Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections

Zhang, Jingjing, Friberg, Ida M., Kift-Morgan, Ann, Parekh, Gita, Morgan, Matt P., Liuzzi, Anna Rita, Lin, Chan-Yu, Donovan, Kieron L., Colmont, Chantal S., Morgan, Peter H., Davis, Paul, Weeks, Ian, Fraser, Donald J., Topley, Nicholas and Eberl, Matthias 2017. Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections. Kidney International 92 (1) , pp. 179-191. 10.1016/j.kint.2017.01.017

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Abstract

The immune system has evolved to sense invading pathogens, control infection, and restore tissue integrity. Despite symptomatic variability in patients, unequivocal evidence that an individual's immune system distinguishes between different organisms and mounts an appropriate response is lacking. We here used a systematic approach to characterize responses to microbiologically well-defined infection in a total of 83 peritoneal dialysis patients on the day of presentation with acute peritonitis. A broad range of cellular and soluble parameters was determined in peritoneal effluents, covering the majority of local immune cells, inflammatory and regulatory cytokines and chemokines as well as tissue damage–related factors. Our analyses, utilizing machine-learning algorithms, demonstrate that different groups of bacteria induce qualitatively distinct local immune fingerprints, with specific biomarker signatures associated with Gram-negative and Gram-positive organisms, and with culture-negative episodes of unclear etiology. Even more, within the Gram-positive group, unique immune biomarker combinations identified streptococcal and non-streptococcal species including coagulase-negative Staphylococcus spp. These findings have diagnostic and prognostic implications by informing patient management and treatment choice at the point of care. Thus, our data establish the power of non-linear mathematical models to analyze complex biomedical datasets and highlight key pathways involved in pathogen-specific immune responses.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine Business (Including Economics)
Additional Information:	This is an open access article under the CC BY license
Publisher:	Elsevier
ISSN:	0085-2538
Funders:	Kidney Research UK, MRC, NIHR, Health and Care Research Wales, EU FP7
Date of First Compliant Deposit:	9 March 2017
Date of Acceptance:	12 January 2017
Last Modified:	18 Jan 2021 17:30
URI:	http://orca.cf.ac.uk/id/eprint/98870

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