Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study.

BACKGROUND: Leprosy is known to be unevenly distributed between and within countries. High risk areas or 'hotspots' are potential targets for preventive interventions, but the underlying epidemiologic mechanisms that enable hotspots to emerge, are not yet fully understood. In this study, we identified and characterized leprosy hotspots in Bangladesh, a country with one of the highest leprosy endemicity levels globally. METHODS: We used data from four high-endemic districts in northwest Bangladesh including 20 623 registered cases between January 2000 and April 2019 (among ~ 7 million population). Incidences per union (smallest administrative unit) were calculated using geospatial population density estimates. A geospatial Poisson model was used to detect incidence hotspots over three (overlapping) 10-year timeframes: 2000-2009, 2005-2014 and 2010-2019. Ordinal regression models were used to assess whether patient characteristics were significantly different for cases outside hotspots, as compared to cases within weak (i.e., relative risk (RR) of one to two), medium (i.e., RR of two to three), and strong (i.e., RR higher than three) hotspots. RESULTS: New case detection rates dropped from 44/100 000 in 2000 to 10/100 000 in 2019. Statistically significant hotspots were identified during all timeframes and were often located at areas with high population densities. The RR for leprosy was up to 12 times higher for inhabitants of hotspots than for people living outside hotspots. Within strong hotspots (1930 cases among less than 1% of the population), significantly more child cases (i.e., below 15 years of age) were detected, indicating recent transmission. Cases in hotspots were not significantly more likely to be detected actively. CONCLUSIONS: Leprosy showed a heterogeneous distribution with clear hotspots in northwest Bangladesh throughout a 20-year period of decreasing incidence. Findings confirm that leprosy hotspots represent areas of higher transmission activity and are not solely the result of active case finding strategies.

Blood RNA signature RISK4LEP predicts leprosy years before clinical onset.

BACKGROUND: Leprosy, a chronic infectious disease caused by Mycobacterium leprae, is often late- or misdiagnosed leading to irreversible disabilities. Blood transcriptomic biomarkers that prospectively predict those who progress to leprosy (progressors) would allow early diagnosis, better treatment outcomes and facilitate interventions aimed at stopping bacterial transmission. To identify potential risk signatures of leprosy, we collected whole blood of household contacts (HC, n=5,352) of leprosy patients, including individuals who were diagnosed with leprosy 4-61 months after sample collection. METHODS: We investigated differential gene expression (DGE) by RNA-Seq between progressors before presence of symptoms (n=40) and HC (n=40), as well as longitudinal DGE within each progressor. A prospective leprosy signature was identified using a machine learning approach (Random Forest) and validated using reverse transcription quantitative PCR (RT-qPCR). FINDINGS: Although no significant intra-individual longitudinal variation within leprosy progressors was identified, 1,613 genes were differentially expressed in progressors before diagnosis compared to HC. We identified a 13-gene prospective risk signature with an Area Under the Curve (AUC) of 95.2%. Validation of this RNA-Seq signature in an additional set of progressors (n=43) and HC (n=43) by RT-qPCR, resulted in a final 4-gene signature, designated RISK4LEP (MT-ND2, REX1BD, TPGS1, UBC) (AUC=86.4%). INTERPRETATION: This study identifies for the first time a prospective transcriptional risk signature in blood predicting development of leprosy 4 to 61 months before clinical diagnosis. Assessment of this signature in contacts of leprosy patients can function as an adjunct diagnostic tool to target implementation of interventions to restrain leprosy development. FUNDING: This study was supported by R2STOP Research grant, the Order of Malta-Grants-for-Leprosy-Research, the Q.M. Gastmann-Wichers Foundation and the Leprosy Research Initiative (LRI) together with the Turing Foundation (ILEP# 702.02.73 and # 703.15.07).

Genomic Characterization of Mycobacterium leprae to Explore Transmission Patterns Identifies New Subtype in Bangladesh.

Mycobacterium leprae, the causative agent of leprosy, is an unculturable bacterium with a considerably reduced genome (3.27 Mb) compared to homologues mycobacteria from the same ancestry. In 2001, the genome of M. leprae was first described and subsequently four genotypes (1-4) and 16 subtypes (A-P) were identified providing means to study global transmission patterns for leprosy. In order to understand the role of asymptomatic carriers we investigated M. leprae carriage as well as infection in leprosy patients (n = 60) and healthy household contacts (HHC; n = 250) from Bangladesh using molecular detection of the bacterial element RLEP in nasal swabs (NS) and slit skin smears (SSS). In parallel, to study M. leprae genotype distribution in Bangladesh we explored strain diversity by whole genome sequencing (WGS) and Sanger sequencing. In the studied cohort in Bangladesh, M. leprae DNA was detected in 33.3% of NS and 22.2% of SSS of patients with bacillary index of 0 whilst in HHC 18.0% of NS and 12.3% of SSS were positive. The majority of the M. leprae strains detected in this study belonged to genotype 1D (55%), followed by 1A (31%). Importantly, WGS allowed the identification of a new M. leprae genotype, designated 1B-Bangladesh (14%), which clustered separately between the 1A and 1B strains. Moreover, we established that the genotype previously designated 1C, is not an independent subtype but clusters within the 1D genotype. Intraindividual differences were present between the M. leprae strains obtained including mutations in hypermutated genes, suggesting mixed colonization/infection or in-host evolution. In summary, we observed that M. leprae is present in asymptomatic contacts of leprosy patients fueling the concept that these individuals contribute to the current intensity of transmission. Our data therefore emphasize the importance of sensitive and specific tools allowing post-exposure prophylaxis targeted at M. leprae-infected or -colonized individuals.

Detection of Mycobacterium leprae DNA in soil: multiple needles in the haystack.

Leprosy is an infectious disease caused by Mycobacterium leprae affecting the skin and nerves. Despite decades of availability of adequate treatment, transmission is unabated and transmission routes are not completely understood. Despite the general assumption that untreated M. leprae infected humans represent the major source of transmission, scarce reports indicate that environmental sources could also play a role as a reservoir. We investigated whether M. leprae DNA is present in soil of regions where leprosy is endemic or areas with possible animal reservoirs (armadillos and red squirrels). Soil samples (n = 73) were collected in Bangladesh, Suriname and the British Isles. Presence of M. leprae DNA was determined by RLEP PCR and genotypes were further identified by Sanger sequencing. M. leprae DNA was identified in 16.0% of soil from houses of leprosy patients (Bangladesh), in 10.7% from armadillos' holes (Suriname) and in 5% from the habitat of lepromatous red squirrels (British Isles). Genotype 1 was found in Bangladesh whilst in Suriname the genotype was 1 or 2. M. leprae DNA can be detected in soil near human and animal sources, suggesting that environmental sources represent (temporary) reservoirs for M. leprae.