Psoriasis and Leprosy: An Arcane Relationship.

Purpose: Psoriasis (Ps) and leprosy are chronic inflammatory skin disorders, characterised by enhanced innate and adaptive immunity. Ps and leprosy rarely coexist. The molecular immune mechanism of the Ps and leprosy rarely coexistence is unclear. Patients and Methods: RNA-sequencing (RNA-seq) was performed on 20 patients with Ps, 5 adults with lepromatous leprosy (L-lep), and 5 patients with tuberculoid leprosy (T-lep) to analyse the differentially expressed genes (DEGs) between them. Moreover, the biological mechanism of Ps and leprosy was explored by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Gene Ontology (GO) analysis, Gene Set Enrichment Analysis analysis, and protein-protein interaction (PPI) analyses. Finally, 13 DEGs of 10 skin biopsies of Ps patients, 6 samples of L-lep patients, 6 samples of T-lep patients and 5 healthy controls were confirmed by quantitative real-time polymerase chain reaction (qRT-PCR). Results: The PPI network was constructed and primarily associated with immune response, IL-17 signalling, and Toll-like receptor pathway between Ps and leprosy. Th17 markers (interleukin (IL)-19, IL-20, IL-36A, IL-36G, IL-22, IL-17A, and lipocalin-2 (LCN2) had higher expression in Ps than in L-lep and T-lep, whereas macrophage biomarkers (CLEC4E and TREM2), SPP1, and dendritic cell (DC)-related hallmarks (ITGAX) and TNF-a had significantly lower expression across Ps and T-lep than in L-lep. Conclusion: To put it simply, Ps patients with IL-17A, IL-19, IL-20, IL-36A, IL-36G, and IL-22 in conjunction with LCN2 with up-graduated expression might be not susceptible to L-lep. However, high levels of CLEC4E, TREM2, and SPP1 in L-lep patients indicated that they unlikely suffered from Ps.

Leprosy among new child cases in China: Epidemiological and clinical analysis from 2011 to 2020.

BACKGROUND: Leprosy, caused by Mycobacterium leprae infection, mainly affects skin and peripheral nerves and may further lead to disability and deformity if not treated timely. The new case detection rate of leprosy in children reflects the active transmission of leprosy infection. This study aims to present the epidemiology and clinical characteristics of new leprosy cases in children in China from 2011 to 2020. METHODOLOGY/PRINCIPAL FINDINGS: All data from leprosy patients younger than 15 years old were extracted from the Leprosy Management Information System in China (LEPMIS). Statistical Package for the Social Sciences (SPSS) version 12.0 was used for descriptive and analytical statistics of the epidemiological and clinical indicators by the Mann-Whitney test, Kruskal-Wallis test, and Fisher's exact test. And geographical distribution was analyzed by ArcGIS 10.5. A total of 152 pediatric new cases of leprosy were found over the last decade. The new case detection rate of pediatric leprosy cases decreased from 0.13 to 0.02 per 1,000,000 population over the last ten years. New pediatric cases had a higher new case detection rate in Guizhou, Sichuan, and Yunnan Provinces. All but 7 provinces in China achieved zero new child case for consecutive five years. The onset of leprosy peaked between 10 and 14 years of age, and the male to female ratio was 1.71:1. Pediatric patients were predominantly infected from symptomatic household adult contacts HHCs. Multibacillary leprosy (MB) was the most common. However, a low proportion of patients developed leprosy reaction and grade 2 disability. CONCLUSIONS/SIGNIFICANCE: The new case detection rate of pediatric leprosy cases has decreased over the past ten years in China. Spatial analysis indicated clusters in high-endemic areas. Leprosy transmission has stopped in the majority of provinces in China. However, sporadic cases may continue to exist for a long time. Active surveillance especially contact tracing should be focused on in future plan for management of leprosy, and interventions in leprosy clusters should be prioritized.

Utility of Multi-target Nested PCR and ELISPOT Assays for the Detection of Paucibacillary Leprosy: A Possible Conclusion of Clinical Laboratory Misdiagnosis.

The diagnosis of paucibacillary (PB) leprosy often possesses a diagnostic challenge, especially for pure neuritic and lesser skin lesions with the zero bacillary load, requiring a sensitive and accurate diagnostic tool. We have included 300 clinically diagnosed new leprosy cases (comprising 98 PB cases) and analyzed the sensitivity and specificity of PB leprosy cases by nested PCR with folP, gyrA, rpoB, RLEP, and 16SrRNA and Enzyme-linked Immunospot Assay test (ELISPOT) with MMPII, NDO-BSA, and LID-1 antigens by detecting interferon gamma (IFN-γ) release. The overall positivity rates of genes tested in 300 clinical specimens were identified as 55% of 16SrRNA, 59% of RLEP, 59.3% of folP, 57.3% of rpoB, 61% of gyrA while 90% of nested folP, 92.6% of nested rpoB, and 95% of nested gyrA, and 285 (95%) of at least one gene positive cases. For PB specimens, 95% PCR positivity was achieved by three tested genes in nested PCR. The data obtained from ELISPOT for three antigens were analyzed for IFN-γ expression with 600 subjects. Among 98 PB leprosy cases, the sensitivity of MMP II, LID-1, and NDO-BSA was 90%, 87%, and 83%, respectively, and the specificity was 90%, 91%, and 86%, respectively. The total number of cases positive for at least one antigen was 90 (91.8%) in PB, which is significantly higher than that in multibacillary (MB) leprosy (56.7%). The combination of multi-targets nested PCR and ELISPOT assay provides a specific tool to early clinical laboratory diagnosis of PB leprosy cases. The two assays are complementary to each other and beneficial for screening PB patients.

Single-Nucleotide Polymorphisms Related to Leprosy Risk and Clinical Phenotypes Among Chinese Population.

BACKGROUND: Genome-wide association studies (GWASs) have identified some immune-related single-nucleotide polymorphisms (SNPs) to be associated with leprosy. METHODS: This study investigated the association of 17 SNPs based on previously published GWAS studies with susceptibility to leprosy, different polar forms and immune states of leprosy in a case-control study from southwestern China, including 1344 leprosy patients and 2732 household contacts (HHCs) (1908 relatives and 824 genetically unrelated contact individuals). The differences of allele distributions were analyzed using chi-squared analysis and logistic regression. RESULTS: After adjusting covariate factors, rs780668 and rs3764147 polymorphisms influenced susceptibilities to genetically related or unrelated leprosy contact individuals. rs142179458 was associated with onset early cases, rs73058713 A allele and rs3764147 A allele increased the risk of reversal reaction, while rs3764147 G allele had higher risk to present lepromatous leprosy and erythema nodosum leprosum. CONCLUSION: Our results demonstrated that genetic variants in the LACC1, HIF1A, SLC29A3 and CDH18 genes were positively correlated with the occurrence of leprosy and leprosy clinical phenotypes, providing new insights into the immunogenetics of the disease.

Predictive nomogram for leprosy using genetic and epidemiological risk factors in Southwestern China: Case-control and prospective analyses.

BACKGROUND: There is a high incidence of leprosy among house-contacts compared with the general population. We aimed to establish a predictive model using these genetic factors along with epidemiological factors to predict leprosy risk of leprosy household contacts (HHCs). METHODS: Weighted genetic risk score (wGRS) encompassing genome wide association studies (GWAS) variants and five non-genetic factors were examined in a case-control design associated with leprosy risk including 589 cases and 647 controls from leprosy HHCs. We constructed a risk prediction nomogram and evaluated its performance by concordance index (C-index) and calibration curve. The results were validated using bootstrap resampling with 1000 resamples and a prospective design including 1100 HHCs of leprosy patients. FINDING: The C-index for the risk model was 0·792 (95% confidence interval [CI] 0·768-0·817), and was confirmed to be 0·780 through bootstrapping validation. The calibration curve for the probability of leprosy showed good agreement between the prediction of the nomogram and actual observation. HHCs were then divided into the low-risk group (nomogram score ≤ 81) and the high-risk group (nomogram score > 81). In prospective analysis, 12 of 1100 participants had leprosy during 63 months' follow-up. We generated the nomogram for leprosy in the validation cohort (C-index 0·773 [95%CI 0·658-0·888], sensitivity75·0%, specificity 66·8%). Interpretation The nomogram achieved an effective prediction of leprosy in HHCs. Using the model, the risk of an individual contact developing leprosy can be determined, which can lead to a rational preventive choice for tracing higher-risk leprosy contacts. FUNDING: The ministry of health of China, ministry of science and technology of China, Chinese academy of medical sciences, Jiangsu provincial department of science and technology, Nanjing municipal science and technology bureau.

Single-Nucleotide Polymorphisms in Genes Predisposing to Leprosy in Leprosy Household Contacts in Zhejiang Province, China.

PURPOSE: Genome-wide association studies (GWAS) have identified multiple genetic variants associated with leprosy. To investigate the single and combined associations between single-nucleotide polymorphisms (SNPs) and the development of leprosy, we therefore performed generalized multi-analytical (GMDR) analysis in Chinese leprosy household contacts and constructed a risk prediction model. PATIENTS AND METHODS: This case-control study included 229 leprosy cases and 233 healthy household contacts in Zhejiang province, China. Participants were genotyped for 17 polymorphisms selected from GWAS. The Pearson χ2 test, logistic regression and GMDR analysis were performed to investigate gene-gene interactions and construct a risk prediction model for leprosy. RESULTS: The genotype and the allele distributions of rs142179458, rs2275606, rs663743 and rs73058713 were significantly different between patients and controls. rs2275606, rs6478108, rs663743 and rs73058713 showed an association after adjusting for sex and age in the logistic regression. A five-way interaction model consisting of rs2058660, rs2275606, rs4720118, rs6478108 and rs780668 was chosen as the optimal model for determining leprosy susceptibility. The model classified 237 (51.3%) into the low-risk group and 225 (48.7%) individuals into the high-risk group. The area under the curve (AUC) of this model was 0.757 (95% CI: 0.712-0.803), and the odds ratio for leprosy between the high- and low-risk groups was 9.733 (95% CI: 6.384-14.960; P<0.001). The sensitivity and specificity of the model were observed to be 74.7% and 76.8%, respectively. CONCLUSION: Our results suggest that rs2058660, rs2275606, rs4720118, rs6478108 and rs780668, five SNPs with a significant sole effect on leprosy, interact to confer a higher risk for the disease in leprosy household contacts (HHCs).