Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease

Zijun Zhang; Natalie Sauerwald; Antonio Cappuccio; Irene Ramos; Venugopalan D. Nair; German Nudelman; Elena Zaslavsky; Yongchao Ge; Angelo Gaitas; Hui Ren; Joel Brockman; Jennifer Geis; Naveen Ramalingam; David King; Micah T. McClain; Christopher W. Woods; Ricardo Henao; Thomas W. Burke; Ephraim L. Tsalik; Carl W. Goforth; Rhonda A. Lizewski; Stephen E. Lizewski; Dawn L. Weir; Andrew G. Letizia; Stuart C. Sealfon; Olga G. Troyanskaya

doi:10.1016/j.crmeth.2023.100395

Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease

Zijun Zhang, Natalie Sauerwald, Antonio Cappuccio, Irene Ramos, Venugopalan D. Nair, German Nudelman, Elena Zaslavsky, Yongchao Ge, Angelo Gaitas, Hui Ren, Joel Brockman, Jennifer Geis, Naveen Ramalingam, David King, Micah T. McClain, Christopher W. Woods, Ricardo Henao, Thomas W. Burke, Ephraim L. Tsalik, Carl W. GoforthRhonda A. Lizewski, Stephen E. Lizewski, Dawn L. Weir, Andrew G. Letizia, Stuart C. Sealfon, Olga G. Troyanskaya

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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.

Original language	English (US)
Journal	Cell Reports Methods
DOIs	https://doi.org/10.1016/j.crmeth.2023.100395
State	Published - Jan 1 2023
Externally published	Yes

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Zhang, Z., Sauerwald, N., Cappuccio, A., Ramos, I., Nair, V. D., Nudelman, G., Zaslavsky, E., Ge, Y., Gaitas, A., Ren, H., Brockman, J., Geis, J., Ramalingam, N., King, D., McClain, M. T., Woods, C. W., Henao, R., Burke, T. W., Tsalik, E. L., ... Troyanskaya, O. G. (2023). Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease. Cell Reports Methods. https://doi.org/10.1016/j.crmeth.2023.100395

@article{916277868bf74b598d31bea75e5f751b,

title = "Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease",

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.",

author = "Zijun Zhang and Natalie Sauerwald and Antonio Cappuccio and Irene Ramos and Nair, {Venugopalan D.} and German Nudelman and Elena Zaslavsky and Yongchao Ge and Angelo Gaitas and Hui Ren and Joel Brockman and Jennifer Geis and Naveen Ramalingam and David King and McClain, {Micah T.} and Woods, {Christopher W.} and Ricardo Henao and Burke, {Thomas W.} and Tsalik, {Ephraim L.} and Goforth, {Carl W.} and Lizewski, {Rhonda A.} and Lizewski, {Stephen E.} and Weir, {Dawn L.} and Letizia, {Andrew G.} and Sealfon, {Stuart C.} and Troyanskaya, {Olga G.}",

note = "Generated from Scopus record by KAUST IRTS on 2023-02-15",

year = "2023",

month = jan,

day = "1",

doi = "10.1016/j.crmeth.2023.100395",

language = "English (US)",

journal = "Cell Reports Methods",

issn = "2667-2375",

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Zhang, Z, Sauerwald, N, Cappuccio, A, Ramos, I, Nair, VD, Nudelman, G, Zaslavsky, E, Ge, Y, Gaitas, A, Ren, H, Brockman, J, Geis, J, Ramalingam, N, King, D, McClain, MT, Woods, CW, Henao, R, Burke, TW, Tsalik, EL, Goforth, CW, Lizewski, RA, Lizewski, SE, Weir, DL, Letizia, AG, Sealfon, SC & Troyanskaya, OG 2023, 'Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease', Cell Reports Methods. https://doi.org/10.1016/j.crmeth.2023.100395

TY - JOUR

T1 - Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease

AU - Zhang, Zijun

AU - Sauerwald, Natalie

AU - Cappuccio, Antonio

AU - Ramos, Irene

AU - Nair, Venugopalan D.

AU - Nudelman, German

AU - Zaslavsky, Elena

AU - Ge, Yongchao

AU - Gaitas, Angelo

AU - Ren, Hui

AU - Brockman, Joel

AU - Geis, Jennifer

AU - Ramalingam, Naveen

AU - King, David

AU - McClain, Micah T.

AU - Woods, Christopher W.

AU - Henao, Ricardo

AU - Burke, Thomas W.

AU - Tsalik, Ephraim L.

AU - Goforth, Carl W.

AU - Lizewski, Rhonda A.

AU - Lizewski, Stephen E.

AU - Weir, Dawn L.

AU - Letizia, Andrew G.

AU - Sealfon, Stuart C.

AU - Troyanskaya, Olga G.

N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15

PY - 2023/1/1

Y1 - 2023/1/1

N2 - 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.

AB - 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.

UR - https://linkinghub.elsevier.com/retrieve/pii/S2667237523000024

UR - http://www.scopus.com/inward/record.url?scp=85147279871&partnerID=8YFLogxK

U2 - 10.1016/j.crmeth.2023.100395

DO - 10.1016/j.crmeth.2023.100395

M3 - Article

C2 - 36936082

SN - 2667-2375

JO - Cell Reports Methods

JF - Cell Reports Methods

ER -

Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease

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