Background
Leprosy, a chronic infectious disease caused by Mycobacterium leprae, continues to pose a public health challenge in many parts of the world. Early and accurate diagnosis is crucial for effective treatment and prevention of disabilities associated with the disease. Molecular techniques such as PCR have demonstrated great potential as a diagnostic tool for directly detecting M. leprae DNA in different clinical samples, providing better sensitivity and specificity than conventional diagnostic techniques. The
Conclusion
Overall, this study's findings suggest that the qPCR technique can be used to detect M. leprae DNA in urine samples of leprosy patients using the Rlep gene target. It can also be used for diagnosing the disease and monitoring the effectiveness of anti-leprosy drugs, including multi-drug therapy (MDT), across various leprosy disease groups.
Methods
Different clinical samples such as smear, blood, and urine samples were collected from leprosy patients and healthy individuals. Leprosy patients were classified by the Ridley-Jopling classification. The Ziehl-Neelsen staining method was used for the slit skin smear (SSS) samples, and the bacteriological index (BI) was calculated for leprosy patients. DNA extraction and qPCR were performed for all three types of clinical samples using the Rlep gene target.
Results
The Mycobacterial leprae DNA was successfully detected and quantified in all clinical samples across all types of leprosy among all the study groups using the Rlep gene (129 bp) target. The Rlep gene target was able to detect the presence of M. leprae DNA in 100% of urine, 96.1% of blood, and 92.2% of SSS samples of leprosy patients. Urine samples showed significant differences (p < 0.001) between the control and the different clinical forms and between borderline tuberculoid (BT) and pure neuritic leprosy (PNL) cases. There are significant differences in cycle threshold (Ct) values between control cases and clinical categories (p < 0.001), as well as specific differences within clinical categories (p < 0.001), reflecting the variability in bacterial load and detection sensitivity across different sample types and clinical manifestations of leprosy.