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      Integrative transcription start site analysis and physiological phenotyping reveal torpor-specific expression program in mouse skeletal muscle

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          Abstract

          Mice enter an active hypometabolic state, called daily torpor when they experience a lowered caloric intake under cold ambient temperature. During torpor, the oxygen consumption rate in some animals drops to less than 30% of the normal rate without harming the body. This safe but severe reduction in metabolism is attractive for various clinical applications; however, the mechanism and molecules involved are unclear. Therefore, here we systematically analyzed the gene expression landscape on the level of the RNA transcription start sites in mouse skeletal muscles under various metabolic states to identify torpor-specific transcribed regulatory patterns. We analyzed the soleus muscles from 38 mice in torpid and non-torpid conditions and identified 287 torpor-specific promoters out of 12,862 detected promoters. Furthermore, we found that the transcription factor ATF3 is highly expressed during torpor deprivation and its binding motif is enriched in torpor-specific promoters. Atf3 was also highly expressed in the heart and brown adipose tissue during torpor and systemically knocking out Atf3 affected the torpor phenotype. Our results demonstrate that mouse torpor combined with powerful genetic tools is useful for studying active hypometabolism.

          Abstract

          Ruslan Deviatiiarov et al. identify torpor-specific promoters with an enriched binding motif and the transcription factor ATF3 highly expressed during torpor deprivation. Knocking out Atf3 affected the torpor phenotype, giving rise to molecular targets for studying and translating hypometabolic states.

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              HISAT: a fast spliced aligner with low memory requirements.

              HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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                Author and article information

                Contributors
                o.gusev.fo@juntendo.ac.jp
                genshiro.sunagawa@riken.jp
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                15 November 2021
                15 November 2021
                2021
                : 4
                : 1290
                Affiliations
                [1 ]GRID grid.77268.3c, ISNI 0000 0004 0543 9688, Regulatory Genomics Research Center, Institute of Fundamental Medicine and Biology, , Kazan Federal University, ; Volkova str.18, Kazan, Tatarstan 420012 Russian Federation
                [2 ]GRID grid.465364.6, ISNI 0000 0004 0619 9372, Endocrinology Research Center, ; Dmitriya Ul’yanova str. 11, 115478 Moscow, Russian Federation
                [3 ]GRID grid.508743.d, Laboratory for Retinal Regeneration, RIKEN Center for Biosystems Dynamics Research, ; 2-2-3 Minatojimaminami-machi, Chuo-ku, Kobe, Hyogo 650-0047 Japan
                [4 ]GRID grid.508743.d, Laboratory for Animal Resources and Genetic Engineering, RIKEN Center for Biosystems Dynamics Research, ; 2-2-3 Minatojimaminami-machi, Chuo-ku, Kobe, Hyogo 650-0047 Japan
                [5 ]GRID grid.258269.2, ISNI 0000 0004 1762 2738, Department of Regulatory Transcriptomics for Medical Genetic Diagnostics, Graduate School of Medicine, , Juntendo University, ; Tokyo, 113-8421 Japan
                [6 ]GRID grid.509459.4, ISNI 0000 0004 0472 0267, RIKEN Center for Integrative Medical Sciences, RIKEN, ; 351-0198 Yokohama, Japan
                Author information
                http://orcid.org/0000-0002-1509-8747
                http://orcid.org/0000-0002-6203-9758
                http://orcid.org/0000-0002-3952-2143
                Article
                2819
                10.1038/s42003-021-02819-2
                8592991
                34782710
                9faceb4a-4a10-497e-a918-25b9b9c940b6
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 November 2020
                : 28 October 2021
                Funding
                Funded by: Ministry of Science and Higher Education of the Russian Federation, agreement no. 075-15-2020-784
                Funded by: FundRef https://doi.org/10.13039/501100001691, MEXT | Japan Society for the Promotion of Science (JSPS);
                Award ID: 18H04706
                Award ID: 19H01066
                Award Recipient :
                Categories
                Article
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                © The Author(s) 2021

                homeostasis,transcriptomics
                homeostasis, transcriptomics

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