Human interleukin 13 (IL-13) is a cytokine that has a profound effect on primary immune cells by inducing immunoglobulin production, proliferation of B cells, and the differentiation of cells of the monocytic lineage. IL-13 can inhibit the production of inflammatory cytokines by both macrophages and monocytes. Previously, IL-13 expression has been reported only in cells of the T-cell lineage and the mast cell line HMC-1. We now report the presence of IL-13 mRNA and protein in human alveolar macrophages (AMs) analyzed by the reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunoabsorbent assay (ELISA), respectively, and IL-13 protein in bronchoalveolar lavage fluid (BALF) of subjects with pulmonary fibrosis. We have investigated 13 patients from 49 to 75 yr of age with forms of pulmonary fibrosis, and eight healthy volunteers from 24 to 61 yr of age. Their AMs were obtained by bronchoalveolar lavage (BAL) and purified by adherence. The proportion of BAL purified AMs expressing IL-13 mRNA was increased in those subjects with fibrotic lung disease, in comparison with those from control subjects (11 of 13 versus 2 of 8, P < 0.01). IL-13 protein was detectable in the BALF of 8 of 13 patients with pulmonary fibrosis, but in none of the control subjects. AMs of four subjects with systemic sclerosis were cultured and IL-13 protein was increased in the culture supernatants when compared to the control subjects, although this did not reach significance. These findings show that IL-13 mRNA is not only a product of T cells, but is also expressed in both normal AMs and those from subjects with pulmonary fibrosis, and that at least some of the IL-13 mRNA is translated into protein and secreted in subjects with pulmonary fibrosis. We hypothesize that IL-13 may be expressed by normal human AMs as part of the homeostatic control process but its production may be increased in the presence of inflammatory lung disease.

BackgroundCharacterization of the Mycobacterium leprae genome has made possible the development of Polymerase Chain Reaction (PCR) systems that can amplify different genomic regions. Increased reliability and technical efficiency of quantitative PCR (qPCR) makes it a promising tool for early diagnosis of leprosy. Index cases that are multibacillary spread the bacillus silently, even before they are clinically diagnosed. Early detection and treatment could prevent transmission in endemic areas.MethodsIn this study, the qPCR technique is used to detect DNA of M. leprae in samples of slit skin smears (SSS) of the ear lobe and blood of leprosy patients and their asymptomatic household contacts residing in Governador Valadares, MG, Brazil, a hyperendemic area for leprosy. A total of 164 subjects participated in the study: 43 index cases, 113 household contacts, and, as negative controls, 8 individuals who reported no contact with patients nor history of leprosy in the family. The qPCR was performed to amplify 16S rRNA fragments and was specifically designed for M. leprae.ResultsOf asymptomatic household contacts, 23.89% showed bacillary DNA by qPCR in samples of SSS and blood. Also, 48.84% of patients diagnosed with leprosy were positive for qPCR while the bacillary load was positive in only 30.23% of patients. It is important to note that most patients were already receiving treatment when the collection of biological material for qPCR was performed. The level of bacillary DNA from household contacts was similar to the DNA levels detected in the group of paucibacillary patients.ConclusionConsidering that household contacts comprise a recognizable group of individuals with a high risk of disease, as they live in close proximity to a source of infection, qPCR can be used to estimate the risk of progress towards leprosy among household contacts and as a routine screening method for a chemoprophylactic protocol.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3056-2) contains supplementary material, which is available to authorized users.

Background Early detection of Mycobacterium leprae is a key strategy for disrupting the transmission chain of leprosy and preventing the potential onset of physical disabilities. Clinical diagnosis is essential, but some of the presented symptoms may go unnoticed, even by specialists. In areas of greater endemicity, serological and molecular tests have been performed and analyzed separately for the follow-up of household contacts, who are at high risk of developing the disease. The accuracy of these tests is still debated, and it is necessary to make them more reliable, especially for the identification of cases of leprosy between contacts. We proposed an integrated analysis of molecular and serological methods using artificial intelligence by the random forest (RF) algorithm to better diagnose and predict new cases of leprosy. Methods The study was developed in Governador Valadares, Brazil, a hyperendemic region for leprosy. A longitudinal study was performed, including new cases diagnosed in 2011 and their respective household contacts, who were followed in 2011, 2012, and 2016. All contacts were diligently evaluated by clinicians from Reference Center for Endemic Diseases (CREDEN-PES) before being classified as asymptomatic. Samples of slit skin smears (SSS) from the earlobe of the patients and household contacts were collected for quantitative polymerase chain reaction (qPCR) of 16S rRNA, and peripheral blood samples were collected for ELISA assays to detect LID-1 and ND-O-LID. Results The statistical analysis of the tests revealed sensitivity for anti-LID-1 (63.2%), anti-ND-O-LID (57.9%), qPCR SSS (36.8%), and smear microscopy (30.2%). However, the use of RF allowed for an expressive increase in sensitivity in the diagnosis of multibacillary leprosy (90.5%) and especially paucibacillary leprosy (70.6%). It is important to report that the specificity was 92.5%. Conclusion The proposed model using RF allows for the diagnosis of leprosy with high sensitivity and specificity and the early identification of new cases among 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 (M. leprae) antigen-specific in vitro assay with subsequent cytokine measurements by ELISA was employed to measure the levels of TNF, IFN-γ, IL-4, and IL-10 in culture supernatants of peripheral blood mononuclear cells from leprosy patients, healthy controls as well as household contacts. Receiver Operating Characteristic (ROC) 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 leprosy patients, respectively. Decision tree classifier algorithms were for TNF analysis categorized subgroups of household contacts according to the operational classification with moderate accuracy (CC:79%, 48/61, and 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.

Background Leprosy is a chronic infectious disease classified into two subgroups for therapeutic purposes: paucibacillary (PB) and multibacillary (MB), closely related to the host immune responses. In this context it is noteworthy looking for immunological biomarkers applicable as complementary diagnostic tools as well as a laboratorial strategy to follow-up leprosy household contacts. Methods The cross-sectional study enrolled 49 participants, including 19 patients and 30 healthy controls. Peripheral blood mononuclear cells (PBMC) were isolated and incubated in the presence of Mycobacterium leprae bacilli. The cells were prepared for surface (CD4+ and CD8+) and intracytoplasmic cytokine staining (IFN-γ, IL-4 and IL-10). Multiple comparisons amongst groups were carried out by ANOVA, Kruskal–Wallis, Student T or Mann–Whitney test. Comparative analysis of categorical variables was performed by Chi-square. Functional biomarker signature analysis was conducted using the global median values for each biomarker index as the cut-off edge to identify the proportion of subjects with high biomarker levels. Results The cytokine signature analysis demonstrated that leprosy patients presented a polyfunctional profile of T-cells subsets, with increased frequency of IFN-γ+ T-cell subsets along with IL-10+ and IL-4+ from CD4+ T-cells, as compared to health Controls (Venn diagram report). Moreover, statistical analysis was carried out using parametric or non-parametric variance analysis followed by pairwise multiple comparisons, according to the data normality distribution. L(PB) displayed a polyfunctional profile characterized by enhanced percentage of IFN-γ+, IL-10+ and IL-4+ produced by most T-cell subsets, as compared to L(MB) that presented a more restricted cytokine functional profile mediated by IL-10+ and IL-4+ T-cells with minor contribution of IFN-γ produced by CD4+ T-cells. Noteworthy was that HHC(MB) exhibited enhanced frequency of IFN-γ+ T-cells, contrasting with HHC(PB) that presented a cytokine profile limited to IL-10 and IL-4. Conclusions Our data demonstrated that L(PB) displayed enhanced percentage of IFN-γ+, IL-10+ and IL-4+ as compared to L(MB) that presented functional profile mediated by IL-10+ and IL-4+ T-cells and HHC(MB) exhibited enhanced frequency of IFN-γ+ T-cells, contrasting with HHC(PB). Together, our findings provide additional immunological features associated with leprosy and household contacts. These data provide evidence that biomarkers of immune response can be useful complementary diagnostic/prognostic tools as well as insights that household contacts should be monitored to access putative subclinical infection.

Background: Characterization of the Mycobacterium leprae genome has made possible the development of Polymerase Chain Reaction (PCR) systems that can amplify different genomic regions. Increased reliability and technical efficiency of quantitative PCR (qPCR) makes it a promising tool for early diagnosis of leprosy. Index cases that are multibacillary spread the bacillus silently, even before they are clinically diagnosed. Early detection and treatment could prevent transmission in endemic areas. Methods: In this study, the qPCR technique is used to detect DNA of M. leprae in samples of slit skin smears (SSS) of the ear lobe and blood of leprosy patients and their asymptomatic household contacts residing in Governador Valadares, MG, Brazil, a hyperendemic area for leprosy. A total of 164 subjects participated in the study: 43 index cases, 113 household contacts, and, as negative controls, 8 individuals who reported no contact with patients nor history of leprosy in the family. The qPCR was performed to amplify 16S rRNA fragments and was specifically designed for M. leprae. Results: Of asymptomatic household contacts, 23.89% showed bacillary DNA by qPCR in samples of SSS and blood. Also, 48.84% of patients diagnosed with leprosy were positive for qPCR while the bacillary load was positive in only 30.23% of patients. It is important to note that most patients were already receiving treatment when the collection of biological material for qPCR was performed. The level of bacillary DNA from household contacts was similar to the DNA levels detected in the group of paucibacillary patients. Conclusion: Considering that household contacts comprise a recognizable group of individuals with a high risk of disease, as they live in close proximity to a source of infection, qPCR can be used to estimate the risk of progress towards leprosy among household contacts and as a routine screening method for a chemoprophylactic protocol.

Slit skin smear and histopathological examinations are currently the main laboratory tools used to aid the diagnosis of leprosy. However, their sensitivity is low, and many cases are not detected. New methodologies have been studied to develop more accurate tests. This narrative review aims to raise attention to the results of molecular (polymerase chain reaction) and serological (Enzyme-Linked Immunosorbent Assay) tests applied to the diagnosis of leprosy, and to summarize the available information about the former. Original scientific articles published in indexed international journals, whose study involved aspects of the diagnosis and classification of leprosy cases or home contacts, were selected. The data were extracted independently using a standardized method that dictated the inclusion of the following information: diagnosis in Paucibacillary and Multibacillary cases and in household contacts; sample number; sample type; study design; studied variables; statistical analysis employed; main results; and limitations identified. In clinical practice, the results from molecular and serological tests are assessed separately, with moderate sensitivity and specificity. However, an integrated study of these methodologies has been suggested for greater accuracy in diagnosis.

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