Research Abstract
- The Identification of various mycobacterial species is critical for understanding their pathogenicity
and epidemiology. Despite the existence of several established methods for identifying mycobacterial
species, each of these methods has several significant limitations, including high costs, substantial
time demands, and a restricted ability to detect a wide range of recoverable species. This study
presents an in silico method using restriction fragment length polymorphism (RFLP) to differentially
identify 75 clinically important mycobacterial species.The present investigation employed specific
primer combinations to identify and generate a distinct hypervariable sequence across the ribosomal
RNA gene. This unique sequence using appropriate restriction enzyme digestion followed by gel
electrophoresis enabled the creation of highly precise and distinct patterns or profiles for each of the
75 medically relevant Mycobacterium species, including members of closely related Mycobacterium
complex groups. This approach can quickly and reliably identify mycobacterial species, allowing for
more timely treatment decisions and contributing to beneficial epidemiological investigations.
Research Date
Research Department
Research Journal
scientific reports-NATURE
Research Publisher
Scientific Reports
Research Year
2025