In this project, the research group proposes to apply next-generation sequencing technology to sequence six genes consistently associated with host susceptibility to leprosy and leprosy reactions.
Comparative sequencing analysis of genes associated with susceptibility to leprosy and its reactive states
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Project summary
Leprosy is a chronic infectious disease caused by Mycobacterium leprae, an obligate, slow growth, intracellular pathogen with tropism for skin and peripheral nervous system. Development of leprosy is highly dependent of host genetic risk factors, as initially demonstrated by observational studies. With the advance of technology, molecular studies successfully identified various candidate genes involved in the control of leprosy phenotypes. However, none of these study designs is suited to pinpoint the true causative genetic variants underlying the observed association effects. This study aimed to identify rare and/or common changes in the DNA of 73 genes previously implicated in the molecular control of host susceptibility to leprosy.
The analysis of variants on the genetic material of the individuals included in the study led to the identification of 37 DNA changes distributed in 24 genes that may increase the chance of an individual developing leprosy after contact with the causative bacteria. Nine of these changes led to modifications in the structure of proteins, and two of these changes are predicted by computer systems to be damaging to the protein function. Six of these genes have more than one DNA change associated with leprosy and, for two out of these six genes, analysis considering combinations of the independent variants confirmed strong evidence for association with leprosy. In 4 of the 37 genes analysed, the combination of rare changes in the genome of the individuals caused an increased risk of development of leprosy. Finally, these genetic variants are being used to predict the risk of a leprosy patient to develop leprosy reactions, through a free-access computer-based, on-line artificial intelligence system that is operating with sensitivity and specificity higher than 85%.
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