargo for clathrin-mediated trafficking from the trans-Golgi network (TGN) to the lysosome. Both GGA and AP proteins are directly recruited to the Golgi apparatus by GTP-bound Arf1 [14]. However, genes encoding GGA proteins and AP-2 are absent from the T. brucei genome [2,15,16], implying either a greater dependence on the remaining AP-1 and AP-3 complexes or the operation of additional uncharacterized sorting mechanisms.
Much of our current knowledge of lysosomal targeting in T. brucei stems from studies on p67, a protein of unknown function that shares structural similarity but not sequence identity with the mammalian LAMP proteins [17]. This protein is trafficked from the ER via the Golgi to the lysosome, utilizing distinct targeting signals in different stages of the parasite life cycle. In PCF cells, the C-terminal cytoplasmic domain of p67 is necessary and sufficient for lysosomal targeting, whereas this region is not required in BSF cells [8]. While the cytoplasmic domain contains two putative dileucine motifs [8], a recent study has analyzed these regions functionally in PCF and demonstrated that both are required to support maximal targeting [18]. It is likely that p67 is sorted in PCF cells via binding to either AP-1 or AP-3 at the TGN, a process that could be inhibited by knockdown of ARF1. While the data presented here suggest that p67 continues to be targeted efficiently for degradation in the lysosome, this does not preclude a defect in the sorting or trafficking of other essential lysosomal factors.
In BSF parasites, the AP complexes may not be directly involved in p67 sorting, given the redundancy of the dileucine motifs in this life cycle stage [8]. As in PCF cells, knockdown of TbARF1 does not prevent p67 localization to the lysosome [2]. However, in contrast, the inducible expression of a constitutively active form of TbARF1 does not inhibit endocytosis but prevents p67 from reaching the lysosome, resulting in extremely rapid cell death [2]. In this situation, excess ARF1 may be sequestering effector proteins required for alternative pathways which would normally facilitate the correct localization of p67.
Given that forward biosynthetic trafficking to the lysosome is not quantitatively impaired in the absence of ARF1, this protein may alternatively influence the recycling of membranes from the lysosome, a defect that might generate multivesicular-type structures such as those observed at higher resolution in Fig. 2. Further studies are required to pinpoint the relationship between TbARF1 and lysosomal function, analysis which may reveal insights into the secretory pathways of lower eukaryotes.

arf1
lysosome
endocytosis
trypanosoma brucei
arf, adp ribosylation factor
bsf, bloodstream form
pcf, procyclic form
rnai, rna interference
t. brucei, trypanosoma brucei