Superiority of transcriptional profiling over procalcitonin for distinguishing bacterial from viral lower respiratory tract infections in hospitalized adults

Abstract

Background: Distinguishing between bacterial and viral lower respiratory tract infection (LRTI) remains challenging. Transcriptional profiling is a promising tool for improving diagnosis in LRTI.

Methods: We performed whole blood transcriptional analysis in 118 patients (median age [interquartile range], 61 [50-76] years) hospitalized with LRTI and 40 age-matched healthy controls (median age, 60 [46-70] years). We applied class comparisons, modular analysis, and class prediction algorithms to identify and validate diagnostic biosignatures for bacterial and viral LRTI.

Results: Patients were classified as having bacterial (n = 22), viral (n = 71), or bacterial-viral LRTI (n = 25) based on comprehensive microbiologic testing. Compared with healthy controls, statistical group comparisons (P < .01; multiple-test corrections) identified 3376 differentially expressed genes in patients with bacterial LRTI, 2391 in viral LRTI, and 2628 in bacterial-viral LRTI. Patients with bacterial LRTI showed significant overexpression of inflammation and neutrophil genes (bacterial > bacterial-viral > viral), and those with viral LRTI displayed significantly greater overexpression of interferon genes (viral > bacterial-viral > bacterial). The K-nearest neighbors algorithm identified 10 classifier genes that discriminated between bacterial and viral LRTI with a 95% sensitivity (95% confidence interval, 77%-100%) and 92% specificity (77%-98%), compared with a sensitivity of 38% (18%-62%) and a specificity of 91% (76%-98%) for procalcitonin.

Conclusions: Transcriptional profiling is a helpful tool for diagnosis of LRTI.

Keywords: bacterial infections; lower respiratory tract infection; microarrays; procalcitonin; viral infections.

Figure 1.

Lower respiratory tract infection (LRTI) whole blood transcriptional signature. A, Heat map representing the transcriptional profile of 20 healthy controls and 59 patients with LRTI based on 3986 transcripts obtained from a nonparametric test (P < .01), 1.25-fold change, and Benjamini–Hochberg multiple-test correction. Transcripts were organized by hierarchical clustering (standard correlation) according to similarities in expression profiles. Transcripts are represented in rows, and individual subjects in columns. Normalized log ratio levels are indicated in red (overexpressed) or blue (underexpressed), as compared with the median expression of the healthy controls. B, Unsupervised hierarchical clustering (distance method) of the transcriptional profiles from the same 3986 transcripts in an independent test cohort comprising 20 healthy controls and 59 patients with LRTI. C, Average modular transcriptional profile for patients with LRTI compared with healthy controls in the training set. D, Average modular transcriptional profile for patients with LRTI as compared with healthy controls in the test set. E, Module functional annotations legend. F, Scatterplot representing the module expression correlation (Spearman) between the training (x-axis) and test (y-axis) sets. Abbreviation: NK, natural killer.

Figure 2.

Transcriptional profiles in patients with bacterial, viral, and bacterial-viral (coinfection) lower respiratory tract infection (LRTI). Heat maps represent the transcriptional profiles of 18 healthy controls and 22 patients with a bacterial LRTI based on 3376 transcripts (A); 18 healthy controls and 71 patients with a viral LRTI based on 2391 transcripts (B), and 18 healthy controls and 25 patients with a bacterial-viral LRTI based on 2628 transcripts (C). All transcripts were identified after applying a nonparametric test (Mann–Whitney) (P < .01), 1.25-fold change, and Benjamini–Hochberg multiple-test correction. D, Venn diagram displaying the overlap among the significant transcripts identified in the 3 LRTI groups.

Figure 3.

Modular transcriptional fingerprint comparison among the 3 lower respiratory tract infection groups. Mean modular transcriptional fingerprint for bacterial (22 patients and 18 matched controls), viral (25 patients and 18 matched controls), and bacterial-viral coinfection (25 patients and 18 matched controls). Modules are organized based on its relation to the innate and adaptive immune response. Abbreviation: NK, natural killer.

Figure 4.

Transcriptional profile discrimination between bacterial and viral lower respiratory tract infection (LRTI). The 10 top-ranked genes that best differentiated bacterial from viral LRTI (Table 2) were identified, after use of a supervised learning K–nearest neighbors (K-NN) algorithm with 12 neighbors and a P value ratio cutoff of .5. A, Use of those genes in a training set correctly classified 21 of 23 individual subjects (91.3%). B, Cross-validation in a test set correctly classified 95.6% of patients. C, Validation in a third cohort, applying an unsupervised hierarchical clustering (distance method), correctly classified 95.6% of patients.