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Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease
Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for dia...
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Published in: | Cell reports methods 2023-02, Vol.3 (2), p.100395-100395, Article 100395 |
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creator | Zhang, Zijun Sauerwald, Natalie Cappuccio, Antonio Ramos, Irene Nair, Venugopalan D. Nudelman, German Zaslavsky, Elena Ge, Yongchao Gaitas, Angelo Ren, Hui Brockman, Joel Geis, Jennifer Ramalingam, Naveen King, David McClain, Micah T. Woods, Christopher W. Henao, Ricardo Burke, Thomas W. Tsalik, Ephraim L. Goforth, Carl W. Lizewski, Rhonda A. Lizewski, Stephen E. Weir, Dawn L. Letizia, Andrew G. Sealfon, Stuart C. Troyanskaya, Olga G. |
description | Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.
[Display omitted]
•We present a computational framework for alternative splicing (AS) diagnostic markers•Our AS biomarkers outperform gene-expression biomarkers in COVID-19 detection•Microfluidic PCR diagnostic assay of AS biomarkers achieves greater than 98% accuracy•We interpret the biological importance of identified AS biomarkers
Host-based response assays (HRAs) can often diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) in HRAs remains unexplored, as existing HRAs are restricted to gene expression signatures. We report a computational framework for the identification, optimization, and evaluation of blood AS-based diagnostic assay development for infectious disease. Using SARS-CoV-2 infection as a case study, we demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis when compared against six reported transcriptome signatures and when implemented as a microfluidic PCR diagnostic assay.
Host-based response assays can diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) remains unexplored. Zhang et al. present a computational framework for AS diagnostic biomarkers. Using SARS-CoV-2 as a case study, they de |
doi_str_mv | 10.1016/j.crmeth.2023.100395 |
format | article |
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[Display omitted]
•We present a computational framework for alternative splicing (AS) diagnostic markers•Our AS biomarkers outperform gene-expression biomarkers in COVID-19 detection•Microfluidic PCR diagnostic assay of AS biomarkers achieves greater than 98% accuracy•We interpret the biological importance of identified AS biomarkers
Host-based response assays (HRAs) can often diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) in HRAs remains unexplored, as existing HRAs are restricted to gene expression signatures. We report a computational framework for the identification, optimization, and evaluation of blood AS-based diagnostic assay development for infectious disease. Using SARS-CoV-2 infection as a case study, we demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis when compared against six reported transcriptome signatures and when implemented as a microfluidic PCR diagnostic assay.
Host-based response assays can diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) remains unexplored. Zhang et al. present a computational framework for AS diagnostic biomarkers. Using SARS-CoV-2 as a case study, they demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis.</description><identifier>ISSN: 2667-2375</identifier><identifier>EISSN: 2667-2375</identifier><identifier>DOI: 10.1016/j.crmeth.2023.100395</identifier><identifier>PMID: 36936082</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>diagnostic biomarker ; host response assays ; infectious disease ; RNA splicing ; SARS-CoV-2 ; viral infection</subject><ispartof>Cell reports methods, 2023-02, Vol.3 (2), p.100395-100395, Article 100395</ispartof><rights>2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c530t-3ff6c4e86dc928168bffe91ba4de715532dd428453f6d3f787e0ab622feb7f003</citedby><cites>FETCH-LOGICAL-c530t-3ff6c4e86dc928168bffe91ba4de715532dd428453f6d3f787e0ab622feb7f003</cites><orcidid>0000-0002-0166-8614 ; 0000-0003-2016-8014</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/PMC10014279/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S2667237523000024$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,3549,27924,27925,45780,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36936082$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Zijun</creatorcontrib><creatorcontrib>Sauerwald, Natalie</creatorcontrib><creatorcontrib>Cappuccio, Antonio</creatorcontrib><creatorcontrib>Ramos, Irene</creatorcontrib><creatorcontrib>Nair, Venugopalan D.</creatorcontrib><creatorcontrib>Nudelman, German</creatorcontrib><creatorcontrib>Zaslavsky, Elena</creatorcontrib><creatorcontrib>Ge, Yongchao</creatorcontrib><creatorcontrib>Gaitas, Angelo</creatorcontrib><creatorcontrib>Ren, Hui</creatorcontrib><creatorcontrib>Brockman, Joel</creatorcontrib><creatorcontrib>Geis, Jennifer</creatorcontrib><creatorcontrib>Ramalingam, Naveen</creatorcontrib><creatorcontrib>King, David</creatorcontrib><creatorcontrib>McClain, Micah T.</creatorcontrib><creatorcontrib>Woods, Christopher W.</creatorcontrib><creatorcontrib>Henao, Ricardo</creatorcontrib><creatorcontrib>Burke, Thomas W.</creatorcontrib><creatorcontrib>Tsalik, Ephraim L.</creatorcontrib><creatorcontrib>Goforth, Carl W.</creatorcontrib><creatorcontrib>Lizewski, Rhonda A.</creatorcontrib><creatorcontrib>Lizewski, Stephen E.</creatorcontrib><creatorcontrib>Weir, Dawn L.</creatorcontrib><creatorcontrib>Letizia, Andrew G.</creatorcontrib><creatorcontrib>Sealfon, Stuart C.</creatorcontrib><creatorcontrib>Troyanskaya, Olga G.</creatorcontrib><title>Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease</title><title>Cell reports methods</title><addtitle>Cell Rep Methods</addtitle><description>Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.
[Display omitted]
•We present a computational framework for alternative splicing (AS) diagnostic markers•Our AS biomarkers outperform gene-expression biomarkers in COVID-19 detection•Microfluidic PCR diagnostic assay of AS biomarkers achieves greater than 98% accuracy•We interpret the biological importance of identified AS biomarkers
Host-based response assays (HRAs) can often diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) in HRAs remains unexplored, as existing HRAs are restricted to gene expression signatures. We report a computational framework for the identification, optimization, and evaluation of blood AS-based diagnostic assay development for infectious disease. Using SARS-CoV-2 infection as a case study, we demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis when compared against six reported transcriptome signatures and when implemented as a microfluidic PCR diagnostic assay.
Host-based response assays can diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) remains unexplored. Zhang et al. present a computational framework for AS diagnostic biomarkers. Using SARS-CoV-2 as a case study, they demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis.</description><subject>diagnostic biomarker</subject><subject>host response assays</subject><subject>infectious disease</subject><subject>RNA splicing</subject><subject>SARS-CoV-2</subject><subject>viral infection</subject><issn>2667-2375</issn><issn>2667-2375</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9UctOHDEQHEVBAQF_EEVzzGU3fttzSURQHkgoSCicrR67vXgzO97Ysyvx9_FmCIJLTra6q6urq5rmLSVLSqj6sF66vMHpfskI47VEeCdfNSdMKb1gXMvXz_7HzXkpa0IIk5Tzjr5pjrnquCKGnTR3n4eUfHv746KFYcI8whT32JbtEF0cVy3ucZxKC6X1EVZjKlN0bR_TBvIvzKUNKbdxDOimmHYHUEEoeNYcBRgKnj--p83d1y8_L78vrm--XV1eXC-c5GRa8BCUE2iUdx0zVJk-BOxoD8KjplJy5r1gRkgelOdBG40EesVYwF6HevRpczXz-gRru82xynqwCaL9W0h5ZSFXxQNa6YQRDhiVvRQaOXQdEA4ePNGMM165Ps1c212_Qe_q3RmGF6QvO2O8t6u0t9V9KpjuKsP7R4acfu-wTHYTi8NhgBGrOZZpYwyRUooKFTPU5VRKxvC0hxJ7SNiu7ZywPSRs54Tr2LvnGp-G_uVZAR9nAFbX9xGzLS7i6NDHXDOqtsT_b_gDwJC6Pg</recordid><startdate>20230227</startdate><enddate>20230227</enddate><creator>Zhang, Zijun</creator><creator>Sauerwald, Natalie</creator><creator>Cappuccio, Antonio</creator><creator>Ramos, Irene</creator><creator>Nair, Venugopalan D.</creator><creator>Nudelman, German</creator><creator>Zaslavsky, Elena</creator><creator>Ge, Yongchao</creator><creator>Gaitas, Angelo</creator><creator>Ren, Hui</creator><creator>Brockman, Joel</creator><creator>Geis, Jennifer</creator><creator>Ramalingam, Naveen</creator><creator>King, David</creator><creator>McClain, Micah T.</creator><creator>Woods, Christopher W.</creator><creator>Henao, Ricardo</creator><creator>Burke, Thomas W.</creator><creator>Tsalik, Ephraim L.</creator><creator>Goforth, Carl W.</creator><creator>Lizewski, Rhonda A.</creator><creator>Lizewski, Stephen E.</creator><creator>Weir, Dawn L.</creator><creator>Letizia, Andrew G.</creator><creator>Sealfon, Stuart C.</creator><creator>Troyanskaya, Olga G.</creator><general>Elsevier Inc</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-0166-8614</orcidid><orcidid>https://orcid.org/0000-0003-2016-8014</orcidid></search><sort><creationdate>20230227</creationdate><title>Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease</title><author>Zhang, Zijun ; Sauerwald, Natalie ; Cappuccio, Antonio ; Ramos, Irene ; Nair, Venugopalan D. ; Nudelman, German ; Zaslavsky, Elena ; Ge, Yongchao ; Gaitas, Angelo ; Ren, Hui ; Brockman, Joel ; Geis, Jennifer ; Ramalingam, Naveen ; King, David ; McClain, Micah T. ; Woods, Christopher W. ; Henao, Ricardo ; Burke, Thomas W. ; Tsalik, Ephraim L. ; Goforth, Carl W. ; Lizewski, Rhonda A. ; Lizewski, Stephen E. ; Weir, Dawn L. ; Letizia, Andrew G. ; Sealfon, Stuart C. ; Troyanskaya, Olga G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c530t-3ff6c4e86dc928168bffe91ba4de715532dd428453f6d3f787e0ab622feb7f003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>diagnostic biomarker</topic><topic>host response assays</topic><topic>infectious disease</topic><topic>RNA splicing</topic><topic>SARS-CoV-2</topic><topic>viral infection</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Zijun</creatorcontrib><creatorcontrib>Sauerwald, Natalie</creatorcontrib><creatorcontrib>Cappuccio, Antonio</creatorcontrib><creatorcontrib>Ramos, Irene</creatorcontrib><creatorcontrib>Nair, Venugopalan D.</creatorcontrib><creatorcontrib>Nudelman, German</creatorcontrib><creatorcontrib>Zaslavsky, Elena</creatorcontrib><creatorcontrib>Ge, Yongchao</creatorcontrib><creatorcontrib>Gaitas, Angelo</creatorcontrib><creatorcontrib>Ren, Hui</creatorcontrib><creatorcontrib>Brockman, Joel</creatorcontrib><creatorcontrib>Geis, Jennifer</creatorcontrib><creatorcontrib>Ramalingam, Naveen</creatorcontrib><creatorcontrib>King, David</creatorcontrib><creatorcontrib>McClain, Micah T.</creatorcontrib><creatorcontrib>Woods, Christopher W.</creatorcontrib><creatorcontrib>Henao, Ricardo</creatorcontrib><creatorcontrib>Burke, Thomas W.</creatorcontrib><creatorcontrib>Tsalik, Ephraim L.</creatorcontrib><creatorcontrib>Goforth, Carl W.</creatorcontrib><creatorcontrib>Lizewski, Rhonda A.</creatorcontrib><creatorcontrib>Lizewski, Stephen E.</creatorcontrib><creatorcontrib>Weir, Dawn L.</creatorcontrib><creatorcontrib>Letizia, Andrew G.</creatorcontrib><creatorcontrib>Sealfon, Stuart C.</creatorcontrib><creatorcontrib>Troyanskaya, Olga G.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Cell reports methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Zijun</au><au>Sauerwald, Natalie</au><au>Cappuccio, Antonio</au><au>Ramos, Irene</au><au>Nair, Venugopalan D.</au><au>Nudelman, German</au><au>Zaslavsky, Elena</au><au>Ge, Yongchao</au><au>Gaitas, Angelo</au><au>Ren, Hui</au><au>Brockman, Joel</au><au>Geis, Jennifer</au><au>Ramalingam, Naveen</au><au>King, David</au><au>McClain, Micah T.</au><au>Woods, Christopher W.</au><au>Henao, Ricardo</au><au>Burke, Thomas W.</au><au>Tsalik, Ephraim L.</au><au>Goforth, Carl W.</au><au>Lizewski, Rhonda A.</au><au>Lizewski, Stephen E.</au><au>Weir, Dawn L.</au><au>Letizia, Andrew G.</au><au>Sealfon, Stuart C.</au><au>Troyanskaya, Olga G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease</atitle><jtitle>Cell reports methods</jtitle><addtitle>Cell Rep Methods</addtitle><date>2023-02-27</date><risdate>2023</risdate><volume>3</volume><issue>2</issue><spage>100395</spage><epage>100395</epage><pages>100395-100395</pages><artnum>100395</artnum><issn>2667-2375</issn><eissn>2667-2375</eissn><abstract>Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.
[Display omitted]
•We present a computational framework for alternative splicing (AS) diagnostic markers•Our AS biomarkers outperform gene-expression biomarkers in COVID-19 detection•Microfluidic PCR diagnostic assay of AS biomarkers achieves greater than 98% accuracy•We interpret the biological importance of identified AS biomarkers
Host-based response assays (HRAs) can often diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) in HRAs remains unexplored, as existing HRAs are restricted to gene expression signatures. We report a computational framework for the identification, optimization, and evaluation of blood AS-based diagnostic assay development for infectious disease. Using SARS-CoV-2 infection as a case study, we demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis when compared against six reported transcriptome signatures and when implemented as a microfluidic PCR diagnostic assay.
Host-based response assays can diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) remains unexplored. Zhang et al. present a computational framework for AS diagnostic biomarkers. Using SARS-CoV-2 as a case study, they demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>36936082</pmid><doi>10.1016/j.crmeth.2023.100395</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-0166-8614</orcidid><orcidid>https://orcid.org/0000-0003-2016-8014</orcidid><oa>free_for_read</oa></addata></record> |
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source | ScienceDirect Journals; PubMed Central |
subjects | diagnostic biomarker host response assays infectious disease RNA splicing SARS-CoV-2 viral infection |
title | Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease |
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