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ABSTRACT Lower respiratory tract infections (LRTIs) remain a significant global cause of infectious disease-related mortality. Accurate discrimination between acute bacterial and viral LRTIs is crucial for optimal patient care, prevention of unnecessary antibiotic prescriptions, and resource allocation. Plasma samples from LRTI patients with bacterial (n = 36), viral (n = 27; excluding SARS-CoV-2), SARS-CoV-2 (n = 22), and mixed bacterial–viral (n = 38) etiology were analyzed for protein profiling. Whole-blood RNA samples from a subset of patients (bacterial, n = 8; viral, n = 8; and SARS-CoV-2, n = 8) were analyzed for transcriptional profiling. Lasso regression modeling identified a seven-protein signature (CRP, IL4, IL9, IP10, MIP1α, MIP1β, and TNFα) that discriminated between patients with bacterial (n = 36) vs viral (n = 27) infections with an area under the curve (AUC) of 0.98. When comparing patients with bacterial and mixed bacterial–viral infections (antibiotics clinically justified; n = 74) vs patients with viral and SARS-CoV-2 infections (antibiotics clinically not justified; n = 49), a 10-protein signature (CRP, bFGF, eotaxin, IFNγ, IL1β, IL7, IP10, MIP1α, MIP1β, and TNFα) with an AUC of 0.94 was identified. The transcriptional profiling analysis identified 232 differentially expressed genes distinguishing bacterial (n = 8) from viral and SARS-CoV-2 (n = 16) etiology. Protein–protein interaction enrichment analysis identified 20 genes that could be useful in the differentiation between bacterial and viral infections. Finally, we examined the performance of selected published gene signatures for bacterial–viral differentiation in our gene set, yielding promising results. Further validation of both protein and gene signatures in diverse clinical settings is warranted to establish their potential to guide the treatment of acute LRTIs.IMPORTANCEAccurate differentiation between bacterial and viral lower respiratory tract infections (LRTIs) is vital for effective patient care and resource allocation. This study investigated specific protein signatures and gene expression patterns in plasma and blood samples from LRTI patients that distinguished bacterial and viral infections. The identified signatures can inform the design of point-of-care tests that can aid healthcare providers in making informed decisions about antibiotic prescriptions in order to reduce unnecessary use, thereby contributing to reduced side effects and antibiotic resistance. Furthermore, the potential for faster and more accurate diagnoses for improved patient management in acute LRTIs is compelling. |
