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

Journal: Kidney international
PMID: 28318629

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.

Authors

  • Jingjing Zhang
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Ida M Friberg
    Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.
  • Ann Kift-Morgan
    Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.
  • Gita Parekh
    Mologic Ltd., Bedford Technology Park, Thurleigh, Bedford, UK.
  • Matt P Morgan
    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.
  • Anna Rita Liuzzi
    Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.
  • Chan-Yu Lin
    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.
  • Kieron L Donovan
    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.
  • Chantal S Colmont
    Wales Kidney Research Unit, Heath Park Campus, Cardiff, UK.
  • Peter H Morgan
    Cardiff Business School, Cardiff University, Cardiff, UK.
  • Paul Davis
    Mologic Ltd., Bedford Technology Park, Thurleigh, Bedford, UK.
  • Ian Weeks
    Systems Immunity Research Institute, Cardiff University, Cardiff, UK.
  • Donald J Fraser
    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.
  • Nicholas Topley
    Wales Kidney Research Unit, Heath Park Campus, Cardiff, UK; Systems Immunity Research Institute, Cardiff University, Cardiff, UK.
  • Matthias Eberl
    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.

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