Genetic Variation Determines PPARγ Function and Anti-diabetic Drug Response In Vivo.

SNPs affecting disease risk often reside in non-coding genomic regions. Here, we show that SNPs are highly enriched at mouse strain-selective adipose tissue binding sites for PPARγ, a nuclear receptor for anti-diabetic drugs. Many such SNPs alter binding motifs for PPARγ or cooperating factors and functionally regulate nearby genes whose expression is strain selective and imbalanced in heterozygous F1 mice. Moreover, genetically determined binding of PPARγ accounts for mouse strain-specific transcriptional effects of TZD drugs, providing proof of concept for personalized medicine related to nuclear receptor genomic occupancy. In human fat, motif-altering SNPs cause differential PPARγ binding, provide a molecular mechanism for some expression quantitative trait loci, and are risk factors for dysmetabolic traits in genome-wide association studies. One PPARγ motif-altering SNP is associated with HDL levels and other metabolic syndrome parameters. Thus, natural genetic variation in PPARγ genomic occupancy determines individual disease risk and drug response.

Circadian enhancers coordinate multiple phases of rhythmic gene transcription in vivo.

Mammalian transcriptomes display complex circadian rhythms with multiple phases of gene expression that cannot be accounted for by current models of the molecular clock. We have determined the underlying mechanisms by measuring nascent RNA transcription around the clock in mouse liver. Unbiased examination of enhancer RNAs (eRNAs) that cluster in specific circadian phases identified functional enhancers driven by distinct transcription factors (TFs). We further identify on a global scale the components of the TF cistromes that function to orchestrate circadian gene expression. Integrated genomic analyses also revealed mechanisms by which a single circadian factor controls opposing transcriptional phases. These findings shed light on the diversity and specificity of TF function in the generation of multiple phases of circadian gene transcription in a mammalian organ.

High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells.

New approach methodologies (NAMs) aim to accelerate the pace of chemical risk assessment while simultaneously reducing cost and dependency on animal studies. High Throughput Transcriptomics (HTTr) is an emerging NAM in the field of chemical hazard evaluation for establishing in vitro points-of-departure and providing mechanistic insight. In the current study, 1201 test chemicals were screened for bioactivity at eight concentrations using a 24-h exposure duration in the human- derived U-2 OS osteosarcoma cell line with HTTr. Assay reproducibility was assessed using three reference chemicals that were screened on every assay plate. The resulting transcriptomics data were analyzed by aggregating signal from genes into signature scores using gene set enrichment analysis, followed by concentration-response modeling of signatures scores. Signature scores were used to predict putative mechanisms of action, and to identify biological pathway altering concentrations (BPACs). BPACs were consistent across replicates for each reference chemical, with replicate BPAC standard deviations as low as 5.6 × 10-3 µM, demonstrating the internal reproducibility of HTTr-derived potency estimates. BPACs of test chemicals showed modest agreement (R2 = 0.55) with existing phenotype altering concentrations from high throughput phenotypic profiling using Cell Painting of the same chemicals in the same cell line. Altogether, this HTTr based chemical screen contributes to an accumulating pool of publicly available transcriptomic data relevant for chemical hazard evaluation and reinforces the utility of cell based molecular profiling methods in estimating chemical potency and predicting mechanism of action across a diverse set of chemicals.

High-Throughput Transcriptomics of Water Extracts Detects Reductions in Biological Activity with Water Treatment Processes.

The presence of numerous chemical contaminants from industrial, agricultural, and pharmaceutical sources in water supplies poses a potential risk to human and ecological health. Current chemical analyses suffer from limitations, including chemical coverage and high cost, and broad-coverage in vitro assays such as transcriptomics may further improve water quality monitoring by assessing a large range of possible effects. Here, we used high-throughput transcriptomics to assess the activity induced by field-derived water extracts in MCF7 breast carcinoma cells. Wastewater and surface water extracts induced the largest changes in expression among cell proliferation-related genes and neurological, estrogenic, and antibiotic pathways, whereas drinking and reclaimed water extracts that underwent advanced treatment showed substantially reduced bioactivity on both gene and pathway levels. Importantly, reclaimed water extracts induced fewer changes in gene expression than laboratory blanks, which reinforces previous conclusions based on targeted assays and improves confidence in bioassay-based monitoring of water quality.

Gene expression networks in the Drosophila Genetic Reference Panel.

A major challenge in modern biology is to understand how naturally occurring variation in DNA sequences affects complex organismal traits through networks of intermediate molecular phenotypes. This question is best addressed in a genetic mapping population in which all molecular polymorphisms are known and for which molecular endophenotypes and complex traits are assessed on the same genotypes. Here, we performed deep RNA sequencing of 200 Drosophila Genetic Reference Panel inbred lines with complete genome sequences and for which phenotypes of many quantitative traits have been evaluated. We mapped expression quantitative trait loci for annotated genes, novel transcribed regions, transposable elements, and microbial species. We identified host variants that affect expression of transposable elements, independent of their copy number, as well as microbiome composition. We constructed sex-specific expression quantitative trait locus regulatory networks. These networks are enriched for novel transcribed regions and target genes in heterochromatin and euchromatic regions of reduced recombination, as well as genes regulating transposable element expression. This study provides new insights regarding the role of natural genetic variation in regulating gene expression and generates testable hypotheses for future functional analyses.

Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments.

'Cell Painting' is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ∼100 µM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.

Combining phenotypic profiling and targeted RNA-Seq reveals linkages between transcriptional perturbations and chemical effects on cell morphology: Retinoic acid as an example.

The United States Environmental Protection Agency has proposed a tiered testing strategy for chemical hazard evaluation based on new approach methods (NAMs). The first tier includes in vitro profiling assays applicable to many (human) cell types, such as high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). The goals of this study were to: (1) harmonize the seeding density of U-2 OS human osteosarcoma cells for use in both assays; (2) compare HTTr- versus HTPP-derived potency estimates for 11 mechanistically diverse chemicals; (3) identify candidate reference chemicals for monitoring assay performance in future screens; and (4) characterize the transcriptional and phenotypic changes in detail for all-trans retinoic acid (ATRA) as a model compound known for its adverse effects on osteoblast differentiation. The results of this evaluation showed that (1) HTPP conducted at low (400 cells/well) and high (3000 cells/well) seeding densities yielded comparable potency estimates and similar phenotypic profiles for the tested chemicals; (2) HTPP and HTTr resulted in comparable potency estimates for changes in cellular morphology and gene expression, respectively; (3) three test chemicals (etoposide, ATRA, dexamethasone) produced concentration-dependent effects on cellular morphology and gene expression that were consistent with known modes-of-action, demonstrating their suitability for use as reference chemicals for monitoring assay performance; and (4) ATRA produced phenotypic changes that were highly similar to other retinoic acid receptor activators (AM580, arotinoid acid) and some retinoid X receptor activators (bexarotene, methoprene acid). This phenotype was observed concurrently with autoregulation of the RARB gene. Both effects were prevented by pre-treating U-2 OS cells with pharmacological antagonists of their respective receptors. Thus, the observed phenotype could be considered characteristic of retinoic acid pathway activation in U-2 OS cells. These findings lay the groundwork for combinatorial screening of chemicals using HTTr and HTPP to generate complementary information for the first tier of a NAM-based chemical hazard evaluation strategy.

Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities.

Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.

Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data.

Estimation of points of departure (PoDs) from high-throughput transcriptomic data (HTTr) represents a key step in the development of next-generation risk assessment (NGRA). Current approaches mainly rely on single key gene targets, which are constrained by the information currently available in the knowledge base and make interpretation challenging as scientists need to interpret PoDs for thousands of genes or hundreds of pathways. In this work, we aimed to address these issues by developing a computational workflow to investigate the pathway concentration-response relationships in a way that is not fully constrained by known biology and also facilitates interpretation. We employed the Pathway-Level Information ExtractoR (PLIER) to identify latent variables (LVs) describing biological activity and then investigated in vitro LVs' concentration-response relationships using the ToxCast pipeline. We applied this methodology to a published transcriptomic concentration-response data set for 44 chemicals in MCF-7 cells and showed that our workflow can capture known biological activity and discriminate between estrogenic and antiestrogenic compounds as well as activity not aligning with the existing knowledge base, which may be relevant in a risk assessment scenario. Moreover, we were able to identify the known estrogen activity in compounds that are not well-established ER agonists/antagonists supporting the use of the workflow in read-across. Next, we transferred its application to chemical compounds tested in HepG2, HepaRG, and MCF-7 cells and showed that PoD estimates are in strong agreement with those estimated using a recently developed Bayesian approach (cor = 0.89) and in weak agreement with those estimated using a well-established approach such as BMDExpress2 (cor = 0.57). These results demonstrate the effectiveness of using PLIER in a concentration-response scenario to investigate pathway activity in a way that is not fully constrained by the knowledge base and to ease the biological interpretation and support the development of an NGRA framework with the ability to improve current risk assessment strategies for chemicals using new approach methodologies.

Deacetylase-independent function of HDAC3 in transcription and metabolism requires nuclear receptor corepressor.

Histone deacetylases (HDACs) are believed to regulate gene transcription by catalyzing deacetylation reactions. HDAC3 depletion in mouse liver upregulates lipogenic genes and results in severe hepatosteatosis. Here we show that pharmacologic HDAC inhibition in primary hepatocytes causes histone hyperacetylation but does not upregulate expression of HDAC3 target genes. Meanwhile, deacetylase-dead HDAC3 mutants can rescue hepatosteatosis and repress lipogenic genes expression in HDAC3-depleted mouse liver, demonstrating that histone acetylation is insufficient to activate gene transcription. Mutations abolishing interactions with the nuclear receptor corepressor (NCOR or SMRT) render HDAC3 nonfunctional in vivo. Additionally, liver-specific knockout of NCOR, but not SMRT, causes metabolic and transcriptomal alterations resembling those of mice without hepatic HDAC3, demonstrating that interaction with NCOR is essential for deacetylase-independent function of HDAC3. These findings highlight nonenzymatic roles of a major HDAC in transcriptional regulation in vivo and warrant reconsideration of the mechanism of action of HDAC inhibitors.

Rev-erbα and Rev-erbß coordinately protect the circadian clock and normal metabolic function.

The nuclear receptor Rev-erbα regulates circadian rhythm and metabolism, but its effects are modest and it has been considered to be a secondary regulator of the cell-autonomous clock. Here we report that depletion of Rev-erbα together with closely related Rev-erbß has dramatic effects on the cell-autonomous clock as well as hepatic lipid metabolism. Mouse embryonic fibroblasts were rendered arrhythmic by depletion of both Rev-erbs. In mouse livers, Rev-erbß mRNA and protein levels oscillate with a diurnal pattern similar to that of Rev-erbα, and both Rev-erbs are recruited to a remarkably similar set of binding sites across the genome, enriched near metabolic genes. Depletion of both Rev-erbs in liver synergistically derepresses several metabolic genes as well as genes that control the positive limb of the molecular clock. Moreover, deficiency of both Rev-erbs causes marked hepatic steatosis, in contrast to relatively subtle changes upon loss of either subtype alone. These findings establish the two Rev-erbs as major regulators of both clock function and metabolism, displaying a level of subtype collaboration that is unusual among nuclear receptors but common among core clock proteins, protecting the organism from major perturbations in circadian and metabolic physiology.

Genome-Wide Analysis of Starvation-Selected Drosophila melanogaster-A Genetic Model of Obesity.

Experimental evolution affords the opportunity to investigate adaptation to stressful environments. Studies combining experimental evolution with whole-genome resequencing have provided insight into the dynamics of adaptation and a new tool to uncover genes associated with polygenic traits. Here, we selected for starvation resistance in populations of Drosophila melanogaster for over 80 generations. In response, the starvation-selected lines developed an obese condition, storing nearly twice the level of total lipids than their unselected controls. Although these fats provide a ∼3-fold increase in starvation resistance, the imbalance in lipid homeostasis incurs evolutionary cost. Some of these tradeoffs resemble obesity-associated pathologies in mammals including metabolic depression, low activity levels, dilated cardiomyopathy, and disrupted sleeping patterns. To determine the genetic basis of these traits, we resequenced genomic DNA from the selected lines and their controls. We found 1,046,373 polymorphic sites, many of which diverged between selection treatments. In addition, we found a wide range of genetic heterogeneity between the replicates of the selected lines, suggesting multiple mechanisms of adaptation. Genome-wide heterozygosity was low in the selected populations, with many large blocks of SNPs nearing fixation. We found candidate loci under selection by using an algorithm to control for the effects of genetic drift. These loci were mapped to a set of 382 genes, which associated with many processes including nutrient response, catabolic metabolism, and lipid droplet function. The results of our study speak to the evolutionary origins of obesity and provide new targets to understand the polygenic nature of obesity in a unique model system.

The nuclear receptor Rev-erbα controls circadian thermogenic plasticity.

Circadian oscillation of body temperature is a basic, evolutionarily conserved feature of mammalian biology. In addition, homeostatic pathways allow organisms to protect their core temperatures in response to cold exposure. However, the mechanism responsible for coordinating daily body temperature rhythm and adaptability to environmental challenges is unknown. Here we show that the nuclear receptor Rev-erbα (also known as Nr1d1), a powerful transcriptional repressor, links circadian and thermogenic networks through the regulation of brown adipose tissue (BAT) function. Mice exposed to cold fare considerably better at 05:00 (Zeitgeber time 22) when Rev-erbα is barely expressed than at 17:00 (Zeitgeber time 10) when Rev-erbα is abundant. Deletion of Rev-erbα markedly improves cold tolerance at 17:00, indicating that overcoming Rev-erbα-dependent repression is a fundamental feature of the thermogenic response to cold. Physiological induction of uncoupling protein 1 (Ucp1) by cold temperatures is preceded by rapid downregulation of Rev-erbα in BAT. Rev-erbα represses Ucp1 in a brown-adipose-cell-autonomous manner and BAT Ucp1 levels are high in Rev-erbα-null mice, even at thermoneutrality. Genetic loss of Rev-erbα also abolishes normal rhythms of body temperature and BAT activity. Thus, Rev-erbα acts as a thermogenic focal point required for establishing and maintaining body temperature rhythm in a manner that is adaptable to environmental demands.

Histone deacetylase 3 is an epigenomic brake in macrophage alternative activation.

Macrophages, a key cellular component of inflammation, become functionally polarized in a signal- and context-specific manner. Th2 cytokines such as interleukin 4 (IL-4) polarize macrophages to a state of alternative activation that limits inflammation and promotes wound healing. Alternative activation is mediated by a transcriptional program that is influenced by epigenomic modifications, including histone acetylation. Here we report that macrophages lacking histone deacetylase 3 (HDAC3) display a polarization phenotype similar to IL-4-induced alternative activation and, furthermore, are hyperresponsive to IL-4 stimulation. Throughout the macrophage genome, HDAC3 deacetylates histone tails at regulatory regions, leading to repression of many IL-4-regulated genes characteristic of alternative activation. Following exposure to Schistosoma mansoni eggs, a model of Th2 cytokine-mediated disease that is limited by alternative activation, pulmonary inflammation was ameliorated in mice lacking HDAC3 in macrophages. Thus, HDAC3 functions in alternative activation as a brake whose release could be of benefit in the treatment of multiple inflammatory diseases.

Genetic and Genomic Response to Selection for Food Consumption in Drosophila melanogaster.

Food consumption is an essential component of animal fitness; however, excessive food intake in humans increases risk for many diseases. The roles of neuroendocrine feedback loops, food sensing modalities, and physiological state in regulating food intake are well understood, but not the genetic basis underlying variation in food consumption. Here, we applied ten generations of artificial selection for high and low food consumption in replicate populations of Drosophila melanogaster. The phenotypic response to selection was highly asymmetric, with significant responses only for increased food consumption and minimal correlated responses in body mass and composition. We assessed the molecular correlates of selection responses by DNA and RNA sequencing of the selection lines. The high and low selection lines had variants with significantly divergent allele frequencies within or near 2081 genes and 3526 differentially expressed genes in one or both sexes. A total of 519 genes were both genetically divergent and differentially expressed between the divergent selection lines. We performed functional analyses of the effects of RNAi suppression of gene expression and induced mutations for 27 of these candidate genes that have human orthologs and the strongest statistical support, and confirmed that 25 (93 %) affected the mean and/or variance of food consumption.

Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates.

Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr). The tPOD calculation methods use data at the level of individual genes and gene set signatures, and compare data processed using the ToxCast Pipeline 2 (tcplfit2), BMDExpress and PLIER (Pathway Level Information ExtractoR). Methods were evaluated by comparing to in vitro PODs from a validated set of high-throughput screening (HTS) assays for a set of estrogenic compounds. Key findings include: (1) for a given chemical and set of experimental conditions, tPODs calculated by different methods can vary by several orders of magnitude; (2) tPODs are at least as sensitive to computational methods as to experimental conditions; (3) in comparison to an external reference set of PODs, some methods give generally higher values, principally PLIER and BMDExpress; and (4) the tPODs from HTTr in this one cell type are mostly higher than the overall PODs from a broad battery of targeted in vitro ToxCast assays, reflecting the need to test chemicals in multiple cell types and readout technologies for in vitro hazard screening.

Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping.

High-throughput transcriptomics (HTTr) uses gene expression profiling to characterize the biological activity of chemicals in in vitro cell-based test systems. As an extension of a previous study testing 44 chemicals, HTTr was used to screen an additional 1,751 unique chemicals from the EPA's ToxCast collection in MCF7 cells using 8 concentrations and an exposure duration of 6 h. We hypothesized that concentration-response modeling of signature scores could be used to identify putative molecular targets and cluster chemicals with similar bioactivity. Clustering and enrichment analyses were conducted based on signature catalog annotations and ToxPrint chemotypes to facilitate molecular target prediction and grouping of chemicals with similar bioactivity profiles. Enrichment analysis based on signature catalog annotation identified known mechanisms of action (MeOAs) associated with well-studied chemicals and generated putative MeOAs for other active chemicals. Chemicals with predicted MeOAs included those targeting estrogen receptor (ER), glucocorticoid receptor (GR), retinoic acid receptor (RAR), the NRF2/KEAP/ARE pathway, AP-1 activation, and others. Using reference chemicals for ER modulation, the study demonstrated that HTTr in MCF7 cells was able to stratify chemicals in terms of agonist potency, distinguish ER agonists from antagonists, and cluster chemicals with similar activities as predicted by the ToxCast ER Pathway model. Uniform manifold approximation and projection (UMAP) embedding of signature-level results identified novel ER modulators with no ToxCast ER Pathway model predictions. Finally, UMAP combined with ToxPrint chemotype enrichment was used to explore the biological activity of structurally related chemicals. The study demonstrates that HTTr can be used to inform chemical risk assessment by determining in vitro points of departure, predicting chemicals' MeOA and grouping chemicals with similar bioactivity profiles.

A new approach methodology to identify tumorigenic chemicals using short-term exposures and transcript profiling.

Current methods for cancer risk assessment are resource-intensive and not feasible for most of the thousands of untested chemicals. In earlier studies, we developed a new approach methodology (NAM) to identify liver tumorigens using gene expression biomarkers and associated tumorigenic activation levels (TALs) after short-term exposures in rats. The biomarkers are used to predict the six most common rodent liver cancer molecular initiating events. In the present study, we wished to confirm that our approach could be used to identify liver tumorigens at only one time point/dose and if the approach could be applied to (targeted) RNA-Seq analyses. Male rats were exposed for 4 days by daily gavage to 15 chemicals at doses with known chronic outcomes and liver transcript profiles were generated using Affymetrix arrays. Our approach had 75% or 85% predictive accuracy using TALs derived from the TG-GATES or DrugMatrix studies, respectively. In a dataset generated from the livers of male rats exposed to 16 chemicals at up to 10 doses for 5 days, we found that our NAM coupled with targeted RNA-Seq (TempO-Seq) could be used to identify tumorigenic chemicals with predictive accuracies of up to 91%. Overall, these results demonstrate that our NAM can be applied to both microarray and (targeted) RNA-Seq data generated from short-term rat exposures to identify chemicals, their doses, and mode of action that would induce liver tumors, one of the most common endpoints in rodent bioassays.

Integrative genomic analysis of CREB defines a critical role for transcription factor networks in mediating the fed/fasted switch in liver.

BACKGROUND: Metabolic homeostasis in mammals critically depends on the regulation of fasting-induced genes by CREB in the liver. Previous genome-wide analysis has shown that only a small percentage of CREB target genes are induced in response to fasting-associated signaling pathways. The precise molecular mechanisms by which CREB specifically targets these genes in response to alternating hormonal cues remain to be elucidated. RESULTS: We performed chromatin immunoprecipitation coupled to high-throughput sequencing of CREB in livers from both fasted and re-fed mice. In order to quantitatively compare the extent of CREB-DNA interactions genome-wide between these two physiological conditions we developed a novel, robust analysis method, termed the 'single sample independence' (SSI) test that greatly reduced the number of false-positive peaks. We found that CREB remains constitutively bound to its target genes in the liver regardless of the metabolic state. Integration of the CREB cistrome with expression microarrays of fasted and re-fed mouse livers and ChIP-seq data for additional transcription factors revealed that the gene expression switches between the two metabolic states are associated with co-localization of additional transcription factors at CREB sites. CONCLUSIONS: Our results support a model in which CREB is constitutively bound to thousands of target genes, and combinatorial interactions between DNA-binding factors are necessary to achieve the specific transcriptional response of the liver to fasting. Furthermore, our genome-wide analysis identifies thousands of novel CREB target genes in liver, and suggests a previously unknown role for CREB in regulating ER stress genes in response to nutrient influx.

Transcriptional regulation via TF-modifying enzymes: an integrative model-based analysis.

Transcription factor activity is largely regulated through post-translational modification. Here, we report the first integrative model of transcription that includes both interactions between transcription factors and promoters, and between transcription factors and modifying enzymes. Simulations indicate that our method is robust against noise. We validated our tool on a well-studied stress response network in yeast and on a STAT1-mediated regulatory network in human B cells. Our work represents a significant step toward a comprehensive model of gene transcription.