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In the present work, we addressed the impact of a human-food web-animal interface on the prevalence of food-borne pathogens in mixed farms of Tamil Nadu, India. We have isolated and identified six strains of Clostridium sp. and five strains of Enterococcus sp. from food and animal sources disposed near to the veterinary and poultry farms. Phylogenetic relationships of these strains were inferred from their homologies in 16S rDNA sequences and rRNA secondary structures. The strain PCP07 was taxonomically equivalent to C. botulinum confirmed by neurotoxin-specific PCR primers, followed by mouse bioassay. Other Clostridial and Enterococcal isolates have shown a phylogenetic similarity to the C. bifermentans and E. durans isolated from veterinary farms, respectively. Results of our study revealed that a humanfood web-animal interface has influenced the disease incidence and prevalence of these isolates in the poultry to veterinary farms, where human food acted as a likely transmittance vehicle for their infections.

Coronavirus disease (COVID-19) has rapidly expanded into a global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Genetic drift in global SARS-CoV-2 isolates and protein evolution have an impact on their ability to escape from current antiviral therapeutics. Hence, our study aimed to reveal how mutations in the folding kinetics of assembly and maturation proteins drive the hijack ability to emerge SARS-CoV-2 variants in humans. In this study, we predicted the folding rate of these proteins using multiple regression analysis and validated the prediction accuracy using machine learning algorithms. Hybrid machine learning using linear regression, random forest, and decision tree was used to evaluate the predicted folding rates compared with other machine learning models. In SARS-CoV-2 variants, the sequence-structure-function-folding rate link stabilizes or retains the mutated residues, making stable near-native protein structures. The folding rates of these protein mutants were increased in their structural classes, particularly β-sheets, which accommodated the hijacking ability of new variants in human host cells. E484A and L432R were identified as potent mutations that resulted in drastic changes in the folding pattern of the spike protein. We conclude that receptor-binding specificity, infectivity, multiplication rate, and hijacking ability are directly associated with an increase in the folding rate of their protein mutants.