Molecular Identification of Uropathogenic Bacteria via 16S rRNA Gene Amplification and Sequencing
Keywords:
Urinary tract infection (UTI), Uropathogenic bacteria, 16S rRNA gene sequencing, Molecular identification, Antibiotic resistance, Escherichia coliAbstract
Urinary tract infections pose challenges to physicians, with antibiotic resistance hampering traditional approaches. Microbiological methods often fail to differentiate closely related bacterial strains, necessitating molecular resolution. This research aimed to identify uropathogenic bacteria using 16S rRNA gene amplification and sequencing.
Urine samples from 40 diagnosed patients were collected at local hospitals and diagnostics centres, Bhubaneswar. Samples were cultured on selective media for uropathogen growth and incubated at 37°C for 24–48 hours. Microbial growth was observed in 32 samples. Colonies with distinct morphology underwent Gram staining and biochemical tests for identification. Genomic DNA was extracted from purified isolates using commercial kits per manufacturer's protocols. The 16S rRNA gene was amplified using universal bacterial primers through PCR. Amplification products were analysed via agarose gel electrophoresis to confirm size. PCR products were purified and sequenced. The sequences were compared against NCBI GenBank using BLAST for species identification based on homology.
Results showed Escherichia coli as the predominant uropathogen, followed by Klebsiella pneumoniae, Pseudomonas aeruginosa, Proteus mirabilis, and Enterococcus faecalis. Molecular identification provided taxonomic precision, corroborating or reclassifying phenotypic designations. Isolates displayed over 99% sequence identity to known uropathogenic strains, deposited in GenBank with accession numbers.
This research demonstrates integrating molecular diagnostics like 16S rRNA sequencing for accurate uropathogen identification. Such approaches enhance epidemiological tracking, diagnosis, and antimicrobial stewardship by linking identity and resistance. Findings contribute to understanding etiology, offering a framework for improved infection management.
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