O'Reilly, E. L. et al. (2024) Biomarker and proteome analysis of milk from dairy cows with clinical mastitis: determining the effect of different bacterial pathogens on the response to infection. Research in Veterinary Science, 172, 105240. (doi: 10.1016/j.rvsc.2024.105240) (PMID:38608347)
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Abstract
Antimicrobial usage (AMU) could be reduced by differentiating the causative bacteria in cases of clinical mastitis (CM) as either Gram-positive or Gram-negative bacteria or identifying whether the case is culture-negative (no growth, NG) mastitis. Immunoassays for biomarker analysis and a Tandem Mass Tag (TMT) proteomic investigation were employed to identify differences between samples of milk from cows with CM caused by different bacteria. A total of 94 milk samples were collected from cows diagnosed with CM across seven farms in Scotland, categorized by severity as mild (score 1), moderate (score 2), or severe (score 3). Bovine haptoglobin (Hp), milk amyloid A (MAA), C-reactive protein (CRP), lactoferrin (LF), α-lactalbumin (LA) and cathelicidin (CATHL) were significantly higher in milk from cows with CM, regardless of culture results, than in milk from healthy cows (all P-values <0.001). Milk cathelicidin (CATHL) was evaluated using a novel ELISA technique that utilises an antibody to a peptide sequence of SSEANLYRLLELD (aa49–61) common to CATHL 1–7 isoforms. A classification tree was fitted on the six biomarkers to predict Gram-positive bacteria within mastitis severity scores 1 or 2, revealing that compared to the rest of the samples, Gram-positive samples were associated with CRP < 9.5 μg/ml and LF ≥ 325 μg/ml and MAA < 16 μg/ml. Sensitivity of the tree model was 64%, the specificity was 91%, and the overall misclassification rate was 18%. The area under the ROC curve for this tree model was 0.836 (95% bootstrap confidence interval: 0.742; 0.917). TMT proteomic analysis revealed little difference between the groups in protein abundance when the three groups (Gram-positive, Gram-negative and no growth) were compared, however when each group was compared against the entirety of the remaining samples, 28 differentially abundant protein were identified including β-lactoglobulin and ribonuclease. Whilst further research is required to draw together and refine a suitable biomarker panel and diagnostic algorithm for differentiating Gram- positive/negative and NG CM, these results have highlighted a potential panel and diagnostic decision tree. Host-derived milk biomarkers offer significant potential to refine and reduce AMU and circumvent the many challenges associated with microbiological culture, both within the lab and on the farm, while providing the added benefit of reducing turnaround time from 14 to 16 h of microbiological culture to just 15 min with a lateral flow device (LFD).
| Item Type: | Articles |
|---|---|
| Additional Information: | This work was supported by Innovate UK Project No: 104295 and by the European Commission FP7 ERA Chair “VetMedZg” Project (grant number 621394). |
| Keywords: | Mastitis, dairy biomarker, acute phase, classification tree diagnostic. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Malcata, Dr Francisco and Eckersall, Professor David and Brady, Mrs Nicola and Viora, Dr Lorenzo and Horvatic, Ms Anita and O'Reilly, Dr Emily and McLaughlin, Dr Mark and Pepler, Dr Theo and Zadoks, Professor Ruth and Kules, Mrs Josipa |
| Creator Roles: | O'Reilly, E. L.Writing – review and editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Data curation, Conceptualization Viora, L.Writing – review and editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization Malcata, F.Investigation, Formal analysis Pepler, P. T.Writing – review and editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Formal analysis, Data curation Zadoks, R.Writing – review and editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization Brady, N.Writing – review and editing, Writing – original draft, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization McLaughlin, M.Writing – review and editing, Writing – original draft, Supervision, Methodology, Formal analysis, Conceptualization Horvatic, A.Writing – review and editing, Validation, Methodology, Formal analysis Kules, J.Writing – review and editing, Validation, Formal analysis Eckersall, P. D.Writing – review and editing, Writing – original draft, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization |
| Authors: | O'Reilly, E. L., Viora, L., Malcata, F., Pepler, P. T., Zadoks, R., Brady, N., Hanh, H. Q., McLaughlin, M., Horvatic, A., Gelemanovic, A., Kules, J., Mrljak, V., and Eckersall, P. D. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine College of Medical Veterinary and Life Sciences > School of Cancer Sciences |
| Journal Name: | Research in Veterinary Science |
| Publisher: | Elsevier |
| ISSN: | 0034-5288 |
| ISSN (Online): | 1532-2661 |
| Published Online: | 29 March 2024 |
| Copyright Holders: | Copyright © 2024 The Authors |
| First Published: | First published in Research in Veterinary Science 172: 105240 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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