Correction: Pannala et al. High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model. Int. J. Mol. Sci. 2023, 24, 17425.

In the original publication [...].

Exposure to PFAS chemicals induces sex-dependent alterations in key rate-limiting steps of lipid metabolism in liver steatosis.

Toxicants with the potential to bioaccumulate in humans and animals have long been a cause for concern, particularly due to their association with multiple diseases and organ injuries. Per- and polyfluoro alkyl substances (PFAS) and polycyclic aromatic hydrocarbons (PAH) are two such classes of chemicals that bioaccumulate and have been associated with steatosis in the liver. Although PFAS and PAH are classified as chemicals of concern, their molecular mechanisms of toxicity remain to be explored in detail. In this study, we aimed to identify potential mechanisms by which an acute exposure to PFAS and PAH chemicals can induce lipid accumulation and whether the responses depend on chemical class, dose, and sex. To this end, we analyzed mechanisms beginning with the binding of the chemical to a molecular initiating event (MIE) and the consequent transcriptomic alterations. We collated potential MIEs using predictions from our previously developed ToxProfiler tool and from published steatosis adverse outcome pathways. Most of the MIEs are transcription factors, and we collected their target genes by mining the TRRUST database. To analyze the effects of PFAS and PAH on the steatosis mechanisms, we performed a computational MIE-target gene analysis on high-throughput transcriptomic measurements of liver tissue from male and female rats exposed to either a PFAS or PAH. The results showed peroxisome proliferator-activated receptor (PPAR)-α targets to be the most dysregulated, with most of the genes being upregulated. Furthermore, PFAS exposure disrupted several lipid metabolism genes, including upregulation of fatty acid oxidation genes (Acadm, Acox1, Cpt2, Cyp4a1-3) and downregulation of lipid transport genes (Apoa1, Apoa5, Pltp). We also identified multiple genes with sex-specific behavior. Notably, the rate-limiting genes of gluconeogenesis (Pck1) and bile acid synthesis (Cyp7a1) were specifically downregulated in male rats compared to female rats, while the rate-limiting gene of lipid synthesis (Scd) showed a PFAS-specific upregulation. The results suggest that the PPAR signaling pathway plays a major role in PFAS-induced lipid accumulation in rats. Together, these results show that PFAS exposure induces a sex-specific multi-factorial mechanism involving rate-limiting genes of gluconeogenesis and bile acid synthesis that could lead to activation of an adverse outcome pathway for steatosis.

Hazard and risk characterization of 56 structurally diverse PFAS using a targeted battery of broad coverage assays using six human cell types.

Per- and poly-fluoroalkyl substances (PFAS) are extensively used in commerce leading to their prevalence in the environment. Due to their chemical stability, PFAS are considered to be persistent and bioaccumulative; they are frequently detected in both the environment and humans. Because of this, PFAS as a class (composed of hundreds to thousands of chemicals) are contaminants of very high concern. Little information is available for the vast majority of PFAS, and regulatory agencies lack safety data to determine whether exposure limits or restrictions are needed. Cell-based assays are a pragmatic approach to inform decision-makers on potential health hazards; therefore, we hypothesized that a targeted battery of human in vitro assays can be used to determine whether there are structure-bioactivity relationships for PFAS, and to characterize potential risks by comparing bioactivity (points of departure) to exposure estimates. We tested 56 PFAS from 8 structure-based subclasses in concentration response (0.1-100 µM) using six human cell types selected from target organs with suggested adverse effects of PFAS - human induced pluripotent stem cell (iPSC)-derived hepatocytes, neurons, and cardiomyocytes, primary human hepatocytes, endothelial and HepG2 cells. While many compounds were without effect; certain PFAS demonstrated cell-specific activity highlighting the necessity of using a compendium of in vitro models to identify potential hazards. No class-specific groupings were evident except for some chain length- and structure-related trends. In addition, margins of exposure (MOE) were derived using empirical and predicted exposure data. Conservative MOE calculations showed that most tested PFAS had a MOE in the 1-100 range; ∼20% of PFAS had MOE<1, providing tiered priorities for further studies. Overall, we show that a compendium of human cell-based models can be used to derive bioactivity estimates for a range of PFAS, enabling comparisons with human biomonitoring data. Furthermore, we emphasize that establishing structure-bioactivity relationships may be challenging for the tested PFAS.

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.

Low-calorie sweeteners and health outcomes: an evaluation of rapid versus traditional evidence mapping.

OBJECTIVE: Scientific evidence related to environmental exposures continues to mount. Tools such as evidence mapping support decision making, but can be resource- and time-intensive. We explored "rapid evidence mapping" to efficiently map scientific evidence using rigorous and transparent methodologies. We undertook a proof-of-concept case study on the topic of low-calorie sweeteners. Our intent was to conduct a traditional evidence map based on the same evidence base from a prior rapid evidence map case study to compare approaches, findings, and conclusions. We searched the literature, screened full text of studies, manually tagged and categorized articles, and created visualizations to map the evidence. RESULTS: We conducted full-text screening of studies from the prior rapid evidence map and identified 255 relevant studies. Our findings corroborated those of the rapid evidence map, identifying most studies as short-term conducted in healthy individuals studying outcomes of appetite, energy sensing and body weight. We identified gaps in research areas related to outcomes of appetite and dietary intake, particularly in study populations with diabetes. Our findings illustrate the promise of rapid evidence mapping as a rigorous approach that can summarize scientific evidence, identify knowledge gaps, and identify areas for a future systematic review in a time-efficient manner.

Whole genome sequencing of low input circulating cell-free DNA obtained from normal human subjects.

Cell-free DNA circulates in plasma at low levels as a normal by-product of cellular apoptosis. Multiple clinical pathologies, as well as environmental stressors can lead to increased circulating cell-free DNA (ccfDNA) levels. Plasma DNA studies frequently employ targeted amplicon deep sequencing platforms due to limited concentrations (ng/ml) of ccfDNA in the blood. Here, we report whole genome sequencing (WGS) and read distribution across chromosomes of ccfDNA extracted from two human plasma samples from normal, healthy subjects, representative of limited clinical samples at <1 ml. Amplification was sufficiently robust with ~90% of the reference genome (GRCh38.p2) exhibiting 10X coverage. Chromosome read coverage was uniform and directly proportional to the number of reads for each chromosome across both samples. Almost 99% of the identified genomic sequence variants were known annotated dbSNP variants in the hg38 reference genome. A high prevalence of C>T and T>C mutations was present along with a strong concordance of variants shared between the germline genome databases; gnomAD (81.1%) and the 1000 Genome Project (93.6%). This study demonstrates isolation and amplification procedures from low input ccfDNA samples that can detect sequence variants across the whole genome from amplified human plasma ccfDNA that can translate to multiple clinical research disciplines.

Saagar-A New, Extensible Set of Molecular Substructures for QSAR/QSPR and Read-Across Predictions.

Molecular structure-based predictive models provide a proven alternative to costly and inefficient animal testing. However, due to a lack of interpretability of predictive models built with abstract molecular descriptors they have earned the notoriety of being black boxes. Interpretable models require interpretable descriptors to provide chemistry-backed predictive reasoning and facilitate intelligent molecular design. We developed a novel set of extensible chemistry-aware substructures, Saagar, to support interpretable predictive models and read-across protocols. Performance of Saagar in chemical characterization and search for structurally similar actives for read-across applications was compared with four publicly available fingerprint sets (MACCS (166), PubChem (881), ECFP4 (1024), ToxPrint (729)) in three benchmark sets (MUV, ULS, and Tox21) spanning ∼145 000 compounds and 78 molecular targets at 1%, 2%, 5%, and 10% false discovery rates. In 18 of the 20 comparisons, interpretable Saagar features performed better than the publicly available, but less interpretable and fixed-bit length, fingerprints. Examples are provided to show the enhanced capability of Saagar in extracting compounds with higher scaffold similarity. Saagar features are interpretable and efficiently characterize diverse chemical collections, thus making them a better choice for building interpretable predictive in silico models and read-across protocols.

Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map.

Background: Given the worldwide spread of the 2019 Novel Coronavirus (COVID-19), there is an urgent need to identify risk and protective factors and expose areas of insufficient understanding. Emerging tools, such as the Rapid Evidence Map (rEM), are being developed to systematically characterize large collections of scientific literature. We sought to generate an rEM of risk and protective factors to comprehensively inform areas that impact COVID-19 outcomes for different sub-populations in order to better protect the public. Methods: We developed a protocol that includes a study goal, study questions, a PECO statement, and a process for screening literature by combining semi-automated machine learning with the expertise of our review team. We applied this protocol to reports within the COVID-19 Open Research Dataset (CORD-19) that were published in early 2020. SWIFT-Active Screener was used to prioritize records according to pre-defined inclusion criteria. Relevant studies were categorized by risk and protective status; susceptibility category (Behavioral, Physiological, Demographic, and Environmental); and affected sub-populations. Using tagged studies, we created an rEM for COVID-19 susceptibility that reveals: (1) current lines of evidence; (2) knowledge gaps; and (3) areas that may benefit from systematic review. Results: We imported 4,330 titles and abstracts from CORD-19. After screening 3,521 of these to achieve 99% estimated recall, 217 relevant studies were identified. Most included studies concerned the impact of underlying comorbidities (Physiological); age and gender (Demographic); and social factors (Environmental) on COVID-19 outcomes. Among the relevant studies, older males with comorbidities were commonly reported to have the poorest outcomes. We noted a paucity of COVID-19 studies among children and susceptible sub-groups, including pregnant women, racial minorities, refugees/migrants, and healthcare workers, with few studies examining protective factors. Conclusion: Using rEM analysis, we synthesized the recent body of evidence related to COVID-19 risk and protective factors. The results provide a comprehensive tool for rapidly elucidating COVID-19 susceptibility patterns and identifying resource-rich/resource-poor areas of research that may benefit from future investigation as the pandemic evolves.

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.

KRAS-retroviral fusion transcripts and gene amplification in arsenic-transformed, human prostate CAsE-PE cancer cells.

CAsE-PE cells are an arsenic-transformed, human prostate epithelial line containing oncogenic mutations in KRAS compared to immortalized, normal KRAS parent cells, RWPE-1. We previously reported increased copy number of mutated KRAS in CAsE-PE cells, suggesting gene amplification. Here, KRAS flanking genomic and transcriptomic regions were sequenced in CAsE-PE cells for insight into KRAS amplification. Comparison of DNA-Seq and RNA-Seq showed increased reads from background aligning to all KRAS exons in CAsE-PE cells, while a uniform DNA-Seq read distribution occurred in RWPE-1 cells with normal transcript expression. We searched for KRAS fusions in DNA and RNA sequencing data finding a portion of reads aligning to KRAS and viral sequence. After generation of cDNA from total RNA, short and long KRAS probes were generated to hybridize cDNA and KRAS enriched fragments were PacBio sequenced. More KRAS reads were captured from CAsE-PE cDNA versus RWPE-1 by each probe set. Only CAsE-PE cDNA showed KRAS viral fusion transcripts, primarily mapping to LTR and endogenous retrovirus sequences on either 5'- or 3'-ends of KRAS. Most KRAS viral fusion transcripts contained 4 to 6 exons but some PacBio sequences were in unusual orientations, suggesting viral insertions within the gene body. Additionally, conditioned media was extracted for potential retroviral particles. RNA-Seq of culture media isolates identified KRAS retroviral fusion transcripts in CAsE-PE media only. Truncated KRAS transcripts suggested multiple retroviral integration sites occurred within the KRAS gene producing KRAS retroviral fusions of various lengths. Findings suggest activation of endogenous retroviruses in arsenic carcinogenesis should be explored.

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.

Arsenite malignantly transforms human prostate epithelial cells in vitro by gene amplification of mutated KRAS.

Inorganic arsenic is an environmental human carcinogen of several organs including the urinary tract. RWPE-1 cells are immortalized, non-tumorigenic, human prostate epithelia that become malignantly transformed into the CAsE-PE line after continuous in vitro exposure to 5µM arsenite over a period of months. For insight into in vitro arsenite transformation, we performed RNA-seq for differential gene expression and targeted sequencing of KRAS. We report >7,000 differentially expressed transcripts in CAsE-PE cells compared to RWPE-1 cells at >2-fold change, q<0.05 by RNA-seq. Notably, KRAS expression was highly elevated in CAsE-PE cells, with pathway analysis supporting increased cell proliferation, cell motility, survival and cancer pathways. Targeted DNA sequencing of KRAS revealed a mutant specific allelic imbalance, 'MASI', frequently found in primary clinical tumors. We found high expression of a mutated KRAS transcript carrying oncogenic mutations at codons 12 and 59 and many silent mutations, accompanied by lower expression of a wild-type allele. Parallel cultures of RWPE-1 cells retained a wild-type KRAS genotype. Copy number analysis and sequencing showed amplification of the mutant KRAS allele. KRAS is expressed as two splice variants, KRAS4a and KRAS4b, where variant 4b is more prevalent in normal cells compared to greater levels of variant 4a seen in tumor cells. 454 Roche sequencing measured KRAS variants in each cell type. We found KRAS4a as the predominant transcript variant in CAsE-PE cells compared to KRAS4b, the variant expressed primarily in RWPE-1 cells and in normal prostate, early passage, primary epithelial cells. Overall, gene expression data were consistent with KRAS-driven proliferation pathways found in spontaneous tumors and malignantly transformed cell lines. Arsenite is recognized as an important environmental carcinogen, but it is not a direct mutagen. Further investigations into this in vitro transformation model will focus on genomic events that cause arsenite-mediated mutation and overexpression of KRAS in CAsE-PE cells.

The Power of Resolution: Contextualized Understanding of Biological Responses to Liver Injury Chemicals Using High-throughput Transcriptomics and Benchmark Concentration Modeling.

Prediction of human response to chemical exposures is a major challenge in both pharmaceutical and toxicological research. Transcriptomics has been a powerful tool to explore chemical-biological interactions, however, limited throughput, high-costs, and complexity of transcriptomic interpretations have yielded numerous studies lacking sufficient experimental context for predictive application. To address these challenges, we have utilized a novel high-throughput transcriptomics (HTT) platform, TempO-Seq, to apply the interpretive power of concentration-response modeling with exposures to 24 reference compounds in both differentiated and non-differentiated human HepaRG cell cultures. Our goals were to (1) explore transcriptomic characteristics distinguishing liver injury compounds, (2) assess impacts of differentiation state of HepaRG cells on baseline and compound-induced responses (eg, metabolically-activated), and (3) identify and resolve reference biological-response pathways through benchmark concentration (BMC) modeling. Study data revealed the predictive utility of this approach to identify human liver injury compounds by their respective BMCs in relation to human internal exposure plasma concentrations, and effectively distinguished drug analogs with varied associations of human liver injury (eg, withdrawn therapeutics trovafloxacin and troglitazone). Impacts of cellular differentiation state (proliferated vs differentiated) were revealed on baseline drug metabolizing enzyme expression, hepatic receptor signaling, and responsiveness to metabolically-activated toxicants (eg, cyclophosphamide, benzo(a)pyrene, and aflatoxin B1). Finally, concentration-response modeling enabled efficient identification and resolution of plausibly-relevant biological-response pathways through their respective pathway-level BMCs. Taken together, these findings revealed HTT paired with differentiated in vitro liver models as an effective tool to model, explore, and interpret toxicological and pharmacological interactions.

Low-calorie sweeteners and health outcomes: A demonstration of rapid evidence mapping (rEM).

BACKGROUND: "Evidence Mapping" is an emerging tool that is increasingly being used to systematically identify, review, organize, quantify, and summarize the literature. It can be used as an effective method for identifying well-studied topic areas relevant to a broad research question along with any important literature gaps. However, because the procedure can be significantly resource-intensive, approaches that can increase the speed and reproducibility of evidence mapping are in great demand. METHODS: We propose an alternative process called "rapid Evidence Mapping" (rEM) to map the scientific evidence in a time-efficient manner, while still utilizing rigorous, transparent and explicit methodological approaches. To illustrate its application, we have conducted a proof-of-concept case study on the topic of low-calorie sweeteners (LCS) with respect to human dietary exposures and health outcomes. During this process, we developed and made publicly available our study protocol, established a PECO (Participants, Exposure, Comparator, and Outcomes) statement, searched the literature, screened titles and abstracts to identify potentially relevant studies, and applied semi-automated machine learning approaches to tag and categorize the included articles. We created various visualizations including bubble plots and frequency tables to map the evidence and research gaps according to comparison type, population baseline health status, outcome group, and study sample size. We compared our results with a traditional evidence mapping of the same topic published in 2016 (Wang et al., 2016). RESULTS: We conducted an rEM of LCS, for which we identified 8122 records from a PubMed search (January 1, 1946-May 1, 2014) and then utilized machine learning (SWIFT-Active Screener) to prioritize relevant records. After screening 2267 (28%) of the total set of titles and abstracts to achieve 95% estimated recall, we ultimately included 297 relevant studies. Overall, our findings corroborated those of Wang et al. (2016) and identified that most studies were acute or short-term in healthy individuals, and studied the outcomes of appetite, energy sensing and body weight. We also identified a lack of studies assessing appetite and dietary intake related outcomes in people with diabetes. The rEM approach required approximately 100 person-hours conducted over 7 calendar months. CONCLUSION: Rapid Evidence Mapping is an expeditious approach based on rigorous methodology that can be used to quickly summarize the available body of evidence relevant to a research question, identify gaps in the literature to inform future research, and contextualize the design of a systematic review within the broader scientific literature, significantly reducing human effort while yielding results comparable to those from traditional methods. The potential time savings of this approach in comparison to the traditional evidence mapping process make it a potentially powerful tool for rapidly translating knowledge to inform science-based decision-making.

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.