Navigating the bridge between wet and dry lab toxicology research to address current challenges with high-dimensional data

TAME 2.0: expanding and improving online data science training for environmental health research

Introduction

Data science training has the potential to propel environmental health research efforts into territories that remain untapped and holds immense promise to change our understanding of human health and the environment. Though data science training resources are expanding, they are still limited in terms of public accessibility, user friendliness, breadth of content, tangibility through real-world examples, and applicability to the field of environmental health science.

Methods

To fill this gap, we developed an environmental health data science training resource, the inTelligence And Machine lEarning (TAME) Toolkit, version 2.0 (TAME 2.0).

Results

TAME 2.0 is a publicly available website that includes training modules organized into seven chapters. Training topics were prioritized based upon ongoing engagement with trainees, professional colleague feedback, and emerging topics in the field of environmental health research (e.g., artificial intelligence and machine learning). TAME 2.0 is a significant expansion upon the original TAME training resource pilot. TAME 2.0 specifically includes training organized into the following chapters: (1) Data management to enable scientific collaborations; (2) Coding in R; (3) Basics of data analysis and visualizations; (4) Converting wet lab data into dry lab analyses; (5) Machine learning; (6) Applications in toxicology and exposure science; and (7) Environmental health database mining. Also new to TAME 2.0 are "Test Your Knowledge" activities at the end of each training module, in which participants are asked additional module-specific questions about the example datasets and apply skills introduced in the module to answer them. TAME 2.0 effectiveness was evaluated via participant surveys during graduate-level workshops and coursework, as well as undergraduate-level summer research training events, and suggested edits were incorporated while overall metrics of effectiveness were quantified.

Discussion

Collectively, TAME 2.0 now serves as a valuable resource to address the growing demand of increased data science training in environmental health research. TAME 2.0 is publicly available at: https://uncsrp.github.io/TAME2/.

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

Burn Pit Smoke Condensate-Mediated Toxicity in Human Airway Epithelial Cells

Burn pits are a method of open-air waste management that was common during military operations in Iraq, Afghanistan, and other regions in Southwest Asia. Veterans returning from deployment have reported respiratory symptoms, potentially from exposure to burn pit smoke, yet comprehensive assessment of such exposure on pulmonary health is lacking. We have previously shown that exposure to condensates from burn pit smoke emissions causes inflammation and cytotoxicity in mice. In this study, we explored the effects of burn pit smoke condensates on human airway epithelial cells (HAECs) to understand their impact on cellular targets in the human lung. HAECs were cultured at the air-liquid interface (ALI) and exposed to burn pit waste smoke condensates (plywood, cardboard, plastic, mixed, and mixed with diesel) generated under smoldering and flaming conditions. Cytotoxicity was evaluated by measuring transepithelial electrical resistance (TEER) and lactate dehydrogenase (LDH) release; toxicity scores (TSs) were quantified for each exposure. Pro-inflammatory cytokine release and modulation of gene expression were examined for cardboard and plastic condensate exposures. Burn pit smoke condensates generated under flaming conditions affected cell viability, with flaming mixed waste and plywood exhibiting the highest toxicity scores. Cardboard and plastic smoke condensates modulated cytokine secretion, with GM-CSF and IL-1β altered in more than one exposure group. Gene expression of detoxifying enzymes (ALDH1A3, ALDH3A1, CYP1A1, CYP1B1, NQO1, etc.), mucins (MUC5AC, MUC5B), and cytokines was affected by several smoke condensates. Particularly, expression of IL6 was elevated following exposure to all burn pit smoke condensates, and polycyclic aromatic hydrocarbon acenaphthene was positively associated with the IL-6 level in the basolateral media of HAECs. These observations demonstrate that exposure to smoke condensates of materials present in burn pits adversely affects HAECs and that aberrant cytokine secretion and altered gene expression profiles following burn pit material smoke exposure could contribute to the development of airway disease.

Wildfires and climate justice: future wildfire events predicted to disproportionally impact socioeconomically vulnerable communities in North Carolina

Wildfire events are becoming increasingly common across many areas of the United States, including North Carolina (NC). Wildfires can cause immediate damage to properties, and wildfire smoke conditions can harm the overall health of exposed communities. It is critical to identify communities at increased risk of wildfire events, particularly in areas with that have sociodemographic disparities and low socioeconomic status (SES) that may exacerbate incurred impacts of wildfire events. This study set out to: (1) characterize the distribution of wildfire risk across NC; (2) implement integrative cluster analyses to identify regions that contain communities with increased vulnerability to the impacts of wildfire events due to sociodemographic characteristics; (3) provide summary-level statistics of populations with highest wildfire risk, highlighting SES and housing cost factors; and (4) disseminate wildfire risk information via our online web application, ENVIROSCAN. Wildfire hazard potential (WHP) indices were organized at the census tract-level, and distributions were analyzed for spatial autocorrelation via global and local Moran's tests. Sociodemographic characteristics were analyzed via k-means analysis to identify clusters with distinct SES patterns to characterize regions of similar sociodemographic/socioeconomic disparities. These SES groupings were overlayed with housing and wildfire risk profiles to establish patterns of risk across NC. Resulting geospatial analyses identified areas largely in Southeastern NC with high risk of wildfires that were significantly correlated with neighboring regions with high WHP, highlighting adjacent regions of high risk for future wildfire events. Cluster-based analysis of SES factors resulted in three groups of regions categorized through distinct SES profiling; two of these clusters (Clusters 2 and 3) contained indicators of high SES vulnerability. Cluster 2 contained a higher percentage of younger (<5 years), non-white, Hispanic and/or Latino residents; while Cluster 3 had the highest mean WHP and was characterized by a higher percentage of non-white residents, poverty, and less than a high school education. Counties of particular SES and WHP-combined vulnerability include those with majority non-white residents, tribal communities, and below poverty level households largely located in Southeastern NC. WHP values per census tract were dispersed to the public via the ENVIROSCAN application, alongside other environmentally-relevant data.