Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections.
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.
Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.
Jingjing
J
Zhang
Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.
Ida M
IM
Friberg
Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.
Ann
A
Kift-Morgan
Mologic Ltd., Bedford Technology Park, Thurleigh, Bedford, UK.
Gita
G
Parekh
Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK; Directorate of Critical Care, Cardiff and Vale University Health Board, University Hospital of Wales, Heath Park, Cardiff, UK.
Matt P
MP
Morgan
Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.
Anna Rita
AR
Liuzzi
Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK; Kidney Research Center, Chang Gung Memorial Hospital, Chang Gung University, College of Medicine, Taoyuan City, Taiwan.
Chan-Yu
CY
Lin
Wales Kidney Research Unit, Heath Park Campus, Cardiff, UK; Directorate of Nephrology and Transplantation, Cardiff and Vale University Health Board, University Hospital of Wales, Heath Park, Cardiff, UK.
Kieron L
KL
Donovan
Wales Kidney Research Unit, Heath Park Campus, Cardiff, UK.
Chantal S
CS
Colmont
Cardiff Business School, Cardiff University, Cardiff, UK.
Peter H
PH
Morgan
Mologic Ltd., Bedford Technology Park, Thurleigh, Bedford, UK.
Paul
P
Davis
Systems Immunity Research Institute, Cardiff University, Cardiff, UK.
Ian
I
Weeks
Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK; Wales Kidney Research Unit, Heath Park Campus, Cardiff, UK; Directorate of Nephrology and Transplantation, Cardiff and Vale University Health Board, University Hospital of Wales, Heath Park, Cardiff, UK; Systems Immunity Research Institute, Cardiff University, Cardiff, UK.
Donald J
DJ
Fraser
Wales Kidney Research Unit, Heath Park Campus, Cardiff, UK; Systems Immunity Research Institute, Cardiff University, Cardiff, UK.
Nicholas
N
Topley
Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK; Systems Immunity Research Institute, Cardiff University, Cardiff, UK. Electronic address: eberlm@cf.ac.uk.
Matthias
M
Eberl
D000208:Acute Disease; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001419:Bacteria / Q000145:classification / Q000276:immunology* / Q000472:pathogenicity; D015415:Biomarkers / Q000378:metabolism; D016022:Case-Control Studies; D005260:Female; D016905:Gram-Negative Bacterial Infections / Q000175:diagnosis* / Q000276:immunology / Q000378:metabolism / Q000382:microbiology; D016908:Gram-Positive Bacterial Infections / Q000175:diagnosis* / Q000276:immunology / Q000378:metabolism / Q000382:microbiology; D054884:Host-Pathogen Interactions; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D010449:Peptide Mapping / Q000379:methods*; D010530:Peritoneal Dialysis / Q000009:adverse effects*; D010538:Peritonitis / Q000175:diagnosis* / Q000276:immunology / Q000378:metabolism / Q000382:microbiology; D019095:Point-of-Care Systems; D000067716:Point-of-Care Testing; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D013997:Time Factors; D055815:Young Adult
D015415:Biomarkers
biomarkers; inflammation; machine learning methods; microbial infection; peritoneal dialysis