The Environmental Health Bioinformatics (EHB) T32 training program leverages the unique confluence of strengths in Data Science and Environmental Health Science at NC State to train scientists fluent in this critical interdisciplinary space. The program includes several enhancements to further the environment for mentored research training and professional development. This data-intensive training program will produce scientists ready to meet the emerging challenges in environmental health research, including the ever-expanding exposome, understanding of variability between individuals, rational integration across massive data sets, and study designs that incorporate diverse community goals and needs.

Exposure to environmental chemicals has been linked to increases in cancer incidence, birth defects, impaired cognitive development, and neurodegenerative disease. Unfortunately, the gap between the ever-expanding number of chemicals in the environment and data on their potential health hazards continues to widen. Although recent advancements that use in vitro, high-throughput screening (HTS) technologies may speed the pace of chemical testing, those platforms cannot detect adverse health effects diagnosable only at a systemic level, such as abnormal development or aberrant behavior. Additionally, an in vivo context is needed to quantify the contribution of interindividual genetic variation to susceptibility differences in developmental or behavioral consequences of exposure. There is strong evidence that gene-environment interactions (GxE) related to individual genetic variation play an important role in health outcomes, and that these interactions are likely a major source of the heterogeneity in response to chemical exposure. Thus, understanding the role of GxE in differential susceptibility to chemical exposure will be key to protecting public health. We propose development of a collaborative bioinformatic + experimental system to study health outcomes affected by gene-environment interactions (Y=GxE) that comprehensively describes (Y), refines characterization of (E), then investigates and probes (G). This system will leverage massive data generated by HTS of chemicals through morphological and behavioral assays in embryonic zebrafish during the critical period (the first 5 days immediately after fertilization) when developmental processes are most highly-conserved between this vertebrate model organism and humans. These data will be analyzed to quantify GxE that elicit differential health outcomes following chemical exposure. The lasting significance of this proposal will be a scalable, efficient system to rapidly address questions of differential genetic susceptibility to an expanding chemical exposome.

The Texas A&M University Superfund Research Center brings together a team of scientists from biomedical, geosciences, data science and engineering disciplines to design comprehensive solutions for complex exposure- and hazard-related challenges. This partnership was formed around a common goal: to develop, apply, and translate a comprehensive set of tools and models that will aid in addressing human health consequences of exposure to mixtures during environmental emergency-related contamination events. Dr. Wright is the lead PI for a subcontract from TAMU, and will act as co-investigator to the Data Sciences Core.

This project aims to identify and validate chromatin regions, genes and pathways that confer susceptibility to environmental chemical-induced and metabolism-associated DNA damage. We directly address the goals of RFA-ES-17-009 to ����������������stimulate the use of population-based model organism resources in exploring environmental health science and toxicology questions relevant to complex human disease outcomes��������������� by proposing a key proof-of-principle study of the interplay between DNA damage induced by 1,3-butadiene, an industrial toxicant and model genotoxic carcinogen, genetics, and epigenetics. This is a study of Gene ������������ Environment ������������ Epigenetics interactions and host susceptibility to a genotoxic environmental exposure. It takes advantage of the largest available mouse in vivo (Collaborative Cross, CC) and human in vitro (1000 Genomes lymphoblast cell lines) population-based models. This work will uncover the associations between DNA damage, tissue-specific chromatin states and genetic polymorphisms, as well as it will deliver data immediately useable in human health assessments by providing the information on dose- and time-dependencies of the associations and the translatability of the findings in a mouse population to a human in vitro population model. Novel methods in statistical genetics and mediation analysis will be developed to enable the wider use of mouse and human population-wide experimental models in the environmental health science field.

Evaluation of composition and hazards of chemical mixtures, or complex products classified as UVCBs (unknown variable composition or biological substances), presents a multitude of challenges. These include the presence of unknown constituents, a limited basis for grouping additive and independent components, and the lack of toxicity data on most constituents, and whole mixtures or UVCBs. Our long-term goal is to ensure timely risk-based assessment of mixtures and UVCBs that ensures human health protection from the toxicity of known/unknown components. We will accomplish this through integration of novel toxicological (i.e., human cell-based assays), analytical (i.e., ion mobility spectrometry-mass spectrometry), and modeling (i.e., interaction, mediation and dose reconstruction) methods. We plan to demonstrate the application of these methods in the context of rapid risk assessment/management of sites contaminated with uncharacterized chemical mixtures. Aim 1 will determine grouping of chemical mixture components for assessment of hazard(s) through integration of multi-phenotype/multi-tissue bioactivity data from a compendium of human induced pluripotent stem cell (iPSC)-derived cells. Aim 2 will develop approaches for prioritization of components in whole mixtures or UVCBs that are likely to contribute most to joint toxicity through mediation and interaction methods that integrate multi-dimensional analytical and in vitro bioactivity data. Aim 3 will evaluate prediction of joint toxicity of whole or defined mixtures and UVCBs through novel probabilistic additivity models of grouped (Aim 1) and prioritized (Aim 2) components. Aim 4 will be a demonstration of the integration of the proposed exposure, toxicological and modeling methods into a tiered hybrid experimental-computational strategy for rapid risk assessment of complex environmental mixtures and UVCBs. The main outcomes of this project will be a suite of analytical, in vitro, and computational methods and tools that can be applied in a tiered strategy for rapid quantitative characterization of the composition and hazards of complex environmental mixtures and UVCBs.

The mission of the Center for Human Health and the Environment (CHHE) is to advance understanding of environmental impacts on human health. Through a systems biology framework integrating all levels of biological organization, CHHE aims to elucidate the fundamental mechanisms through which environmental exposures/stressors interface with biomolecules, pathways, the genome, and epigenome to influence human disease. CHHE will develop three interdisciplinary research teams that represent NC State������������������s distinctive strengths. CHHE will implement specific mechanisms to promote intra- and inter-team interactions and build interdisciplinary bridges to advance basic science discovery and translational research in environmental health science along the continuum from genes to population. These teams are; - The Molecular/Cellular-Based Systems and Model Organisms Team will utilize cutting edge molecular/cellular-based systems and powerful vertebrate and invertebrate model organisms to define mechanisms, pathways, GxE interactions, and individual susceptibility to environmental agents. - The Human Population Science Team will integrate expertise on environmental exposures, epidemiology, genomics and epigenomics to identify key human pathways and link exposure and disease across populations. - Bioinformatics Team will develop novel analytics and computational tools to translate Big Data generated across high-throughput and multiscale experiments into systems-level discoveries To further increase the impact and translational capacity of these teams, CHHE will develop three new facility cores that will provide instrumentation, expertise, and training to facilitate basic mechanism- to population-based research. - The Integrative Health Sciences Facility Core will expand the ability of CHHE members to translate basic science discoveries across species and provide mechanistic insights into epidemiological studies by partnering with: a) NC State������������������s Comparative Toxicogenomics Database (CTD); b) East Carolina University Brody School of Medicine and c) NC Dept. of Health and Human Services. - The Comparative Pathobiology Core will be located at NC State������������������s top-ranked College of Veterinary Medicine and its nationally recognized veterinary pathology group to facilitate assessment of the effects of environmental stressors in the many model organisms utilized by CHHE members. - The Systems Technologies Core will introduce state-of-the-art proteomics capabilities and dedicated bioinformatics support to expand the ability of CHHE members to analyze the Next Generation Sequencing data involving the genome, transcriptome and epigenome. As a land-grant university, NC State has an extensive and active Cooperative Extension Service network throughout North Carolina. CHHE will utilize this unique network to develop a highly effective, multi-directional Community Outreach and Engagement Core to disseminate findings that will contribute to addressing disparity in exposures and health outcomes and to educate communities about environmental influences on health. A strong Career Development Core for early stage scientists that is coordinated with a robust Pilot Project Program will support cutting-edge, collaborative and multidisciplinary environmental health projects to enhance the research success and impact of our membership. Through these activities and the purposeful interfacing of different disciplines CHHE will build on NC State������������������s unique research and community outreach strengths to become a premier transformative and synergistic EHS Core Center.

Only ~3% of human genome encodes protein. The remaining 97% of the human genome is referred to as noncoding DNA. Initially, much of the intergenic noncoding sequence was referred to as ����������������junk DNA��������������� as it was considered to have no function. While some intergenic sequences contain DNA elements important in gene regulation, many intergenic sequences can be transcribed into RNA. In fact, ~85% of the human genome is transcribed into RNA. RNAs that lack protein coding function are referred to as noncoding RNAs (ncRNAs) and of these the long noncoding RNAs (lncRNAs >200 nt) represent the majority. LncRNAs are one of the largest and more diverse classes of cellular transcripts with over 10,000 lncRNA transcripts reported in the human genome; in most cases their biological function is unknown. Emerging evidence indicates they play an important role in regulating gene expression and are associated with human diseases such as cancer, Alzheimer������������������s and heart disease. There is a critical need to determine whether associations of lncRNAs with specific disease are functionally significant and to define and characterize the function of lncRNAs using in vivo disease model systems. Given that the etiology of most chronic human diseases involves interactions with environment, it is also important to determine how environmental factors impact the expression, activity and function of lncRNAs. Nonmelanoma skin cancer (NMSC) is the most common cancer in the United States. The majority of NMSCs is caused by solar UVB radiation. p53 plays a key role in the response of skin keratinocytes to UVB-induced DNA damage by inducing cell cycle arrest and apoptosis. In skin cancer, the incidence of p53 mutations ranges from 50-90%. UVB-induced mutation of p53 allows keratinocytes upon successive UVB exposures to evade apoptosis and cell cycle arrest and these defects have a critical role in skin cancer development. LincRNA-p21 is a lncRNA and was recently discovered to be a direct transcriptional target of p53 where it serves as a mediator of p53-dependent transcriptional repression. We observed that; i) lincRNA-p21 is highly inducible by UVB in the mouse skin in vivo and in human/mouse keratinocytes, ii) UVB-induction of lincRNA-p21 is p53-dependent and iii) lincRNA-p21 has a key role in UVB-induced apoptotic cell death in keratinocytes. Our plan is to characterize the regulation and function of lincRNA-p21 in keratinocytes and skin in vivo in response to UVB and to define the role of lincRNA-p21 in UVB-induced skin cancer. The central hypotheses are i) lincRNA-p21 is induced by UVB in keratinocytes through a p53-dependent pathway to produce apoptotic cell death and ii) lincRNA-p21 functions as a tumor suppressor in NMSC whereby the loss of lincRNA-p21 expression allows mutant p53 and non-mutant p53 keratinocytes to evade UVB-induced apoptotic death leading to skin cancer. The proposed studies are significant as they represent the first characterization of a lncRNA function in an in vivo disease model with a highly relevant environmental component; moreover if the hypothesis is correct this will be the first demonstration of a lncRNA functioning as a tumor suppressor in vivo.

There is variability in the severity of clinical disease in cystic fibrosis (CF), which reflects non-CFTR genetic variants, i.e., ����������������genetic modifiers���������������, and environmental influences. The Gene Modifier Study (GMS, UNC), the Twin and Sibling Study (TSS, JHU), and the EPIC Observational Cohort Study (EPIC, Univ. Washington) have assembled the world's largest cohort of CF patients with comprehensive clinical data and DNA samples. These resources have enabled genome-wide association studies (GWAS) which discovered common genetic variants affecting CF lung disease, CF-related diabetes (CFRD), pseudomonas infection, meconium ileus, and CF-related liver disease (CFLD). Sequencing the entire genome presents the opportunity to discover novel modifier variants, including rare (possibly high effect) variants, thereby providing new therapeutic targets for all individuals with CF. CFTR modulator treatments have become available for most people with CF, but not all genotypes of CFTR and not all complications are fully treated. To discover CF gene modifiers, we will carry out whole genome sequencing (WGS) in 5,200 individuals with CF from the GMS, TSS, and EPIC cohorts. To date, phenotype harmonization demonstrates that the three independent studies have similarities and distinctive, complementary features. Over the past 11 months, these collaborative investigators have made remarkable progress, and initial results have been reported in 9 abstracts at the NACF Conference. To continue this research, we will pursue 3 Specific Aims with focused effort at UNC on CF lung disease and liver disease, including 1) Create, filter, and annotate high-quality sequence data for the consortium samples; 2) Discover rare variants associated with key CF phenotypes; and 3) Validate and extend GWAS analyses for common CF genetic modifiers. We anticipate a continuing need for novel CF treatments, which may result from discovery of non-CFTR genetic modifiers.

Important progress continues to be made in the treatment of most common cancers, but therapeutic benefit remains difficult to predict and severe or fatal adverse events occur frequently. The Human Genome Project has fueled the notion that genetic information can produce effective and cost-efficient selection of therapies for individual patients, but validated genetic signatures that predict response to most chemotherapy regimens remain to be identified. Numerous genes potentially influence drug response, but current candidate-gene approaches are limited by the requirement of a priori knowledge about the genes involved and the moderate size of most clinical trials often limits the power of in vitro genome wide association studies (GWAS) for cancer pharmacogenomics discovery. In response to these limitations, we have undertaken a thorough, pharmacogenomic assessment of cytotoxic effect of the majority of FDA approved anti-cancer compounds using an ex vivo model system to determine the heritability of drug-induced cell killing to prioritize drugs for pharmacogenomic mapping. These results are an important first step, and while high heritability of a trait does not guarantee successful association mapping results, it represents an important first step and the results will be used to prioritize drugs with high heritabilities for genome-wide association mapping. In the current proposal, GWAS mapping of cytotoxic agents will be performed in a European American population, and then replication GWAS mapping will be performed in an East Asian population. In addition to discovering and validating genetic variants that predict drug response, the wealth of data collected will be used to dissect the underlying etiology of drug response traits, including assessing the relative contribution of genetic, environmental, and interaction components of variation. These results will provide crucial insight to prioritize genetic variants for follow-up in precious clinical population resources, and potentially reveal new insight into the overall etiology of drug responses.

Renewal is requested for a long-standing and successful training program in bioinformatics at North Carolina State University, with support for 8 predoctoral trainees and 2 postdoctoral trainees. The application builds on the success of the bioinformatics graduate program at NC State, which has produced nearly 50 Master's and 60 PhD graduates in Bioinformatics since the beginning of the program in 1999. The program and environment have been substantially updated to focus on environmental health bioinformatics (EHB) training, supported by additional faculty and new research programs. Past doctoral graduates are employed in academic, government, or industry positions. The graduate training program has prerequisites in mathematics, statistics, computing, and genetics. Ph.D. candidates are engaged in coursework in bioinformatics, computer science, genetics, genomics, and statistics for two years, and are expected to complete their program within five years. The substantial re-focusing of the program on environmental and related health sciences has included recruitment of additional faculty members, the addition of existing NC State faculty who bring substantial environmental expertise, and a new strength in spatial-environmental analysis. A new combined data/informatics structure for student committees will ensure appropriate progress and focus to ensure student success.