Co-Expression Network Analysis Identifies miRNA⁻mRNA Networks Potentially Regulating Milk Traits and Blood Metabolites
MicroRNAs (miRNA) regulate mRNA networks to coordinate cellular functions. In this study, we constructed gene co-expression networks to detect miRNA modules (clusters of miRNAs with similar expression patterns) and miRNA⁻mRNA pairs associated with blood (triacylglyceride and nonesterified fatty acid...
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description | MicroRNAs (miRNA) regulate mRNA networks to coordinate cellular functions. In this study, we constructed gene co-expression networks to detect miRNA modules (clusters of miRNAs with similar expression patterns) and miRNA⁻mRNA pairs associated with blood (triacylglyceride and nonesterified fatty acids) and milk (milk yield, fat, protein, and lactose) components and milk fatty acid traits following dietary supplementation of cows' diets with 5% linseed oil (LSO) (
= 6 cows) or 5% safflower oil (SFO) (
= 6 cows) for 28 days. Using miRNA transcriptome data from mammary tissues of cows for co-expression network analysis, we identified three consensus modules: blue, brown, and turquoise, composed of 70, 34, and 86 miRNA members, respectively. The hub miRNAs (miRNAs with the most connections with other miRNAs) were miR-30d, miR-484 and miR-16b for blue, brown, and turquoise modules, respectively. Cell cycle arrest, and p53 signaling and transforming growth factor⁻beta (TGF-β) signaling pathways were the common gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for target genes of the three modules. Protein percent (
= 0.03) correlated with the turquoise module in LSO treatment while protein yield (
= 0.003) and milk yield (
= 7 × 10
) correlated with the turquoise model, protein and milk yields and lactose percent (
< 0.05) correlated with the blue module and fat percent (
= 0.04) correlated with the brown module in SFO treatment. Several fatty acids correlated (
< 0.05) with the blue (CLA:9,11) and brown (C4:0, C12:0, C22:0, C18:1n9c and CLA:10,12) modules in LSO treatment and with the turquoise (C14:0, C18:3n3 and CLA:9,11), blue (C14:0 and C23:0) and brown (C6:0, C16:0, C22:0, C22:6n3 and CLA:10,12) modules in SFO treatment. Correlation of miRNA and mRNA data from the same animals identified the following miRNA⁻mRNA pairs: miR-183/
(
= 0.003), miR-484/
(
= 0.011) and miR-130a/
(
= 0.004) with lowest
-values for the blue, brown, and turquoise modules, respectively. Milk yield, protein yield, and protein percentage correlated (
< 0.05) with 28, 31 and 5 miRNA⁻mRNA pairs, respectively. Our results suggest that, the blue, brown, and turquoise modules miRNAs, hub miRNAs, miRNA⁻mRNA networks, cell cycle arrest GO term, p53 signaling and TGF-β signaling pathways have considerable influence on milk and blood phenotypes following dietary supplementation of dairy cows' diets with 5% LSO or 5% SFO. |
doi_str_mv | 10.3390/ijms19092500 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6164576</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2126869515</sourcerecordid><originalsourceid>FETCH-LOGICAL-c412t-e3fcec9c87f75d474d4775a87d2bebfa7f5434f763457acdd967753818b86a653</originalsourceid><addsrcrecordid>eNpdkctuFDEQRa0IlITALuvIEhsWNPjt9ibSMAoQKQ8UhbXl7nZPPHG3B9sNmSXfxd_wJXiUh4YsSlVSHV3dqgvAIUYfKFXoo1sOCSukCEdoB-xjRkiFkJAvtuY98CqlJUKEEq52wR5FmCmO1D64m4fq5G4VbUoujPDC5l8h3sLZaPw6uQRPOztm1zub4OCuLmZ_f_8ZSnsEE_wW8oYw3q_hlV1M3mQ3LuC587fwOhqXEzRjBz_5EDp4brNpgnfZptfgZW98sm8e-gH4_vnkev61Orv8cjqfnVUtwyRXlvatbVVby17yjklWSnJTy440tumN7DmjrJeCMi5N23VKlD2tcd3UwghOD8Dxve5qagbbtcVsNF6vohtMXOtgnP5_M7obvQg_tcCiSIoi8O5BIIYfk01ZDy611nsz2jAlTZDivDy6ZgV9-wxdhimWVxYKE1ELxfHG0ft7qo0hpWj7JzMY6U2kejvSgh9tH_AEP2ZI_wE0W5_l</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2126869515</pqid></control><display><type>article</type><title>Co-Expression Network Analysis Identifies miRNA⁻mRNA Networks Potentially Regulating Milk Traits and Blood Metabolites</title><source>MEDLINE</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>PubMed Central</source><creator>Ammah, Adolf A ; Do, Duy N ; Bissonnette, Nathalie ; Gévry, Nicolas ; Ibeagha-Awemu, Eveline M</creator><creatorcontrib>Ammah, Adolf A ; Do, Duy N ; Bissonnette, Nathalie ; Gévry, Nicolas ; Ibeagha-Awemu, Eveline M</creatorcontrib><description>MicroRNAs (miRNA) regulate mRNA networks to coordinate cellular functions. In this study, we constructed gene co-expression networks to detect miRNA modules (clusters of miRNAs with similar expression patterns) and miRNA⁻mRNA pairs associated with blood (triacylglyceride and nonesterified fatty acids) and milk (milk yield, fat, protein, and lactose) components and milk fatty acid traits following dietary supplementation of cows' diets with 5% linseed oil (LSO) (
= 6 cows) or 5% safflower oil (SFO) (
= 6 cows) for 28 days. Using miRNA transcriptome data from mammary tissues of cows for co-expression network analysis, we identified three consensus modules: blue, brown, and turquoise, composed of 70, 34, and 86 miRNA members, respectively. The hub miRNAs (miRNAs with the most connections with other miRNAs) were miR-30d, miR-484 and miR-16b for blue, brown, and turquoise modules, respectively. Cell cycle arrest, and p53 signaling and transforming growth factor⁻beta (TGF-β) signaling pathways were the common gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for target genes of the three modules. Protein percent (
= 0.03) correlated with the turquoise module in LSO treatment while protein yield (
= 0.003) and milk yield (
= 7 × 10
) correlated with the turquoise model, protein and milk yields and lactose percent (
< 0.05) correlated with the blue module and fat percent (
= 0.04) correlated with the brown module in SFO treatment. Several fatty acids correlated (
< 0.05) with the blue (CLA:9,11) and brown (C4:0, C12:0, C22:0, C18:1n9c and CLA:10,12) modules in LSO treatment and with the turquoise (C14:0, C18:3n3 and CLA:9,11), blue (C14:0 and C23:0) and brown (C6:0, C16:0, C22:0, C22:6n3 and CLA:10,12) modules in SFO treatment. Correlation of miRNA and mRNA data from the same animals identified the following miRNA⁻mRNA pairs: miR-183/
(
= 0.003), miR-484/
(
= 0.011) and miR-130a/
(
= 0.004) with lowest
-values for the blue, brown, and turquoise modules, respectively. Milk yield, protein yield, and protein percentage correlated (
< 0.05) with 28, 31 and 5 miRNA⁻mRNA pairs, respectively. Our results suggest that, the blue, brown, and turquoise modules miRNAs, hub miRNAs, miRNA⁻mRNA networks, cell cycle arrest GO term, p53 signaling and TGF-β signaling pathways have considerable influence on milk and blood phenotypes following dietary supplementation of dairy cows' diets with 5% LSO or 5% SFO.</description><identifier>ISSN: 1422-0067</identifier><identifier>ISSN: 1661-6596</identifier><identifier>EISSN: 1422-0067</identifier><identifier>DOI: 10.3390/ijms19092500</identifier><identifier>PMID: 30149509</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Animals ; Biomarkers ; Blood ; Cancer ; Cattle ; Cell cycle ; Computational Biology - methods ; Correlation ; Disease ; Encyclopedias ; Fatty acids ; Gene expression ; Gene Expression Profiling ; Gene Expression Regulation ; Gene Ontology ; Gene Regulatory Networks ; Genes ; Genetic Association Studies ; Genetic engineering ; Genomes ; Growth factors ; Lactose ; Linseed oil ; Meat quality ; Metabolism ; Metabolites ; Metabolome ; Metabolomics - methods ; MicroRNAs ; MicroRNAs - genetics ; Milk ; miRNA ; Network analysis ; Oils & fats ; p53 Protein ; Phenotype ; Phenotypes ; Proteins ; Quantitative Trait, Heritable ; RNA Interference ; RNA, Messenger - genetics ; Signal transduction ; Transcriptome ; Transforming growth factor-b ; Triglycerides</subject><ispartof>International journal of molecular sciences, 2018-08, Vol.19 (9), p.2500</ispartof><rights>2018. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 by her Majesty the Queen in Right of Canada as represented by the Minister of Agriculture and Agri-Food. 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c412t-e3fcec9c87f75d474d4775a87d2bebfa7f5434f763457acdd967753818b86a653</citedby><cites>FETCH-LOGICAL-c412t-e3fcec9c87f75d474d4775a87d2bebfa7f5434f763457acdd967753818b86a653</cites><orcidid>0000-0001-8810-9294 ; 0000-0003-3281-209X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164576/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164576/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30149509$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ammah, Adolf A</creatorcontrib><creatorcontrib>Do, Duy N</creatorcontrib><creatorcontrib>Bissonnette, Nathalie</creatorcontrib><creatorcontrib>Gévry, Nicolas</creatorcontrib><creatorcontrib>Ibeagha-Awemu, Eveline M</creatorcontrib><title>Co-Expression Network Analysis Identifies miRNA⁻mRNA Networks Potentially Regulating Milk Traits and Blood Metabolites</title><title>International journal of molecular sciences</title><addtitle>Int J Mol Sci</addtitle><description>MicroRNAs (miRNA) regulate mRNA networks to coordinate cellular functions. In this study, we constructed gene co-expression networks to detect miRNA modules (clusters of miRNAs with similar expression patterns) and miRNA⁻mRNA pairs associated with blood (triacylglyceride and nonesterified fatty acids) and milk (milk yield, fat, protein, and lactose) components and milk fatty acid traits following dietary supplementation of cows' diets with 5% linseed oil (LSO) (
= 6 cows) or 5% safflower oil (SFO) (
= 6 cows) for 28 days. Using miRNA transcriptome data from mammary tissues of cows for co-expression network analysis, we identified three consensus modules: blue, brown, and turquoise, composed of 70, 34, and 86 miRNA members, respectively. The hub miRNAs (miRNAs with the most connections with other miRNAs) were miR-30d, miR-484 and miR-16b for blue, brown, and turquoise modules, respectively. Cell cycle arrest, and p53 signaling and transforming growth factor⁻beta (TGF-β) signaling pathways were the common gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for target genes of the three modules. Protein percent (
= 0.03) correlated with the turquoise module in LSO treatment while protein yield (
= 0.003) and milk yield (
= 7 × 10
) correlated with the turquoise model, protein and milk yields and lactose percent (
< 0.05) correlated with the blue module and fat percent (
= 0.04) correlated with the brown module in SFO treatment. Several fatty acids correlated (
< 0.05) with the blue (CLA:9,11) and brown (C4:0, C12:0, C22:0, C18:1n9c and CLA:10,12) modules in LSO treatment and with the turquoise (C14:0, C18:3n3 and CLA:9,11), blue (C14:0 and C23:0) and brown (C6:0, C16:0, C22:0, C22:6n3 and CLA:10,12) modules in SFO treatment. Correlation of miRNA and mRNA data from the same animals identified the following miRNA⁻mRNA pairs: miR-183/
(
= 0.003), miR-484/
(
= 0.011) and miR-130a/
(
= 0.004) with lowest
-values for the blue, brown, and turquoise modules, respectively. Milk yield, protein yield, and protein percentage correlated (
< 0.05) with 28, 31 and 5 miRNA⁻mRNA pairs, respectively. Our results suggest that, the blue, brown, and turquoise modules miRNAs, hub miRNAs, miRNA⁻mRNA networks, cell cycle arrest GO term, p53 signaling and TGF-β signaling pathways have considerable influence on milk and blood phenotypes following dietary supplementation of dairy cows' diets with 5% LSO or 5% SFO.</description><subject>Animals</subject><subject>Biomarkers</subject><subject>Blood</subject><subject>Cancer</subject><subject>Cattle</subject><subject>Cell cycle</subject><subject>Computational Biology - methods</subject><subject>Correlation</subject><subject>Disease</subject><subject>Encyclopedias</subject><subject>Fatty acids</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Gene Expression Regulation</subject><subject>Gene Ontology</subject><subject>Gene Regulatory Networks</subject><subject>Genes</subject><subject>Genetic Association Studies</subject><subject>Genetic engineering</subject><subject>Genomes</subject><subject>Growth factors</subject><subject>Lactose</subject><subject>Linseed oil</subject><subject>Meat quality</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Metabolome</subject><subject>Metabolomics - methods</subject><subject>MicroRNAs</subject><subject>MicroRNAs - genetics</subject><subject>Milk</subject><subject>miRNA</subject><subject>Network analysis</subject><subject>Oils & fats</subject><subject>p53 Protein</subject><subject>Phenotype</subject><subject>Phenotypes</subject><subject>Proteins</subject><subject>Quantitative Trait, Heritable</subject><subject>RNA Interference</subject><subject>RNA, Messenger - genetics</subject><subject>Signal transduction</subject><subject>Transcriptome</subject><subject>Transforming growth factor-b</subject><subject>Triglycerides</subject><issn>1422-0067</issn><issn>1661-6596</issn><issn>1422-0067</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</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>eNpdkctuFDEQRa0IlITALuvIEhsWNPjt9ibSMAoQKQ8UhbXl7nZPPHG3B9sNmSXfxd_wJXiUh4YsSlVSHV3dqgvAIUYfKFXoo1sOCSukCEdoB-xjRkiFkJAvtuY98CqlJUKEEq52wR5FmCmO1D64m4fq5G4VbUoujPDC5l8h3sLZaPw6uQRPOztm1zub4OCuLmZ_f_8ZSnsEE_wW8oYw3q_hlV1M3mQ3LuC587fwOhqXEzRjBz_5EDp4brNpgnfZptfgZW98sm8e-gH4_vnkev61Orv8cjqfnVUtwyRXlvatbVVby17yjklWSnJTy440tumN7DmjrJeCMi5N23VKlD2tcd3UwghOD8Dxve5qagbbtcVsNF6vohtMXOtgnP5_M7obvQg_tcCiSIoi8O5BIIYfk01ZDy611nsz2jAlTZDivDy6ZgV9-wxdhimWVxYKE1ELxfHG0ft7qo0hpWj7JzMY6U2kejvSgh9tH_AEP2ZI_wE0W5_l</recordid><startdate>20180824</startdate><enddate>20180824</enddate><creator>Ammah, Adolf A</creator><creator>Do, Duy N</creator><creator>Bissonnette, Nathalie</creator><creator>Gévry, Nicolas</creator><creator>Ibeagha-Awemu, Eveline M</creator><general>MDPI AG</general><general>MDPI</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>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-8810-9294</orcidid><orcidid>https://orcid.org/0000-0003-3281-209X</orcidid></search><sort><creationdate>20180824</creationdate><title>Co-Expression Network Analysis Identifies miRNA⁻mRNA Networks Potentially Regulating Milk Traits and Blood Metabolites</title><author>Ammah, Adolf A ; Do, Duy N ; Bissonnette, Nathalie ; Gévry, Nicolas ; Ibeagha-Awemu, Eveline M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c412t-e3fcec9c87f75d474d4775a87d2bebfa7f5434f763457acdd967753818b86a653</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Animals</topic><topic>Biomarkers</topic><topic>Blood</topic><topic>Cancer</topic><topic>Cattle</topic><topic>Cell cycle</topic><topic>Computational Biology - methods</topic><topic>Correlation</topic><topic>Disease</topic><topic>Encyclopedias</topic><topic>Fatty acids</topic><topic>Gene expression</topic><topic>Gene Expression Profiling</topic><topic>Gene Expression Regulation</topic><topic>Gene Ontology</topic><topic>Gene Regulatory Networks</topic><topic>Genes</topic><topic>Genetic Association Studies</topic><topic>Genetic engineering</topic><topic>Genomes</topic><topic>Growth factors</topic><topic>Lactose</topic><topic>Linseed oil</topic><topic>Meat quality</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>Metabolome</topic><topic>Metabolomics - methods</topic><topic>MicroRNAs</topic><topic>MicroRNAs - genetics</topic><topic>Milk</topic><topic>miRNA</topic><topic>Network analysis</topic><topic>Oils & fats</topic><topic>p53 Protein</topic><topic>Phenotype</topic><topic>Phenotypes</topic><topic>Proteins</topic><topic>Quantitative Trait, Heritable</topic><topic>RNA Interference</topic><topic>RNA, Messenger - genetics</topic><topic>Signal transduction</topic><topic>Transcriptome</topic><topic>Transforming growth factor-b</topic><topic>Triglycerides</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ammah, Adolf A</creatorcontrib><creatorcontrib>Do, Duy N</creatorcontrib><creatorcontrib>Bissonnette, Nathalie</creatorcontrib><creatorcontrib>Gévry, Nicolas</creatorcontrib><creatorcontrib>Ibeagha-Awemu, Eveline M</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>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</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 Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</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>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Publicly Available Content 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>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of molecular sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ammah, Adolf A</au><au>Do, Duy N</au><au>Bissonnette, Nathalie</au><au>Gévry, Nicolas</au><au>Ibeagha-Awemu, Eveline M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Co-Expression Network Analysis Identifies miRNA⁻mRNA Networks Potentially Regulating Milk Traits and Blood Metabolites</atitle><jtitle>International journal of molecular sciences</jtitle><addtitle>Int J Mol Sci</addtitle><date>2018-08-24</date><risdate>2018</risdate><volume>19</volume><issue>9</issue><spage>2500</spage><pages>2500-</pages><issn>1422-0067</issn><issn>1661-6596</issn><eissn>1422-0067</eissn><abstract>MicroRNAs (miRNA) regulate mRNA networks to coordinate cellular functions. In this study, we constructed gene co-expression networks to detect miRNA modules (clusters of miRNAs with similar expression patterns) and miRNA⁻mRNA pairs associated with blood (triacylglyceride and nonesterified fatty acids) and milk (milk yield, fat, protein, and lactose) components and milk fatty acid traits following dietary supplementation of cows' diets with 5% linseed oil (LSO) (
= 6 cows) or 5% safflower oil (SFO) (
= 6 cows) for 28 days. Using miRNA transcriptome data from mammary tissues of cows for co-expression network analysis, we identified three consensus modules: blue, brown, and turquoise, composed of 70, 34, and 86 miRNA members, respectively. The hub miRNAs (miRNAs with the most connections with other miRNAs) were miR-30d, miR-484 and miR-16b for blue, brown, and turquoise modules, respectively. Cell cycle arrest, and p53 signaling and transforming growth factor⁻beta (TGF-β) signaling pathways were the common gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for target genes of the three modules. Protein percent (
= 0.03) correlated with the turquoise module in LSO treatment while protein yield (
= 0.003) and milk yield (
= 7 × 10
) correlated with the turquoise model, protein and milk yields and lactose percent (
< 0.05) correlated with the blue module and fat percent (
= 0.04) correlated with the brown module in SFO treatment. Several fatty acids correlated (
< 0.05) with the blue (CLA:9,11) and brown (C4:0, C12:0, C22:0, C18:1n9c and CLA:10,12) modules in LSO treatment and with the turquoise (C14:0, C18:3n3 and CLA:9,11), blue (C14:0 and C23:0) and brown (C6:0, C16:0, C22:0, C22:6n3 and CLA:10,12) modules in SFO treatment. Correlation of miRNA and mRNA data from the same animals identified the following miRNA⁻mRNA pairs: miR-183/
(
= 0.003), miR-484/
(
= 0.011) and miR-130a/
(
= 0.004) with lowest
-values for the blue, brown, and turquoise modules, respectively. Milk yield, protein yield, and protein percentage correlated (
< 0.05) with 28, 31 and 5 miRNA⁻mRNA pairs, respectively. Our results suggest that, the blue, brown, and turquoise modules miRNAs, hub miRNAs, miRNA⁻mRNA networks, cell cycle arrest GO term, p53 signaling and TGF-β signaling pathways have considerable influence on milk and blood phenotypes following dietary supplementation of dairy cows' diets with 5% LSO or 5% SFO.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>30149509</pmid><doi>10.3390/ijms19092500</doi><orcidid>https://orcid.org/0000-0001-8810-9294</orcidid><orcidid>https://orcid.org/0000-0003-3281-209X</orcidid><oa>free_for_read</oa></addata></record> |
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source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; MDPI - Multidisciplinary Digital Publishing Institute; PubMed Central |
subjects | Animals Biomarkers Blood Cancer Cattle Cell cycle Computational Biology - methods Correlation Disease Encyclopedias Fatty acids Gene expression Gene Expression Profiling Gene Expression Regulation Gene Ontology Gene Regulatory Networks Genes Genetic Association Studies Genetic engineering Genomes Growth factors Lactose Linseed oil Meat quality Metabolism Metabolites Metabolome Metabolomics - methods MicroRNAs MicroRNAs - genetics Milk miRNA Network analysis Oils & fats p53 Protein Phenotype Phenotypes Proteins Quantitative Trait, Heritable RNA Interference RNA, Messenger - genetics Signal transduction Transcriptome Transforming growth factor-b Triglycerides |
title | Co-Expression Network Analysis Identifies miRNA⁻mRNA Networks Potentially Regulating Milk Traits and Blood Metabolites |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-26T04%3A04%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Co-Expression%20Network%20Analysis%20Identifies%20miRNA%E2%81%BBmRNA%20Networks%20Potentially%20Regulating%20Milk%20Traits%20and%20Blood%20Metabolites&rft.jtitle=International%20journal%20of%20molecular%20sciences&rft.au=Ammah,%20Adolf%20A&rft.date=2018-08-24&rft.volume=19&rft.issue=9&rft.spage=2500&rft.pages=2500-&rft.issn=1422-0067&rft.eissn=1422-0067&rft_id=info:doi/10.3390/ijms19092500&rft_dat=%3Cproquest_pubme%3E2126869515%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2126869515&rft_id=info:pmid/30149509&rfr_iscdi=true |