Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping.

High-throughput transcriptomics (HTTr) uses gene expression profiling to characterize the biological activity of chemicals in in vitro cell-based test systems. As an extension of a previous study testing 44 chemicals, HTTr was used to screen an additional 1,751 unique chemicals from the EPA's ToxCast collection in MCF7 cells using 8 concentrations and an exposure duration of 6 h. We hypothesized that concentration-response modeling of signature scores could be used to identify putative molecular targets and cluster chemicals with similar bioactivity.

High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells.

New approach methodologies (NAMs) aim to accelerate the pace of chemical risk assessment while simultaneously reducing cost and dependency on animal studies. High Throughput Transcriptomics (HTTr) is an emerging NAM in the field of chemical hazard evaluation for establishing in vitro points-of-departure and providing mechanistic insight. In the current study, 1201 test chemicals were screened for bioactivity at eight concentrations using a 24-h exposure duration in the human- derived U-2 OS osteosarcoma cell line with HTTr.

Searching for LINCS to Stress: Using Text Mining to Automate Reference Chemical Curation.

Adaptive stress response pathways (SRPs) restore cellular homeostasis following perturbation but may activate terminal outcomes like apoptosis, autophagy, or cellular senescence if disruption exceeds critical thresholds. Because SRPs hold the key to vital cellular tipping points, they are targeted for therapeutic interventions and assessed as biomarkers of toxicity. Hence, we are developing a public database of chemicals that perturb SRPs to enable new data-driven tools to improve public health.

High-Throughput Transcriptomics of Water Extracts Detects Reductions in Biological Activity with Water Treatment Processes.

The presence of numerous chemical contaminants from industrial, agricultural, and pharmaceutical sources in water supplies poses a potential risk to human and ecological health. Current chemical analyses suffer from limitations, including chemical coverage and high cost, and broad-coverage assays such as transcriptomics may further improve water quality monitoring by assessing a large range of possible effects. Here, we used high-throughput transcriptomics to assess the activity induced by field-derived water extracts in MCF7 breast carcinoma cells.

Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments.

'Cell Painting' is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens.

Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities.

Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures).

Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data.

Estimation of points of departure (PoDs) from high-throughput transcriptomic data (HTTr) represents a key step in the development of next-generation risk assessment (NGRA). Current approaches mainly rely on single key gene targets, which are constrained by the information currently available in the knowledge base and make interpretation challenging as scientists need to interpret PoDs for thousands of genes or hundreds of pathways. In this work, we aimed to address these issues by developing a computational workflow to investigate the pathway concentration-response relationships in a way that is not fully constrained by known biology and also facilitates interpretation.

Evaluating adaptive stress response gene signatures using transcriptomics.

Stress response pathways (SRPs) mitigate the cellular effects of chemicals, but excessive perturbation can lead to adverse outcomes. Here, we investigated a computational approach to evaluate SRP activity from transcriptomic data using gene set enrichment analysis (GSEA). We extracted published gene signatures for DNA damage response (DDR), unfolded protein response (UPR), heat shock response (HSR), response to hypoxia (HPX), metal-associated response (MTL), and oxidative stress response (OSR) from the Molecular Signatures Database (MSigDB).

Estimating Hepatotoxic Doses Using High-Content Imaging in Primary Hepatocytes.

Using in vitro data to estimate point of departure (POD) values is an essential component of new approach methodologies (NAMs)-based chemical risk assessments. In this case study, we evaluated a NAM for hepatotoxicity based on rat primary hepatocytes, high-content imaging (HCI), and toxicokinetic modeling. First, we treated rat primary hepatocytes with 10 concentrations (0.

Mechanistic lessons learned from studies of planktonic bacteria with metallic nanomaterials: implications for interactions between nanomaterials and biofilm bacteria.

Metal and metal-oxide nanoparticles (NPs) are used in numerous applications and have high likelihood of entering engineered and natural environmental systems. Careful assessment of the interaction of these NPs with bacteria, particularly biofilm bacteria, is necessary. This perspective discusses mechanisms of NP interaction with bacteria and identifies challenges in understanding NP-biofilm interaction, considering fundamental material attributes and inherent complexities of biofilm structure.

Effects of chloride and ionic strength on physical morphology, dissolution, and bacterial toxicity of silver nanoparticles.

In this study, we comprehensively evaluate chloride- and ionic-strength-mediated changes in the physical morphology, dissolution, and bacterial toxicity of silver nanoparticles (AgNPs), which are one of the most-used nanomaterials. The findings isolate the impact of ionic strength from that of chloride concentration. As ionic strength increases, AgNP aggregation likewise increases (such that the hydrodynamic radius [HR] increases), fractal dimension (Df) strongly decreases (providing increased available surface relative to suspensions with higher Df), and the release of Ag(aq) increases.