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Heterogeneity within and across Pediatric Pulmonary Infections: From Bipartite Networks to At-Risk Subphenotypes

Although influenza (flu) and respiratory syncytial virus (RSV) infections are extremely common in children under two years and resolve naturally, a subset develop severe disease resulting in hospitalization despite having no identifiable clinical risk factors. However, little is known about inherent...

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Autores principales: Bhavnani, Suresh K., Dang, Bryant, Caro, Maria, Bellala, Gowtham, Visweswaran, Shyam, Mejias, Asuncion, Divekar, Rohit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333711/
https://www.ncbi.nlm.nih.gov/pubmed/25717396
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author Bhavnani, Suresh K.
Dang, Bryant
Caro, Maria
Bellala, Gowtham
Visweswaran, Shyam
Mejias, Asuncion
Divekar, Rohit
author_facet Bhavnani, Suresh K.
Dang, Bryant
Caro, Maria
Bellala, Gowtham
Visweswaran, Shyam
Mejias, Asuncion
Divekar, Rohit
author_sort Bhavnani, Suresh K.
collection PubMed
description Although influenza (flu) and respiratory syncytial virus (RSV) infections are extremely common in children under two years and resolve naturally, a subset develop severe disease resulting in hospitalization despite having no identifiable clinical risk factors. However, little is known about inherent host-specific genetic and immune mechanisms in this at-risk subpopulation. We therefore conducted a secondary analysis of statistically significant, differentially-expressed genes from a whole genome-wide case-control study of children less than two years of age hospitalized with flu or RSV, through the use of bipartite networks. The analysis revealed three clusters of cases common to both types of infection: core cases with high expression of genes in the network core implicated in hyperimmune responsiveness; periphery cases with lower expression of the same set of genes indicating medium-responsiveness; and control-like cases with a gene signature resembling that of the controls, indicating normal-responsiveness. These results provide testable hypotheses for at-risk subphenotypes and their respective pathophysiologies in both types of infections. We conclude by discussing alternate hypotheses for the results, and insights about how bipartite networks of gene expression across multiple phenotypes can help to identify complex patterns related to subphenotypes, with the translational goal of identifying targeted therapeutics.
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spelling pubmed-43337112015-02-25 Heterogeneity within and across Pediatric Pulmonary Infections: From Bipartite Networks to At-Risk Subphenotypes Bhavnani, Suresh K. Dang, Bryant Caro, Maria Bellala, Gowtham Visweswaran, Shyam Mejias, Asuncion Divekar, Rohit AMIA Jt Summits Transl Sci Proc Articles Although influenza (flu) and respiratory syncytial virus (RSV) infections are extremely common in children under two years and resolve naturally, a subset develop severe disease resulting in hospitalization despite having no identifiable clinical risk factors. However, little is known about inherent host-specific genetic and immune mechanisms in this at-risk subpopulation. We therefore conducted a secondary analysis of statistically significant, differentially-expressed genes from a whole genome-wide case-control study of children less than two years of age hospitalized with flu or RSV, through the use of bipartite networks. The analysis revealed three clusters of cases common to both types of infection: core cases with high expression of genes in the network core implicated in hyperimmune responsiveness; periphery cases with lower expression of the same set of genes indicating medium-responsiveness; and control-like cases with a gene signature resembling that of the controls, indicating normal-responsiveness. These results provide testable hypotheses for at-risk subphenotypes and their respective pathophysiologies in both types of infections. We conclude by discussing alternate hypotheses for the results, and insights about how bipartite networks of gene expression across multiple phenotypes can help to identify complex patterns related to subphenotypes, with the translational goal of identifying targeted therapeutics. American Medical Informatics Association 2014-04-07 /pmc/articles/PMC4333711/ /pubmed/25717396 Text en ©2014 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose
spellingShingle Articles
Bhavnani, Suresh K.
Dang, Bryant
Caro, Maria
Bellala, Gowtham
Visweswaran, Shyam
Mejias, Asuncion
Divekar, Rohit
Heterogeneity within and across Pediatric Pulmonary Infections: From Bipartite Networks to At-Risk Subphenotypes
title Heterogeneity within and across Pediatric Pulmonary Infections: From Bipartite Networks to At-Risk Subphenotypes
title_full Heterogeneity within and across Pediatric Pulmonary Infections: From Bipartite Networks to At-Risk Subphenotypes
title_fullStr Heterogeneity within and across Pediatric Pulmonary Infections: From Bipartite Networks to At-Risk Subphenotypes
title_full_unstemmed Heterogeneity within and across Pediatric Pulmonary Infections: From Bipartite Networks to At-Risk Subphenotypes
title_short Heterogeneity within and across Pediatric Pulmonary Infections: From Bipartite Networks to At-Risk Subphenotypes
title_sort heterogeneity within and across pediatric pulmonary infections: from bipartite networks to at-risk subphenotypes
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333711/
https://www.ncbi.nlm.nih.gov/pubmed/25717396
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