Effective eradication of high-risk primary prostate cancer (HRPCa) could significantly decrease mortality from prostate cancer. However, the discovery of curative therapies for HRPCa is hampered by the lack of authentic preclinical models.We improved upon tumorgraft models that have been shown to predict drug response in other cancer types by implanting thin, precision-cut slices of HRPCa under the renal capsule of immunodeficient mice. Tissue slice grafts (TSGs) from 6 cases of HRPCa were established in mice. Following androgen deprivation by castration, TSGs were recovered and the presence and phenotype of cancer cells were evaluated.High-grade cancer in TSGs generated from HRPCa displayed characteristic Gleason patterns and biomarker expression. Response to androgen deprivation therapy (ADT) was as in humans, with some cases exhibiting complete pathologic regression and others showing resistance to castration. As in humans, ADT decreased cell proliferation and prostate-specific antigen expression in TSGs. Adverse pathological features of parent HRPCa were associated with lack of regression of cancer in corresponding TSGs after ADT. Castration-resistant cancer cells remaining in TSGs showed upregulated expression of androgen receptor target genes, as occurs in castration-resistant prostate cancer (CRPC) in humans. Finally, a rare subset of castration-resistant cancer cells in TSGs underwent epithelial-mesenchymal transition, a process also observed in CRPC in humans.in vivo setting. The authentic response of TSGs to ADT, which has been extensively characterized in humans, suggests that TSGs can serve as a surrogate model for clinical trials to achieve rapid and less expensive screening of therapeutics for HRPCa and primary CRPC.Our study demonstrates the feasibility of generating TSGs from multiple patients and of generating a relatively large number of TSGs from the same HRPCa specimen with similar cell composition and histology among control and experimental samples in an  Mortality from prostate cancer (PCa) is confined to those men who have either advanced disease  or high-risk localized PCa (HRPCa) ,2. The dAndrogen receptor (AR) signaling plays a central role in all stages of PCa. For HRPCa, the standard of care is either radical prostatectomy or radiation therapy combined with androgen-deprivation therapy (ADT) -8. In spin vivo model of HRPCa and primary CRPC is critical for pre-clinical assessment and comparison of different treatment options. Such a model will not only accelerate the discovery of effective therapies by minimizing the number of costly and time-consuming clinical trials, but also help enhance our understanding of mechanisms of therapeutic resistance.New primary therapies that can be used to eradicate HRPCa alone or in combination with ADT before CRPC arises, or to treat primary CRPC effectively after ADT, are urgently needed. Because experimental models of primary human PCa are extremely limited, new generations of compounds targeting AR signaling at different levels as well as other essential pathways in PCa have been developed using pre-clinical models of metastatic CRPC -18. WhetRemarkable correlations between drug activity in “tumorgrafts” derived directly from patient tissues and clinical outcomes have been observed ,20. For in vivo tissue slice graft (TSG) model: 1) retains PCa histopathology, allowing for analysis of almost all of the cell types present in PCa and their interactions; 2) provides accurate assessment of the effects of interventions when tissues from the same specimen with similar cell composition are used as control and experimental samples; 3) ensures sufficient samples obtained for large experiments; and 4) permits optimal exchange of nutrients, oxygen, and drugs between TSG and the host.We developed methodology to establish tumorgrafts from thin, precision-cut tissue slices of human PCa to overcome at least some of the problems . This noHere we characterized TSGs generated from 6 HRPCa cases as well as the castration-resistant cancer that remained in TSGs from 3 of 5 cases after ADT. We focused on high-grade components of the tumors as the likely cause of recurrence and/or castration-resistance after primary therapy. The main questions we addressed were whether cancers in TSGs maintained in intact mice retained the histology and biomarker expression of parent tumors, and whether androgen deprivation affected cell proliferation, AR-regulated gene expression and EMT of cancers in TSGs similarly to that in humans. We provide evidence that TSGs are the first realistic model of primary HRPCa and CRPC that can be used with high predictive power to evaluate an exponentially growing number of molecularly targeted therapies and to discriminate the most effective therapeutics for further clinical development.All animal studies were approved by the Stanford Administrative Panel on Laboratory Animal Care (APLAC) and done in compliance with the regulations for animal studies at Stanford University. Patient-derived tissues were obtained immediately after surgery under a protocol approved by the Stanford Institutional Review Board. The participants provided their written informed consent to participate in this study.Clinical and histopathologic parameters of the donors are summarized in Table −/−γC−/− mice bred at Stanford University or NIH III mice  between 6 and 8 weeks of age were used. All animal studies were done in compliance with regulations at Stanford University. Precision-cutting and subrenal implantation of tissue slices were described previously [Male recombination activating gene-2 (RAG2)eviously . A 25-mgCastration of mice was performed one month after subrenal implantation as previously described .Immunohistochemistry was performed as previously described . AntigenThe proliferation index, defined as percentage of proliferating cells, was established by dividing the number of Ki67-positive cells by the total area of cancer (based on histology and/or AMACR expression) in ten 20X-microscopic fields, randomly chosen for each TSG. Similarly, the percentage of CCNA-positive cells was determined by dividing the number of CCNA-positive cells by the total area of cancer in five 20X-microscopic fields for each TSG. Student’s t-test with a significant level set at α < 0.05 was performed to determine statistical significance.We generated 6–10 TSGs from each of 6 fresh HRPCa tissues obtained immediately following radical prostatectomy Tables . Since h−/−γc−/−[−/−γc−/− mice were significantly higher than in NIH III mice  were down-graded on final pathology of the radical prostatectomy specimens to Gleason score 7 from 9 and had no adverse pathological features such as positive surgical margin, seminal vesicle invasion, or extracapsular extension Table . These TAfter ADT, remaining cancer cells in CR-TSGs derived from HRPCa-2 demonstrated similar histological features and biomarkers to those in control TSGs, including expression of AR Figure O-P. As eWe next examined the expression of AR-regulated genes that have been reported to be up-regulated in human CRPC including TOP2A and CCNA . In CR-TTo determine the effects of ADT on EMT in TSGs and compare to its effects in PCa in humans, we examined EMT marker expression in three human prostates removed by radical prostatectomy following neoadjuvant ADT (Flutamide or Lupron treatment of 6 to 10 weeks in duration) and two untreated specimens as controls. In untreated PCa, high-grade cancers of Gleason patterns 4 and 5 showed strong cytoplasmic expression of K18 (Figure A similar staining pattern was observed in CR-TSGs derived from HRPCa-2, -3 and −4. Specifically, a rare population of cancer cells expressing both VIM and K18 was observed in CR-TSGs Figure M-P but nWe have shown that TSGs derived from HRPCa recapitulate characteristics of parent tumors including histopathological features, biomarker expression, and responses to ADT. Our study differs from previous reports -26 of gein vivo setting. This capability is particularly useful since PCa specimens are becoming smaller due to early cancer detection. Our model can be used to test a variety of therapeutic strategies, including potential curative therapies for HRPCa that can either prevent CRPC from arising during ADT or kill CRPC cells after disease progression. Since ADT may be associated with numerous side effects such as increased cardiovascular mortality, other alternative therapies should also be investigated [HRPCa is the target of adjuvant and neoadjuvant therapies since low-risk PCa is largely curable by surgery or radiation or needs no treatment. A growing inventory of new agents has been discovered that may improve the clinical outcome of HRPCa. Clinical trials evaluating such candidate compounds require a large number of patients, are expensive and time-consuming, and expose patients to certain risks. The TSG model of HRPCa provides a much-needed pre-clinical screening platform that can be used to rapidly narrow down the number of agents or regimens for further investigation in clinical trials. The authenticity of the model in recapitulating the features of the parent tumors increases confidence in the likelihood of similar drug responses in humans. In addition, our study demonstrates the feasibility of generating a relatively large number of TSGs from the same HRPCa specimen with similar cell composition and histology among control and experimental samples in an stigated . FinallyAs proof-of-principle, we have demonstrated that our model closely mimics the response of PCa in humans to ADT. First, ADT decreased cell proliferation and reduced graft weight of TSGs. Second, ADT downregulated the conventional AR target gene PSA while selectively upregulating CCNA and TOP2A in CR-TSGs, as in human PCa , suggestOur findings are the first to link seminal vesicle invasion, positive surgical margin and extracapsular extension to lack of complete pathologic response to ADT by HRPCa. The efficacy of neoadjuvant ADT in the TSG model appears much better than in patients determined by histology ,44. SincIt is interesting to note that endothelial and stromal cells in TSGs are mostly of human origin, rather than replaced by their host counterparts. This is consistent with a recent report demonstrating a burst of angiogenesis by endogenous human blood vessels in primary xenografts of benign prostate or PCa tissues that occurred between days 6–14 after transplantation into SCID mice pre-implanted with testosterone pellets . In contWe provide evidence that TSGs are a realistic model of primary HRPCa and CRPC that may be used with high predictive power to evaluate the exponentially growing number of molecularly targeted therapies and to discriminate the most effective therapeutics for further clinical development.ADT: Androgen deprivation therapy; AR: Androgen receptor; CRPC: Castration-resistant prostate cancer; CR-TSGs: Castration-resistant tissue slice grafts; EMT: Epithelial-mesenchymal transition; PCa: Prostate cancer; PSA: Prostate-specific antigen; TSG: Tissue slice graft.The authors declare that they have no competing interests.HZ conceived of the study, participated in its design, carried out the TSG generation and castration, analyzed data and drafted the manuscript. AT and AI assisted with TSG generation and castration. RN, SWR, and JS carried out the immunostaining. DMP conceived of the study, and participated in its design and revision of the manuscript. All authors read and approved the final manuscript.


Drosophila species and in D. melanogaster inbred strains for both embryos and adults. In the embryos we found that expression divergence is on average more than 20% higher for genes on the X chromosome relative to the autosomes; but in contrast, in the inbred strains, gene expression variation is significantly lower on the X chromosome. Furthermore, expression divergence of genes on Muller's D element is significantly greater along the branch leading to the obscura sub-group, in which this element segregates as a neo-X chromosome. In the adults, divergence is greatest on the X chromosome for males, but not for females, yet in both sexes inbred strains harbour the lowest level of gene expression variation on the X chromosome. We consider different explanations for our results and conclude that they are most consistent within the framework of the faster-X hypothesis.The X chromosome is present as a single copy in the heterogametic sex, and this hemizygosity is expected to drive unusual patterns of evolution on the X relative to the autosomes. For example, the hemizgosity of the X may lead to a lower chromosomal effective population size compared to the autosomes, suggesting that the X might be more strongly affected by genetic drift. However, the X may also experience stronger positive selection than the autosomes, because recessive beneficial mutations will be more visible to selection on the X where they will spend less time being masked by the dominant, less beneficial allele—a proposal known as the faster-X hypothesis. Thus, empirical studies demonstrating increased genetic divergence on the X chromosome could be indicative of either adaptive or non-adaptive evolution. We measured gene expression in  Drosophila species and also in several inbred strains of D. melanogaster for both embryos and adults. We found that gene expression is evolving significantly faster between species in the embryos, yet harbours significantly less variation within inbred strains. In adults, evolution between species appears to be much slower than in the embryos, yet they also harbour significantly lower levels of gene expression variation on the X chromosome in inbred strains. Overall, our results are consistent with there being an excess of adaptive evolution on the X chromosome in Drosophila embryos. Finally, we underscore the importance of biological context for understanding how chromosomes evolve in different species.There is a single copy of the X chromosome in males, yet two copies in females. This unique inheritance pattern has long been predicted to influence how the X chromosome evolves. In particular, the theory suggests that the single copy of the X in males could facilitate faster evolution of the X, although this faster evolution could be either adaptive or non-adaptive. We measured gene expression across the chromosomes in several different  It has long been suspected that the distinct properties of the X chromosome might in turn produce distinct patterns of evolution on the X relative to the autosomes DrosophilaDetermining the relative importance of different evolutionary forces in shaping the X chromosome is crucial for understanding several phenomena related to the X. For example, Haldane's rule, which is a classic generalization stating that in the hybrids of inter-species crosses the heterogametic sex is most often the inviable or sterile sex Drosophila species, whole-genome comparisons do not reveal any bias towards higher rates of protein evolution on the X chromosome Drosophila studies, which may use biased samples of genes D. mirandaEmpirical studies have sought to quantify the importance of adaptative processes in driving the evolution of the X. While many studies have found that the differences between species can often be attributed to X-linked loci of large effect Drosophila, it is conceivable that adaptive differences in this group of species are most often seen in cis-regulatory, and therefore non-coding, regions of the genome While the evidence for adaptive evolution of the X remains somewhat patchy, such discrepancies suggest that differences in the biology of different groups of species could strongly influence their chromosomal evolution. An important parameter in the faster-X theory is the presence or absence of dosage compensation in the heterogametic sex; that is, whether the presence of a single copy of a gene in the heterogametic sex is compensated, in terms of gene expression, to an extent that it is selectively equivalent to the two copies in the homogametic sex. Theory shows that beneficial mutations will evolve faster on the X compared to the autosomes, only if mutations are at least partially recessive Drosophila X chromosome relative to the autosomes at the level of gene expression divergence. By focusing on gene expression, we relax the implicit assumption of previous studies that a majority of adaptive evolution occurs via changes in amino acid sequences. Additionally, by measuring divergence in terms of gene expression rather than coding sequences, we could compare expression divergence in embryos relative to adults and therefore ask whether gene expression is free to evolve independently in different stages of the animal's life-cycle. Our results show that mean gene expression divergence is higher for the X chromosome relative to autosomes and, more surprisingly, this effect is much stronger in the Drosophila embryos relative to the adults.We aimed to address evolution on the Drosophila has been sought in the adaptive evolution of protein sequences, but has so far produced mixed results Drosophila chromosomes we used gene expression data from two distinct stages of the life-cycle – the embryo D. melanogaster and hybridised the samples to whole-genome microarrays to provide insight into the maintenance of gene expression variation across chromosomes but within a single species. Similarly, for adult stages we used whole-genome microarray data from 40 adult inbred strains of D. melanogaster separated into males and females Evidence for accelerated evolution of the X in D. melanogaster strains  has become X-linked and is referred to as a neo-X chromosome. If X-linkage were the cause of increased expression divergence, then we would expect to see accelerated evolution of gene expression on this chromosome relative to the remaining autosomes in this lineage In the obscura sub-group, Muller's element D  complex is not fully active until after the blastoderm stage DrosophilaRecent evidence suggests that epistatic interactions between genes constitutes a substantial fraction of the variation of quantitative traits in D. melanogaster there is also no recombination occurring between pairs of homologous autosomes in males, and therefore such an effect would contribute to increased evolution on the autosomes. To quantify the magnitude of this effect, we compared the X-linked case to a scenario in which there is no recombination between autosomally linked loci in males. The results show that the effect of a lack of recombination in males cannot account for the advantage enjoyed by X-linked loci, which when compared against the autosomal case in which there is male recombination shows that the advantage in this case is weak and dependent upon high-levels of genetic variance , and therefore provides a powerful, independent test for faster evolution of the X chromosome. In addition, we find that gene expression evolves faster on the X chromosome in embryos when we employ a more global measure of expression divergence , and not necessarily its effect on fitness. Theory suggests that mutations could have fitness consequences that are non-linear even if they have additive phenotypic effects cis-acting elements fail to capture their effects on fitness.If we adopt a faster-X interpretation of the data, then we must provide some explanation as to why beneficial To understand the fitness effect of a mutation in an organismal context, we must focus on the biology of the organism, and not just on its genetics. One potential route towards non-additive intra-locus effects on fitness is canalisation. The canalisation of embryonic development, such that it is resistant to environmental or genetic perturbations, has long been recognized as a crucial element contributing to the evolution of robustness in developmental systems Drosophila embryos. While our results are consistent with adaptive evolutionary processes, more work is required to unravel the details underpinning this excess of divergence at the genetic, phenotypic, and fitness levels. We contend that variations in biological and life-history details, such as differences in dosage compensation menchanisms, can strongly impact how the chromosomes of different species evolve. We therefore stress the importance of appreciating biological context when attempting to understand chromosomal evolution. Deciphering the relationship between species-specific biology and chromosomal patterns of evolution promises to provide fertile ground for future research.We report evidence that gene expression evolves faster on the X chromosome in D. melanogaster, originally collected from farmer's markets in North Carolina and provided as a resource by the Drosophila Genetic Reference Panel  We used inbred strains of Populations of healthy adults from 3–7 days of age, were reared at 25°C and used for embryo collections. To synchronize the age of the embryos in each sample, we pre-laid the flies three times for 1 hour with a fresh apple juice plate with yeast paste before every collection. Another fresh plate with yeast was used to collect the embryos. After collection, embryos were rinsed with distilled water and then dechorionated in 100% bleach for 2 minutes before being washed in desalinated water. The embryos were then transferred into a 1.5-ml tube and snap-frozen in liquid nitrogen and stored at To isolate RNA, embryos were thawed on ice and homogenized with a pellet pestle and a pellet pestle cordless motor (Kontes). RNA was isolated with the RNeasy Mini kit (Qiagen) and eluted with 30 ml of distilled water. The RNA concentration was measured with the NanoDrop spectrophotometer and RNA quality was assessed with Bioanalyser using the Agilent RNA 6000 Nano kit.To prepare samples for hybridization to the chip, we followed the Agilent One-Colour Microarray-Based Gene Expression Analysis protocol version 6.5 (Low Input Quick Amp Labeling). The starting amount of RNA was normalized to 100 ng for all samples.Drosophila was taken from a species-specific microarray data set, in which eight time-points were sampled for the duration of embryogenesis of D. melanogaster, D. simulans, D. ananassae, D. pseudoobscura, D. persimilis, and D. virilisDrosophila expression was collected from a microarray experiment that measured the gene expression of whole flies sorted into males and females and taken from D. melanogaster, D. ananassae, D. mojavensis, D. pseudoobscura, D. simulans, D. virilis, and D. yakubaD. melanogasterD. melanogaster strain data was taken from a whole-genome microarray study of gene expression in whole adult flies from 40 inbred strains separated into males and females Embryonic expression in To quantify gene expression divergence in a chromosomal context, we fitted the following linear model hole see .Differentiation of gene expression between inbred strains was determined using the R package ‘limma’ Absolute pairwise species contrasts of the D. pseudoobscura and D. persimilis but not including the terminal branches  consists of genes that are not expressed in at least two species in at least one time point (1502 genes). The second set (“Six”) consists of genes that are not expressed in at least six species in at least one time point (849 genes), and the final set (“Six-Eight”) consists of genes that are not expressed in at least six species at every time point (536 genes). Expression distributions for these gene sets shows that they increasingly capture more weakly expressed genes as the criteria for exclusion becomes more stringent . When weTo determine whether the lower effective population size of the X chromosome might increase the chance that it fixes weakly deleterious mutations, we used gene expression mutation accumulation data to assess potential chromosomal biases in the accumulation of gene expression differences. We used jack-knifed mutational variance estimates scaled by residual variances to provide estimates of the mutational heritability of gene expression changes between lines A hierarchically-arranged controlled vocabulary (CV) of embryonic tissue expression terms based on an in situ expression data set In all of our models, we assume that selection coefficients are equal in the two sexes, which corresponds to the assumption of complete dosage compensation in cis-acting beneficial allele between generations, We derived genotype frequency recurrence equations to describe the evolutionary dynamics in our models and then solved the equations numerically. To compare evolution in the equivalent X versus autosomal scenarios, we extracted the change in allele frequency of the al cases .Spearman's parately . For theparately .The correlation approach captures the extent to which chromosomal subsets of genes tend to conserve their expression relationships in pairs of species. However, this approach fails to capture the level of conservation of gene expression in a chromosomal subset relative to a separate chromosomal subset across pairs of species. For example, we might wish to ask whether the expression relationship of genes on the X chromosome relative to the autosomal arm 2L shares a conserved pattern in a pair of species. To answer questions of this nature, we introduce a variant of Spearman's correlation coefficient which allows us to rank genes in a chromosomal subset relative to genes in a separate chromosomal subset for pairs of species. For the correlation of subset As it is established that correlation coefficients within subsets can vary, sometimes dramatically, from correlation at the level of aggregates Click here for additional data file.Figure S2Divergence of gene expression across chromosomes in both adult males and females for genes with sex-biased expression patterns.(PDF)Click here for additional data file.Figure S3Embryonic expression divergence on the X is not driven by extreme expression levels. Bootstrapped divergence measures generated by resampling genes according to their expression levels. Genes were resampled per chromosome using 10,000 bootstrap replicates for both embryos, A, and adults, B. There are more expression levels sampled for embryos because they have a broader gene expression level distribution than the adults.(PDF)Click here for additional data file.Figure S4Mosaic plots for all pair-wise species comparisons of normalized gene expression categorised as up or down relative to one of the species. Mosaic plots visualize categorical data (contingency table) using rectangles that are proportional to the number of counts in each row-column combination, and highlight in red variable combinations that have less than expected numbers and in blue those that have more than expected based on Pearson residuals (PDF)Click here for additional data file.Figure S5D. persimilis and D. pseudoobscura as a fraction of the total branch length for Drosophila embryos.The lengths of the summed terminal branches leading to (PDF)Click here for additional data file.Figure S6Selection gradient ratios when there is no recombination between homologous pairs of male autosomes. The left panel shows the ratio when both loci are X-linked versus both loci being linked on the same autosome but with no male recombination. The right panel shows the ratio for autosomes when there is no recombination in males versus the case when there is. Parameter values: recombination rates, (PDF)Click here for additional data file.Figure S7D. melanogaster or D. simulans.Divergence of gene expression across chromosomes in both adult males and females for 656 genes with male-biased expression in either (PDF)Click here for additional data file.Figure S8Bootstrapped  Spearman's (PDF)Click here for additional data file.Figure S9Bootstrapped  Mean Canberra distances for adult males and females for all pair-wise species comparisons.(PDF)Click here for additional data file.Figure S10A schematic depicting gene expression in two genes showing why Spearman's (PDF)Click here for additional data file.Figure S11All bootstrapped Spearman's (PDF)Click here for additional data file.Figure S12D. melanogaster showing that an initially bimodal distribution, where the lower mode represents unexpressed zygotic genes, becomes a unimodal distribution through time as the zygotic genome is activated.The distribution of gene expression levels during embryogenesis of (PDF)Click here for additional data file.Figure S132 gene expression time course for the X-linked gene Vinculin (Vinc) for D. ananassae and D. virilis showing divergence across the whole time course.Log(PDF)Click here for additional data file.Figure S14The distributions of resampled mean divergences for each autosome with the mean of the X chromosome indicated by a dashed red line for embryos and adults. Autosomal genes were resampled so that they matched the number of genes on the X chromosome and in each of 10,000 resamples the mean divergence per chromosome was recorded.(PDF)Click here for additional data file.Figure S15Divergence of gene expression in adult males for genes that show unbiased, male-biased, and female-biased expression patterns.(PDF)Click here for additional data file.Figure S16Divergence of gene expression in adult females for genes that show unbiased, male-biased, and female-biased expression patterns.(PDF)Click here for additional data file.Figure S17D. melanogaster or D. simulans relative to all genes in the dataset.Divergence of gene expression in adult males for 656 genes with male-biased expression in either (PDF)Click here for additional data file.Figure S182 expression level from the global mean.Gene expression level by chromosome for embryos and adults in the Drosophila data sets. Expression level is shown as the deviation of each gene's mean log(PDF)Click here for additional data file.Figure S19The relationship between expression level and divergence for embryos and adults in the Drosophila data sets. Genes are ranked by expression, from lowest to highest, binned into groups of 50, and their mean divergence deviation from the global mean (log divergence) is shown as a T-statistic, with significant values highlighted in red. A LOESS curve is fitted to the data.(PDF)Click here for additional data file.Figure S20D. persimilis-D. pseudoobscura species comparison of normalized gene expression categorised as up or down relative to one of the species. Mosaic plots visualize categorical data (contingency table) using rectangles that are proportional to the number of counts in each row-column combination, and highlight in red variable combinations that have less than expected numbers and in blue those that have more than expected based on Pearson residuals Mosaic plots for the (PDF)Click here for additional data file.Figure S21D. persimilis and D. pseudoobscura.Gene expression divergence per chromosome along the branches leading to (PDF)Click here for additional data file.Figure S22Fast-evolving genes tend to diverge more on the X in embryos and adult males. The mean ratio of chromosomal divergence to divergence in the rest of the genome. Mean divergence is plotted for genes belonging to each percentile of a particular chromosome's divergence distribution  relative to genes in the same percentile of the divergence distribution of the the rest of the genome . The results show that, for the X chromosome, the excess of X/A divergence is higher for faster-evolving genes in both embryos and adult males. Lines are LOESS fits to the data and dashed lines indicate ratios of 1.(PDF)Click here for additional data file.Figure S23Log expression distributions for gene sets excluded for being non-expressed in at least two species in at least one time point (“Two”), in at least six species in at least one time point (“Six”), and in all species at all time points (“Six-Eight”). See (PDF)Click here for additional data file.Figure S24Gene expression divergence on the X chromosome relative to the autosomes for sets of genes with groups of non-expressed genes removed using various different criteria: non-expressed in at least two species in at least one time point (“Two”), non-expressed in at least six species in at least one time point (“Six”), and non-expressed in all species at all time points (“Six-Eight”). See (PDF)Click here for additional data file.Figure S25Simulated bivariate data illustrating the Yule-Simpson effect (PDF)Click here for additional data file.Figure S26Distributions of pairwise species chromosome correlations for embryos, adult males, and adult females. In light blue are the distributions of a relativised Spearman's rank correlation coefficient see . The suf(PDF)Click here for additional data file.Table S1The chromosomal distribution of genes in the expression datasets.(PDF)Click here for additional data file.Table S2Drosophila embryo species comparisons. Aut - all autosomes. W - Wilcoxon rank sum test statistic. P-values adjusted according to Benjamini-Hochberg correction.Contrasts for (PDF)Click here for additional data file.Table S3D. melanogaster embryo strain comparisons. Aut - all autosomes. W - Wilcoxon rank sum test statistic. P-values adjusted according to Benjamini-Hochberg correction.Contrasts for (PDF)Click here for additional data file.Table S4Drosophila adult female species comparisons. Aut - all autosomes. W - Wilcoxon rank sum test statistic. P-values adjusted according to Benjamini-Hochberg correction.Contrasts for (PDF)Click here for additional data file.Table S5D. melanogaster female adult strain comparisons. Aut - all autosomes. W - Wilcoxon rank sum test statistic. P-values adjusted according to Benjamini-Hochberg correction.Contrasts for (PDF)Click here for additional data file.Table S6Drosophila adult male species comparisons. Aut - all autosomes. W - Wilcoxon rank sum test statistic. P-values adjusted according to Benjamini-Hochberg correction.Contrasts for (PDF)Click here for additional data file.Table S7D. melanogaster male adult strain comparisons. Aut - all autosomes. W - Wilcoxon rank sum test statistic. P-values adjusted according to Benjamini-Hochberg correction.Contrasts for (PDF)Click here for additional data file.Table S8Drosophila embryos for a common set of 2072 genes and 5 species. W - Wilcoxon rank sum test statistic. P-values adjusted according to Benjamini-Hochberg correction.Contrasts for (PDF)Click here for additional data file.Table S9Drosophila adults for a common set of 2072 genes and 5 species. W - Wilcoxon rank sum test statistic. P-values adjusted according to Benjamini-Hochberg correction.Contrasts for (PDF)Click here for additional data file.Table S10Drosophila. Enrichment is based on the ‘parent-child’ algorithm in the topGO R package and Fisher's exact test applied to 2228 genes that reside on the X chromosome in Drosophila, and enrichment is relative to the whole genome. Terms with uncorrected P-values below 0.05 are shown. # - total number of genes with this annotation in the dataset. Sig. - significant, Exp. - expected. Characterisation of the embryonic expression patterns of genes residing on the X chromosome in (PDF)Click here for additional data file.Table S11Fitnesses in a diploid two-locus epistatic model with X-linkage. Fitnesses of different male-female gametic combinations when both the loci are located on the X chromosome. T/t - trans-acting gene; C/c - cis-acting locus; 00 - indicates a male gamete carrying a Y chromosome; (PDF)Click here for additional data file.Table S12Characterisation of genes with a percentile X/A divergence ratio greater than 1.015 in adult males. Enrichment is based on the ‘parent-child’ algorithm in the topGO R package and Fisher's exact test applied to 352 genes that have an X/A percentile divergence ratio of (PDF)Click here for additional data file.Table S13Fitnesses in a diploid two-locus epistatic model. Fitnesses of different male-female gametic combinations when there is a beneficial partially recessive interaction between an autosomal allele and an X-linked allele . T/t - trans-acting autosomal gene; C/c - cis-acting X-linked locus; 0 - indicates a male gamete carrying a Y chromosome; (PDF)Click here for additional data file.


The goal of this work is to introduce new metrics to assess risk of Alzheimer's disease (AD) which we call AD Pattern Similarity (AD-PS) scores. These metrics are the conditional probabilities modeled by large-scale regularized logistic regression. The AD-PS scores derived from structural MRI and cognitive test data were tested across different situations using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The scores were computed across groups of participants stratified by cognitive status, age and functional status. Cox proportional hazards regression was used to evaluate associations with the distribution of conversion times from mild cognitive impairment to AD. The performances of classifiers developed using data from different types of brain tissue were systematically characterized across cognitive status groups. We also explored the performance of anatomical and cognitive-anatomical composite scores generated by combining the outputs of classifiers developed using different types of data. In addition, we provide the AD-PS scores performance relative to other metrics used in the field including the Spatial Pattern of Abnormalities for Recognition of Early AD (SPARE-AD) index and total hippocampal volume for the variables examined. The development of biomarkers for early detection of Alzheimer's disease (AD) has become an area of intensive research in neuroimaging and genetics. AD has no known cure and is one of the ten main causes of death in US, making it a leading public health concern and imposing a huge economic burden on individuals and society. It is believed that the neurodegenerative processes that lead to AD start many years before the symptoms appear. Earlier detection of the disease would allow earlier interventions and may provide clues to its causes. The Alzheimer's Disease Neuroimaging Initiative (ADNI-1) project In this work we introduce new metrics for assessing AD risk based on structural MRI (sMRI) and cognitive performance data using large-scale machine learning methods. There are several existing indices for AD risk assessment that often are generated by severely reducing the dimensionality of imaging and/or genetic data before applying a classification algorithm such as a support vector machine (SVM). For example, the Spatial Pattern of Abnormalities for Recognition of Early AD (SPARE-AD) index We propose new metrics for assessment of AD risk based on probabilities as modeled by high-dimensional regularized classifiers. We have recently introduced an approach to automatic classification of brain MRI images in AD that is based on large-scale regularization www.adni-info.org.The ADNI (adni.loni.ucla.edu) was launched in 2003 by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, the Food and Drug Administration (FDA), private pharmaceutical companies, and non-profit organizations. Its primary goal was to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessment could be combined to measure the progression of MCI and early AD. Determination of sensitive and specific markers of very early AD progression could help researchers and clinicians develop new treatments and monitor their effectiveness, and reduce the time and cost of clinical trials. The Principal Investigator of this initiative is Michael W. Weiner, MD, VA Medical Center and University of California – San Francisco. ADNI recruited from over 50 sites across the U.S. and Canada. The initial goal of ADNI was to recruit 800 adult participants, ages 55 to 90, composed of approximately 200 cognitively normal older individuals to be followed for 3 years, 400 people with MCI to be followed for 3 years, and 200 people with early AD to be followed for 2 years. For up-to-date information about the cohort, see We used ADNI-1 subject data collected from 50 clinic sites. Ethics approval was obtained for each institution involved including our Institutional Review Board at Wake Forest Baptist Health. This study was conducted according to Good Clinical Practice guidelines, the Declaration of Helsinki, US 21CFR Part 50– Protection of Human Subjects, and Part 56– Institutional Review Boards, and pursuant to state and federal HIPAA regulations. Study subjects gave written informed consent at enrollment for data collection, sample storage and subsequent use of samples for research, and completed questionnaires approved by each participating site's Institutional Review Board. The data were anonymized before being shared.For the present analysis, we used baseline structural MRI, DNA, and cognitive data from 694 Caucasians. Of those, 188 were CN, 171 had AD, and 335 had MCI at baseline We used baseline 1.5T T1-weighted MRI data as described in the ADNI acquisition protocol The images were segmented and normalized using the Statistical Parametric Mapping (SPM) software package. Segmentation of the original images into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF) was performed using the NewSegment tool. Normalization was carried out using Diffeomorphic Anatomical Registration using the Exponentiated Lie algebra (DARTEL) method In addition, we used as a classical control measure in our analyses, total hippocampal volume (THV). The data were available in the ADNI website and generated using the software for automated segmentation and parcellation FreeSurfer (FS) V4 We used 25 cognitive scores from four memory tests see  availablth sample or feature vector containing the ith participant cognitive or MRI data, th label , http://www-stat.stanford.edu/~tibs/glmnet-matlab/). Most computations were made on a LINUX computer with 16 CPUs and 96 GBs of RAM. We actively used parallel computing features provided by the MATLAB parallel computing toolbox. The AD-PS scores can be computed overnight using 15 CPUs.In previous work we proposed the use of regularized logistic regression (RLR) with elastic net regularization for high-dimensional classification of AD sMRI images 1-fold CV where at each step we leave one fold for testing and use the remaining K1-1 folds for training and validation. These last two procedures are implemented by using a nested K2-fold CV. We divide the K1-1 folds into K2 folds and we leave one fold for validation and K2-1 folds for training combined with a grid search to determine the optimal parameters. The grid we used in our analyses was 2 folds of the internal CV procedure. The classifier is then retrained using the data in the K1-1 folds left for training and validation and the selected optimal regularization parameters. The classifier's generalization capability is then evaluated by computing the classification accuracy, sensitivity and specificity using the fold originally left for testing in the external CV. This is repeated K1 times and the average classification accuracy is reported. For cognitive data the procedure was similar, but since the problem size is small, we optimized both parameters using a two-dimensional grid  with 10-fold cross-validations (CV) and grid search  CN versus AD; 2) CN versus cMCI; 3) CN versus ncMCI; and 4) ncMCI versus cMCI. In each case RLR models were estimated independently for GM, WM, CSF and cognitive data using in each case all the available samples. Classification accuracy, sensitivity and specificity were estimated based on the nested CV method described above. To account for variability due to CV partitioning, the process was repeated 100 times and the median values were reported.Second, the cognitive and sMRI AD-PS scores for all the subjects were computed as described in sections 2.5–2.7. We evaluated the performance of the AD-PS scores across cognitive status groups and for groups of ADNI participants stratified according to age (<75 versus > = 75), and by the functional status. We used a cutoff value of 2 to stratify participants by their FAQ results. Discriminative maps for the different types of tissues were generated. In addition, to assess associations of all metrics with the distribution of times of MCI to AD conversion we performed survival analyses based on proportional hazards regression using SAS.Finally, in all analyses described above, we evaluated two composite metrics: 1) Anatomical AD-PS scores, which represent the sum of GM, WM, and CSF probabilities; and 2) Cognitive-anatomical AD-PS scores, which is the sum of the anatomical and cognitive scores. Since our scores are probabilities, their combinations can be interpreted as metrics defined in a unit multidimensional hypercube. In Results of the first analyses are presented in The AD-PS scores significantly distinguished participants grouped according to clinical status see . All AD-In The main goal of this analysis was to introduce and test new metrics for assessment of AD risk. Similar metrics, such as the SPARE-AD and STAND score We systematically evaluated the relevance of different types of brain tissue when discriminating cognitive status groups in the voxel space. If we take as a reference recent work in the ADNI literature Our approach generates discriminative maps at a voxel level which uncover brain regions that have been associated with AD before . They are similar in interpretation to those generated by a linear SVM There is a growing body of literature indicating increasing interest in the role of white matter in AD We proposed here metrics for AD risk assessments which integrate information from different sources by combining probabilities generated by classifiers. Similar ideas have been used previously in face and voice recognition based on Bayesian theory Here the AD-PS scores were estimated using cognitive and structural data independently. We generated combinations of the scores to seek a composite cognitive-anatomical metric for AD risk assessment with increased performance compared with metrics based on a specific source of information. While many different composite metrics could be devised, in this work we used the sum of the scores across sources of information to illustrate the concept. In some situations, our composite metrics improved detection of differences of distributions between clinical groups, such as ncMCI from cMCI participants, but very often it did not. This could suggest that collapsing all the multimodal information in a single score may not always be useful and/or the non-optimality of the composite metric used here.The AD-PS and SPARE-AD scores very often detected more significant differences between groups than THV. Although in general the AD-PS scores often produced more significant results, in several situations they were outperformed by the SPARE-AD scores and THV. These relative results do not represent a rigorous comparison of these three metrics. For example, the AD-PS and SPARE-AD scores are estimated using different image processing approaches, sample sizes and smoothing kernels. On the other hand, the THV used here were based on FreeSurfer estimates; there are other estimators available that were not tested here that could be more accurate. The SPARE-AD and THV performances were instead provided as a reference to help assess and validate the AD-PS scores. Additional information about correlations of the three metrics across cognitive groups can be found in Tables S1 and S2 in Our study has several limitations. A potential confounding factor here is the quality of the brain tissue segmentation. Although we made an effort to generate masks covering each type of tissue, there could be overlap among areas. We centered our analyses on the use of only sMRI and cognitive data because these were available for most ADNI-1 participants at baseline. In the future, we will estimate the AD-PS scores for amyloid PET imaging, amyloid and tau levels in CSF, etc. Our composite cognitive scores included only a portion of the cognitive data available in ADNI: memory scores. We chose these parameters because of their well-documented association with AD and their use in previous work by other researchers. We will include additional cognitive information in the future. High performance of cognitive data-based classifiers is in large part the result of these data being used to define cognitive groups in advance, which gives these classifiers an unfair advantage. Our sMRI AD-PS scores were based on images normalized using DARTEL; although this is a method easy to use and less time-consuming than other methods in the field, it may not be the best option. We expect significant improvements of AD-PS anatomical scores by using more sophisticated normalization methods. It is very likely the AD-PS scores will benefit from increasing the sample size, which could be implemented by integrating the ADNI databases to be available worldwide in the coming years Our analyses provided evidence of the validity of the AD-PS scores. In general the AD-PS scores distinguished well between AD-like cognitive and anatomical patterns across clinical status, as seen in the gradient of values across clinical groups ordered by severity. The structural AD-PS scores often detected greater AD-like abnormalities in older and less functional ADNI participants according to the FAQ. The differences in AD-like patterns detected by the AD-PS scores were always in the expected directions across cognitive status, age and functional groups. In addition, they also were consistent with directions detected by other known metrics such as SPARE-AD and THV. The survival analyses showed that the AD-PS scores are strongly associated to the MCI to AD conversion times. The AD-PS metrics can be a powerful tool in AD research to detect AD-like cognitive and anatomical effects across given groups of subjects stratified by clinical, risk factors or intervention groups. Finally, the approaches presented here can be extended to other neurodegenerative diseases such as Parkinson's, amyotrophic lateral sclerosis, etc. This will be the subject of future work.File S1File S1 contains supplementary materials. Figure S1. The CV procedure with nested 10 fold CV is illustrated. The RLR model is estimated for all different values of the grid  using the internal training data. The values of the regularization parameters that produced maximum accuracy when tested on the internal testing dataset are recorded. The process is repeated 10 times using different internal folds as testing dataset. At the end the average value of the recorded regularization parameters is computed and the RLR model is recomputed using the external training data set. The external testing dataset is used to estimate classification accuracy, sensitivity and specificity which are recorded. The above process is repeated ten times across the ten external folds and the final estimator of the three metrics is computed as their average across the ten external folds. Table S1. Correlations  between AD-PS and SPARE-AD scores across cognitive statuses computed using the Spearman's rank sum test are presented. Table S2. Correlations  between AD-PS and SPARE-AD scores and THV across cognitive statuses computed using the Spearman's rank sum test are presented. Table S3. The IDs of the 188 CN participants are listed. Table S4. The IDs of the 171 participants are listed. Table S5. The IDs of the 153 MCI converters participants are listed. Table S6. The IDs of the 182 MCI non-converters participants are listed.(DOCX)Click here for additional data file.


Pancreatic cancer (PC) has a complex etiology and displays a wide range of cellular escape pathways that allow it to resist different treatment modalities. Crucial signaling molecules that function downstream of the survival pathways, particularly at points where several of these pathways crosstalk, provide valuable targets for the development of novel anti-cancer drugs. Bcl-2 family member proteins are anti-apoptotic molecules that are known to be overexpressed in most cancers including PC. The anti-apoptotic machinery has been linked to the observed resistance developed to chemotherapy and radiation and therefore is important from the targeted drug development point of view. Over the past ten years, our group has extensively studied a series of small molecule inhibitors of Bcl-2 against PC and provide solid preclinical platform for testing such novel drugs in the clinic. This review examines the efficacy, potency, and function of several small molecule inhibitor drugs targeted to the Bcl-2 family of proteins and their preclinical progress against PC. This article further focuses on compounds that have been studied the most and also discusses the anti-cancer potential of newer class of Bcl-2 drugs. AmongstK-ras oncogene and loss of the CDKN2A, p53, and/or SMAD4 tumor suppressors along with upregulation of pro-survival Bcl-2 . ABT-737, when tested in dogs, rapidly decreases platelets that return to baseline within three days and is compatible with a mechanism involving platelet destruction rather than a mechanism involving decreased production from megakaryocytes as observed following conventional cytotoxic chemotherapy. As Bcl-XL is critical in limiting the proapoptotic activity of Bak in platelets, the observed thrombocytopenia appears to be because of inhibition of Bcl-XL resulting in activation of Bak [131]. Older platelets contain less Bcl-XL than younger platelets and are more susceptible to ABT-737, which helps to explain the almost full recovery of platelets in mice dosed daily with ABT-737. However, it should be noted that non specificity can also be beneficial in certain cases. For example, our group has shown that both ApoG2 and TW-37 can target Par-4 activation that leads to cancer cell specific killing ), B-cell malignancies (with Rituxan® [rituximab]), small cell lung cancer (with Hycamtin® [topotecan]), glioma  and esophageal cancer . Clinical trials are ongoing in the US and Europe and Ascenta is collaborating with Ascenta Pharma Group Corporation (located in Hong Kong and Shanghai) for the clinical development of AT-101 in China. Even though AT-101 is in so many different clinical trials, however, it is still far from a specific Bcl-2 inhibitor. Future research will focus on developing a more potent inhibitor with better specificity against Bcl-2 family proteins. Due to the dynamics of Bcl-2 protein structure, chemistry, mode of binding and efficacy, it is not an easy task to develop a perfect inhibitor. For example, both TW-37 and ApoG2 showed immense potency in the preclinical laboratory setting, however, they have not advanced to the clinic due to pharmacokinetic and toxicity related issues.Over the years different groups have shown that Bcl-2 inhibitors can target non Bcl-2 targets as well. Therefore, in order to delineate the entire set of pathways modulated by these inhibitors our laboratory is pursuing a systems biology approach (on AT-101) to obtain drug target gene signatures of these. This will help in understanding the mechanism of action of these inhibitors and aid in the rational design of single agent and combination regimens in genetically pre-defined subset of cancer patients. Other approaches include nano-encapsulation of Bcl-2 inhibitors to increase bioavailability and tumor targeted combinations with vaso-active peptide receptor engrafted sterically stabilized Bcl-2 inhibitor micelle formulations.4.PC is a deadly disease that is considered incurable. The dismal survival rate points towards the urgent need for the development of newer drugs against this malignancy. Deregulation of apoptotic machinery in PC has been acknowledged as a major contributor to the observed resistance to chemotherapy. Therefore, agents that target the apoptotic machinery leading to PC cell death may offer a better strategy against this disease. Over the last two decades the advancement of our understanding of apoptosis and the recognition of its key players has led to development of various strategies to selectively destroy cancer cells. Initial strategy to target anti-apoptotic protein translation by using antisense technology did not bring much success. However, small molecule inhibitors have shown promise against PC and have brought back enthusiasm among researchers. Small molecule inhibitors of Bcl-2 family proteins are specific and targeted agents that can be given orally and do not have any appreciable toxicity. This new class of drugs is increasingly being realized as effective anticancer agents either alone or to sensitize cancer cells to standard chemotherapeutics agents. Their emerging potential as enhancers of chemotherapeutic drugs can be a significant development that can be used as a future strategy against deadly tumors. We believe that in the next decade or so, as more key regulators of apoptosis are uncovered, new SMIs will continue to emerge to target these apoptotic proteins and will significantly improve the PC care.


In recent years, genome-wide ChIP-on-Chip, ChIP-Seq and DamID assays have been extensively used to unravel the combinatorial interaction of TFs with cis-regulatory modules (CRMs) in the genome. Even though genome-wide binding profiles are increasingly becoming available for different TFs, single TF binding profiles are in most cases not sufficient for dissecting complex regulatory networks. Thus, potent computational tools detecting statistically significant and biologically relevant TF-motif co-occurrences in genome-wide datasets are essential for analyzing context-dependent transcriptional regulation. We have developed COPS (Co-Occurrence Pattern Search), a new bioinformatics tool based on a combination of association rules and Markov chain models, which detects co-occurring TF binding sites (BSs) on genomic regions of interest. COPS scans DNA sequences for frequent motif patterns using a Frequent-Pattern tree based data mining approach, which allows efficient performance of the software with respect to both data structure and implementation speed, in particular when mining large datasets. Since transcriptional gene regulation very often relies on the formation of regulatory protein complexes mediated by closely adjoining TF binding sites on CRMs, COPS additionally detects preferred short distance between co-occurring TF motifs. The performance of our software with respect to biological significance was evaluated using three published datasets containing genomic regions that are independently bound by several TFs involved in a defined biological process. In sum, COPS is a fast, efficient and user-friendly tool mining statistically and biologically significant TFBS co-occurrences and therefore allows the identification of TFs that combinatorially regulate gene expression.In multi-cellular organisms, spatiotemporal activity of  Preferred TFBS spacing may indicate formation of regulatory protein complexes mediated by closely adjoining TFBSs (Δbp ≈ 10 bp) Cell-type specific gene expression results from the combinatorial interaction of transcription factors (TFs) with in vivo binding maps of numerous TFs. In several cases, the determination of in vivo binding patterns for a set of TFs involved in the same biological process has allowed the dissection of combinatorial TF interactions at the genome-wide level. Prominent examples include the elucidation of the transcriptional networks controlling muscle and nervous system development in DrosophilaIn recent years, the wide-spread use of genome-wide chromatin-profiling methods such as chromatin immunoprecipitation (ChIP) followed by microarray analysis (ChIP-on-Chip) or massively parallel sequencing (ChIP-Seq) and DNA-adenine methyltransferase identification (DamID) has generated the Despite the increasing availability of genome-wide DNA interaction data for a vast number of TFs, the acquisition of binding profiles for all TFs essential for the regulation of cell- or tissue-type specific developmental processes is so far almost impossible in higher organisms. Furthermore, TFs are often expressed in many different cell types and yet manage to co-ordinate cell-type specific transcriptional programs via their collaboration with different co-regulators. Therefore, as single or few TF binding profiles are not sufficient for dissecting complex regulatory networks, computational programs mining statistically significant and biologically relevant co-occurrences of TF motifs are crucial for the identification of co-regulatory TFs. Computational tools that scan pre-selected sequences or whole genomes for homotypic or heterotypic clusters of TFBSs are the most effective among the existing approaches implemented for the identification of combinatorially regulated CRMs (reviewed in Co-Occurrence Pattern Search), a computational tool mining frequent co-occurrences of TF motifs in genome-wide data using a combination of association rules and Markov chain models. In addition to detecting motif co-occurrences, COPS reports the preferred spatial arrangement of the TFBSs, an important feature of cell-type specific CRM activities. The performance of COPS was evaluated by analyzing cell type-specific in vivo binding regions for two Drosophila and one mouse genome-wide datasets in vivo binding data for known co-regulatory TFs allowed us to validate the in vivo significance of the detected motif co-occurrences. Importantly, in all cases COPS calculated a substantial number of co-occurring motifs for TFs with known functions in the selected tissue-specific biological processes, suggesting their combinatorial activity on selected CRMs. In comparison to existing computational approaches, COPS is more suitable for handling large datasets combined with extensive motif collections. Furthermore, it detects preferred spatial arrangements of BSs, an important aspect of regulatory TF complex formation. In sum, we show that COPS is a powerful, time-efficient and user-friendly bioinformatics tool for identifying co-regulatory TFs that control cell- and tissue-specific target gene expression in a combinatorial manner.In this study, we present COPS (The genomic regions bound by Twist (Twi), Myocyte enhancer factor 2 (Mef2), Tinman (Tin), Bagpipe (Bap) and Biniou (Bin) at the embryonic stages 10 to 11 (6–8 h of development) Genomic regions bound by the neurogenic factors Prospero (Pros) (2611 sequences), Asense (Ase) (2745 sequences), Deadpan (1426 sequences) and Snail (4000 sequences) at the embryonic stages 10 to 11 were defined by extending the DamID-identified regions We analyzed the genomic regions bound by Mef2a (883 sequences), GATA-binding protein 4 (GATA4) (473 sequences), NK2 TF related, locus 5 (Nkx2.5) (386 sequences) and Serum response factor (Srf) (1291 sequences), as defined by ChIP-on-Chip in HL-1 cells Drosophila datasets. The Pros PWM described by Down et al. (2007) Drosophila datasets. The PWM of GATA1  was used for detecting GATA4 BSs, as motifs recovered from GATA4 bound regions were reported to match the GATA1 motif All TF binding motifs annotated in open source databases JASPAR  were useDrosophila and mouse TFs was retrieved from FlyBase Information concerning the tissue-specific expression and developmental functions of in vivo overlap with the expected (background) overlap, an overlap analysis was performed for the frequent motif patterns for which genome-wide data was available. The expected overlap was measured by randomly permuting (1000 times) the same number of regions bound by one TF through the genome and the mean overlap was subsequently calculated. The significance of the observed compared to the expected overlap was calculated by assuming that the overlap follows a Poisson distribution.In order to compare the Motif position frequency matrices (PFMs) describing the probability of nucleotide distribution at each position were used for detecting TFBSs. First a weight sum of log scaled score was calculated as described in A data mining method based on a frequent pattern (FP) growth algorithm was used. This method uses an extended prefix tree structure for storing quantitative information about frequent patterns. The FP-tree is composed of a header table and nodes representing items, and each node has three member variables node-name, node-count and node-link. The node-name represents the item name, the node-count records the path reaching this node and the node-link links to the next node in the FP-tree carrying the same node-name. The header table consists of the item name and head of node-link, which points to the first node in the FP-tree carrying the item name. The nodes were arranged in a way that frequent co-occurrence nodes would have higher chance for sharing nodes Let The FP-tree is gradually built by incorporating results from scanning each sequence. A list of motifs found on the sequence will be first sorted according to The header table together with the constructed FP-tree were used for mining frequent patterns. Starting from the node-link of each node-name in the header table, the frequent pattern is generated by concatenating the nodes in the same prefix path. Their corresponding number of occurrence is calculated by nodes accumulation and prefix path count adjustment.In our approach, a log likelihood score based on a Markov model was used to calculate the statistical significance of the frequent patterns. Assuming both Markov models are ergodic, the empirical Kullback-Leibler divergence (KLD) between two Markov models isA distance analysis was carried out in order to detect distance preferences between motif pairs in the frequent motif patterns. The motifs were scanned on the sample sequences and their corresponding coordinates were used to calculate the pair-wise distance. The pair-wise distance is always representing the distance between the last nucleotide of the first motif and the first nucleotide of the second motif of the pair. The calculated distances between the motifs in the pair were distributed in equally spaced intervals of 10 bp. The background was estimated by analyzing the pair-wise distance of the motifs in the pair in randomly selected sequences from non-coding regions in the genome. The significance of the interval was calculated by fitting the interval data to a binomial distribution and the p-value was calculated for each interval.Drosophila genome were scanned using a 500 base pair window, in order to detect short distance motif pairs . The GO terms of the genes associated with regions containing the respective short distance pairs were compared to the rest of the genes in the genome using the Fisher exact test.The non-coding regions upstream and downstream of every gene in the The relative performance of COPS was compared to ModuleDigger Drosophila genome (background). The size of the sequence subsets ranged from 50 to 300 sequences. The number of times a program could identify the motif pairs Bap/Twi, Tin/Twi in the Twi dataset and the motif pairs Ase/Pros, Snail/Pros in the Pros dataset was considered as True Positive count (TP). The False Positive count (FP) corresponds to the number of times these motif pairs were detected in the background. The Matthews Correlation Coefficient (MCC) was calculated as follows:For comparing COPS with these programs, we used all three tools to analyze subsets of sequences from the Twi and Pros dataset and from all non-coding regions in the http://homes.esat.kuleuven.be/~kmarchal/Supplementary_Information_Sun_2009_ModuleDigger/Index.html) and  respectively.ModuleDigger and CPModule were accessed at (COPS is implemented in Python (which is platform independent) and can be used directly after download and installation of the SciPy package. We exemplarily cite the memory requirements and data processing time for some of the datasets described in the manuscript.Drosophila melanogaster Twist dataset (scanned for 75 motifs): 484 MB, 158.978 sec.Drosophila melanogaster Pros dataset (scanned for 75 motifs): 552 MB 216.774 sec.Mus musculus Mef2 dataset (scanned for 49 motifs): 2.1GB 283.928 sec.The main memory consumption is due to the calculation of the score distribution of the PWMs. Therefore longer motifs (i.e. mouse TFs) increase memory usage.in vivo, as shown by ChIP-on-Chip, ChIP-Seq or DamID experiments. An analysis with COPS aims at identifying candidate co-regulators of the main TF involved in the control of cell type-specific processes.In this study we present COPS , a compulog likelihood score calculated using two Markov models. The models are trained separately using the adequate input sequences and background sequences assembled from all the non-coding sequences of the respective genomes. The statistical significance cut-off score for motif co-occurrence (Z score) is calculated by comparing the log likelihood score of the frequent pattern to the log likelihood score distribution of the background. An additional feature implemented in COPS is the calculation of the preferred distances between co-occurring DNA motifs and its statistical significance.Initially, COPS scans the input sequences using all TF binding motifs annotated in open source databases JASPAR  or retriCOPS ultimately generates a list of co-occurring TFBS pairs and reports for each of them the statistical significance of the co-occurrence (Z score) and the percentage of overlap between the BSs of the TF pair. Highly overlapping motifs can generate ambiguous results and therefore COPS users may decide to discard such pairs from the list of results. However, before a pair is discarded, users need to consider that TFs of the same family very often bind to highly similar sequences, therefore the properties of the TFs of the pair should be examined. Furthermore, COPS calculates the preferred distance arrangement between the motifs of the detected TF pairs and its statistical significance . As the requirements of individual users may vary when they employ COPS for data analysis, the parameters provided by the program for each motif pair can be considered accordingly. Generally, a high Z score and a low BS overlap are good indicators of true positive results. Nevertheless, motif pairs are likely to be ranked with a rather low statistical significance score in cases when the reported PWMs fail to represent the actual binding profile of the respective TFs or they are poorly annotated. Therefore, additional parameters defined by the users are sometimes required for further analysis of the results. For instance, the users can resort to databases in order to address whether the co-occurring TFs are expressed in the same tissues as the main TF or whether they are suggested to physically interact with the main TF. Examples of additional means employed for result validation are given in the following sections.in vivo binding regions for five Drosophila mesoderm specification TFs, namely Twist (Twi), Myocyte enhancer factor 2 (Mef2), Tinman (Tin), Bagpipe (Bap) and Biniou (Bin), identified by ChIP-on-Chip in stage 10 to 11 embryos in vivo by all the other mesoderm specifying TFs mentioned above In order to evaluate the biological relevance of COPS predictions, we analyzed three published genome-wide datasets containing CRMs interacting with several co-regulatory TFs. The first dataset contains the Drosophila embryos in vivo binding, we used genomic regions shown by ChIP-on-Chip to be combinatorially bound by Twi/Dorsal/Snail in vivo in stage 5 to 7 Drosophila embryos Scanning of Twi-interacting regions using all annotated PWMs as well as a number of optimized PWMs described in the literature In addition to the four known Twi co-regulators, we detected co-occurring BSs for five TFs described either to be expressed in mesoderm-derived tissues or to be implicated in muscle development . Thus, win vivo implications for the co-occurrence of Twi with neuronal TFs. Since Twi, in addition to its prominent role in mesoderm specification, has been suggested to be important for patterning the neurogenic ectoderm during early embryonic development Interestingly, COPS detected Twi BSs in combination with BSs for TFs related to nervous system development, among which Deadpan (Dpn), Asense (Ase) and Snail . The subIn addition to the Twi-bound genomic regions, we independently analyzed the genomic regions bound by Bin, Tin, Bap and Mef2. In all four sequence-sets, COPS detected co-occurrence of the motif of the respective TF with the motifs of other mesoderm specification factors , i.e. TwDrosophila embryos The second genome-wide dataset consists of genomic regions bound by the neural stem cell TFs, Prospero (Pros), Ase, Dpn and Snail, as defined by DamID in stage 10 to 11 The genomic regions bound by Ase, Dpn and Snail were independently analyzed and in all cases COPS detected co-occurrence of the motif of the respective TF with the motifs of all other neural stem cell specification factors .Drosophila datasets, we analyzed the genome-wide data generated by Schlesinger et al. (2011) using mouse HL-1 cardiomyocytes. In this study In addition to the two The genomic regions bound by Mef2a were independently analyzed and COPS detected co-occurrence of the Mef2a motif with the BS of the co-regulator Nkx2.5 . SRF andSystematic analyses of TFBS arrangements on developmental enhancers have highlighted that random distributions of TFBSs on CRMs are not sufficient for precise regulation of target gene expression Drosophila genome for sequences containing the above mentioned TFBS pairs at a distance of -1–9 bp as reported by COPS and examining the GO terms of the genes associated with these regions. In all three cases, genes associated with these specific short-distance TF combinations exhibited similar functional annotations as the respective TF pair  COPS will have to scan the sequences for a total number of 67525 frequent patterns. Therefore when possible, we advice COPS users to preselect a smaller subset of motifs in analyses involving longer patterns, in order to facilitate their analysis.Notably, COPS is not restricted to the detection of motif pairs, but it can also be used for identifying longer co-occurrence patterns, namely combinations of three or more TFBSs. However, the number of motif combinations dramatically expands when scanning for longer patterns using large motif collections, hence resulting in increased memory requirements and processing time. We exemplarily mention that when a One feature of COPS not found in other sequence-screening tools is the detection of distance preferences between co-occurring motifs. The defined spatial organization of TFBSs on CRMs is critical for the proper assembly of functional regulatory complexes, since protein-protein interactions very often depend on favourable arrangements of BSs in vivo is not always easy to detect due to a decrease of the signal to noise ratio. Moreover, similarly to all computational routines, COPS cannot capture several aspects of in vivo transcriptional regulation. First of all, recruitment of TFs can occur without the presence of recognizable TF motifs on the respective genomic region. In such cases, genomic localization of the TF is mediated via binding at distal sites followed by DNA looping or via protein-protein interactions in vivoWhen analyzing sequences with bioinformatics tools such as COPS, a parameter that should be taken into consideration is the important role of PWMs. TF co-occurrences are likely to be falsely omitted if the reported PWMs fail to represent the actual binding profile of the respective TFs or if they are poorly annotated. Furthermore, false positive results may be obtained due to degenerate PWMs that are frequently encountered in the genome and are therefore likely to be part of co-occurrence patterns on multiple genomic regions. An additional critical parameter is the definition of the genomic regions that will be considered as “TF-bound DNA regions”. In contrast to the regions defined by ChIP-Seq, which are usually in the range of a few hundreds base-pairs long, DamID- and ChIP-on-Chip- detected regions often extend up to several kilo base-pairs. In such cases, the region that is bound by the TF In sum, COPS is a potent computational tool applicable for identifying potential transcriptional co-regulators that define context-dependent transcriptional outputs. In combination with genome-wide data for TF-DNA interactions, histone modifications and protein-protein interactions COPS allows the elucidation of cell type-specific regulatory networks.Figure S1Motif logos for the main TFs analyzed in the study. The logos of the motifs used for scanning for BSs of the main TFs from all three datasets are depicted in this figure. All other motif logos can be found in the open source databases TRANSFAC and JASPAR.(TIF)Click here for additional data file.Table S1Drosophila embryos. The table includes all results generated by analyzing Twi-, Tin-, Mef2-, Bin- and Bap-bound genomic regions using COPS. The TF motif pairs found to co-occur are listed in column A. Known co-regulators are shown in bold. Candidate co-regulatory TFs expressed in the muscle/mesoderm are highlighted in yellow. Column B shows the statistical significance of the co-occurrence pattern (Z score) and column C the percentage of motif overlap as calculated by the program. The tissue-specific expression of the co-occurring TF of each pair as reported in FlyBase is shown in column D and the literature supporting a co-regulatory role with the main TF in column E. Columns G-M show the preferred distance arrangements between the TF motifs of the different pairs and their statistical significance.Analysis of genomic regions bound by mesoderm specification TFs in stage 10 to 11 (XLS)Click here for additional data file.Table S2Drosophila embryos. The table includes all the results generated by analyzing Pros-, Ase, Dpn- and Snail-bound genomic regions using COPS. The TF motif pairs found to co-occur are listed in column A. Known co-regulators are shown in bold. Candidate co-regulatory TFs expressed in neuroblasts/nervous system are highlighted in light blue. Column B shows the statistical significance of the co-occurrence pattern (Z score) and column C the percentage of motif overlap as calculated by the program. The tissue-specific expression of the co-occurring TF of each pair as reported in FlyBase is shown in column D and the literature supporting a co-regulatory role with the main TF in column E. Columns G-O show the preferred distance arrangements between the TF motifs of the different pairs and their statistical significance.Analysis of genomic regions bound by neuroblast differentiation TFs in stage 10 to 11 (XLS)Click here for additional data file.Table S3Analysis of genomic regions bound by cardiac mesoderm specification TFs in HL-1 mouse cardiomyocytes. The table includes all the results generated by analyzing Nkx2.5- and Mef2a-bound genomic regions using COPS. The TF motif pairs found to co-occur are listed in column A. Known co-regulators are shown in bold. Candidate co-regulatory TFs expressed in the heart are highlighted in orange. Column B shows the statistical significance of the co-occurrence pattern (Z score) and column C the percentage of motif overlap as calculated by the program. The tissue-specific expression and developmental functions of the co-occurring TF of each pair as reported in UniProtKB is shown in column D and the literature supporting a co-regulatory role with the main TF in column E. Columns G-K show the preferred distance arrangements between the TF motifs of the different pairs and their statistical significance.(XLS)Click here for additional data file.Table S4Overrepresented GO terms of genes associated to genomic regions showing close distance arrangements of TFBS pairs. The table shows the GO terms  that were found overrepresented among the genes associated with genomic regions containing the motif pairs Bin/Twi, Mad/Twi and Ase/Dpn (Column A of each worksheet) at a close distance arrangement of −1–9 bp. The P value for each overrepresented GO term is shown in Column G. The P value was calculated by comparing the number of genes linked to each GO term to the total number of genes associated with genomic regions containing the short distance motif pair (Column C vs Column D) in relation to the number of genes linked to the GO term and associated to regions without the short distance motif pair (Column E vs Column F).(XLS)Click here for additional data file.


While regional heterogeneity in islet distribution has been well studied in rodents, less is known about human pancreatic histology. To fill gaps in our understanding, regional differences in the adult human pancreas were quantitatively analyzed including the pathogenesis of type 2 diabetes (T2D). Cadaveric pancreas specimens were collected from the head, body and tail regions of each donor, including subjects with no history of diabetes or pancreatic diseases (n = 23) as well as patients with T2D (n = 12). The study further included individuals from whom islets were isolated (n = 7) to study islet yield and function in a clinical setting of islet transplantation. The whole pancreatic sections were examined using an innovative large-scale image capture and unbiased detailed quantitative analyses of the characteristics of islets from each individual . Islet distribution/density is similar between the head and body regions, but is >2-fold higher in the tail region. In contrast to rodents, islet cellular composition and architecture were similar throughout the pancreas and there was no difference in glucose-stimulated insulin secretion in islets isolated from different regions of the pancreas. Further studies revealed preferential loss of large islets in the head region in patients with T2D. The present study has demonstrated distinct characteristics of the human pancreas, which should provide a baseline for the future studies integrating existing research in the field and helping to advance bi-directional research between humans and preclinical models. Insulin-secreting pancreatic beta-cells play a key role in glucose homeostasis and the pathophysiology of diabetes. Beta-cells are organized into distinct structures termed the islet of Langerhans together with other endocrine cells, which is a micro-organ and a functional unit. Recent studies on the histology of the human pancreas have shown distinct islet architecture with an increased fraction of alpha-cells intermingled with beta-cells, in contrast to rodent islets with the central core of beta-cells and less alpha-cells residing in the periphery In a series of recent works, we have demonstrated that islet architecture is size dependent in humans where such drastic morphological changes occur selectively in large islets (>∼100 µm in diameter), but small islets show similar architecture with mice in vitro experiment with the presence of excess exogenous glucagon in the culture media that compensated the functional differences During embryogenesis, the pancreas arises from dorsal and ventral pancreatic protrusions from the primitive gut endoderm. Regions of the adult pancreas are anatomically referred to head, body and tail regions. The head region is located on the right side of the abdomen that attached to the duodenum. The body and tail region extends to the left side of the abdomen next to the spleen. Regional heterogeneities in the histology of islets have been well studied in rodents with largely similar observations that the density of beta-cell mass in the body and tail regions is higher than in the head region In the head region of the human pancreas, past studies reported that 55–90% of the islet cell volume in this location was represented by pancreatic polypeptide (PP)-cells Our aim was to examine regional differences in islet distribution, cellular composition and architecture as well as glucose-stimulated insulin secretion, by highlighting the similarities and differences between humans and rodents. Here we report that: (1) Islet distribution/density is similar between the head and body regions, but is >2-fold higher in the tail region; (2) Similar cellular composition and architecture is present throughout the pancreas; and (3) There is no regional difference in glucose-stimulated insulin secretion in isolated islets. Further studies include the pathophysiology of T2D.The use of human tissues in the study was approved by the Institutional Review Board at the University of Chicago.Human pancreata were generously provided by the Gift of Hope-Organ Procurement Organization in Chicago. Written informed consent from a donor or the next of kin was obtained for use of a sample in research. Specimens were collected within 12 hours of cold ischemia.Paraffin-embedded sections (5 µm) were stained with the following primary antibodies : polyclonal guinea pig anti-porcine insulin , mouse monoclonal anti-human glucagon , polyclonal goat anti-somatostatin , polyclonal rabbit anti-pancreatic polypeptide (DAKO) and DAPI . The primary antibodies were detected using a combination of DyLight 488, 549, and 649-conjugated secondary antibodies .http://rsbweb.nih.gov/ij/). To measure coordinates of each islet-cell type, DAPI fluorescent signals were converted to 8-bit masks and watershed to obtain masks of individual nuclei. The pixels surrounding nuclei masks were quantified with respect to each endocrine hormone staining  to identify which hormone was most prevalent around each nucleus and to record islet-cell coordinates. DAPI signals outside of islets were not included. Based on the coordinates of every cell within individual islets, neighborhood of each cell was identified and contact probabilities between cell types were calculated Microscopic images were taken with an Olympus IX8 DSU spinning disk confocal microscope  with imaging software StereoInvestigator . A modified method of “virtual slice capture” was used Human pancreata from multi-organ donors with consent for research and/or clinical use were processed at the Human Islet Cell Processing Facility of the Diabetes Research Institute at the University of Miami. Islets were isolated using a modified automated method Function of isolated islets based on insulin release in response to glucose was determined using the simulation index (SI), i.e. the ratio between stimulated (28 mM glucose) and basal (2.8 mM glucose) insulin releases in culture media for 2 h at 37°C. Supernatant was collected and insulin concentrations were assessed by ELISA .P<0.05.Data are expressed as mean ±SEM. Pairwise t-tests were used to compare mean percent distributions of beta-, alpha- and delta-cell populations across the pancreas regions: head, body, and tail. Corrections to p-values were made for multiple testing using the Bonferroni method  We first examined islet size distribution and cellular composition in the head, body and tail regions of the pancreas from cadaveric specimens with no history of diabetes or pancreatic diseases . Clinical information about the pancreas tissue-donors is summarized in t-test  that it was >2-fold higher in the body-tail region compared to the head region. Note that here the islet size distribution is in a clinically relevant range (>∼50 µm in diameter). In contrast to rodents We examined regional heterogeneity in the human pancreas comparing head, body and tail regions from each individual to understand species differences, whereas past studies characterized the pancreatic regions to a limited extent using point-count morphometry The present study may have an important implication for the future studies on tissue harvesting, particularly the need to identify anatomical regions of pancreas specimens. In cancer research, several studies report that the majority of pancreatic cancer involves the head region, whereas the less frequent tumors in the body and tail region are more malignant