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

Heather Chick, Megan Rees, Matthew Lewis, Lisa Williams, Owen Bodger Orcid Logo, Llinos Harris Orcid Logo, Steven Rushton, Thomas Wilkinson Orcid Logo

Biomedicines, Volume: 12, Issue: 4, Start page: 724

Swansea University Authors: Heather Chick, Megan Rees, Matthew Lewis, Lisa Williams, Owen Bodger Orcid Logo, Llinos Harris Orcid Logo, Thomas Wilkinson Orcid Logo

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DOI (Published version): 10.3390/biomedicines12040724

Abstract

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 s...

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Published in: Biomedicines
ISSN: 2227-9059
Published: MDPI AG 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa65918
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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. 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spelling v2 65918 2024-03-26 Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses 00e95531dda8486188b1e44f7d27af77 Heather Chick Heather Chick true false 8b1bcd1353bb073cfaf4291e06b2c549 Megan Rees Megan Rees true false d7abbd0311803af9852f5cf8d9cde925 Matthew Lewis Matthew Lewis true false 47b8363ea06585d818ea53124498e3bd Lisa Williams Lisa Williams true false 8096440ab42b60a86e6aba678fe2695a 0000-0002-4022-9964 Owen Bodger Owen Bodger true false dc70f9d4badbbdb5d467fd321986d173 0000-0002-0295-3038 Llinos Harris Llinos Harris true false 86cca6bf31bfe8572de27c1b441420d8 0000-0003-0397-6079 Thomas Wilkinson Thomas Wilkinson true false 2024-03-26 BMS 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. Journal Article Biomedicines 12 4 724 MDPI AG 2227-9059 ex vivo whole blood models; host immune responses; bacterial discrimination; pair-wise comparison; multivariate analysis; Staphylococcus epidermidis; Staphylococcus aureus; Escherichia coli 25 3 2024 2024-03-25 10.3390/biomedicines12040724 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University This work was funded by a Health Care Research Wales (HCRW-HS-18-32) PhD studentship for M.L.L. awarded to T.S.W., funded by a Swansea University scholarship for H.M.C. awarded to T.S.W., partially funded by a BBSRC grant (BB0191421/1) awarded to T.S.W. 2024-04-15T17:03:15.4143760 2024-03-26T16:48:11.4500172 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Heather Chick 1 Megan Rees 2 Matthew Lewis 3 Lisa Williams 4 Owen Bodger 0000-0002-4022-9964 5 Llinos Harris 0000-0002-0295-3038 6 Steven Rushton 7 Thomas Wilkinson 0000-0003-0397-6079 8 65918__29876__35f80dd3417c4e31a41ba2c0751af4c3.pdf 65918.pdf 2024-04-03T10:42:37.5162752 Output 2174813 application/pdf Version of Record true This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng https://creativecommons.org/licenses/by/4.0/
title Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses
spellingShingle Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses
Heather Chick
Megan Rees
Matthew Lewis
Lisa Williams
Owen Bodger
Llinos Harris
Thomas Wilkinson
title_short Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses
title_full Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses
title_fullStr Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses
title_full_unstemmed Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses
title_sort Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses
author_id_str_mv 00e95531dda8486188b1e44f7d27af77
8b1bcd1353bb073cfaf4291e06b2c549
d7abbd0311803af9852f5cf8d9cde925
47b8363ea06585d818ea53124498e3bd
8096440ab42b60a86e6aba678fe2695a
dc70f9d4badbbdb5d467fd321986d173
86cca6bf31bfe8572de27c1b441420d8
author_id_fullname_str_mv 00e95531dda8486188b1e44f7d27af77_***_Heather Chick
8b1bcd1353bb073cfaf4291e06b2c549_***_Megan Rees
d7abbd0311803af9852f5cf8d9cde925_***_Matthew Lewis
47b8363ea06585d818ea53124498e3bd_***_Lisa Williams
8096440ab42b60a86e6aba678fe2695a_***_Owen Bodger
dc70f9d4badbbdb5d467fd321986d173_***_Llinos Harris
86cca6bf31bfe8572de27c1b441420d8_***_Thomas Wilkinson
author Heather Chick
Megan Rees
Matthew Lewis
Lisa Williams
Owen Bodger
Llinos Harris
Thomas Wilkinson
author2 Heather Chick
Megan Rees
Matthew Lewis
Lisa Williams
Owen Bodger
Llinos Harris
Steven Rushton
Thomas Wilkinson
format Journal article
container_title Biomedicines
container_volume 12
container_issue 4
container_start_page 724
publishDate 2024
institution Swansea University
issn 2227-9059
doi_str_mv 10.3390/biomedicines12040724
publisher MDPI AG
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 1
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description 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.
published_date 2024-03-25T17:03:11Z
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