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Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses

Lookup NU author(s): Professor Stephen Rushton

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2024 by the authors.Whole blood models are rapid and versatile for determining immune responses to inflammatory and infectious stimuli, but they have not been used for bacterial discrimination. Staphylococcus aureus, S. epidermidis and Escherichia coli are the most common causes of invasive disease, and rapid testing strategies utilising host responses remain elusive. Currently, immune responses can only discriminate between bacterial ‘domains’ (fungi, bacteria and viruses), and very few studies can use immune responses to discriminate bacteria at the species and strain level. Here, whole blood was used to investigate the relationship between host responses and bacterial strains. Results confirmed unique temporal profiles for the 10 parameters studied: IL-6, MIP-1α, MIP-3α, IL-10, resistin, phagocytosis, S100A8, S100A8/A9, C5a and TF3. Pairwise analysis confirmed that IL-6, resistin, phagocytosis, C5a and S100A8/A9 could be used in a discrimination scheme to identify to the strain level. Linear discriminant analysis (LDA) confirmed that (i) IL-6, MIP-3α and TF3 could predict genera with 95% accuracy; (ii) IL-6, phagocytosis, resistin and TF3 could predict species at 90% accuracy and (iii) phagocytosis, S100A8 and IL-10 predicted strain at 40% accuracy. These data are important because they confirm the proof of concept that host biomarker panels could be used to identify bacterial pathogens.


Publication metadata

Author(s): Chick HM, Rees ME, Lewis ML, Williams LK, Bodger O, Harris LG, Rushton S, Wilkinson TS

Publication type: Article

Publication status: Published

Journal: Biomedicines

Year: 2024

Volume: 12

Issue: 4

Online publication date: 25/03/2024

Acceptance date: 22/03/2024

Date deposited: 08/05/2024

ISSN (electronic): 2227-9059

Publisher: Multidisciplinary Digital Publishing Institute (MDPI)

URL: https://doi.org/10.3390/biomedicines12040724

DOI: 10.3390/biomedicines12040724

Data Access Statement: Datasets presented in this manuscript are available upon request from the corresponding author.


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Funding

Funder referenceFunder name
BB0191421/1
BBSRC

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