Untargeted metabolomic analysis of urine samples for diagnosis of inherited metabolic disorders
Metabolomics has become an important tool for clinical research, especially for analyzing inherited metabolic disorders (IMDs). The purpose of this study was to explore the performance of metabolomics in diagnosing IMDs using an untargeted metabolomic approach. A total of 40 urine samples were colle...
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| Veröffentlicht in: | Functional & integrative genomics 2021-11, Vol.21 (5-6), p.645-653 |
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| creator | Liu, Hao Zhu, Jiang Li, Qiu Wang, Dongjuan Wan, Kexing Yuan, Zhaojian Zhang, Juan Zou, Lin He, Xiaoyan Miao, Jingkun |
| description | Metabolomics has become an important tool for clinical research, especially for analyzing inherited metabolic disorders (IMDs). The purpose of this study was to explore the performance of metabolomics in diagnosing IMDs using an untargeted metabolomic approach. A total of 40 urine samples were collected: 20 samples from healthy children and 20 from pediatric patients, of whom 13 had confirmed IMDs and seven had suspected IMDs. Samples were analyzed by Orbitrap mass spectrometry in positive and negative mode alternately, coupled with ultra-high liquid chromatography. Raw data were processed using Compound Discovery 2.0 ™ and then exported for partial least squares discriminant analysis (PLS-DA) by SIMCA-P 14.1. After comparing with m/zCloud and chemSpider libraries, compounds with similarity above 80% were selected and normalized for subsequent relative quantification analysis. The uncommon compounds discovered were analyzed based on the Kyoto Encyclopedia of Genes and Genomes to explore their possible metabolic pathways. All IMDs patients were successfully distinguished from controls in the PLS-DA. Untargeted metabolomics revealed a broader metabolic spectrum in patients than what is observed using routine chromatographic methods for detecting IMDs. Higher levels of certain compounds were found in all 13 confirmed IMD patients and 5 of 7 suspected IMD patients. Several potential novel markers emerged after relative quantification. Untargeted metabolomics may be able to diagnose IMDs from urine and may deepen insights into the disease by revealing changes in various compounds such as amino acids, acylcarnitines, organic acids, and nucleosides. Such analyses may identify biomarkers to improve the study and treatment of IMDs. |
| doi_str_mv | 10.1007/s10142-021-00804-w |
| format | Article |
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The purpose of this study was to explore the performance of metabolomics in diagnosing IMDs using an untargeted metabolomic approach. A total of 40 urine samples were collected: 20 samples from healthy children and 20 from pediatric patients, of whom 13 had confirmed IMDs and seven had suspected IMDs. Samples were analyzed by Orbitrap mass spectrometry in positive and negative mode alternately, coupled with ultra-high liquid chromatography. Raw data were processed using Compound Discovery 2.0 ™ and then exported for partial least squares discriminant analysis (PLS-DA) by SIMCA-P 14.1. After comparing with m/zCloud and chemSpider libraries, compounds with similarity above 80% were selected and normalized for subsequent relative quantification analysis. The uncommon compounds discovered were analyzed based on the Kyoto Encyclopedia of Genes and Genomes to explore their possible metabolic pathways. All IMDs patients were successfully distinguished from controls in the PLS-DA. Untargeted metabolomics revealed a broader metabolic spectrum in patients than what is observed using routine chromatographic methods for detecting IMDs. Higher levels of certain compounds were found in all 13 confirmed IMD patients and 5 of 7 suspected IMD patients. Several potential novel markers emerged after relative quantification. Untargeted metabolomics may be able to diagnose IMDs from urine and may deepen insights into the disease by revealing changes in various compounds such as amino acids, acylcarnitines, organic acids, and nucleosides. Such analyses may identify biomarkers to improve the study and treatment of IMDs.</description><identifier>ISSN: 1438-793X</identifier><identifier>EISSN: 1438-7948</identifier><identifier>DOI: 10.1007/s10142-021-00804-w</identifier><identifier>PMID: 34585279</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Amino Acids - metabolism ; Amino Acids - urine ; Animal Genetics and Genomics ; Biochemistry ; Bioinformatics ; Biomarkers - metabolism ; Biomarkers - urine ; Biomedical and Life Sciences ; Carnitine - analogs & derivatives ; Carnitine - metabolism ; Carnitine - urine ; Cell Biology ; Child ; Discriminant analysis ; Genomes ; Humans ; Life Sciences ; Liquid chromatography ; Mass Spectrometry ; Mass spectroscopy ; Metabolic Diseases - diagnosis ; Metabolic Diseases - metabolism ; Metabolic Diseases - urine ; Metabolic disorders ; Metabolic pathways ; Metabolomics ; Microbial Genetics and Genomics ; Nucleosides - metabolism ; Nucleosides - urine ; Organic acids ; Original Article ; Patients ; Pediatrics ; Plant Genetics and Genomics ; Urine</subject><ispartof>Functional & integrative genomics, 2021-11, Vol.21 (5-6), p.645-653</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-439743daea712587e2026af6f1bc05fafd2dc8429550851b69eabe7742bfeb263</citedby><cites>FETCH-LOGICAL-c375t-439743daea712587e2026af6f1bc05fafd2dc8429550851b69eabe7742bfeb263</cites><orcidid>0000-0003-4649-116X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10142-021-00804-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10142-021-00804-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34585279$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Hao</creatorcontrib><creatorcontrib>Zhu, Jiang</creatorcontrib><creatorcontrib>Li, Qiu</creatorcontrib><creatorcontrib>Wang, Dongjuan</creatorcontrib><creatorcontrib>Wan, Kexing</creatorcontrib><creatorcontrib>Yuan, Zhaojian</creatorcontrib><creatorcontrib>Zhang, Juan</creatorcontrib><creatorcontrib>Zou, Lin</creatorcontrib><creatorcontrib>He, Xiaoyan</creatorcontrib><creatorcontrib>Miao, Jingkun</creatorcontrib><title>Untargeted metabolomic analysis of urine samples for diagnosis of inherited metabolic disorders</title><title>Functional & integrative genomics</title><addtitle>Funct Integr Genomics</addtitle><addtitle>Funct Integr Genomics</addtitle><description>Metabolomics has become an important tool for clinical research, especially for analyzing inherited metabolic disorders (IMDs). The purpose of this study was to explore the performance of metabolomics in diagnosing IMDs using an untargeted metabolomic approach. A total of 40 urine samples were collected: 20 samples from healthy children and 20 from pediatric patients, of whom 13 had confirmed IMDs and seven had suspected IMDs. Samples were analyzed by Orbitrap mass spectrometry in positive and negative mode alternately, coupled with ultra-high liquid chromatography. Raw data were processed using Compound Discovery 2.0 ™ and then exported for partial least squares discriminant analysis (PLS-DA) by SIMCA-P 14.1. After comparing with m/zCloud and chemSpider libraries, compounds with similarity above 80% were selected and normalized for subsequent relative quantification analysis. The uncommon compounds discovered were analyzed based on the Kyoto Encyclopedia of Genes and Genomes to explore their possible metabolic pathways. All IMDs patients were successfully distinguished from controls in the PLS-DA. Untargeted metabolomics revealed a broader metabolic spectrum in patients than what is observed using routine chromatographic methods for detecting IMDs. Higher levels of certain compounds were found in all 13 confirmed IMD patients and 5 of 7 suspected IMD patients. Several potential novel markers emerged after relative quantification. Untargeted metabolomics may be able to diagnose IMDs from urine and may deepen insights into the disease by revealing changes in various compounds such as amino acids, acylcarnitines, organic acids, and nucleosides. Such analyses may identify biomarkers to improve the study and treatment of IMDs.</description><subject>Amino Acids - metabolism</subject><subject>Amino Acids - urine</subject><subject>Animal Genetics and Genomics</subject><subject>Biochemistry</subject><subject>Bioinformatics</subject><subject>Biomarkers - metabolism</subject><subject>Biomarkers - urine</subject><subject>Biomedical and Life Sciences</subject><subject>Carnitine - analogs & derivatives</subject><subject>Carnitine - metabolism</subject><subject>Carnitine - urine</subject><subject>Cell Biology</subject><subject>Child</subject><subject>Discriminant analysis</subject><subject>Genomes</subject><subject>Humans</subject><subject>Life Sciences</subject><subject>Liquid chromatography</subject><subject>Mass Spectrometry</subject><subject>Mass spectroscopy</subject><subject>Metabolic Diseases - diagnosis</subject><subject>Metabolic Diseases - metabolism</subject><subject>Metabolic Diseases - urine</subject><subject>Metabolic disorders</subject><subject>Metabolic pathways</subject><subject>Metabolomics</subject><subject>Microbial Genetics and Genomics</subject><subject>Nucleosides - metabolism</subject><subject>Nucleosides - urine</subject><subject>Organic acids</subject><subject>Original Article</subject><subject>Patients</subject><subject>Pediatrics</subject><subject>Plant Genetics and Genomics</subject><subject>Urine</subject><issn>1438-793X</issn><issn>1438-7948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kMtKAzEUhoMotlZfwIUMuHEzmusks5TiDQpuLLgLmZmTmjIzqckMpW_v1NYLLlydwPn-75AfoXOCrwnG8iYSTDhNMSUpxgrzdH2AxoQzlcqcq8PvN3sdoZMYlxhjgXN2jEaMCyWozMdIz9vOhAV0UCUNdKbwtW9cmZjW1JvoYuJt0gfXQhJNs6ohJtaHpHJm0fr92rVvENwvwRCvXPShghBP0ZE1dYSz_Zyg-f3dy_QxnT0_PE1vZ2nJpOhSznLJWWXASEKFkkAxzYzNLClKLKyxFa1KxWkuBFaCFFkOpgApOS0sFDRjE3S1866Cf-8hdrpxsYS6Ni34PmoqpJQ0V1IN6OUfdOn7MPx3S6ksExlXWyHdUWXwMQawehVcY8JGE6y39etd_XqoX3_Wr9dD6GKv7osGqu_IV98DwHZAHFbtAsLP7X-0Hz_Skdc</recordid><startdate>20211101</startdate><enddate>20211101</enddate><creator>Liu, Hao</creator><creator>Zhu, Jiang</creator><creator>Li, Qiu</creator><creator>Wang, Dongjuan</creator><creator>Wan, Kexing</creator><creator>Yuan, Zhaojian</creator><creator>Zhang, Juan</creator><creator>Zou, Lin</creator><creator>He, Xiaoyan</creator><creator>Miao, Jingkun</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><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>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</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>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>P64</scope><scope>PADUT</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4649-116X</orcidid></search><sort><creationdate>20211101</creationdate><title>Untargeted metabolomic analysis of urine samples for diagnosis of inherited metabolic disorders</title><author>Liu, Hao ; Zhu, Jiang ; Li, Qiu ; Wang, Dongjuan ; Wan, Kexing ; Yuan, Zhaojian ; Zhang, Juan ; Zou, Lin ; He, Xiaoyan ; Miao, Jingkun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-439743daea712587e2026af6f1bc05fafd2dc8429550851b69eabe7742bfeb263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Amino Acids - 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Academic</collection><jtitle>Functional & integrative genomics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Hao</au><au>Zhu, Jiang</au><au>Li, Qiu</au><au>Wang, Dongjuan</au><au>Wan, Kexing</au><au>Yuan, Zhaojian</au><au>Zhang, Juan</au><au>Zou, Lin</au><au>He, Xiaoyan</au><au>Miao, Jingkun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Untargeted metabolomic analysis of urine samples for diagnosis of inherited metabolic disorders</atitle><jtitle>Functional & integrative genomics</jtitle><stitle>Funct Integr Genomics</stitle><addtitle>Funct Integr Genomics</addtitle><date>2021-11-01</date><risdate>2021</risdate><volume>21</volume><issue>5-6</issue><spage>645</spage><epage>653</epage><pages>645-653</pages><issn>1438-793X</issn><eissn>1438-7948</eissn><abstract>Metabolomics has become an important tool for clinical research, especially for analyzing inherited metabolic disorders (IMDs). The purpose of this study was to explore the performance of metabolomics in diagnosing IMDs using an untargeted metabolomic approach. A total of 40 urine samples were collected: 20 samples from healthy children and 20 from pediatric patients, of whom 13 had confirmed IMDs and seven had suspected IMDs. Samples were analyzed by Orbitrap mass spectrometry in positive and negative mode alternately, coupled with ultra-high liquid chromatography. Raw data were processed using Compound Discovery 2.0 ™ and then exported for partial least squares discriminant analysis (PLS-DA) by SIMCA-P 14.1. After comparing with m/zCloud and chemSpider libraries, compounds with similarity above 80% were selected and normalized for subsequent relative quantification analysis. The uncommon compounds discovered were analyzed based on the Kyoto Encyclopedia of Genes and Genomes to explore their possible metabolic pathways. All IMDs patients were successfully distinguished from controls in the PLS-DA. Untargeted metabolomics revealed a broader metabolic spectrum in patients than what is observed using routine chromatographic methods for detecting IMDs. Higher levels of certain compounds were found in all 13 confirmed IMD patients and 5 of 7 suspected IMD patients. Several potential novel markers emerged after relative quantification. Untargeted metabolomics may be able to diagnose IMDs from urine and may deepen insights into the disease by revealing changes in various compounds such as amino acids, acylcarnitines, organic acids, and nucleosides. Such analyses may identify biomarkers to improve the study and treatment of IMDs.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>34585279</pmid><doi>10.1007/s10142-021-00804-w</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-4649-116X</orcidid></addata></record> |
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| subjects | Amino Acids - metabolism Amino Acids - urine Animal Genetics and Genomics Biochemistry Bioinformatics Biomarkers - metabolism Biomarkers - urine Biomedical and Life Sciences Carnitine - analogs & derivatives Carnitine - metabolism Carnitine - urine Cell Biology Child Discriminant analysis Genomes Humans Life Sciences Liquid chromatography Mass Spectrometry Mass spectroscopy Metabolic Diseases - diagnosis Metabolic Diseases - metabolism Metabolic Diseases - urine Metabolic disorders Metabolic pathways Metabolomics Microbial Genetics and Genomics Nucleosides - metabolism Nucleosides - urine Organic acids Original Article Patients Pediatrics Plant Genetics and Genomics Urine |
| title | Untargeted metabolomic analysis of urine samples for diagnosis of inherited metabolic disorders |
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