Contribution of infection and vaccination to population-level seroprevalence through two COVID waves in Tamil Nadu, India.

This study employs repeated, large panels of serological surveys to document rapid and substantial waning of SARS-CoV-2 antibodies at the population level and to calculate the extent to which infection and vaccination separately contribute to seroprevalence estimates. Four rounds of serological surveys were conducted, spanning two COVID waves (October 2020 and April-May 2021), in Tamil Nadu (population 72 million) state in India. Each round included representative populations in each district of the state, totaling ≥ 20,000 persons per round. State-level seroprevalence was 31.5% in round 1 (October-November 2020), after India's first COVID wave. Seroprevalence fell to 22.9% in round 2 (April 2021), a roughly one-third decline in 6 months, consistent with dramatic waning of SARS-Cov-2 antibodies from natural infection. Seroprevalence rose to 67.1% by round 3 (June-July 2021), with infections from the Delta-variant induced second COVID wave accounting for 74% of the increase. Seroprevalence rose to 93.1% by round 4 (December 2021-January 2022), with vaccinations accounting for 63% of the increase. Antibodies also appear to wane after vaccination. Seroprevalence in urban areas was higher than in rural areas, but the gap shrunk over time (35.7 v. 25.7% in round 1, 89.8% v. 91.4% in round 4) as the epidemic spread even in low-density rural areas.

Factors Associated With the Decay of Anti-SARS-CoV-2 S1 IgG Antibodies Among Recipients of an Adenoviral Vector-Based AZD1222 and a Whole-Virion Inactivated BBV152 Vaccine.

Background: The magnitude of protection conferred following recovery from COVID-19 or by vaccine administration, and the duration of protective immunity developed, remains ambiguous. Methods: We investigated the factors associated with anti-SARS-CoV-2 S1 IgG decay in 519 individuals who recovered from COVID-19 illness or received COVID-19 vaccination with two commercial vaccines, viz., an adenoviral vector-based (AZD1222) and a whole-virion-based inactivated (BBV152) vaccine in Chennai, India from March to December 2021. Blood samples collected during regular follow-up post-infection/-vaccination were examined for anti-SARS-CoV-2 S1 IgG by a commercial automated chemiluminescent immunoassay (CLIA). Results: Age and underlying comorbidities were the two variables that were independently associated with the development of a breakthrough infection. Individuals who were >60 years of age with underlying comorbid conditions (viz., hypertension, diabetes mellitus and cardiovascular disease) had a ~15 times and ~10 times greater odds for developing a breakthrough infection and hospitalization, respectively. The time elapsed since the first booster dose was associated with attrition in anti-SARS-CoV-2 IgG, where each month passed was associated with an ebb in the anti-SARS-CoV-2 IgG antibody levels by a coefficient of -6 units. Conclusions: Our findings advocate that the elderly with underlying comorbidities be administered with appropriate number of booster doses with AZD1222 and BBV152 against COVID-19.

A novel in silico approach to identify potential therapeutic targets in human bacterial pathogens.

UNLABELLED: In recent years, genome-sequencing projects of pathogens and humans have revolutionized microbial drug target identification. Of the several known genomic strategies, subtractive genomics has been successfully utilized for identifying microbial drug targets. The present work demonstrates a novel genomics approach in which codon adaptation index (CAI), a measure used to predict the translational efficiency of a gene based on synonymous codon usage, is coupled with subtractive genomics approach for mining potential drug targets. The strategy adopted is demonstrated using respiratory pathogens, namely, Streptococcus pneumoniae and Haemophilus influenzae as examples. Our approach identified 8 potent target genes (Streptococcus pneumoniae-2, H. influenzae-6), which are functionally significant and also play key role in host-pathogen interactions. This approach facilitates swift identification of potential drug targets, thereby enabling the search for new inhibitors. These results underscore the utility of CAI for enhanced in silico drug target identification. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11568-011-9152-7) contains supplementary material, which is available to authorized users.

Modeling of phosphomethyl pyrimidine kinase from Leptospira interrogans serovar lai strain 56601.

Many microorganisms, as well as plants and fungi, synthesize thiamin, but vertebrates do not produce it. Phosphomethyl pyrimidine kinase is an enzyme involved in an intermediary step of thiamin biosynthesis from purine molecules. This enzyme is absent in humans. Thus, it is a potential chemotherapeutic target for antileptospiral treatment. Structure of this enzyme from Leptospira interrogans serovar lai strain 56601 has not yet been elucidated. We used the structural template of phosphomethyl pyrimidine kinase from Thermus thermophilus HB8 for modeling the phosphomethyl pyrimidine kinase structure from Leptospira interrogans serovar lai strain 56601 . The model is deposited in Protein Data Bank (PDB ID: 2G53) at RCSB. Thus, we analyse and propose the usefulness of the modeled phosphomethyl pyrimidine kinase for the design of suitable inhibitors towards the treatment of leptospirosis.