Computational Systems Biology Approach Predicts Regulators and Targets of microRNAs and Their Genomic Hotspots in Apoptosis Process
Novel computational systems biology tools such as common targets analysis, common regulators analysis, pathway discovery, and transcriptomic-based hotspot discovery provide new opportunities in understanding of apoptosis molecular mechanisms. In this study, after measuring the global contribution of...
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| Veröffentlicht in: | Molecular biotechnology 2016-07, Vol.58 (7), p.460-479 |
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| description | Novel computational systems biology tools such as common targets analysis, common regulators analysis, pathway discovery, and transcriptomic-based hotspot discovery provide new opportunities in understanding of apoptosis molecular mechanisms. In this study, after measuring the global contribution of microRNAs in the course of apoptosis by Affymetrix platform, systems biology tools were utilized to obtain a comprehensive view on the role of microRNAs in apoptosis process. Network analysis and pathway discovery highlighted the crosstalk between transcription factors and microRNAs in apoptosis. Within the transcription factors,
PRDM1
showed the highest upregulation during the course of apoptosis, with more than 9-fold expression increase compared to non-apoptotic condition. Within the microRNAs, MIR1208 showed the highest expression in non-apoptotic condition and downregulated by more than 6 fold during apoptosis. Common regulators algorithm showed that
TNF
receptor is the key upstream regulator with a high number of regulatory interactions with the differentially expressed microRNAs.
BCL2
and
AKT1
were the key downstream targets of differentially expressed microRNAs. Enrichment analysis of the genomic locations of differentially expressed microRNAs led us to the discovery of chromosome bands which were highly enriched (
p
|
| doi_str_mv | 10.1007/s12033-016-9938-x |
| format | Article |
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PRDM1
showed the highest upregulation during the course of apoptosis, with more than 9-fold expression increase compared to non-apoptotic condition. Within the microRNAs, MIR1208 showed the highest expression in non-apoptotic condition and downregulated by more than 6 fold during apoptosis. Common regulators algorithm showed that
TNF
receptor is the key upstream regulator with a high number of regulatory interactions with the differentially expressed microRNAs.
BCL2
and
AKT1
were the key downstream targets of differentially expressed microRNAs. Enrichment analysis of the genomic locations of differentially expressed microRNAs led us to the discovery of chromosome bands which were highly enriched (
p
< 0.01) with the apoptosis-related microRNAs, such as 13q31.3, 19p13.13, and Xq27.3 This study opens a new avenue in understanding regulatory mechanisms and downstream functions in the course of apoptosis as well as distinguishing genomic-enriched hotspots for apoptosis process.</description><identifier>ISSN: 1073-6085</identifier><identifier>EISSN: 1559-0305</identifier><identifier>DOI: 10.1007/s12033-016-9938-x</identifier><identifier>PMID: 27178576</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Apoptosis ; Apoptosis - drug effects ; Biochemistry ; Biological Techniques ; Biotechnology ; Cell Biology ; Cell Line ; Chemistry ; Chemistry and Materials Science ; Epidermal Growth Factor - pharmacology ; Genomics ; Genomics - methods ; Human Genetics ; Humans ; MicroRNAs ; MicroRNAs - genetics ; Original Paper ; Positive Regulatory Domain I-Binding Factor 1 ; Protein Science ; Proteomics - methods ; Repressor Proteins - genetics ; Systems Biology ; Transcription Factors - genetics ; Transcriptome - drug effects</subject><ispartof>Molecular biotechnology, 2016-07, Vol.58 (7), p.460-479</ispartof><rights>Springer Science+Business Media New York 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c405t-4228041f54a99dc46068fb84a56db09a386be2d4a5745cc9eb1c70d31d65a2f53</citedby><cites>FETCH-LOGICAL-c405t-4228041f54a99dc46068fb84a56db09a386be2d4a5745cc9eb1c70d31d65a2f53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12033-016-9938-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12033-016-9938-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27178576$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Alanazi, Ibrahim O.</creatorcontrib><creatorcontrib>Ebrahimie, Esmaeil</creatorcontrib><title>Computational Systems Biology Approach Predicts Regulators and Targets of microRNAs and Their Genomic Hotspots in Apoptosis Process</title><title>Molecular biotechnology</title><addtitle>Mol Biotechnol</addtitle><addtitle>Mol Biotechnol</addtitle><description>Novel computational systems biology tools such as common targets analysis, common regulators analysis, pathway discovery, and transcriptomic-based hotspot discovery provide new opportunities in understanding of apoptosis molecular mechanisms. In this study, after measuring the global contribution of microRNAs in the course of apoptosis by Affymetrix platform, systems biology tools were utilized to obtain a comprehensive view on the role of microRNAs in apoptosis process. Network analysis and pathway discovery highlighted the crosstalk between transcription factors and microRNAs in apoptosis. Within the transcription factors,
PRDM1
showed the highest upregulation during the course of apoptosis, with more than 9-fold expression increase compared to non-apoptotic condition. Within the microRNAs, MIR1208 showed the highest expression in non-apoptotic condition and downregulated by more than 6 fold during apoptosis. Common regulators algorithm showed that
TNF
receptor is the key upstream regulator with a high number of regulatory interactions with the differentially expressed microRNAs.
BCL2
and
AKT1
were the key downstream targets of differentially expressed microRNAs. Enrichment analysis of the genomic locations of differentially expressed microRNAs led us to the discovery of chromosome bands which were highly enriched (
p
< 0.01) with the apoptosis-related microRNAs, such as 13q31.3, 19p13.13, and Xq27.3 This study opens a new avenue in understanding regulatory mechanisms and downstream functions in the course of apoptosis as well as distinguishing genomic-enriched hotspots for apoptosis process.</description><subject>Algorithms</subject><subject>Apoptosis</subject><subject>Apoptosis - drug effects</subject><subject>Biochemistry</subject><subject>Biological Techniques</subject><subject>Biotechnology</subject><subject>Cell Biology</subject><subject>Cell Line</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Epidermal Growth Factor - pharmacology</subject><subject>Genomics</subject><subject>Genomics - methods</subject><subject>Human Genetics</subject><subject>Humans</subject><subject>MicroRNAs</subject><subject>MicroRNAs - genetics</subject><subject>Original Paper</subject><subject>Positive Regulatory Domain I-Binding Factor 1</subject><subject>Protein Science</subject><subject>Proteomics - methods</subject><subject>Repressor Proteins - genetics</subject><subject>Systems Biology</subject><subject>Transcription Factors - genetics</subject><subject>Transcriptome - drug effects</subject><issn>1073-6085</issn><issn>1559-0305</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkU9v1DAQxSMEoqXwAbggS1y4BMaJ_x6XFbRIFaBSzpbjONtUSRw8idQ988WZsgtCSIiDZXvmN8_ye0XxnMNrDqDfIK-grkvgqrS2NuXdg-KUS2lLqEE-pDPoulRg5EnxBPEWoOJS1I-Lk0pzbaRWp8X3bRrndfFLnyY_sC97XOKI7G2fhrTbs8085-TDDfucY9uHBdlV3K2DX1JG5qeWXfu8i1ROHRv7kNPVx82xcRP7zM7jlKjOLtKCMy3WT6SZ5iVhjySaQkR8Wjzq_IDx2XE_K76-f3e9vSgvP51_2G4uyyBALqWoKgOCd1J4a9sgFCjTNUZ4qdoGrK-NamLV0l0LGYKNDQ8a2pq3Svqqk_VZ8eqgS3_6tkZc3NhjiMPgp5hWdNyAUZwsqv6PaiuNFhYEoS__Qm_TmsnMn5TQlIcwRPEDRR4h5ti5Ofejz3vHwd2H6Q5hOgrT3Yfp7mjmxVF5bcbY_p74lR4B1QFAak27mP94-p-qPwARP6st</recordid><startdate>20160701</startdate><enddate>20160701</enddate><creator>Alanazi, Ibrahim O.</creator><creator>Ebrahimie, Esmaeil</creator><general>Springer US</general><general>Springer Nature B.V</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>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</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>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20160701</creationdate><title>Computational Systems Biology Approach Predicts Regulators and Targets of microRNAs and Their Genomic Hotspots in Apoptosis Process</title><author>Alanazi, Ibrahim O. ; Ebrahimie, Esmaeil</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-4228041f54a99dc46068fb84a56db09a386be2d4a5745cc9eb1c70d31d65a2f53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Apoptosis</topic><topic>Apoptosis - drug effects</topic><topic>Biochemistry</topic><topic>Biological Techniques</topic><topic>Biotechnology</topic><topic>Cell Biology</topic><topic>Cell Line</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Epidermal Growth Factor - pharmacology</topic><topic>Genomics</topic><topic>Genomics - methods</topic><topic>Human Genetics</topic><topic>Humans</topic><topic>MicroRNAs</topic><topic>MicroRNAs - genetics</topic><topic>Original Paper</topic><topic>Positive Regulatory Domain I-Binding Factor 1</topic><topic>Protein Science</topic><topic>Proteomics - methods</topic><topic>Repressor Proteins - genetics</topic><topic>Systems Biology</topic><topic>Transcription Factors - genetics</topic><topic>Transcriptome - drug effects</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alanazi, Ibrahim O.</creatorcontrib><creatorcontrib>Ebrahimie, Esmaeil</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>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</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>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Molecular biotechnology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alanazi, Ibrahim O.</au><au>Ebrahimie, Esmaeil</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computational Systems Biology Approach Predicts Regulators and Targets of microRNAs and Their Genomic Hotspots in Apoptosis Process</atitle><jtitle>Molecular biotechnology</jtitle><stitle>Mol Biotechnol</stitle><addtitle>Mol Biotechnol</addtitle><date>2016-07-01</date><risdate>2016</risdate><volume>58</volume><issue>7</issue><spage>460</spage><epage>479</epage><pages>460-479</pages><issn>1073-6085</issn><eissn>1559-0305</eissn><abstract>Novel computational systems biology tools such as common targets analysis, common regulators analysis, pathway discovery, and transcriptomic-based hotspot discovery provide new opportunities in understanding of apoptosis molecular mechanisms. In this study, after measuring the global contribution of microRNAs in the course of apoptosis by Affymetrix platform, systems biology tools were utilized to obtain a comprehensive view on the role of microRNAs in apoptosis process. Network analysis and pathway discovery highlighted the crosstalk between transcription factors and microRNAs in apoptosis. Within the transcription factors,
PRDM1
showed the highest upregulation during the course of apoptosis, with more than 9-fold expression increase compared to non-apoptotic condition. Within the microRNAs, MIR1208 showed the highest expression in non-apoptotic condition and downregulated by more than 6 fold during apoptosis. Common regulators algorithm showed that
TNF
receptor is the key upstream regulator with a high number of regulatory interactions with the differentially expressed microRNAs.
BCL2
and
AKT1
were the key downstream targets of differentially expressed microRNAs. Enrichment analysis of the genomic locations of differentially expressed microRNAs led us to the discovery of chromosome bands which were highly enriched (
p
< 0.01) with the apoptosis-related microRNAs, such as 13q31.3, 19p13.13, and Xq27.3 This study opens a new avenue in understanding regulatory mechanisms and downstream functions in the course of apoptosis as well as distinguishing genomic-enriched hotspots for apoptosis process.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>27178576</pmid><doi>10.1007/s12033-016-9938-x</doi><tpages>20</tpages></addata></record> |
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| subjects | Algorithms Apoptosis Apoptosis - drug effects Biochemistry Biological Techniques Biotechnology Cell Biology Cell Line Chemistry Chemistry and Materials Science Epidermal Growth Factor - pharmacology Genomics Genomics - methods Human Genetics Humans MicroRNAs MicroRNAs - genetics Original Paper Positive Regulatory Domain I-Binding Factor 1 Protein Science Proteomics - methods Repressor Proteins - genetics Systems Biology Transcription Factors - genetics Transcriptome - drug effects |
| title | Computational Systems Biology Approach Predicts Regulators and Targets of microRNAs and Their Genomic Hotspots in Apoptosis Process |
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