Characterization and optimization of variability in a human colonic epithelium culture model.

Animal models have historically been poor preclinical predictors of gastrointestinal (GI) directed therapeutic efficacy and drug-induced GI toxicity. Human stem and primary cell-derived culture systems are a major focus of efforts to create biologically relevant models that enhance preclinical predictive value of intestinal efficacy and toxicity. The inherent variability in stem-cell-based cultures makes development of useful models a challenge; the stochastic nature of stem-cell differentiation interferes with the ability to build and validate reproducible assays that query drug responses and pharmacokinetics. In this study, we aimed to characterize and reduce sources of variability in a complex stem cell-derived intestinal epithelium model, termed RepliGut® Planar, across cells from multiple human donors, cell lots, and passage numbers. Assessment criteria included barrier formation and integrity, gene expression, and cytokine responses. Gene expression and culture metric analyses revealed that controlling cell passage number reduces variability and maximizes physiological relevance of the model. In a case study where passage number was optimized, distinct cytokine responses were observed among four human donors, indicating that biological variability can be detected in cell cultures originating from diverse human sources. These findings highlight key considerations for designing assays that can be applied to additional primary-cell derived systems, as well as establish utility of the RepliGut® Planar platform for robust development of human-predictive drug-response assays.

Animal models are frequently used as tools for studying gastrointestinal (GI) disease, but they poorly replicate the complexities of the human gut limiting the clinical translation of new therapeutics in development. Human stem cell derived models can better recapitulate human GI physiology, but the inherent dynamic nature of stem cells introduces variability in culture performance. We identified sources of variability in the primary stem-cell derived RepliGut® Planar model to develop robust and reliable assays that can improve preclinical therapeutic development for GI disease. Analysis of barrier formation, gene expression, and cytokine responses demonstrated that controlling cell passage number reduces variability and maximizes physiological relevance of the model. These findings highlight key assay design considerations that can be applied to additional primary-cell derived systems. Availability of reliable and physiologically relevant cell-based models can reduce animal testing, improve research accuracy, and make new treatments more relevant and effective for patients.

Characterization and optimization of variability in a human colonic epithelium culture model.

Animal models have historically been poor preclinical predictors of gastrointestinal (GI) directed therapeutic efficacy and drug-induced GI toxicity. Human stem and primary cell-derived culture systems are a major focus of efforts to create biologically relevant models that enhance preclinical predictive value of intestinal efficacy and toxicity. The inherent variability in stem-cell-based complex cultures makes development of useful models a challenge; the stochastic nature of stem-cell differentiation interferes with the ability to build and validate robust, reproducible assays that query drug responses and pharmacokinetics. In this study, we aimed to characterize and reduce potential sources of variability in a complex stem cell-derived intestinal epithelium model, termed RepliGut® Planar, across cells from multiple human donors, cell lots, and passage numbers. Assessment criteria included barrier formation and integrity, gene expression, and cytokine responses. Gene expression and culture metric analyses revealed that controlling for stem/progenitor-cell passage number reduces variability and maximizes physiological relevance of the model. After optimizing passage number, donor-specific differences in cytokine responses were observed in a case study, suggesting biologic variability is observable in cell cultures derived from multiple human sources. Our findings highlight key considerations for designing assays that can be applied to additional primary-cell derived systems, as well as establish utility of the RepliGut® Planar platform for robust development of human-predictive drug-response assays.

Whole exome and transcript profiling of liver following aflatoxin B1 exposure in rats.

We recently developed a rat whole exome sequencing (WES) panel and used it to evaluate early somatic mutations in archival liver tissues from F344/N rats exposed to the hepatocarcinogen, Aflatoxin B1 (AFB1), a widely studied, potent mutagen and hepatocarcinogen associated with hepatocellular carcinoma (HCC). Rats were exposed to 1-ppm AFB1 in feed for 14, 90, and 90 days plus a recovery 60-day, non-exposure period (150-day) timepoint. Isolated liver DNA was exome sequenced. We identified 172 sequence variants across all timepoints, of which 101 were non-synonymous variants. Well-annotated genes carried a diverse set of 29 non-synonymous mutations at 14 days, increasing to 39 mutations at 90 days and then decreasing to 33 mutations following the 60-day recovery. Gene Set Enrichment Analysis conducted on previously reported, available RNA expression data of the same exome sequenced archival samples identified altered transcripts in pathways associated with malignant transformation. These included HALLMARK gene sets associated with cell proliferation (MYC Targets Version 1 and Version 2, E2F targets), cell cycle (G2M checkpoint, mitotic spindle), cell death (apoptosis), and DNA damage (DNA repair, UV response Up, Reactive oxygen species) pathways. DriverNet Impact analysis integrated exome-seq and expression data to reveal somatic mutations in Mcm8, Bdp1, and Cct6a that may drive cancer formation. Connectivity with transcript expression changes identified these genes as the top-ranked candidate driver genes associated with hepatocellular transformation. In conclusion, exome sequencing revealed early somatic mutations that may play a role in cancer cell transformation that are translatable to aflatoxin-induced HCC.

Transcriptional pathways linked to fetal and maternal hepatic dysfunction caused by gestational exposure to perfluorooctanoic acid (PFOA) or hexafluoropropylene oxide-dimer acid (HFPO-DA or GenX) in CD-1 mice.

Per- and polyfluoroalkyl substances (PFAS) comprise a diverse class of chemicals used in industrial processes, consumer products, and fire-fighting foams which have become environmental pollutants of concern due to their persistence, ubiquity, and associations with adverse human health outcomes, including in pregnant persons and their offspring. Multiple PFAS are associated with adverse liver outcomes in adult humans and toxicological models, but effects on the developing liver are not fully described. Here we performed transcriptomic analyses in the mouse to investigate the molecular mechanisms of hepatic toxicity in the dam and its fetus after exposure to two different PFAS, perfluorooctanoic acid (PFOA) and its replacement, hexafluoropropylene oxide-dimer acid (HFPO-DA, known as GenX). Pregnant CD-1 mice were exposed via oral gavage from embryonic day (E) 1.5-17.5 to PFOA (0, 1, or 5 mg/kg-d) or GenX (0, 2, or 10 mg/kg-d). Maternal and fetal liver RNA was isolated (N = 5 per dose/group) and the transcriptome analyzed by Affymetrix Array. Differentially expressed genes (DEG) and differentially enriched pathways (DEP) were obtained. DEG patterns were similar in maternal liver for 5 mg/kg PFOA, 2 mg/kg GenX, and 10 mg/kg GenX (R2: 0.46-0.66). DEG patterns were similar across all 4 dose groups in fetal liver (R2: 0.59-0.81). There were more DEGs in fetal liver compared to maternal liver at the low doses for both PFOA (fetal = 69, maternal = 8) and GenX (fetal = 154, maternal = 93). Upregulated DEPs identified across all groups included Fatty Acid Metabolism, Peroxisome, Oxidative Phosphorylation, Adipogenesis, and Bile Acid Metabolism. Transcriptome-phenotype correlation analyses demonstrated > 1000 maternal liver DEGs were significantly correlated with maternal relative liver weight (R2 >0.92). These findings show shared biological pathways of liver toxicity for PFOA and GenX in maternal and fetal livers in CD-1 mice. The limited overlap in specific DEGs between the dam and fetus suggests the developing liver responds differently than the adult liver to these chemical stressors. This work helps define mechanisms of hepatic toxicity of two structurally unique PFAS and may help predict latent consequences of developmental exposure.

Single nucleotide polymorphism patterns associated with a cancer resistant phenotype.

BACKGROUND AND AIMS: In this study ten mouse strains representing ~90% of genetic diversity in laboratory mice (B6C3F1/J, C57BL/6J, C3H/HeJ, A/J, NOD.B1oSnH2/J, NZO/HILtJ, 129S1/SvImJ, WSB/EiJ, PWK/PhJ, CAST/EiJ) were examined to identify the mouse strain with the lowest incidence of cancer. The unique single polymorphisms (SNPs) associated with this low cancer incidence are reported. METHODS: Evaluations of cancer incidence in the 10 mouse strains were based on gross and microscopic diagnosis of tumors. Single nucleotide polymorphisms (SNPs) in the coding regions of the genome were derived from the respective mouse strains located in the Sanger mouse sequencing database and the B6C3F1/N genome from the National Toxicology Program (NTP). RESULTS: The WSB strain had an overall lower incidence of both benign and malignant tumors compared to the other mouse strains. At 2 years, the incidence of total malignant tumors (Poly-3 incidence rate) ranged from 2% (WSB) to 92% (C3H) in males, and 14% (WSB) to 93% (NZO) in females, and the total incidence of benign and malignant tumor incidence ranged from 13% (WSB) to 99% (C3H) in males and 25% (WSB) to 96% (NOD) in females. Single nucleotide polymorphism (SNP) patterns were examined in the following strains: B6C3F1/N, C57BL/6J, C3H/HeJ, 129S1/SvImJ, A/J, NZO/HILtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ. We identified 7519 SNPs (involving 5751 Ensembl transcripts of 3453 Ensembl Genes) that resulted in a unique amino acid change in the coding region of the WSB strain. CONCLUSIONS: The inherited genetic patterns in the WSB cancer-resistant mouse strain occurred in genes involved in multiple cell functions including mitochondria, metabolic, immune, and membrane-related cell functions. The unique SNP patterns in a cancer resistant mouse strain provides insights for understanding and developing strategies for cancer prevention.

Impact of Aligner, Normalization Method, and Sequencing Depth on TempO-seq Accuracy.

High-throughput transcriptomics has advanced through the introduction of TempO-seq, a targeted alternative to traditional RNA-seq. TempO-seq platforms use 50 nucleotide probes, each specifically designed to target a known transcript, thus allowing for reduced sequencing depth per sample compared with RNA-seq without compromising the accuracy of results. Thus far, studies using the TempO-seq method have relied on existing tools for processing the resulting short read data. However, these tools were originally designed for other data types. While they have been used for processing of early TempO-seq data, they have not been systematically assessed for accuracy or compared to determine an optimal framework for processing and analyzing TempO-seq data. In this work, we re-analyze several publicly available TempO-seq data sets covering a range of experimental designs and use corresponding RNA-seq data sets as a gold standard to rigorously assess accuracy at multiple levels. We compare 6 aligners and 5 normalization methods across various accuracy and performance metrics. Our results demonstrate the overall robust accuracy of the TempO-seq platform, independent of data processing methods. Complex aligners and advanced normalization methods do not appear to have any general advantage over simpler methods when it comes to analyzing TempO-seq data. The reduced complexity of the sequencing space, and the fact that TempO-seq probes are all equal length, appears to reduce the need for elaborate bioinformatic or statistical methods used to address these factors in RNA-seq data.

Utility of Extrapolating Human S1500+ Genes to the Whole Transcriptome: Tunicamycin Case Study.

The TempO-Seq S1500+ platform(s), now available for human, mouse, rat, and zebrafish, measures a discrete number of genes that are representative of biological and pathway co-regulation across the entire genome in a given species. While measurement of these genes alone provides a direct assessment of gene expression activity, extrapolating expression values to the whole transcriptome (~26 000 genes in humans) can estimate measurements of non-measured genes of interest and increases the power of pathway analysis algorithms by using a larger background gene expression space. Here, we use data from primary hepatocytes of 54 donors that were treated with the endoplasmic reticulum (ER) stress inducer tunicamycin and then measured on the human S1500+ platform containing ~3000 representative genes. Measurements for the S1500+ genes were then used to extrapolate expression values for the remaining human transcriptome. As a case study of the improved downstream analysis achieved by extrapolation, the "measured only" and "whole transcriptome" (measured + extrapolated) gene sets were compared. Extrapolation increased the number of significant genes by 49%, bringing to the forefront many that are known to be associated with tunicamycin exposure. The extrapolation procedure also correctly identified established tunicamycin-related functional pathways reflected by coordinated changes in interrelated genes while maintaining the sample variability observed from the "measured only" genes. Extrapolation improved the gene- and pathway-level biological interpretations for a variety of downstream applications, including differential expression analysis, gene set enrichment pathway analysis, DAVID keyword analysis, Ingenuity Pathway Analysis, and NextBio correlated compound analysis. The extrapolated data highlight the role of metabolism/metabolic pathways, the ER, immune response, and the unfolded protein response, each of which are key activities associated with tunicamycin exposure that were unrepresented or underrepresented in one or more of the analyses of the original "measured only" dataset. Furthermore, the inclusion of the extrapolated genes raised "tunicamycin" from third to first upstream regulator in Ingenuity Pathway Analysis and from sixth to second most correlated compound in NextBio analysis. Therefore, our case study suggests an approach to extend and enhance data from the S1500+ platform for improved insight into biological mechanisms and functional outcomes of diseases, drugs, and other perturbations.

Evaluation of 5-day In Vivo Rat Liver and Kidney With High-throughput Transcriptomics for Estimating Benchmark Doses of Apical Outcomes.

A 5-day in vivo rat model was evaluated as an approach to estimate chemical exposures that may pose minimal risk by comparing benchmark dose (BMD) values for transcriptional changes in the liver and kidney to BMD values for toxicological endpoints from traditional toxicity studies. Eighteen chemicals, most having been tested by the National Toxicology Program in 2-year bioassays, were evaluated. Some of these chemicals are potent hepatotoxicants (eg, DE71, PFOA, and furan) in rodents, some exhibit toxicity but have minimal hepatic effects (eg, acrylamide and α,ß-thujone), and some exhibit little overt toxicity (eg, ginseng and milk thistle extract) based on traditional toxicological evaluations. Male Sprague Dawley rats were exposed once daily for 5 consecutive days by oral gavage to 8-10 dose levels for each chemical. Liver and kidney were collected 24 h after the final exposure and total RNA was assayed using high-throughput transcriptomics (HTT) with the rat S1500+ platform. HTT data were analyzed using BMD Express 2 to determine transcriptional gene set BMD values. BMDS was used to determine BMD values for histopathological effects from chronic or subchronic toxicity studies. For many of the chemicals, the lowest transcriptional BMDs from the 5-day assays were within a factor of 5 of the lowest histopathological BMDs from the toxicity studies. These data suggest that using HTT in a 5-day in vivo rat model provides reasonable estimates of BMD values for traditional apical endpoints. This approach may be useful to prioritize chemicals for further testing while providing actionable data in a timely and cost-effective manner.

SWIFT-Active Screener: Accelerated document screening through active learning and integrated recall estimation.

BACKGROUND: In the screening phase of systematic review, researchers use detailed inclusion/exclusion criteria to decide whether each article in a set of candidate articles is relevant to the research question under consideration. A typical review may require screening thousands or tens of thousands of articles in and can utilize hundreds of person-hours of labor. METHODS: Here we introduce SWIFT-Active Screener, a web-based, collaborative systematic review software application, designed to reduce the overall screening burden required during this resource-intensive phase of the review process. To prioritize articles for review, SWIFT-Active Screener uses active learning, a type of machine learning that incorporates user feedback during screening. Meanwhile, a negative binomial model is employed to estimate the number of relevant articles remaining in the unscreened document list. Using a simulation involving 26 diverse systematic review datasets that were previously screened by reviewers, we evaluated both the document prioritization and recall estimation methods. RESULTS: On average, 95% of the relevant articles were identified after screening only 40% of the total reference list. In the 5 document sets with 5,000 or more references, 95% recall was achieved after screening only 34% of the available references, on average. Furthermore, the recall estimator we have proposed provides a useful, conservative estimate of the percentage of relevant documents identified during the screening process. CONCLUSION: SWIFT-Active Screener can result in significant time savings compared to traditional screening and the savings are increased for larger project sizes. Moreover, the integration of explicit recall estimation during screening solves an important challenge faced by all machine learning systems for document screening: when to stop screening a prioritized reference list. The software is currently available in the form of a multi-user, collaborative, online web application.

Genome-wide promoter DNA methylation profiling of hepatocellular carcinomas arising either spontaneously or due to chronic exposure to Ginkgo biloba extract (GBE) in B6C3F1/N mice.

Epigenetic modifications, such as DNA methylation, play an important role in carcinogenesis. In a recent NTP study, chronic exposure of B6C3F1/N mice to Ginkgo biloba extract (GBE) resulted in a high incidence of hepatocellular carcinomas (HCC). Genome-wide promoter methylation profiling on GBE-exposed HCC (2000 mg/kg group), spontaneous HCC (vehicle-control group), and age-matched vehicle control liver was performed to identify differentially methylated genes in GBE-exposed HCC and spontaneous HCC. DNA methylation alterations were correlated to the corresponding global gene expression changes. Compared to control liver, 1296 gene promoters (719 hypermethylated, 577 hypomethylated) in GBE-exposed HCC and 738 (427 hypermethylated, 311 hypomethylated) gene promoters in spontaneous HCC were significantly differentially methylated, suggesting an impact of methylation on GBE-exposed HCC. Differential methylation of promoter regions in relevant cancer genes (cMyc, Spry2, Dusp5) and their corresponding differential gene expression was validated by quantitative pyrosequencing and qRT-PCR, respectively. In conclusion, we have identified differentially methylated promoter regions of relevant cancer genes altered in GBE-exposed HCC compared to spontaneous HCC. Further study of unique sets of differentially methylated genes in chemical-exposed mouse HCC could potentially be used to differentiate treatment-related tumors from spontaneous-tumors in cancer bioassays and provide additional understanding of the underlying epigenetic mechanisms of chemical carcinogenesis.

Development of a Zebrafish S1500+ Sentinel Gene Set for High-Throughput Transcriptomics.

Sentinel gene sets have been developed with the purpose of maximizing the information from targeted transcriptomic platforms. We recently described the development of an S1500+ sentinel gene set, which was built for the human transcriptome, utilizing a data- and knowledge-driven hybrid approach to select a small subset of genes that optimally capture transcriptional diversity, correlation with other genes based on large-scale expression profiling, and known pathway annotation within the human genome. While this detailed bioinformatics approach for gene selection can in principle be applied to other species, the reliability of the resulting gene set depends on availability of a large body of transcriptomics data. For the model organism zebrafish, we aimed to create a similar sentinel gene set (Zf S1500+ gene set); however, there is insufficient standardized expression data in the public domain to train the gene correlation model. Therefore, our strategy was to use human-zebrafish ortholog mapping of the human S1500+ genes and nominations from experts in the zebrafish scientific community. In this study, we present the bioinformatics curation and refinement process to produce the final Zf S1500+ gene set, explore whole transcriptome extrapolation using this gene set, and assess pathway-level inference. This gene set will add value to targeted high-throughput transcriptomics in zebrafish for toxicogenomic screening and other research domains.

BMDExpress 2: enhanced transcriptomic dose-response analysis workflow.

SUMMARY: A new version (version 2) of the genomic dose-response analysis software, BMDExpress, has been created. The software addresses the increasing use of transcriptomic dose-response data in toxicology, drug design, risk assessment and translational research. In this new version, we have implemented additional statistical filtering options (e.g. Williams' trend test), curve fitting models, Linux and Macintosh compatibility and support for additional transcriptomic platforms with up-to-date gene annotations. Furthermore, we have implemented extensive data visualizations, on-the-fly data filtering, and a batch-wise analysis workflow. We have also significantly re-engineered the code base to reflect contemporary software engineering practices and streamline future development. The first version of BMDExpress was developed in 2007 to meet an unmet demand for easy-to-use transcriptomic dose-response analysis software. Since its original release, however, transcriptomic platforms, technologies, pathway annotations and quantitative methods for data analysis have undergone a large change necessitating a significant re-development of BMDExpress. To that end, as of 2016, the National Toxicology Program assumed stewardship of BMDExpress. The result is a modernized and updated BMDExpress 2 that addresses the needs of the growing toxicogenomics user community. AVAILABILITY AND IMPLEMENTATION: BMDExpress 2 is available at https://github.com/auerbachs/BMDExpress-2/releases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics.

Changes in gene expression can help reveal the mechanisms of disease processes and the mode of action for toxicities and adverse effects on cellular responses induced by exposures to chemicals, drugs and environment agents. The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in in vitro model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. Our approach is modular, applicable to any species, and facilitates a robust, quantitative evaluation of performance. In particular, we were able to perform gene selection such that the resulting set of "sentinel genes" adequately represents all known canonical pathways from Molecular Signature Database (MSigDB v4.0) and can be used to infer expression changes for the remainder of the transcriptome. The resulting computational model allowed us to choose a purely data-driven subset of 1500 sentinel genes, referred to as the S1500 set, which was then augmented using a knowledge-driven selection of additional genes to create the final S1500+ gene set. Our results indicate that the sentinel genes selected can be used to accurately predict pathway perturbations and biological relationships for samples under study.

Transcriptome and DNA Methylome Analysis in a Mouse Model of Diet-Induced Obesity Predicts Increased Risk of Colorectal Cancer.

Colorectal cancer (CRC) tends to occur at older age; however, CRC incidence rates have been rising sharply among young age groups. The increasing prevalence of obesity is recognized as a major risk, yet the mechanistic underpinnings remain poorly understood. Using a diet-induced obesity mouse model, we identified obesity-associated molecular changes in the colonic epithelium of young and aged mice, and we further investigated whether the changes were reversed after weight loss. Transcriptome analysis indicated that obesity-related colonic cellular metabolic switch favoring long-chain fatty acid oxidation happened in young mice, while obesity-associated downregulation of negative feedback regulators of pro-proliferative signaling pathways occurred in older mice. Strikingly, colonic DNA methylome was pre-programmed by obesity at young age, priming for a tumor-prone gene signature after aging. Furthermore, obesity-related changes were substantially preserved after short-term weight loss, but they were largely reversed after long-term weight loss. We provided mechanistic insights into increased CRC risk in obesity.

DNA Methylation Changes in Tbx3 in a Mouse Model Exposed to Polybrominated Diphenyl Ethers.

DE-71, a commercial mixture of polybrominated diphenyl ethers widely used in flame retardants, is a pervasive environmental contaminant due to its continuing release from waste material and its long half-life in humans. Although the genotoxic potential of DE-71 appears to be low based on bacterial mutagenicity, it remains a public health concern due to its reported involvement in tumor development. Molecular mechanisms by which DE-71 influences tumor incidence or progression remain understudied. We used liver carcinoma tissue from mice exposed to DE-71 to test the hypothesis that epigenetic alterations consistent with tumor development, specifically DNA methylation, result from long-term DE-71 exposure. We profiled DNA methylation status using the methylated-CpG island recovery assay coupled with microarray analysis of hepatocellular carcinoma DNA from animals exposed to DE-71. DE-71 exposure had little impact on global DNA methylation. However, we detected gene body-specific hypomethylation within the Tbx3 locus, a transcription factor important in liver tumorigenesis and in embryonic and cancer stem cell proliferation. This nonpromoter hypomethylation was accompanied by upregulation of Tbx3 mRNA and protein and by alterations in downstream cell cycle-associated marker expression. Thus, exposure to DE-71 may facilitate tumor development by inducing epigenetic programs that favor expansion of progenitor cell populations.

SWIFT-Review: a text-mining workbench for systematic review.

BACKGROUND: There is growing interest in using machine learning approaches to priority rank studies and reduce human burden in screening literature when conducting systematic reviews. In addition, identifying addressable questions during the problem formulation phase of systematic review can be challenging, especially for topics having a large literature base. Here, we assess the performance of the SWIFT-Review priority ranking algorithm for identifying studies relevant to a given research question. We also explore the use of SWIFT-Review during problem formulation to identify, categorize, and visualize research areas that are data rich/data poor within a large literature corpus. METHODS: Twenty case studies, including 15 public data sets, representing a range of complexity and size, were used to assess the priority ranking performance of SWIFT-Review. For each study, seed sets of manually annotated included and excluded titles and abstracts were used for machine training. The remaining references were then ranked for relevance using an algorithm that considers term frequency and latent Dirichlet allocation (LDA) topic modeling. This ranking was evaluated with respect to (1) the number of studies screened in order to identify 95 % of known relevant studies and (2) the "Work Saved over Sampling" (WSS) performance metric. To assess SWIFT-Review for use in problem formulation, PubMed literature search results for 171 chemicals implicated as EDCs were uploaded into SWIFT-Review (264,588 studies) and categorized based on evidence stream and health outcome. Patterns of search results were surveyed and visualized using a variety of interactive graphics. RESULTS: Compared with the reported performance of other tools using the same datasets, the SWIFT-Review ranking procedure obtained the highest scores on 11 out of 15 of the public datasets. Overall, these results suggest that using machine learning to triage documents for screening has the potential to save, on average, more than 50 % of the screening effort ordinarily required when using un-ordered document lists. In addition, the tagging and annotation capabilities of SWIFT-Review can be useful during the activities of scoping and problem formulation. CONCLUSIONS: Text-mining and machine learning software such as SWIFT-Review can be valuable tools to reduce the human screening burden and assist in problem formulation.

RNA-Seq-based toxicogenomic assessment of fresh frozen and formalin-fixed tissues yields similar mechanistic insights.

Formalin-fixed, paraffin-embedded (FFPE) pathology specimens represent a potentially vast resource for transcriptomic-based biomarker discovery. We present here a comparison of results from a whole transcriptome RNA-Seq analysis of RNA extracted from fresh frozen and FFPE livers. The samples were derived from rats exposed to aflatoxin B1 (AFB1 ) and a corresponding set of control animals. Principal components analysis indicated that samples were separated in the two groups representing presence or absence of chemical exposure, both in fresh frozen and FFPE sample types. Sixty-five percent of the differentially expressed transcripts (AFB1 vs. controls) in fresh frozen samples were also differentially expressed in FFPE samples (overlap significance: P < 0.0001). Genomic signature and gene set analysis of AFB1 differentially expressed transcript lists indicated highly similar results between fresh frozen and FFPE at the level of chemogenomic signatures (i.e., single chemical/dose/duration elicited transcriptomic signatures), mechanistic and pathology signatures, biological processes, canonical pathways and transcription factor networks. Overall, our results suggest that similar hypotheses about the biological mechanism of toxicity would be formulated from fresh frozen and FFPE samples. These results indicate that phenotypically anchored archival specimens represent a potentially informative resource for signature-based biomarker discovery and mechanistic characterization of toxicity.

Obesity, rather than diet, drives epigenomic alterations in colonic epithelium resembling cancer progression.

While obesity represents one of several risk factors for colorectal cancer in humans, the mechanistic underpinnings of this association remain unresolved. Environmental stimuli, including diet, can alter the epigenetic landscape of DNA cis-regulatory elements affecting gene expression and phenotype. Here, we explored the impact of diet and obesity on gene expression and the enhancer landscape in murine colonic epithelium. Obesity led to the accumulation of histone modifications associated with active enhancers at genomic loci downstream of signaling pathways integral to the initiation and progression of colon cancer. Meanwhile, colon-specific enhancers lost the same histone mark, poising cells for loss of differentiation. These alterations reflect a transcriptional program with many features shared with the program driving colon cancer progression. The interrogation of enhancer alterations by diet in colonic epithelium provides insights into the biology underlying high-fat diet and obesity as risk factors for colon cancer.

Fine-tuning of epigenetic regulation with respect to promoter CpG content in a cell type-specific manner.

Epigenetic regulation of gene expression is fundamental for cell type-specific gene expression. However, integrated comparative transcriptomic and epigenomic analyses in various adult primary differentiated cells remain underrepresented. We generated promoter landscapes of DNA methylation and three important histone methylation marks (H3K4me3, H3K9me2, and H3K27me3) in two primary cell types (B lymphocytes and liver) from adult mice. In line with previous studies, we also observed distinct H3K4me3 patterns at promoters dictated by CpG content in differentiated primary cells. We further explored the distribution of initiating RNA polymerase II and elongating RNA polymerase II across genes within different promoter classes, suggesting different rate-limiting steps at CpG-rich vs. CpG-poor genes. Examination of differentially expressed genes revealed that regulation of tissue-specific genes is closely related to gene function regardless of promoter type. Although repressive chromatin marks displayed differential preference to promoters based on CpG content, we observed fine-tuning of the pattern of association of these marks with specific promoter types in a cell type-specific manner. The distribution of H3K9me2 and H3K27me3, relative to CpG content, differed substantially between the two cell types. Cell-type specific accumulation of repressive chromatin marks was also observed at silent genes in both cell types, suggesting that differentiated primary cells may exhibit cell-type specificity in the distribution of repressive chromatin marks. Epigenetic regulation of gene expression and the association of specific histone marks with promoter sequence classes are fine-tuned in a cell type-specific manner. This unexpected finding underscores the value of extensive study of epigenetic marks across cell and tissue types.

Deep sequencing identification of novel glucocorticoid-responsive miRNAs in apoptotic primary lymphocytes.

Apoptosis of lymphocytes governs the response of the immune system to environmental stress and toxic insult. Signaling through the ubiquitously expressed glucocorticoid receptor, stress-induced glucocorticoid hormones induce apoptosis via mechanisms requiring altered gene expression. Several reports have detailed the changes in gene expression mediating glucocorticoid-induced apoptosis of lymphocytes. However, few studies have examined the role of non-coding miRNAs in this essential physiological process. Previously, using hybridization-based gene expression analysis and deep sequencing of small RNAs, we described the prevalent post-transcriptional repression of annotated miRNAs during glucocorticoid-induced apoptosis of lymphocytes. Here, we describe the development of a customized bioinformatics pipeline that facilitates the deep sequencing-mediated discovery of novel glucocorticoid-responsive miRNAs in apoptotic primary lymphocytes. This analysis identifies the potential presence of over 200 novel glucocorticoid-responsive miRNAs. We have validated the expression of two novel glucocorticoid-responsive miRNAs using small RNA-specific qPCR. Furthermore, through the use of Ingenuity Pathways Analysis (IPA) we determined that the putative targets of these novel validated miRNAs are predicted to regulate cell death processes. These findings identify two and predict the presence of additional novel glucocorticoid-responsive miRNAs in the rat transcriptome, suggesting a potential role for both annotated and novel miRNAs in glucocorticoid-induced apoptosis of lymphocytes.