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Preprint | EuropePMC | ID: ppcovidwho-296935


Heterogeneity in susceptibility among individuals to COVID-19 has been evident through the pandemic worldwide. Protective cytotoxic T lymphocyte (CTL) responses generated against pathogens in certain individuals are known to impose selection pressure on the pathogen, thus driving emergence of new variants. In this study, we focus on the role played by host genetic heterogeneity in terms of HLA-genotypes in determining differential COVID-19 severity in patients and dictating mechanisms of immune evasion adopted by SARS-CoV-2 due to the imposed immune pressure at global and cohort levels. We use bioinformatic tools for CTL epitope prediction to identify epitopes under immune pressure. Using HLA-genotype data of COVID-19 patients from a local cohort, we observe that asymptomatic individuals recognize a larger number of pressured epitopes which could facilitate emergence of mutations at these epitopic regions to overcome the protectivity they offer to the host. Based on the severity of COVID-19, we also identify HLA-alleles and epitopes that offer higher protectivity against severe disease in infected individuals. Finally, we shortlist a set of pressured and protective epitopes that represent regions in the viral proteome that are under higher immune pressure across SARS-CoV-2 variants due to the protectivity they offer. Identification of such epitopes could potentially aid in prediction of indigenous variants of SARS-CoV-2 and other pathogens, defined by the distribution of HLA-genotypes among members of a population.

EBioMedicine ; 67: 103352, 2021 May.
Article in English | MEDLINE | ID: covidwho-1205123


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 knowledge-based 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: 0.96-0.99) in eleven independent datasets (n = 898). We devise a new stand-alone patient-wise score (VB10) based on the panel, which shows high diagnostic accuracy with a weighted mean AUROC of 0.94 (95% CI 0.91-0.98) in 2996 patient samples from 56 public datasets from 19 different countries. Further, we evaluate VB10 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 VB10 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 VB10 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 VB10 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. FUNDING: Grand Challenges India, Biotechnology Industry Research Assistance Council (BIRAC), Department of Biotechnology, Govt. of India.

Bacterial Infections/diagnosis , Biomarkers/blood , Computational Biology/methods , Virus Diseases/diagnosis , Adult , Bacterial Infections/blood , Bacterial Infections/genetics , Databases, Factual , Decision Support Systems, Clinical , Diagnosis, Differential , Female , Gene Expression Profiling , Humans , India , Male , Middle Aged , Observational Studies as Topic , Predictive Value of Tests , Protein Interaction Maps , Virus Diseases/blood , Virus Diseases/genetics