Perception of a moving visual stimulus can be suppressed or enhanced by surrounding context in adjacent parts of the visual field. We studied the neural processes underlying such contextual modulation with fMRI. We selected motion selective regions of interest (ROI) in the occipital and parietal lobes with sufficiently well defined topography to preclude direct activation by the surround. BOLD signal in the ROIs was suppressed when surround motion direction matched central stimulus direction, and increased when it was opposite. With the exception of hMT+/V5, inserting a gap between the stimulus and the surround abolished surround modulation. This dissociation between hMT+/V5 and other motion selective regions prompted us to ask whether motion perception is closely linked to processing in hMT+/V5, or reflects the net activity across all motion selective cortex. The motion aftereffect (MAE) provided a measure of motion perception, and the same stimulus configurations that were used in the fMRI experiments served as adapters. Using a linear model, we found that the MAE was predicted more accurately by the BOLD signal in hMT+/V5 than it was by the BOLD signal in other motion selective regions. However, a substantial improvement in prediction accuracy could be achieved by using the net activity across all motion selective cortex as a predictor, suggesting the overall conclusion that visual motion perception depends upon the integration of activity across different areas of visual cortex. Segregating moving objects from their background and from each other is an essential step in visual processing, and the key source of information for achieving this is retinal image motion Given these characteristics of retinal image motion, one low level spatial mechanism that could successfully detect object motion consists of a centre-surround organisation that compares the direction of motion in the centre and surround, with the result of the comparison being that overall activity is reduced when centre and surround motion directions are similar, and enhanced when central motion direction differs from surround motion direction. Such centre-surround spatial processing arrangements appear to exist at multiple levels of the visual system, and are not confined to the processing of motion information. Below, we briefly review evidence for centre-surround organization of the visual receptive fields of individual neurons, as well as centre-surround effects in psychophysical and brain imaging experiments that are produced by the net activity of a population of neurons whose individual receptive fields are not spatially aligned.Nakayama & Loomis Recent psychophysical and fMRI studies have also investigated centre-surround interactions in motion processing, effects which can be traced back at least to the work of Loomis and Nakayama The results of the psychophysical studies described above reflect modulation of neural activity at the population level, consistent with the existence of centre-surround organization at the population level, which might be produced by lateral connections between individual visual neurons within visual topographic maps, or by connections between multiple topographic maps. In principle these effects should be detectable using fMRI by exploiting the topographic organization of visual areas. In an fMRI study of surround modulation using static sinusoidal gratings, Williams et al. To establish that our brain imaging findings reflected actual motion perception we used the BOLD signal results to predict a perceptual effect, measured outside the scanner with a different set of participants. Prolonged viewing of an area of uniform motion produces a motion after effect (MAE), in which illusory motion is generated in the opposite direction to the motion that was stared at. Previous reports have indicated that the strength of the MAE can be influenced by the presence of a surround Two sets of visual stimuli were used in this experiment, all presented to participants within the same sessions. The first set was used to define regions of interest (ROIs) in which the BOLD response to an alternately expanding and contracting circular grating presented in the central 3° of the visual field was greater than the response to a static grating. We used radial motion as an alternative to translation because, in a pilot study, translating stimuli of the necessary duration produced optokinetic nystagmaus, which prevented effective fixation. Since radial motion translates equally across all directions, fixation is more stable and it allowed us to successfully segregate the centre and surround responses. The use of radial motion was also helpful in generating a strong BOLD response to our central motion area - radially moving dots have been shown to elicit greater BOLD responses than translation at small eccentricities within the lower visual areas moving centre – static centre contrast was conservative , while the threshold for the moving surround – static surround contrast was liberal , resulting in exclusion of voxels that had even a weak motion selectivity for surround stimulation. Thus we ensured that the ROIs contained populations of visual neurons whose topographic mapping corresponded only to the central stimulus.As well as the moving and static stimuli that directly stimulated the central 3° of the visual field, the first set also included circular gratings, either alternating between expansion and contraction or static, presented within an annulus, whose width was 9°, surrounding a central 3° diameter blank area. These two stimuli were used to isolate voxels where the BOLD response to surround motion was greater than that to a static surrounding grating. In the final stage of ROI definition, voxels that showed motion selectivity for both central and peripheral presentations were excluded from the ROI. The statistical threshold for the The second set of stimuli was designed to test for inhibition or facilitation of the BOLD response in the ROIs by motion in the surrounding region. One stimulus consisted of uniform expansion and contraction spanning both the central and surround regions, while in a second stimulus the surround motion was in antiphase with the central motion. We repeated the presentation of the central motion stimulus alone to provide a baseline measure of the BOLD signal change in the ROI, produced by direct stimulation of the central regions without surround modulation, which was independent of the stimuli used in ROI definition. The final stimulus was the fixation cross alone. We reasoned that suppression by a surround would result in a lower signal in the ROI than that produced by the central stimulus alone, while facilitation by a surround would produce a larger signal. The various centre/surround combinations are shown in 2) and the darker background (18.2 cd/m2) calculated as (Lmax−Lmin)/(Lmax+Lmin). The luminance of the background when only the fixation point was shown was also 18.2 cd/m2. Each ring was 0.5° wide, and there were 0.5° gaps between rings, defined by the same luminance as the general background, resulting in a spatial frequency of 1 cycle/deg.The stimuli were generated using Matlab and PsychToolBox Stimuli were presented in a block design, in which each block lasted 16 sec. Each experimental condition was repeated seven times. The experiment was divided into three sessions, each lasting 370 sec. We chose to divide the total scan time in this way to maximise the efficiency of fixation by allowing periods for recovery and movement of the eyes at reasonable intervals.MRI images were obtained using the standard twelve-channel head array coil of a 3-Tesla Siemens Magnetom Trio scanner. Functional volumes consisted of 36 interleaved coronal slices positioned to cover the occipital and parietal lobes  with zero interslice gap, TR 2500 ms, TE = 30 ms, and flip angle = 90 deg. Prospective motion correction (PACE) was used to track any small head movements made by participants, allowing slices to be automatically repositioned between each TR The data from each participant were processed with Statistical Parametric Mapping software (SPM2). The SPM2 motion correction algorithm was applied to identify any scans in which inter-scan motion had not been negated by PACE prospective motion correction. No such scans were identified. Volumes from each scanning session were all co-registered to the first volume of the first scan session to adjust any differences in head position or orientation between sessions. The functional series was registered to the high-resolution anatomical image before the latter image was normalized to the Montreal Neurological Institute (MNI) template. The transformation parameters computed to achieve this normalization were also applied to the functional data series. Spatial smoothing was limited to a 4 mm kernel in order to avoid blurring the boundary between direct topographic activation produced by the central stimulus and that produced by the surround. The time series was high-pass filtered using a 128 sec cut off. The activation for each participant was modelled using a linear combination of functions obtained by convolving the known temporal profile of the experimental conditions with the standard haemodynamic function of SPM plus its time derivative. As the TR was relatively short, temporal autocorrelation between volumes was a potential problem, and so the SPM2 correction for serial correlations was applied. After using contrasts to functionally define ROIs, percentage signal change caused by the experimental stimuli was extracted using the MarsBaR (MARSeille Boîte À Région d'Intérêt), Participants gave their informed consent in accordance with the standard procedure of the Combined Universities Brain Imaging Centre (CUBIC). All the experiments reported here were approved by the Research Ethics Committee of The University of Reading.Six healthy volunteers, aged 20–33, with normal or corrected vision took part in this experiment. All were familiarised with the stimuli and the demands of fixation during an initial training session on the day before they were scanned. All volunteers had previous experience in the MRI environment, and appreciated the need to remain still and fixate. They were screened according to standard procedures and written informed consent was obtained. They were paid for their time.In this experiment we asked whether surround modulation of the BOLD response in ROIs defined in the same way as Experiment 1 was reduced or abolished by inserting a gap between the directly stimulated region and the surround region. In a psychophysical investigation of surround modulation of motion processing, Kim and Wilson 2 for the background, 5.27 cd/m2 for the darker rings, and 18.3 cd/m2 for the brightest rings. Therefore, the Michelson contrast defining the moving pattern was 55%.The second aim of Experiment 2, in conjunction with Experiment 3, was to determine the relationship between the effects of surround modulation on the BOLD response and its effects on a perceptual measure. We used the static motion after effect (MAE – the apparent motion of a stationary test field which follows prolonged inspection of a moving adaptation field) as a measure of the perceptual effects of surround modulation, reported here as Experiment 3. Pilot studies revealed that measurable modulation of the MAE by surrounds could only be obtained if the perceptual salience of the surround was increased to match that of the centre. In order to maintain direct comparability between the stimuli used to elicit BOLD responses and those used to measure perceptual effects we also increased the perceptual salience of the surrounds used in Experiment 2. In both experiments, this was achieved by M-scaling the stimuli. Previous studies of lateral interactions within visual areas have also employed stimuli that control for the cortical magnification factor in this way 2) in the same position as the boundary created by opposing motion directions in the antiphase condition.Finally, we addressed a potential confound arising from the presence of a visible boundary between centre and surround in the antiphase surround condition of Experiment 1. The uniform surround condition did not contain a boundary, and therefore any observed differences in BOLD signal between the antiphase and uniform surround conditions might be due either to the different motion direction or the boundary. In Experiment 2, to isolate the effect of a boundary we included a version of the uniform stimulus with an imposed boundary between centre and surround. The boundary consisted of a static circle  at 500 Hz, with a spatial resolution of 0.1 degree. We replicated the various stimuli used in both fMRI experiments. Stimuli were presented in 16 sec blocks, as in the fMRI experiment, and the task was simply to gaze steadily at the fixation point.We first identified the total set of ROIs  made up of motion-sensitive voxels from occipital or parietal cortex that responded directly to central motion, but did not respond directly to surround motion. We then identified the subset of these regions that fell within hMT+/V5 . Our third set of ROIs was made up of voxels in early visual areas V1/V2 that corresponded retinotopically to the central stimulus area. moving centre - static centre  and then excluded voxels if their activity was significant in the contrast moving surround - static surround, . We also excluded any voxels where the initial beta weight for the moving central stimulus was negative. The moving > static contrast is a standard technique for identifying motion selective areas, particularly hMT+/V5 3, SE 11 mm3). The procedure resulted in ROIs that were in striate, extrastriate, and parietal cortex, that had some degree of retinotopic organisation and in which a moving stimulus produced a stronger signal than a static one.To isolate the central motion ROIs, we first used the contrast moving centre – static centre contrast, setting the False Discovery Rate at p = 0.001. This resulted in one significantly activated cluster within each hemisphere that lay close to the ascending limb of inferior temporal sulcus and lateral occipital sulcus. This is in agreement with the MT location reported in previous studies 3, SE 14 mm3).To define the subset of the central motion ROIs corresponding to hMT+V5, we first defined hMT+/V5 in each hemisphere as the cluster with the strongest activation in the static central - rest , with voxels activated even weakly (p<0.05 uncorrected) by the static surround excluded. This produced a cluster in the calcarine fissure which combined the central visual field representations of V1 and V2. For each hemisphere only the region with the peak activation, which was also the cluster with the most voxels in/around the calcarine fissure was included in the analysis .We used the fact that early visual cortex has a strong response for static visual stimuli and fine retinotopic organisation to produce an independent localizer for that region. V1/V2 ROIs were defined by the contrast We calculated the percentage BOLD signal change in each of the central motion ROIs under the three experimental conditions . p < 0.001. The effect size of the difference was 0.43 . Antiphase motion in the surround significantly increased the percentage signal change compared to the central motion only baseline, t (78) = 5.75, p < 0.001, with Cohen's d = 0.65.The mean signal change in the set of central motion ROIs for the uniform motion surround and the antiphase surround relative to the central motion baseline is shown in p < 0.001, and d = 0.81. Antiphase motion in the surround significantly increased the percentage signal change compared to the central motion baseline, t (27) = 4.05, p < 0.001, d = 0.77. The effect sizes were larger in the hMT+/V5 central motion ROIs than in the full set of motion selective regions due to relatively lower variation between regions in hMT+/V5.The mean signal changes in the set of hMT+/V5 central motion ROIs for the uniform motion surround and the antiphase surround relative to the central motion baseline are shown in p < 0.05, d = 0.66. Antiphase motion in the surround significantly increased the percentage signal change compared to the central motion baseline, t (11) = 6.36, p < 0.001, d = 1.83. The very large effect size for the antiphase versus baseline comparison was due to the combination of a relatively large mean difference and relatively low variation between regions.The mean signal changes in the set of V1/V2 ROIs for the uniform motion surround and the antiphase surround relative to the central motion baseline are shown in 3, SE 27 mm3), 14 of which were hMT+/V5 ROIs , and 12 V1/V2 ROIs  that were selective for the central 3 degrees of the visual field, but not specifically motion selective. In order to permit necessary statistical comparisons, we also defined a fourth category of ROI, consisting of the 50 motion selective areas that were not included in the hMT+/V5 subcategory, which we refer to as central motion not MT+/V5 ROIs . The stereotaxic coordinates in MNI space of the hMT+/V5 and V1/V2 ROIs are given in ROIs were defined using the same procedures as Experiment 1, the only difference being that the stimuli were M-scaled (see Methods). In 12 hemispheres, we identified 64 central motion ROIs  = 2.60, p < 0.05, d = 0.32. Conditions with imposed static boundaries or gaps produced a BOLD signal that was not significantly different from baseline, consistent with abolition of surround modulation. Further statistical evidence that surround modulation was abolished by gaps is provided by the direct comparison between antiphase surround motion with and without a gap, which was significant, t(63) = −2.22, p < 0.05, d = −0.28, as was the comparison between uniform surround motion with and without a gap, t(63) = 2.40, p < 0.05, d = 0.30. Imposing a static boundary between centre and surround in the uniform motion condition abolished suppression relative to the central motion baseline, whereas the increase in the BOLD signal relative to uniform motion without a boundary approached significance, t(63) = −1.62, p = 0.11, d = 0.20. While the presence of a boundary acted to prevent suppression, inspection of Considering first the full set of 64 motion selective regions, only the uniform and antiphase conditions resulted in a BOLD signal that was significantly different from the central motion baseline, uniform t(63) = −2.03, p<0.05, d = 0.31, while there was a trend towards significance for the reduced BOLD signal in the uniform motion condition, t(49) = −1.87, p = 0.067, d = 0.26. None of the conditions with gaps or boundaries produced a BOLD signal that was significantly different from baseline. Further statistical evidence that surround modulation was reduced by gaps is provided by the direct comparison between antiphase surround motion with and without a gap, which was significant, t(49) = 3.04, p < 0.01, d = 0.42 as was the comparison between uniform surround motion with and without a gap, t(49) = 2.45, p < 0.05, d = 0.34.The 50 motion selective regions see  that werp < 0.05, d = 0.68. These results are in agreement with the role of MT in integrating motion signals across visual space.Turning to the subset of motion selective regions that were identified as hMT+/V5 central motion ROIs, the pattern of results is qualitatively different see  from thap < 0.05, d = 0.53 and the uniform gap condition, t(53.07) = 2.19, p < 0.05, d = 0.55. However, there was no significant difference in BOLD response to the antiphase gap condition, t(42.48) = 1.331, NS, d = 0.46.To confirm that the effect of the boundary and the gaps on BOLD responses in hMT+/V5 was different from their effects in the other motion selective ROI we made direct comparisons between the two categories of ROI using independent samples t tests. Levene's test indicated that the variance of the two ROI categories was significantly different in all the following comparisons, and therefore t statistics and degrees of freedom have been adjusted to take this into account. As suggested by p < 0.001, d = 1.39. This condition also produced higher signal change than the uniform surround, t(11) = 3.33, p < 0.01, d = 0.96, and the uniform surround with an imposed boundary, t(11) = 2.59, p < 0.05, d = 0.74. In contrast to the comparable condition of Experiment 1, uniform surround motion did not produce suppression relative to the baseline.In the V1/V2 ROIs, only antiphase surround motion produced strong and significant surround modulation when compared to the central motion baseline, t(11) = 4.81, Experiment 2 revealed that introducing a 1.5° gap between centre and surround generally removed surround modulation effects on the BOLD signal, with the notable exception of ROIs located within hMT+/V5, where surround modulation persisted despite the gap. The fMRI results suggested two possible effects of the gap in the MAE experiment. If the MAE depends primarily on processing in the specialized motion processing region hMT+/V5 then surround modulation of the MAE would not be influenced by the gap. However, if the MAE depends upon the net firing rates across motion selective areas of cortex, then the gap would abolish surround modulation of the MAE.MAEs were stronger when the direction of motion in the central region of the adapting stimulus was contracting p < 0.05, d = −0.84, antiphase t(11) = 2.34, p < 0.05, d = 0.67. Conditions with imposed static boundaries or gaps produced an MAE that was not significantly different from baseline, consistent with abolition of surround modulation. Further statistical evidence that surround modulation was abolished by gaps is provided by the direct comparison between uniform surround motion with and without a gap, which was significant, t(11) = −2.67, p < 0.05, d = −0.77, as was the comparison between antiphase surround motion with and without a gap, t(11) = 2.44, p < 0.05, d = 0.71. Imposing a static boundary between centre and surround in the uniform motion condition abolished suppression relative to the central motion baseline, but the increase in the MAE relative to uniform motion without a boundary only approached significance, t(11) = 2.12, p = 0.058, d = 0.61.The pattern of MAE results was strikingly similar to the pattern of BOLD signal changes obtained for the full set of central motion ROIs in fMRI Experiment 2. This suggests that the subjective MAE reflects the net activity across all motion selective areas of visual cortex rather than hMT+/V5 specifically, because hMT+/V5 had a different pattern of BOLD responses to the centre-surround stimulus configurations we used . StatistAlthough the MAE and fMRI experiments were conducted with different groups of participants, and the MAE is an indirect measure of centre-surround interaction, the pattern of results for the MAE was strikingly similar to the BOLD signal averaged across the full set of motion selective regions that we identified. We made the assumption that the BOLD signal, as a proxy for neural activity during adaptation, predicted the MAE, rather than the other way around. To render the BOLD data from the four different ROI categories and the MAE data directly comparable, we converted both sets of data to the percentage change from their respective central motion baselines. Thus, the change in the BOLD signal relative to baseline in each of the five surround conditions was converted to a percentage of the raw percent signal change observed in the baseline itself . For the MAE, the differences were converted to a percentage of the mean strength rating received by the baseline . An example of this, illustrating the good correspondence between the MAE and BOLD in the central motion ROIs can be seen in To provide a quantitative comparison, for each ROI category in turn, linear regression was used to predict the MAE from the BOLD signal. For regression lines to describe a meaningful relationship between BOLD and the MAE the fits had to conform to an underlying assumption that when the BOLD signal  was at its baseline level, the MAE should also be at its baseline level. Therefore, we required the fitted regression lines to pass through the origin. A further assumption required for BOLD to meaningfully predict the MAE was that when BOLD was below baseline, the MAE should also be below baseline, and when the BOLD signal was above baseline the MAE should also be above baseline. This constrained us to exclude any fitted regression lines with negative slopes (there were none).The best fit was obtained using the BOLD signal from the full set of 64 motion selective ROIs as the predictor. This is shown in 2 quantifying the strength of the relationship between BOLD and MAE. Across the 10000 subsamples both the R2 values and the number of voxels showed substantial variation, the mean R2 being 0.45 (SD 0.24) and the mean number of voxels in a set of 14 regions being 152 (SD 94), but almost none of this variation was shared (Pearson's r = 0.029).While the subsampling procedure controlled for the influence of the number of ROIs on the model fit, the hMT+/V5 ROIs were on average smaller than the other motion selective regions, and exhibited less variability in size (see above). Therefore, each subsample of 14 regions typically contained a larger number of voxels than the hMT+/V5 set, and the greater signal averaging may have resulted in a less noisy BOLD signal than that obtained from hMT+/V5. If this was the case then it is possible that our analysis overestimated the predictive power of the non-hMT+/V5 motion-selective cortex relative to the hMT+/V5 regions. We checked for this possibility by asking whether variation in the total number of voxels in each random subsample of 14 regions covaried with the obtained fit to the MAE data. If the fit improved as the number of voxels increased, then this would indicate an important role of the number of voxels in determining the fit that was independent of the role of number of ROIs. In practice, this possibility was ruled out because there was no systematic linear or nonlinear relationship between total number of voxels used to predict the MAE and the RThe regression analyses for all the ROI categories are shown in p < 0.05, d = 1.27.If the extent of small deviations from fixation differed between experimental conditions, and the pattern of differences was similar to the differences in the BOLD signal and MAE between conditions, then eye movements might potentially form the basis of an alternative explanation for our findings. However, this is not borne out in the eye movement data presented in We located regions of the occipital and parietal lobes that responded to moving stimuli presented in the central visual field, and which were not activated by visual motion occurring at eccentricities of 1.5 deg or greater. BOLD responses in these regions were suppressed if a surround moved in the same direction, so that central visual stimulation became part of a large field of uniform motion. On the other hand, when the surrounding motion generated a motion-defined boundary with the central region, BOLD responses were facilitated. This mechanism potentially enables motion selective cortex to distinguish which of two otherwise identical local motion signals arise from background motion and which arises from object motion. In the context of the figure-ground segregation problem, the ability to integrate signals across a greater range, which we only found evidence for in hMT+/V5, may have a role in allowing regions of visual space to be grouped and classified on the basis of their direction of motion despite intervening areas of occlusion produced by objects closer than the depth plane of interest.Our other main finding was that the strength of MAE produced by adapting stimuli with the same center-surround configurations as those used in the fMRI experiment was most accurately described by a linear function of the BOLD signal averaged across all the motion selective cortex identified in the fMRI experiment. The most likely explanation of this linear result is that there is an approximately linear relationship between the BOLD signal and neural activity as often assumed by fMRI analysis methods Previous psychophysical studies of contextual modulation using both moving and static stimuli have found that the effects fall off as the spatial separation between the main stimulus and the context increases Given the emphasis on hMT+/V5 as the main centre of motion processing, it was a reasonable expectation that the effects of contextual modulation on a measure of motion perception would be predicted by the BOLD effects in hMT+/V5, rather than elsewhere in the brain. We did find that hMT+/V5 provided a better fit to the MAE than other motion selective regions if the number of other motion selective ROIs contributing to the fit was limited to be the same as the number of hMT+/V5 ROI. The fit provided by hMT+/V5 was also considerably better than that from the V1/V2 ROIs. This is consistent with the established view that hMT+/V5 is the main centre of motion processing. However, our findings also emphasize the role of other motion selective regions in generating the conscious experience of motion. When the averaged signal from the other motion selective regions was used to predict the MAE the achieved fit was actually higher than that for hMT+/V5 alone, and the best fit was achieved by averaging the signal from hMT+/V5 and the other regions. The involvement of centre-surround interactions in regions other than hMT+/V5 in generating the MAE has been also suggested by studies on surround interactions during binocular rivalry Previous investigations of surround modulation in human visual cortex have used static stimuli, and have focused on early retinotopic visual areas BOLD responses in visual cortex can be modulated by attention Turning to the top-down deployment of attention, we think it is unlikely that this process influenced our data. Participants were given the task of maintaining central fixation, and the eye tracking experiment indicated that they were largely successful in this. Nonetheless, it might be argued that attention was shifted (or distracted) covertly towards surrounds when they were present and formed some kind of boundary with the central region of the stimulus, and that such shifts accounted for variation in the BOLD response produced by the central stimulus. However, if participants did shift spatial attention towards surrounds in the gap conditions of Experiment 2, then the BOLD response to the central stimulus should have declined relative to the central stimulus only baseline. This should have happened regardless of whether surround motion was in or out of phase with motion in the central region. Contradicting this account, the BOLD response to the central region was simply uninfluenced by a surround if there was a gap, except in hMT+/V5, where there was an effect, but not of the unidirectional type predicted by this attentional distraction account. Furthermore, the attentional distraction account does not predict the different pattern of results we found in hMT+/V5 compared to other motion sensitive ROIs. Therefore, our results are more likely to reflect the low level mechanisms of stimulus driven saliency than top-down attentional selection.Our paradigm relies upon the topographic organisation of visual areas, as well as the ability to present visual stimuli at a consistent retinal location. The concentric pattern of expansion and contraction we used was specifically designed to remove any visual signal that would produce optokinetic nystagmus (OKN). Although attempting to fixate, participants inadvertently make small eye movements, and if these were more prevalent in some conditions our findings might be confounded by BOLD activation produced by making eye movements, or by changes in the topographic location of visual stimulation that eye movements cause. However, analysis of eye movements showed that this explanation of the findings is highly unlikely - ocular behaviour was similar in all the experimental conditions we tested. The small differences between conditions that did occur showed no consistent relationship with the experimental conditions, unlike BOLD and the MAE.Although our paradigm relied upon the well documented topography of visual areas to define ROIs, we did not acquire full retinotopic maps of our participants' visual systems. Such maps would have been of limited use in locating our functionally defined ROIs within specific visual areas because our test stimuli were foveal, and conventional retinotopic mapping possesses insufficient spatial resolution to separate the different visual areas within the central 2 deg of visual space. Given that we measured signal change in ROIs corresponding to the central 3 deg of visual space, retinotopic mapping data would, at best, have, allowed us to separate V3A/B from V1/V2, which would not have influenced our main conclusions. In fact, our ROI definition procedure was a simple form of visual eccentricity mapping because voxels were only included in a ROI if they responded to stimulation of the central 3 degrees of visual space, but did not respond to more eccentric visual stimulation. Centering our centre-surround stimulus at several degrees of eccentricity instead of at the fovea would potentially have allowed us to take advantage of information provided by retinotopic mapping. This was attempted in a pilot study, but due to the much smaller volume of cortex that represents peripheral visual space we were unable to locate voxels that responded to the centre of the stimulus but not the surround, which was a necessary criterion for testing our hypotheses.


Lack of understanding of the response of hepatocellular carcinoma (HCC) to anticancer drugs causes the high mortality of HCC patients. Bleomycin (BLM) that induces DNA damage is clinically used for cancer therapy, while the mechanism underlying BLM-induced DNA damage response (DDR) in HCC cells remains ambiguous. Given that 14-3-3 proteins are broadly involved in regulation of diverse biological processes (BPs)/pathways, we investigate how a 14-3-3 isoform coordinates particular BPs/pathways in BLM-induced DDR in HCC.via its associations with multiple pathway-specific proteins coordinates multiple pathways including chromosome remodeling, DNA/RNA binding/processing, DNA repair, protein ubiquitination/degradation, cell cycle arrest, signal transduction and apoptosis. Further, “zoom-in” investigation of the 14-3-3ε interacting network indicated that the BLM-induced interaction between 14-3-3ε and a MAP kinase TAK1 plays a critical role in determining cell propensity of apoptosis. Functional characterization of this interaction further revealed that BLM triggers site-specific phosphorylations in the kinase domain of TAK1. These BLM-induced changes of phosphorylations directly correlate to the strength of the TAK1 binding to 14-3-3ε, which govern the phosphorylation-dependent TAK1 activation. The enhanced 14-3-3ε-TAK1 association then inhibits the anti-apoptotic activity of TAK1, which ultimately promotes BLM-induced apoptosis in HCC cells. In a data-dependent manner, we then derived a mechanistic model where 14-3-3ε plays the pivotal role in integrating diverse biological pathways for cellular DDR to BLM in HCC.Using dual-tagging quantitative proteomic approach, we dissected the 14-3-3ε interactome formed during BLM-induced DDR, which revealed that 14-3-3ε Our data demonstrated on a systems view that 14-3-3ε coordinates multiple biological pathways involved in BLM-induced DDR in HCC cells. Specifically, 14-3-3ε associates with TAK1 in a phosphorylation-dependent manner to determine the cell fate of BLM-treated HCC cells. Not only individual proteins but also those critical links in the network could be the potential targets for BLM-mediated therapeutic intervention of HCC. Streptomyces verticillusHepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide with a high recurrence and therefore an extremely low 5-year survival rate etc14-3-3 proteins, the small, acidic, and abundant proteins with propensity to form both homo- and hetero-dimers, belong to a family of conserved regulatory molecules expressed in all eukaryotic cells Further, emerging data of the identified isoform-specific interactions has suggested that an individual isoform probably has its specific biological roles Previously, several reports identified a few putative proteins associated with 14-3-3ε through yeast two-hybrid approach or immunoprecipitation. For example, 14-3-3ε was found to interact with human calmodulin in a yeast two-hybrid approach, and their association may regulate cell signal transduction and cell proliferation Similar to what previously described Given that BLM is a DNA damage response (DDR) inducer, we first examined both dose- and time-dependent response of the 14-3-3ε stable HCC cells to BLM to determine the maximum DDR, where the DDR-associated composition of the 14-3-3ε interactome in BLM-treated HCC cells was profiled/determined. First, we evaluated the dose-dependent BLM effect on the HCC cells collected at 2 and 4 hours following BLM treatment, respectively. As the results shown in e.g., 4 hours, could promote elevated DDR but with apoptosis  was further validated based on the protein ratio variability of all quantifiable proteins, which were calculated by the latest version of Proteome Discoverer software  with a ‘SILAC Quantitation’ module  and DAVID (http://www.david.abcc.ncifcrf.gov) to search for their known functions, the BLM-induced 14-3-3ε interactors were found in multiple functional categories/biological processes (BPs) such as gene transcriptional regulation (14%), signal transduction (14%), DNA/RNA binding and splicing (20%), and protein degradation (12%) along with protein transport, cytoskeleton/cell motility, cell cycle, apoptosis, nucleoside metabolic process and protein translation  to promote DSBs repair Kip1 to cause cell cycle arrest We then biologically validated the physiological accuracy of newly MS-identified BLM-inducible 14-3-3ε interactors representing different BPs/pathways. The representative candidates of 14-3-3ε interactors include HDAC1, HDAC6, NONO, DDB1, TAK1 and TAB1. Their known relevance to cellular DDR is briefly described: Histone deacetylases (HDACs) such as HDAC1 and HDAC6 are known to be associated with other factors such as RBBP4, MTA2 and SMARCC2 to form macroprotein complexes that provide epigenetic control of gene expression By performing co-immunoprecipitation/immunoblotting experiments, proteins such as HDAC1, HDAC6, NONO, DDB1, TAK1 and TAB1 were indeed shown to be BLM-induced 14-3-3ε interactors -S5. Furthttp://string.embl.de/) to construct the BLM-induced 14-3-3ε interaction network , and the truncated forms of TAK1 including its kinase domain  and the C-terminal domain , and then co-transfected each one of these constructs respectively with FLAG-tagged 14-3-3ε into HCC cells. As the results shown in Phosphorylations at certain Ser/Thr residues of TAK1, including Thr184, Thr187 and Ser192 that reside in the TAK1-N domain (activation loop) We constructed a series of HA-tagged phosphoSer/Thr-depleted TAK1 mutants including T184A, T187A, and S192A in the TAK1-N domain, S268A involved in putative 14-3-3 binding motif, as well as S412A and S432A in the TAK1-C domain, in which each of the serine or threonine residues was substituted by alanine.Following the co-transfection of either wild-type (WT) TAK1 or each of the mutants respectively with FLAG-14-3-3ε, comparative immunoblotting analyses of the immunoprecipitates pulled-down by anti-FLAG beads from BLM-treated versus non-treated cells revealed the followings: Firstly, although BLM treatment clearly enhanced the bindings between 14-3-3ε and either wild-type (WT) TAK1 or each of the mutants at Ser412 or Ser432, no difference was found in terms of their binding strengths to 14-3-3ε . These rGiven that the known role of TAK1 in promoting cell survival (anti-apoptotic activity) when cells were exposed to various stresses and inflammatory cytokines etc. Based on our proteomic identification of the key composition of the BLM-induced 14-3-3ε interactome, the data-dependent functional categorization and interaction mapping analysis have revealed that 14-3-3ε indeed plays both central and coordinated roles in a network involving multiple pathways or BPs for signal transduction, chromosome remodeling, DNA/RNA binding and processing, DNA repair, cell cycle, protein ubiquitination/degradation, cytoskeleton and cell motility, and apoptosis  in the cell fate-determining sub-networks associated with the BPs of chromosome remodeling, DNA/RNA binding and processing, DNA repair, and protein degradation. Notably, our identification of the BLM-induced interactions of 14-3-3ε with nuclear localized proteins including HDAC1 and NONO In biological validation of the network-linked functions of 14-3-3ε interactions, we have further demonstrated that 14-3-3ε enhances its interactions with these pathway-specific/representing factors including HDAC1, NONO, and DDB1 in HCC DDR to high-dose BLM-induced apoptosis , implicaStrategically, as a discovery-to-hypothesis approach, our phenotype-specific interactome screening provides a systemic basis for “zoom-in” functional characterization of any novel proteins/enzyme interactions in the context of their cooperative and concerted actions within the 14-3-3ε interactome, which in turn facilitates understanding of phenotype-specific systemic function of the interactome on a pathway scale. As a result of our ‘pathway-scale’ characterization of the composition of the BLM-induced 14-3-3ε interactome, we have obtained a data-derived mechanistic model  that pro3) was purchased from Cambridge Isotope Laboratories ; Sequencing-grade trypsin was purchased from Promega . Fetal bovine serum (A15-151) was purchased from PAA laboratories ; Dialyzed FBS (26400-044), and lipofectamine 2000 (11668-019) were obtained from Invitrogen; G418 sulfate (345810) was purchased from Calbiochem; FLAG peptide (F3290) was purchased from Sigma-Aldrich; Bleomycin Sulfate (1076308) was purchased from United States Pharmacopeia (USP).Deuterium-labeled leucine  was purchased from Stratagene; anti-FLAG beads (A2220) was obtained from Sigma-Aldrich; 14-3-3ζ antibody (sc-1019) was purchased from Santa Cruz Biotechnology, Inc.; antibodies against 14-3-3 epsilon (ab40117), HDAC1(ab11966) and gamma-H2AX(ab22551) were obtained from Abcam; antibodies against NONO (11058-1-AP), DDB1(11380-1-AP), NCL (10556-1-AP), NFKBIA(IκB-α) (10268-1-AP) and p65(10745-1-AP) were purchased from Proteintech group, Inc.; TAK1(#4505), phospho-Chk1-S345(#2348), phospho-TAK1-T187(#4536), phospho-SPAK/JNK(Thr183/Tyr185) (#9251), phospho-ERK1/2(Thr202/Tyr204) (#9101), Caspase-3(#9662), cleaved Caspase-3 (#9661), and PARP1(#9542) antibodies were purchased from Cell Signaling Tech.; HDAC6 antibody (AP1106a) were purchased from Abgent.Primers for constructing all expressing plasmids are listed in Hepatocellular carcinoma (HCC) cell line QGY-7703, derived from primary hepatocellular carcinoma of a 35 years old female, was purchased from the Cell Bank of Chinese Academy of Science  Stable HCC cells were treated either with or without BLM at indicated dose or for different time periods, the whole cell lysates (WCL) was extracted by lysis buffer (Beyotime P0013) supplemented with protease inhibitor cocktail  and phosphatase inhibitor cocktail .To detect the nuclear translocation of p65 (NF-κB) in the HCC cells expressing FLAG-14-3-3ε with or without BLM treatment at indicated dose and time points, nuclear fractions were isolated by a nuclear isolation kit  according to the manufacturer's protocol.oC overnight to extract histone/γ-H2AX. After extraction, neutralize HCl with NaOH before the following use.For the isolation of histone/gamma-H2AX (γ-H2AX), cells were lysed by the lysis buffer  and 2 mM PSMF) on ice for 15 min. After centrifugation, wash the pellets twice with the lysis buffer. Added 0.2 M HCl into pellets and kept them at 43 isotope labeling has been described previously 9 HCC cells stably expressing FLAG-14-3-3ε grown in regular (unlabeled) RPMI-1640 medium were treated with 20mU/ml BLM for 2 h while the untreated cells were maintained in the medium containing Leu-d3. Each cell pool was lysed with the lysis buffer  supplemented with protease inhibitor cocktail  and phosphatase inhibitor cocktail , respectively. Equal amounts of protein derived from each cell pool were incubated with 350 μl anti-FLAG beads  at 4°C for 4 h to immunoprecipitate FLAG-14-3-3ε interacting proteins, respectively. After immunoprecipitation, anti-FLAG beads were washed four times with TBS buffer  to eliminate the non-specific binding. Immunoprecipitates were eluted by 100 ug/ml of FLAG peptide, then elutes were combined and concentrated to the appropriate volume for the following SDS-PAGE separation. The procedure of visible bands cutting, in-gel Trypsin digestion, and peptides extraction was performed as previously described The detailed procedure of Leu-d2O, 5% ACN, 0.1% formic acid), and peptides were eluted by 5% solvent B  for 5 min followed by a linear gradient to 45% solvent B in the next 90 min, ramping to 95% solvent B in 4 min and dropping to 90% solvent B for 4 min before re-equilibrating the system to 10% solvent B for 7 min. The entire eluant was sprayed into the mass spectrometer via a dynamic nanospray probe (Thermo Fisher Scientific) and analyzed in positive mode. The 3 most abundant precursor ions detected in the full MS survey scan  were isolated for further MS/MS analyzing. Spectra were acquired under automatic gain control (AGC) for survey spectra (AGC: 106) and for MS/MS spectra (AGC: 104). The spray voltage was 1.6 kV and the temperature of ion transfer capillary was 180°C.LC-MS/MS experiments were performed on a hybrid linear quadruple ion trap/Orbitrap (LTQ-Orbitrap) mass spectrometer (Thermo Scientific) coupled to a Shimadzu LC-20AD LC system  and SIL-20AC autosampler . Peptide mixtures were separated by a PICOFRIT C18 reverse-phase column  at a flow rate of 300 nL/min with a 110 min-gradient. Samples were loaded in solvent A (95% H3 (+3.0188), phosphorylated Ser/Thr/Tyr (+79.9663) and oxidized Met (15.9994). TurboSequest results were filtered by Xcorr, peptide probability and Delta Cn. Xcorr was set to 1.9, 2.6, 3.6 for 1+, 2+ and 3+ charged precursor ions, respectively; peptide probability<0.001, delta Cn >0.1, and keratin (most of them are contaminants) was excluded. The data's false positive rate was estimated under 1% by reverse database search, the reverse database was generated from IPI human 3.45 (71983 entries). Each protein identified requires the match of at least two unique peptides.Tandem mass spectra were extracted by Bioworks  and submitted to human International Protein Index database  using TurboSequest V.28 (rev. 13) search engine. Searches were performed with the following parameters: Trypsin enzyme specificity with allowing 2 missed cleavages; a precursor tolerance of 10 ppm and fragment tolerance of 1 Da; variable modifications include Leu-dThe light-to-heavy ratio (L/H) of each leucine-containing peptide was calculated using the extracted ion chromatogram (XIC) of both versions with PepQuan SILAC software integrated in Bioworks 3.3.1 SP1. For the default parameter, we selected leucine as the “modified” amino acid residue with a mass shift of 3.0188 Da between its light and heavy isotope version with the mass tolerance of 0.01Da. Protein ratio (L/H) was calculated by averaging the ratios of all leucine-containing peptides sequenced for each protein, and standard deviation (SD) was given to evaluate the accuracy of protein ratio. The proteins with their L/H SD larger than 20% were further validated by manually checking their MS signals. In addition, to assess the quality of our quantitative results, we re-processed all the raw data generated by LTQ-Orbitrap through the Proteome Discoverer platform , which integrates both Mascot and Sequest algorithms. Therefore, the output from different search engines can be cross-validated or merged to and maximize the protein identifications. The “Quantitation” node in Proteome Discoverer was used for global protein quantitation. Only unique peptides are used for protein ratio calculation. And the protein ratio is the median value of peptide ratios. The protein ratio variability is calculated as a coefficient-of-variation (CV) for log-normal distributed data. The quantitative results from Proteome Discoverer can be found in http://www.geneontology.org/) and DAVID (http://www.david.abcc.ncifcrf.gov) for categorization according to their previously known associations with different functions. Network analysis on our proteomic dataset by using STRING mapping tool (http://string.embl.de/), the scheme of BLM-induced 14-3-3ε interacting network was constructed by the Cytoscape 2.6 (http://www.cytoscape.org/).The all identified BLM-induced HCC-specific 14-3-3ε inteactors were submitted to Gene Ontology  specific to human 14-3-3ε were designed and synthesized by Shanghai GenePharma Co., Ltd, China. The siRNAs target sequences were as follows: #1, #2 and #3(5′-CGCUGAGUGAAGAAAGCUATT-3′/ 5′-UAGCUUUCUUCACUCAGCGTT-3′). Negative Control (NC) siRNA (AM4611) was purchased from Ambion, Inc. The siRNAs (40nM) were transfected into QGY-7703 cells using Lipofectamine 2000 transfection reagent. Cells were analyzed after 48 h transfection.SQ Membrane, 0.2 µm). The membranes were blocked with 5% nonfat milk and probed with specified primary antibody followed by incubation with secondary antibody conjugated with horseradish peroxidase, and then ECL substrate was added on membrane and exposed by ECL system.The procedures of protein isolation and immunoprecipitation were essentially the same as described in the purification of FLAG-14-3-3ε complexes for MS identification. The immunoprecipitated FLAG-14-3-3ε complexes were eluted by FLAG peptide or by directly boiling the anti-FLAG beads. Samples were separated by SDS-PAGE and transferred on PVDF membrane  is less than that of endogenous form in HCC cells. Due to FLAG tag is immediately fused on the N-terminus of 14-3-3ε and the short length nature of FLAG tag, the exogenous (FLAG-14-3-3ε) and endogenous 14-3-3ε were not able to be separated by conventional SDS-PAGE gel. To estimate the expression level of FLAG-14-3-3ε, we roughly normalized the densitometry of loading control (GAPDH) between parental cells and FLAG-14-3-3ε expressing cells according to immunoblotting (IB) assay as 1.0, and then, we determined that the expression level of FLAG-tagged 14-3-3ε is about 50 percent of that of endogenous 14-3-3ε. B. FLAG-14-3-3ε heterodimerized with endogenous 14-3-3ζ in FLAG-14-3-3ε expressing HCC.(PDF)Click here for additional data file.Figure S2Schematic design of dual-tagging quantitative proteomic approach. HCC cells stably expressing FLAG-14-3-3ε maintained in “light” (Leu) medium were treated with 20mU/ml BLM for 2h. In parallel, cells maintained in “heavy” medium (Leu-d3) were left untreated. The FLAG-14-3-3ε complexes were immunoprecipitated (IP) from the whole cell lysate derived from each cell pool using anti-FLAG beads and eluted by 1×FLAG peptide, respectively. IP products were mixed at 1:1 based on the total protein mass followed by SDS-PAGE separation, in-gel trypsin digestion, and LC-MS/MS analysis.(PDF)Click here for additional data file.Figure S3The plot of AACT/SILAC ratio distribution of identified peptides/proteins and the ratio variability of all quantifiable proteins. All the raw data generated by LTQ-Orbitrap (Thermo Scientific) was re-processed on Proteome Discoverer platform  and searched against the UniProt protein database . The top 32 most abundant proteins (PSM≥100 and unique peptide≥2) were highlighted by red diamonds. Those proteins with significant changes identified by both platforms (Bioworks v3.3.1SP1 and Proteome Discoverer v1.3) were indicated. As a result, over 94% of overall quantifiable proteins are showing less that 40% ratio variability, and more than 70% have variabilities less than 20%. Also the most abundant proteins in the complex have ratio varabilities varying from 3.2% to 21.9%. The average is at 13.39 (±4.88 SD). This variation is consistent with the Bioworks result and our manual inspection. Therefore, we chose the cutoff value at three times of the variability or 1.4.(PDF)Click here for additional data file.Figure S4Representative MS spectra of BLM-induced 14-3-3ε interactors.A. 14-3-3ε (bait protein). B. HDAC1. C. NONO. D. HDAC6. E. DDB1. F. MAP3K7 (TAK1). G. MAP3K7IP1 (TAB1).(PDF)Click here for additional data file.Figure S5MS and MS/MS spectra of the peptides matched to HDAC1. All raw data generated on LTQ-Orbitrap (Thermo Scientific) were re-processed using Proteome Discoverer software , which resulted in one more unique peptide (YYAVNYPLR) of HDAC1 compared to the previous results from Bioworks (v3.3.1SP1) search . The AACT/SILAC pairs of both peptides and a representative MS/MS are shown.(PDF)Click here for additional data file.Figure S6Ser412 on TAK1 was identified as a BLM-induced phosphorylation site by MS/MS. The MS/MS spectra of both phosphorylated and unphosphorylated sequence (SIQDLTVTGTEPGQVSSR) were shown.(PDF)Click here for additional data file.Table S1The list of all proteins identified in 14-3-3ε immunoprecipitates by MS.(XLSX)Click here for additional data file.Table S2Analysis and categorization of all quantified proteins identified in 14-3-3ε immunoprecipitates.(XLSX)Click here for additional data file.Table S3Categorization of the identified BLM-induced 14-3-3ε interactors and their associations with different biological processes or pathways. All of MS-indentified BLM-induced HCC-specific 14-3-3ε interactors were grouped into the specific biological processes (BPs) or pathways by using online database-Gene Ontology (http://geneontology.org/) and DAVID (http://david.abcc.ncifcrf.gov).(PDF)Click here for additional data file.Table S4The list of TAK1 sequence and primers for constructing the expressing plasmids.(PDF)Click here for additional data file.


Breast cancer outcome, including response to therapy, risk of metastasis and survival, is difficult to predict using currently available methods, highlighting the urgent need for more informative biomarkers. Androgen receptor (AR) has been implicated in breast carcinogenesis however its potential to be an informative biomarker has yet to be fully explored. In this study, AR protein levels were determined in a cohort of 73 Grade III invasive breast ductal adenocarcinomas.The levels of Androgen receptor protein in a cohort of breast tumour samples was determined by immunohistochemistry and the results were compared with clinical characteristics, including survival. The role of defects in the regulation of Androgen receptor gene expression were examined by mutation and methylation screening of the 5' end of the gene, reporter assays of the 5' and 3' end of the AR gene, and searching for miRNAs that may regulate AR gene expression.AR promoter is associated with loss of AR expression in breast cancer cells but not in primary breast tumours. In AR negative breast tumours, mutation screening identified the same mutation (T105A) in the 5'UTR of two AR negative breast cancer patients but not reported in the normal human population. Reporter assay analysis of this mutation however found no evidence for a negative impact on AR 5'UTR activity. The role of miR-124 in regulating AR expression was also investigated, however no evidence for this was found.AR was expressed in 56% of tumours and expression was significantly inversely associated with 10-year survival (P = 0.004). An investigation into the mechanisms responsible for the loss of AR expression revealed that hypermethylation of the This study highlights the potential for AR expression to be an informative biomarker for breast cancer survival and sets the scene for a more comprehensive investigation of the molecular basis of this phenomenon. Breast cancer is a heterogeneous disease comprising tumour subtypes associated with variable clinical characteristics . VariablAndrogen receptor (AR) is a member of the nuclear receptor superfamily and is known to be involved in a complex network of signalling pathways that collectively regulate cell proliferation ,4. ExpreAR gene comprises 9 exons spanning 180.25 kilobases located on chromosome Xq12. Functional analyses have identified two independently regulated transcription initiation sites (TIS), AR-TIS I (-12/-11/-10) and AR-TIS II (-1/+1)  motifs 1 and 2 [AR promoter and extend into Exon 1. Hypermethylation of these CGI have been shown to silence AR transcription in prostate cancer cells and primary tumours [AR gene have been also observed in prostate cancer cell lines, xenografts [The S I -12/-/-10 and a GC-box . Additio 1 and 2  and a cA 1 and 2 . Two CpG tumours . Geneticnografts . In breaAR promoter is associated with loss of AR expression in breast cancer cells, although this is not the case in our cohort of tumours from patients with Stage III breast cancer. We subsequently assessed whether somatic mutations in AR regulatory regions or miRNAs bioinformatically predicted to target the human AR 3'UTR might contribute to the observed changes in AR expression.In this study, we show that loss of AR expression is significantly associated with poor 10 year survival outcome in Grade III invasive breast ductal adenocarcinomas. We then evaluated potential regulatory mechanisms that may account for the loss of AR expression. For the first time we show that DNA hypermethylation in the To assess the prognostic value of AR expression in breast cancer patients, IHC analysis was performed in a cohort of 73 Grade III lymph node positive ductal adenocarcinomas from patients with Stage III disease. Patient and tumour characteristics are summarised in Table AR promoter region and AR expression levels in breast cancer cell lines. DNA methylation was determined by MS-HRM analysis of bisulfite treated DNA in three regions; Regions 1 and 2 correspond to the CpG island in the AR minimal promoter [AR promoter region was associated with the level of AR mRNA  in breast cancer cell lines. Our results revealed no sequence variations in MDAMB157, MDAMB231, MDAMB436, MDAMB468, MCF7, T47D, ZR75-1, Hs578T and BT549 cells. We also sequenced the AR promoter region from -6 to +133 in 32 primary breast tumours. Amplification of the AR promoter region from -165 to -7, which corresponds to the homopurine repeat and GC box  or, to our knowledge, any previously reported AR variants.Somatic mutation of regulatory regions of the AR 5'UTR T105A variant, we performed bioinformatics analysis on the wild-type and variant sequence. In the wild-type sequence the T position is invariant in mammals and is a component of the binding site for RNApolII (based on ChIP-seq data) and the predicted and conserved binding site for several transcription factors, including RUNX1, En1 and Pax6. Based on the currently available ChIP-seq data however, there is currently no evidence that these transcription factors bind to this sequence in vivo. The substitution from T to A results in an abolishment of these predicted sites and the creation of predicted and conserved binding sites for NHLH1 (data not shown).To investigate the potential significance of the AR or the SV40 promoter, in pGL3-basic and pGL3-promoter vectors, respectively. The AR 5'UTR T105A sequence variant did not have a negative impact on SV40 or AR driven reporter activity in either MCF7 or T47D cells   in the tability . A recenan women . HoweverAR 3'UTR for potential miRNA target sites. Bioinformatic analysis revealed that miR-124 was the only miRNA predicted to target the human AR 3'UTR using miRanda and TargetScan. To examine whether miR-124 regulates the expression of the AR transcript, we used a reporter gene assay. MCF7 and T47D cells were transfected with pcDNA 3.1(+)-mir-124 vector and expression of miR-124 verified. Cells transfected with pcDNA 3.1(+)-mir-124 expressed high levels of mature miR-124 at 12, 24, 39 and 48 hr time intervals post-transfection, whereas no endogenous expression was detected in control-transfected cells  but not in T47D cells , MDAMB231 (ER-PR-HER2-), MDAMB436 (ER-PR-), MDAMB468 (ER-PR-), MCF7 (ER + PR + HER2-), T47D (ER + PR + HER2-), ZR75-1 (ER + PR-), Hs578T (ER-PR-HER2-), and BT549 (ER-PR-) were obtained from American Type Culture Collection (ATCC) and cultured according to the manufacturer's recommendations. Hormone receptor status sourced from [http://www.neaf.gov.au) and The University of Queensland Human Ethics committee. Tumour tissues were formalin fixed and paraffin embedded (FFPE), sectioned and stained using hematoxylin and eosin (H&E), and confirmed by a qualified pathologist (G.D.F.) as invasive Grade III ductal adenocarcinomas. After the exclusion of patients for whom there was no follow-up data, non-breast cancer associated deaths and unreadable IHC expression, 73 patients were available for further analysis. None of the patients had received preoperative radiochemotherapy. The characteristics of the patients and tumours is shown in Table Primary breast tumours were sourced from the Princess Alexandra Hospital  following procedures endorsed by both The National Ethics Application process of the National Health and Medical Research Council of Australia . Tissue microarrays were constructed in duplicate from tumour-rich tissue cores (1 mm × 0.6 mm) using an automated tissue microarray (TMA) instrument . 4 μM sections of the TMA blocks were used for immunohistochemical (IHC) analysis. Sections were transferred on to glass slides, deparaffinised and immunostained using anti-ER (SP1), anti-PR, (SP2), anti-HER2/neu (4B5)  or anti-AR  antibodies, and counterstained with 3,3'-Diaminobenzidine (DAB) and hematoxylin. Staining was performed with the BenchMark automated slide stainer (Ventana) using the iVIEW DAB detection kit with additional Avidin and Biotin Blockers according to the manufacturer's instructions. Analysis of stained sections was performed by a qualified pathologist (G.D.F) and the presence of tumour tissue confirmed by examining the counterstain. Expression was scored as positive when visible staining ≥ 1% was observed in the nucleus for ER, PR and AR. For HER2 IHC was semiquantitatively evaluated with a score of 3+ regarded as positive, 2+ as equivocal, and 1+ or 0 as negative. In instances of an equivocal evaluation, silver-enhanced in situ hybridisation was performed as previously described .Genomic DNA was isolated from cell lines using the NucleoSpin Tissue kit (MachereyNagel) according to the manufacturer's instructions. For each human tumour sample, four FFPE tumour-rich tissue cores (1 mm × 0.6 mm) were crushed and digested with proteinase K at 55°C for 2 days and treated with 20 mg RNase A for 1 hr at 37°C. DNA was isolated using the PureGene kit (Qiagen) and subjected to bisulfite modification using the MethylEasy Xceed kit (Human Genetic Signatures) according to the manufacturer's instructions.PCR amplification and methylation sensitive high resolution melt analysis (MS-HRM) was performed in duplicate on the RotorGene™ Q (Qiagen). Primers were designed according to the principles outlined in  to contr® Complete Whole Genome Amplification kit (Sigma) were used as positive/methylated and negative/unmethylated controls, respectively. Triplicate PCR reactions were pooled and Shrimp Alkaline Phosphatase  treatment performed followed by transcription and RNaseA Cleavage for the T-cleavage reaction. Purified samples were nanodispensed onto silicon chips preloaded with matrix . Mass spectra were collected using a MassARRAY mass spectrometer (Bruker-Sequenom) and results analysed by the EpiTYPER software V 1.0. Methylation readings with overlapping signals and silent peaks were eliminated from the calculation.Sequenom MassARRAY methylation analysis was performed as described previously . The forAR mRNA from cell lines, total RNA was extracted using Trizol (Invitrogen). cDNA was synthesised using 500 ng of RNA and Superscript First Strand Synthesis System III (Invitrogen), according to the manufacturer's instructions. β-actin was used to normalise mRNA concentration and primers are shown in Additional file To quantitate In breast cancer cell lines, a region spanning -659 to +280 with respect to the start of transcription +1) were examined for somatic mutations. In FFPE tumours, the fragmented nature and limited availability of DNA meant that analysis was constrained to a smaller region (-165 to +133) and was examined in the 32 tumours for which IHC indicated negative AR expression. Primer sequences are shown in Additional file  were exahttp://genome.ucsc.edu). Transcription factor binding sites were predicted by MOODS (MOtif Occurrence Detection Suite) [P-value less than or equal to 0.01. We tested T105A 5'UTR variant and WT sequences for overlap with the TFBS models in the JASPAR database [http://jaspar.cgb.ki.se/.Bioinformatic analysis initially involved an analysis of UCSC Genome Browser (n Suite) . MOODS udatabase . TFBS loAR 5'UTR T105A mutation was introduced into the wild-type AR 5'UTR sequence  by site-directed mutagenesis using a two-step PCR procedure using the primers listed in Additional file AR 5'UTR wild-type and T105A mutant alone and together with the AR promoter were cloned into the HindIII/NcoI sites of pGL3-promoter and the KpnI/NcoI sites of pGL3-basic (Promega), respectively. Constructs were confirmed by sequencing performed by AGRF .The AR promoter, together with either the wild-type or mutant AR 5'UTR sequence and 100 ng Renilla reporter in a 24-well plate, using Lipofectamine 2000 (Invitrogen). Forty-eight hours after the initial transfections, relative luciferase activities were determined using the Dual-Glo luciferase assay kit (Promega) and a DTX880 Multimode Detector (Beckman Coulter) according to the manufacturer's instructions. Statistical analysis was performed using unpaired, two-tailed t tests, with p values < 0.05 considered significant.MCF7 and T47D cells were transiently transfected with 800 ng pGL3+/- either the SV40 or the Two algorithms, miRanda and TargetScan 5.1, were used to predict target sites for miRNA in the AR 3'UTR ,37. A toAR 3'UTR  was ligated downstream of the luciferase coding sequence in the pSG5 vector (Stratagene). The mir-124-1 stem-loop  ± 200 bp of flanking sequences was cloned into the KpnI/XbaI sites of the pcDNA 3.1(+) expression vector (Invitrogen). MCF7 and T47D cells were transiently transfected with 400 ng pcDNA 3.1(+) constructs, 200 ng pSG5-luciferase constructs and 10 ng of Renilla reporter in a 24-well plate, using FuGene 6 (Roche). Forty-eight hours after the initial transfections, relative luciferase activities were determined using the Dual-Glo luciferase assay kit (Promega) and a DTX880 Multimode Detector (Beckman Coulter) according to the manufacturer's instructions. Statistical analysis was performed using unpaired, two-tailed t tests, with p values < 0.05 considered significant.The Survival time was calculated from the date of tumour removal to the date of last follow-up or death attributable to breast cancer. Overall survival probabilities were estimated non-parametrically with the use of the Kaplan-Meier product limit method (GraphPad Prism version 5.0a) and statistical significance accepted as P ≤ 0.05.The authors declare that they have no competing interests.1: Contributed to AR IHC analysis, developed AR MS-HRM assay, determined expression levels in Breast cancer cell-lines. Drafted manuscript. Stacey L Edwards1: Performed AR 5'UTR reporter assays. Shalima S Nair2: Performed AR Sequenom methylation assays on clinical samples and cell-lines. Juliet D French1: Peter J Bailey1: performed bioinformatics analysis of AR 5'UTR and promoter region. Kathryn Salkield1: Contributed to AR IHC analysis. Sandra Stein3: Managed breast tumour biobank, created tumour tissue array, optimized AR antibody IHC, performed AR, ER, PR and HER2 IHC. Sarah Wagner3: Contributed to creation of tumour tissue array and optimization of AR antibody IHC. Glenn D Francis3: Pathologist responsible to tumour bank, pathological review of tumour samples and analysis of ER, PR, HER2 and AR IHC results. Susan J Clark2: Contributed to project design, attracting research funding, interpreting results and review of manuscript. Melissa A Brown1 Project leader. Contributed to project design, attracting research funding, interpreting results and reviewing, editing, submission and post-review revision of manuscript. All authors read and approved the final manuscript.Kate M PetersThe pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/12/132/prepubTable S1. Oligonucleotide primers.Click here for file


This prompted us to analyse whether the 22 chemicals with AR antagonistic properties would produce mixture effects in an in vitro AR antagonism assay when combined at concentrations found in human serum. Other antiandrogenic modalities could not be considered. Two scenarios were investigated, one representative of average serum levels reported in European countries, the other in line with levels towards the high exposures. In both situations, the in vitro potency of the 22 selected AR antagonists was too low to produce combined AR antagonistic effects at the concentrations found in human serum, although the high exposure scenario came quite close to measurable effects. Nevertheless, our analysis exposes an explanation gap which can only be bridged by conjuring up as yet undiscovered high potency AR antagonists or, alternatively, high exposures to unknown agents of average potency.Several countries have experienced rises in cryptorchidisms, hypospadias and testicular germ cell cancer. The reasons for these trends are largely unknown, but Skakkebaek has proposed that these disorders form a testicular dysgenesis syndrome and can be traced to androgen insufficiency in foetal life. This suggests that antiandrogenic chemicals might contribute to risks, but few chemicals have been linked to these diseases in epidemiological studies. In animal studies with  In recent years, several countries have experienced increases in the incidence of cryptorchisms  and certain 3,3′,4,4′,5-pentachlorobiphenyls (PCBs) have been detected in several epidemiological studies, but the magnitude of effects was relatively small  dosed pregnant dams with p,p-DDE (10 and 100 mg/kg per day) from gestational day (GD) 14 to 18, during the male programming window. Effects on landmarks of male sexual differentiation (changes in AGD and retained nipples) were only observed at the higher dose of 100 mg/kg per day. The concentrations of p,p-DDE measured in the male foetuses on GD15, 17 and 19 reached values between 2 and 7 μmol/l, a concentration range associated with 20–70% AR antagonism in in vitro AR antagonist assays based on MDA-kb2 cells  were too low to produce in vitro AR antagonism and in fact did not elicit AGD changes or retained nipples in the study by Since antiandrogenicity is central to the TDS hypothesis, we wondered whether antiandrogens known to be present in human tissues are sufficiently potent, and present in sufficiently high levels and numbers to result in antiandrogenic effects. We combined tissue dosimetry with data sets describing in vitro potency. In approaching this issue, we could rely on extensive evidence that the joint effects of multi-component mixtures of in vitro AR antagonists (up to 30 components) can be approximated quite well by using the mixture assessment concept of dose addition  can be replaced with several equi-effective fractions of other active chemicals, without loss of effect. This opened the way for making an attempt of predicting the combined effects of antiandrogens in human tissues entirely by modelling, without conducting the actual mixture experiments.This interesting concordance led us to assess the magnitude of combined antiandrogenic effects that can be expected on the basis of published measurements of the human tissue levels of antiandrogenic chemicals and their addition . Accordiin vitro AR antagonist assay meant that we had to restrict our analysis to AR antagonists. The possible contribution of antiandrogens that operate through other modes of action, for example phthalates, could not be taken into account directly, because such agents are without effect in in vitro AR antagonist assays. It is therefore not possible to integrate the effects of AR antagonists and those of chemicals that suppress foetal androgen synthesis at the level of AR antagonism. This can only be achieved by conducting in vivo studies, but considering that only a handful of chemicals have been evaluated in vivo, exclusive reliance on in vivo data would have severely limited the scope of our analysis. The exclusion of phthalates from the analysis will have to be reflected on carefully when it comes to assessing the implications of our observations (see ‘Discussion’ section).However, the dose addition principle together with application of an in vitro had been described in detail. At the same time, information about human serum or lipid levels had to be available for each substance. This restriction reduced the range of chemicals that could be considered for analysis, mainly because the number of agents identified as in vitro AR antagonists by far exceeds the number of chemicals for which tissue level data are also available. For example, quite a few pesticides were found to be active , n-propyl paraben , perfluorooctane sulphonate  , 2,2′,4,4′-tetrahydroxybenzophenone , 2-hydroxy-4-methoxybenzophenone (benzophenone 3 (BP3), CAS# 131-57-7, 98%), butylated hydroxyanisole , butylated hydroxytoluene , and benzo(α)pyrene  were purchased from Sigma–Aldrich Co . Ethyl paraben  and methyl paraben  were obtained from Acros Organics  and PCB 126 (CAS# 57465-28-8), hexahydrohexamethylcyclopentabenzopyran  and 6-acetyl-1,1,2,4,4,7-hexamethyltetraline  from LGC Promochem . 3-Benzylidene camphor  was provided by Induchem AG , 2,2′,4,4′,6-pentabromodiphenyl ether  by Cambridge Isotope Laboratories, Inc. , p,p′-dichlorodiphenyldichloroethylene  by Greyhound Chromatography  and 4-methylenbenzylidene camphor  by Merck & Co .5α-Dihydrotestosterone  was purchased from Steraloids Ltd,  bisphenol-A , PCB 118 (CAS# 31508-00-6) was obtained from Ultra Scientific . 2,2′,3,4,4′,5′-Hexachlorobiphenyl , 2,2′,4,4′,5,5′-hexachlorobiphenyl  and 2,2′,3,4,4′,5,5′-heptachlorobiphenyl  were purchased from Riedel-de-Haen . Stock solutions were made by dissolving the compounds in ethanol . Stock solutions and all dilution series were stored at −20 °C. All other reagents were obtained from Sigma–Aldrich Co. or Invitrogen .2. We employed a modified version of the original assay protocol by Wilson as described in MDA-kb2 cells with the MMTV.luciferase.neo reporter gene construct , generally under the assumption of ‘non-interaction’ . The mathematical and statistical procedures used for calculating mixture effects according to CA were the same as those described in xi is the effect concentration of the ith compound in the mixture that on its own produces the same quantitative effect x as the mixture and pi the relative proportion of the corresponding individual concentration present in the total mixture concentration. The individual effect concentrations were derived from the concentration–response functions for the compounds .where ECOwing to its mathematical features, the CA concept cannot be used to calculate the effect concentrations associated with mixture effects that exceed the maximal AR antagonistic effect of the least efficacious compound present in the mixture. In the case investigated here, this limitation was introduced by BaP which showed AR antagonistic effects of a magnitude not exceeding 70% of the effect seen with DHT, corresponding to an AR antagonistic effect of 30%. To construct CA prediction curves that covered the entire range of antagonistic effects, we assumed that BaP did not contribute to the overall mixture effect at effect concentrations beyond the 30% effect.p,p′-DDE by p,p′-DDE Data of human tissue levels of AR antagonistic chemicals were collected and converted to molar concentrations , with anSupplementary Table 1) to predict their combined additive effects (dose addition) in the MDA-kb2 assay, for two scenarios.For the 22 chemicals listed in 10 (for details of the mixture ratios refer to Supplementary Table 2). However, the sum of the average serum concentrations of all 22 compounds reaches only 50 nmol/l, which is by a factor of 48 lower than the predicted IC10 for the combination. AR antagonistic effects would therefore not become apparent in the MDA-kb2 assay, in which the 22 selected chemicals were tested at the average molar concentrations in human serum.For the first scenario, we modelled average exposures. Accordingly, In the second scenario, we evaluated the expected joint effects for serum levels equivalent to high exposures. This time, the sum of serum concentrations of all compounds (1.1 μmol/l) was only by a factor of 2.6 lower than the total mixture concentration predicted to produce 10% AR antagonistic effects  and ECxi (the effect concentration of the ith compound in the mixture that on its own produces the same quantitative effect x as the mixture) have the same values for all mixture components.Previous mixture studies from our laboratory were intended to assess the predictability of combination effects , and to Supplementary Table 2). We then produced rank orders by arranging the risk units according to their magnitude and generated cumulative plots of the sums of risk units  AR antagonists at levels equivalent to high-end serum concentrations will have to be combined to achieve a 10% AR antagonistic mixture effect. This number is closer to the number of chemicals with known We have based our predictions on a 10% AR antagonistic effect, a magnitude that can be captured with the statistical power afforded by the MDA-kb2 assay  and 11 (20% of 57) in the case of average and high-end exposures, respectively, needed to provoke a 10% antagonistic effect in the MDA-kb2 assay. In the case of an effect magnitude of 50%, these numbers reduce to 1126 (20% of 5632) or 48 (20% of 242) for average or high exposures respectively.The cumulative risk unit summations depicted in in vitro assay only last for 24 h, sensitive foetal tissues come into contact with the chemicals of interest for much longer periods of time.We had to base our calculations on a number of largely unproven assumptions which will be discussed here, in terms of their impact on our estimated antiandrogen numbers  with the chemicals investigated here is largely unexplored, but should depend primarily on toxicokinetic factors. It is quite conceivable that the doses of certain in vitro active compounds that have to be administered singly to pregnant dams to attain foetal AR antagonistic tissue levels are so high that maternal toxicity is induced, in which case the in vitro activity of such chemicals will be irrelevant because it cannot be expressed in vivo. Nevertheless, in the light of the available evidence of the behaviour of in vitro AR antagonists in combination  will also need to be taken into account. But even if we consider joint effects between phthalates and known AR antagonists, critical effect magnitudes may be hard to reach.in vitro  and other analgesics in pregnancy is associated with an increased risk of cryptorchidism in boys : Your provocative presentation is very interesting and your conclusions might be valid, but would be more convincing if you built in a degree of susceptibility. Thus, vulnerability should also be included in your Monte Carlo model. You modelled the sensitivity to androgen receptor antagonists as being tenfold higher in utero than suggested by in vitro assays, but there are possibly also a variety of factors, particularly on as yet unknown genetic predisposition, which must be present to explain the gap. This is a variable which you must consider to determine if genetic predisposition rather than the number of chemicals determines the outcome.A Kortenkamp : That is a good suggestion. Rather than modelling, we are trying to define a framework which will isolate factors that will make a big difference from those which do not have such a strong impact. The sensitivity, vulnerability and predisposition of the subjects will have a massive impact on the number of chemicals we have to invoke. In this context, the magnitude of the effect is not as important.Louis Guillette : It has been emphasised at this meeting that mixtures are important. Studies in humans, wildlife and laboratory animals show that multiple endocrine activities are taking place simultaneously. Earl Gray has examined multiple modalities with antiandrogenic activity and has described ‘something from nothing’ effects due to the summation of minor events. You should consider what modalities other than antiandrogenic substances could have oestrogenic activity. Glucocorticoid signalling might also be involved. Michael Eisenberg (this volume) described many isoforms of the androgen receptor (AR). Most laboratory tests are targeted on single clones, but there is a marked diversity in the AR in the human population especially between different ethnic populations.A Kortenkamp: We have conducted various AR in vitro antagonist assays and find that the potency is very comparable with agreement between different assays. Modalities other than AR antagonism are difficult to model because it is not clear how these different modalities result in common mixture effects at a level of biological organisation further removed from interaction with receptors. We have taken account of this by qualifying our estimates with strong provisos. There is currently no other way of dealing with this.Shanna Swan : There are many non-chemical endocrine disrupters such as stress, nutrition, sleep and lack of exercise. We have seen that in low stress mothers there is a twofold increased susceptibility to phthalates in relation to the anogenital distance (AGD) of the offspring. Some of these multiple non-chemical factors can act as antiandrogens.A Kortenkamp: Your point is much appreciated. These non-chemical factors probably act by altering sensitivity to chemical compounds and can be taken into account in our framework by making different assumptions about the vulnerability of the foetus.Anne Marie Vinggaard : Your estimations of the hazard index might not be valid because these are based only on AR antagonism. You have not included chemicals which inhibit testosterone production and these have an additive effect to AR antagonism. Also, the IC50 or IC10 values might not be appropriate for your calculations because they fail to take account of protein binding when extrapolating from in vitro to the in vivo situation. For example, the effect of PAHs has been shown to be more potent in vivo if protein binding in vitro is considered.A Kortenkamp: I agree that other modalities are relevant to the disorders that we see, but it is difficult to take these into account with our tentative modelling approach other than by stating the direction in which the presence of phthalates and other chemicals with different modes of action will affect our estimates. They will likely reduce the number of chemicals we need to invoke. That is currently the only way to take account of the factors you mentioned because it is impossible to aggregate the effects of phthalates and AR antagonists at the tissue level, where we measure the concentrations in tissue and at the cellular level. Animal models are required to explore the combined effect.Greet Schoeters : You complicate matters by studying mixtures rather than single compounds, but the outcome of your in vitro assay addresses a single target. Endocrine disrupters cause syndromes such as testicular dysgenesis syndrome (TDS), which are a mixture of different events. How does this fit into your model?A Kortenkamp: We were investigating the magnitude of the problem by considering mixture effects. It will be near impossible to explain current trends in male reproductive health without taking account of mixtures. In order to make progress, we had to simplify matters somewhat by making sometimes quite crude assumptions.Jerry Heindel : How many mixtures have you tested and are you sure you are correct? If you predict that a mixture will have no effect, have you tested the mixture to make sure?A Kortenkamp: We have performed the experiments and found that mixtures had no effect as calculated.http://dx.doi.org/10.1530/REP-13-0440.This is linked to the online version of the paper at


Forty-four soybean genotypes with different photoperiod response were selected after screening of 1000 soybean accessions under artificial condition and were profiled using 40 SSR and 5 AFLP primer pairs. The average polymorphism information content (PIC) for SSR and AFLP marker systems was 0.507 and 0.120, respectively. Clustering of genotypes was done using UPGMA method for SSR and AFLP and correlation was 0.337 and 0.504, respectively. Mantel's correlation coefficients between Jaccard's similarity coefficient and the cophenetic values were fairly high in both the marker systems  indicating very good fit for the clustering pattern. UPGMA based cluster analysis classified soybean genotypes into four major groups with fairly moderate bootstrap support. These major clusters corresponded with the photoperiod response and place of origin. The results indicate that the photoperiod insensitive genotypes, 11/2/1939 (EC 325097) and MACS 330 would be better choice for broadening the genetic base of soybean for this trait. The photoperiod response is a major criterion, which determines the latitudinal adaptation of a soybean variety . A consiBetter knowledge of the genetic similarity of breeding materials could help to maintain genetic diversity and sustain long-term selection gains. Furthermore, monitoring the genetic variability within the gene pool of elite breeding material could make crop improvement more efficient by the directed accumulation of favored alleles thus decreasing the amount of material to be screened. Several studies have used molecular markers to help in identification of genetically diverse genotypes to use in crosses in cultivar improvement programme. These studies have more success than conventional selection programme in producing productive lines from plant introduction/exotic lines crosses with elite lines . Among tOne thousand soybean genotypes obtained from India, USA, Hungary, Philippines and Taiwan were screened for sensitivity to photoperiodism as described by Simple sequence repeat (SSR)/ microsatellite analysis was carried out using 40 mapped markers distributed on all the 20 chromosomes . AmplifiAFLP fingerprints were generated based on the protocol of pij2 where pij is the frequency of the jth allele of ith marker (The scoring of bands was done as present (1) or absent (0) for each AFLP and SSR marker allele and data was entered in a binary data matrix as discrete variables. Jaccard's coefficient of similarity was calculated and a dendrogram was constructed by using Unweighted Pair Group Method of Arithmetic Mean (UPGMA). The computer package NTSYS-PC Version 2.02  was usedh marker  while PIAmong the 40 SSR primer pairs used in the present study, 34 (85.0%) were polymorphic, while six primers revealed monomorphic patterns. In total, 120 alleles were detected for the 34 polymorphic SSR primers, with an average of 3.53 alleles per locus. Allele sizes ranged from 90 bp to 300 bp. Summarized data for the SSR loci and their PIC values are presented in The five AFLP primer combinations used in this study were selected on the basis of a high number of scorable polymorphic bands. It was possible to discriminate each one of the 44 soybean genotypes using five primer combinations. Band sizes ranged from 100 to 700 bp. The five primer pairs revealed a total of 449 different bands that were of sufficient intensity to be scored, and 208 (46.3%) of these were polymorphic. The percentage of polymorphic bands per assay unit ranged from 34.0% (E-ACT/M-CAT) to 57% (E-AAG/M-CTT), with an average of 46.3%. The average PIC score for AFLP primer combination was 0.12, with a range of 0.08 to 0.16 . A similvs. AFLP), r = 0.771 (SSR vs. combination) and 0.971 (AFLP vs. combination), respectively. All these were statistically significant. The slightly lower level of correlation between SSR and AFLP in the present study could probably reflect that these markers are known to target different genomic fractions involving repeat and/or unique sequences, which may have differentially evolved or been preserved during the course of natural or artificial selection.The similarity coefficients based on shared SSR and AFLP bands revealed that the average genetic similarity (GS) between genotypes was 0.446, with a range of 0.220 to 0.765. GS estimates for AFLP and SSR were 0.504 and 0.337, respectively. As expected, the level of polymorphism was higher for SSR (0.507) than for AFLP (0.12), reflecting the hypervariability of SSR markers. SSR/microsatellite analysis thus revealed significantly lower mean genetic similarity values (0.337) than AFLP (0.504). Similar results have been reported for soybean  and one from the USA (Maple Arrow), which grouped together with 0.545 similarities. Though genotype PI 437418 did not group with cluster III, it showed a reasonable level of similarity with this cluster and, thus can be considered as an outlier of this group. The genotypes of this group showed delayed flowering from 1-5 days in extended photoperiod. Cluster IV included one genotype each from Hungary and India. This cluster consisted of diverse genotypes  X EC95937 (USSR), and 11/2/1939, a line from Hungary) which showed no delay in flowering under extended photoperiod. The cluster formed by these two genotypes is not strong and showed only 0.50 similarity between each of its members, which, in turn, showed 0.36 similarity with other genotypes of the present study. It is evident from dendrogram  that soySoybean producing regions in India range from the lower Himalayan Hills and Northern Plain in the north to the Deccan Plateau in the south. The soybean varieties cultivated in these areas were developed through separate breeding programs, because most of the Indian soybean varieties are photoperiod sensitive, restricting their cultivation to localized areas only. The genotypes, 11/2/1939 (Hungary) and MACS330 (India) identified as photoperiod insensitive in the present study formed a separate group, as clearly shown by UPGMA . Literat