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      The dynamic effect of genetic variation on the in vivo ER stress transcriptional response in different tissues


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          The genetic regulation of gene expression varies greatly across tissue-type and individuals and can be strongly influenced by the environment. Many variants, under healthy control conditions, may be silent or even have the opposite effect under diseased stress conditions. This study uses an in vivo mouse model to investigate how the effect of genetic variation changes with cellular stress across different tissues. Endoplasmic reticulum stress occurs when misfolded proteins accumulate in the endoplasmic reticulum. This triggers the unfolded protein response, a large transcriptional response which attempts to restore homeostasis. This transcriptional response, despite being a conserved, basic cellular process, is highly variable across different genetic backgrounds, making it an ideal system to study the dynamic effects of genetic variation. In this study, we sought to better understand how genetic variation alters expression across tissues, in the presence and absence of endoplasmic reticulum stress. The use of different mouse strains and their F1s allow us to also identify context-specific cis- and trans- regulatory variation underlying variable transcriptional responses. We found hundreds of genes that respond to endoplasmic reticulum stress in a tissue- and/or genotype-dependent manner. The majority of the regulatory effects we identified were acting in cis-, which in turn, contribute to the variable endoplasmic reticulum stress- and tissue-specific transcriptional response. This study demonstrates the need for incorporating environmental stressors across multiple different tissues in future studies to better elucidate the effect of any particular genetic factor in basic biological pathways, like the endoplasmic reticulum stress response.

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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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            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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              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/.

                Author and article information

                Role: Editor
                G3 (Bethesda)
                G3: Genes|Genomes|Genetics
                Oxford University Press
                June 2022
                29 April 2022
                29 April 2022
                : 12
                : 6
                : jkac104
                Department of Human Genetics, University of Utah School of Medicine , Salt Lake City, UT 84112, USA
                Author notes
                Corresponding author: Eccles Institute of Human Genetics, 15 N 2030 E, Room 5200, Salt Lake City, UT 84112, USA. Email: cchow@ 123456genetics.utah.edu
                Author information
                © The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 24 February 2022
                : 16 April 2022
                : 12 May 2022
                Page count
                Pages: 17
                Funded by: NIH NIGMS R35;
                Award ID: R35GM124780
                Funded by: Glenn Award from the Glenn Foundation for Medical Research, and the University of Utah Mario R Capecchi Endowed Chair in Genetics;
                Funded by: NIH, Interdisciplinary Training Grant T32 Program in Computational Approaches to Diabetes and Metabolism Research;
                Award ID: T32DK110966

                er stress,regulatory variation,g×e,tissue effects,genetic variation,in vivo mouse
                er stress, regulatory variation, g×e, tissue effects, genetic variation, in vivo mouse


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