Multiple studies have provided compelling evidence that the FTO gene variants are associated with obesity measures. The objective of the study was to investigate whether FTO variants are associated with a broad range of obesity related anthropometric traits in an island population.P-values ranging from 0.0004 to 0.01. Similar but less significant associations were also observed between these markers and bicondilar upper arm width and upper arm circumference. Most of these significant findings could be explained by a mediating effect of “body fatness”. However, one unique association signal between upper arm width and rs16952517  could not be explained by this mediating effect. In addition, using a principle component analysis and conditional association tests adjusted for “body fatness”, two novel association signals were identified between upper arm circumference and rs11075986  and rs16945088 .We examined genetic association between 29 FTO SNPs and a comprehensive set of anthropometric traits in 843 unrelated individuals from an island population in the eastern Adriatic coast of Croatia. The traits include 11 anthropometrics  and two derived measures (BMI and WHR). Using single locus score tests, 15 common SNPs were found to be significantly associated with “body fatness” measures such as weight, BMI, hip and waist circumferences with The current study confirmed the association of common variants of FTO gene with “body fatness” measures in an isolated island population. We also observed evidence of pleiotropic effects of FTO gene on fat-free mass, such as frame size and muscle mass assessed by bicondilar upper arm width and upper arm circumference respectively and these pleiotropic effects might be influenced by variants that are different from the ones associated with “body fatness”. The worldwide prevalence of obesity has reached epidemic proportions in recent decades and is associated with increased risks of type 2 diabetes and cardiovascular diseases. The increase in overweight and obesity has been attributed to modernization, calorie-rich nutritionally poor diets and sedentary lifestyles Although the aforementioned studies provide compelling evidence that the FTO gene is involved in altering fat mass, the reported associations are typically limited to “body fatness” measures such as body weight, body mass index (BMI) This study was approved by the Institutional Review Board of the University of Cincinnati and the Ethics Committee of the Institute for Anthropological Research, Zagreb. Written informed consent was obtained from each study participant.th century The sample was derived from a study on genetics of metabolic syndrome in an isolated population from the middle Dalmatian island of Hvar in the eastern Adriatic coast of Croatia . The Crowww.hapmap.org) based on a pairwise r2 of ≥0.8 among all common SNPs with minor allele frequency of ≥0.05. We also included eight significant SNPs from previous GWAS . These 36 SNPs span 60kb and fall in intron 1 and intron 2 of the FTO gene. Genotyping was performed using the SNPlex protocol (Applied Biosystems), which is based on multiple oligonucleotide ligation/PCR assay with a universal ZipChute™ probe detection for high-throughput multiplexed SNP genotyping. Details of the SNPlex genotyping methods were described previously The FTO gene (MIM 610966) is large encompassing >410kb of genomic region. Therefore, we adopted a targeted approach to search for common tagging SNPs within 30kb upstream and 30kb downstream of the original and most significantly associated SNP, rs9939609 reported by Frayling et al. r2) between markers was estimated by Haploview (version 4.1) Statistical analyses were performed using R (version 2.8.0) and the GenABEL library N = 360) and females (N = 483) separately. Since these measures are correlated with gender and age (data not shown), we first adjusted the effects of gender and age and their interaction term by linear regression. Unless otherwise mentioned, all data analyses were performed on these adjusted measures.Summary statistics of the 11 anthropometrics and two derivatives (BMI and WHR) are listed in The anthropometric measures we studied correlated to each other . We tested the genetic association of the remaining 29 SNPs with each of the adjusted anthropometric trait using the single-locus score test under additive model (1df test). The distribution and the pairwise linkage disequilibrium (LD) of these 29 SNPs are shown in Among the 36 genotyped SNPs, seven were excluded from the analysis either due to low minor allele frequency (5 with MAF<0.05) or deviation from HWE . In contrast, “body fatness” markers were less significantly associated with UAC (a member of cluster 1 traits) compared with UAW. WHR and two skin fold measures (TrS and SbS) of cluster 2 also showed nominally significant associations with some of these markers, significance of which were not supported by the permutation test.Interestingly, UAW was found to be significantly associated with “body fatness” markers, although it is less closely correlated with the “body fatness” traits . In addiSince multiple cluster 1 measures and UAW were found to be associated with similar sets of SNP markers, we performed a principle component analysis (PCA) to evaluate whether some “lower-dimensional summary” of these variables provide more information about the observed associations. We identified three major components  that could explain up to 90% of the total variance of these measures  observed between PC1 and the “body fatness” markers shared similar pattern with individual body fatness measures  and the highest association peak was found at rs1421085   after the adjustment for PC1 (Supplement The first component (PC1) explained more than 70% of the total variance and was approximately an average of all the included measures with similar loading coefficients (around −0.40∼−0.45), except for UAW (with a loading coefficient of −0.264). The association signals (.000307) . TherefoP-value = 0.00531) and PC1 adjusted-UAW .The second component (PC2) had a prominent UAW constituent (with a loading coefficient of 0.935) and it mainly represented the effect of UAW with adjustments of other variables. Not surprisingly, PC2 was not associated with “body fatness” markers because PC2 was independent to PC1 and the later already absorbed most of the variance of “body fatness”. In this sense, PC2 could be approximately regarded as UAW adjusted for PC1 or “body fatness”  and rs16945088 . Similar association signals exist between PC1 adjusted UAC (UAC|PC1) and rs11075986  and rs16945088 .Similar to PC2, the third component (PC3) mainly represents the effect of UAC (with a loading coefficient of −0.878) and could be approximately regarded as UAC adjusted for PC1 (or “body fatness”) represented as UAC|PC1 in We investigated genetic association between a set of obesity related anthropometric measures and 29 common SNPs in the FTO gene. These markers were selected either based on significant findings of previous GWAS or by pairwise tagging approach. The anthropometric measures we studied were grouped into two major clusters: the first cluster included four closely correlated “body fatness” measures Although the sample size of present study was relatively small compared with the previous GWAS r2 = 1.0) or near perfect (r2 = 0.961) LD based on the HapMap data of Caucasian samples (CEU). Since these significant SNPs are in strong pairwise LD (average r2 = 0.65) and show similar association patterns with different “body fatness” measures, we referred these SNPs as “body fatness” markers. Conditional association analysis on rs1421085 indicated no major effect of allelic heterogeneity on “body fatness” measures, since adjusting for rs1421085 completely eliminated the associations between “body fatness” measures and “body fatness” markers.Consequently, we confirmed association between a set of high frequency (MAF>0.3) SNPs in FTO gene and “body fatness” measures in our samples. Specifically, the most significant associations were observed with six previously reported GWAS SNPs including rs1421085 2 (16.1%SD) which correspond to the explained variances (under additive model) of 1.23% (Wt), 1.27% (HC), 1.49% (WC) and 1.29% (BMI) respectively. The higher effect size estimates we obtained from the present study may probably be attributed to the relatively homogenous genetic background and environmental exposures in our study population.The estimated effect size of these “body fatness” markers on “body fatness” measures from our sample were comparable but of higher magnitude compared to those of previous reports Interestingly, similar significant association was observed between UAW and the “body fatness” markers, although UAW is loosely correlated with “body fatness” measures. UAW, also known as elbow breadth, is a robust measure for frame size and has been suggested to be used for the interpretation of body weight UAC, WHR and two skin fold measures (TrS and SbS) only showed nominal significant associations with some of these “body fatness” markers. The effect of these “body fatness” SNPs on UAC, WHR and two skin fold measures (TrS and SbS) appeared to be mediated through adiposity, since adjusting for PC1 (as a surrogate measure for “body fatness”) completely eliminated the associations (data not shown).Through principle component analysis of the four “body fatness” measures together with UAC and UAW, we identified three major components. The first component (PC1) could be regarded as a summary for “body fatness” measures and it could surrogate the associations between “body fatness” measures and “body fatness” markers, since adjusting for this component completely eliminated the associations between “body fatness” traits and “body fatness” markers. The second and third components mainly represented the PC1 or “body fatness” adjusted UAW and UAC, respectively. Of particular note were the association between PC2 (or PC1 adjusted UAW) and rs16952517 and associations between PC3 (or PC1 adjusted UAC) and rs11075986 and rs16945088. These significant associations demonstrated that variants other than the “body fatness” markers of FTO gene might have pleiotropic effects on frame size (UAW) and muscle mass (UAC) and these effects are independent from the effect of the FTO gene on “body fatness”, which are influenced by a different set of markers.Fto null mice I367FFto mice In summary, our study confirmed the association of common variants of the FTO gene with body fatness measures in an island population from eastern Adriatic coast of Croatia. In addition to the previously reported SNPs, we identified a set of high frequency tagging SNPs associated with body fatness measures with similar magnitudes of significance. The associations between “body fatness” traits and the “body fatness” markers could be explained by a single SNP without any clear sign for allelic heterogeneity. Based on principle component analysis and conditional association tests adjusted for “body fatness” measures, we observed evidences to support the pleiotropic effects of FTO gene on fat-free mass, such as frame size and muscle mass as indicated by UAW and UAC, respectively. These possible pleiotropic effects might be influenced by variant(s) different from the ones associated with “body fatness”. This new observation is at least partially consistent with results from recent functional studies of the FTO gene in mice It should be noted that although our genetic association tests and principle component analysis provide strong evidence to support pleiotropic effects of the FTO gene on fat-free mass, these results should be interpreted with caution. Considering the possibilities of statistical fluctuation due to inadequate power and multiple comparisons, further replication studies in independent populations and functional studies are required to confirm these observations. In addition, the 29 common SNPs we examined encompass only a small fraction of the FTO gene (intron 1 and intron 2). Therefore, we cannot rule out the possibility that the true causal or functional SNP(s) might be located in other regions of the gene that are in high LD with the SNPs in our study. These limitations call for additional validation in an independent sample with detailed anthropometric measurements and more comprehensive studies to clarify the pleiotropic effects of the FTO gene. These should include deep sequencing to identify the specific causal variant(s) and functional studies to further examine the multifaceted mechanism by which FTO influences body growth and energy homeostasis.Table S1Pairwise correlation of anthropometric measures.(0.06 MB PDF)Click here for additional data file.Table S2Permutation test results between SNP markers and anthropometric traits.(0.16 MB PDF)Click here for additional data file.Table S3Principle components of “body fatness” phenotypic measures.(0.08 MB PDF)Click here for additional data file.Figure S1P values of association tests between “body fatness” measures  adjusted by PC1.(0.50 MB TIF)Click here for additional data file.Figure S2P values of association tests between “body fatness” measures  and PC1 adjusted for rs1421085.(0.52 MB TIF)Click here for additional data file.


Transduction of graded synaptic input into trains of all-or-none actionpotentials (spikes) is a crucial step in neural coding. Hodgkin identified threeclasses of neurons with qualitatively different analog-to-digital transductionproperties. Despite widespread use of this classification scheme, ageneralizable explanation of its biophysical basis has not been described. Werecorded from spinal sensory neurons representing each class and reproducedtheir transduction properties in a minimal model. With phase plane andbifurcation analysis, each class of excitability was shown to derive fromdistinct spike initiating dynamics. Excitability could be converted between allthree classes by varying single parameters; moreover, several parameters, whenvaried one at a time, had functionally equivalent effects on excitability. Fromthis, we conclude that the spike-initiating dynamics associated with each ofHodgkin's classes represent different outcomes in a nonlinearcompetition between oppositely directed, kinetically mismatched currents. Class1 excitability occurs through a saddle node on invariant circle bifurcation whennet current at perithreshold potentials is inward (depolarizing) at steadystate. Class 2 excitability occurs through a Hopf bifurcation when, despite netcurrent being outward (hyperpolarizing) at steady state, spike initiation occursbecause inward current activates faster than outward current. Class 3excitability occurs through a quasi-separatrix crossing when fast-activatinginward current overpowers slow-activating outward current during a stimulustransient, although slow-activating outward current dominates during constantstimulation. Experiments confirmed that different classes of spinal lamina Ineurons express the subthreshold currents predicted by our simulations and,further, that those currents are necessary for the excitability in each cellclass. Thus, our results demonstrate that all three classes of excitabilityarise from a continuum in the direction and magnitude of subthreshold currents.Through detailed analysis of the spike-initiating process, we have explained afundamental link between biophysical properties and qualitative differences inhow neurons encode sensory input. Information is transmitted through the nervous system in the form of actionpotentials or spikes. Contrary to popular belief, a spike is not generatedinstantaneously when membrane potential crosses some preordained threshold. Infact, different neurons employ different rules to determine when and why theyspike. These different rules translate into diverse spiking patterns that havebeen observed experimentally and replicated time and again in computationalmodels. In this study, our aim was not simply to replicate different spikingpatterns; instead, we sought to provide deeper insight into the connectionbetween biophysics and neural coding by relating each to the process of spikeinitiation. We show that Hodgkin's three classes of excitability resultfrom a nonlinear competition between oppositely directed, kinetically mismatchedcurrents; the outcome of that competition is manifested as dynamically distinctspike-initiating mechanisms. Our results highlight the benefits of forwardengineering minimal models capable of reproducing phenomena of interest and thendissecting those models in order to identify general explanations of how thosephenomena arise. Furthermore, understanding nonlinear dynamical processes suchas spike initiation is crucial for definitively explaining how biophysicalproperties impact neural coding. Action potentials, or spikes, are responsible for transmitting information throughthe nervous system f–I) curves f–I curve, whereas class 2 neuronshave a discontinuous f–I curve because of their inabilityto maintain spiking below a critical frequency. Class 3 neurons fail to spikerepetitively, typically spiking only once at the onset of stimulation; theirf–I curve is undefined since calculation of firingrate requires at least two spikes for an interspike interval (ISI) to be measured.Although neuronal coding properties may change on slow time scales , Hodgkin's classification provides a fundamentaldescription of analog-to-digital transduction occurring on the time scale of singleISIs, and therefore addresses the very essence of how individual spikes areinitiated.Hodgkin recognized this and identified three basic classes of neurons distinguishedby their frequency-current  bifurcation (sometimes referred tosimply as a saddle-node bifurcation) whereas class 2 neurons spike repetitively whentheir fixed point is destabilized through a subcritical Hopf bifurcation The distinction between class 1 and 2 excitability has proven extremely useful fordistinguishing neurons with different coding properties This study set out to identify the biophysical basis for qualitative differences inneural coding exemplified by Hodgkin's three classes. By relating spikeinitiating dynamics with transduction properties, and by identifying the biophysicalbasis for those dynamics, we explain how Hodgkin's three classes ofexcitability result from a nonlinear, time-dependent competition between oppositelydirected currents.f–I curves  f–Icurve.Spinal sensory neurons fall into several categories based on spiking patternI curves . Spikingf–I curve characteristic of class 2excitability was observed when parameter βw(see below) was set between values giving class 1 or 3 excitability, butphasic-spiking could not be reproduced in the 2D model. Phasic-spiking wasachieved by incorporating adaptation  andsingle-spiking neurons never exhibited class 2 excitability forIstim as high as 500 pA. So, although a neuronmay exhibit both class 2 and class 3 excitability, lamina I neurons exhibitalmost entirely one or the other class over thephysiologically relevant stimulus range. We therefore label tonic-, phasic-, andsingle-spiking spinal lamina I neurons as class 1, 2, and 3 neurons,respectively. Although practical, such labeling may be inappropriate for othercell populations if a more balanced mix of class 2 and 3 excitability existswithin a single cell type.Thus, neurons should not strictly be labeled class 2 or 3 but, rather, asexhibiting predominantly class 2 or 3 excitability based on the range ofV and a slower recovery variable w.This interaction can be visualized by plotting V againstw to create a phase portrait. Behavior of the model can beunderstood by how the V- and w-nullclinesintersect; nullclines represent everywhere in phase space whereV or w remain constant.Having reproduced each class of excitability in a 2D model, our next step was toexploit the simplicity of that model to uncover the spike initiating dynamicsassociated with each class. Because the model is 2D, its behavior can beexplained entirely by the interaction between two variables: a fast activationvariable V- and w-nullclinesintersect at three points; the leftmost intersection constitutes a stable fixedpoint that controls membrane potential. Stimulation shifts theV-nullcline upwards. If the V-nullcline wasshifted far enough , two of the intersection points were destroyed and the class 1 modelbegan to spike repetitively. Disappearance of the two fixed points and thequalitative change in behavior that results  is referred to most precisely as a saddle-nodeon invariant circle (SNIC) bifurcation Right panels of V- and w-nullclinesintersect at a single, stable point. Spiking began when the vertical shift inthe V-nullcline caused by stimulation destabilized that pointthrough a Hopf bifurcation V- and w-nullclines intersected to the rightof the local minimum in the V-nullcline whereas the fixed pointwas stable when the nullclines intersected to the left of the minimum . Destabilization of thefixed point (in class 2 excitability) is distinct from destruction of the stablefixed point (in class 1 excitability), but both cause the neuron to spikerepetitively rather than remaining at a stable, subthreshold voltage.Center panels of V- and w-nullclinesintersected at a single, stable point that remained stable forIstim>rheobase, meaning spike initiationoccurred without a bifurcation. The system moved to the newlypositioned stable fixed point, but could do so via different paths:trajectories starting below the quasi-separatrix made a long excursion aroundthe elbow of the V-nullcline, resulting in a single spike;trajectories starting above the quasi-separatrix followed a more direct,subthreshold route. We refer to the boundary in phase space from whichtrajectories diverge that was above the quasi-separatrix before stimulation ends up below thequasi-separatrix during stimulation. We refer to this mechanism as aquasi-separatrix-crossing (QSC) since spike initiation requires that systemvariables cross from one side of the quasi-separatrix to the other. Such amechanism was first described by Fitzhugh Left panels of erge see  as a quaThe quasi-separatrix was plotted by integrating backward in time (−0.01ms time step) from point * indicated on βw = −10mV, which, on the phase plane, corresponds to the V- andw-nullclines meeting tangentially at the minimum of theV-nullcline so that rheobasic stimulation simultaneouslydestroys and destabilizes the fixed point (data not shown). There was nobifurcation forβw = −21mV and Istim<80 µA/cm2;stronger stimulation eventually caused a Hopf bifurcation, but such stimulationis unphysiological and is likely to activate other processes not included in themodel (see above). The range of Istim over whichspike initiation occurred through a QSC is indicated with gray shading. Noticethat a neuron that generated repetitive spiking through a Hopf bifurcation(βw = −13mV) would, for a narrow range of weaker Istim,generate a single spike through a QSC, consistent with data in Since model parameters were chosen to produce one or another spiking pattern,simply reproducing a given pattern is not necessarily informative—thisconstitutes an inverse problem akin to circular reasoning. To ensure that themodel does not simply mimic spiking pattern, it must predict behaviors separatefrom those used to choose parameters. The model makes such a prediction: spikesinitiated through different dynamical mechanisms are predicted to exhibitdifferent variability in their amplitudes. Specifically, spikes initiatedthrough an SNIC bifurcation should have uniform amplitudes because allsuprathreshold trajectories follow the invariant circle formed when the stablemanifolds in the 3D model the same way that varying βwaffected Islow in the 2D model ; recall that the 2D model lies at the interface betweenclass 1 and 2 excitability whenβw = −10mV  . For a given value ofIstim, class 1 and 2 excitability were mutuallyexclusive whereas class 2 and 3 excitability coexisted. Furthermore, the 3Dmodel exhibited constant or variably sized spikes depending on whether the modelwas class 1 or 3, respectively  . Withβcompare . Thetraectively . This deThus, expression of distinct subthreshold currents accounts for the differentclasses of excitability observed amongst spinal lamina I neurons. But can otherbiophysical properties also account for differences in excitability? And, if so,do those properties confer the same or different spike initiating dynamics thanthose described above? In other words, can we generalize our biophysicalexplanation of excitability?βw . A Hopfbifurcation occurs when the w-nullcline crosses theV-nullcline on its middle arm(βw<−10 mV); althoughnecessary, this is not strictly sufficient for the bifurcation (see below), butit is a close enough approximation for our demonstration. A QSC can occur whenthe w-nullcline crosses the V-nullcline on itsleft arm. Given this connection between phase plane geometry and spikeinitiating dynamics, one can predict the effects on excitability of changing theshape or relative positioning of the two nullclines regardless of which specificparameters are varied.We summarize here how spike initiating dynamics can be inferred from the phaseplane geometry of the 2D model. We start by consideringβw  since itV-nullcline in one direction (viaβm) should have the same effect asmoving the w-nullcline in the opposite direction (viaβw), which indeed it did  with different voltage-dependency thanthe classic Na+ current comprising most ofIfast. Increasingḡfast in the 2D model withoutaltering its voltage-dependency should also have an effect comparable toreducing βm, which indeed it did  in essentially the same way and withequivalent consequences for spike initiating dynamics. Although the specificbiophysical mechanism is different in each case , the common outcome is achange in the balance of fast and slow currents near threshold.Moving the d it did . The biod it did . Thus,βḡslow (whereIslow is outward) should have effects comparableto increasing ḡfast, which indeed it did, changes in maximal conductance should have small effects if theconductance is only marginally activated near threshold. By comparison, smallchanges in ḡsub were sufficient to alterspike initiating dynamics in the 3D model  extended the tail of theactivation curve for gslow so thatIslow was more strongly activated atperithreshold potentials; this predictably encouraged class 3 excitability, fast-activating inward current canoverpower slow-activating outward current—the latter is stronger whenfully activated, but can only partially activate (because of its slow kinetics)before a spike is inevitable. Through this mechanism, a single spike can beinitiated before negative feedback forces the system back to its stable fixedpoint, hence class 3 excitability. Speeding up the kinetics ofIslow predictably allowsIslow to compete more effectively withIfast (see below).Interpretation of the phase plane geometry can be formalized by doing localstability analysis near the fixed points . In clase Ifast . HoweverI–V curve (V high enough that fast-activating inward current competeswith slow-activating outward current. As in the class 3 neuron (see above), itis crucial that Ifast activates more rapidly thanIslow in order for positive feedback to outrunnegative feedback, since the latter would dominate and prohibit spiking if givenenough time to fully activate. The difference from class 3 excitability is thatfast positive feedback can outrun slow negative feedback with constantstimulation in a class 2 neuron; in the class 3 neuron, positive feedback canoutrun negative feedback only during the stimulus transient.In class 2 neurons, –V curve  is monotIss/∂V = 0at the saddle-node, meaning the steady state I–Vcurve is non-monotonic with a local maximum above which depolarization activatesnet inward current at steady state  thus reveals the strength anddirection of feedback with which Ifast must compete.Changing the direction and magnitude of Isub in the3D model had the same consequences on the steady stateI–V curve (data not shown) as changingβw in the 2D model. Changing otherparameters in the 2D model, such as ḡNa,also had similar effects . To investigate this further, we changed thekinetics of Islow by varyingφw in the 2D model. Consistent withour dynamical explanations of spike initiation, speeding upIslow increased the minimum stimulation requiredto produce class 2 or 3 excitability , whereas slowingdown Islow had the opposite effects; the minimumstimulation required to produce class 1 excitability was unaffected (φz had the same effects in the 3Dmodel (data not shown).The affected . Increasinactivation of a slow outward current  shouldencourage class 1 excitability the same way perithreshold activation of a slowinward current does. To test this, we incorporatedIK,A by warping the w-nullcline tocreate a 2D model similar to that of Wilson V-nullclineintercepted the negatively sloping region of the w-nullclinetangentially (f–I curve (not shown) typical of class 1excitability. Furthermore, inactivation of IK,Aintroduced a region of negative slope into the steady stateI–V curve that overlapped the apex of theinstantaneous I–V curve . Thus, IK,A increasedrheobase but its slow inactivation as voltage passed through threshold amountedto a slow positive feedback process that encouraged class 1 excitability. Theconverse has been shown in medial superior olive neurons, where inactivation ofINa encourages coincidence detection associatedwith class 3 excitability If the balance of fast and slow currents at perithreshold potentials is thecrucial determinant of excitability, then perithresholdentially and repeentially , which r–V curve . The samOur results demonstrate that the three classes of excitability first described byHodgkin Our approach for uncovering the biophysical basis for Hodgkin'sclassification was to forward engineer a simple model in order to help reverseengineer complex neurons. The benefit of such an approach is that the model startssimple and is made only as complex as required to reproduce the phenomena ofinterest; extraneous details are thus excluded. Building a biologically realistic,high-dimensional model that exhibits one or another firing pattern is reasonablystraightforward, but such a model almost certainly contains extraneous detail andmay fail to provide greater insight than the experiments upon which it is based. Thechallenge when forward engineering simple models is that one may reproduce thephenomena of interest through a mechanism that is not the same as that used by realneurons; for example, a QSC produces a single-spiking pattern, but single-spikingneurons may use a completely different mechanism to produce the same result. Thisconstitutes an inverse problem that requires careful consideration in order tovalidate the forward engineered model, as we demonstrated in The forward engineering approach gave a model complex enough to reproduce each classof excitability yet simple enough for its spike initiating dynamics to be rigorouslycharacterized using phase plane analysis. By expressing the problem geometrically,we were able to visualize and uncover the functional equivalence of changingdifferent model parameters . As a reThe mechanistic explanation of excitability afforded by quantitative analysis  isprecisely what is needed to make sense of the ever accumulating mass of experimentaldata. It provides an organizing framework for understanding which parameters areimportant and why, for instance, by explaining the functional equivalence ofdifferent biophysical changes see . By compkinetics ofcurrents activating or inactivating at perithreshold potentials influence theprocess of spike initiation. Previous studies have identified the competitionbetween inward and outward currents as a critical determinant of excitability  depends on Istim, hence the diagonalborder between class 2 and 3 excitability on Istim, hence the vertical border betweenclass 1 and 2 excitability on the same plots.According to our results, the direction, magnitude, and ty e.g.,, but ourThe adaptation observed in phasic-spiking neurons is also interesting insofar as itindicates that shifting the balance of fast and slow currents has importantconsequences for coding, and that a given neuron is not restricted to a unique spikeinitiation mechanism. Effects of activating an outward current or inactivating aninward current on ultra-slow time scales  can be predicted fromplots like accommodation,but this does not provide as satisfying a dynamical explanation as a QSC, which canaccurately predict whether a spike will be initiated based on where the trajectorystarts relative to the quasi-separatrix . Indeed, an unhealthyneuron will often fail to spike repetitively, but many other indices of neuronalhealth  have proven that a single-spiking patternis not synonymous with dysfunction. Indeed, “healthy”single-spiking neurons have been described not only in the superficial dorsal hornof the spinal cord + current + current factors into thecoding properties of those neurons, arguing that well-timed spikes are generated byrapid depolarizing input that minimizes activation ofIK,lt and inactivation ofINa; ideally, that rapid depolarization is preceded byhyperpolarizing input that primes the neuron by deactivatingIK,lt and deinactivatingINa. That biophysical explanation is consistent with ourdata. Single-spiking cells in the auditory system also exhibit variably sized spikes and is liable to varywith temperature Class 3 excitability has been most extensively studied in the auditory system wherethe single- (or onset-) spiking pattern has been shown to result from alow-threshold Kese.g., , which iese.g., , as is tAs explained in the Introduction, Hodgkin identified three classes of neurons basedon phenomenological differences in their spiking pattern To conclude, Hodgkin's three classes of excitability result from differentoutcomes in a competition between fast and slow currents. The kinetic mismatchbetween currents is crucial for allowing single-spiking (class 3 excitability) orrepetitive spiking faster than a critical frequency (class 2 excitability) despitethe net steady state current being outward at threshold. Moreover, reproduction ofqualitatively different spiking patterns in a 2D model emphasizes that rich dynamicsare possible in simple systems based on their nonlinearities. Identifyingfunctionally important nonlinearities and then determining how they are biologicallyimplemented represents a powerful way of deciphering the functional significance ofbiophysical properties.2 and5% CO2) sucrose-substituted artificial cerebrospinal fluid(S-ACSF) containing (in mM) 252 sucrose, 2.5 KCl, 2 CaCl2, 2MgCl2, 10 glucose, 26 NaHCO3, 1.25NaH2PO4, and 5 kynurenic acid; pH 7.35;340–350 mOsm. The spinal cord was removed by hydraulic extrusion andsliced in the parasagittal plane as previously described All experiments were performed in accordance with regulations of the CanadianCouncil on Animal Care. Adult male Sprague Dawley rats were anesthetized withintraperitoneal injection of sodium pentobarbital (30 mg/kg) and perfusedintracardially with ice-cold oxygenated (95% O2 and 5%CO2), room temperature ACSF. Lamina I neurons were visualized withgradient-contrast optics on a modified Zeiss Axioplan2 microscope  and were patched on with pipettes filled with (in mM) 135KMeSO4, 5 KCl, 10 HEPES, and 2 MgCl2, 4 ATP , 0.4 GTP (Sigma); pH was adjusted to 7.2 with KOH and osmolarityranged from 270–290 mOsm. Whole cell current clamp recordings wereperformed using an Axopatch 200B amplifier .Functional classification was determined from responses to 900 ms-long currentsteps N is a random number drawn from aGaussian distribution with average 0 and unit variance, which is then adjustedaccording to size of the time step. Noise amplitude(σnoise) and filtering(τnoise) are reported in the text. DCoffset (Iavg) was adjusted to give roughlyequivalent firing rates across different neurons.Slices were transferred to a recording chamber constantly perfused at ∼2ml/min with oxygenated .Recordings were later sampled at 10–20 KHz on a computer usingStrathclyde Electrophysiology software .V and a slower recovery variablew. V represents voltage and controlsinstantaneous activation of fast currents(Ifast); w is a function ofvoltage and controls activation of slower currents(Islow). BothIfast and Islow maycomprise more than one current (see below); currents with similar kineticswere bundled together in order to create a low-dimensional model. The systemis described byENa = 50 mV,EK = −100mV,Eleak = −70mV,ḡfast = 20mS/cm2,ḡslow = 20mS/cm2,gleak = 2mS/cm2,φw = 0.15,C = 2µF/cm2,βm = −1.2mV,γm = 18mV,γw = 10mV, and βw was varied as explainedbelow.Our starting model was derived from the Morris-Lecar model βw was found to be sufficient to convertthe model between tonic-spiking (class 1 excitability) and single-spiking(class 3 excitability); varying other parameters includingḡfast,ḡslow,βm orγw also affected excitabilitythrough the same geometrical changes associated with varyingβw  or outward(Esub = EK = −100mV) and ḡsub was varied. Kinetics ofIsub were adjusted to match experimentaldata so thatφz = 0.5for inward current andφz = 0.15for outward current. The steady-state activation curve forz was the same for all models withβz = −21mV andγz = 15mV.φy = 0.15,βy = −10mV,γy = 10mV, and all other parameters were the same as in the 2D model.To make the model more biophysically realistic, we converted the 2D modelinto a 3D model see  by splitIstim (inµA/cm2) was varied to produce steps, noise, orramps comparable to stimuli used in experiments. Equations were integratednumerically in XPP To stimulate the model, t, all variables notassociated with the nullcline were held constant at their value at timet. Stability of fixed points was determined from theeigenvalues found by local stability analysis near the fixed point. Aquasi-separatrix is distinct from a nullcline and was identified by theseparation of flow on the phase plane . The quasi-separatrix was plotted byintegrating backward in time (−0.01 ms time step) from point* indicated on relevant figures; see Nullclines were calculated in XPP Istim was systematicallyvaried to determine at what point the system qualitatively changes behavior, which corresponds to a bifurcation. Whereasrepetitive spiking is generated through a bifurcation, single-spikinggenerated through a QSC is not evident on a bifurcation diagram since thesystem's steady state has not changed. The stimulus range overwhich a QSC occurs is therefore indicated based on independentsimulations.In bifurcation analysis,


Incstudies . They hmmation , 4. Howelopment .δ agonistsfor the treatment of metabolic disease is highlighted by 3 key articles. Theyinclude a review from de Lange et al. on the regulation of the oxidative capacity of muscle by PPAR-δ, anarticle by Perreault et al. which tackles opportunities and issues with thedevelopment of PPAR-δ agonist for the treatment ofobesity, and finally a review from Wang that addresses the effect of PPAR-δ activationon vascular pathophysiological processes. A key question regarding the resultof PPAR-δactivation, either via natural or via synthetic ligands, is its effect on cellproliferation and the risk of inducing cancer. This has been an area of intensedebate as both pro- and antitumorigenic effectshave been reported. This topic is concisely reviewed in this issue by Muller etal. Last but not least, two interesting and not well-characterized portions ofPPAR-δbiology are presented. First, as PPAR-δ is expressed at high levelin the brain, Hall et al. investigate the potentialneuroprotective role of PPAR-δ activation in thisorgan. Second, although the role ofPPAR-δ inembryo implantation was recognized early on with studies in knockout mice [δ are still unclear. Thistopic and the projected potential applications of PPAR-δligands in assisted reproductive technology are addressed in Huang’s review.In this special issue of PPAR Research, thebroad potential of PPAR-ut mice ,the repδ function. The currentavailability of synthetic ligands should help to further dissect PPAR-δ-mediated responses in the brain as well as in other functions not addressed inthis issue, including gut and skin homesotasis. Although challenges for the development of PPAR-δagonists remains, they clearly holdgreat therapeutic promise, ashighlighted by recent clinical findings indicating that MBX-8025, one of themost advanced PPAR-δ agonists currently in phaseII clinical trial for dyslipidemia, displays hypolipidemic features not observed with the currentlyavailable dyslipidemia therapies [Taken together, it isobvious that there is an urgent need for additional basic research to bettercharacterize PPAR-herapies , 7.Francine M. GregoireFrancine M. Gregoire


Since human epidermal growth factor receptor 2 (HER2) is known to participate with the epidermal growth factor receptor (EGFR) in mitogenic signalling, we hypothesised that HER2 overexpression might indicate responsiveness to EGFR targeted therapies. MCF7 breast cancer cells transfected with the HER2 gene were subcloned to establish a set of genetically related cell lines expressing graded levels of HER2 by immunoblot analysis. The subcloned cell lines and parental MCF7 cells were characterised by their growth characteristics, and cell by cell patterns of EGFR, HER2 and HER3 expression as well as levels of phosphorylated mitogen-activated protein kinase (MAPK) and AKT by laser scanning cytometry (LSC). Growth inhibition assays were used to characterise response to EGFR targeted therapy, and to determine the relationship between therapeutic response and levels of tyrosine kinase expression. The levels of growth inhibition of AG1478 and of the AG1478-trastuzumab combinations were correlated with levels of HER2 expression among the different cell lines. Among EGFR, HER2 and HER3, HER2 overexpression was the best single predictive marker, but combinations of two markers provided additional predictive information. It has The EGFR is expressed in 15–35% of breast cancers, and is also associated with a poor prognosis .Tumour EGFR expression has not proven to be a useful predictive marker of clinical response to EGFR-targeted therapies , to techRobust predictive markers are needed in order to identify the relatively small subsets of patients whose tumours are likely to respond to EGFR-targeted therapies . CandidaHuman epidermal growth factor receptor 2 and the EGFR are known to heterodimerise with one another to drive mitogenic signalling pathways, and heterodimerisation has been shown to inhibit internalisation and degradation of the EGFR , and incubated at 4°C for 10 min on ice. The transfection mixture was electroporated at 220 V and 950 μF and plated into two 100 mm tissue culture plates. The transfected cells were cultured overnight for recovery and then cultured in the appropriate selective media for 4–8 weeks. Resultant colonies or cell pools were subcloned by colony transfer and plating of cell pools into 24-well plates at an average ratio of 2 cells per well. Individual neomycin resistant clones were expanded and assayed for HER2 expression by immunoblot analysis. SKBR3 (an unrelated HER2 overexpressing breast cancer cell line) was used as an independent comparative reference for HER2 expression in the immunoblots.cDNA encoding human HER2 was excised from a pLXSN retroviral expression construct . The HER2 transfected clonal cell lines were cultured in the same media supplemented with 500 μg ml−1 geneticin.JC 1939, MCF7, and SKBR3 breast cancer cell lines were maintained in RPMI 1640 containing 100 U mlμg) was subject to SDS–PAGE on 4–20% tris/glycine gels, transferred onto nitrocellulose filters, and blocked for 0.5 h in TTBS  with 5% nonfat dry milk. A monoclonal antibody against HER2  was used at a 1 : 1000 dilution. The secondary antibody was goat anti-mouse horseradish peroxidase (HRP) conjugated antibody (Santa Cruz Biotechnology), and was used at a 1 : 1000 dilution. Antibody signal was visualised using the SuperSignal West Pico Chemiluminescent Substrate (Pierce) exposed to Kodak BioMax XAR film. Densitometry was performed using a Personal Densitometer SI (Molecular Dynamics), and the data were analyzed using Scion Image for Windows .Cell lysate (50 2 flasks at 2.5 × 106 cells flask−1 and harvested in log phase growth by trypsinisation were treated with cold (4°C) 5 mM dithiothreitol (DTT) for 15 min at room temperature to reduce clumping and fixed in 0.5% paraformaldehyde and 70% methanol as described previously , and centrifuged at 200 g for 2 min. For EGFR family member analysis, monoclonal antibodies against HER2 , EGFR , and HER3 , directly conjugated to FITC, PE and Cy5, respectively, were used at a 1 : 10 dilution in a 1-h incubation at room temperature in the dark. For the phosphorylated (P) MAPK and AKT analysis, monoclonal antibodies against P-MAPK  and P-AKT  directly conjugated to Cy5 and Cy3, respectively, were used. Cell suspensions were washed 1 × with PBS and resuspended in 100 μl of 4′,6-diamidino-2-phenylindole (DAPI) (Sigma) at a final concentration of 1 μg ml−1 in v v−1 1 : 1 glycerol : PBS. 100 μl of cell suspension was pipetted into a HybriWell chamber (22 × 22 × 0.15 mm3) (Schleicher&Schuell) affixed to precleaned glass microscope slides. Fluorescence measurements were made using a laser scanning cytometer (LSC) (CompuCyte) with the WinCyte (version 3.6) program, equipped with an air-cooled violet diode laser emitting at a wavelength of 405 nm, an air-cooled argon laser emitting at a wavelength of 488 nm, and an a HeNe laser emitting at a wavelength of 633 nm. DAPI fluorescence was measured using a 463/39 nm band pass filter, FITC fluorescence was measured using a 530/30 nm band pass filter with a 555-nm dichroic long pass filter, CY3 fluorescence was measured using a 580/30-nm band pass filter with 605-nm dichroic long pass filter and CY5 fluorescence was measured using a 650-nm long pass filter with a full mirror. A fixed scan area of 1.6 × 108 μm2 centred on the HybriWell chamber was used with all samples. DNA was used as the contouring parameter with a threshold of 700 and 30 pixels added to threshold. For quantitation of EGFR, HER2 and HER3 in molecules per cell, the reference cell line JC 1939 was subjected to ELISAs  with the appropriate antibodies. Fluorescence signal in each cell line from LSC analysis was normalised to the signal from JC 1939 to determine molecules per cell.For LSC analysis, cell lines plated into 75 cmM stock in DMSO. Trastuzumab was a gift from Genentech. Cell stocks were grown to 80% confluence, trypsinised, and plated in triplicate into 12-well tissue culture plates at 2 × 104 cells well−1. The following day, duplicate wells (for day 0 counts) were trypsinised and each well was counted in triplicate with a haemocytometer. The remaining wells were fed every other day with regular cell culture media supplemented with appropriate inhibitor or DMSO as vehicle control. On day 6, the remaining wells were trypsinised and counted in triplicate with a hemocytometer, and the data were graphed in Excel.The EGFR inhibitor AG1478 was purchased from Calbiochem, La Jolla, CA, USA, and was stored at −20°C as a 10 mpost hoc for the determination of differences between groups. Pearson correlation coefficients of linear regression were calculated for −log of surviving cell fraction vs relative HER2 expression.Statistical analyses were performed using SPSS version 12.0 for Windows. Human epidermal growth factor receptor 2 expression, cell growth and drug treatment data were analyzed by one-way ANOVA followed by a two-sided Dunnett's test A and SFB, and the surviving cell fractions of cells treated with the drug combination, SFAB, where CI=SFAB/(SFA × SFB). Triplicate analyses were subjected to two-sided statistical tests  to determine if the mean CI value for each cell line was significantly different from a CI of 1.0 at the P<0.05 level. Synergy was defined by a CI value that was significantly >1.0, and antagonism was defined by a CI value significantly greater than 1.0. Effects were considered additive if the CI was not significantly different from 1.0.The combination index (CI) for drug effect in each cell line was calculated from the surviving cell fractions of cells treated with each drug alone, SFk) to mean expression levels of EGFR, HER2 and HER3 using the formula a1, a2 and a3 were derived to provide a least-squares fit between observed and predicted efficacy ratios across the cell lines examined. Least-squares regression was further attempted for each of the three assayed genes in isolation, using models of the form ã1, ã2 and ã3 constants was derived to provide a least-squares best-fit between the observed efficacy ratios and those predicted from the single gene in isolation. Each of these linear regression analyses was tested by leave-one-out cross validation. For each cell line, parameters were fit without using the data provided by that cell line and the efficacy ratio for the missing cell line was predicted from the derived parameters. The root mean square errors of the predictions were assessed for all regression analyses with and without the cross-validation.Least-squares linear regression analysis was applied to correlate protein expression levels with efficacy ratios of trastuzumab and AG1478 on the parental MCF7 and HER2 expressing subclones. Mean expression of EGFR, HER2 and HER3 were derived from cell-based measurements of molecules per cell by LSC. Drug efficacy ratios were derived for each treatment by taking one minus the ratio of cells counted following treatment to cells counted without treatment. A linear regression model was defined to relate efficacy ratio . The subclones in the high HER2 overexpressing group exhibit higher growth rates than the subclones with intermediate levels of HER2 expression. These findings do not support a monotonic relationship between mean HER2 expression level and growth rate.The relative growth rates of the transfected cell lines are shown in Parental MCF7 cells and HER2 transfected MCF7 subclones were grown to subconfluence, and fed with normal culture media 24 h prior to trypsinisation and cell fixation. The fixed cells were stained with fluorochrome conjugated antibodies against the EGFR, HER2 and HER3 and analyzed by LSC. The results, expressed in molecules per cell and correlated cell by cell, are shown in It is apparent that despite the overall correlations among levels of HER2, EGFR and HER3 in the same cells, there are substantial differences among the different subclones with respect to the ranges of levels of expression of these cell constituents. Human epidermal growth factor receptor 2 levels are lowest in the parental MCF7 cell line and highest in NH27 cells, but NH27 cells have neither high levels of HER3 nor the highest levels of EGFR per cell. In most of the MCF7 subclones that contain high EGFR overexpressing cells, the cells with EGFR levels exceeding 80 000 molecules per cell exhibit only moderately elevated levels of HER2 (mostly in the range of 200 000–600 000 molecules per cell). However, in NH27 cells, among the few that exhibit EGFR levels in excess of 80 000 molecules per cell, most also have HER2 levels that exceed 600 000 molecules per cell. Thus, across subclones, there is a relationship between levels of EGFR expression and HER3 expression per cell that is worth pursuing (see below), but there does not appear to be a strict quantitative relationship between increased EGFR levels and/or HER3 levels on the one hand and increasing HER2 levels on the other.r=0.078, P=0.868; see Parental MCF7 cells and HER2 transfected MCF7 subclones were prepared for LSC, and stained with antibodies against the phosphorylated forms of MAPK and AKT. Correlated cellular levels of P-MAPK and P-AKT were determined by LSC . Quantitr=−0.067, P=0.886; see There was considerable variation in levels of P-MAPK per cell, both within and among the different cell lines . Across Within individual cell lines, the simultaneous measurement of P-MAPK and P-AKT in each cell by LSC reveals a strong correlation between the cellular levels of these two phospho-proteins . HoweverμM AG1478, 1 μg ml−1 trastuzumab, and a combination of 0.5 μM AG1478 and 1 μg ml−1 trastuzumab on cell proliferation are shown in P=0.005). In the high HER2 expressing group, trastuzumab produced an average decrease in cell proliferation that is 24% greater than in parental MCF7 cells (P=0.004), but the difference in the effects of trastuzumab on intermediate and high HER2 overexpressing MCF7 subclones is not statistically significant (P=0.244).The effects of 0.5 P=0.05) and high (P=0.007) HER2 expressing clones, respectively. However, the difference in response between the intermediate and high HER2 expressing clones is not statistically significant (P=0.077).AG1478 treatment does not substantially inhibit the growth of parental MCF7 or the low HER2 overexpressing subclones .Combined treatment of these cell lines with both AG1478 and trastuzumab  was achieved using a combination of HER2 and HER3 expression levels. Interestingly, the overall smallest root mean square error for prediction of AG1478 efficacy (0.180) is achieved using a combination of EGFR and HER3 expression levels. Predictions using all three receptor expression levels together result in errors that were not as low as those derived from the best individual or two-receptor predictions, suggesting that the data set may be too small to draw robust inferences from the three-protein model.It is generally agreed that there is a need for better clinical biomarkers for response to EGFR targeted therapy . OverallHuman epidermal growth factor receptor 2 is known to heterodimerise preferentially with the EGFR (Our panel of HER2-transfected subclones was intended to provide the opportunity to study the effects of targeted therapeutic agents in a family of cell lines that have few overall genotypic differences, and that express graded levels of HER2 protein. Nonetheless, there are differences among these subclones in quantitative levels of intracellular co-expression of key proteins, including HER2, EGFR and HER3 , and relcombination of predictors for the efficacy of AG1478 or combined AG1478-trastuzumab is increased expression of the EGFR and HER3 (Other cell constituents might also predict response to anti-EGFR therapy, and some might provide independent predictive information. The data on EGFR, HER2 and HER3 expression levels by LSC support this possibility. We have noted that high cellular levels of EGF receptor are often accompanied by high levels of HER3 in the same cells . The linand HER3 .in vitro can be translated directly into the clinic. Additional support from experimental studies in vivo would be highly desirable. Such studies would require the development and validation of a suitable panel of tumour lines that stably overexpress graded levels of HER2 in vivo.Of course, one cannot assume that results obtained on cell lines Several published clinical studies suggest that our findings in breast cancer cell lines might be of some relevance in the clinical setting. In a recent study of breast cancer patients, 35% of 306 HER2-overexpressing tumours were found to express EGFR Conversely, 87% of EGFR-overexpressing tumours were found to overexpress HER2 as well (In summary, we show that in human breast cancer cell lines increased levels of HER2 expression alone are associated with increased effectiveness of anti-EGFR therapy, alone or in combination with anti-HER2 therapy, and that the combination of EGFR and HER3 overexpression may be an even better predictor of response. This would suggest the possibility that HER2 overexpression alone and/or the combination of EGFR and HER3 expression levels might be useful clinical markers for response to EGFR and combined EGFR–HER2 targeted therapy in patients with breast cancer.


The major histocompatibility complex (MHC) of immunity genes has been reported to influence mate choice in vertebrates, and a recent study presented genetic evidence for this effect in humans. Specifically, greater dissimilarity at the MHC locus was reported for European-American mates (parents in HapMap Phase 2 trios) than for non-mates. Here we show that the results depend on a few extreme data points, are not robust to conservative changes in the analysis procedure, and cannot be reproduced in an equivalent but independent set of European-American mates. Although some evidence suggests an avoidance of extreme MHC similarity between mates, rather than a preference for dissimilarity, limited sample sizes preclude a rigorous investigation. In summary, fine-scale molecular-genetic data do not conclusively support the hypothesis that mate selection in humans is influenced by the MHC locus. There is evidence in numerous species that genes involved in immunity influence mate choice. Factors like body odor may subconsciously favor partners with different immunity alleles, to avoid inbreeding and/or endow offspring with broad resistance to pathogens. A previous study, based on HapMap genotypes, reported that European-American mates were extremely dissimilar from each other in immunity alleles compared to non-mates. Upon re-examining the results and methods, and visually comparing mates and non-mates, we found that this effect was weak, strongly dependent on extreme pairs and on arbitrary choices in methodology, and not significant after correcting for the multiple hypotheses tested. More importantly, examination of new couples from the same population did not support this hypothesis. Rare instances of very high MHC similarity among non-mates suggest that mates may avoid extreme similarity, rather than favoring dissimilarity. However, too few samples are readily available to test this prospect rigorously. We conclude that HapMap samples do not conclusively support the hypothesis that MHC genotypes exert an influence on mate choice. The same previous study reported that Yorubans appear to prefer mates who are more genetically similar to themselves overall. Our analyses suggest that the effect is driven by a subset of the sample. The MHC locus contains genes central to acquired immunity, as well as numerous olfactory receptors Despite the advent of MHC-based matchmaking services bona fide mate selection rather than a stated preference. A recent study by Chaix et al. sought signs of mate selection in the genotypes of HapMap Phase 2 (Hap2) parents by comparing the genetic relatedness of mated and unmated opposite-sex couples, both at the MHC locus and overall  P<0.001) overall, but no significant difference in MHC relatedness was detected between mates and non-mates. By contrast, European-American mates (N = 28 couples) did not differ significantly from non-mates in autosomal relatedness, but were less similar at the MHC locus than were non-mates (P = 0.015).HapMap genotypes of father-mother-child trios afford the chance to test for an association of MHC similarity with The latter result was interpreted as supporting a role for the MHC locus in mate choice, and outliers were excluded as an explanation The availability of HapMap Phase 3 (Hap3) data now permits a test of these findings using independent samples from the same population.http://www.hapmap.org). There were 30 mated couples in each cohort . While Chaix et al. referred to Q as the proportion of identical variants, a comparison of an individual with themselves (self-self) using the het-het  = 50% score yields values of Q that are variable and lower than unity , mean(cohort)Q, was then derived, and the relatedness coefficient for a and b was calculated as a,bR  =  Q)/(1−mean(cohort)Q). The mean R (meanR) for couples, mean(mates)R, was calculated; its significance was then assessed by randomly pairing males and females 1,000 times and recording the frequency P with which the absolute value of meanR from a random trial was equal to or greater than that observed for real couples. Following the previous report, actual couples were allowed in random trials; however, we found that excluding them had little impact on the results . Q, R and P values were also calculated based on SNPs at the MHC locus, defined as spanning positions 29,700,000 to 33,300,000 on chromosome 6 for NCBI build 35 The mean P values, the difference in relatedness for mates and non-mates was quantified as a Z score, namely (mean(mates)R−mean(non-mates))R/sd(non-mates)R, where mean(non-mates)R and sd(non-mates)R are the mean and standard deviation, respectively, of the relatedness of non-mate pairs. Because we wished to assess our results using the methods of the previous report Z score on standard deviations rather than median deviations. We note that P values are based on random trials in which real mate pairs were allowed, for consistency with the previous report Z scores compare mates with the non-mate distribution; however, this distinction has a negligible impact on results (data not shown).The methods were modified in various ways, both to explore the robustness of results to changes in the procedure, and to establish the applicability of the analysis to Hap3 couples; details are provided in P values derived from different sets of 1,000 random trials Q and all pairwise R values were first calculated with all samples included in order to detect relatives  but found them unchanged. MAFs were based on all available samples, including children and unmated individuals, although this was found to have a negligible impact on the number of remaining SNPs (not shown). The minimum MAF was lowered to 1% to conform more closely to the standard definition of common variant, yielding identity coefficients consistent with those based on MAF ≥5% Q), and therefore did not influence comparisons of relatedness (R) in mate and non-mate pairs. Concordance of results with phased genotypes was verified for Hap2  was conducted separately for the subset of Hap3 samples also present in Hap2, for all Hap3 samples, and for samples present in Hap3 only. Because the latter subset of samples allows an independent test of findings reported previously in Hap2, a one-tailed test of significance was conducted in this case for the previously reported hypothesis of interest: 1) relatedness is lower than expected for MHC SNPs in European mates, or 2) relatedness is higher than expected for autosomal SNPs in Yoruban mates.Unphased genotypes were obtained for Hap2 (release 24) and Hap3 (release 2) populations with parent-child trios. Mate-pair relatedness analyses were only conducted for Europeans and Yorubans due to insufficient numbers of couples in the other Hap 3 population samples (N≤16 following exclusions to avoid the presence of close relatives). Uncalled alleles, which are corrected in phased genotypes for Hap2 . In Hap3et al. calculated the recombination rate and mean relatedness between mates mean(mates)R for 3.6 Mbp segments throughout the genome et al., the genome was divided into segments of 3.6 Mbp tiled every 300 Kbp, segments with fewer than 1000 SNPs or overlapping centromeres were excluded, and mean(mates)R was calculated for each segment based on all SNPs falling within that segment. Additional steps, which may differ from the methods of Chaix et al., were as follows. First, all mated individuals were included in the analyses but pairs of relatives were not included in the mean population-wide identity coefficient, mean(cohort)Q. Second, the recombination rate for each segment (in cM/Mbp) was calculated from the difference in recombination (cM) between the two data points closest to each end of the segment, with the denominator fixed at 3.6 Mbp. Third, we also conducted this analysis for male-female non-mate pairs; in this case, pairs of relatives were excluded from mean(cohort)Q and from the mean relatedness for non-mate pairs, mean(non-mates)R. Lastly, segments overlapping the MHC locus were excluded.Chaix et al. tested four specific locus/population combinations , reporting that two of these revealed significant phenomena and two did not; however, no adjustment was made for multiple hypothesis testing et al. reported a P value of 0.015 as significant α = 0.05. For k hypotheses tested, the corrected threshold according to the Dunn-Šidák method α' = 1-(1-α)k1/, so that α' = 0.0253 for k = 2 and α' = 0.0127 for k = 4.Chaix P values reported in this study are two-sided unless specified otherwise. They are also nominal (uncorrected for multiple testing), so that any correction would further reduce significance.We note that all et al.Z = −0.41) and was diminished in significance by small changes in the analysis (P = 0.014 to P = 0.022) when the number of random trials was increased , and was lost (P = 0.052) upon the subsequent exclusion of the single most MHC-dissimilar couple . In addition, results varied depending on which couples were excluded to avoid the presence of related individuals (0.019≤P≤0.034). Chaix et al. reported results based on an identity measure that assigns a score of 50% to two heterozygous genotypes (het-het  = 50%), but noted that results were similar with het-het  = 100% P = 0.0546 and P = 0.0592) in two of the four possible permutations of excluded couples.We successfully replicated each of the specific results of Chaix analysis . SignifiSubsequent analyses were based on unphased genotypes, SNPs with MAF ≥1%, het-het  = 100%, 100,000 random trials, and aggregate values of relatedness and significance calculated for all possible ways of excluding samples such that pairs of related invididuals are eliminated see . In Hap2After replicating previously reported results, we sought to test the hypothesis of MHC-dependent mate selection in an independent sample from the same population. Of the 50 couples genotyped in Hap3, 26 “Hap2∩3” couples were also present in Hap2 while 24 “Hap3-only” couples were unique to Hap3 , the consistency of results obtained with Hap2 and Hap3 genotypes suggested that Hap3-only couples represented a valid test of previously reported findings.First, we verified that Hap2 and Hap3 genotypes were concordant for each Hap2∩3 sample  and yielZ = −0.08, one-tailed P = 0.351; Z = −0.24, P = 0.14; The 24 independent European mated Hap3-only couples showed negligible and insignificant dissimilarity at the MHC locus relative to random pairs , even when only common SNPs were considered .Standards vary on when, whether and how to correct significance tests when multiple hypotheses are examined. Chaix 0253 see , so thatper seα' = 0.0127, so that MHC relatedness in Hap2 European mates would no longer differ significantly from expectation (P = 0.015≥α'), whereas overall relatedness in Yoruban couples would (P<0.001).Analyses of autosomal SNPs were introduced in the previous report as negative controls; however, the excess autosomal relatedness in Yoruban mates was presented as a significant result We found that the previously reported MHC dissimilarity among Hap2 European-American mate pairs P value. It is certainly true that multiple testing corrections are a matter of ongoing debate and diverse preference, and overly conservative approaches can lead to a substantial loss of power. However, even stricter approaches to multiple testing can be argued. For example, the authors might also have corrected for the several instances wherein they tested two alternative methods and chose one.This conclusion is supported by the observation that a stringent correction for multiple-hypothesis testing renders the original finding of MHC dissimilarity insignificant, even given the previously-reported nominal (uncorrected) Although our analysis of HapMap genotypes does not support a broad and significant dependence of mate selection on MHC, a weak effect is apparent in Hap2 even when the most extreme couples are excluded Click here for additional data file.Figure S2Sporadic cases of high MHC similarity in HapMap European non-mate pairs.(0.10 MB PDF)Click here for additional data file.Table S1Replication of previous findings in Hap2 mate pairs.(0.06 MB PDF)Click here for additional data file.Table S2Detailed results for MHC relatedness in European couples.(0.08 MB PDF)Click here for additional data file.Table S3Detailed results for autosomal relatedness in Yoruban couples.(0.08 MB PDF)Click here for additional data file.Text S1Overview of procedure and datasets.(0.10 MB PDF)Click here for additional data file.Text S2Sample pairs identified as relatives.(0.08 MB PDF)Click here for additional data file.Text S3Modification of methods, tests of concordance, and application to Hap3 cohorts.(0.55 MB PDF)Click here for additional data file.Text S4Critical impact of an extreme couple on mate-pair MHC dissimilarity.(0.10 MB PDF)Click here for additional data file.Text S5Analysis of Hap2∩3 couples with Hap2 and Hap3 genotypes.(0.12 MB PDF)Click here for additional data file.Text S6Discrepancy detected in Hap2 Yorubans.(0.09 MB PDF)Click here for additional data file.Text S7Genome-wide relatedness and recombination rate.(0.09 MB PDF)Click here for additional data file.


TGFβ is both neuroprotective and a key immune system modulator and is likely to be an important target for future stroke therapy. The precise function of increased TGF-β1 after stroke is unknown and its pleiotropic nature means that it may convey a neuroprotective signal, orchestrate glial scarring or function as an important immune system regulator. We therefore investigated the time course and cell-specificity of TGFβ signaling after stroke, and whether its signaling pattern is altered by gender and aging.We performed distal middle cerebral artery occlusion strokes on 5 and 18 month old TGFβ reporter mice to get a readout of TGFβ responses after stroke in real time. To determine which cell type is the source of increased TGFβ production after stroke, brain sections were stained with an anti-TGFβ antibody, colocalized with markers for reactive astrocytes, neurons, and activated microglia. To determine which cells are responding to TGFβ after stroke, brain sections were double-labelled with anti-pSmad2, a marker of TGFβ signaling, and markers of neurons, oligodendrocytes, endothelial cells, astrocytes and microglia.TGFβ signaling increased 2 fold after stroke, beginning on day 1 and peaking on day 7. This pattern of increase was preserved in old animals and absolute TGFβ signaling in the brain increased with age. Activated microglia and macrophages were the predominant source of increased TGFβ after stroke and astrocytes and activated microglia and macrophages demonstrated dramatic upregulation of TGFβ signaling after stroke. TGFβ signaling in neurons and oligodendrocytes did not undergo marked changes.We found that TGFβ signaling increases with age and that astrocytes and activated microglia and macrophages are the main cell types that undergo increased TGFβ signaling in response to post-stroke increases in TGFβ. Therefore increased TGFβ after stroke likely regulates glial scar formation and the immune response to stroke. Transforming Growth Factor β1 (TGF-β1) is universally induced by acute and chronic brain injury, including stroke, trauma, seizure, multiple sclerosis, and Alzheimer's disease . In the After brain injury, TGFβ signaling can be neuroprotective, but also promote glial scarring and fibrosis ,4-6. WhiIn stroke, TGF-β1 mRNA is elevated for at least a week afterwards  and cleaWe therefore investigated the timecourse and cell-specificity of TGFβ signaling after stroke, and whether its signaling pattern is altered by gender and aging in a mouse model of stroke. We report here that TGFβ signaling increases 2 fold after the dMCAO model of stroke, beginning on day 1 and peaking on day 7. This pattern of increase is similar in each gender and preserved in old animals, although absolute TGFβ signaling was much higher in the brains of the aged animals. CD68+ activated microglia and macrophages were the predominant source of increased TGF-β1 after stroke and astrocytes and CD68+ cells were the main cell types that responded to the post-stroke increase in TGF-β1. Our results demonstrate that TGFβ signaling peaks at 7 days after dMCAO stroke, increases with aging, and that the cells with increased responses during this time period, astrocytes and microglia, are those involved in glial scarring and innate immune responses. This may indicate that TGFβ signaling plays a role in regulating both the immune response and the glial scar after stroke.SBE-LucRT mice were a gift from Dr Tony Wyss-Coray . Nine feDistal middle cerebral artery occlusion (dMCAO) was induced as described previously . Briefly2 per steradian (sr) by using LIVINGIMAGE software (Xenogen) and integrated over 5 min. For photon quantification, a region of interest was manually selected and kept constant within all experiments. Baseline imaging was performed 1 week before stroke was induced and bioluminescence was expressed as fold induction over baseline levels.Bioluminescence was detected as described previously using the In Vivo Imaging System  . Mice weFor determination of stage of estrus, sterile cotton swabs moistened with saline were inserted into the vagina and gently rotated. Swabs were then rolled onto glass slides and allowed to dry. Polymorphonuclear lymphocytes (PMNs) and epithelial cells were visualized with a Hema 3 Stat Pack (Fisher Cat#23-123-869) used according to the manufactuer's directions. Stage of estrus cycle was determined based on the ratio of PMN's to nucleated and cornified epithlieal cells. Diestrus was defined as the stage when cells consisted mainly of polymorphonuclear lymphocytes (PMNs). Proestrus was defined as the stage when nucleated and cornified epithelial cells were observed, along with a few PMNs. Estrus was classified as the stage when cornified epithelial cells were predominant, and metestrus was defined as the stage when cornified epithelial cells and PMNs were detected.To visualize the region of infarction, representative animals from each age group (n ≥ 5) were sacrificed at 24 hours post stroke and their brains processed for TTC staining. Brains were sectioned into 1 mm thick slices and each slice was immersed in 1.5% TTC in PBS at 37°C for 15 minutes and then fixed in 10% formalin. The area of the ipsilateral hemisphere and the area of the infarct on each section was measured in a blinded fashion using NIH ImageJ 1.43u. The measurements were multiplied by the distance between sections (1 mm) and then summed over the entire brain to yield volume measurements.Immunoflorescence was performed on PFA fixed free-floating coronal brain sections (40 μm) using standard techniques. The following primary antibodies were used: anti-TGF-β1 , anti-pSmad2 ; Milli-Mark Pan Neuronal marker ; anti-GFAP ; anti-CD68 ; anti CAII  and anti-β-dystroglycan . For quantification of Milli-Mark Pan Neuronal marker, CAII, CD68 and GFAP colocalization with pSmad2, two sections from each mouse spaced 300 μm apart were immunostained. From each section four random fields of view in the penumbra (40× objective) were analyzed. The total number of cells positive for each cell marker was averaged and the percentage of each population that was also positive for pSmad2 was calculated.Unstroked and stroked brains were lysed in glo lysis buffer (Promega). Protein concentrations were equalized and lysates mixed with 4× NuPage LDS loading buffer (Invitrogen). Samples were loaded onto 4-12% NuPage Bis-Tris gels (Invitrogen) and subsequently transferred onto PVDF membranes. Blots were incubated with rabbit polyclonal antibodies (1:1000) against pSmad2  and actin (cat# A5060 Sigma-Aldritch) and horseradish peroxidase-conjugated goat anti-rabbit IgG as secondary antibody. Protein signals were detected using an ECL kit (Amersham Pharmacia Biotech) and band intensities were quantified on the original digital image using UN-Scan-It Gel 6.1.Data was acquired in a blinded and unbiased fashion. Data are expressed as mean +/- standard error of the mean (SEM). Statistical analyses were performed with Prism 5 software . Means between two groups were compared with two-tailed, unpaired Student's t tests; comparisons of means from multiple groups with one control were analyzed with one-way ANOVA and Dunnet's post hoc test.TGFβ pathway activation is transmitted primarily through the Smad system. Receptor-regulated Smads  are phosphorylated, associate with a co-Smad (Smad 4) and translocate to the nucleus, where they bind to Smad binding elements (SBE) to activate transcription of target genes ,16. SBE-The dMCAO model of stroke produces a small lesion in the cortex with a clearly defined border and a small penumbra. In line with previous reports infarct volume was significantly larger in the aged mice and astrogliosis (GFAP immunoreactivity) was accelerated Fig  18,19].,19.18,19To confirm the increase in TGFβ signaling we detected by luciferase activity, we performed a western blot on brain extracts from the ipsilateral hemisphere of mice sacrificed before, and 7 days after stroke, for phosphorylated Smad2 (pSmad2), a downstream mediator of TGFβ signaling. The pSmad2 antibody we used for immunoflorescence (Millipore AB3849) detected a protein with the correct molecular weight for pSmad2 (58 kDa) and bound the same band as an alternative pSmad2 antibody , neurons , and monocytic immune cells (CD68). Although TGF-β1 colocalized with all three cell types it was predominantly co-localized with CD68, which marks both activated microglia and macrophages. These cells were located in both the ischemic penumbra and the stroke core. These data suggest that endogenous microglia and invading macrophages are the predominant cell types that make TGF-β1 after ischemic brain injury Fig .To determine which cells are responding to TGFβ in the uninjured brain and after stroke, brain sections from mice sacrificed at days 1, 3, 7, 14 and 21 after stroke and at 21 days after sham surgery were double-labeled with anti-pSmad2, a marker of TGFβ signaling, and markers of neurons , oligodendrocytes (CAII), endothelial cells (β-dystroglycan), astrocytes (GFAP) and activated microglia and macrophages (CD68). We found that neurons uniformly respond to TGFβ in the absence of injury and at all time points after stroke, with 100% of neurons co-localizing with pSmad2 (Fig Although we found no difference in TGFβ signaling in neurons, oligodendrocytes and endothelial cells after stroke, we did find a qualitative increase in TGFβ signaling in astrocytes and microglia/macrophages. In the days following stroke, pSmad2 immunoflorescence became brighter in the border of the lesion Fig  comparedWe show here for the first time that TGFβ signaling in the brain increases in the first week after stroke in both young and old mice. We incorporated 18 month old mice into this study because nearly three quarters of all strokes occur in people over the age of 65 and there is a large knowledge gap regarding how mechanisms of recovery change with age. Similarly, we performed this study on mice of both genders in order to determine if there is a sex difference in TGFβ signaling after stroke. In 5 month old mice, TGFβ signaling increased 2 fold over baseline in the first week after stroke, before beginning to return to pre-stroke levels. This profile of increased TGFβ signaling was similar in 18 month old mice, although the absolute level of TGFβ signaling was significantly higher in the older animals. This could be in response to greater damage in the older animals as we found that lesion size was over double the volume compared to the younger mice. Baseline TGFβ signaling was also higher in the aged animals and so increased TGFβ signaling after stroke may also be a reflection of their higher baseline level. Our finding that infarct volume is increased in older animals conflicts with other studies that report that infarct volume is not increased in older subjects . HoweverEvery cell type in the brain has been shown to be capable of making TGF-β1 and increases in TGF-β1 mRNA have been demonstrated after stroke . To discWhile there is ample evidence that TGFβ production is increased after stroke, as it is after many kinds of brain injury, it was not known whether increased TGFβ production translates to an increased response, because all TGFβ isoforms are excreted as an inactive form that lies inert in the extracellular matrix . TGFβ reTGF-β1 is strongly neuroprotective, at least in part due to direct effects in neurons ,24-28. ISurprisingly, we also found that the majority of oligodendrocytes in the brain are responding to TGFβ in the absence of injury and after stroke. This suggests that TGFβ signaling plays a physiological role in oligodendrocytes that was heretofore unappreciated. As with neurons, co-localization of pSmad2 with oligodendrocytes did not increase after stroke suggesting that the role of increased TGF-β1 after stroke is not to communicate to this cell type. Whether constitutive TGFβ signaling in oligodendrocytes is important for their function or survival remains to be elucidated.We rarely found co-localization of pSmad2 with endothelial cells. Signal transduction by TGFβ family members is mediated via specific heteromeric complexes of type I and type II serine/threonine kinase receptors. In most cells TGFβ signals via the type I receptor ALK5 but in endothelial cells it can also signal via the type I receptor ALK1 ,30. ALK5Our data demonstrates that CD68+ activated microglia and macrophages respond in an autocrine manner to the TGF-β1 they produce. We found that co-localization of CD68 with pSmad2 increased in parallel with TGFβ signaling reporter gene expression after stroke. TGFβ can exert either anti-inflammatory or pro-inflammatory effects in a context-dependent fashion. However, in the majority of contexts TGF-β1 appears to play an anti-inflammatory role and moderate the production of neurotoxic pro-inflammatory cytokines. It drives the differentiation of regulatory T cells and M2c macrophages that resolve immune responses, and resolves the inflammatory process in myocardial ischemia ,31,32. Ain vitro and in vivo evidence that TGFβ plays an important role in generating reactive astrogliosis, and that reactive astrocytes participate in innate immune responses. Overexpression of TGFβ chronically leads to reactive astrogliosis, and these mice display exacerbated reactive astrogliosis in a stab wound model [Our data also demonstrates that co-localization of pSmad2 with activated astrocytes increases after stroke. There is strong nd model . Injectind model , while ind model ,34. Finand model . Prior tnd model .As the brain ages it becomes characterized by a low grade chronic pro-inflammatory state and responds with an earlier and stronger innate immune response following stroke . We founThese results show that TGFβ signaling in the brain does not oscillate with the estrus cycle and that it increases 2 fold after the dMCAO model of stroke in young and old animals of each gender. Astrocytes and CD68+ innate immune cells are the cell types that respond to increased levels of TGFβ after stroke and they do so during the first week after stroke, coincident with glial scar formation. Given TGFβs effects on monocytic lineage cells and astrocytes we hypothesize that the role of increased TGFβ signaling after stroke is to regulate glial scar formation and the polarization of invading innate immune cells and resident microglia.The authors declare that they have no competing interests.KPD performed the surgery, estrus determination and live imaging. KPD, EC and LEM carried out the immunoflorescence. KPD and MSB conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.3D projection of neurons colocalizing with pSmad2. 3D projection of Figure Click here for file3D projection of oligodendrocytes colocalizing with pSmad2. 3D projection of Figure Click here for file3D projection of lack of colocalization of endothelial cells with pSmad2. 3D projection of Figure Click here for file3D projection of astrocytes colocalizing with pSmad2. 3D projection of Figure Click here for file3D projection of microglia colocalizing with pSmad2. 3D projection of Figure Click here for file


T0070907 does not alter microtubule polymerization in vitro, and does not appear to work by triggering modulation of tubulin RNA levels subsequent to decreased polymerization. This observation suggests the possible development of antimicrotubule drugs that work by a novel mechanism, and implies the presence of cancer therapeutic targets that have not yet been exploited. This review summarizes what is known about PPARγ inhibitors and cancer cell death, with emphasis on the tubulin phenotype and PPAR-dependence, and identifies potential mechanisms of action.The microtubule-targeting agents (MTAs) are a very successful class of cancer drugs with therapeutic benefits in both hematopoietic and solid tumors. However, resistance to these drugs is a significant problem. Current MTAs bind to microtubules, and/or to their constituent tubulin heterodimers, and affect microtubule polymerization and dynamics. The PPAR They have important roles incontrol of metabolism, inflammation, and cell growth and differentiation. There are three PPAR isoforms (unctions. Much evr target .γ (NR1C3) is able to both activate and repress transcription, depending on the promoter that is involved [γ binds to promoters containingPPAR-response elements (PPREs) in combination with its heterodimer partner, theretinoid X receptor. Activator ligand binding to PPARγ causes a structural shift that increases its ability torecruit transcriptional coactivators while decreasing its basal ability to bindto corepressors [γ also exhibits transrepressive functions at promoters lacking aPPRE [γ binding inhibitstranscription, either by binding/sequestering the transcription factors or bypreventing clearance of repressor complexes. In at least one case of transrepression, the specific PPARγconformational shift required is different from that required for cofactorrecruitment at PPREs [γ has basal ligand-independentrepression [γ inhibitorbinding and PPARγ knockdown may not be the same.PPARγ NR1C is able pressors . PPARγ ag aPPRE , by bindat PPREs . It is wpression  and actiγ NR1C is able γ can be activated pharmacologically by thiazolidenedione (TZD) compounds, including the antidiabetic drugspioglitazone and rosiglitazone. There are multiple studies showing that high doses of TZDs can inhibit tumor growthin cell lines and mouse models. Clinical trials are currently underway testing TZDs as chemopreventive and therapeuticagents in human cancers [γ activity, theyalso have multiple PPARγ-independent effects, and the specific role of PPARγ activationitself in the therapeutic effects of TZDs is still an active area of research. These topics are reviewed, from the point ofview of cancer therapeutic effects, in several recent reviews [PPAR research.PPAR cancers . While T reviews –18 and eγ inhibitor compounds are also able to reduce tumor growth in preclinical models [γ activity in cell death is an active research area, and may depend on thespecific cell type. Our recent observation that PPARγ inhibitors can cause rapid dissolution of the microtubule network in colon cancer cells [Vinca alkaloids that are in current clinical use. However, unlike MTAs [α and β tubulin proteins long before a commitment toapoptosis, and do not strongly affect microtubule polymerization in vitro. This review will focus on the strong possibilitythat PPARγ inhibitor compounds represent a new class of tubulin-targetingagents [Several studies indicate that PPARl models , 19–29. er cells suggestsike MTAs , they magagents .γ ligand-binding pocket can accommodate a variety of lipophilic molecules [γ, including oxidized low-density lipoproteins, unsaturated fatty acids, 15-hydroxyeicosatetraenoic acid, and 9-and 13-hydroxyoctadecadienoic acids. In addition, the putative endogenous ligand prostaglandin 15-deoxy delta--prostaglandin J2, as well as the TZD anti-diabetic drugs [γ. The anti-inflammatory drug 5-aminosalicylicacid binds to PPARγ at therapeutic doses [γ conformational shifts that favor recruitment of transcriptional coactivators over corepressorsor that promote specific posttranslational modifications, and it is these changesthat dictate the transcriptional activity of PPARγ. All of these ligands also have multipleeffects that are independent of PPARγ, especially at high doses [The PPARolecules . Many ceic drugs , are ablic doses , as do oic doses , althouggh doses . In addiγ also binds to a number of compounds that are able to inhibit TZD-mediated PPARγ activation  at high doses. At low micromolar doses, T0070907 and GW9662 also bind to and inhibit PPARα and PPARδ  than those needed forinhibitory effects [Interpreting the effects of the cancer-targeting PPARnd PPARδ . In addi effects , 40.α/β tubulin heterodimers , and also movetubulin heterodimers from one end of the polymer to the other (treadmilling) [Microtubules are long, tube-shaped polymers, formed by ordered arrays of rodimers , thatmamilling) . These pmilling) –46. Bothmilling) .Vinca domain, or the colchicine domain. Historically, MTAs have been divided into microtubule-stabilizing and microtubule-destabilizingagents, based on their effects at high doses on polymer mass in in vitro polymerization assays. However, while the classification remains inuse, and these effects clearly occur in vivo at high doses, it is becoming generally accepted that MTAs atclinically relevant concentrations primarily act by disrupting microtubuledynamics, rather than by affecting bulk polymerization [Given the importance of rapid cell proliferation and migration to tumor development, it is not surprising that themicrotubule-targeting agents (MTAs) are one of the most successful classes of cancer therapeutics, with clinical applications in hematological cancers and solid tumors of the head/neck, breast, ovaries, testes, lung, gastric tissue,and prostate . MTAs arrization , 49. Therization , 51.γ inhibitors reduce tubulin levels in HT-29 colon cancercells, before a commitment to apoptosis [Discovery and development of new microtubule-binding compounds is an area of active research. Incontrast, less effort has been spent on considering whether reducing tubulinlevels directly, and thereby altering microtubule function, could be used to impaircancer cells. Our recent results, showing that PPARpoptosis , suggestγ inhibitors can cause cell death. T0070907 and/or GW9662 exhibitedantiproliferative effects in both hematopoietic cell lines from non-Hodgkin'slymphoma and multiple myeloma [μM for T0070907 and 10–50 μM forGW9662. While the reasons for this reduction in cell number have not been explored in all cases, T0070907 andGW9662 clearly caused apoptosis in several epithelial cell lines. BADGE also exhibited cytotoxic effects againstcolon cancer cell lines [μM range.Experiments with many different cancer cell lines show thathigh doses of PPAR myeloma  andepit myeloma , breast myeloma , 22, 27, myeloma , 52, ora myeloma ,esophag myeloma , prostat myeloma . IC50 coll lines , 31 and ll lines , 20, atμM T0070907 led to dissolution of the microtubule network within 12 hours [γ inhibitor GW9662 , it will be difficult to do geneticreplacement experiments to address this issue in the absence of otherinformation about the reasons for tubulin loss. Regardless of whether or not microtubule disruption is the first triggerfor apoptosis, the reduction in tubulin should serve as a barrier that isimpossible for the tumor cells to surmount. This effect could have profound advantages, in that simply modulatingthe levels of anti-apoptotic proteins in response to chemotherapy would not besufficient to allow tumor escape.The effects of T0070907 and GW9662 on tubulin are striking, especially as there have not been any reports of cancer cell-targeting smallmolecules that affect tubulin levels without dramatic effects on microtubule polymerization. However, it is not clearthat the ultimate cause of cell death is loss of tubulin. These compounds may independently target acombination of signaling pathways that ultimately trigger the apoptoticresponse as well as modulating tubulin levels. Given the fact that the PPARSaccharomyes cerevisiae, which wasable to tolerate a 50% reduction in either α or β tubulin, as long as therewere not excess unpaired β tubulin molecules [Intriguingly, commitment to apoptosis in HT-29 cells occurredat about the same time that tubulin levels dropped below a threshold level fornormal function observed in yeast. After 12 hours of T0070907 treatment, whenthe tumor cells had lost ∼50% of their tubulin, the effects of the drug werestill reversible. By the time, 50% ofthe cells had committed to apoptosis (∼20 h), the average tubulin level was lessthan 10% of control  of tubulin loss will serve as an importantstep in identifying the therapeutic targets that are exploited by T0070907, andin design of better ways to target them. In HT-29 cells, rodimers , and disBecause the protein half-life of tubulin is believed to belong (∼50 hours) , 57, detautoregulatory control. This mechanism is associated with largereductions in tubulin mRNA; later work showed that polysomal mRNA in theprocess of being translated was specifically susceptible to an increased mRNAdecay [μM)doses increases the amount of soluble tubulin by inhibiting bulk microtubulepolymerization [T0070907-induced tubulin loss is unlikely to be the result ofacute increases in tubulin monomer protein. Many eukaryotic cells respond to a sudden increase in unpolymerizedtubulin by reducing synthesis of tubulin –65 in a NAdecay , 67. T00NAdecay . In addirization , did notγ activators have been shown tosuppress translation initiation in a PPARγ-independent manner through amechanism involving intracellular Ca2+ store depletion andsubsequent inhibition of the eIF2 translation inititation factor [There is also the potential for T0070907 to controltranslation initiation or other aspects of protein synthesis. The TZD PPARn factor . It is pγ inhibitors may interfere withthis pathway. Tubulin production and assembly into α/β heterodimers require the presence of multiplechaperone proteins, including the multisubunit chaperones prefoldin [γ inhibitors bind directly to andinhibit some of the chaperone proteins. Alternatively, they may change the expression or function of any of thechaperone subunits or cofactors.The loss of tubulin and cell death phenotypes induced byT0070907 can be mimicked by knockdown of chaperone proteins involved in foldingand assembly of tubulin, suggesting that PPARrefoldin  and CCT refoldin   and fewer microtubules in Drosophila oocytes [Knockdown of microtubule-associated proteins (MAPs) can also cause loss of tubulin, and PPARdynamics. Others  oocytes . In the  oocytes . In bothγ. Although results in two cell lines [γ knockdown causes cell death or potentiates the effects of the inhibitors, this resultdoes not occur in all cell lines [μM for T0070907 and 10–50 μM for GW9662) in all cell lines tested are much higher than those neededto inhibit the transcriptional effect of PPARγ by at least 90%, given anapproximate effective dissociation constant in the low nanomolar range , the compounds did not cause weightloss or malaise in mice . In addiγ (or PPARα/δ) function is required for the tubulin-targeting effects ofT0070907 and/or GW9662. If suppression of PPARγ cannot be separated from the tubulin targeting effects, it will benecessary to carefully balance the therapeutic effects of PPARγ inhibitors ontubulin with the possible deleterious effects of PPARγ inhibition onphysiologic processes that affect tumor growth. In addition to the question of whether PPARγ is a tumor suppressor orhas tumor-promoting activity in each cancer cell type, the effects of PPARγ on angiogenesis[A major question is whether suppression of PPARogenesis, 16 and ogenesis must alsγ itself in angiogenesis, in contrastto the effects of TZD PPARγ activators, is still relatively unclear [γ-dependent. It is also noteworthy that standard microtubule-targeting agents in clinical usedisrupt tumor-specific vasculature [γ inhibitors also suppress tumor angiogenesis, and whether the effectsare linked to PPARγ and/or tubulin.The role of PPAR unclear . TZDs ca unclear  and decr unclear  areknowculature , 91. It γ inhibition on the immunesystem will depend upon the individual characteristics of the tumor, including thesite of the tumor, the role of PPARγ in tumor-intrinsic biology, and thepresence and type of immune infiltration. A variety of immunecells infiltrate tumors, including macrophage lineage cells, T lymphocytes, mastcells, and natural killer (NK) cells [reg) functions are modulated byPPARγ. Monocytes can differentiate into M1 or M2 macrophages in response to different stimuli [γ activation of humanmonocytes promotes M2 polarization [γinhibitors might be expected to favor production of M1 tumor-suppressinginflammatory macrophages. Treg cells lacking PPARγ are unable to suppress colitis in a regulatory Tcell-dependent model of inflammatory bowel disease [γ is required for suppressive regulatory function. As Treg activity can impair tumor rejection [γ inhibitors should suppress Treg thereby aiding tumor rejection.In all probability, the net effect of PPARK) cells . All of  stimuli , 94. In  stimuli . Since Prization , PPARγi disease , arguingejection , PPARγ γ inhibitor compounds reduce tubulin protein levels, withoutaffecting in vitro polymerization rates, suggests the exciting possibility that targeting tubulin levelsdirectly, rather than microtubule dynamics, might be an additional way tomanipulate microtubule biology to kill cancer cells. Several questions, including whetherinhibition of PPAR function is required for the tubulin effect, the nature of thetumor specificity, the ultimate targets of these compounds, and whether bettercompounds with similar tubulin targeting effects can be designed, must beanswered before this strategy can be fully realized.The microtubule-targeting agents are one of the mostsuccessful classes of cancer therapeutics, but ongoing issues with resistancemake the development of additional strategies for targeting microtubules extremelydesirable. The recent discovery that the small molecule PPAR