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      Developmental exposures to common environmental contaminants, DEHP and lead, alter adult brain and blood hydroxymethylation in mice

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          Abstract

          Introduction: The developing epigenome changes rapidly, potentially making it more sensitive to toxicant exposures. DNA modifications, including methylation and hydroxymethylation, are important parts of the epigenome that may be affected by environmental exposures. However, most studies do not differentiate between these two DNA modifications, possibly masking significant effects.

          Methods: To investigate the relationship between DNA hydroxymethylation and developmental exposure to common contaminants, a collaborative, NIEHS-sponsored consortium, TaRGET II, initiated longitudinal mouse studies of developmental exposure to human-relevant levels of the phthalate plasticizer di(2-ethylhexyl) phthalate (DEHP), and the metal lead (Pb). Exposures to 25 mg DEHP/kg of food (approximately 5 mg DEHP/kg body weight) or 32 ppm Pb-acetate in drinking water were administered to nulliparous adult female mice. Exposure began 2 weeks before breeding and continued throughout pregnancy and lactation, until offspring were 21 days old. At 5 months, perinatally exposed offspring blood and cortex tissue were collected, for a total of 25 male mice and 17 female mice ( n = 5–7 per tissue and exposure). DNA was extracted and hydroxymethylation was measured using hydroxymethylated DNA immunoprecipitation sequencing (hMeDIP-seq). Differential peak and pathway analysis was conducted comparing across exposure groups, tissue types, and animal sex, using an FDR cutoff of 0.15.

          Results: DEHP-exposed females had two genomic regions with lower hydroxymethylation in blood and no differences in cortex hydroxymethylation. For DEHP-exposed males, ten regions in blood (six higher and four lower) and 246 regions (242 higher and four lower) and four pathways in cortex were identified. Pb-exposed females had no statistically significant differences in blood or cortex hydroxymethylation compared to controls. Pb-exposed males, however, had 385 regions (all higher) and six pathways altered in cortex, but no differential hydroxymethylation was identified in blood.

          Discussion: Overall, perinatal exposure to human-relevant levels of two common toxicants showed differences in adult DNA hydroxymethylation that was specific to sex, exposure type, and tissue, but male cortex was most susceptible to hydroxymethylation differences by exposure. Future assessments should focus on understanding if these findings indicate potential biomarkers of exposure or are related to functional long-term health effects.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              BEDTools: a flexible suite of utilities for comparing genomic features

              Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                Front Cell Dev Biol
                Front Cell Dev Biol
                Front. Cell Dev. Biol.
                Frontiers in Cell and Developmental Biology
                Frontiers Media S.A.
                2296-634X
                13 June 2023
                2023
                : 11
                : 1198148
                Affiliations
                [1] 1 Department of Environmental Health Sciences , University of Michigan School of Public Health , Ann Arbor, MI, United States
                [2] 2 Epigenomics Core , Biomedical Research Core Facilities , Michigan Medicine , Ann Arbor, MI, United States
                [3] 3 Department of Nutritional Sciences , University of Michigan School of Public Health , Ann Arbor, MI, United States
                [4] 4 Department of Pharmacology , University of Michigan Medical School , Ann Arbor, MI, United States
                [5] 5 Department of Computational Medicine and Bioinformatics , Michigan Medicine , Ann Arbor, MI, United States
                [6] 6 Department of Biostatistics , University of Michigan School of Public Health , Ann Arbor, MI, United States
                Author notes

                Edited by: Mellissa Mann, University of Pittsburgh, United States

                Reviewed by: Naoko Hattori, Hoshi University, Japan

                Michelle Holland, King’s College London, United Kingdom

                Michael Cowley, North Carolina State University, United States

                *Correspondence: Dana C. Dolinoy, ddolinoy@ 123456umich.edu
                Article
                1198148
                10.3389/fcell.2023.1198148
                10294071
                3327ebc2-917f-410f-814e-a74b5446dafe
                Copyright © 2023 Petroff, Cavalcante, Colacino, Goodrich, Jones, Lalancette, Morgan, Neier, Perera, Rygiel, Svoboda, Wang, Sartor and Dolinoy.

                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
                : 31 March 2023
                : 25 May 2023
                Funding
                Funded by: National Institute of Environmental Health Sciences , doi 10.13039/100000066;
                Award ID: P30 ES017885 U24 ES026697 K01 ES032048 R35 ES031686 R01 ES028802 T32 ES007062
                Funded by: National Human Genome Research Institute , doi 10.13039/100000051;
                Award ID: T32 HG000040
                Funded by: National Institute on Aging , doi 10.13039/100000049;
                Award ID: R01 AG072396
                This work was supported by funding from the following sources: National Institute of Environmental Health Sciences (NIEHS) TaRGET II Consortium U24 (ES026697), NIEHS Grant K01 (ES032048), NIEHS Grant R35 (ES031686), NIEHS Grant R01 (ES028802), the Michigan Lifestage Environmental Exposures and Disease (M-LEEaD) NIEHS Core Center (P30 ES017885), Institutional Training Grant T32 (NIEHS T32 ES007062 and National Human Genome Research Institute T32 HG000040), and National Institute on Aging (NIA) Grant R01 (AG072396).
                Categories
                Cell and Developmental Biology
                Original Research
                Custom metadata
                Developmental Epigenetics

                dna methylation,dna hydroxymethylation,lead (pb),phthalate,dehp (di-(2-ethylhexyl) phthalate),toxicoepigenetics,5-hydroxymethylcytosine

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