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Front Immunol ; 13: 1034159, 2022.
Article in English | MEDLINE | ID: covidwho-2198881


Introduction: Despite numerous efforts to describe COVID-19's immunological landscape, there is still a gap in our understanding of the virus's infections after-effects, especially in the recovered patients. This would be important to understand as we now have huge number of global populations infected by the SARS-CoV-2 as well as variables inclusive of VOCs, reinfections, and vaccination breakthroughs. Furthermore, single-cell transcriptome alone is often insufficient to understand the complex human host immune landscape underlying differential disease severity and clinical outcome. Methods: By combining single-cell multi-omics (Whole Transcriptome Analysis plus Antibody-seq) and machine learning-based analysis, we aim to better understand the functional aspects of cellular and immunological heterogeneity in the COVID-19 positive, recovered and the healthy individuals. Results: Based on single-cell transcriptome and surface marker study of 163,197 cells (124,726 cells after data QC) from the 33 individuals (healthy=4, COVID-19 positive=16, and COVID-19 recovered=13), we observed a reduced MHC Class-I-mediated antigen presentation and dysregulated MHC Class-II-mediated antigen presentation in the COVID-19 patients, with restoration of the process in the recovered individuals. B-cell maturation process was also impaired in the positive and the recovered individuals. Importantly, we discovered that a subset of the naive T-cells from the healthy individuals were absent from the recovered individuals, suggesting a post-infection inflammatory stage. Both COVID-19 positive patients and the recovered individuals exhibited a CD40-CD40LG-mediated inflammatory response in the monocytes and T-cell subsets. T-cells, NK-cells, and monocyte-mediated elevation of immunological, stress and antiviral responses were also seen in the COVID-19 positive and the recovered individuals, along with an abnormal T-cell activation, inflammatory response, and faster cellular transition of T cell subtypes in the COVID-19 patients. Importantly, above immune findings were used for a Bayesian network model, which significantly revealed FOS, CXCL8, IL1ß, CST3, PSAP, CD45 and CD74 as COVID-19 severity predictors. Discussion: In conclusion, COVID-19 recovered individuals exhibited a hyper-activated inflammatory response with the loss of B cell maturation, suggesting an impeded post-infection stage, necessitating further research to delineate the dynamic immune response associated with the COVID-19. To our knowledge this is first multi-omic study trying to understand the differential and dynamic immune response underlying the sample subtypes.

Antigen Presentation , COVID-19 , Humans , Bayes Theorem , Multiomics , SARS-CoV-2
Front Microbiol ; 12: 653399, 2021.
Article in English | MEDLINE | ID: covidwho-1389208


Co-infection with ancillary pathogens is a significant modulator of morbidity and mortality in infectious diseases. There have been limited reports of co-infections accompanying SARS-CoV-2 infections, albeit lacking India specific study. The present study has made an effort toward elucidating the prevalence, diversity and characterization of co-infecting respiratory pathogens in the nasopharyngeal tract of SARS-CoV-2 positive patients. Two complementary metagenomics based sequencing approaches, Respiratory Virus Oligo Panel (RVOP) and Holo-seq, were utilized for unbiased detection of co-infecting viruses and bacteria. The limited SARS-CoV-2 clade diversity along with differential clinical phenotype seems to be partially explained by the observed spectrum of co-infections. We found a total of 43 bacteria and 29 viruses amongst the patients, with 18 viruses commonly captured by both the approaches. In addition to SARS-CoV-2, Human Mastadenovirus, known to cause respiratory distress, was present in a majority of the samples. We also found significant differences of bacterial reads based on clinical phenotype. Of all the bacterial species identified, ∼60% have been known to be involved in respiratory distress. Among the co-pathogens present in our sample cohort, anaerobic bacteria accounted for a preponderance of bacterial diversity with possible role in respiratory distress. Clostridium botulinum, Bacillus cereus and Halomonas sp. are anaerobes found abundantly across the samples. Our findings highlight the significance of metagenomics based diagnosis and detection of SARS-CoV-2 and other respiratory co-infections in the current pandemic to enable efficient treatment administration and better clinical management. To our knowledge this is the first study from India with a focus on the role of co-infections in SARS-CoV-2 clinical sub-phenotype.

Wellcome Open Res ; 5: 184, 2020.
Article in English | MEDLINE | ID: covidwho-808195


Background: India first detected SARS-CoV-2, causal agent of COVID-19 in late January 2020, imported from Wuhan, China. From March 2020 onwards, the importation of cases from countries in the rest of the world followed by seeding of local transmission triggered further outbreaks in India. Methods: We used ARTIC protocol-based tiling amplicon sequencing of SARS-CoV-2 (n=104) from different states of India using a combination of MinION and MinIT sequencing from Oxford Nanopore Technology to understand how introduction and local transmission occurred. Results: The analyses revealed multiple introductions of SARS-CoV-2 genomes, including the A2a cluster from Europe and the USA, A3 cluster from Middle East and A4 cluster (haplotype redefined) from Southeast Asia (Indonesia, Thailand and Malaysia) and Central Asia (Kyrgyzstan). The local transmission and persistence of genomes A4, A2a and A3 was also observed in the studied locations. The most prevalent genomes with patterns of variance (confined in a cluster) remain unclassified, and are here proposed as A4-clade based on its divergence within the A cluster. Conclusions: The viral haplotypes may link their persistence to geo-climatic conditions and host response. Multipronged strategies including molecular surveillance based on real-time viral genomic data is of paramount importance for a timely management of the pandemic.