VB10, a new blood biomarker for differential diagnosis and recovery monitoring of acute viral and bacterial infections

Abstract

Background: Precise differential diagnosis between acute viral and bacterial infections is important to enable appropriate therapy, avoid unnecessary antibiotic prescriptions and optimize the use of hospital resources. A systems view of host response to infections provides opportunities for discovering sensitive and robust molecular diagnostics. Methods: We combine blood transcriptomes from six independent datasets (n = 756) with a knowledgebased human protein-protein interaction network, identifies subnetworks capturing host response to each infection class, and derives common response cores separately for viral and bacterial infections. We subject the subnetworks to a series of computational filters to identify a parsimonious gene panel and a standalone diagnostic score that can be applied to individual samples. We rigorously validate the panel and the diagnostic score in a wide range of publicly available datasets and in a newly developed Bangalore-Viral Bacterial (BL-VB) cohort. Finding: We discover a 10-gene blood-based biomarker panel (Panel-VB) that demonstrates high predictive performance to distinguish viral from bacterial infections, with a weighted mean AUROC of 0.97 (95% CI: 0.96À0.99) in eleven independent datasets (n = 898). We devise a new stand-alone patient-wise score (VB 10 ) based on the panel, which shows high diagnostic accuracy with a weighted mean AUROC of 0.94 (95% CI 0.91À0.98) in 2996 patient samples from 56 public datasets from 19 different countries. Further, we evaluate VB 10 in a newly generated South Indian (BL-VB, n = 56) cohort and find 97% accuracy in the confirmed cases of viral and bacterial infections. We find that VB 10 is (a) capable of accurately identifying the infection class in culture-negative indeterminate cases, (b) reflects recovery status, and (c) is applicable across different age groups, covering a wide spectrum of acute bacterial and viral infections, including uncharacterized pathogens. We tested our VB 10 score on publicly available COVID-19 data and find that our score detected viral infection in patient samples. Interpretation: Our results point to the promise of VB 10 as a diagnostic test for precise diagnosis of acute infections and monitoring recovery status. We expect that it will provide clinical decision support for antibiotic prescriptions and thereby aid in antibiotic stewardship efforts.

A new blood-based RNA signature (R9), for monitoring effectiveness of tuberculosis treatment in a South Indian longitudinal cohort

“…, and Ravichandran et al. have earlier shown that a similar network approach is capable of identifying condition-specific perturbations that are biologically relevant and thus useful for biomarker discovery ( Sambarey et al., 2017b ; Metri et al., 2017 ; Ravichandran et al., 2021 ). Briefly, our method uses a knowledge-based comprehensive human protein–protein interaction network (hPPiN) previously constructed by us ( Table S2 A), renders it specific to each given condition by integrating gene expression data into it, and sensitively mines most perturbed subnetworks and their most influential epicentric nodes ( Sambaturu et al., 2016 , 2021 ; Ravichandran and Chandra, 2019 ).…”

A 9-gene biomarker panel identifies bacterial coinfections in culture-negative COVID-19 cases

“…Hence, a ‘suspected coinfection’ diagnosis was made by the attending physicians based on the clinical parameters (PCT, CRP, TLC, NLR or ESR). The clinical parameters were considered suggestive of bacterial coinfection at the following thresholds - PCT ≥ 0.25 ng ml −1 , 10 CRP ≥ 10 mg dl −1 , 11 TLC ≥ 12 500 cells per μl, 9 NLR ≥ 12.5, 12 and ESR ≥ 60 mm h −1 . 9 Since all the patients were culture-negative, they were classified into 3 categories of different confidence in clinical diagnosis based on these parameters.…”

“…The clinical parameters were considered suggestive of bacterial coinfection at the following thresholds - PCT ≥ 0.25 ng ml −1 , 10 CRP ≥ 10 mg dl −1 , 11 TLC ≥ 12 500 cells per μl, 9 NLR ≥ 12.5, 12 and ESR ≥ 60 mm h −1 . 9 Since all the patients were culture-negative, they were classified into 3 categories of different confidence in clinical diagnosis based on these parameters. If 3 or more parameters were suggestive of bacterial infection, the diagnosis of bacterial coinfection is of higher confidence and therefore the patients were classified as ‘probable bacterial coinfection’.…”

“…This method has been described in detail in Ravichandran et al, 2021 and Banerjee et al, 2021. 9,18 Using a similar approach, Top Perturbed Response networks were generated for the 'Probable Bacterial Coinfection' condition with respect to 'Only COVID-19', which captures the most important differences in the hostresponse between bacterial coinfection and only COVID-19.…”

“…Previously in a large-scale meta-analysis to identify a gene signature to discriminate between bacterial and viral infections, we reported a set of differentially regulated and functionally important genes from peripheral blood of patients with culture-confirmed bacterial infections with different species. 9 In this study, we have utilized this knowledge of the gene expression profile in culture-confirmed bacterial infections along with whole-blood transcriptome integrated response network analysis for patients hospitalized with only COVID-19 infections and clinically suspected probable bacterial coinfections to identify a 9-gene signature that can confirm the presence of bacterial coinfection. This 9-gene biomarker panel has also been transformed into a coinfection score that can be measured for individual patients with the simple qPCR method to assess the probability of the presence of bacterial coinfection with high accuracy.…”