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      Molecular and Metabolic Analysis of Arsenic-Exposed Humanized AS3MT Mice

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          Abstract

          Background:

          Chronic exposure to inorganic arsenic (iAs) has been associated with type 2 diabetes (T2D). However, potential sex divergence and the underlying mechanisms remain understudied. iAs is not metabolized uniformly across species, which is a limitation of typical exposure studies in rodent models. The development of a new “humanized” mouse model overcomes this limitation. In this study, we leveraged this model to study sex differences in the context of iAs exposure.

          Objectives:

          The aim of this study was to determine if males and females exhibit different liver and adipose molecular profiles and metabolic phenotypes in the context of iAs exposure.

          Methods:

          Our study was performed on wild-type (WT) 129S6/SvEvTac and humanized arsenic + 3 methyl transferase (human AS3MT) 129S6/SvEvTac mice treated with 400 ppb of iAs via drinking water ad libitum. After 1 month, mice were sacrificed and the liver and gonadal adipose depots were harvested for iAs quantification and sequencing-based microRNA and gene expression analysis. Serum blood was collected for fasting blood glucose, fasting plasma insulin, and homeostatic model assessment for insulin resistance (HOMA-IR).

          Results:

          We detected sex divergence in liver and adipose markers of diabetes (e.g., miR-34a, insulin signaling pathways, fasting blood glucose, fasting plasma insulin, and HOMA-IR) only in humanized (not WT) mice. In humanized female mice, numerous genes that promote insulin sensitivity and glucose tolerance in both the liver and adipose are elevated compared to humanized male mice. We also identified Klf11 as a putative master regulator of the sex divergence in gene expression in humanized mice.

          Discussion:

          Our study underscored the importance of future studies leveraging the humanized mouse model to study iAs-associated metabolic disease. The findings suggested that humanized males are at increased risk for metabolic dysfunction relative to humanized females in the context of iAs exposure. Future investigations should focus on the detailed mechanisms that underlie the sex divergence. https://doi.org/10.1289/EHP12785

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          Most cited references119

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          Is Open Access

          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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              Cytoscape: a software environment for integrated models of biomolecular interaction networks.

              Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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                Author and article information

                Journal
                Environ Health Perspect
                Environ Health Perspect
                EHP
                Environmental Health Perspectives
                Environmental Health Perspectives
                0091-6765
                1552-9924
                27 December 2023
                December 2023
                : 131
                : 12
                : 127021
                Affiliations
                [ 1 ]departmentDepartment of Biomedical Sciences, College of Veterinary Medicine , Cornell University , Ithaca, New York, USA
                [ 2 ]departmentDepartment of Nutrition, Gillings School of Global Public Health , University of North Carolina , Chapel Hill, North Carolina, USA
                [ 3 ]departmentDepartment of Genetics, School of Medicine , University of North Carolina , Chapel Hill, North Carolina, USA
                Author notes
                Address correspondence to Praveen Sethupathy. Email: pr46@ 123456cornell.edu
                Article
                EHP12785
                10.1289/EHP12785
                10752418
                38150313
                95fd98ce-3a83-41d4-98a0-56500f0fc4bf

                EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted.

                History
                : 23 January 2023
                : 30 October 2023
                : 04 December 2023
                Categories
                Research

                Public health
                Public health

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