Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients

Abstract Finding novel biomarkers for human pathologies and predicting clinical outcomes for patients is challenging. This stems from the heterogeneous response of individuals to disease and is reflected in the inter-individual variability of gene expression responses that obscures differential gene...

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Autores principales:	Tristan V. de Jong, Victor Guryev, Yuri M. Moshkin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b9aef1fb8b2c43d68c53a1e6fda14b93
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spelling	oai:doaj.org-article:b9aef1fb8b2c43d68c53a1e6fda14b932021-12-02T14:42:01ZEstimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients10.1038/s41598-021-90192-92045-2322https://doaj.org/article/b9aef1fb8b2c43d68c53a1e6fda14b932021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90192-9https://doaj.org/toc/2045-2322Abstract Finding novel biomarkers for human pathologies and predicting clinical outcomes for patients is challenging. This stems from the heterogeneous response of individuals to disease and is reflected in the inter-individual variability of gene expression responses that obscures differential gene expression analysis. Here, we developed an alternative approach that could be applied to dissect the disease-associated molecular changes. We define gene ensemble noise as a measure that represents a variance for a collection of genes encoding for either members of known biological pathways or subunits of annotated protein complexes and calculated within an individual. The gene ensemble noise allows for the holistic identification and interpretation of gene expression disbalance on the level of gene networks and systems. By comparing gene expression data from COVID-19, H1N1, and sepsis patients we identified common disturbances in a number of pathways and protein complexes relevant to the sepsis pathology. Among others, these include the mitochondrial respiratory chain complex I and peroxisomes. This suggests a Warburg effect and oxidative stress as common hallmarks of the immune host–pathogen response. Finally, we showed that gene ensemble noise could successfully be applied for the prediction of clinical outcome namely, the mortality of patients. Thus, we conclude that gene ensemble noise represents a promising approach for the investigation of molecular mechanisms of pathology through a prism of alterations in the coherent expression of gene circuits.Tristan V. de JongVictor GuryevYuri M. MoshkinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tristan V. de Jong Victor Guryev Yuri M. Moshkin Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients
description	Abstract Finding novel biomarkers for human pathologies and predicting clinical outcomes for patients is challenging. This stems from the heterogeneous response of individuals to disease and is reflected in the inter-individual variability of gene expression responses that obscures differential gene expression analysis. Here, we developed an alternative approach that could be applied to dissect the disease-associated molecular changes. We define gene ensemble noise as a measure that represents a variance for a collection of genes encoding for either members of known biological pathways or subunits of annotated protein complexes and calculated within an individual. The gene ensemble noise allows for the holistic identification and interpretation of gene expression disbalance on the level of gene networks and systems. By comparing gene expression data from COVID-19, H1N1, and sepsis patients we identified common disturbances in a number of pathways and protein complexes relevant to the sepsis pathology. Among others, these include the mitochondrial respiratory chain complex I and peroxisomes. This suggests a Warburg effect and oxidative stress as common hallmarks of the immune host–pathogen response. Finally, we showed that gene ensemble noise could successfully be applied for the prediction of clinical outcome namely, the mortality of patients. Thus, we conclude that gene ensemble noise represents a promising approach for the investigation of molecular mechanisms of pathology through a prism of alterations in the coherent expression of gene circuits.
format	article
author	Tristan V. de Jong Victor Guryev Yuri M. Moshkin
author_facet	Tristan V. de Jong Victor Guryev Yuri M. Moshkin
author_sort	Tristan V. de Jong
title	Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients
title_short	Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients
title_full	Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients
title_fullStr	Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients
title_full_unstemmed	Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients
title_sort	estimates of gene ensemble noise highlight critical pathways and predict disease severity in h1n1, covid-19 and mortality in sepsis patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b9aef1fb8b2c43d68c53a1e6fda14b93
work_keys_str_mv	AT tristanvdejong estimatesofgeneensemblenoisehighlightcriticalpathwaysandpredictdiseaseseverityinh1n1covid19andmortalityinsepsispatients AT victorguryev estimatesofgeneensemblenoisehighlightcriticalpathwaysandpredictdiseaseseverityinh1n1covid19andmortalityinsepsispatients AT yurimmoshkin estimatesofgeneensemblenoisehighlightcriticalpathwaysandpredictdiseaseseverityinh1n1covid19andmortalityinsepsispatients
_version_	1718389831985266688

Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients

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