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.
Highlights
The immune system is an intricate network of specialized cell types and molecular structures evolved to sense and target invading pathogens, control and clear the infection, and repair and restore the integrity of affected tissues and organs
As alternative to organism-based diagnostics, we aimed at exploiting the human host response and used a systematic approach based on machine learning algorithms to identify diagnostically relevant, pathogen-specific local immune fingerprints in peritoneal dialysis (PD) patients who presented with acute peritonitis
By combining biomarker measurements during acute peritonitis and feature selection approaches based on Support Vector Machines (SVMs), Artificial neural networks (ANNs), and Random Forests (RFs), our findings demonstrate the power of advanced mathematical models to analyze complex biomedical datasets and highlight key pathways involved in pathogen-specific inflammatory responses at the site of infection
Summary
The immune system is an intricate network of specialized cell types and molecular structures evolved to sense and target invading pathogens, control and clear the infection, and repair and restore the integrity of affected tissues and organs. Wrapped feature selection methods have proved highly efficient for finding the best feature combination compared with time-consuming exhaustive searches.[18] Support Vector Machines (SVMs) are data-driven methods that try to find a separating hyperplane with the maximal “margin” for classification problems and that can be used for regression or density estimation.[19,20,21] Artificial neural networks (ANNs) are inspired by biological neural networks with data processing from the input through a network of multiple nodes that are connected with each other in different layers.[22,23,24] Random Forests (RFs) are ensemble methods constructed on multiple decision trees for classification and regression.[25,26,27] By combining biomarker measurements during acute peritonitis and feature selection approaches based on SVMs, ANNs, and RFs, our findings demonstrate the power of advanced mathematical models to analyze complex biomedical datasets and highlight key pathways involved in pathogen-specific inflammatory responses at the site of infection. The observation that different infecting bacteria induce consistent and unique local immune responses has immediate diagnostic implications at the point of care by directing appropriate antibiotic treatment before conventional microbiological culture results become available
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