bioRxivGenomicsPreprint
"Decoding Mobile Genetic Elements, Virulence Factors, and Antibiotic Resistance Genes Driving Biofilm Formation in MRSA via Network Analysis"
Daraksha IramiD, Manish Singh SansiiD, Indrajit GangulyiD, Sanjeev SinghiD, Satpal Dixit
Abstract
This study aimed to analyze the genome of the Methicillin-resistant Staphylococcus aureus (MRSA) strain d1418m22 through whole-genome sequencing (WGS) and comprehensive bioinformatics analysis with six references strains. De novo assembly resulted in 61 scaffolds with a total genome size of 2.78 Mb. Functional annotation revealed 2,625 predicted genes, including those involved in metabolism, cellular processes, and virulence. Comparative genomic analysis identified 20 antibiotic resistance genes, including those conferring resistance to beta-lactams, fluoroquinolones, and aminoglycosides. In addition, 21 virulence factors (VFs) were identified, including Panton-Valentine leukocidin (PVL) and various enterotoxins. The presence of staphylococcal cassette chromosome mec (SCCmec) type IVa and mobile genetic elements (MGEs), such as prophages and transposons, underscored the role of horizontal gene transfer (HGT) in the evolution and transmission of resistance and virulence factors. Interaction network analysis identified key hub genes involved in various cellular processes, including biofilm formation. This comprehensive genomic analysis provides valuable insights into the genetic makeup of MRSA strain d1418m22, contributing to a better understanding of its pathogenicity and potential public health implications. Six reference MRSA strains were isolated from (MRSA-AMRF4 and MRSA-AMRF5) eye infections, (MRSA and MRSA-15) wound pus, (VMRSA-WC071, and VMRSA-WC081) urine samples. The analysis identified resistance genes, virulence factors, GC content, ANI values, and SCCmec elements, which were found to be similar to those present in the resistant MRSA d1418m22 strain genome.
- organism: human
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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