A Hidradenitis Suppurativa molecular disease signature derived from patient samples by high-throughput RNA sequencing and re-analysis of previously reported transcriptomic data sets

Hidradenitis suppurativa (HS) is a common, debilitating inflammatory skin disease linked to immune dysregulation and abnormalities in follicular structure and function. Several studies have characterized the transcriptomic profile of affected and unaffected skin in small populations. In this study of 20 patients, RNA from lesional and matching non-lesional skin biopsies in 20 subjects were used to identify an expression-based HS disease signature. This was followed by differential expression and pathway enrichment analyses, as well as jointly reanalyzing our findings with previously published transcriptomic profiles. We establish an RNA-Seq based HS expression disease signature that is mostly consistent with previous reports. Bulk-RNA profiles from 104 subjects in 7 previously reported data sets identified a disease signature of 118 differentially regulated genes compared to three control data sets from non-lesional skin. We confirmed previously reported expression profiles and further characterized dysregulation in complement activation and host response to bacteria in disease pathogenesis. Changes in the transcriptome of lesional skin in this cohort of HS patients is consistent with smaller previously reported populations. The findings further support the significance of immune dysregulation, in particular with regard to bacterial response mechanisms. Joint analysis of this and previously reported cohorts indicate a remarkably consistent expression profile.

HIF prolyl hydroxylase inhibition protects skeletal muscle from eccentric contraction-induced injury

Background

In muscular dystrophy and old age, skeletal muscle repair is compromised leading to fibrosis and fatty tissue accumulation. Therefore, therapies that protect skeletal muscle or enhance repair would be valuable medical treatments. Hypoxia-inducible factors (HIFs) regulate gene transcription under conditions of low oxygen, and HIF target genes EPO and VEGF have been associated with muscle protection and repair. We tested the importance of HIF activation following skeletal muscle injury, in both a murine model and human volunteers, using prolyl hydroxylase inhibitors that stabilize and activate HIF.

Methods

Using a mouse eccentric limb injury model, we characterized the protective effects of prolyl hydroxylase inhibitor, GSK1120360A. We then extended these studies to examine the impact of EPO modulation and infiltrating immune cell populations on muscle protection. Finally, we extended this study with an experimental medicine approach using eccentric arm exercise in untrained volunteers to measure the muscle-protective effects of a clinical prolyl hydroxylase inhibitor, daprodustat.

Results

GSK1120360A dramatically prevented functional deficits and histological damage, while accelerating recovery after eccentric limb injury in mice. Surprisingly, this effect was independent of EPO, but required myeloid HIF1α-mediated iNOS activity. Treatment of healthy human volunteers with high-dose daprodustat reduced accumulation of circulating damage markers following eccentric arm exercise, although we did not observe any diminution of functional deficits with compound treatment.

Conclusion

The results of these experiments highlight a novel skeletal muscle protective effect of prolyl hydroxylase inhibition via HIF-mediated expression of iNOS in macrophages. Partial recapitulation of these findings in healthy volunteers suggests elements of consistent pharmacology compared to responses in mice although there are clear differences between these two systems.

Electronic supplementary material

The online version of this article (10.1186/s13395-018-0179-5) contains supplementary material, which is available to authorized users.

Uncovering new disease indications for G-protein coupled receptors and their endogenous ligands

Background

The Open Targets Platform integrates different data sources in order to facilitate identification of potential therapeutic drug targets to treat human diseases. It currently provides evidence for nearly 2.6 million potential target-disease pairs. G-protein coupled receptors are a drug target class of high interest because of the number of successful drugs being developed against them over many years. Here we describe a systematic approach utilizing the Open Targets Platform data to uncover and prioritize potential new disease indications for the G-protein coupled receptors and their ligands.

Results

Utilizing the data available in the Open Targets platform, potential G-protein coupled receptor and endogenous ligand disease association pairs were systematically identified. Intriguing examples such as GPR35 for inflammatory bowel disease and CXCR4 for viral infection are used as illustrations of how a systematic approach can aid in the prioritization of interesting drug discovery hypotheses. Combining evidences for G-protein coupled receptors and their corresponding endogenous peptidergic ligands increases confidence and provides supportive evidence for potential new target-disease hypotheses. Comparing such hypotheses to the global pharma drug discovery pipeline to validate the approach showed that more than 93% of G-protein coupled receptor-disease pairs with a high overall Open Targets score involved receptors with an existing drug discovery program.

Conclusions

The Open Targets gene-disease score can be used to prioritize potential G-protein coupled receptors-indication hypotheses. In addition, availability of multiple different evidence types markedly increases confidence as does combining evidence from known receptor-ligand pairs. Comparing the top-ranked hypotheses to the current global pharma pipeline serves validation of our approach and identifies and prioritizes new therapeutic opportunities.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2392-y) contains supplementary material, which is available to authorized users.

Assessment of rosacea symptom severity by genome-wide association study and expression analysis highlights immuno-inflammatory and skin pigmentation genes

Rosacea is a common, chronic skin disease of variable severity with limited treatment options. The cause of rosacea is unknown, but it is believed to be due to a combination of hereditary and environmental factors. Little is known about the genetics of the disease. We performed a genome-wide association study (GWAS) of rosacea symptom severity with data from 73 265 research participants of European ancestry from the 23andMe customer base. Seven loci had variants associated with rosacea at the genome-wide significance level (P < 5 × 10-8). Further analyses highlighted likely gene regions or effector genes including IRF4 (P = 1.5 × 10-17), a human leukocyte antigen (HLA) region flanked by PSMB9 and HLA-DMB (P = 2.2 × 10-15), HERC2-OCA2 (P = 4.2 × 10-12), SLC45A2 (P = 1.7 × 10-10), IL13 (P = 2.8 × 10-9), a region flanked by NRXN3 and DIO2 (P = 4.1 × 10-9), and a region flanked by OVOL1and SNX32 (P = 1.2 × 10-8). All associations with rosacea were novel except for the HLA locus. Two of these loci (HERC-OCA2 and SLC45A2) and another precedented variant (rs1805007 in melanocortin 1 receptor) with an association P value just below the significance threshold (P = 1.3 × 10-7) have been previously associated with skin phenotypes and pigmentation, two of these loci are linked to immuno-inflammation phenotypes (IL13 and PSMB9-HLA-DMA) and one has been associated with both categories (IRF4). Genes within three loci (PSMB9-HLA-DMA, HERC-OCA2 and NRX3-DIO2) were differentially expressed in a previously published clinical rosacea transcriptomics study that compared lesional to non-lesional samples. The identified loci provide specificity of inflammatory mechanisms in rosacea, and identify potential pathways for therapeutic intervention.

The epigenetic signature of systemic insulin resistance in obese women

AIMS/HYPOTHESIS

Insulin resistance (IR) links obesity to type 2 diabetes. The aim of this study was to explore whether white adipose tissue (WAT) epigenetic dysregulation is associated with systemic IR by genome-wide CG dinucleotide (CpG) methylation and gene expression profiling in WAT from insulin-resistant and insulin-sensitive women. A secondary aim was to determine whether the DNA methylation signature in peripheral blood mononuclear cells (PBMCs) reflects WAT methylation and, if so, can be used as a marker for systemic IR.

METHODS

From 220 obese women, we selected a total of 80 individuals from either of the extreme ends of the distribution curve of HOMA-IR, an indirect measure of systemic insulin sensitivity. Genome-wide transcriptome and DNA CpG methylation profiling by array was performed on subcutaneous (SAT) and visceral (omental) adipose tissue (VAT). CpG methylation in PBMCs was assayed in the same cohort.

RESULTS

There were 647 differentially expressed genes (false discovery rate [FDR] 10%) in SAT, all of which displayed directionally consistent associations in VAT. This suggests that IR is associated with dysregulated expression of a common set of genes in SAT and VAT. The average degree of DNA methylation did not differ between the insulin-resistant and insulin-sensitive group in any of the analysed tissues/cells. There were 223 IR-associated genes in SAT containing a total of 336 nominally significant differentially methylated sites (DMS). The 223 IR-associated genes were over-represented in pathways related to integrin cell surface interactions and insulin signalling and included COL5A1, GAB1, IRS2, PFKFB3 and PTPRJ. In VAT there were a total of 51 differentially expressed genes (FDR 10%); 18 IR-associated genes contained a total of 29 DMS.

CONCLUSIONS/INTERPRETATION

In individuals discordant for insulin sensitivity, the average DNA CpG methylation in SAT and VAT is similar, although specific genes, particularly in SAT, display significantly altered expression and DMS in IR, possibly indicating that epigenetic regulation of these genes influences metabolism.

Significant obesity-associated gene expression changes occur in the stomach but not intestines in obese mice

The gastrointestinal (GI) tract can have significant impact on the regulation of the whole‐body metabolism and may contribute to the development of obesity and diabetes. To systemically elucidate the role of the GI tract in obesity, we performed a transcriptomic analysis in different parts of the GI tract of two obese mouse models: ob/ob and high‐fat diet (HFD) fed mice. Compared to their lean controls, significant changes in the gene expression were observed in both obese mouse groups in the stomach (ob/ob: 959; HFD: 542). In addition, these changes were quantitatively much higher than in the intestine. Despite the difference in genetic background, the two mouse models shared 296 similar gene expression changes in the stomach. Among those genes, some had known associations to obesity, diabetes, and insulin resistance. In addition, the gene expression profiles strongly suggested an increased gastric acid secretion in both obese mouse models, probably through an activation of the gastrin pathway. In conclusion, our data reveal a previously unknown dominant connection between the stomach and obesity in murine models extensively used in research.

Network-Based Identification and Prioritization of Key Regulators of Coronary Artery Disease Loci

OBJECTIVE

Recent genome-wide association studies of coronary artery disease (CAD) have revealed 58 genome-wide significant and 148 suggestive genetic loci. However, the molecular mechanisms through which they contribute to CAD and the clinical implications of these findings remain largely unknown. We aim to retrieve gene subnetworks of the 206 CAD loci and identify and prioritize candidate regulators to better understand the biological mechanisms underlying the genetic associations.

APPROACH AND RESULTS

We devised a new integrative genomics approach that incorporated (1) candidate genes from the top CAD loci, (2) the complete genetic association results from the 1000 genomes-based CAD genome-wide association studies from the Coronary Artery Disease Genome Wide Replication and Meta-Analysis Plus the Coronary Artery Disease consortium, (3) tissue-specific gene regulatory networks that depict the potential relationship and interactions between genes, and (4) tissue-specific gene expression patterns between CAD patients and controls. The networks and top-ranked regulators according to these data-driven criteria were further queried against literature, experimental evidence, and drug information to evaluate their disease relevance and potential as drug targets. Our analysis uncovered several potential novel regulators of CAD such as LUM and STAT3, which possess properties suitable as drug targets. We also revealed molecular relations and potential mechanisms through which the top CAD loci operate. Furthermore, we found that multiple CAD-relevant biological processes such as extracellular matrix, inflammatory and immune pathways, complement and coagulation cascades, and lipid metabolism interact in the CAD networks.

CONCLUSIONS

Our data-driven integrative genomics framework unraveled tissue-specific relations among the candidate genes of the CAD genome-wide association studies loci and prioritized novel network regulatory genes orchestrating biological processes relevant to CAD.

Abstract 58: Network-based Identification and Prioritization of Key Regulators of Coronary Artery Disease Loci

Objective: Recent genome-wide association studies (GWAS) of coronary artery disease (CAD) have revealed 58 genome-wide significant and 148 suggestive genetic loci. However, the molecular mechanisms through which they contribute to CAD and the clinical implications of these findings remain largely unknown. We aim to retrieve gene subnetworks of the 206 CAD loci and identify and prioritize candidate regulators to better understand the biological mechanisms underlying the genetic associations.

Approach and Results: We devised a new integrative genomics approach that incorporated i) candidate genes from the top CAD loci, ii) the complete genetic association results from the CARDIoGRAM-C4D CAD GWAS, iii) tissue-specific gene regulatory networks that depict the potential relationship and interactions between genes, and iv) tissue-specific gene expression patterns between CAD patients and controls. The networks and top ranked regulators according to these data-driven criteria were further queried against literature, experimental evidence, and drug information to evaluate their disease relevance and potential as drug targets. Our analysis uncovered several potential novel regulators of CAD such as LUM and STAT3 , which possess properties suitable as drug targets. We also revealed molecular relations and potential mechanisms through which the top CAD loci operate. Furthermore, we found that extracellular matrix genes coordinate multiple CAD-relevant biological processes such as complement and coagulation cascades and lipid metabolism through tissue-specific interactions in the CAD networks.

Conclusion: Our data-driven integrative genomics framework unraveled tissue-specific relations among the candidate genes of the CAD GWAS loci and prioritized novel network regulatory genes orchestrating biological processes relevant to CAD.

Effect of Roux-en-Y gastric bypass surgery on bile acid metabolism in normal and obese diabetic rats

In addition to classic functions of facilitating hepatobiliary secretion and intestinal absorption of lipophilic nutrients, bile acids (BA) are also endocrine factors and regulate glucose and lipid metabolism. Recent data indicate that antiobesity bariatric procedures e.g. Roux-en-Y gastric bypass surgery (RYGB), which also remit diabetes, increase plasma BAs in humans, leading to the hypothesis that BAs may play a role in diabetes resolution following surgery. To investigate the effect of RYGB on BA physiology and its relationship with glucose homeostasis, we undertook RYGB and SHAM surgery in Zucker diabetic fatty (ZDF) and normoglycemic Sprague Dawley (SD) rats and measured plasma and fecal BA levels, as well as plasma glucose, insulin, Glucagon like peptide 1 (GLP-1) and Peptide YY (PYY), 2 days before and 3, 7, 14 and 28 days after surgery. RYGB decreased body weight and increased plasma GLP-1 in both SD and ZDF rats while decreasing plasma insulin and glucose in ZDF rats starting from the first week. Compared to SHAM groups, both SD-RYGB and ZDF-RYGB groups started to have increases in plasma total BAs in the second week, which might not contribute to early post-surgery metabolic changes. While there was no significant difference in fecal BA excretion between SD-RYGB and SD-SHAM groups, the ZDF-RYGB group had a transient 4.2-fold increase (P<0.001) in 24-hour fecal BA excretion on post-operative day 3 compared to ZDF-SHAM, which paralleled a significant increase in plasma PYY. Ratios of plasma and fecal cholic acid/chenodeoxycholic acid derived BAs were decreased in RYGB groups. In addition, tissue mRNA expression analysis suggested early intestinal BA reabsorption and potentially reduced hepatic cholic acid production in RYGB groups. In summary, we present novel data on RYGB-mediated changes in BA metabolism to further understand the role of BAs in RYGB-induced metabolic effects in humans.

Integrative framework for identification of key cell identity genes uncovers determinants of ES cell identity and homeostasis

Identification of genes associated with specific biological phenotypes is a fundamental step toward understanding the molecular basis underlying development and pathogenesis. Although RNAi-based high-throughput screens are routinely used for this task, false discovery and sensitivity remain a challenge. Here we describe a computational framework for systematic integration of published gene expression data to identify genes defining a phenotype of interest. We applied our approach to rank-order all genes based on their likelihood of determining ES cell (ESC) identity. RNAi-mediated loss-of-function experiments on top-ranked genes unearthed many novel determinants of ESC identity, thus validating the derived gene ranks to serve as a rich and valuable resource for those working to uncover novel ESC regulators. Underscoring the value of our gene ranks, functional studies of our top-hit Nucleolin (Ncl), abundant in stem and cancer cells, revealed Ncl's essential role in the maintenance of ESC homeostasis by shielding against differentiation-inducing redox imbalance-induced oxidative stress. Notably, we report a conceptually novel mechanism involving a Nucleolin-dependent Nanog-p53 bistable switch regulating the homeostatic balance between self-renewal and differentiation in ESCs. Our findings connect the dots on a previously unknown regulatory circuitry involving genes associated with traits in both ESCs and cancer and might have profound implications for understanding cell fate decisions in cancer stem cells. The proposed computational framework, by helping to prioritize and preselect candidate genes for tests using complex and expensive genetic screens, provides a powerful yet inexpensive means for identification of key cell identity genes.

Integrative clinical transcriptomics analyses for new therapeutic intervention strategies: a psoriasis case study

Psoriasis is a chronic inflammatory skin disease with complex pathological features and unmet pharmacotherapy needs. Here, we present a framework for developing new therapeutic intervention strategies for psoriasis by utilizing publicly available clinical transcriptomics data sets. By exploring the underlying molecular mechanisms of psoriasis, the effects of subsequent perturbation of these mechanisms by drugs and an integrative analysis, we propose a psoriasis disease signature, identify potential drug repurposing opportunities and present novel target selection methodologies. We anticipate that the outlined methodology or similar approaches will further support biomarker discovery and the development of new drugs for psoriasis.

Fip1 regulates mRNA alternative polyadenylation to promote stem cell self-renewal

mRNA alternative polyadenylation (APA) plays a critical role in post-transcriptional gene control and is highly regulated during development and disease. However, the regulatory mechanisms and functional consequences of APA remain poorly understood. Here, we show that an mRNA 3' processing factor, Fip1, is essential for embryonic stem cell (ESC) self-renewal and somatic cell reprogramming. Fip1 promotes stem cell maintenance, in part, by activating the ESC-specific APA profiles to ensure the optimal expression of a specific set of genes, including critical self-renewal factors. Fip1 expression and the Fip1-dependent APA program change during ESC differentiation and are restored to an ESC-like state during somatic reprogramming. Mechanistically, we provide evidence that the specificity of Fip1-mediated APA regulation depends on multiple factors, including Fip1-RNA interactions and the distance between APA sites. Together, our data highlight the role for post-transcriptional control in stem cell self-renewal, provide mechanistic insight on APA regulation in development, and establish an important function for APA in cell fate specification.

Human and mouse skeletal muscle stem cells: convergent and divergent mechanisms of myogenesis

Satellite cells are the chief contributor to skeletal muscle growth and regeneration. The study of mouse satellite cells has accelerated in recent years due to technical advancements in the isolation of these cells. The study of human satellite cells has lagged and thus little is known about how the biology of mouse and human satellite cells compare. We developed a flow cytometry-based method to prospectively isolate human skeletal muscle progenitors from the satellite cell pool using positive and negative selection markers. Results show that this pool is enriched in PAX7 expressing cells that possess robust myogenic potential including the ability to give rise to de novo muscle in vivo. We compared mouse and human satellite cells in culture and identify differences in the elaboration of the myogenic genetic program and in the sensitivity of the cells to cytokine stimulation. These results indicate that not all mechanisms regulating mouse satellite cell activation are conserved in human satellite cells and that such differences may impact the clinical translation of therapeutics validated in mouse models. Thus, the findings of this study are relevant to developing therapies to combat muscle disease.

Mining emerging biomedical literature for understanding disease associations in drug discovery

Systematically evaluating the exponentially growing body of scientific literature has become a critical task that every drug discovery organization must engage in in order to understand emerging trends for scientific investment and strategy development. Developing trends analysis uses the number of publications within a 3-year window to determine concepts derived from well-established disease and gene ontologies to aid in recognizing and predicting emerging areas of scientific discoveries relevant to that space. In this chapter, we describe such a method and use obesity and psoriasis as use-case examples by analyzing the frequency of disease-related MeSH terms in PubMed abstracts over time. We share how our system can be used to predict emerging trends at a relatively early stage and we analyze the literature-identified genes for genetic associations, druggability, and biological pathways to explore any potential biological connections between the two diseases that could be utilized for drug discovery.

Diverse stresses dramatically alter genome-wide p53 binding and transactivation landscape in human cancer cells

The effects of diverse stresses on promoter selectivity and transcription regulation by the tumor suppressor p53 are poorly understood. We have taken a comprehensive approach to characterizing the human p53 network that includes p53 levels, binding, expression and chromatin changes under diverse stresses. Human osteosarcoma U2OS cells treated with anti-cancer drugs Doxorubicin (DXR) or Nutlin-3 (Nutlin) led to strikingly different p53 gene binding patterns based on chromatin immunoprecipitation with high-throughput sequencing experiments. Although two contiguous RRRCWWGYYY decamers is the consensus binding motif, p53 can bind a single decamer and function in vivo. Although the number of sites bound by p53 was six times greater for Nutlin than DXR, expression changes induced by Nutlin were much less dramatic compared with DXR. Unexpectedly, the solvent dimethylsulphoxide (DMSO) alone induced p53 binding to many sites common to DXR; however, this binding had no effect on target gene expression. Together, these data imply a two-stage mechanism for p53 transactivation where p53 binding only constitutes the first stage. Furthermore, both p53 binding and transactivation were associated with increased active histone modification histone H3 lysine 4 trimethylation. We discovered 149 putative new p53 target genes including several that are relevant to tumor suppression, revealing potential new targets for cancer therapy and expanding our understanding of the p53 regulatory network.

Evaluation of analytical methods for connectivity map data

Connectivity map data and associated methodologies have become a valuable tool in understanding drug mechanism of action (MOA) and discovering new indications for drugs. However, few systematic evaluations have been done to assess the accuracy of these methodologies. One of the difficulties has been the lack of benchmarking data sets. Iskar et al. (PLoS. Comput. Biol. 6, 2010) predicted the Anatomical Therapeutic Chemical (ATC) drug classification based on drug-induced gene expression profile similarity (DIPS), and quantified the accuracy of their method by computing the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve. We adopt the same data and extend the methodology, by using a simpler eXtreme cosine (XCos) method, and find it does better in this limited setting than the Kolmogorov-Smirnov (KS) statistic. In fact, for partial AUC (a more relevant statistic for actual application to repositioning) XCos does 17% better than the DIPS method (p=1.2e-7). We also observe that smaller gene signatures (with 100 probes) do better than larger ones (with 500 probes), and that DMSO controls from within the same batch obviate the need for mean centering. As expected there is heterogeneity in the prediction accuracy amongst the various ATC codes. We find that good transcriptional response to drug treatment appears necessary but not sufficient to achieve high AUCs. Certain ATC codes, such as those corresponding to corticosteroids, had much higher AUCs possibly due to strong transcriptional responses and consistency in MOA.

Gastrointestinal weight-loss surgery: glimpses at the molecular level

Pharmacotherapy for obesity remains a key challenge, and gastrointestinal weight-loss surgery remains a preferred option to help reduce excess body weight along with resolution of several comorbidities associated with obesity. This offers a unique opportunity to study the underlying mechanisms of gastro-intestinal weight-loss surgery to develop effective and less invasive long-term therapeutic interventions potentially mimicking the benefits of gastrointestinal weight-loss surgery. Here, we present an integrative analysis of currently available human transcriptomics data sets pre- and post-surgery and propose a computational biology strategy for selecting putative drug targets. We anticipate that approaches similar to the one that we outline here, would help elucidate underlying mechanisms that result in metabolic improvements and provide guidance on pharmaceutical targets to develop effective and less invasive therapies for obesity and related comorbidities.

Cnot1, Cnot2, and Cnot3 maintain mouse and human ESC identity and inhibit extraembryonic differentiation

Embryonic stem cell (ESC) identity and self-renewal is maintained by extrinsic signaling pathways and intrinsic gene regulatory networks. Here, we show that three members of the Ccr4-Not complex, Cnot1, Cnot2, and Cnot3, play critical roles in maintaining mouse and human ESC identity as a protein complex and inhibit differentiation into the extraembryonic lineages. Enriched in the inner cell mass of blastocysts, these Cnot genes are highly expressed in ESC and downregulated during differentiation. In mouse ESCs, Cnot1, Cnot2, and Cnot3 are important for maintenance in both normal conditions and the 2i/LIF medium that supports the ground state pluripotency. Genetic analysis indicated that they do not act through known self-renewal pathways or core transcription factors. Instead, they repress the expression of early trophectoderm (TE) transcription factors such as Cdx2. Importantly, these Cnot genes are also necessary for the maintenance of human ESCs, and silencing them mainly lead to TE and primitive endoderm differentiation. Together, our results indicate that Cnot1, Cnot2, and Cnot3 represent a novel component of the core self-renewal and pluripotency circuitry conserved in mouse and human ESCs.

Acute depletion of Tet1-dependent 5-hydroxymethylcytosine levels impairs LIF/Stat3 signaling and results in loss of embryonic stem cell identity

The TET family of FE(II) and 2-oxoglutarate-dependent enzymes (Tet1/2/3) promote DNA demethylation by converting 5-methylcytosine to 5-hydroxymethylcytosine (5hmC), which they further oxidize into 5-formylcytosine and 5-carboxylcytosine. Tet1 is robustly expressed in mouse embryonic stem cells (mESCs) and has been implicated in mESC maintenance. Here we demonstrate that, unlike genetic deletion, RNAi-mediated depletion of Tet1 in mESCs led to a significant reduction in 5hmC and loss of mESC identity. The differentiation phenotype due to Tet1 depletion positively correlated with the extent of 5hmC loss. Meta-analyses of genomic data sets suggested interaction between Tet1 and leukemia inhibitory factor (LIF) signaling. LIF signaling is known to promote self-renewal and pluripotency in mESCs partly by opposing MAPK/ERK-mediated differentiation. Withdrawal of LIF leads to differentiation of mESCs. We discovered that Tet1 depletion impaired LIF-dependent Stat3-mediated gene activation by affecting Stat3's ability to bind to its target sites on chromatin. Nanog overexpression or inhibition of MAPK/ERK signaling, both known to maintain mESCs in the absence of LIF, rescued Tet1 depletion, further supporting the dependence of LIF/Stat3 signaling on Tet1. These data support the conclusion that analysis of mESCs in the hours/days immediately following efficient Tet1 depletion reveals Tet1's normal physiological role in maintaining the pluripotent state that may be subject to homeostatic compensation in genetic models.

WebGimm: An integrated web-based platform for cluster analysis, functional analysis, and interactive visualization of results

Cluster analysis methods have been extensively researched, but the adoption of new methods is often hindered by technical barriers in their implementation and use. WebGimm is a free cluster analysis web-service, and an open source general purpose clustering web-server infrastructure designed to facilitate easy deployment of integrated cluster analysis servers based on clustering and functional annotation algorithms implemented in R. Integrated functional analyses and interactive browsing of both, clustering structure and functional annotations provides a complete analytical environment for cluster analysis and interpretation of results. The Java Web Start client-based interface is modeled after the familiar cluster/treeview packages making its use intuitive to a wide array of biomedical researchers. For biomedical researchers, WebGimm provides an avenue to access state of the art clustering procedures. For Bioinformatics methods developers, WebGimm offers a convenient avenue to deploy their newly developed clustering methods. WebGimm server, software and manuals can be freely accessed at http://ClusterAnalysis.org/.

Generalized random set framework for functional enrichment analysis using primary genomics datasets

MOTIVATION

Functional enrichment analysis using primary genomics datasets is an emerging approach to complement established methods for functional enrichment based on predefined lists of functionally related genes. Currently used methods depend on creating lists of 'significant' and 'non-significant' genes based on ad hoc significance cutoffs. This can lead to loss of statistical power and can introduce biases affecting the interpretation of experimental results.

RESULTS

We developed and validated a new statistical framework, generalized random set (GRS) analysis, for comparing the genomic signatures in two datasets without the need for gene categorization. In our tests, GRS produced correct measures of statistical significance, and it showed dramatic improvement in the statistical power over other methods currently used in this setting. We also developed a procedure for identifying genes driving the concordance of the genomics profiles and demonstrated a dramatic improvement in functional coherence of genes identified in such analysis.

AVAILABILITY

GRS can be downloaded as part of the R package CLEAN from http://ClusterAnalysis.org/. An online implementation is available at http://GenomicsPortals.org/.

Research resource: estrogen-driven prolactin-mediated gene-expression networks in hormone-induced prostatic intraepithelial neoplasia

Cotreatment with testosterone (T) and 17β-estradiol (E2) is an established regimen for inducing of prostatic intraepithelial neoplasia (PIN) and prostate cancer in rodent models. We previously used the pure antiestrogen ICI 182,780 (ICI) and bromocriptine, a dopamine receptor agonist, to inhibit PIN induction and systemic hyperprolactinemia in Noble rats and found that the carcinogenic action of T+E2 is mediated directly by the effects of E2 on the prostate and/or indirectly via E2-induced hyperprolactinemia. In this study, we delineate the specific action(s) of E2 and prolactin (PRL) in early prostate carcinogenesis by an integrated approach combining global transcription profiling, gene ontology, and gene-network mapping. We identified 2504 differentially expressed genes in the T+E2-treated lateral prostate. The changes in expression of a subset of 1990 genes (∼80%) were blocked upon cotreatment with ICI and bromocriptine, respectively, whereas those of 262 genes (∼10%) were blocked only by treatment with ICI, suggesting that E2-induced pituitary PRL is the primary mediator of the prostatic transcriptional response to the altered hormone milieu. Bioinformatics analyses identified hormone-responsive gene networks involved in immune responses, stromal tissue remodeling, and the ERK pathway. In particular, our data suggest that IL-1β may mediate, at least in part, hormone-induced changes in gene expression during PIN formation. Together, these data highlight the importance of pituitary PRL in estrogen-induced prostate tumorigenesis. The identification of both E2- and pituitary PRL-responsive genes provides a comprehensive resource for future investigations of the complex mechanisms by which changes in the endocrine milieu contribute to prostate carcinogenesis in vivo.

A semi-parametric Bayesian model for unsupervised differential co-expression analysis

Background

Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined sets of biological samples, such analysis aims at identifying genes that are co-expressed in one, but not in the other set of samples.

Results

We developed a novel probabilistic framework for jointly uncovering contexts (i.e. groups of samples) with specific co-expression patterns, and groups of genes with different co-expression patterns across such contexts. In contrast to current clustering and bi-clustering procedures, the implicit similarity measure in this model used for grouping biological samples is based on the clustering structure of genes within each sample and not on traditional measures of gene expression level similarities. Within this framework, biological samples with widely discordant expression patterns can be placed in the same context as long as the co-clustering structure of genes is concordant within these samples. To the best of our knowledge, this is the first method to date for unsupervised differential co-expression analysis in this generality. When applied to the problem of identifying molecular subtypes of breast cancer, our method identified reproducible patterns of differential co-expression across several independent expression datasets. Sample groupings induced by these patterns were highly informative of the disease outcome. Expression patterns of differentially co-expressed genes provided new insights into the complex nature of the ERα regulatory network.

Conclusions

We demonstrated that the use of the co-clustering structure as the similarity measure in the unsupervised analysis of sample gene expression profiles provides valuable information about expression regulatory networks.

CLEAN: CLustering Enrichment ANalysis

Background

Integration of biological knowledge encoded in various lists of functionally related genes has become one of the most important aspects of analyzing genome-wide functional genomics data. In the context of cluster analysis, functional coherence of clusters established through such analyses have been used to identify biologically meaningful clusters, compare clustering algorithms and identify biological pathways associated with the biological process under investigation.

Results

We developed a computational framework for analytically and visually integrating knowledge-based functional categories with the cluster analysis of genomics data. The framework is based on the simple, conceptually appealing, and biologically interpretable gene-specific functional coherence score (CLEAN score). The score is derived by correlating the clustering structure as a whole with functional categories of interest. We directly demonstrate that integrating biological knowledge in this way improves the reproducibility of conclusions derived from cluster analysis. The CLEAN score differentiates between the levels of functional coherence for genes within the same cluster based on their membership in enriched functional categories. We show that this aspect results in higher reproducibility across independent datasets and produces more informative genes for distinguishing different sample types than the scores based on the traditional cluster-wide analysis. We also demonstrate the utility of the CLEAN framework in comparing clusterings produced by different algorithms. CLEAN was implemented as an add-on R package and can be downloaded at . The package integrates routines for calculating gene specific functional coherence scores and the open source interactive Java-based viewer Functional TreeView (FTreeView).

Conclusion

Our results indicate that using the gene-specific functional coherence score improves the reproducibility of the conclusions made about clusters of co-expressed genes over using the traditional cluster-wide scores. Using gene-specific coherence scores also simplifies the comparisons of clusterings produced by different clustering algorithms and provides a simple tool for selecting genes with a "functionally coherent" expression profile.

Influence of fatty acid diets on gene expression in rat mammary epithelial cells

BACKGROUND

This study examines the impact of dietary fatty acids on regulation of gene expression in mammary epithelial cells before and during puberty.

METHODS

Diets primarily consisted of n-9 monounsaturated fatty acids (olive oil), n-6 polyunsaturated fatty acids (safflower), saturated acids (butter), and the reference AIN-93G diet (soy oil). The dietary regimen mimics the repetitive nature of fatty acid exposure in Western diets. Diet-induced changes in gene expression were examined in laser capture microdissected mammary ductal epithelial cells at day of weaning and end of puberty. PCNA immunohistochemistry analysis compared proliferation rates between diets.

RESULTS

Genes differentially expressed between each test diets and the reference diet were significantly enriched by cell cycle genes. Some of these genes were involved in activation of the cell cycle pathway or the G2/M check point pathway. Although there were some differences in the level of differential expression, all diets showed qualitatively the same pattern of differential expression compared to the reference diet. Cluster analysis identified an expanded set of cell cycle as well as immunity and sterol metabolism related clusters of differentially expressed genes.

CONCLUSION

Fatty acid-enriched diets significantly upregulated proliferation above normal physiological levels during puberty. Higher cellular proliferation during puberty caused by enriched fatty acid diets poses a potential increase risk of mammary cancer in later life. The human homologs of 27 of 62 cell cycle rat genes are included in a human breast cancer cluster of 45 cell cycle genes, further emphasizing the importance of our findings in the rat model.