Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.

An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September 2021. This study is focused on finding potential vaccine candidates and designing an in-silico subunit multi-epitope vaccine candidates using a unique computational pipeline, integrating reverse vaccinology, molecular docking and simulation methods. A protein named spike protein of SARS-CoV-2 with the GenBank ID QHD43416.1 was shortlisted as a potential vaccine candidate and was examined for presence of B-cell and T-cell epitopes. We also investigated antigenicity and interaction with distinct polymorphic alleles of the epitopes. High ranking epitopes such as DLCFTNVY (B cell epitope), KIADYNKL (MHC Class-I) and VKNKCVNFN (MHC class-II) were shortlisted for subsequent analysis. Digestion analysis verified the safety and stability of the shortlisted peptides. Docking study reported a strong binding of proposed peptides with HLA-A*02 and HLA-B7 alleles. We used standard methods to construct vaccine model and this construct was evaluated further for its antigenicity, physicochemical properties, 2D and 3D structure prediction and validation. Further, molecular docking followed by molecular dynamics simulation was performed to evaluate the binding affinity and stability of TLR-4 and vaccine complex. Finally, the vaccine construct was reverse transcribed and adapted for E. coli strain K 12 prior to the insertion within the pET-28-a (+) vector for determining translational and microbial expression followed by conservancy analysis. Also, six multi-epitope subunit vaccines were constructed using different strategies containing immunogenic epitopes, appropriate adjuvants and linker sequences. We propose that our vaccine constructs can be used for downstream investigations using in-vitro and in-vivo studies to design effective and safe vaccine against different strains of COVID-19.

Endothelin 1 (EDN1) encodes a potent endogenous vasoconstrictor, ET1, to maintain vascular homeostasis and redistribution of tissue blood flow during exercise. One of the EDN1 missense polymorphisms, rs5370 G/T, has strongly been associated with cardiopulmonary diseases. This study investigated the impact of rs5370 polymorphism in high-altitude pulmonary oedema (HAPE) disorder or maladaptation and adaptation physiology in a well-characterized case–control study of high-altitude and low-altitude populations comprising 310 samples each of HAPE-patients, HAPE-free controls and native highlanders. The rs5370 polymorphism was genotyped, and the gene expression and plasma level of EDN1 were evaluated. The functional relevance of each allele was investigated in the human embryonic kidney 293 cell line after exposure to hypoxia and computationally. The T allele was significantly more prevalent in HAPE-p compared to HAPE-f and HLs. The EDN1 gene expression and ET1 bio-level were significantly elevated in HAPE-p compared to controls. Compared to the G allele, the T allele was significantly associated with elevated levels of ET-1 in all three study groups and cells exposed to hypoxia. The in silico studies further confirmed the stabilizing effect of the T allele on the structural integrity and function of ET1 protein. The ET1 rs5370 T allele is associated with an increased concentration of ET-1 in vivo and in vitro, establishing it as a potent marker in the adaptation/maladaptation physiology under the high-altitude environment. This could also be pertinent in endurance exercises at high altitudes.

The year 2019 has seen an emergence of the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease of 2019 (COVID-19). Since the onset of the pandemic, biological and interdisciplinary research is being carried out across the world at a rapid pace to beat the pandemic. There is an increased need to comprehensively understand various aspects of the virus from detection to treatment options including drugs and vaccines for effective global management of the disease. In this review, we summarize the salient findings pertaining to SARS-CoV-2 biology, including symptoms, hosts, epidemiology, SARS-CoV-2 genome, and its emerging variants, viral diagnostics, host-pathogen interactions, alternative antiviral strategies and application of machine learning heuristics and artificial intelligence for effective management of COVID-19 and future pandemics.

Background SARS-CoV-2 has affected every demography disproportionately, including even the native highland populations. Hypobaric-hypoxic settings at high-altitude (HA, >2,500 masl) present an extreme environment that impacts the survival of permanent residents, possibly including SARS-CoV-2. Conflicting hypotheses have been presented for COVID-19 incidence and fatality at HA. Objectives To evaluate protection or risk against COVID-19 incidence and fatality in humans under hypobaric-hypoxic environment of high-altitude (>2,501 masl). Methods Global COVID-19 data of March 2020-21, employed from official websites of the Indian Government, John Hopkins University, and Worldometer were clustered into 6 altitude categories. Clinical cofactors and comorbidities data were evaluated with COVID-19 incidence and fatality. Extensive comparisons and correlations using several statistical tools estimated the risk and protection. Results Of relevance, data analyses revealed four distinct responses, namely, partial risk, total risk, partial protection, and total protection from COVID-19 at high-altitude indicating a mixed baggage and complexity of the infection. Surprisingly, it included the countries within the same geographic region. Moreover, body mass index, hypertension, and diabetes correlated significantly with COVID-19 incidence and fatality rate (P ≤ 0.05). Conclusions Varied patterns of protection and risk against COVID-19 incidence and fatality were observed among the high-altitude populations. It is though premature to generalize COVID-19 effects on any particular demography without further extensive studies.

Clostridium difficile (C. difficile) is a multi-strain, spore-forming, Gram-positive, opportunistic enteropathogen bacteria, majorly associated with nosocomial infections, resulting in severe diarrhoea and colon inflammation. Several antibiotics including penicillin, tetracycline, and clindamycin have been employed to control C. difficile infection, but studies have suggested that injudicious use of antibiotics has led to the development of resistance in C. difficile strains. However, many proteins from its genome are still considered uncharacterized proteins that might serve crucial functions and assist in the biological understanding of the organism. In this study, we aimed to annotate and characterise the 6 C. difficile strains using in silico approaches. We first analysed the complete genome of 6 C. difficile strains using standardised approaches and analysed hypothetical proteins (HPs) employing various bioinformatics approaches coalescing, including identifying contigs, coding sequences, phage sequences, CRISPR-Cas9 systems, antimicrobial resistance determination, membrane helices, instability index, secretory nature, conserved domain, and vaccine target properties like comparative homology analysis, allergenicity, antigenicity determination along with structure prediction and binding-site analysis. This study provides crucial supporting information about the functional characterization of the HPs involved in the pathophysiology of the disease. Moreover, this information also aims to assist in mechanisms associated with bacterial pathogenesis and further design candidate inhibitors and bona fide pharmaceutical targets.