Multi-omics integration reveals a nonlinear signature that precedes progression of lung fibrosis
Idiopathic pulmonary fibrosis (IPF) is a devastating progressive interstitial lung disease with poor outcomes. While decades of research have shed light on pathophysiological mechanisms associated with the disease, our understanding of the early molecular events driving IPF and its progression is li...
Gespeichert in:
Veröffentlicht in: | Clinical & Translational Immunology 2024-01, Vol.13 (1), p.e1485 |
---|---|
Hauptverfasser: | , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 1 |
container_start_page | e1485 |
container_title | Clinical & Translational Immunology |
container_volume | 13 |
creator | Pattaroni, Céline Begka, Christina Cardwell, Bailey Jaffar, Jade Macowan, Matthew Harris, Nicola L Westall, Glen P Marsland, Benjamin J |
description | Idiopathic pulmonary fibrosis (IPF) is a devastating progressive interstitial lung disease with poor outcomes. While decades of research have shed light on pathophysiological mechanisms associated with the disease, our understanding of the early molecular events driving IPF and its progression is limited. With this study, we aimed to model the leading edge of fibrosis using a data-driven approach.
Multiple omics modalities (transcriptomics, metabolomics and lipidomics) of healthy and IPF lung explants representing different stages of fibrosis were combined using an unbiased approach. Multi-Omics Factor Analysis of datasets revealed latent factors specifically linked with established fibrotic disease (Factor1) and disease progression (Factor2).
Features characterising Factor1 comprised well-established hallmarks of fibrotic disease such as defects in surfactant, epithelial-mesenchymal transition, extracellular matrix deposition, mitochondrial dysfunction and purine metabolism. Comparatively, Factor2 identified a signature revealing a nonlinear trajectory towards disease progression. Molecular features characterising Factor2 included genes related to transcriptional regulation of cell differentiation, ciliogenesis and a subset of lipids from the endocannabinoid class. Machine learning models, trained upon the top transcriptomics features of each factor, accurately predicted disease status and progression when tested on two independent datasets.
This multi-omics integrative approach has revealed a unique signature which may represent the inflection point in disease progression, representing a promising avenue for the identification of therapeutic targets aimed at addressing the progressive nature of the disease. |
doi_str_mv | 10.1002/cti2.1485 |
format | Article |
fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10807351</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A781040324</galeid><sourcerecordid>A781040324</sourcerecordid><originalsourceid>FETCH-LOGICAL-c471t-8a004de88e2bae3b475d2b83f0d0ac982aab7b44a9444017c8afd5799d1417183</originalsourceid><addsrcrecordid>eNpdkU1r3DAQhkVpacI2h_6BIuilPXgzkuW1fCoh9AsScknP6lgeOwpeaSvJgf77ymwa0jIHDdIz72jmZeytgK0AkOc2O7kVSjcv2KmEBiqAnX75LD9hZyndA4CoFTRi95qd1FruOqnqU_bzepmzq8Le2cSdzzRFzC54HumBcE4cuQ9-dp4w8uQmj3mJxPMdZn6IZGmgVJIwRUpprQsjnxc_8dH1MSSX3rBXY9Ghs8dzw358-Xx7-a26uvn6_fLiqrKqFbnSCKAG0ppkj1T3qm0G2et6hAHQdloi9m2vFHZKKRCt1TgOTdt1g1CiFbresE9H3cPS72mw5HPE2Ryi22P8bQI68--Ld3dmCg9GgIa2bkRR-PCoEMOvhVI2e5cszTN6CksyshO6YGWDBX3_H3oflujLfCtVolFQF2p7pCacyTg_htLYlhiorDt4Gl25v2i1gIIXOzbs47HAltWlSOPT9wWY1Wyzmm1Wswv77vm8T-Rfa-s_Nr6l2A</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2919195403</pqid></control><display><type>article</type><title>Multi-omics integration reveals a nonlinear signature that precedes progression of lung fibrosis</title><source>PubMed Central Free</source><source>DOAJ Directory of Open Access Journals</source><source>Access via Wiley Online Library</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Wiley Online Library (Open Access Collection)</source><creator>Pattaroni, Céline ; Begka, Christina ; Cardwell, Bailey ; Jaffar, Jade ; Macowan, Matthew ; Harris, Nicola L ; Westall, Glen P ; Marsland, Benjamin J</creator><creatorcontrib>Pattaroni, Céline ; Begka, Christina ; Cardwell, Bailey ; Jaffar, Jade ; Macowan, Matthew ; Harris, Nicola L ; Westall, Glen P ; Marsland, Benjamin J</creatorcontrib><description>Idiopathic pulmonary fibrosis (IPF) is a devastating progressive interstitial lung disease with poor outcomes. While decades of research have shed light on pathophysiological mechanisms associated with the disease, our understanding of the early molecular events driving IPF and its progression is limited. With this study, we aimed to model the leading edge of fibrosis using a data-driven approach.
Multiple omics modalities (transcriptomics, metabolomics and lipidomics) of healthy and IPF lung explants representing different stages of fibrosis were combined using an unbiased approach. Multi-Omics Factor Analysis of datasets revealed latent factors specifically linked with established fibrotic disease (Factor1) and disease progression (Factor2).
Features characterising Factor1 comprised well-established hallmarks of fibrotic disease such as defects in surfactant, epithelial-mesenchymal transition, extracellular matrix deposition, mitochondrial dysfunction and purine metabolism. Comparatively, Factor2 identified a signature revealing a nonlinear trajectory towards disease progression. Molecular features characterising Factor2 included genes related to transcriptional regulation of cell differentiation, ciliogenesis and a subset of lipids from the endocannabinoid class. Machine learning models, trained upon the top transcriptomics features of each factor, accurately predicted disease status and progression when tested on two independent datasets.
This multi-omics integrative approach has revealed a unique signature which may represent the inflection point in disease progression, representing a promising avenue for the identification of therapeutic targets aimed at addressing the progressive nature of the disease.</description><identifier>ISSN: 2050-0068</identifier><identifier>EISSN: 2050-0068</identifier><identifier>DOI: 10.1002/cti2.1485</identifier><identifier>PMID: 38269243</identifier><language>eng</language><publisher>Australia: John Wiley & Sons, Inc</publisher><subject>Biological analysis ; Cell differentiation ; Datasets ; Development and progression ; Explants ; Extracellular matrix ; Factor analysis ; Fibrosis ; Gene expression ; Gene regulation ; Genes ; Genetic transcription ; Health aspects ; Investigations ; Lipids ; Lung diseases ; Machine learning ; Medical research ; Medicine, Experimental ; Metabolism ; Metabolomics ; Metadata ; Original ; Pathophysiology ; Principal components analysis ; Pulmonary fibrosis ; Regression analysis ; Surface active agents ; Therapeutic targets ; Transcriptomics</subject><ispartof>Clinical & Translational Immunology, 2024-01, Vol.13 (1), p.e1485</ispartof><rights>2024 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of Australian and New Zealand Society for Immunology, Inc.</rights><rights>COPYRIGHT 2024 John Wiley & Sons, Inc.</rights><rights>2024. This work is published 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>2024 The Authors. published by John Wiley & Sons Australia, Ltd on behalf of Australian and New Zealand Society for Immunology, Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c471t-8a004de88e2bae3b475d2b83f0d0ac982aab7b44a9444017c8afd5799d1417183</citedby><cites>FETCH-LOGICAL-c471t-8a004de88e2bae3b475d2b83f0d0ac982aab7b44a9444017c8afd5799d1417183</cites><orcidid>0000-0002-9920-1181</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/PMC10807351/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10807351/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38269243$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pattaroni, Céline</creatorcontrib><creatorcontrib>Begka, Christina</creatorcontrib><creatorcontrib>Cardwell, Bailey</creatorcontrib><creatorcontrib>Jaffar, Jade</creatorcontrib><creatorcontrib>Macowan, Matthew</creatorcontrib><creatorcontrib>Harris, Nicola L</creatorcontrib><creatorcontrib>Westall, Glen P</creatorcontrib><creatorcontrib>Marsland, Benjamin J</creatorcontrib><title>Multi-omics integration reveals a nonlinear signature that precedes progression of lung fibrosis</title><title>Clinical & Translational Immunology</title><addtitle>Clin Transl Immunology</addtitle><description>Idiopathic pulmonary fibrosis (IPF) is a devastating progressive interstitial lung disease with poor outcomes. While decades of research have shed light on pathophysiological mechanisms associated with the disease, our understanding of the early molecular events driving IPF and its progression is limited. With this study, we aimed to model the leading edge of fibrosis using a data-driven approach.
Multiple omics modalities (transcriptomics, metabolomics and lipidomics) of healthy and IPF lung explants representing different stages of fibrosis were combined using an unbiased approach. Multi-Omics Factor Analysis of datasets revealed latent factors specifically linked with established fibrotic disease (Factor1) and disease progression (Factor2).
Features characterising Factor1 comprised well-established hallmarks of fibrotic disease such as defects in surfactant, epithelial-mesenchymal transition, extracellular matrix deposition, mitochondrial dysfunction and purine metabolism. Comparatively, Factor2 identified a signature revealing a nonlinear trajectory towards disease progression. Molecular features characterising Factor2 included genes related to transcriptional regulation of cell differentiation, ciliogenesis and a subset of lipids from the endocannabinoid class. Machine learning models, trained upon the top transcriptomics features of each factor, accurately predicted disease status and progression when tested on two independent datasets.
This multi-omics integrative approach has revealed a unique signature which may represent the inflection point in disease progression, representing a promising avenue for the identification of therapeutic targets aimed at addressing the progressive nature of the disease.</description><subject>Biological analysis</subject><subject>Cell differentiation</subject><subject>Datasets</subject><subject>Development and progression</subject><subject>Explants</subject><subject>Extracellular matrix</subject><subject>Factor analysis</subject><subject>Fibrosis</subject><subject>Gene expression</subject><subject>Gene regulation</subject><subject>Genes</subject><subject>Genetic transcription</subject><subject>Health aspects</subject><subject>Investigations</subject><subject>Lipids</subject><subject>Lung diseases</subject><subject>Machine learning</subject><subject>Medical research</subject><subject>Medicine, Experimental</subject><subject>Metabolism</subject><subject>Metabolomics</subject><subject>Metadata</subject><subject>Original</subject><subject>Pathophysiology</subject><subject>Principal components analysis</subject><subject>Pulmonary fibrosis</subject><subject>Regression analysis</subject><subject>Surface active agents</subject><subject>Therapeutic targets</subject><subject>Transcriptomics</subject><issn>2050-0068</issn><issn>2050-0068</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpdkU1r3DAQhkVpacI2h_6BIuilPXgzkuW1fCoh9AsScknP6lgeOwpeaSvJgf77ymwa0jIHDdIz72jmZeytgK0AkOc2O7kVSjcv2KmEBiqAnX75LD9hZyndA4CoFTRi95qd1FruOqnqU_bzepmzq8Le2cSdzzRFzC54HumBcE4cuQ9-dp4w8uQmj3mJxPMdZn6IZGmgVJIwRUpprQsjnxc_8dH1MSSX3rBXY9Ghs8dzw358-Xx7-a26uvn6_fLiqrKqFbnSCKAG0ppkj1T3qm0G2et6hAHQdloi9m2vFHZKKRCt1TgOTdt1g1CiFbresE9H3cPS72mw5HPE2Ryi22P8bQI68--Ld3dmCg9GgIa2bkRR-PCoEMOvhVI2e5cszTN6CksyshO6YGWDBX3_H3oflujLfCtVolFQF2p7pCacyTg_htLYlhiorDt4Gl25v2i1gIIXOzbs47HAltWlSOPT9wWY1Wyzmm1Wswv77vm8T-Rfa-s_Nr6l2A</recordid><startdate>20240101</startdate><enddate>20240101</enddate><creator>Pattaroni, Céline</creator><creator>Begka, Christina</creator><creator>Cardwell, Bailey</creator><creator>Jaffar, Jade</creator><creator>Macowan, Matthew</creator><creator>Harris, Nicola L</creator><creator>Westall, Glen P</creator><creator>Marsland, Benjamin J</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IAO</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-9920-1181</orcidid></search><sort><creationdate>20240101</creationdate><title>Multi-omics integration reveals a nonlinear signature that precedes progression of lung fibrosis</title><author>Pattaroni, Céline ; Begka, Christina ; Cardwell, Bailey ; Jaffar, Jade ; Macowan, Matthew ; Harris, Nicola L ; Westall, Glen P ; Marsland, Benjamin J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c471t-8a004de88e2bae3b475d2b83f0d0ac982aab7b44a9444017c8afd5799d1417183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Biological analysis</topic><topic>Cell differentiation</topic><topic>Datasets</topic><topic>Development and progression</topic><topic>Explants</topic><topic>Extracellular matrix</topic><topic>Factor analysis</topic><topic>Fibrosis</topic><topic>Gene expression</topic><topic>Gene regulation</topic><topic>Genes</topic><topic>Genetic transcription</topic><topic>Health aspects</topic><topic>Investigations</topic><topic>Lipids</topic><topic>Lung diseases</topic><topic>Machine learning</topic><topic>Medical research</topic><topic>Medicine, Experimental</topic><topic>Metabolism</topic><topic>Metabolomics</topic><topic>Metadata</topic><topic>Original</topic><topic>Pathophysiology</topic><topic>Principal components analysis</topic><topic>Pulmonary fibrosis</topic><topic>Regression analysis</topic><topic>Surface active agents</topic><topic>Therapeutic targets</topic><topic>Transcriptomics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pattaroni, Céline</creatorcontrib><creatorcontrib>Begka, Christina</creatorcontrib><creatorcontrib>Cardwell, Bailey</creatorcontrib><creatorcontrib>Jaffar, Jade</creatorcontrib><creatorcontrib>Macowan, Matthew</creatorcontrib><creatorcontrib>Harris, Nicola L</creatorcontrib><creatorcontrib>Westall, Glen P</creatorcontrib><creatorcontrib>Marsland, Benjamin J</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale Academic OneFile</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech 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>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</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>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Biological Science Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Clinical & Translational Immunology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pattaroni, Céline</au><au>Begka, Christina</au><au>Cardwell, Bailey</au><au>Jaffar, Jade</au><au>Macowan, Matthew</au><au>Harris, Nicola L</au><au>Westall, Glen P</au><au>Marsland, Benjamin J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-omics integration reveals a nonlinear signature that precedes progression of lung fibrosis</atitle><jtitle>Clinical & Translational Immunology</jtitle><addtitle>Clin Transl Immunology</addtitle><date>2024-01-01</date><risdate>2024</risdate><volume>13</volume><issue>1</issue><spage>e1485</spage><pages>e1485-</pages><issn>2050-0068</issn><eissn>2050-0068</eissn><abstract>Idiopathic pulmonary fibrosis (IPF) is a devastating progressive interstitial lung disease with poor outcomes. While decades of research have shed light on pathophysiological mechanisms associated with the disease, our understanding of the early molecular events driving IPF and its progression is limited. With this study, we aimed to model the leading edge of fibrosis using a data-driven approach.
Multiple omics modalities (transcriptomics, metabolomics and lipidomics) of healthy and IPF lung explants representing different stages of fibrosis were combined using an unbiased approach. Multi-Omics Factor Analysis of datasets revealed latent factors specifically linked with established fibrotic disease (Factor1) and disease progression (Factor2).
Features characterising Factor1 comprised well-established hallmarks of fibrotic disease such as defects in surfactant, epithelial-mesenchymal transition, extracellular matrix deposition, mitochondrial dysfunction and purine metabolism. Comparatively, Factor2 identified a signature revealing a nonlinear trajectory towards disease progression. Molecular features characterising Factor2 included genes related to transcriptional regulation of cell differentiation, ciliogenesis and a subset of lipids from the endocannabinoid class. Machine learning models, trained upon the top transcriptomics features of each factor, accurately predicted disease status and progression when tested on two independent datasets.
This multi-omics integrative approach has revealed a unique signature which may represent the inflection point in disease progression, representing a promising avenue for the identification of therapeutic targets aimed at addressing the progressive nature of the disease.</abstract><cop>Australia</cop><pub>John Wiley & Sons, Inc</pub><pmid>38269243</pmid><doi>10.1002/cti2.1485</doi><orcidid>https://orcid.org/0000-0002-9920-1181</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2050-0068 |
ispartof | Clinical & Translational Immunology, 2024-01, Vol.13 (1), p.e1485 |
issn | 2050-0068 2050-0068 |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10807351 |
source | PubMed Central Free; DOAJ Directory of Open Access Journals; Access via Wiley Online Library; EZB-FREE-00999 freely available EZB journals; Wiley Online Library (Open Access Collection) |
subjects | Biological analysis Cell differentiation Datasets Development and progression Explants Extracellular matrix Factor analysis Fibrosis Gene expression Gene regulation Genes Genetic transcription Health aspects Investigations Lipids Lung diseases Machine learning Medical research Medicine, Experimental Metabolism Metabolomics Metadata Original Pathophysiology Principal components analysis Pulmonary fibrosis Regression analysis Surface active agents Therapeutic targets Transcriptomics |
title | Multi-omics integration reveals a nonlinear signature that precedes progression of lung fibrosis |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T03%3A00%3A24IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multi-omics%20integration%20reveals%20a%20nonlinear%20signature%20that%20precedes%20progression%20of%20lung%20fibrosis&rft.jtitle=Clinical%20&%20Translational%20Immunology&rft.au=Pattaroni,%20C%C3%A9line&rft.date=2024-01-01&rft.volume=13&rft.issue=1&rft.spage=e1485&rft.pages=e1485-&rft.issn=2050-0068&rft.eissn=2050-0068&rft_id=info:doi/10.1002/cti2.1485&rft_dat=%3Cgale_pubme%3EA781040324%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2919195403&rft_id=info:pmid/38269243&rft_galeid=A781040324&rfr_iscdi=true |