Guided optimization of ToxPi model weights using a Semi-Automated approach.

The Toxicological Prioritization Index (ToxPi) is a visual analysis and decision support tool for dimension reduction and visualization of high throughput, multi-dimensional feature data. ToxPi was originally developed for assessing the relative toxicity of multiple chemicals or stressors by synthesizing complex toxicological data to provide a single comprehensive view of the potential health effects. It continues to be used for profiling chemicals and has since been applied to other types of "sample" entities, including geospatial (e.g. county-level Covid-19 risk and sites of historical PFAS exposure) and other profiling applications. For any set of features (data collected on a set of sample entities), ToxPi integrates the data into a set of weighted slices that provide a visual profile and a score metric for comparison. This scoring system is highly dependent on user-provided feature weights, yet users often lack knowledge of how to define these feature weights. Common methods for predicting feature weights are generally unusable due to inappropriate statistical assumptions and lack of global distributional expectation. However, users often have an inherent understanding of expected results for a small subset of samples. For example, in chemical toxicity, prior knowledge can often place subsets of chemicals into categories of low, moderate or high toxicity (reference chemicals). Ordinal regression can be used to predict weights based on these response levels that are applicable to the entire feature set, analogous to using positive and negative controls to contextualize an empirical distribution. We propose a semi-supervised method utilizing ordinal regression to predict a set of feature weights that produces the best fit for the known response ("reference") data and subsequently fine-tunes the weights via a customized genetic algorithm. We conduct a simulation study to show when this method can improve the results of ordinal regression, allowing for accurate feature weight prediction and sample ranking in scenarios with minimal response data. To ground-truth the guided weight optimization, we test this method on published data to build a ToxPi model for comparison against expert-knowledge-driven weight assignments.

Adaptive selection at G6PD and disparities in diabetes complications.

Diabetes complications occur at higher rates in individuals of African ancestry. Glucose-6-phosphate dehydrogenase deficiency (G6PDdef), common in some African populations, confers malaria resistance, and reduces hemoglobin A1c (HbA1c) levels by shortening erythrocyte lifespan. In a combined-ancestry genome-wide association study of diabetic retinopathy, we identified nine loci including a G6PDdef causal variant, rs1050828 -T (Val98Met), which was also associated with increased risk of other diabetes complications. The effect of rs1050828 -T on retinopathy was fully mediated by glucose levels. In the years preceding diabetes diagnosis and insulin prescription, glucose levels were significantly higher and HbA1c significantly lower in those with versus without G6PDdef. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, participants with G6PDdef had significantly higher hazards of incident retinopathy and neuropathy. At the same HbA1c levels, G6PDdef participants in both ACCORD and the Million Veteran Program had significantly increased risk of retinopathy. We estimate that 12% and 9% of diabetic retinopathy and neuropathy cases, respectively, in participants of African ancestry are due to this exposure. Across continentally defined ancestral populations, the differences in frequency of rs1050828 -T and other G6PDdef alleles contribute to disparities in diabetes complications. Diabetes management guided by glucose or potentially genotype-adjusted HbA1c levels could lead to more timely diagnoses and appropriate intensification of therapy, decreasing the risk of diabetes complications in patients with G6PDdef alleles.

Gene-environment interactions within a precision environmental health framework.

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Evaluation of Point-of-Care Tests for Identification of Pathogens to Inform Clinical Mastitis Treatment Decisions in Pasture- and Confinement-Managed Dairy Cows in Australia.

To support antimicrobial stewardship in livestock production, there is a growing array of point of care diagnostics to guide antimicrobial treatment. The primary objective of this observational study was to evaluate the diagnostic performance of 5 point of care tests currently available in Australia for guiding lactational treatment of non-severe clinical mastitis. A secondary objective was to describe the pathogen profiles of mastitis-causing organisms in cows managed in barns ("intensive") and on pasture ("non-intensive"). Foremilk samples (n = 641) were collected by farm staff in dairy herds in Australia (n = 30) and tested at a university laboratory using a reference test and 5 index tests. The reference test was aerobic culture on Trypticase Soy Agar with 5% sheep blood followed by MALDI-TOF for identification of isolates. The following point of care tests were evaluated as index tests: Accumast®, biplate, Check-Up, Mastatest®, and 3M Petrifilm. We found that 23% of samples were contaminated, with the median herd contamination prevalence being 22%. After excluding contaminated samples, the most common diagnoses (according to the reference test) in intensive herds were no growth (31.7%), Klebsiella spp. (28.1%), E. coli (15.0%), and Strep. uberis (8.4%). The most common diagnoses in non-contaminated samples from cows in non-intensive herds were Strep. uberis (35.0%), no growth (26.9%), and E. coli (13.3%). After 24 h of incubation, all index tests demonstrated limited diagnostic sensitivity for identification of pathogens of interest (range: 0.06 to 0.63). Diagnostic performance was better at the group-level, with sensitivity and specificity for identification of non-contaminated gram-positive growths (i.e., cases that are widely considered to be candidates for antimicrobial treatment) being 0.84 and 0.75 (biplate), 0.76 and 0.90 (Accumast), 0.89 and 0.79 (Check-Up), 0.67 and 0.83 (Petrifilm), and 0.55 and 0.81 (Mastatest). In intensive herds, 22.7 to 40% of cases were classified as antimicrobial treatment candidates by index tests, which was less than for cows in non-intensive herds (41.3 to 61.0%). Despite limited diagnostic reliability at genus and species level, and the need to ensure samples are collected aseptically, our findings indicate that implementation of selective treatment protocols using the tests evaluated in this study would likely reduce antimicrobial usage in Australian herds.

Adam19 Deficiency Impacts Pulmonary Function: Human GWAS Follow-up in Mouse.

Purpose: Over 550 loci have been associated with human pulmonary function in genome-wide association studies (GWAS); however, the causal role of most remains uncertain. Single nucleotide polymorphisms in a disintegrin and metalloprotease domain 19 (ADAM19) are consistently related to pulmonary function in GWAS. Thus, we used a mouse model to investigate the causal link between Adam19 and pulmonary function. Methods: We created an Adam19 knockout (KO) mouse model and validated the gene targeting using RNA-Seq and RT-qPCR. Contrary to prior publications, the KO was not neonatal lethal. Thus, we phenotyped the Adam19 KO. Results: KO mice had lower body weight and shorter tibial length than wild type (WT). Dual-energy X-ray Absorptiometry indicated lower soft weight, fat weight, and bone mineral content in KO mice. In lung function analyses using flexiVent, compared to WT, Adam19 KO had decreased baseline respiratory system elastance, minute work of breathing, tissue damping, tissue elastance, and forced expiratory flow at 50% forced vital capacity but higher FEV0.1 and FVC. Adam19 KO had attenuated tissue damping and tissue elastance in response to methacholine following LPS exposure. Adam19 KO also exhibited attenuated neutrophil extravasation into the airway after LPS administration compared to WT. RNA-Seq analysis of KO and WT lungs identified several differentially expressed genes (Cd300lg, Kpna2, and Pttg1) implicated in lung biology and pathogenesis. Gene set enrichment analysis identified negative enrichment for TNF pathways. Conclusion: Our murine findings support a causal role of ADAM19, implicated in human GWAS, in regulating pulmonary function.

Interactive data sharing for multiple questionnaire-based exposome-wide association studies and exposome correlations in the Personalized Environment and Genes Study.

The correlations among individual exposures in the exposome, which refers to all exposures an individual encounters throughout life, are important for understanding the landscape of how exposures co-occur, and how this impacts health and disease. Exposome-wide association studies (ExWAS), which are analogous to genome-wide association studies (GWAS), are increasingly being used to elucidate links between the exposome and disease. Despite increased interest in the exposome, tools and publications that characterize exposure correlations and their relationships with human disease are limited, and there is a lack of data and results sharing in resources like the GWAS catalog. To address these gaps, we developed the PEGS Explorer web application to explore exposure correlations in data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS) that were rigorously calculated to account for differing data types and previously published results from ExWAS. Through globe visualizations, PEGS Explorer allows users to explore correlations between exposures found to be associated with complex diseases. The exposome data used for analysis includes not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The web application addresses the lack of accessible data and results sharing, a major challenge in the field, and enables users to put results in context, generate hypotheses, and, importantly, replicate findings in other cohorts. PEGS Explorer will be updated with additional results as they become available, ensuring it is an up-to-date resource in exposome science.

Questionnaire-based exposome-wide association studies for common diseases in the Personalized Environment and Genes Study.

The exposome collectively refers to all exposures, beginning in utero and continuing throughout life, and comprises not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The exposome interacts with individual genetic and epigenetic characteristics to affect human health and disease, but large-scale studies that characterize the exposome and its relationships with human disease are limited. To address this gap, we used extensive questionnaire data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS, n = 9, 429) to evaluate exposure associations in relation to common diseases. We performed an exposome-wide association study (ExWAS) to examine single exposure models and their associations with 11 common complex diseases, namely allergic rhinitis, asthma, bone loss, fibroids, high cholesterol, hypertension, iron-deficient anemia, ovarian cysts, lower GI polyps, migraines, and type 2 diabetes. Across diseases, we found associations with lifestyle factors and socioeconomic status as well as asbestos, various dust types, biohazardous material, and textile-related exposures. We also found disease-specific associations such as fishing with lead weights and migraines. To differentiate between a replicated result and a novel finding, we used an AI-based literature search and database tool that allowed us to examine the current literature. We found both replicated findings, especially for lifestyle factors such as sleep and smoking across diseases, and novel findings, especially for occupational exposures and multiple diseases.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs).

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Etiology and epidemiology of digital dermatitis in Australian dairy herds.

Bovine digital dermatitis (BDD) is an important cause of lameness in dairy cows worldwide. However, there is very little known about this disease in Australian herds, which are predominantly managed on pasture. The primary objectives of this cross-sectional study were to describe the presence and prevalence of BDD in Australian dairy herds and to characterize the microbiota of healthy skin and M4 lesions of BDD-affected, pasture-managed cows. Cows from 71 dairy herds were examined at milking time to identify the presence of BDD lesions. True prevalence was estimated using Bayesian methods with informative priors for sensitivity and specificity. Biopsy samples (n = 60) were collected from cows with and without BDD-lesions in 7 pasture-based herds. The microbiota in the superficial and deep strata of each tissue biopsy were characterized via sequencing of the V3-V4 region of the bacterial ribosomal RNA gene. Lesions were detected in 1,817 (11.5%) of 15,813 cows, and in 68 of 71 (95.8%) herds. The median herd-level apparent and true prevalences of BDD were 8.5 and 18.1%, respectively, but this varied considerably between farms. On farms with BDD, M4 lesions accounted for 70 to 100% of all lesions (interquartile range = 95.1 to 100%, median = 100%). M2 lesions (i.e., large ulcerative lesions) were observed at low prevalence (<2.2%) in the few herds (7/71, 9.9%) where they were found. There was a statistically significant difference in the composition of the microbiota between healthy skin and M4 lesions but not between superficial and deep tissue layers. Several gut- and effluent-associated bacterial taxa, including Lentimicrobium and Porphyromonas, which have previously been associated with BDD, were abundant in BDD lesions but not in control biopsies. Our study supports the idea that such taxa are involved in, though possibly not essential to, lesion development and persistence in pasture-managed cows in Australia. Our results also suggest that Dichelobacter may contribute to the disease process. We conclude that BDD is likely to occur in most Australian dairy farms, but that further studies are needed to identify its impact on cow welfare and productivity. Further investigation of the etiology of BDD in Australian dairy herds is also necessary to inform prevention and control strategies.

Dynamic Molecular Markers of Otosclerosis in the Human Cochlea.

OBJECTIVE: To investigate the role and distribution of various molecular markers using immunohistochemistry and immunofluorescence to further elucidate and understand the pathogenesis of otosclerosis. METHODS: Archival celloidin formalin-fixed 20-micron thick histologic sections from 7 patients diagnosed with otosclerosis were studied and compared to controls. Sections in the mid-modiolar region were immunoreacted with rabbit polyclonal antibodies against nidogen-1, ß2-laminin, collagen-IX, BSP, and monoclonal antibodies against TGF ß-1 and ubiquitin. Digital images were acquired using a high-resolution light and laser confocal microscope. RESULTS: Nidogen-1, BSP, and collagen-IX were expressed in the otospongiotic regions, and to lesser extent, in the otosclerotic regions, the latter previously believed to be inactive. ß2-laminin and ubiquitin were uniformly expressed in both otospongiotic and otosclerotic regions. There was a basal level of expression of all of these markers in the normal hearing and sensorineural hearing loss specimens utilized as control. TGF ß -1, however, though present in the otosclerosis bones, was absent in the normal hearing and sensorineural hearing loss controls. CONCLUSIONS: Our results propose that the activity and function of TGF-1 may play a key role in the development and pathogenesis of otosclerosis. Further studies utilizing a higher number of temporal bone specimens will be helpful for future analysis and to help decipher its role as a potential target in therapeutic interventions.

Maternal educational attainment in pregnancy and epigenome-wide DNA methylation changes in the offspring from birth until adolescence.

Maternal educational attainment (MEA) shapes offspring health through multiple potential pathways. Differential DNA methylation may provide a mechanistic understanding of these long-term associations. We aimed to quantify the associations of MEA with offspring DNA methylation levels at birth, in childhood and in adolescence. Using 37 studies from high-income countries, we performed meta-analysis of epigenome-wide association studies (EWAS) to quantify the associations of completed years of MEA at the time of pregnancy with offspring DNA methylation levels at birth (n = 9 881), in childhood (n = 2 017), and adolescence (n = 2 740), adjusting for relevant covariates. MEA was found to be associated with DNA methylation at 473 cytosine-phosphate-guanine sites at birth, one in childhood, and four in adolescence. We observed enrichment for findings from previous EWAS on maternal folate, vitamin-B12 concentrations, maternal smoking, and pre-pregnancy BMI. The associations were directionally consistent with MEA being inversely associated with behaviours including smoking and BMI. Our findings form a bridge between socio-economic factors and biology and highlight potential pathways underlying effects of maternal education. The results broaden our understanding of bio-social associations linked to differential DNA methylation in multiple early stages of life. The data generated also offers an important resource to help a more precise understanding of the social determinants of health.

Multi-tissue transcriptomic and serum metabolomic assessment reveals systemic implications of acute ozone-induced stress response in male Wistar Kyoto rats.

Air pollutant exposures have been linked to systemic disease; however, the underlying mechanisms between responses of the target tissue and systemic effects are poorly understood. A prototypic inducer of stress, ozone causes respiratory and systemic multiorgan effects through activation of a neuroendocrine stress response. The goal of this study was to assess transcriptomic signatures of multiple tissues and serum metabolomics to understand how neuroendocrine and adrenal-derived stress hormones contribute to multiorgan health outcomes. Male Wistar Kyoto rats (12-13 weeks old) were exposed to filtered air or 0.8 ppm ozone for 4-hours, and blood/tissues were collected immediately post-exposure. Each tissue had distinct expression profiles at baseline. Ozone changed 1,640 genes in lung, 274 in hypothalamus, 2,516 in adrenals, 1,333 in liver, 1,242 in adipose, and 5,102 in muscle (adjusted p-value < 0.1, absolute fold-change > 50%). Serum metabolomic analysis identified 863 metabolites, of which 447 were significantly altered in ozone-exposed rats (adjusted p-value < 0.1, absolute fold change > 20%). A total of 6 genes were differentially expressed in all 6 tissues. Glucocorticoid signaling, hypoxia, and GPCR signaling were commonly changed, but ozone induced tissue-specific changes in oxidative stress, immune processes, and metabolic pathways. Genes upregulated by TNF-mediated NFkB signaling were differentially expressed in all ozone-exposed tissues, but those defining inflammatory response were tissue-specific. Upstream predictor analysis identified common mediators of effects including glucocorticoids, although the specific genes responsible for these predictors varied by tissue. Metabolomic analysis showed major changes in lipids, amino acids, and metabolites linked to the gut microbiome, concordant with transcriptional changes identified through pathway analysis within liver, muscle, and adipose tissues. The distribution of receptors and transcriptional mechanisms underlying the ozone-induced stress response are tissue-specific and involve induction of unique gene networks and metabolic phenotypes, but the shared initiating triggers converge into shared pathway-level responses. This multi-tissue transcriptomic analysis, combined with circulating metabolomic assessment, allows characterization of the systemic inhaled pollutant-induced stress response.

The Effect of Month and District on 100-Day In-Calf Rate in Year-Round Calving Dairy Herds.

Monitoring 100-day in-calf rate (100DICR) is an integral part of the assessment of reproductive performance in year-round calving dairy herds. The objective of this study was to investigate the effect of month on 100DICR in year-round calving herds in New South Wales (NSW), Australia and determine whether a fluctuating 100DICR target is an appropriate alternative to a constant 100DICR target. The 100DICR is defined as the percentage of all current lactating cows over 100 days in milk (DIM) that conceive on or before 100 DIM. As dairy cows are typically dried off 7 months after conception, 100DICR was an approximate 7-month rolling average. Mean monthly 100DICRs were calculated with a generalised linear model for six NSW north coast herds located 15-140 km from the coast and four NSW south coast herds located less than 10 km from the coast, over a two-year period. The mean 100DICR was lowest in May at 28.62% (95%CI 28.31-28.93) and increased during winter and spring, peaking in December at 34.74% (95%CI 34.32-35.15). The observed trend was similar for north and south coast herds, although north coast herds experienced a greater change in 100DICR from the peak to a nadir of 27.58% (95%CI 27.18-27.98), a 7.15-point difference, compared to south coast herds with a nadir of 30.18% (95%CI 29.69-30.67), a 4.67-point difference between the peak and nadir. In conclusion, 100DICR is affected by month with the lowest 100DICRs observed in late autumn and the highest 100DICRs observed in late spring and early summer. Therefore, a fluctuating target 100DICR is an appropriate alternative to a constant target when assessing reproductive performance in year-round calving herds. While the district does not affect mean 100DICR per se, the district does affect the difference between peak and nadir 100DICR.

Antimicrobial susceptibility testing to evaluate minimum inhibitory concentration values of clinically relevant antibiotics.

Antimicrobial susceptibility testing is used to determine the minimum inhibitory concentration (MIC), the standard measurement of antibiotic activity. Here, we present a protocol for evaluating MIC values of clinically relevant antibiotics against bacterial isolates cultured in standard bacteriologic medium and in mammalian cell culture medium. We describe steps for pathogen identification, culturing bacteria, preparing MIC plates, MIC assay incubation, and determining MIC. This protocol can potentially optimize the use of existing antibiotics while enhancing efforts to discover new ones. For complete details on the use and execution of this protocol, please refer to Heithoff et al.1.

Race/ethnicity-stratified fine-mapping of the MHC locus reveals genetic variants associated with late-onset asthma.

Introduction: Asthma is a chronic disease of the airways that impairs normal breathing. The etiology of asthma is complex and involves multiple factors, including the environment and genetics, especially the distinct genetic architecture associated with ancestry. Compared to early-onset asthma, little is known about genetic predisposition to late-onset asthma. We investigated the race/ethnicity-specific relationship among genetic variants within the major histocompatibility complex (MHC) region and late-onset asthma in a North Carolina-based multiracial cohort of adults. Methods: We stratified all analyses by self-reported race (i.e., White and Black) and adjusted all regression models for age, sex, and ancestry. We conducted association tests within the MHC region and performed fine-mapping analyses conditioned on the race/ethnicity-specific lead variant using whole-genome sequencing (WGS) data. We applied computational methods to infer human leukocyte antigen (HLA) alleles and residues at amino acid positions. We replicated findings in the UK Biobank. Results: The lead signals, rs9265901 on the 5' end of HLA-B, rs55888430 on HLA-DOB, and rs117953947 on HCG17, were significantly associated with late-onset asthma in all, White, and Black participants, respectively (OR = 1.73, 95%CI: 1.31 to 2.14, p = 3.62 × 10-5; OR = 3.05, 95%CI: 1.86 to 4.98, p = 8.85 × 10-6; OR = 19.5, 95%CI: 4.37 to 87.2, p = 9.97 × 10-5, respectively). For the HLA analysis, HLA-B*40:02 and HLA-DRB1*04:05, HLA-B*40:02, HLA-C*04:01, and HLA-DRB1*04:05, and HLA-DRB1*03:01 and HLA-DQB1 were significantly associated with late-onset asthma in all, White, and Black participants. Conclusion: Multiple genetic variants within the MHC region were significantly associated with late-onset asthma, and the associations were significantly different by race/ethnicity group.

Re-evaluation of FDA-approved antibiotics with increased diagnostic accuracy for assessment of antimicrobial resistance.

Accurate assessment of antibiotic susceptibility is critical for treatment of antimicrobial resistant (AMR) infections. Here, we examine whether antimicrobial susceptibility testing in media more physiologically representative of in vivo conditions improves prediction of clinical outcome relative to standard bacteriologic medium. This analysis reveals that ∼15% of minimum inhibitory concentration (MIC) values obtained in physiologic media predicted a change in susceptibility that crossed a clinical breakpoint used to categorize patient isolates as susceptible or resistant. The activities of antibiotics having discrepant results in different media were evaluated in murine sepsis models. Testing in cell culture medium improves the accuracy by which MIC assays predict in vivo efficacy. This analysis identifies several antibiotics for treatment of AMR infections that standard testing failed to identify and those that are ineffective despite indicated use by standard testing. Methods with increased diagnostic accuracy mitigate the AMR crisis via utilizing existing agents and optimizing drug discovery.

C/EBPß deficiency enhances the keratinocyte innate immune response to direct activators of cytosolic pattern recognition receptors.

The skin is the first line of defense to cutaneous microbes and viruses, and epidermal keratinocytes play a critical role in preventing infection by viruses and pathogens through activation of the type I interferon (IFN) response. Using RNAseq analysis, here we report that the conditional deletion of C/EBPß transcription factor in mouse epidermis (CKOß mice) resulted in the upregulation of IFNß and numerous keratinocyte interferon-stimulated genes (ISGs). The expression of cytosolic pattern recognition receptors (cPRRs), that recognize viral RNA and DNA, were significantly increased, and enriched in the RNAseq data set. cPRRs stimulate a type I IFN response that can trigger cell death to eliminate infected cells. To determine if the observed increases in cPRRs had functional consequences, we transfected CKOß primary keratinocytes with the pathogen and viral mimics poly(I:C) (dsRNA) or poly(dA:dT) (synthetic B-DNA) that directly activate PRRs. Transfected CKOß primary keratinocytes displayed an amplified type I IFN response which was accompanied by increased activation of IRF3, enhanced ISG expression, enhanced activation of caspase-8, caspase-3 and increased apoptosis. Our results identify C/EBPß as a critical repressor of the keratinocyte type I IFN response, and demonstrates that the loss of C/EBPß primes keratinocytes to the activation of cytosolic PRRs by pathogen RNA and DNA to induce cell death mediated by caspase-8 and caspase-3.

A tiered testing strategy based on in vitro phenotypic and transcriptomic data for selecting representative petroleum UVCBs for toxicity evaluation in vivo.

Hazard evaluation of substances of "unknown or variable composition, complex reaction products and biological materials" (UVCBs) remains a major challenge in regulatory science because their chemical composition is difficult to ascertain. Petroleum substances are representative UVCBs and human cell-based data have been previously used to substantiate their groupings for regulatory submissions. We hypothesized that a combination of phenotypic and transcriptomic data could be integrated to make decisions as to selection of group-representative worst-case petroleum UVCBs for subsequent toxicity evaluation in vivo. We used data obtained from 141 substances from 16 manufacturing categories previously tested in 6 human cell types (induced pluripotent stem cell [iPSC]-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, and MCF7 and A375 cell lines). Benchmark doses for gene-substance combinations were calculated, and both transcriptomic and phenotype-derived points of departure (PODs) were obtained. Correlation analysis and machine learning were used to assess associations between phenotypic and transcriptional PODs and to determine the most informative cell types and assays, thus representing a cost-effective integrated testing strategy. We found that 2 cell types-iPSC-derived-hepatocytes and -cardiomyocytes-contributed the most informative and protective PODs and may be used to inform selection of representative petroleum UVCBs for further toxicity evaluation in vivo. Overall, although the use of new approach methodologies to prioritize UVCBs has not been widely adopted, our study proposes a tiered testing strategy based on iPSC-derived hepatocytes and cardiomyocytes to inform selection of representative worst-case petroleum UVCBs from each manufacturing category for further toxicity evaluation in vivo.

ToxicR: A computational platform in R for computational toxicology and dose-response analyses.

The need to analyze the complex relationships observed in high-throughput toxicogenomic and other omic platforms has resulted in an explosion of methodological advances in computational toxicology. However, advancements in the literature often outpace the development of software researchers can implement in their pipelines, and existing software is frequently based on pre-specified workflows built from well-vetted assumptions that may not be optimal for novel research questions. Accordingly, there is a need for a stable platform and open-source codebase attached to a programming language that allows users to program new algorithms. To fill this gap, the Biostatistics and Computational Biology Branch of the National Institute of Environmental Health Sciences, in cooperation with the National Toxicology Program (NTP) and US Environmental Protection Agency (EPA), developed ToxicR, an open-source R programming package. The ToxicR platform implements many of the standard analyses used by the NTP and EPA, including dose-response analyses for continuous and dichotomous data that employ Bayesian, maximum likelihood, and model averaging methods, as well as many standard tests the NTP uses in rodent toxicology and carcinogenicity studies, such as the poly-K and Jonckheere trend tests. ToxicR is built on the same codebase as current versions of the EPA's Benchmark Dose software and NTP's BMDExpress software but has increased flexibility because it directly accesses this software. To demonstrate ToxicR, we developed a custom workflow to illustrate its capabilities for analyzing toxicogenomic data. The unique features of ToxicR will allow researchers in other fields to add modules, increasing its functionality in the future.