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      Diverse PFAS produce unique transcriptomic changes linked to developmental toxicity in zebrafish

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          Abstract

          Per- and polyfluoroalkyl substances (PFAS) are a widespread and persistent class of contaminants posing significant environmental and human health concerns. Comprehensive understanding of the modes of action underlying toxicity among structurally diverse PFAS is mostly lacking. To address this need, we recently reported on our application of developing zebrafish to evaluate a large library of PFAS for developmental toxicity. In the present study, we prioritized 15 bioactive PFAS that induced significant morphological effects and performed RNA-sequencing to characterize early transcriptional responses at a single timepoint (48 h post fertilization) after early developmental exposures (8 h post fertilization). Internal concentrations of 5 of the 15 PFAS were measured from pooled whole fish samples across multiple timepoints between 24–120 h post fertilization, and additional temporal transcriptomics at several timepoints (48–96 h post fertilization) were conducted for Nafion byproduct 2. A broad range of differentially expressed gene counts were identified across the PFAS exposures. Most PFAS that elicited robust transcriptomic changes affected biological processes of the brain and nervous system development. While PFAS disrupted unique processes, we also found that similarities in some functional head groups of PFAS were associated with the disruption in expression of similar gene sets. Body burdens after early developmental exposures to select sulfonic acid PFAS, including Nafion byproduct 2, increased from the 24–96 h post fertilization sampling timepoints and were greater than those of sulfonamide PFAS of similar chain lengths. In parallel, the Nafion byproduct 2-induced transcriptional responses increased between 48 and 96 h post fertilization. PFAS characteristics based on toxicity, transcriptomic effects, and modes of action will contribute to further prioritization of PFAS structures for testing and informed hazard assessment.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Contributors
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                URI : https://loop.frontiersin.org/people/819357/overviewRole:
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                URI : https://loop.frontiersin.org/people/558506/overviewRole:
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                URI : https://loop.frontiersin.org/people/40352/overviewRole: Role: Role: Role: Role:
                Journal
                Front Toxicol
                Front Toxicol
                Front. Toxicol.
                Frontiers in Toxicology
                Frontiers Media S.A.
                2673-3080
                22 July 2024
                2024
                : 6
                : 1425537
                Affiliations
                [1] 1 Environmental and Molecular Toxicology Department , College of Agricultural Sciences , Oregon State University , Corvallis, OR, United States
                [2] 2 Sinnhuber Aquatic Research Laboratory , Oregon State University , Corvallis, OR, United States
                [3] 3 Pacific Northwest National Laboratory , Biological Sciences Division , Richland, WA, United States
                [4] 4 Biological Sciences Department , College of Sciences , North Carolina State University , Raleigh, NC, United States
                Author notes

                Edited by: Ella Atlas, Health Canada, Canada

                Reviewed by: Arianna Giorgetti, University of Bologna, Italy

                Wen-Jun Shi, South China Normal University, China

                Jason O’Brien, Environment and Climate Change Canada (ECCC), Canada

                *Correspondence: Robyn L. Tanguay, robyn.tanguay@ 123456oregonstate.edu
                [ † ]

                These authors have contributed equally to this work

                Article
                1425537
                10.3389/ftox.2024.1425537
                11298493
                39104825
                428be710-2ffb-4dd2-8b71-19a69de2aeba
                Copyright © 2024 Rericha, St. Mary, Truong, McClure, Martin, Leonard, Thunga, Simonich, Waters, Field and Tanguay.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 April 2024
                : 21 June 2024
                Funding
                Funded by: U.S. Environmental Protection Agency , doi 10.13039/100000139;
                Award ID: 83948101
                Funded by: National Institute of Environmental Health Sciences , doi 10.13039/100000066;
                Award ID: T32 ES007060 P30 ES030287 P42 ES016465 R35 ES031709
                Funded by: U.S. Department of Energy , doi 10.13039/100000015;
                Award ID: DE-AC05-76RL01830
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by EPA grant #83948101, NIH grants R35 ES031709, P42 ES016465, P30 ES030287, and T32 ES007060. Pacific Northwest National Laboratory is a multi-program laboratory operated by Battelle for the U.S. Department of Energy under Contract DE-AC05-76RL01830.
                Categories
                Toxicology
                Original Research
                Custom metadata
                Environmental Toxicology

                pfas,developmental toxicity,transcriptomics,bioconcentration,phenotypically anchored

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