Structural and functional characterization of avian influenza H9N2 virus neuraminidase with a combination of five novel mutations

The influenza virus neuraminidase (NA) protein is responsible for actively cleaving the sialic acid (SA) bound to the viral hemagglutinin. In the present study, we identified a combination of five novel amino acid substitutions in the NA, conferring increased substrate binding and altered surface characteristics to a low pathogenic avian influenza (LPAI) H9N2 virus strain. The H9N2 strain reported from India, A/Environmental/India/1726265/2017 (H9N2-1726265) showed the combination of amino acid substitutions T149I, R249W, G346A, W403R and G435R, which were in the vicinity of the enzyme active site cavity. The strain A/chicken/India/99321/2009 (H9N2-99321) did not show these substitutions and was used for comparison. Virus elution was studied using turkey red blood cells (tRBCs). NA enzyme kinetics assays were carried out using the MUNANA substrate, which is an SA analogue. Homology modelling and molecular docking were performed to determine alterations in the surface characteristics and substrate binding. H9N2-1726265 showed enhanced elution from tRBCs. Enzyme kinetics revealed a lower KM of H9N2-1726265 (111.5 μM) as compared to H9N2-99321 (135.2 μM), indicating higher substrate binding affinity of H9N2-1726265, due to which the NA enzyme cleaved the SA more efficiently, leading to faster elution. Molecular docking revealed a greater number of binding interactions of H9N2-1726265 to SA as compared to H9N2-99321 corroborating the greater substrate binding affinity. Changes in the surface charge, hydrophobicity, and contour, were observed in H9N2-1726265 NA due to the five substitutions. Thus, the novel combination of five amino acids near the sialic acid binding site of NA, resulted in altered surface characteristics, higher substrate binding affinity, and virus elution.

District-wise estimation of Basic reproduction number (R0) for COVID-19 in India in the initial phase

SARS-CoV-2 or COVID-19 was introduced into India by multiple sources generating local clusters and leading to the nationwide spread. A retrospective study has been conducted on the epidemiological features and spatial spread of COVID-19 in India during February 2020–March 2021. For each district, the cumulative number of confirmed COVID-19 cases were fitted to exponential growth model for the initial phase of the outbreak (the first 7–15 days). From the estimated growth rate, epidemiological parameters like the Basic reproduction number (R₀) and epidemic doubling time (τ) were determined. Using Q-GIS software, we have generated the all India distribution maps for R₀ and τ. COVID-19 spread rapidly covering majority of the districts of India between March and June 2020. As on 1st March 2021, a total of 715 out of 717 districts have been affected. The R₀ range is at par with the global average. A few districts, where outbreaks were caused by migrant workers coming home, intense transmission was recorded R₀ > 7. We also found that the spread of COVID-19 was not uniform across the different districts of India. The methodology developed in the study can be used by researchers and public health professionals to analyze and study epidemics in future.

Design of a multi-epitope peptide vaccine candidate against chandipura virus: an immuno-informatics study

Chandipura virus (CHPV) is an emerging pathogen responsible for acute encephalitic syndrome (AES) in pediatric population in India. Several outbreaks of CHPV have been reported from different states of India since the year 2003. At present there is no vaccine or therapeutic measures available to curtail the disease. In this study, we have identified both T-cell and B-cell epitopes of different antigenic proteins of CHPV like Nucleoprotein (N), Phosphoprotein (P) and Matrix protein (M) along with the immuno-dominant glycoprotein (G) and conducted in silico characterization for the same. The idea is to design a multi-epitope peptide construct using the epitopes, which were found to be non-toxic, non-allergenic and possessing high immunogenicity. The final multi-epitope construct named as: MEC-CHPV, comprised of β-defensin adjuvant at N-terminal for enhancement of immunogenicity followed by fourteen B-cell epitopes, four Helper T-cell epitopes and six Cytotoxic T-cell epitopes. The characterization of designed construct was carried out in terms of physicochemical parameters, antigenicity and allergenicity. The 3D structure prediction was performed. Molecular docking and molecular-dynamics simulation of MEC-CHPV with Toll like receptors (TLR-3 and TLR-8) showed stable interactions. In silico cloning of MEC-CHPV in pET30a(+) expression vector was also conducted using codon optimization. The in silico immune-simulation indicated a typical immune response against MEC-CHPV when used as a potential vaccine. This study provides a cost-effective and time-saving way to design a peptide vaccine candidate against CHPV using immuno-informatics approach. Development of the MEC-CHPV construct may pave the way for future laboratory experiments.Communicated by Ramaswamy H. Sarma.