Combining the host transcriptome and molecular pathogen diagnostics to better understand febrile illness in children

Abstract

Background: The significant morbidity and mortality associated with infectious diseases if untreated and the rise of antimicrobial resistance has prompted the development of novel diagnostics for infectious diseases. These diagnostics have performed well at identifying patients with confident diagnosis of bacterial or viral infection, but perform less well when the aetiology is unclear. The introduction of molecular diagnostics has shown there is more pathogen data than what is revealed by conventional diagnostics. In this study I explore whether molecular diagnostic data, when analysed in conjunction with gene expression data can help us to understand febrile illnesses more clearly. Methods: I analysed molecular diagnostic pathogen data from 4611 febrile children and 1061 controls recruited to the PERFORM study, an EU funded study recruiting patients with fever or suspected infection across Europe and supplemented this with host gene expression data from a 12-gene signature. Results: Pathogen detection did not always align with the patient’s clinical course, with bacteria detected in patients presumed to have viral disease and controls and viruses detected in patients presumed to have viral disease and controls. In addition, we found that pathogen co-detection was common and non-random. Gene expression analysis of a 12-gene signature showed that the detection of some pathogens and syndromes resulted in a “bacterial-like” response, others resulted in a “viral-like” response, while many resulted in a mixed picture, with both bacterial and viral-like 6 responses in different patients. The host response was also influenced by site of infection and the presence of co-detections. Conclusions: This study shows the utility of gene expression studies in understanding infections. Further analysis of additional pathogen data, and expression data from a wider set of genes at multiple timepoints, will allow for better understanding of when pathogen detection requires intervention. This could form the foundation for novel diagnostics for infectious diseases.