Molecular detection and analysis of virulence genes in multi-drug resistant Escherichia coli from infected broilers.

Escherichia coli associated infections are major threats in poultry industry owing to severe economic losses each year. This study was conducted to identify E. coli isolates, to evaluate their antibiotic sensitivity and to find out their virulence patterns from infected broilers of Sylhet city in Bangladesh. Using polymerase chain reaction, a total 20 isolates were identified as E. coli from 11 chickens, exhibiting symptoms like colibacillosis and/or diarrhea. All isolates were positive for type-1 fimbrial adhesion (fimH), followed by putative avian hemolysin (hlyF) in 17 isolates; while none of the isolates was amplified with intimin (eaeA). Among 10 tested antibiotics, 100% of the isolates (n = 20) showed resistance to ampicillin, amoxicillin and tetra-cycline; but they were 100% sensitive to gentamicin. Organ specific correlations of antibiotic sensitivity were obtained among the isolates through principal component analysis (PCA) and Agglomerative Hierarchical Clustering (AHC). The 16S rRNA data of two multi-drug resistant isolates revealed closed clustering with clinical E. coli strains which could be indication of their zoonotic potential. In conclusion, the results depict higher prevalence of fimH and hlyF genes and drug resistance patterns of E. coli isolates from broilers in Sylhet city of Bangladesh.

Identification and host response interaction study of SARS-CoV-2 encoded miRNA-like sequences: an in silico approach.

COVID-19, a global pandemic caused by an RNA virus named SARS-CoV-2 has brought the world to a standstill in terms of infectivity, casualty, and commercial plummet. RNA viruses can encode microRNAs (miRNAs) capable of modulating host gene expression, and with that notion, we aimed to predict viral miRNA like sequences of MERS-CoV, SARS-CoV and SARS-CoV-2, analyze sequence reciprocity and investigate SARS-CoV-2 encoded potential miRNA-human genes interaction using bioinformatics tools. In this study, we retrieved 206 SARS-CoV-2 genomes, executed phylogenetic analysis, and the selected reference genome (MT434792.1) exhibited about 99% similarities among the retrieved genomes. We predicted 402, 137, and 85 putative miRNAs of MERS-CoV (NC_019843.3), SARS-CoV (NC_004718.3), and SARS-CoV-2 (MT434792.1) genome, respectively. Sequence similarity was analyzed among 624 miRNAs which revealed that the predicted miRNAs of SARS-CoV-2 share a cluster with the clad of miRNAs from MERS-CoV and SARS-CoV. Only SARS-CoV-2 derived 85 miRNAs were encountered for target prediction and 29 viral miRNAs seemed to target 119 human genes. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis suggested the involvement of respective genes in various pathways and biological processes. Finally, we focused on eight putative miRNAs influencing 14 genes that are involved in the adaptive hypoxic response, neuroinvasion and hormonal regulation, and tumorigenic progression in patients with COVID-19. SARS-CoV-2 encoded miRNAs may cause misexpression of some critical regulators and facilitate viral neuroinvasion, altered hormonal axis, and tumorigenic events in the human host. However, these propositions need validation from future studies.