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MicrobioLink: An Integrated Computational Pipeline to Infer Functional Effects of Microbiome-Host Interactions

dc.contributor.authorAndrighetti, Tahila [UNESP]
dc.contributor.authorBohar, Balazs
dc.contributor.authorLemke, Ney [UNESP]
dc.contributor.authorSudhakar, Padhmanand
dc.contributor.authorKorcsmaros, Tamas
dc.contributor.institutionEarlham Institute
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionEötvös Loránd University
dc.contributor.institutionQuadram Institute Bioscience
dc.contributor.institutionMetabolism and Ageing
dc.date.accessioned2020-12-12T02:42:34Z
dc.date.available2020-12-12T02:42:34Z
dc.date.issued2020-05-21
dc.description.abstractMicrobiome-host interactions play significant roles in health and in various diseases including autoimmune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe-host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating, and prioritizing predictions-as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn's disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral, etc.), is freely available on GitHub.en
dc.description.affiliationEarlham Institute, Norwich Research Park
dc.description.affiliationInstitute of Biosciences São Paulo University (UNESP)
dc.description.affiliationDepartment of Genetics Eötvös Loránd University
dc.description.affiliationQuadram Institute Bioscience, Norwich Research Park
dc.description.affiliationDepartment of Chronic Diseases Metabolism and Ageing
dc.description.affiliationUnespInstitute of Biosciences São Paulo University (UNESP)
dc.description.sponsorshipBiotechnology and Biological Sciences Research Council
dc.description.sponsorshipIdBiotechnology and Biological Sciences Research Council: BB/J004529/1, BB/P016774/1 and BB/CSP17270/1
dc.description.sponsorshipIdBiotechnology and Biological Sciences Research Council: BB/R012490/1, BBS/E/F/000PR10353 and BBS/E/F/000PR10355
dc.identifierhttp://dx.doi.org/10.3390/cells9051278
dc.identifier.citationCells, v. 9, n. 5, 2020.
dc.identifier.doi10.3390/cells9051278
dc.identifier.issn2073-4409
dc.identifier.lattes7977035910952141
dc.identifier.scopus2-s2.0-85085539271
dc.identifier.urihttp://hdl.handle.net/11449/201815
dc.language.isoeng
dc.relation.ispartofCells
dc.sourceScopus
dc.subjectcomputational pipeline
dc.subjectmicrobiota–host interactions
dc.subjectnetwork diffusion
dc.subjectnetworks
dc.subjectprotein–protein interactions
dc.subjectsystems biology
dc.titleMicrobioLink: An Integrated Computational Pipeline to Infer Functional Effects of Microbiome-Host Interactionsen
dc.typeArtigopt
dspace.entity.typePublication
relation.isOrgUnitOfPublicationab63624f-c491-4ac7-bd2c-767f17ac838d
relation.isOrgUnitOfPublication.latestForDiscoveryab63624f-c491-4ac7-bd2c-767f17ac838d
unesp.advisor.lattes7977035910952141
unesp.author.orcid0000-0003-1907-4491 0000-0003-1907-4491 0000-0003-1907-4491[4]
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Biociências, Botucatupt
unesp.departmentFísica e Biofísica - IBBpt

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