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2565. A Novel Prognostic Gene Set for the Prediction of Severe Dengue

BACKGROUND: There is an urgent need for the identification of biomarkers predictive of severe dengue. Single cohort transcriptomic studies have not yielded a parsimonious gene set predictive of severe dengue. We hypothesized that integration of gene expression data from heterogeneous patient populat...

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Autores principales: Robinson, Makeda L, Sweeney, Timothy E, Barouch-Bentov, Rina, Sahoo, Malaya K, Sanz, Ana Maria, Pu, Szu-Yuan, Ortiz, Eliana, Albornoz, Luis, Suarez, Fernando Rosso, Montoya, Jose G, Pinsky, Benjamin, Khatri, Purvesh, Einav, Shirit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6253061/
http://dx.doi.org/10.1093/ofid/ofy209.173
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author Robinson, Makeda L
Sweeney, Timothy E
Barouch-Bentov, Rina
Sahoo, Malaya K
Sanz, Ana Maria
Pu, Szu-Yuan
Ortiz, Eliana
Albornoz, Luis
Suarez, Fernando Rosso
Montoya, Jose G
Pinsky, Benjamin
Khatri, Purvesh
Einav, Shirit
author_facet Robinson, Makeda L
Sweeney, Timothy E
Barouch-Bentov, Rina
Sahoo, Malaya K
Sanz, Ana Maria
Pu, Szu-Yuan
Ortiz, Eliana
Albornoz, Luis
Suarez, Fernando Rosso
Montoya, Jose G
Pinsky, Benjamin
Khatri, Purvesh
Einav, Shirit
author_sort Robinson, Makeda L
collection PubMed
description BACKGROUND: There is an urgent need for the identification of biomarkers predictive of severe dengue. Single cohort transcriptomic studies have not yielded a parsimonious gene set predictive of severe dengue. We hypothesized that integration of gene expression data from heterogeneous patient populations with dengue infection would yield a set of conserved genes that is predictive of severe dengue and generalizable across cohorts. METHODS: Ten dengue gene expression datasets were identified in publicly available microarray repositories. A novel integrated multicohort platform was used to detect differentially expressed gene transcripts between uncomplicated and severe dengue patients and validate the identified putative signature in silico and prospectively in a new cohort of 34 dengue patients in Colombia. Dengue diagnosis was made by NS1 antigen and anti-DENV IgM antibody and confirmed by RT-PCR assays, ELISA, and IgG avidity measurements. The expression level of the signature genes was measured via microfluidic qRT-PCR assays in blood samples collected longitudinally during the course of illness. RESULTS: Using the multicohort analysis to analyze 446 peripheral blood samples of patients with dengue infection from 7 publicly available gene expression datasets, we identified a 20 gene set that predicts the development of severe dengue. We in silico validated the diagnostic power of this gene set to separate severe dengue from dengue with or without warning signs in 3 independent datasets composed of 84 samples with a global area under the ROC curve (AUC) of 0.80 [95% CI 0.68–0.88]. We prospectively validated the gene set in a new cohort composed of 34 dengue patients from Colombia with an AUC of 0.89 [95% CI 0.81–0.97]. The severity scores measured in patients with severe dengue progressively declined in longitudinal samples. CONCLUSION: Our data indicate that the identified 20 gene signature predicts the development of severe dengue in patients prior to its onset and suggest that dengue infection itself triggers this host response. These findings may provide new insight into the pathogenesis of severe dengue and have implications for the development of a prognostic molecular assay to identify patients at risk to develop severe dengue. DISCLOSURES: T. E. Sweeney, Inflammatix, Inc.: Employee and Shareholder, Salary. P. Khatri, Inflammatix, Inc: Scientific Advisor and Shareholder, Licensing agreement or royalty.
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spelling pubmed-62530612018-11-28 2565. A Novel Prognostic Gene Set for the Prediction of Severe Dengue Robinson, Makeda L Sweeney, Timothy E Barouch-Bentov, Rina Sahoo, Malaya K Sanz, Ana Maria Pu, Szu-Yuan Ortiz, Eliana Albornoz, Luis Suarez, Fernando Rosso Montoya, Jose G Pinsky, Benjamin Khatri, Purvesh Einav, Shirit Open Forum Infect Dis Abstracts BACKGROUND: There is an urgent need for the identification of biomarkers predictive of severe dengue. Single cohort transcriptomic studies have not yielded a parsimonious gene set predictive of severe dengue. We hypothesized that integration of gene expression data from heterogeneous patient populations with dengue infection would yield a set of conserved genes that is predictive of severe dengue and generalizable across cohorts. METHODS: Ten dengue gene expression datasets were identified in publicly available microarray repositories. A novel integrated multicohort platform was used to detect differentially expressed gene transcripts between uncomplicated and severe dengue patients and validate the identified putative signature in silico and prospectively in a new cohort of 34 dengue patients in Colombia. Dengue diagnosis was made by NS1 antigen and anti-DENV IgM antibody and confirmed by RT-PCR assays, ELISA, and IgG avidity measurements. The expression level of the signature genes was measured via microfluidic qRT-PCR assays in blood samples collected longitudinally during the course of illness. RESULTS: Using the multicohort analysis to analyze 446 peripheral blood samples of patients with dengue infection from 7 publicly available gene expression datasets, we identified a 20 gene set that predicts the development of severe dengue. We in silico validated the diagnostic power of this gene set to separate severe dengue from dengue with or without warning signs in 3 independent datasets composed of 84 samples with a global area under the ROC curve (AUC) of 0.80 [95% CI 0.68–0.88]. We prospectively validated the gene set in a new cohort composed of 34 dengue patients from Colombia with an AUC of 0.89 [95% CI 0.81–0.97]. The severity scores measured in patients with severe dengue progressively declined in longitudinal samples. CONCLUSION: Our data indicate that the identified 20 gene signature predicts the development of severe dengue in patients prior to its onset and suggest that dengue infection itself triggers this host response. These findings may provide new insight into the pathogenesis of severe dengue and have implications for the development of a prognostic molecular assay to identify patients at risk to develop severe dengue. DISCLOSURES: T. E. Sweeney, Inflammatix, Inc.: Employee and Shareholder, Salary. P. Khatri, Inflammatix, Inc: Scientific Advisor and Shareholder, Licensing agreement or royalty. Oxford University Press 2018-11-26 /pmc/articles/PMC6253061/ http://dx.doi.org/10.1093/ofid/ofy209.173 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Robinson, Makeda L
Sweeney, Timothy E
Barouch-Bentov, Rina
Sahoo, Malaya K
Sanz, Ana Maria
Pu, Szu-Yuan
Ortiz, Eliana
Albornoz, Luis
Suarez, Fernando Rosso
Montoya, Jose G
Pinsky, Benjamin
Khatri, Purvesh
Einav, Shirit
2565. A Novel Prognostic Gene Set for the Prediction of Severe Dengue
title 2565. A Novel Prognostic Gene Set for the Prediction of Severe Dengue
title_full 2565. A Novel Prognostic Gene Set for the Prediction of Severe Dengue
title_fullStr 2565. A Novel Prognostic Gene Set for the Prediction of Severe Dengue
title_full_unstemmed 2565. A Novel Prognostic Gene Set for the Prediction of Severe Dengue
title_short 2565. A Novel Prognostic Gene Set for the Prediction of Severe Dengue
title_sort 2565. a novel prognostic gene set for the prediction of severe dengue
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6253061/
http://dx.doi.org/10.1093/ofid/ofy209.173
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