ABSTRACT
Background: SARS-COV-2 is an enveloped RNA virus that is responsible for the global pandemic COVID-19. The virus is reported to cause dysbiosis of the Human Nasopharyngeal microbiota, consequently regulating the host immunity and infection pathophysiology. The compositional change in microbial diversity due to the virus has been reported by independent authors in smaller cohorts and different geographical regions, with a few correlating with fungal and bacterial co-infections. Here, we study for the first time, the nasopharyngeal microbial diversity in the COVID-19 patients, across the three waves in India and explore its correlation with the causative virus variant (and/or the severity of symptoms, if any). Methods: We profiled the nasopharyngeal microbiota of 589 Indian subjects, across the three waves (First; n=181, Second; n=217, Third; n=191), which were further categorized as COVID-19 positives and COVID-19 negatives. These respective groups were further divided into subgroups based on the symptoms as Asymptomatic and Symptomatic. The nasopharyngeal swabs were collected from subjects providing samples for diagnostics purposes at the Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India. Using high throughput 16S rRNA gene amplicon-based sequencing, we sequenced and profiled the nasopharyngeal DNA microbiome prior to subjecting them to diversity, composition and network analyses. Results: Patients infected with SARS-COV-2 showed a reduced microbial alpha diversity compared to the COVID-19 negatives, in a wave-dependent manner, as implicated by measuring the alpha diversity indices. Furthermore, the compositional change in the community was found to be significantly associated with the viral load as well as the severity of the symptoms observed in the patients. Preliminary taxonomic analysis indicated that, overall, Firmicutes, Proteobacteria, and Actinobacteriota were amongst the dominating Phyla, while Staphylococcaceae and Corynebacteriaceae were the most abundant Families. Also, the microbiota signatures of the first and third wave were more similar to each other at the phylum level compared to the second wave. However, the abundance of microbes varied greatly between the major groups i.e COVID-19 positives and the negatives at the family level, in the respective waves. A similar observation was made where both the commensals and pathobionts differed in abundance between the patient subgroups. Interestingly, the change in microbial network architecture from first to second wave was driven by opportunistic pathogens such as Paenibacillus, Peptostreptococcus, and Solobacterium while Leptotrichia and Actinomyces were noted to be taxonomic groups driving the changes during the third wave when compared to the second wave. Conclusion: In the Indian cohort examined, SARS-COV-2 infection perturbs the nasopharyngeal microbiome, resulting in lower & varied diversity in the niche, irrespective of the virus variant (& thus, the COVID wave) and the disease severity. Whether these changes assist in COVID-19 disease onset & progression, would be interesting to explore in the future.
Subject(s)
COVID-19 , Dysbiosis , Severe Acute Respiratory Syndrome , Bacterial InfectionsABSTRACT
Genomes have an inherent context dictated by the order in which the nucleotides and higher order genomic elements are arranged in the DNA/RNA. Learning this context is a daunting task, governed by the combinatorial complexity of interactions possible between ordered elements of genomes. Can natural language processing be employed on these orderly, complex and also evolving datatypes (genomic sequences) to reveal the latent patterns or context of genomic elements (e.g Mutations)? Here we present an approach to understand the mutational landscape of Covid-19 by treating the temporally changing (continuously mutating) SARS-CoV-2 genomes as documents. We demonstrate how the analogous interpretation of evolving genomes to temporal literature corpora provides an opportunity to use dynamic topic modeling (DTM) and temporal Word2Vec models to delineate mutation signatures corresponding to different Variants-of-Concerns and tracking the semantic drift of Mutations-of-Concern (MoC). We identified and studied characteristic mutations affiliated to Covid-infection severity and tracked their relationship with MoCs. Our ground work on utility of such temporal NLP models in genomics could supplement ongoing efforts in not only understanding the Covid pandemic but also provide alternative strategies in studying dynamic phenomenon in biological sciences through data science (especially NLP, AI/ML).
Subject(s)
COVID-19ABSTRACT
MotivationContinuous emergence of new variants through appearance, accumulation and disappearance of mutations in viruses is a hallmark of many viral diseases. SARS-CoV-2 and its variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of the variants and huge scale of genome sequence data available for Covid19 have added to the challenges of traceability of mutations of concern. The latter however provides an opportunity to utilize SARS-CoV-2 genomes and the mutations therein as big data records to comprehensively classify the variants through the (machine) learning of mutation patterns. The unprecedented sequencing effort and tracing of disease outcomes provide an excellent ground for identifying important mutations by developing machine learnt models or severity classifiers using mutation profile of SARS-CoV-2. This is expected to provide a significant impetus to the efforts towards not only identifying the mutations of concern but also exploring the potential of mutation driven predictive prognosis of SARS-CoV-2. ResultsWe describe how a graduated approach of building various severity specific machine learning classifiers, using only the mutation corpus of SARS-CoV-2 genomes, can potentially lead to the identification of important mutations and guide potential prognosis of infection. We demonstrate the applicability of model derived important mutations and use of Shapley values in order to identify the significant mutations of concern as well as for developing sparse models of outcome classification. A total of 77,284 outcome traced SARS-CoV-2 genomes were employed in this study which represented a total corpus of 30346 unique nucleotide mutations and 18647 amino acid mutations. Machine learning models pertaining to graduated classifiers of target outcomes namely Asymptomatic, Mild, Symptomatic/Moderate, Severe and Fatal were built considering the TRIPOD guidelines for predictive prognosis. Shapley values for model linked important mutations were employed to select significant mutations leading to identification of less than 20 outcome driving mutations from each classifier. We additionally describe the significance of adopting a temporal modeling approach to benchmark the predictive prognosis linked with continuously evolving pathogens. A chronologically distinct sampling is important in evaluating the performance of models trained on past data in accurately classifying prognosis linked with genomes of future (observed with new mutations). We conclude that while machine learning approach can play a vital role in identifying relevant mutations, caution should be exercised in using the mutation signatures for predictive prognosis in cases where new mutations have accumulated along with the previously observed mutations of concern. Contactsharmila.mande@tcs.com Supplementary informationSupplementary data are enclosed.
Subject(s)
COVID-19 , Virus DiseasesABSTRACT
The SARS-CoV2 mediated Covid-19 pandemic has impacted humankind at an unprecedented scale. While substantial research efforts have focused towards understand the mechanisms of viral infection and developing vaccines/ therapeutics, factors affecting the susceptibility to SARS-CoV2 infection and manifestation of Covid-19 remain less explored. Given that the Human Leukocyte Antigen (HLA) system is known to vary among ethnic populations, it is likely to affect the recognition of the virus, and in turn, the susceptibility to Covid-19. To understand this, we used bioinformatic tools to probe all SARS-CoV2 peptides which could elicit T-cell response in humans. We also tried to answer the intriguing question of whether these potential epitopes were equally immunogenic across ethnicities, by studying the distribution of HLA alleles among different populations and their share of cognate epitopes. We provide evidence that the newer mutations in SARS-CoV2 are unlikely to alter the T-cell mediated immunogenic responses among the studied ethnic populations. The work presented herein is expected to bolster our understanding of the pandemic, by providing insights into differential immunological response of ethnic populations to the virus as well as by gauging the possible effects of mutations in SARS-CoV2 on efficacy of potential epitope-based vaccines through evaluating ~40000 viral genomes.