Natural selection plays a significant role in governing the codon usage bias in the novel SARS-CoV-2 variants of concern (VOC).

The ongoing prevailing COVID-19 pandemic caused by SARS-CoV-2 is becoming one of the major global health concerns worldwide. The SARS-CoV-2 genome encodes spike (S) glycoprotein that plays a very crucial role in viral entry into the host cell via binding of its receptor binding domain (RBD) to the host angiotensin converting enzyme 2 (ACE2) receptor. The continuously evolving SARS-CoV-2 genome results in more severe and transmissible variants characterized by the emergence of novel mutations called 'variants of concern' (VOC). The currently designated alpha, beta, gamma, delta and omicron VOC are the focus of this study due to their high transmissibility, increased virulence, and concerns for decreased effectiveness of the available vaccines. In VOC, the spike (S) gene and other non-structural protein mutations may affect the efficacies of the approved COVID-19 vaccines. To understand the diversity of SARS-CoV-2, several studies have been performed on a limited number of sequences. However, only a few studies have focused on codon usage bias (CUBs) pattern analysis of all the VOC strains. Therefore, to evaluate the evolutionary divergence of all VOC S-genes, we performed CUBs analysis on 300,354 sequences to understand the evolutionary relationship with its adaptation in different hosts, i.e., humans, bats, and pangolins. Base composition and RSCU analysis revealed the presence of 20 preferred AU-ended and 10 under-preferred GC-ended codons. In addition, CpG was found to be depleted, which may be attributable to the adaptive response by viruses to escape from the host defense process. Moreover, the ENC values revealed a higher bias in codon usage in the VOC S-gene. Further, the neutrality plot analysis demonstrated that S-genes analyzed in this study are under 83.93% influence of natural selection, suggesting its pivotal role in shaping the CUBs. The CUBs pattern of S-genes was found to be very similar among all the VOC strains. Interestingly, we observed that VOC strains followed a trend of antagonistic codon usage with respect to the human host. The identified CUBs divergence would help to understand the virus evolution and its host adaptation, thus help design novel vaccine strategies against the emerging VOC strains. To the best of our knowledge, this is the first report for identifying the evolution of CUBs pattern in all the currently identified VOC.

Identification of COVID-19 prognostic markers and therapeutic targets through meta-analysis and validation of Omics data from nasopharyngeal samples.

BACKGROUND: While our battle with the COVID-19 pandemic continues, a multitude of Omics data have been generated from patient samples in various studies. Translation of these data into clinical interventions against COVID-19 remains to be accomplished. Exploring host response to COVID-19 in the upper respiratory tract can unveil prognostic markers and therapeutic targets. METHODS: We conducted a meta-analysis of published transcriptome and proteome profiles of respiratory samples of COVID-19 patients to shortlist high confidence upregulated host factors. Subsequently, mRNA overexpression of selected genes was validated in nasal swabs from a cohort of COVID-19 positive/negative, symptomatic/asymptomatic individuals. Guided by this analysis, we sought to check for potential drug targets. An FDA-approved drug, Auranofin, was tested against SARS-CoV-2 replication in cell culture and Syrian hamster challenge model. FINDINGS: The meta-analysis and validation in the COVID-19 cohort revealed S100 family genes (S100A6, S100A8, S100A9, and S100P) as prognostic markers of severe COVID-19. Furthermore, Thioredoxin (TXN) was found to be consistently upregulated. Auranofin, which targets Thioredoxin reductase, was found to mitigate SARS-CoV-2 replication in vitro. Furthermore, oral administration of Auranofin in Syrian hamsters in therapeutic as well as prophylactic regimen reduced viral replication, IL-6 production, and inflammation in the lungs. INTERPRETATION: Elevated mRNA level of S100s in the nasal swabs indicate severe COVID-19 disease, and FDA-approved drug Auranofin mitigated SARS-CoV-2 replication in preclinical hamster model. FUNDING: This study was supported by the DBT-IISc partnership program (DBT (IED/4/2020-MED/DBT)), the Infosys Young Investigator award (YI/2019/1106), DBT-BIRAC grant (BT/CS0007/CS/02/20) and the DBT-Wellcome Trust India Alliance Intermediate Fellowship (IA/I/18/1/503613) to ST lab.

Machine Learning Assisted Prediction of Prognostic Biomarkers Associated With COVID-19, Using Clinical and Proteomics Data.

With the availability of COVID-19-related clinical data, healthcare researchers can now explore the potential of computational technologies such as artificial intelligence (AI) and machine learning (ML) to discover biomarkers for accurate detection, early diagnosis, and prognosis for the management of COVID-19. However, the identification of biomarkers associated with survival and deaths remains a major challenge for early prognosis. In the present study, we have evaluated and developed AI-based prediction algorithms for predicting a COVID-19 patient's survival or death based on a publicly available dataset consisting of clinical parameters and protein profile data of hospital-admitted COVID-19 patients. The best classification model based on clinical parameters achieved a maximum accuracy of 89.47% for predicting survival or death of COVID-19 patients, with a sensitivity and specificity of 85.71 and 92.45%, respectively. The classification model based on normalized protein expression values of 45 proteins achieved a maximum accuracy of 89.01% for predicting the survival or death, with a sensitivity and specificity of 92.68 and 86%, respectively. Interestingly, we identified 9 clinical and 45 protein-based putative biomarkers associated with the survival/death of COVID-19 patients. Based on our findings, few clinical features and proteins correlate significantly with the literature and reaffirm their role in the COVID-19 disease progression at the molecular level. The machine learning-based models developed in the present study have the potential to predict the survival chances of COVID-19 positive patients in the early stages of the disease or at the time of hospitalization. However, this has to be verified on a larger cohort of patients before it can be put to actual clinical practice. We have also developed a webserver CovidPrognosis, where clinical information can be uploaded to predict the survival chances of a COVID-19 patient. The webserver is available at http://14.139.62.220/covidprognosis/.

In-silico profiling and structural insights into the impact of nSNPs in the P. falciparum acetyl-CoA transporter gene to understand the mechanism of drug resistance in malaria.

The continuous emergence of resistance to the available drugs poses major constraints in the development of effective therapeutics against malaria. Malaria drug resistance has been attributed to be the manifestation of numerous factors. For example, mutations in the parasite transporter protein acetyl-CoA transporter (Pfact) can remarkably affect its uptake affinity for a drug molecule against malaria, and hence enhance its susceptibility to resistance. To identify major contributors to its loss of functionality, we have thoroughly scrutinized eight such recently reported resistant mutants, via in-silico tools in terms of alterations in different properties. We performed molecular dynamics simulations of the selected Pfact mutants to gain deeper insights into the structural perturbation and dynamicity. Comparison of residue interaction network map of mutants with that of Wild type (WT) protein suggests structural changes as a result of the mutation(s) that translate into the weakening of intra-protein interactions, especially around the drug binding pocket. This, in turn, diminishes the affinity of drug molecules towards the binding site, which was validated by docking analysis. Finally, collating all the observations, we have delineated R108K mutant to deviate the most from WT protein, which, intriguingly suggests that replacing an amino acid with another of similar nature can even translate into greater functional effects as those with dissimilar substitutions.Communicated by Ramaswamy H. Sarma.

Identification of Novel SARS-CoV-2 Drug Targets by Host MicroRNAs and Transcription Factors Co-regulatory Interaction Network Analysis.

Understanding the host regulatory mechanisms opposing virus infection and virulence can provide actionable insights to identify novel therapeutics against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We have used a network biology approach to elucidate the crucial factors involved in host responses involving host-microRNA (miRNA) interactions with host and virus genes using recently published experimentally verified protein-protein interaction data. We were able to identify 311 host genes to be potentially targetable by 2,197 human miRNAs. These miRNAs are known to be involved in various biological processes, such as T-cell differentiation and activation, virus replication, and immune system. Among these, the anti-viral activity of 38 miRNAs to target 148 host genes is experimentally validated. Six anti-viral miRNAs, namely, hsa-miR-1-3p, hsa-miR-17-5p, hsa-miR-199a-3p, hsa-miR-429, hsa-miR-15a-5p, and hsa-miR-20a-5p, are previously reported to be anti-viral in respiratory diseases and were found to be downregulated. The interaction network of the 2,197 human miRNAs and interacting transcription factors (TFs) enabled the identification of 51 miRNAs to interact with 77 TFs inducing activation or repression and affecting gene expression of linked genes. Further, from the gene regulatory network analysis, the top five hub genes HMOX1, DNMT1, PLAT, GDF1, and ITGB1 are found to be involved in interferon (IFN)-α2b induction, epigenetic modification, and modulation of anti-viral activity. The comparative miRNAs target identification analysis in other respiratory viruses revealed the presence of 98 unique host miRNAs targeting SARS-CoV-2 genome. Our findings identify prioritized key regulatory interactions that include miRNAs and TFs that provide opportunities for the identification of novel drug targets and development of anti-viral drugs.

Dataset of mutational analysis, miRNAs targeting SARS-CoV-2 genes and host gene expression in SARS-CoV and SARS-CoV-2 infections.

The identification of host-miRNAs targeting mutated virus genes is crucial to understand the miRNA mediated host-defense mechanism in virus infections. To understand the mechanism in COVID-19 infections, we collected genome sequences of SARS-CoV-2 with its metadata from the GISAID database (submitted till April 2020) and identified mutational changes in the sequences. The dataset consists of genes with mutation event count and entropy scores. We predicted host-miRNAs targeting the genes in the genomes and compared it with that in related viral species. We have identified 2284 miRNAs targeting MERS genomes, 2074 miRNAs targeting SARS genomes, and 1599 miRNAs targeting SARS-CoV-2 genomes, identified using the miRNA target prediction software miRanda. The host miRNAs targeting SARS-CoV-2 genes were further validated to be anti-viral miRNAs and their role in respiratory diseases through a literature survey, which helped in the identification of 42 conserved antiviral miRNAs. The data could be used to validate the anti-viral role of the predicted miRNAs and design miRNA-based therapeutics against SARS-CoV-2.

Integrative analyses of SARS-CoV-2 genomes from different geographical locations reveal unique features potentially consequential to host-virus interaction, pathogenesis and clues for novel therapies.

We have performed an integrative analysis of SARS-CoV-2 genome sequences from different countries. Apart from mutational analysis, we have predicted host antiviral miRNAs targeting virus genes, PTMs in the virus proteins and antiviral peptides. A comparison of the analyses with other coronavirus genomes has been performed, wherever possible. Our analysis confirms unique features in the SARS-CoV-2 genomes absent in other evolutionarily related coronavirus family genomes, which presumably confer unique infection, transmission and virulence capabilities to the virus. For understanding the crucial factors involved in host-virus interactions, we have performed Bioinformatics aided analysis integrated with experimental data related to other corona viruses. We have identified 42 conserved miRNAs that can potentially target SARS-CoV-2 genomes. Interestingly, out of these, 3 are previously reported to exhibit antiviral activity against other respiratory viruses. Gene expression analysis of known host antiviral factors reveals significant over-expression of IFITM3 and down regulation of cathepsins during SARS-CoV-2 infection, suggesting its active role in pathogenesis and delayed immune response. We also predicted antiviral peptides which can be used in designing peptide based drugs against SARS-CoV-2. Our analysis explores the functional impact of the virus mutations on its proteins and interaction of its genes with host antiviral mechanisms.

ApicoTFdb: the comprehensive web repository of apicomplexan transcription factors and transcription-associated co-factors.

Despite significant progress in apicomplexan genome sequencing and genomics, the current list of experimentally validated transcription factors (TFs) in these genomes is incomplete and mainly consists of AP2 family of proteins, with only a limited number of non-AP2 family TFs and transcription-associated co-factors (TcoFs). We have performed a systematic bioinformatics-aided prediction of TFs and TcoFs in apicomplexan genomes and developed the ApicoTFdb database which consists of experimentally validated as well as computationally predicted TFs and TcoFs in 14 apicomplexan species. The predicted TFs are manually curated to complement the existing annotations. The current version of the database includes 1292 TFs which includes experimentally validated and computationally predicted TFs, representing 20 distinct families across 14 apicomplexan species. The predictions include TFs of TUB, NAC, BSD, HTH, Cupin/Jumonji, winged helix and FHA family proteins, not reported earlier as TFs in the genomes. Apart from TFs, ApicoTFdb also classifies TcoFs into three main subclasses: TRs, CRRs and RNARs, representing 2491 TcoFs in 14 apicomplexan species, are analyzed in this study. The database is designed to integrate different tools for comparative analysis. All entries in the database are dynamically linked with other databases, literature reference, protein-protein interactions, pathways and annotations associated with each protein. ApicoTFdb will be useful to the researchers interested in less-studied gene regulatory mechanisms mediating the complex life cycle of the apicomplexan parasites. The database will aid in the discovery of novel drug targets to much needed combat the growing drug resistance in the parasites.

A genome-wide analysis of coatomer protein (COP) subunits of apicomplexan parasites and their evolutionary relationships.

BACKGROUND: Protein secretion is an essential process in all eukaryotes including organisms belonging to the phylum Apicomplexa, which includes many intracellular parasites. The apicomplexan parasites possess a specialized collection of secretory organelles that release a number of proteins to facilitate the invasion of host cells and some of these proteins also participate in immune evasion. Like in other eukaryotes, these parasites possess a series of membrane-bound compartments, namely the endoplasmic reticulum (ER), the intermediate compartments (IC) or vesicular tubular clusters (VTS) and Golgi complex through which proteins pass in a sequential and vectorial fashion. Two sets of proteins; COPI and COPII are important for directing the sequential transfer of material between the ER and Golgi complex. RESULTS: Here, using in silico approaches, we identify the components of COPI and COPII complexes in the genome of apicomplexan organisms. The results showed that the COPI and COPII protein complexes are conserved in most apicomplexan genomes with few exceptions. Diversity among the components of COPI and COPII complexes in apicomplexan is either due to the absence of a subunit or due to the difference in the number of protein domains. For example, the COPI epsilon subunit and COPII sec13 subunit is absent in Babesia bovis, Theileria parva, and Theileria annulata genomes. Phylogenetic and domain analyses for all the proteins of COPI and COPII complexes was performed to predict their evolutionary relationship and functional significance. CONCLUSIONS: The study thus provides insights into the apicomplexan COPI and COPII coating machinery, which is crucial for parasites secretory network needed for the invasion of host cells.