RNA sequencing reveals potential interacting networks between the altered transcriptome and ncRNome in the skeletal muscle of diabetic mice
For a global epidemic like Type 2 diabetes mellitus (T2DM), while impaired gene regulation is identified as a primary cause of aberrant cellular physiology; in the past few years, non-coding RNAs (ncRNAs) have emerged as important regulators of cellular metabolism. However, there are no reports of c...
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| description | For a global epidemic like Type 2 diabetes mellitus (T2DM), while impaired gene regulation is identified as a primary cause of aberrant cellular physiology; in the past few years, non-coding RNAs (ncRNAs) have emerged as important regulators of cellular metabolism. However, there are no reports of comprehensive in-depth cross-talk between these regulatory elements and the potential consequences in the skeletal muscle during diabetes. Here, using RNA sequencing, we identified 465 mRNAs and 12 long non-coding RNAs (lncRNAs), to be differentially regulated in the skeletal muscle of diabetic mice and pathway enrichment analysis of these altered transcripts revealed pathways of insulin, FOXO and AMP-activated protein kinase (AMPK) signaling to be majorly over-represented. Construction of networks showed that these pathways significantly interact with each other that might underlie aberrant skeletal muscle metabolism during diabetes. Gene-gene interaction network depicted strong interactions among several differentially expressed genes (DEGs) namely, Prkab2, Irs1, Pfkfb3, Socs2 etc. Seven altered lncRNAs depicted multiple interactions with the altered transcripts, suggesting possible regulatory roles of these lncRNAs. Inverse patterns of expression were observed between several of the deregulated microRNAs (miRNAs) and the differentially expressed transcripts in the tissues. Towards validation, overexpression of miR-381-3p and miR-539-5p in skeletal muscle C2C12 cells significantly decreased the transcript levels of their targets, Nfkbia, Pik3r1 and Pi3kr1, Cdkn2d, respectively. Collectively, the findings provide a comprehensive understanding of the interactions and cross-talk between the ncRNome and transcriptome in the skeletal muscle during diabetes and put forth potential therapeutic options for improving insulin sensitivity. |
| doi_str_mv | 10.1042/BSR20210495 |
| format | Article |
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However, there are no reports of comprehensive in-depth cross-talk between these regulatory elements and the potential consequences in the skeletal muscle during diabetes. Here, using RNA sequencing, we identified 465 mRNAs and 12 long non-coding RNAs (lncRNAs), to be differentially regulated in the skeletal muscle of diabetic mice and pathway enrichment analysis of these altered transcripts revealed pathways of insulin, FOXO and AMP-activated protein kinase (AMPK) signaling to be majorly over-represented. Construction of networks showed that these pathways significantly interact with each other that might underlie aberrant skeletal muscle metabolism during diabetes. Gene-gene interaction network depicted strong interactions among several differentially expressed genes (DEGs) namely, Prkab2, Irs1, Pfkfb3, Socs2 etc. Seven altered lncRNAs depicted multiple interactions with the altered transcripts, suggesting possible regulatory roles of these lncRNAs. Inverse patterns of expression were observed between several of the deregulated microRNAs (miRNAs) and the differentially expressed transcripts in the tissues. Towards validation, overexpression of miR-381-3p and miR-539-5p in skeletal muscle C2C12 cells significantly decreased the transcript levels of their targets, Nfkbia, Pik3r1 and Pi3kr1, Cdkn2d, respectively. Collectively, the findings provide a comprehensive understanding of the interactions and cross-talk between the ncRNome and transcriptome in the skeletal muscle during diabetes and put forth potential therapeutic options for improving insulin sensitivity.</description><identifier>ISSN: 0144-8463</identifier><identifier>EISSN: 1573-4935</identifier><identifier>DOI: 10.1042/BSR20210495</identifier><identifier>PMID: 34190986</identifier><language>eng</language><publisher>England: Portland Press Ltd The Biochemical Society</publisher><subject>AMP-activated protein kinase ; Animals ; Biology ; Cell Line ; Deregulation ; Diabetes ; Diabetes & Metabolic Disorders ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus - genetics ; Diabetes Mellitus - metabolism ; Disease Models, Animal ; Epigenetics ; Experiments ; Forkhead protein ; Gene Expression Profiling ; Gene regulation ; Gene Regulatory Networks ; Gene sequencing ; Genes ; Genomics ; Glucose ; Human subjects ; Insulin ; Insulin - metabolism ; Insulin resistance ; Insulin Resistance - genetics ; Kinases ; Male ; Metabolism ; Metabolites ; Mice ; Mice, Inbred C57BL ; MicroRNAs ; MicroRNAs - genetics ; MicroRNAs - metabolism ; miRNA ; Molecular Bases of Health & Disease ; Muscle, Skeletal - metabolism ; Muscles ; Musculoskeletal system ; Myogenesis ; Non-coding RNA ; Physiology ; Regulatory sequences ; Ribonucleic acid ; RNA ; RNA, Long Noncoding - genetics ; RNA, Long Noncoding - metabolism ; RNA, Messenger - genetics ; RNA, Messenger - metabolism ; RNA-Seq ; Signal Transduction ; Skeletal muscle ; Transcriptome ; Transcriptomes</subject><ispartof>Bioscience reports, 2021-07, Vol.41 (7)</ispartof><rights>2021 The Author(s).</rights><rights>2021. 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However, there are no reports of comprehensive in-depth cross-talk between these regulatory elements and the potential consequences in the skeletal muscle during diabetes. Here, using RNA sequencing, we identified 465 mRNAs and 12 long non-coding RNAs (lncRNAs), to be differentially regulated in the skeletal muscle of diabetic mice and pathway enrichment analysis of these altered transcripts revealed pathways of insulin, FOXO and AMP-activated protein kinase (AMPK) signaling to be majorly over-represented. Construction of networks showed that these pathways significantly interact with each other that might underlie aberrant skeletal muscle metabolism during diabetes. Gene-gene interaction network depicted strong interactions among several differentially expressed genes (DEGs) namely, Prkab2, Irs1, Pfkfb3, Socs2 etc. Seven altered lncRNAs depicted multiple interactions with the altered transcripts, suggesting possible regulatory roles of these lncRNAs. Inverse patterns of expression were observed between several of the deregulated microRNAs (miRNAs) and the differentially expressed transcripts in the tissues. Towards validation, overexpression of miR-381-3p and miR-539-5p in skeletal muscle C2C12 cells significantly decreased the transcript levels of their targets, Nfkbia, Pik3r1 and Pi3kr1, Cdkn2d, respectively. Collectively, the findings provide a comprehensive understanding of the interactions and cross-talk between the ncRNome and transcriptome in the skeletal muscle during diabetes and put forth potential therapeutic options for improving insulin sensitivity.</description><subject>AMP-activated protein kinase</subject><subject>Animals</subject><subject>Biology</subject><subject>Cell Line</subject><subject>Deregulation</subject><subject>Diabetes</subject><subject>Diabetes & Metabolic Disorders</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus - genetics</subject><subject>Diabetes Mellitus - metabolism</subject><subject>Disease Models, Animal</subject><subject>Epigenetics</subject><subject>Experiments</subject><subject>Forkhead protein</subject><subject>Gene Expression Profiling</subject><subject>Gene regulation</subject><subject>Gene Regulatory Networks</subject><subject>Gene sequencing</subject><subject>Genes</subject><subject>Genomics</subject><subject>Glucose</subject><subject>Human subjects</subject><subject>Insulin</subject><subject>Insulin - metabolism</subject><subject>Insulin resistance</subject><subject>Insulin Resistance - genetics</subject><subject>Kinases</subject><subject>Male</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Mice</subject><subject>Mice, Inbred C57BL</subject><subject>MicroRNAs</subject><subject>MicroRNAs - genetics</subject><subject>MicroRNAs - metabolism</subject><subject>miRNA</subject><subject>Molecular Bases of Health & Disease</subject><subject>Muscle, Skeletal - metabolism</subject><subject>Muscles</subject><subject>Musculoskeletal system</subject><subject>Myogenesis</subject><subject>Non-coding RNA</subject><subject>Physiology</subject><subject>Regulatory sequences</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>RNA, Long Noncoding - genetics</subject><subject>RNA, Long Noncoding - metabolism</subject><subject>RNA, Messenger - genetics</subject><subject>RNA, Messenger - metabolism</subject><subject>RNA-Seq</subject><subject>Signal Transduction</subject><subject>Skeletal muscle</subject><subject>Transcriptome</subject><subject>Transcriptomes</subject><issn>0144-8463</issn><issn>1573-4935</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpdkVtrFTEUhYNY7Gn1yXcJ-CKUqblNMnkRavEGpcJRn0Mms6dNO5OcJpmKv8E_bQ6nlrZPWWR_rH1ZCL2m5JgSwd5__LFmhFWp22doRVvFG6F5-xytCBWi6YTk--gg5ytCKsPFC7TPBdVEd3KF_q7PT3CGmwWC8-ECJ7gFO2W8iQVC8XbCPhRI1pVtNUD5HdN1xn0VAAGXS8B2qgAMuCQbskt-U-Jcf8OAg1ufb7XfgfkaJijVcl6ymwDHEQ_eVivv8OwdvER7Y-0Nr-7eQ_Tr86efp1-bs-9fvp2enDVOEF0aN4CWYhz5wCVXgtNB90K2_WB1p9hohZO0V1p1lHCu3UAU08CpE9oSUNzyQ_Rh57tZ-hkGVxdNdjKb5Geb_phovXlcCf7SXMRb0zEl69mqwbs7gxTr5XIxs88OpskGiEs2rBVSK9mJtqJvn6BXcUmhrmeY5kpTxSSv1NGOcinmnGC8H4YSsw3ZPAi50m8ezn_P_k-V_wPcnaTE</recordid><startdate>20210730</startdate><enddate>20210730</enddate><creator>Kesharwani, Devesh</creator><creator>Kumar, Amit</creator><creator>Poojary, Mukta</creator><creator>Scaria, Vinod</creator><creator>Datta, Malabika</creator><general>Portland Press Ltd The Biochemical Society</general><general>Portland Press Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7QO</scope><scope>7T7</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M2P</scope><scope>M7N</scope><scope>M7P</scope><scope>MBDVC</scope><scope>P64</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-2559-9991</orcidid></search><sort><creationdate>20210730</creationdate><title>RNA sequencing reveals potential interacting networks between the altered transcriptome and ncRNome in the skeletal muscle of diabetic mice</title><author>Kesharwani, Devesh ; Kumar, Amit ; Poojary, Mukta ; Scaria, Vinod ; Datta, Malabika</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c409t-cde964ff3d3637431d9b465bda9872fa4c61b797810339cd0729e31c49a0e73a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>AMP-activated protein kinase</topic><topic>Animals</topic><topic>Biology</topic><topic>Cell Line</topic><topic>Deregulation</topic><topic>Diabetes</topic><topic>Diabetes & Metabolic Disorders</topic><topic>Diabetes mellitus</topic><topic>Diabetes mellitus (non-insulin dependent)</topic><topic>Diabetes Mellitus - genetics</topic><topic>Diabetes Mellitus - metabolism</topic><topic>Disease Models, Animal</topic><topic>Epigenetics</topic><topic>Experiments</topic><topic>Forkhead protein</topic><topic>Gene Expression Profiling</topic><topic>Gene regulation</topic><topic>Gene Regulatory Networks</topic><topic>Gene sequencing</topic><topic>Genes</topic><topic>Genomics</topic><topic>Glucose</topic><topic>Human subjects</topic><topic>Insulin</topic><topic>Insulin - metabolism</topic><topic>Insulin resistance</topic><topic>Insulin Resistance - genetics</topic><topic>Kinases</topic><topic>Male</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>Mice</topic><topic>Mice, Inbred C57BL</topic><topic>MicroRNAs</topic><topic>MicroRNAs - genetics</topic><topic>MicroRNAs - metabolism</topic><topic>miRNA</topic><topic>Molecular Bases of Health & Disease</topic><topic>Muscle, Skeletal - metabolism</topic><topic>Muscles</topic><topic>Musculoskeletal system</topic><topic>Myogenesis</topic><topic>Non-coding RNA</topic><topic>Physiology</topic><topic>Regulatory sequences</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>RNA, Long Noncoding - genetics</topic><topic>RNA, Long Noncoding - metabolism</topic><topic>RNA, Messenger - genetics</topic><topic>RNA, Messenger - metabolism</topic><topic>RNA-Seq</topic><topic>Signal Transduction</topic><topic>Skeletal muscle</topic><topic>Transcriptome</topic><topic>Transcriptomes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kesharwani, Devesh</creatorcontrib><creatorcontrib>Kumar, Amit</creatorcontrib><creatorcontrib>Poojary, Mukta</creatorcontrib><creatorcontrib>Scaria, Vinod</creatorcontrib><creatorcontrib>Datta, Malabika</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioscience reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kesharwani, Devesh</au><au>Kumar, Amit</au><au>Poojary, Mukta</au><au>Scaria, Vinod</au><au>Datta, Malabika</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>RNA sequencing reveals potential interacting networks between the altered transcriptome and ncRNome in the skeletal muscle of diabetic mice</atitle><jtitle>Bioscience reports</jtitle><addtitle>Biosci Rep</addtitle><date>2021-07-30</date><risdate>2021</risdate><volume>41</volume><issue>7</issue><issn>0144-8463</issn><eissn>1573-4935</eissn><abstract>For a global epidemic like Type 2 diabetes mellitus (T2DM), while impaired gene regulation is identified as a primary cause of aberrant cellular physiology; in the past few years, non-coding RNAs (ncRNAs) have emerged as important regulators of cellular metabolism. However, there are no reports of comprehensive in-depth cross-talk between these regulatory elements and the potential consequences in the skeletal muscle during diabetes. Here, using RNA sequencing, we identified 465 mRNAs and 12 long non-coding RNAs (lncRNAs), to be differentially regulated in the skeletal muscle of diabetic mice and pathway enrichment analysis of these altered transcripts revealed pathways of insulin, FOXO and AMP-activated protein kinase (AMPK) signaling to be majorly over-represented. Construction of networks showed that these pathways significantly interact with each other that might underlie aberrant skeletal muscle metabolism during diabetes. Gene-gene interaction network depicted strong interactions among several differentially expressed genes (DEGs) namely, Prkab2, Irs1, Pfkfb3, Socs2 etc. Seven altered lncRNAs depicted multiple interactions with the altered transcripts, suggesting possible regulatory roles of these lncRNAs. Inverse patterns of expression were observed between several of the deregulated microRNAs (miRNAs) and the differentially expressed transcripts in the tissues. Towards validation, overexpression of miR-381-3p and miR-539-5p in skeletal muscle C2C12 cells significantly decreased the transcript levels of their targets, Nfkbia, Pik3r1 and Pi3kr1, Cdkn2d, respectively. Collectively, the findings provide a comprehensive understanding of the interactions and cross-talk between the ncRNome and transcriptome in the skeletal muscle during diabetes and put forth potential therapeutic options for improving insulin sensitivity.</abstract><cop>England</cop><pub>Portland Press Ltd The Biochemical Society</pub><pmid>34190986</pmid><doi>10.1042/BSR20210495</doi><orcidid>https://orcid.org/0000-0002-2559-9991</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | AMP-activated protein kinase Animals Biology Cell Line Deregulation Diabetes Diabetes & Metabolic Disorders Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetes Mellitus - genetics Diabetes Mellitus - metabolism Disease Models, Animal Epigenetics Experiments Forkhead protein Gene Expression Profiling Gene regulation Gene Regulatory Networks Gene sequencing Genes Genomics Glucose Human subjects Insulin Insulin - metabolism Insulin resistance Insulin Resistance - genetics Kinases Male Metabolism Metabolites Mice Mice, Inbred C57BL MicroRNAs MicroRNAs - genetics MicroRNAs - metabolism miRNA Molecular Bases of Health & Disease Muscle, Skeletal - metabolism Muscles Musculoskeletal system Myogenesis Non-coding RNA Physiology Regulatory sequences Ribonucleic acid RNA RNA, Long Noncoding - genetics RNA, Long Noncoding - metabolism RNA, Messenger - genetics RNA, Messenger - metabolism RNA-Seq Signal Transduction Skeletal muscle Transcriptome Transcriptomes |
| title | RNA sequencing reveals potential interacting networks between the altered transcriptome and ncRNome in the skeletal muscle of diabetic mice |
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