Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children
Pediatric tuberculosis (TB) remains a major global health problem. Improved pediatric diagnostics using readily available biosources are urgently needed. We used liquid chromatography-mass spectrometry to analyze plasma metabolite profiles of Indian children with active TB (n = 16) and age- and sex-...
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creator | Dutta, Noton K. Tornheim, Jeffrey A. Fukutani, Kiyoshi F. Paradkar, Mandar Tiburcio, Rafael T. Kinikar, Aarti Valvi, Chhaya Kulkarni, Vandana Pradhan, Neeta Shivakumar, Shri Vijay Bala Yogendra Kagal, Anju Gupte, Akshay Gupte, Nikhil Mave, Vidya Gupta, Amita Andrade, Bruno B. Karakousis, Petros C. |
description | Pediatric tuberculosis (TB) remains a major global health problem. Improved pediatric diagnostics using readily available biosources are urgently needed. We used liquid chromatography-mass spectrometry to analyze plasma metabolite profiles of Indian children with active TB (n = 16) and age- and sex-matched,
Mycobacterium tuberculosis
-exposed but uninfected household contacts (n = 32). Metabolomic data were integrated with whole blood transcriptomic data for each participant at diagnosis and throughout treatment for drug-susceptible TB. A decision tree algorithm identified 3 metabolites that correctly identified TB status at distinct times during treatment. N-acetylneuraminate achieved an area under the receiver operating characteristic curve (AUC) of 0.66 at diagnosis. Quinolinate achieved an AUC of 0.77 after 1 month of treatment, and pyridoxate achieved an AUC of 0.87 after successful treatment completion. A set of 4 metabolites (gamma-glutamylalanine, gamma-glutamylglycine, glutamine, and pyridoxate) identified treatment response with an AUC of 0.86. Pathway enrichment analyses of these metabolites and corresponding transcriptional data correlated N-acetylneuraminate with immunoregulatory interactions between lymphoid and non-lymphoid cells, and correlated pyridoxate with p53-regulated metabolic genes and mitochondrial translation. Our findings shed new light on metabolic dysregulation in children with TB and pave the way for new diagnostic and treatment response markers in pediatric TB. |
doi_str_mv | 10.1038/s41598-020-75513-8 |
format | Article |
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Mycobacterium tuberculosis
-exposed but uninfected household contacts (n = 32). Metabolomic data were integrated with whole blood transcriptomic data for each participant at diagnosis and throughout treatment for drug-susceptible TB. A decision tree algorithm identified 3 metabolites that correctly identified TB status at distinct times during treatment. N-acetylneuraminate achieved an area under the receiver operating characteristic curve (AUC) of 0.66 at diagnosis. Quinolinate achieved an AUC of 0.77 after 1 month of treatment, and pyridoxate achieved an AUC of 0.87 after successful treatment completion. A set of 4 metabolites (gamma-glutamylalanine, gamma-glutamylglycine, glutamine, and pyridoxate) identified treatment response with an AUC of 0.86. Pathway enrichment analyses of these metabolites and corresponding transcriptional data correlated N-acetylneuraminate with immunoregulatory interactions between lymphoid and non-lymphoid cells, and correlated pyridoxate with p53-regulated metabolic genes and mitochondrial translation. Our findings shed new light on metabolic dysregulation in children with TB and pave the way for new diagnostic and treatment response markers in pediatric TB.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-020-75513-8</identifier><identifier>PMID: 33177551</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/326/2521 ; 631/326/41 ; 631/326/421 ; 631/553 ; 692/53 ; Adolescent ; Antitubercular Agents - therapeutic use ; Biomarkers - blood ; Blood ; Case-Control Studies ; Child ; Children ; Chromatography, Liquid ; Diagnosis ; Family Characteristics ; Female ; Gene Expression Profiling ; Global health ; Glutamine ; Humanities and Social Sciences ; Humans ; Immunoregulation ; Liquid chromatography ; Longitudinal Studies ; Lymphoid cells ; Male ; Mass spectrometry ; Mass Spectrometry - methods ; Mass spectroscopy ; Metabolism ; Metabolites ; Metabolomics ; Metabolomics - methods ; Mitochondria ; multidisciplinary ; p53 Protein ; Pediatrics ; Public health ; Science ; Science (multidisciplinary) ; Transcription ; Treatment Outcome ; Tuberculosis ; Tuberculosis - blood ; Tuberculosis - diagnosis ; Tuberculosis - drug therapy</subject><ispartof>Scientific reports, 2020-11, Vol.10 (1), p.19527, Article 19527</ispartof><rights>The Author(s) 2020</rights><rights>The Author(s) 2020. 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><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-4e46438b6088e2a5dae3a3a696bb9fa3e9eac6c9d516d4d95daa0341661bd9393</citedby><cites>FETCH-LOGICAL-c474t-4e46438b6088e2a5dae3a3a696bb9fa3e9eac6c9d516d4d95daa0341661bd9393</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658223/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658223/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,41120,42189,51576,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33177551$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Dutta, Noton K.</creatorcontrib><creatorcontrib>Tornheim, Jeffrey A.</creatorcontrib><creatorcontrib>Fukutani, Kiyoshi F.</creatorcontrib><creatorcontrib>Paradkar, Mandar</creatorcontrib><creatorcontrib>Tiburcio, Rafael T.</creatorcontrib><creatorcontrib>Kinikar, Aarti</creatorcontrib><creatorcontrib>Valvi, Chhaya</creatorcontrib><creatorcontrib>Kulkarni, Vandana</creatorcontrib><creatorcontrib>Pradhan, Neeta</creatorcontrib><creatorcontrib>Shivakumar, Shri Vijay Bala Yogendra</creatorcontrib><creatorcontrib>Kagal, Anju</creatorcontrib><creatorcontrib>Gupte, Akshay</creatorcontrib><creatorcontrib>Gupte, Nikhil</creatorcontrib><creatorcontrib>Mave, Vidya</creatorcontrib><creatorcontrib>Gupta, Amita</creatorcontrib><creatorcontrib>Andrade, Bruno B.</creatorcontrib><creatorcontrib>Karakousis, Petros C.</creatorcontrib><title>Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>Pediatric tuberculosis (TB) remains a major global health problem. Improved pediatric diagnostics using readily available biosources are urgently needed. We used liquid chromatography-mass spectrometry to analyze plasma metabolite profiles of Indian children with active TB (n = 16) and age- and sex-matched,
Mycobacterium tuberculosis
-exposed but uninfected household contacts (n = 32). Metabolomic data were integrated with whole blood transcriptomic data for each participant at diagnosis and throughout treatment for drug-susceptible TB. A decision tree algorithm identified 3 metabolites that correctly identified TB status at distinct times during treatment. N-acetylneuraminate achieved an area under the receiver operating characteristic curve (AUC) of 0.66 at diagnosis. Quinolinate achieved an AUC of 0.77 after 1 month of treatment, and pyridoxate achieved an AUC of 0.87 after successful treatment completion. A set of 4 metabolites (gamma-glutamylalanine, gamma-glutamylglycine, glutamine, and pyridoxate) identified treatment response with an AUC of 0.86. Pathway enrichment analyses of these metabolites and corresponding transcriptional data correlated N-acetylneuraminate with immunoregulatory interactions between lymphoid and non-lymphoid cells, and correlated pyridoxate with p53-regulated metabolic genes and mitochondrial translation. Our findings shed new light on metabolic dysregulation in children with TB and pave the way for new diagnostic and treatment response markers in pediatric TB.</description><subject>631/326/2521</subject><subject>631/326/41</subject><subject>631/326/421</subject><subject>631/553</subject><subject>692/53</subject><subject>Adolescent</subject><subject>Antitubercular Agents - therapeutic use</subject><subject>Biomarkers - blood</subject><subject>Blood</subject><subject>Case-Control Studies</subject><subject>Child</subject><subject>Children</subject><subject>Chromatography, Liquid</subject><subject>Diagnosis</subject><subject>Family Characteristics</subject><subject>Female</subject><subject>Gene Expression Profiling</subject><subject>Global health</subject><subject>Glutamine</subject><subject>Humanities and Social Sciences</subject><subject>Humans</subject><subject>Immunoregulation</subject><subject>Liquid chromatography</subject><subject>Longitudinal Studies</subject><subject>Lymphoid cells</subject><subject>Male</subject><subject>Mass spectrometry</subject><subject>Mass Spectrometry - methods</subject><subject>Mass spectroscopy</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Metabolomics</subject><subject>Metabolomics - methods</subject><subject>Mitochondria</subject><subject>multidisciplinary</subject><subject>p53 Protein</subject><subject>Pediatrics</subject><subject>Public health</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Transcription</subject><subject>Treatment Outcome</subject><subject>Tuberculosis</subject><subject>Tuberculosis - blood</subject><subject>Tuberculosis - diagnosis</subject><subject>Tuberculosis - drug therapy</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><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>eNp9kU9rFTEUxUNR2lL7BVxIwPVo_s9kI0ixtVBwo-uQTO68lzoveSaZB4If3rxOrXVjNrnk_u65JxyEXlPyjhI-vC-CSj10hJGul5LybjhB54wI2THO2Itn9Rm6LOWetCOZFlSfojPOaX-cOke_bmOFTbY1pIjThHdQrUtz2oWxYBs9rtnGMuawr-tbhgPYueCYDjBjF9LO5u-QCw4R1y1gN6fk8ZQyrouDPC5zKqFgH-wmPlSNG7dh9hniK_Ryalpw-XhfoG_Xn75efe7uvtzcXn2860bRi9oJEErwwSkyDMCs9Ba45VZp5ZyeLAcNdlSj9pIqL7xugCVcUKWo85prfoE-rLr7xe3AjxDbr2azz6GZ_2mSDebfTgxbs0kH0ys5MMabwNtHgZx-LFCquU9Ljs2zYaKnUg69OFJspcacSskwPW2gxBxDM2topoVmHkIzQxt689zb08ifiBrAV6C0VtxA_rv7P7K_AcpGpqA</recordid><startdate>20201111</startdate><enddate>20201111</enddate><creator>Dutta, Noton K.</creator><creator>Tornheim, Jeffrey A.</creator><creator>Fukutani, Kiyoshi F.</creator><creator>Paradkar, Mandar</creator><creator>Tiburcio, Rafael T.</creator><creator>Kinikar, Aarti</creator><creator>Valvi, Chhaya</creator><creator>Kulkarni, Vandana</creator><creator>Pradhan, Neeta</creator><creator>Shivakumar, Shri Vijay Bala Yogendra</creator><creator>Kagal, Anju</creator><creator>Gupte, Akshay</creator><creator>Gupte, Nikhil</creator><creator>Mave, Vidya</creator><creator>Gupta, Amita</creator><creator>Andrade, Bruno B.</creator><creator>Karakousis, Petros C.</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>C6C</scope><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>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</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>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>5PM</scope></search><sort><creationdate>20201111</creationdate><title>Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children</title><author>Dutta, Noton K. ; Tornheim, Jeffrey A. ; Fukutani, Kiyoshi F. ; Paradkar, Mandar ; Tiburcio, Rafael T. ; Kinikar, Aarti ; Valvi, Chhaya ; Kulkarni, Vandana ; Pradhan, Neeta ; Shivakumar, Shri Vijay Bala Yogendra ; Kagal, Anju ; Gupte, Akshay ; Gupte, Nikhil ; Mave, Vidya ; Gupta, Amita ; Andrade, Bruno B. ; Karakousis, Petros C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-4e46438b6088e2a5dae3a3a696bb9fa3e9eac6c9d516d4d95daa0341661bd9393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>631/326/2521</topic><topic>631/326/41</topic><topic>631/326/421</topic><topic>631/553</topic><topic>692/53</topic><topic>Adolescent</topic><topic>Antitubercular Agents - 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Improved pediatric diagnostics using readily available biosources are urgently needed. We used liquid chromatography-mass spectrometry to analyze plasma metabolite profiles of Indian children with active TB (n = 16) and age- and sex-matched,
Mycobacterium tuberculosis
-exposed but uninfected household contacts (n = 32). Metabolomic data were integrated with whole blood transcriptomic data for each participant at diagnosis and throughout treatment for drug-susceptible TB. A decision tree algorithm identified 3 metabolites that correctly identified TB status at distinct times during treatment. N-acetylneuraminate achieved an area under the receiver operating characteristic curve (AUC) of 0.66 at diagnosis. Quinolinate achieved an AUC of 0.77 after 1 month of treatment, and pyridoxate achieved an AUC of 0.87 after successful treatment completion. A set of 4 metabolites (gamma-glutamylalanine, gamma-glutamylglycine, glutamine, and pyridoxate) identified treatment response with an AUC of 0.86. Pathway enrichment analyses of these metabolites and corresponding transcriptional data correlated N-acetylneuraminate with immunoregulatory interactions between lymphoid and non-lymphoid cells, and correlated pyridoxate with p53-regulated metabolic genes and mitochondrial translation. Our findings shed new light on metabolic dysregulation in children with TB and pave the way for new diagnostic and treatment response markers in pediatric TB.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>33177551</pmid><doi>10.1038/s41598-020-75513-8</doi><oa>free_for_read</oa></addata></record> |
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subjects | 631/326/2521 631/326/41 631/326/421 631/553 692/53 Adolescent Antitubercular Agents - therapeutic use Biomarkers - blood Blood Case-Control Studies Child Children Chromatography, Liquid Diagnosis Family Characteristics Female Gene Expression Profiling Global health Glutamine Humanities and Social Sciences Humans Immunoregulation Liquid chromatography Longitudinal Studies Lymphoid cells Male Mass spectrometry Mass Spectrometry - methods Mass spectroscopy Metabolism Metabolites Metabolomics Metabolomics - methods Mitochondria multidisciplinary p53 Protein Pediatrics Public health Science Science (multidisciplinary) Transcription Treatment Outcome Tuberculosis Tuberculosis - blood Tuberculosis - diagnosis Tuberculosis - drug therapy |
title | Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children |
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