Biology subjects

Kuntala, P. K.

Publications and source records attributed to Kuntala, P. K..

5 recordsLinked to original sources

bioRxiv2025

Comprehensive Transcriptomic and Epigenomic Insights into Environmental Toxicant Exposures: The TaRGET II Resource

Environmental exposures to toxic chemicals can profoundly alter the transcriptome and epigenome in both humans and animals, contributing to disease development across the lifespan. To elucidate how early-life exposure to toxicants exerts such persistent effects, the Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription II (TaRGET II) Consortium generated a landmark resource comprising 2,564 epigenomes and 1,043 transcriptomes from longitudinal studies in mice. All data are publicly available through the TaRGET II data portal and the WashU Epigenome Browser. This resource from target (liver, brain, lung, heart) and surrogate (blood) tissues at weaning (3 weeks) and two adult time-points (5 and 10 months) characterized the molecular response to arsenic (As), lead (Pb), bisphenol-A (BPA), di-2-ethylhexyl phthalate(DEHP), tributyltin (TBT), tetrachlorodibenzo-p-dioxin (TCDD), and particulate matter with a diameter of <2.5m (PM2.5). The findings revealed persistent, toxicant-specific, sex-dependent epigenomic and transcriptomic perturbations, resulting in disrupted expression of 14,908 genes, altered chromatin accessibility at 87,409 regulatory elements, DNA methylation changes at 113,186 genomic regions, and chromatin state switching of histone modifications. The resulting high-resolution map of how environmental exposures reprogram the epigenome and transcriptome is broadly accessible via ToxiTaRGET database, offering unparalleled opportunities for the scientific community to investigate the molecular underpinnings of environmental toxicant exposures and their contributions to disease pathogenesis.

pharmacology and toxicology↗

bioRxiv2025

ToxiTaRGET: a multi-omics resource for toxicant-responsive molecular targets

Environmental toxicant exposures can induce widespread alterations in both the transcriptome and epigenome of mammals, and directly contribute to the increased risk of various diseases, including cardiovascular disorders, cancer, and neurological disorders. To evaluate how early-life toxicants produce long-term impacts on the transcriptome and epigenome in mice, the Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription II (TaRGET II) Consortium generated a landmark resource comprising 3,607 multi-omics from longitudinal studies in mice. The molecular changes in responding to distinct environmental toxicants, including arsenic (As), lead (Pb), bisphenol A (BPA), tributyltin (TBT), di-2-ethylhexyl phthalate (DEHP), dioxin (TCDD), and fine particulate matter (PM2.5), were systematically identified and visualized on an integrative platform, ToxiTaRGET, to allow quickly search and browse by researchers. ToxiTaRGET houses a rich repository of molecular signatures, including gene expression, chromatin accessibility, and DNA methylation profiles, in response to early-life toxicant exposures. These molecular signatures span multiple biologically important tissues in both male and female mice at three distinct life stages, offering a valuable resource for the environmental health and toxicogenomic research communities.

bioinformatics↗

bioRxiv2025

A comprehensive data repository of environmental toxicants exposed mouse epigenomes from the TaRGET II Consortium

The Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription (TaRGET) is a multiphase program that aims to understand how environmental factors contribute to disease susceptibility using toxicant-exposed mouse models. Here, we introduce the TaRGET II Data Portal (https://data.targetepigenomics.org/), a repository of exposomes from mice exposed to environmental toxicants, including arsenic (As), lead (Pb), bisphenol A (BPA), tributyltin (TBT), di(2-ethylhexyl) phthalate (DEHP), tetrachlorodibenzo-p-dioxin (TCDD), and air pollution (PM2.5). Sequencing assays capturing changes in chromatin accessibility, DNA methylation, gene expression, and post-translational histone modifications from multiple centers were quality-controlled and uniformly processed. The datasets cover multiple tissues collected at four time points: 3 weeks, 5 weeks, 20 weeks, and 40 weeks. The TaRGET II Data Portal offers an efficient way to browse, search, visualize, and download relevant datasets and associated metadata, serving as a key resource for studying the impact of environmental toxicant exposures on disease susceptibility for the broader scientific community.

genomics↗

bioRxiv2024

Characterizing cytosine methylation of polymorphic human transposable element insertions using human pangenome resources

Cytosine methylation, a crucial epigenetic modification, plays a vital role in genomic regulation. Leveraging the advancements in third-generation sequencing, we investigated the methylation patterns of non-reference insertions of human lymphoblastoid cell lines (LCLs), particularly polymorphic transposable elements (TEs). We validated the high concordance between long-read methylation calls and conventional whole genome bisulfite sequencing (WGBS) method. By characterizing thousands of polymorphic TE insertions genome-wide using long reads from the draft Human Pangenome Reference, we aimed to establish general rules of TE methylation by addressing three key questions: 1) what is the methylation profile of each insertion? 2) do newly inserted TEs adopt the methylation pattern of their genomic context? and 3) do new TE insertions affect the methylation of their flanking regions? While most non-TE insertions exhibit DNA methylation patterns consistent with their genomic context, TE insertions are generally highly methylated, exhibiting distinct, class-specific patterns, and with profound variation within TE bodies. A small percentage of Alu insertions are hypomethylated, particularly those inserted within hypomethylated CpG islands. By comparing DNA methylation of flanking regions of TE insertions between individuals with and without the TE insertions, we revealed that majority of TEs exhibited minimal impact on nearby regions, although numerous exceptions exist where the methylation status of both L1 and Alu insertions "leak" into nearby regions, leading to either methylation spreading or hypomethylation sloping shores. In conclusion, we demonstrated the methylation calling capability of third-generation sequencing and its unique advantage in characterizing epigenomic features within non-reference positions. While TE insertions primarily exhibit methylation patterns restricted within their boundaries, some TEs are able to engage in context-dependent complex interactions with genomic neighborhood.

bioinformatics↗

bioRxiv2021

STENCIL: A web templating engine for visualizing and sharing life science datasets

The ability to aggregate experimental data analysis and results into a concise and interpretable format is a key step in evaluating the success of an experiment. This critical step determines baselines for reproducibility and is a key requirement for data dissemination. However, in practice it can be difficult to consolidate data analyses that encapsulates the broad range of datatypes available in the life sciences. We present STENCIL, a web templating engine designed to organize, visualize, and enable the sharing of interactive data visualizations. STENCIL leverages a flexible web framework for creating templates to render highly customizable visual front ends. This flexibility enables researchers to render small or large sets of experimental outcomes, producing high-quality downloadable and editable figures that retain their original relationship to the source data. REST API based back ends provide programmatic data access and supports easy data sharing. STENCIL is a lightweight tool that can stream data from Galaxy, a popular bioinformatic analysis web platform. STENCIL has been used to support the analysis and dissemination of two large scale genomic projects containing the complete data analysis for over 2,400 distinct datasets. Code and implementation details are available on GitHub: https://github.com/CEGRcode/stencil

bioinformatics↗