Markers distinguish viral from bacterial infections in respiratory illness

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Scientists from the National Institutes of Health-funded Respiratory Pathogens Research Centre have identified 11 genetic markers in blood that accurately distinguished between viral and bacterial infections in the respiratory tract.

The finding is important because physicians don’t have a good way to confirm bacterial infections like pneumonia and more-often-than-not default to an antibiotic.

Antibiotics are lifesaving drugs, but overuse is leading to one of the world’s most pressing health threats: antibiotic resistance. Researchers at the University of Rochester Medical Centre are developing a tool to help physicians prescribe antibiotics to patients who really need them, and avoid giving them to individuals who don’t.

“It’s extremely difficult to interpret what’s causing a respiratory tract infection, especially in very ill patients who come to the hospital with a high fever, cough, shortness of breath and other concerning symptoms,” said Dr Ann R Falsey, lead study author, professor and interim chief of the infectious diseases division at UR Medicine’s Strong Memorial Hospital. “My goal is to develop a tool that physicians can use to rule out a bacterial infection with enough certainty that they are comfortable, and their patients are comfortable, foregoing an antibiotic.”

Falsey’s project caught the attention of the US government; she’s one of 10 semifinalists in the Antimicrobial Resistance Diagnostic Challenge, a competition sponsored by NIH and the Biomedical Advanced Research and Development Authority to help combat the development and spread of drug resistant bacteria. Selected from among 74 submissions, Falsey received $50,000 to continue her research and develop a prototype diagnostic test, such as a blood test, using the genetic markers her team identified.

A group of 94 adults hospitalised with lower respiratory tract infections were recruited to participate in Falsey’s study. The team gathered clinical data, took blood from each patient, and conducted a battery of microbiologic tests to determine which individuals had a bacterial infection (41 patients) and which had a non-bacterial or viral infection (53 patients). Dr Thomas J Mariani, professor of paediatrics and biomedical genetics at URMC, used complex genetic and statistical analysis to pinpoint markers in the blood that correctly classified the patients with bacterial infections 80% to 90% of the time.

“Our genes react differently to a virus than they do to bacteria,” said Mariani, a member of the Respiratory Pathogens Research Centre (RPRC). “Rather than trying to detect the specific organism that’s making an individual sick, we’re using genetic data to help us determine what’s affecting the patient and when an antibiotic is appropriate or not.”

Falsey, co-director of the RPRC, and Mariani say that the main limitation of their study is the small sample size and that the genetic classifiers selected from the study population may not prove to be universal to all patients.

Abstract
Lower respiratory tract infection (LRTI) commonly causes hospitalization in adults. Because bacterial diagnostic tests are not accurate, antibiotics are frequently prescribed. Peripheral blood gene expression to identify subjects with bacterial infection is a promising strategy. We evaluated whole blood profiling using RNASeq to discriminate infectious agents in adults with microbiologically defined LRTI. Hospitalized adults with LRTI symptoms were recruited. Clinical data and blood was collected, and comprehensive microbiologic testing performed. Gene expression was measured using RNASeq and qPCR. Genes discriminatory for bacterial infection were identified using the Bonferroni-corrected Wilcoxon test. Constrained logistic models to predict bacterial infection were fit using screened LASSO. We enrolled 94 subjects who were microbiologically classified; 53 as “non-bacterial” and 41 as “bacterial”. RNAseq and qPCR confirmed significant differences in mean expression for 10 genes previously identified as discriminatory for bacterial LRTI. A novel dimension reduction strategy selected three pathways (lymphocyte, α-linoleic acid metabolism, IGF regulation) including eleven genes as optimal markers for discriminating bacterial infection (naïve AUC = 0.94; nested CV-AUC = 0.86). Using these genes, we constructed a classifier for bacterial LRTI with 90% (79% CV) sensitivity and 83% (76% CV) specificity. This novel, pathway-based gene set displays promise as a method to distinguish bacterial from nonbacterial LRTI.

Authors
Soumyaroop Bhattacharya, Alex F Rosenberg, Derick R Peterson, Katherine Grzesik, Andrea M Baran, John M Ashton, Steven R Gill, Anthony M Corbett, Jeanne Holden-Wiltse, David J Topham, Edward E Walsh, Thomas J Mariani, Ann R Falsey

University of Rochester Medical Centre material
Scientific Reports abstract


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