A novel in silico molecular tool for comprehensive differentiation of Mycobacterium species

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.