An OMICs-based meta-analysis to support infection state stratification

Myall, Ashleigh C, Perkins, Simon, Rushton, David, David, Jonathan, Spencer, Phillippa, Jones, Andrew R ORCID: 0000-0001-6118-9327 and Antczak, Philipp ORCID: 0000-0001-9600-7757 (2021) An OMICs-based meta-analysis to support infection state stratification. BIOINFORMATICS, 37 (16). pp. 2347-2355.

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Abstract

<h4>Motivation</h4>A fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure an individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can in theory be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral/bacterial/no-infection), we conducted a meta-analysis of human blood infection studies using machine learning.<h4>Results</h4>We focused on publicly available gene expression data from two widely used platforms, Affymetrix and Illumina microarrays as they represented a significant proportion of the available data. We were able to develop multi-class models with high accuracies with our best model predicting 93% of bacterial and 89% viral samples correctly. To compare the selected features in each of the different technologies, we reverse-engineered the underlying molecular regulatory network and explored the neighbourhood of the selected features. The networks highlighted that although on the gene-level the models differed, they contained genes from the same areas of the network. Specifically, this convergence was to pathways including the Type I interferon Signalling Pathway, Chemotaxis, Apoptotic Processes and Inflammatory/Innate Response.<h4>Availability</h4>Data and code are available on the Gene Expression Omnibus and github.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.

Item Type:	Article
Uncontrolled Keywords:	Biotechnology, Emerging Infectious Diseases, Prevention, Infectious Diseases, Vaccine Related, Genetics, Biodefense, 4 Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Infection, 3 Good Health and Well Being
Depositing User:	Symplectic Admin
Date Deposited:	11 Feb 2021 11:42
Last Modified:	14 Mar 2024 18:37
DOI:	10.1093/bioinformatics/btab089
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3115499