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 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.

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“…Taken together, SARS-CoV-2 would be directly targeting the innate immunity and inflammation signalling pathways by interfering with the NF-κB activity. Thus, it is not surprising that TLE1 is a top-ranking regulator of inflammation that allows to transcriptionally distinguish mild from severe COVID-19 [29].…”

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“…The gene-wise, mean-centered was projected to eigenvectors: , . The eigenvalues ( , ) were estimated by GAMLSS using R ’s gamlss package [ 7 , 17 , 44 ]. The estimated and were compared between surrogate mother groups by the Wald test: ( —GAMLSS estimated parameter ( , , etc.…”

“…Composite FA is inversely related to individuals’ quality/fitness [ 14 , 15 ] and increases with age [ 16 ]. Estimating individuals’ developmental/transcriptional instability could also be practical for diagnostics, as some diseases, such as those that are infectious and inflammatory, destabilize the transcriptional landscape [ 17 ].…”