CovidFranceV1, Main, Exploration, bibRecord, 000428

The current landscape of coronavirus-host protein-protein interactions.

Identifieur interne : 000428 ( Main/Exploration ); précédent : 000427; suivant : 000429

The current landscape of coronavirus-host protein-protein interactions.

Auteurs : Laure Perrin-Cocon [France] ; Olivier Diaz [France] ; Clémence Jacquemin [France] ; Valentine Barthel [France] ; Eva Ogire [France] ; Christophe Ramière [France] ; Patrice André [France] ; Vincent Lotteau [France] ; Pierre-Olivier Vidalain [France]

Source :

Journal of translational medicine [ 1479-5876 ] ; 2020.

RBID : pubmed:32811513

Descripteurs français

KwdFr :
- Animaux (MeSH), Bases de données de protéines (MeSH), Betacoronavirus (physiologie), Cartes d'interactions protéiques (MeSH), Coronavirus (composition chimique), Coronavirus (métabolisme), Facteurs de transcription (métabolisme), Humains (MeSH), Infections à coronavirus (métabolisme), Infections à coronavirus (virologie), Interactions hôte-pathogène (physiologie), Pandémies (MeSH), Pneumopathie virale (métabolisme), Pneumopathie virale (virologie), Protéines mitochondriales (métabolisme), Protéines virales (métabolisme), Réplication virale (génétique).
MESH :
- composition chimique : Coronavirus.
- génétique : Réplication virale.
- métabolisme : Coronavirus, Facteurs de transcription, Infections à coronavirus, Pneumopathie virale, Protéines mitochondriales, Protéines virales.
- physiologie : Betacoronavirus, Interactions hôte-pathogène.
- virologie : Infections à coronavirus, Pneumopathie virale.
- Animaux, Bases de données de protéines, Cartes d'interactions protéiques, Humains, Pandémies.

English descriptors

KwdEn :
- Animals (MeSH), Betacoronavirus (physiology), Coronavirus (chemistry), Coronavirus (metabolism), Coronavirus Infections (metabolism), Coronavirus Infections (virology), Databases, Protein (MeSH), Host-Pathogen Interactions (physiology), Humans (MeSH), Mitochondrial Proteins (metabolism), Pandemics (MeSH), Pneumonia, Viral (metabolism), Pneumonia, Viral (virology), Protein Interaction Maps (MeSH), Transcription Factors (metabolism), Viral Proteins (metabolism), Virus Replication (genetics).
MESH :
- chemical , metabolism : Mitochondrial Proteins, Transcription Factors, Viral Proteins.
- chemistry : Coronavirus.
- genetics : Virus Replication.
- metabolism : Coronavirus, Coronavirus Infections, Pneumonia, Viral.
- physiology : Betacoronavirus, Host-Pathogen Interactions.
- virology : Coronavirus Infections, Pneumonia, Viral.
- Animals, Databases, Protein, Humans, Pandemics, Protein Interaction Maps.

Abstract

In less than 20 years, three deadly coronaviruses, SARS-CoV, MERS-CoV and SARS-CoV-2, have emerged in human population causing hundreds to hundreds of thousands of deaths. Other coronaviruses are causing epizootic representing a significant threat for both domestic and wild animals. Members of this viral family have the longest genome of all RNA viruses, and express up to 29 proteins establishing complex interactions with the host proteome. Deciphering these interactions is essential to identify cellular pathways hijacked by these viruses to replicate and escape innate immunity. Virus-host interactions also provide key information to select targets for antiviral drug development. Here, we have manually curated the literature to assemble a unique dataset of 1311 coronavirus-host protein-protein interactions. Functional enrichment and network-based analyses showed coronavirus connections to RNA processing and translation, DNA damage and pathogen sensing, interferon production, and metabolic pathways. In particular, this global analysis pinpointed overlooked interactions with translation modulators (GIGYF2-EIF4E2), components of the nuclear pore, proteins involved in mitochondria homeostasis (PHB, PHB2, STOML2), and methylation pathways (MAT2A/B). Finally, interactome data provided a rational for the antiviral activity of some drugs inhibiting coronaviruses replication. Altogether, this work describing the current landscape of coronavirus-host interactions provides valuable hints for understanding the pathophysiology of coronavirus infections and developing effective antiviral therapies.

DOI: 10.1186/s12967-020-02480-z
PubMed: 32811513
PubMed Central: PMC7432461

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000559
to stream Main, to step Curation: 000559

Le document en format XML

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<author><name sortKey="Andre, Patrice" sort="Andre, Patrice" uniqKey="Andre P" first="Patrice" last="André">Patrice André</name>
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<series><title level="j">Journal of translational medicine</title>
<idno type="eISSN">1479-5876</idno>
<imprint><date when="2020" type="published">2020</date>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals (MeSH)</term>
<term>Betacoronavirus (physiology)</term>
<term>Coronavirus (chemistry)</term>
<term>Coronavirus (metabolism)</term>
<term>Coronavirus Infections (metabolism)</term>
<term>Coronavirus Infections (virology)</term>
<term>Databases, Protein (MeSH)</term>
<term>Host-Pathogen Interactions (physiology)</term>
<term>Humans (MeSH)</term>
<term>Mitochondrial Proteins (metabolism)</term>
<term>Pandemics (MeSH)</term>
<term>Pneumonia, Viral (metabolism)</term>
<term>Pneumonia, Viral (virology)</term>
<term>Protein Interaction Maps (MeSH)</term>
<term>Transcription Factors (metabolism)</term>
<term>Viral Proteins (metabolism)</term>
<term>Virus Replication (genetics)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Animaux (MeSH)</term>
<term>Bases de données de protéines (MeSH)</term>
<term>Betacoronavirus (physiologie)</term>
<term>Cartes d'interactions protéiques (MeSH)</term>
<term>Coronavirus (composition chimique)</term>
<term>Coronavirus (métabolisme)</term>
<term>Facteurs de transcription (métabolisme)</term>
<term>Humains (MeSH)</term>
<term>Infections à coronavirus (métabolisme)</term>
<term>Infections à coronavirus (virologie)</term>
<term>Interactions hôte-pathogène (physiologie)</term>
<term>Pandémies (MeSH)</term>
<term>Pneumopathie virale (métabolisme)</term>
<term>Pneumopathie virale (virologie)</term>
<term>Protéines mitochondriales (métabolisme)</term>
<term>Protéines virales (métabolisme)</term>
<term>Réplication virale (génétique)</term>
</keywords>
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<term>Transcription Factors</term>
<term>Viral Proteins</term>
</keywords>
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</keywords>
<keywords scheme="MESH" qualifier="composition chimique" xml:lang="fr"><term>Coronavirus</term>
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<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Virus Replication</term>
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<keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Coronavirus</term>
<term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
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<term>Facteurs de transcription</term>
<term>Infections à coronavirus</term>
<term>Pneumopathie virale</term>
<term>Protéines mitochondriales</term>
<term>Protéines virales</term>
</keywords>
<keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Betacoronavirus</term>
<term>Interactions hôte-pathogène</term>
</keywords>
<keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Betacoronavirus</term>
<term>Host-Pathogen Interactions</term>
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<keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Animals</term>
<term>Databases, Protein</term>
<term>Humans</term>
<term>Pandemics</term>
<term>Protein Interaction Maps</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Animaux</term>
<term>Bases de données de protéines</term>
<term>Cartes d'interactions protéiques</term>
<term>Humains</term>
<term>Pandémies</term>
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<front><div type="abstract" xml:lang="en">In less than 20 years, three deadly coronaviruses, SARS-CoV, MERS-CoV and SARS-CoV-2, have emerged in human population causing hundreds to hundreds of thousands of deaths. Other coronaviruses are causing epizootic representing a significant threat for both domestic and wild animals. Members of this viral family have the longest genome of all RNA viruses, and express up to 29 proteins establishing complex interactions with the host proteome. Deciphering these interactions is essential to identify cellular pathways hijacked by these viruses to replicate and escape innate immunity. Virus-host interactions also provide key information to select targets for antiviral drug development. Here, we have manually curated the literature to assemble a unique dataset of 1311 coronavirus-host protein-protein interactions. Functional enrichment and network-based analyses showed coronavirus connections to RNA processing and translation, DNA damage and pathogen sensing, interferon production, and metabolic pathways. In particular, this global analysis pinpointed overlooked interactions with translation modulators (GIGYF2-EIF4E2), components of the nuclear pore, proteins involved in mitochondria homeostasis (PHB, PHB2, STOML2), and methylation pathways (MAT2A/B). Finally, interactome data provided a rational for the antiviral activity of some drugs inhibiting coronaviruses replication. Altogether, this work describing the current landscape of coronavirus-host interactions provides valuable hints for understanding the pathophysiology of coronavirus infections and developing effective antiviral therapies.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32811513</PMID>
<DateCompleted><Year>2020</Year>
<Month>08</Month>
<Day>26</Day>
</DateCompleted>
<DateRevised><Year>2020</Year>
<Month>08</Month>
<Day>26</Day>
</DateRevised>
<Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1479-5876</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>18</Volume>
<Issue>1</Issue>
<PubDate><Year>2020</Year>
<Month>08</Month>
<Day>18</Day>
</PubDate>
</JournalIssue>
<Title>Journal of translational medicine</Title>
<ISOAbbreviation>J Transl Med</ISOAbbreviation>
</Journal>
<ArticleTitle>The current landscape of coronavirus-host protein-protein interactions.</ArticleTitle>
<Pagination><MedlinePgn>319</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1186/s12967-020-02480-z</ELocationID>
<Abstract><AbstractText>In less than 20 years, three deadly coronaviruses, SARS-CoV, MERS-CoV and SARS-CoV-2, have emerged in human population causing hundreds to hundreds of thousands of deaths. Other coronaviruses are causing epizootic representing a significant threat for both domestic and wild animals. Members of this viral family have the longest genome of all RNA viruses, and express up to 29 proteins establishing complex interactions with the host proteome. Deciphering these interactions is essential to identify cellular pathways hijacked by these viruses to replicate and escape innate immunity. Virus-host interactions also provide key information to select targets for antiviral drug development. Here, we have manually curated the literature to assemble a unique dataset of 1311 coronavirus-host protein-protein interactions. Functional enrichment and network-based analyses showed coronavirus connections to RNA processing and translation, DNA damage and pathogen sensing, interferon production, and metabolic pathways. In particular, this global analysis pinpointed overlooked interactions with translation modulators (GIGYF2-EIF4E2), components of the nuclear pore, proteins involved in mitochondria homeostasis (PHB, PHB2, STOML2), and methylation pathways (MAT2A/B). Finally, interactome data provided a rational for the antiviral activity of some drugs inhibiting coronaviruses replication. Altogether, this work describing the current landscape of coronavirus-host interactions provides valuable hints for understanding the pathophysiology of coronavirus infections and developing effective antiviral therapies.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Perrin-Cocon</LastName>
<ForeName>Laure</ForeName>
<Initials>L</Initials>
<AffiliationInfo><Affiliation>CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 69007, Lyon, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Diaz</LastName>
<ForeName>Olivier</ForeName>
<Initials>O</Initials>
<AffiliationInfo><Affiliation>CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 69007, Lyon, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Jacquemin</LastName>
<ForeName>Clémence</ForeName>
<Initials>C</Initials>
<AffiliationInfo><Affiliation>CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 69007, Lyon, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Barthel</LastName>
<ForeName>Valentine</ForeName>
<Initials>V</Initials>
<AffiliationInfo><Affiliation>CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 69007, Lyon, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ogire</LastName>
<ForeName>Eva</ForeName>
<Initials>E</Initials>
<AffiliationInfo><Affiliation>CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 69007, Lyon, France.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>UMR Processus Infectieux en Milieu Insulaire Tropical, Université de La Réunion, CNRS, 9192 INSERM U1187, IRD 249, Plateforme de Recherche CYROI, Sainte Clotilde La Réunion, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ramière</LastName>
<ForeName>Christophe</ForeName>
<Initials>C</Initials>
<AffiliationInfo><Affiliation>CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 69007, Lyon, France.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Laboratoire de Virologie, Hôpital de la Croix-Rousse, Hospices Civils de Lyon, Lyon, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>André</LastName>
<ForeName>Patrice</ForeName>
<Initials>P</Initials>
<AffiliationInfo><Affiliation>CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 69007, Lyon, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Lotteau</LastName>
<ForeName>Vincent</ForeName>
<Initials>V</Initials>
<AffiliationInfo><Affiliation>CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 69007, Lyon, France. vincent.lotteau@inserm.fr.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Vidalain</LastName>
<ForeName>Pierre-Olivier</ForeName>
<Initials>PO</Initials>
<Identifier Source="ORCID">0000-0003-3082-7407</Identifier>
<AffiliationInfo><Affiliation>CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 69007, Lyon, France. pierre-olivier.vidalain@inserm.fr.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<GrantList CompleteYN="Y"><Grant><GrantID>DIRED 20181189</GrantID>
<Agency>Conseil Régional de l'île de La Réunion</Agency>
<Country>International</Country>
</Grant>
</GrantList>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
<PublicationType UI="D016454">Review</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2020</Year>
<Month>08</Month>
<Day>18</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>England</Country>
<MedlineTA>J Transl Med</MedlineTA>
<NlmUniqueID>101190741</NlmUniqueID>
<ISSNLinking>1479-5876</ISSNLinking>
</MedlineJournalInfo>
<ChemicalList><Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D024101">Mitochondrial Proteins</NameOfSubstance>
</Chemical>
<Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D014157">Transcription Factors</NameOfSubstance>
</Chemical>
<Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D014764">Viral Proteins</NameOfSubstance>
</Chemical>
</ChemicalList>
<SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName>
<SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName>
</SupplMeshList>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName>
<QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D017934" MajorTopicYN="N">Coronavirus</DescriptorName>
<QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName>
<QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName>
<QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName>
<QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D030562" MajorTopicYN="N">Databases, Protein</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D054884" MajorTopicYN="N">Host-Pathogen Interactions</DescriptorName>
<QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D024101" MajorTopicYN="N">Mitochondrial Proteins</DescriptorName>
<QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName>
<QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName>
<QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D060066" MajorTopicYN="Y">Protein Interaction Maps</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D014157" MajorTopicYN="N">Transcription Factors</DescriptorName>
<QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D014764" MajorTopicYN="N">Viral Proteins</DescriptorName>
<QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D014779" MajorTopicYN="N">Virus Replication</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName>
</MeshHeading>
</MeshHeadingList>
<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Coronavirus</Keyword>
<Keyword MajorTopicYN="Y">Interactome</Keyword>
<Keyword MajorTopicYN="Y">Protein–protein interactions</Keyword>
<Keyword MajorTopicYN="Y">SARS-CoV-2</Keyword>
<Keyword MajorTopicYN="Y">Virus-host interactions</Keyword>
</KeywordList>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year>
<Month>05</Month>
<Day>23</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2020</Year>
<Month>08</Month>
<Day>07</Day>
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<Month>8</Month>
<Day>20</Day>
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The current landscape of coronavirus-host protein-protein interactions.

The current landscape of coronavirus-host protein-protein interactions.

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