Expression of SmATPDases 1 and 2 in Schistosoma mansoni eggs favours IL-10 production in infected individuals.

A role of IL-10 is down-regulating T-cell responses to schistosome antigens. Since SmATPDases can be correlated to modulation of the immune response, we evaluated the expression of enzymes in S. mansoni eggs. Faecal samples were collected from 40 infected individuals to detect coding regions of the SmATPDases. The cytokines were measured in supernatants of PBMC. The analysis was performed by the global median determination and set up high producers (HP) of cytokines. Six individuals expressed SmATPDase1, six expressed SmATPDase2 and six expressed both enzymes. The group who expressed only SmATPDase1 showed a high frequency of IFN-γ, TNF IL-4 HP; individuals who expressed only SmATPDase2 showed a high frequency of IFN-γ, IL-6 and IL-4 HP; and individuals who expressed both enzymes showed a high frequency of IL-10 HP. The comparison of the IFN-γ/IL-10 ratio presented higher indices in the group who had SmATPDase 2 expression than those who had the expression of both enzymes. The positive correlation between infection intensity and IL-10 levels remained only in the positive SmATPDase group. The IL-10 is the only cytokine induced by the expression of both enzymes. Our data suggest that the expression of both enzymes seems to be a factor that modulates the host immune response by inducing high IL-10 production.

Interplay among differential exposure to Mycobacterium leprae and TLR4 polymorphism impacts the immune response in household contacts of leprosy patients.

Introduction: The aim of the present study was to investigate the association between the single nucleotide polymorphism (SNP) rs1927914 A/G in TLR4 gene and the immunological profile of household contacts (HHC) of leprosy patients. Leprosy classification is usually complex and requires the assessment of several clinical and laboratorial features. Methods: Herein, we have applied distinct models of descriptive analysis to explore qualitative/quantitative changes in chemokine and cytokine production in HHC further categorized according to operational classification [HHC(PB) and HHC(MB)] and according to TLR4SNP. Results and discussion: Our results showed that M. leprae stimuli induced an outstanding production of chemokines (CXCL8;CCL2; CXCL9; CXCL10) by HHC(PB), while increase levels of pro-inflammatory cytokines (IL-6; TNF; IFN-γ; IL-17) were observed for HHC(MB). Moreover, the analysis of chemokine and cytokine signatures demonstrated that A allele was associated with a prominent soluble mediator secretion (CXCL8; CXCL9; IL-6; TNF; IFN-γ). Data analysis according to TLR4 SNP genotypes further demonstrated that AA and AG were associated with a more prominent secretion of soluble mediators as compared to GG, supporting the clustering of AA and AG genotypes into dominant genetic model. CXCL8, IL-6, TNF and IL-17 displayed distinct profiles in HHC(PB) vs HHC(MB) or AA+AG vs GG genotype. In general, chemokine/cytokine networks analysis showed an overall profile of AA+GA-selective (CXCL9-CXCL10) and GG-selective (CXCL10-IL-6) axis regardless of the operational classification. However, mirrored inverted CCL2-IL-10 axis and a (IFN-γ-IL-2)-selective axis were identified in HHC(MB). CXCL8 presented outstanding performance to classify AA+AG from GG genotypes and HHC(PB) from HHC(MB). TNF and IL-17 presented elevated accuracy to classify AA+AG from GG genotypes and HHC(PB) (low levels) from HHC(MB) (high levels), respectively. Our results highlighted that both factors: i) differential exposure to M. leprae and ii) TLR4 rs1927914 genetic background impact the immune response of HHC. Our main results reinforce the relevance of integrated studies of immunological and genetic biomarkers that may have implications to improve the classification and monitoring of HHC in future studies.

Algorithm Design for a Cytokine Release Assay of Antigen-Specific In Vitro Stimuli of Circulating Leukocytes to Classify Leprosy Patients and Household Contacts.

BACKGROUND: Immunological biomarkers have often been used as a complementary approach to support clinical diagnosis in several infectious diseases. The lack of commercially available laboratory tests for conclusive early diagnosis of leprosy has motivated the search for novel methods for accurate diagnosis. In the present study, we describe an integrated analysis of a cytokine release assay using a machine learning approach to create a decision tree algorithm. This algorithm was used to classify leprosy clinical forms and monitor household contacts. METHODS: A model of Mycobacterium leprae antigen-specific in vitro assay with subsequent cytokine measurements by enzyme-linked immunosorbent assay was employed to measure the levels of tumor necrosis factor (TNF), interferon-γ, interleukin 4, and interleukin 10 (IL-10) in culture supernatants of peripheral blood mononuclear cells from patients with leprosy, healthy controls, and household contacts. Receiver operating characteristic curve analysis was carried out to define each cytokine's global accuracy and performance indices to identify clinical subgroups. RESULTS: Data demonstrated that TNF (control culture [CC]: AUC = 0.72; antigen-stimulated culture [Ml]: AUC = 0.80) and IL-10 (CC: AUC = 0.77; Ml: AUC = 0.71) were the most accurate biomarkers to classify subgroups of household contacts and patients with leprosy, respectively. Decision tree classifier algorithms for TNF analysis categorized subgroups of household contacts according to the operational classification with moderate accuracy (CC: 79% [48/61]; Ml: 84% [51/61]). Additionally, IL-10 analysis categorized leprosy patients' subgroups with moderate accuracy (CC: 73% [22/30] and Ml: 70% [21/30]). CONCLUSIONS: Together, our findings demonstrated that a cytokine release assay is a promising method to complement clinical diagnosis, ultimately contributing to effective control of the disease.

Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App.

BACKGROUND: According to the World Health Organization, achieving targets for control of leprosy by 2030 will require disease elimination and interruption of transmission at the national or regional level. India and Brazil have reported the highest leprosy burden in the last few decades, revealing the need for strategies and tools to help health professionals correctly manage and control the disease. OBJECTIVE: The main objective of this study was to develop a cross-platform app for leprosy screening based on artificial intelligence (AI) with the goal of increasing accessibility of an accurate method of classifying leprosy treatment for health professionals, especially for communities further away from major diagnostic centers. Toward this end, we analyzed the quality of leprosy data in Brazil on the National Notifiable Diseases Information System (SINAN). METHODS: Leprosy data were extracted from the SINAN database, carefully cleaned, and used to build AI decision models based on the random forest algorithm to predict operational classification in paucibacillary or multibacillary leprosy. We used Python programming language to extract and clean the data, and R programming language to train and test the AI model via cross-validation. To allow broad access, we deployed the final random forest classification model in a web app via shinyApp using data available from the Brazilian Institute of Geography and Statistics and the Department of Informatics of the Unified Health System. RESULTS: We mapped the dispersion of leprosy incidence in Brazil from 2014 to 2018, and found a particularly high number of cases in central Brazil in 2014 that further increased in 2018 in the state of Mato Grosso. For some municipalities, up to 80% of cases showed some data discrepancy. Of a total of 21,047 discrepancies detected, the most common was "operational classification does not match the clinical form." After data processing, we identified a total of 77,628 cases with missing data. The sensitivity and specificity of the AI model applied for the operational classification of leprosy was 93.97% and 87.09%, respectively. CONCLUSIONS: The proposed app was able to recognize patterns in leprosy cases registered in the SINAN database and to classify new patients with paucibacillary or multibacillary leprosy, thereby reducing the probability of incorrect assignment by health centers. The collection and notification of data on leprosy in Brazil seem to lack specific validation to increase the quality of the data for implementations via AI. The AI models implemented in this work had satisfactory accuracy across Brazilian states and could be a complementary diagnosis tool, especially in remote areas with few specialist physicians.