A combination of transcriptional and microRNA regulation improves the stability of the relative concentrations of target genes.
Identifieur interne : 000208 ( PubMed/Curation ); précédent : 000207; suivant : 000209A combination of transcriptional and microRNA regulation improves the stability of the relative concentrations of target genes.
Auteurs : Andrea Riba [Italie] ; Carla Bosia [Italie] ; Mariama El Baroudi [Italie] ; Laura Ollino [Italie] ; Michele Caselle [Italie]Source :
- PLoS computational biology [ 1553-7358 ] ; 2014.
Descripteurs français
- KwdFr :
- Algorithmes, Biologie informatique, Facteur de transcription E2F1 (métabolisme), Facteurs de transcription (métabolisme), Gènes du rétinoblastome, Humains, Instabilité du génome, Maturation post-transcriptionnelle des ARN, Modèles génétiques, Processus stochastiques, Réseaux de régulation génique, microARN (génétique), microARN (métabolisme).
- MESH :
- génétique : microARN.
- métabolisme : Facteur de transcription E2F1, Facteurs de transcription, microARN.
- Algorithmes, Biologie informatique, Gènes du rétinoblastome, Humains, Instabilité du génome, Maturation post-transcriptionnelle des ARN, Modèles génétiques, Processus stochastiques, Réseaux de régulation génique.
English descriptors
- KwdEn :
- Algorithms, Computational Biology, E2F1 Transcription Factor (metabolism), Gene Regulatory Networks, Genes, Retinoblastoma, Genomic Instability, Humans, MicroRNAs (genetics), MicroRNAs (metabolism), Models, Genetic, RNA Processing, Post-Transcriptional, Stochastic Processes, Transcription Factors (metabolism).
- MESH :
- chemical , genetics : MicroRNAs.
- chemical , metabolism : E2F1 Transcription Factor, MicroRNAs, Transcription Factors.
- Algorithms, Computational Biology, Gene Regulatory Networks, Genes, Retinoblastoma, Genomic Instability, Humans, Models, Genetic, RNA Processing, Post-Transcriptional, Stochastic Processes.
Abstract
It is well known that, under suitable conditions, microRNAs are able to fine tune the relative concentration of their targets to any desired value. We show that this function is particularly effective when one of the targets is a Transcription Factor (TF) which regulates the other targets. This combination defines a new class of feed-forward loops (FFLs) in which the microRNA plays the role of master regulator. Using both deterministic and stochastic equations, we show that these FFLs are indeed able not only to fine-tune the TF/target ratio to any desired value as a function of the miRNA concentration but also, thanks to the peculiar topology of the circuit, to ensure the stability of this ratio against stochastic fluctuations. These two effects are due to the interplay between the direct transcriptional regulation and the indirect TF/Target interaction due to competition of TF and target for miRNA binding (the so called "sponge effect"). We then perform a genome wide search of these FFLs in the human regulatory network and show that they are characterized by a very peculiar enrichment pattern. In particular, they are strongly enriched in all the situations in which the TF and its target have to be precisely kept at the same concentration notwithstanding the environmental noise. As an example we discuss the FFL involving E2F1 as Transcription Factor, RB1 as target and miR-17 family as master regulator. These FFLs ensure a tight control of the E2F/RB ratio which in turns ensures the stability of the transition from the G0/G1 to the S phase in quiescent cells.
DOI: 10.1371/journal.pcbi.1003490
PubMed: 24586138
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wicri:level="1"><nlm:affiliation>Department of Physics and INFN, University of Torino, Torino, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Department of Physics and INFN, University of Torino, Torino</wicri:regionArea></affiliation></author><author><name sortKey="Caselle, Michele" sort="Caselle, Michele" uniqKey="Caselle M" first="Michele" last="Caselle">Michele Caselle</name><affiliation wicri:level="1"><nlm:affiliation>Department of Physics and INFN, University of Torino, Torino, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Department of Physics and INFN, University of Torino, Torino</wicri:regionArea></affiliation></author></analytic><series><title level="j">PLoS computational biology</title><idno type="eISSN">1553-7358</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Computational Biology</term><term>E2F1 Transcription Factor (metabolism)</term><term>Gene Regulatory Networks</term><term>Genes, Retinoblastoma</term><term>Genomic Instability</term><term>Humans</term><term>MicroRNAs (genetics)</term><term>MicroRNAs (metabolism)</term><term>Models, Genetic</term><term>RNA Processing, Post-Transcriptional</term><term>Stochastic Processes</term><term>Transcription Factors (metabolism)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Biologie informatique</term><term>Facteur de transcription E2F1 (métabolisme)</term><term>Facteurs de transcription (métabolisme)</term><term>Gènes du rétinoblastome</term><term>Humains</term><term>Instabilité du génome</term><term>Maturation post-transcriptionnelle des ARN</term><term>Modèles génétiques</term><term>Processus stochastiques</term><term>Réseaux de régulation génique</term><term>microARN (génétique)</term><term>microARN (métabolisme)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>MicroRNAs</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>E2F1 Transcription Factor</term><term>MicroRNAs</term><term>Transcription Factors</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>microARN</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Facteur de transcription E2F1</term><term>Facteurs de transcription</term><term>microARN</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Computational Biology</term><term>Gene Regulatory Networks</term><term>Genes, Retinoblastoma</term><term>Genomic Instability</term><term>Humans</term><term>Models, Genetic</term><term>RNA Processing, Post-Transcriptional</term><term>Stochastic Processes</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Biologie informatique</term><term>Gènes du rétinoblastome</term><term>Humains</term><term>Instabilité du génome</term><term>Maturation post-transcriptionnelle des ARN</term><term>Modèles génétiques</term><term>Processus stochastiques</term><term>Réseaux de régulation génique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">It is well known that, under suitable conditions, microRNAs are able to fine tune the relative concentration of their targets to any desired value. We show that this function is particularly effective when one of the targets is a Transcription Factor (TF) which regulates the other targets. This combination defines a new class of feed-forward loops (FFLs) in which the microRNA plays the role of master regulator. Using both deterministic and stochastic equations, we show that these FFLs are indeed able not only to fine-tune the TF/target ratio to any desired value as a function of the miRNA concentration but also, thanks to the peculiar topology of the circuit, to ensure the stability of this ratio against stochastic fluctuations. These two effects are due to the interplay between the direct transcriptional regulation and the indirect TF/Target interaction due to competition of TF and target for miRNA binding (the so called "sponge effect"). We then perform a genome wide search of these FFLs in the human regulatory network and show that they are characterized by a very peculiar enrichment pattern. In particular, they are strongly enriched in all the situations in which the TF and its target have to be precisely kept at the same concentration notwithstanding the environmental noise. As an example we discuss the FFL involving E2F1 as Transcription Factor, RB1 as target and miR-17 family as master regulator. These FFLs ensure a tight control of the E2F/RB ratio which in turns ensures the stability of the transition from the G0/G1 to the S phase in quiescent cells.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">24586138</PMID><DateCreated><Year>2014</Year><Month>03</Month><Day>03</Day></DateCreated><DateCompleted><Year>2015</Year><Month>05</Month><Day>11</Day></DateCompleted><DateRevised><Year>2015</Year><Month>05</Month><Day>15</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1553-7358</ISSN><JournalIssue CitedMedium="Internet"><Volume>10</Volume><Issue>2</Issue><PubDate><Year>2014</Year><Month>Feb</Month></PubDate></JournalIssue><Title>PLoS computational biology</Title><ISOAbbreviation>PLoS Comput. Biol.</ISOAbbreviation></Journal><ArticleTitle>A combination of transcriptional and microRNA regulation improves the stability of the relative concentrations of target genes.</ArticleTitle><Pagination><MedlinePgn>e1003490</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pcbi.1003490</ELocationID><Abstract><AbstractText>It is well known that, under suitable conditions, microRNAs are able to fine tune the relative concentration of their targets to any desired value. We show that this function is particularly effective when one of the targets is a Transcription Factor (TF) which regulates the other targets. This combination defines a new class of feed-forward loops (FFLs) in which the microRNA plays the role of master regulator. Using both deterministic and stochastic equations, we show that these FFLs are indeed able not only to fine-tune the TF/target ratio to any desired value as a function of the miRNA concentration but also, thanks to the peculiar topology of the circuit, to ensure the stability of this ratio against stochastic fluctuations. These two effects are due to the interplay between the direct transcriptional regulation and the indirect TF/Target interaction due to competition of TF and target for miRNA binding (the so called "sponge effect"). We then perform a genome wide search of these FFLs in the human regulatory network and show that they are characterized by a very peculiar enrichment pattern. In particular, they are strongly enriched in all the situations in which the TF and its target have to be precisely kept at the same concentration notwithstanding the environmental noise. As an example we discuss the FFL involving E2F1 as Transcription Factor, RB1 as target and miR-17 family as master regulator. These FFLs ensure a tight control of the E2F/RB ratio which in turns ensures the stability of the transition from the G0/G1 to the S phase in quiescent cells.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Riba</LastName><ForeName>Andrea</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Department of Physics and INFN, University of Torino, Torino, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bosia</LastName><ForeName>Carla</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Department of Physics and INFN, University of Torino, Torino, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>El Baroudi</LastName><ForeName>Mariama</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>National Research Council (CNR), Institute of Informatics and Telematics (IIT) and Institute of Clinical Physiology (IFC), Laboratory for Integrative System Medicine (LISM), Pisa, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ollino</LastName><ForeName>Laura</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of Physics and INFN, University of Torino, Torino, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Caselle</LastName><ForeName>Michele</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Physics and INFN, University of Torino, Torino, Italy.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>02</Month><Day>27</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS Comput 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Mar;10(3):e1003582</RefSource></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D019295">Computational Biology</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D050687">E2F1 Transcription Factor</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000378">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D053263">Gene Regulatory Networks</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D016161">Genes, Retinoblastoma</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D042822">Genomic Instability</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D035683">MicroRNAs</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000235">genetics</QualifierName><QualifierName MajorTopicYN="N" UI="Q000378">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D008957">Models, Genetic</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012323">RNA Processing, Post-Transcriptional</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D013269">Stochastic Processes</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D014157">Transcription Factors</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000378">metabolism</QualifierName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC3937125</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="ecollection"><Year>2014</Year><Month>2</Month><Day></Day></PubMedPubDate><PubMedPubDate 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|texte= A combination of transcriptional and microRNA regulation improves the stability of the relative concentrations of target genes.
}}
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| HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Curation/biblio.hfd \
| NlmPubMed2Wicri -a TelematiV1
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