Unveiling the role of NK cells, NKT-like cells, and γδ cells in pathogenesis of type 1 reactions in leprosy.

Leprosy is a disease with spectral clinical manifestations along with two types of reactions, type 1 reaction (T1R) and type 2 reaction (T2R). T1R especially occurs because of the defensive upgradation of cell-mediated immunity (CMI) to M. leprae antigens. T1R is the main cause of disability in leprosy. The role of conventional adaptive T cells has been well studied to understand T1R. A comprehensive understanding of the role of unconventional T cells in the manifestation of inflammation during T1R is crucial and has not been studied. In our study, we found significantly higher plasma levels of TNFα, IL1ß, IL17, and IP10 in T1R when compared to non-reaction (NR). Gene expression for cytokines in blood circulation by qPCR showed significantly higher expression of IFNγ, IP10, TNFα, IL6, IL17A and chemokines CCL3, CCR1, CCR5, and CXCR3 in T1R as compared to NR. Frequencies of NKT-like cells (48.7 %) and NK cells (22.3 %) were found significantly higher in T1R in comparison to NR (36.9 %, 18.3 %, respectively) (p = 0.0001). Significantly lower levels of γδT cells (3.32 %) were observed in T1R in comparison to NR (5.16 %). The present study has provided evidence for the first time on the role of plausible unconventional T cells in the immunopathogenesis of T1R in leprosy.

Genomic characterization of Mycobacterium lepromatosis from ENL patients from India.

BACKGROUND: Leprosy is caused by Mycobacterium leprae and Mycobacterium lepromatosis. Both organisms cannot be cultured in vitro. M. lepromatosis was found to be associated mainly with diffuse lepromatous leprosy and with Lucio's phenomena initially. Later, M. lepromatosis was observed in borderline leprosy cases (BL), lepromatous leprosy cases (LL) and leprosy reactional cases (T1R and ENL). Although many cases are being reported with similar clinical features like Lucio phenomenon in India but M. lepromatosis was not isolated from these cases. The aim of this study was to screen MB patients and patients with type 2 reaction for the presence of M. lepromatosis. METHODOLOGY: We recruited a total of 75 multibacillary leprosy cases (45 MB cases without reaction and 30 type 2 reaction (ENL) cases) from TLM hospitals Purulia (West Bengal), Barabanki (Uttar Pradesh), Shahdara (Delhi) and PGIMER (Chandigarh), India. Punch biopsies of 5 mm were collected in 70% ethanol from all the study subjects. DNA was extracted followed by Hemi-nested PCR targeting 16S rRNA gene specific for M. lepromatosis. Further, PCR products were processed for Sanger sequencing for an absolute confirmation of M. lepromatosis. Whole genome sequencing was done to confirm the presence of M. lepromatosis. RESULT: We observed presence of M. lepromatosis in 4 necrotic ENL patients by heminested PCR. There was 100% 16S rRNA sequence similarity with M. lepromatosis FJ924 in one case, 98.96% in two cases and in one case it was 90.9% similarity by nucleotide BLAST (BLASTn) by using the NCBI website. On the basis of Sanger sequencing, we noted presence of M. lepromatosis in 3 necrotic ENL patients as one sample only gave 90.9% similarity by BLASTn. On the basis of de novo assembly and genome obtained, only one sample S4 with a 2.9 mb genome size was qualified for downstream analysis. Sixteen M. lepromatosis- specific proteins were identified in this case and the closest species was M. lepromatosis strain FJ924 based on whole genome level phylogeny. CONCLUSION: These results provide valuable insights into the prevalence of M. lepromatosis in ENL patients in different regions of India and contribute to our understanding of the genetic characteristics of this pathogen in the context of leprosy.

Corroboration of cross-reactivity between Mycobacterium leprae and hosts' salivary and cutaneous proteins: A hope for prognostic biomarkers for the pathogenesis of reactions in leprosy.

Introduction: Immunological reactions are frequent complications that may occur either before, during, or after treatment and affect 30-50% of leprosy patients. The presence of autoantibodies like rheumatoid factor, antinuclear factor, and antibodies to host collagen, keratin, actin, myosin, endothelial cells, and myelin basic protein (MBP) has been earlier reported in leprosy patients. The purpose of this study was to identify cross-reactive proteins in clinical samples such as saliva and slit skin scrapings (SSS) of leprosy patients which could be utilised as prognostic biomarkers for Type 1 Reaction (T1R) in leprosy. Method: A total of 10 leprosy patients in T1R and 5 healthy volunteers were recruited. The protein was extracted from their SSS and saliva samples, thereafter, isoelectric focusing (IEF) and two-dimensional PAGE were performed to analyse the proteins. Furthermore, the cross-reactivity was identified by western blotting host proteins in gel against purified IgG from Mycobacterium leprae soluble antigen (MLSA)- hyperimmunized rabbit sera, thereafter, cross-reactive proteins were identified by MS/MS. The cross-reactive host proteins were analysed for homologous bacterial proteins and B cell epitopes (BCEs) were predicted by using bioinformatic tools. Results: A total of five spots of salivary proteins namely S100-A9, 35.3 kDa, and 41.5 kDa proteins, Serpin peptidase inhibitor (clade A), Cystatin SA-III, and four spots of SSS namely 41.4 kDa protein, Alpha-1 antitrypsin, vimentin, and keratin 1, were identified as cross-reactive. Further, a total of 22 BCEs of cross-reactive host proteins were predicted and visualised. Discussion: This data provides strong evidence of cross-reactivity/molecular mimicry between host and pathogen in leprosy patients with reaction. These BCEs of cross-reactive proteins could be further studied to predict reactions and may be utilised as an early diagnostic biomarker for T1R in leprosy.

Ofloxacin resistance in multibacillary new leprosy cases from Purulia, West Bengal: a threat to effective secondary line treatment for rifampicin-resistant leprosy cases.

OBJECTIVES: Purulia is one of the high-endemic districts for leprosy in West Bengal (the eastern part of India). The annual new case detection rate (ANCDR) of leprosy in West Bengal is 6.04/100000 (DGHS 2019-20). Our earlier report provided evidence of secondary drug resistance in relapse cases of leprosy. The aim of the current study was to observe primary drug resistance patterns for dapsone, rifampicin, and ofloxacin amongst new leprosy patients from Purulia, West Bengal in order to better understand the emergence of primary resistance to these drugs. METHODS: In the present study, slit-skin smear samples were collected from 145 newly diagnosed leprosy cases from The Leprosy Mission (TLM) Purulia hospital between 2017 and 2018. DNA was extracted from these samples and the Mycobacterium leprae genome was analyzed for genes associated with drug resistance by polymerase chain reaction (PCR), followed by Sanger sequencing. Wild-type strain (Thai-53) and mouse footpad-derived drug-resistant strain (Z-4) were used as reference strains. RESULTS: Of 145 cases, 25 cases showed mutations in genes associated with resistance to rifampicin, dapsone, and ofloxacin (as described by the World Health Organization, rpoB, folP, and gyrA, respectively) through Sanger sequencing. Of these 25 cases, 16 cases showed mutations in ofloxacin, two cases showed mutations in combinations of ofloxacin and rifampicin, four cases showed a mutation only in rifampicin, one case showed mutations in combinations of rifampicin and dapsone, and two cases showed mutations only in dapsone. CONCLUSION: Results from this study indicated the emergence of resistance to antileprosy drugs in new cases of leprosy. As ofloxacin is the alternate drug for the treatment of rifampicin-resistant cases, the emergence of new cases with resistance to ofloxacin indicates that ofloxacin-resistant M. leprae strains are actively circulating in this endemic region (i.e., Purulia, West Bengal), posing challenges for the effective treatment of rifampicin-resistant cases.

Mimicking B and T cell epitopes between Mycobacterium leprae and host as predictive biomarkers in type 1 reaction in leprosy.

Several Mycobacterial infections including leprosy and tuberculosis are known to evoke autoimmune responses by modulating homeostatic mechanism of the host. Presence of autoantibodies like, rheumatoid factor, anti-nuclear factor and antibodies to host, collagen, keratin, myelin basic protein (MBP) and myosin, have been earlier reported in leprosy patients. In the present study, we detected the role of mimicking epitopes between Mycobacterium leprae and host components in the induction of autoimmune response in leprosy. Based on our previous findings, we predicted and synthesized a total of 15 mimicking linear B cell epitopes (BCE) and 9 mimicking linear T cell epitopes (TCE) of keratin and MBP. Humoral and cell-mediated immune responses against these epitopes were investigated in Non-reaction (NR), Type 1 reaction (T1R) leprosy patients, and healthy controls. We observed significantly higher levels of antibodies against 8 BCE in T1R in comparison to NR leprosy patients. Further, we also found 5 TCE significantly associated with lymphocyte proliferation in the T1R group. Our results indicated that these epitopes play a key role in the induction of autoimmune response in leprosy and are also strongly associated with the inflammatory episodes of T1R. Conclusively, these molecules may be employed as a biomarker to predict the inflammatory episodes of T1R.

Deep learning based microscopic cell images classification framework using multi-level ensemble.

BACKGROUND AND OBJECTIVES: Advancement of the ultra-fast microscopic images acquisition and generation techniques give rise to the automated artificial intelligence (AI)-based microscopic images classification systems. The earlier cell classification systems classify the cell images of a specific type captured using a specific microscopy technique, therefore the motivation behind the present study is to develop a generic framework that can be used for the classification of cell images of multiple types captured using a variety of microscopic techniques. METHODS: The proposed framework for microscopic cell images classification is based on the transfer learning-based multi-level ensemble approach. The ensemble is made by training the same base model with different optimisation methods and different learning rates. An important contribution of the proposed framework lies in its ability to capture different granularities of features extracted from multiple scales of an input microscopic cell image. The base learners used in the proposed ensemble encapsulates the aggregation of low-level coarse features and high-level semantic features, thus, represent the different granular microscopic cell image features present at different scales of input cell images. The batch normalisation layer has been added to the base models for the fast convergence in the proposed ensemble for microscopic cell images classification. RESULTS: The general applicability of the proposed framework for microscopic cell image classification has been tested with five different public datasets. The proposed method has outperformed the experimental results obtained in several other similar works. CONCLUSIONS: The proposed framework for microscopic cell classification outperforms the other state-of-the-art classification methods in the same domain with a comparatively lesser amount of training data.

Ensemble based machine learning approach for prediction of glioma and multi-grade classification.

Glioma is the most pernicious cancer of the nervous system, with histological grade influencing the survival of patients. Despite many studies on the multimodal treatment approach, survival time remains brief. In this study, a novel two-stage ensemble of an ensemble-type machine learning-based predictive framework for glioma detection and its histograde classification is proposed. In the proposed framework, five characteristics belonging to 135 subjects were considered: human telomerase reverse transcriptase (hTERT), chitinase-like protein (YKL-40), interleukin 6 (IL-6), tissue inhibitor of metalloproteinase-1 (TIMP-1) and neutrophil/lymphocyte ratio (NLR). These characteristics were examined using distinctive ensemble-based machine learning classifiers and combination strategies to develop a computer-aided diagnostic system for the non-invasive prediction of glioma cases and their grade. In the first stage, the analysis was conducted to classify glioma cases and control subjects. Machine learning approaches were applied in the second stage to classify the recognised glioma cases into three grades, from grade II, which has a good prognosis, to grade IV, which is also known as glioblastoma. All experiments were evaluated with a five-fold cross-validation method, and the classification results were analysed using different statistical parameters. The proposed approach obtained a high value of accuracy and other statistical parameters compared with other state-of-the-art machine learning classifiers. Therefore, the proposed framework can be utilised for designing other intervention strategies for the prediction of glioma cases and their grades.

Automated detection of bioimages using novel deep feature fusion algorithm and effective high-dimensional feature selection approach.

The classification of bioimages plays an important role in several biological studies, such as subcellular localisation, phenotype identification and other types of histopathological examinations. The objective of the present study was to develop a computer-aided bioimage classification method for the classification of bioimages across nine diverse benchmark datasets. A novel algorithm was developed, which systematically fused the features extracted from nine different convolution neural network architectures. A systematic fusion of features boosts the performance of a classifier but at the cost of the high dimensionality of the fused feature set. Therefore, non-discriminatory and redundant features need to be removed from a high-dimensional fused feature set to improve the classification performance and reduce the time complexity. To achieve this aim, a method based on analysis of variance and evolutionary feature selection was developed to select an optimal set of discriminatory features from the fused feature set. The proposed method was evaluated on nine different benchmark datasets. The experimental results showed that the proposed method achieved superior performance, with a significant reduction in the dimensionality of the fused feature set for most bioimage datasets. The performance of the proposed feature selection method was better than that of some of the most recent and classical methods used for feature selection. Thus, the proposed method was desirable because of its superior performance and high compression ratio, which significantly reduced the computational complexity.

A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images.

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.

Machine learning-based identification of radiofrequency electromagnetic radiation (RF-EMR) effect on brain morphology: a preliminary study.

The brain of a human and other organisms is affected by the electromagnetic field (EMF) radiations, emanating from the cell phones and mobile towers. Prolonged exposure to EMF radiations may cause neurological changes in the brain, which in turn may bring chemical as well as morphological changes in the brain. Conventionally, the identification of EMF radiation effect on the brain is performed using cellular-level analysis. In the present work, an automatic image processing-based approach is used where geometric features extracted from the segmented brain region has been analyzed for identifying the effect of EMF radiation on the morphology of a brain, using drosophila as a specimen. Genetic algorithm-based evolutionary feature selection algorithm has been used to select an optimal set of geometrical features, which, when fed to the machine learning classifiers, result in their optimal performance. The best classification accuracy has been obtained with the neural network with an optimally selected subset of geometrical features. A statistical test has also been performed to prove that the increase in the performance of classifier post-feature selection is statistically significant. This machine learning-based study indicates that there exists discrimination between the microscopic brain images of the EMF-exposed drosophila and non-exposed drosophila. Graphical abstract Proposed Methodology for identification of radiofrequency electromagnetic radiation (RF-EMR) effect on the morphology of brain of Drosophila.

Utility of multiplex PCR for early diagnosis and household contact surveillance for leprosy.

Early diagnosis of leprosy is important for limiting the severity of disease, which may lead to disabilities and deformities if not treated timely. Multiplex PCR employing more than one gene, specific to target DNA, is more efficient detection tool. In the present study, slit skin scrapings, blood, nasal swabs and saliva from Paucibacillary (PB) and Multibacillary (MB) cases as well as household contacts of PB cases were tested by multiplex PCR using three different gene targets namely RLEP, 16SrRNA and sodA. We found an increase in overall diagnostic positivity for M. leprae DNA detection by M-PCR as compared to individual PCR. In case of nasal swabs using M-PCR the PPV, NPV were 0.5454, 0.8333 respectively. There is remarkable increase in PPV in SSS of PB cases and nasal swabs of HHCs using M-PCR. Conclusively, our finding suggests the utility of M-PCR for early diagnosis and household contact surveillance for leprosy.

Association of non-tuberculous mycobacteria with Mycobacterium leprae in environment of leprosy endemic regions in India.

Non-tuberculous mycobacteria (NTM) are environmental mycobacteria found ubiquitously in nature. The present study was conducted to find out the presence of various species of NTM in leprosy endemic region along with Mycobacterium (M) leprae. Water and wet soil samples from the periphery of ponds used by the community were collected from districts of Purulia of West Bengal and Champa of Chhattisgarh, India. Samples were processed and decontaminated followed by culturing on Lowenstein Jensen (LJ) media. Polymerase chain reaction (PCR) was performed using 16S rRNA gene target of mycobacteria and species was confirmed by sequencing method. Indirect immune-fluorescent staining of M. leprae from soil was performed using M. leprae-PGL-1 rabbit polyclonal antibody. The phylogenetic tree was constructed by using MEGA-X software. From 380 soil samples 86 NTM were isolated, out of which 34(40%) isolates were rapid growing mycobacteria (RGM) and 52(60%) isolates were slow growing mycobacteria (SGM). Seventy-seven NTM isolates were obtained from 250 water samples, out of which 35(45%) were RGM and 42(55%) were SGM. Amongst all the RGM, we isolated M. porcinum, M. psychrotolerans, M. alsenase, M. arabiense and M. asiaticum from Indian environmental samples. M. fortuitum was the most commonly isolated species of all RGM. Out of all SGM, M. holsaticum, M. yongonense, M. seoulense, M. szulgai, M. europaeum, M. simiae and M. chimaera were isolated for the first time from Indian environment. M. intracellulare was the commonest of all isolated SGM. Presence of M. leprae was confirmed by indirect immunofluorescent microcopy and PCR method from the same environmental samples. Phylogenetic tree was showing a close association between these NTMs and M. leprae in these samples. Several NTM species of pathogenic and nonpathogenic in nature along with M. leprae were isolated from soil and pond water samples from leprosy endemic regions and these might be playing a role in causing disease and maintaining leprosy endemicity in India.

VDR polymorphism, gene expression and vitamin D levels in leprosy patients from North Indian population.

BACKGROUND: Leprosy is a chronic infectious disease caused by Mycobacterium leprae and mainly affects skin, peripheral nerves. Vitamin D receptor (VDR) gene polymorphism has been found to be associated with leprosy. Vitamin D has been shown to control several host immunomodulating properties through VDR gene. Vitamin D deficiency was also found to be linked to an increased risk for several infections and metabolic diseases. OBJECTIVE: In the present study, we investigated the association of VDR gene polymorphism, mRNA gene expression of VDR and the vitamin D levels with leprosy and its reactional states. METHODOLOGY: A total of 305 leprosy patients consisting of tuberculoid (TT), borderline tuberculoid (BT), borderline lepromatous (BL), lepromatous leprosy (LL), as well as 200 healthy controls were enrolled in the study. We identified single nucleotide polymorphisms (SNPs) of VDR Taq1, Fok1 and Apa1, as well as the expression of VDR mRNA gene using PCR-based restriction fragment length polymorphism (RFLP) analysis and real-time PCR respectively. We also performed ELISA to measure vitamin D levels. RESULT: We observed that SNP of VDR gene (Fok1 and Taq1) are associated with the leprosy disease. The allelic frequency distribution of T and t allele (p = 0.0037), F and f allele (p = 0.0024) was significantly higher in leprosy patients and healthy controls. ff genotype of Fok1 was found to be associated with leprosy patients [p = 0.0004; OR (95% CI) 3.148 (1.662-5.965)]. The recessive model of Fok1 genotype was also found to be significantly associated in leprosy patients in comparison to healthy controls [p = 0.00004; OR (95% CI) 2.85 (1.56-5.22)]. Leprosy patients are significantly associated with t-F-a haplotype. Further, VDR gene expression was found to be lower in non-reaction group compared to that of reaction group of leprosy and healthy controls. Paradoxically, we noted no difference in the levels of vitamin D between leprosy patients and healthy controls. CONCLUSION: Blood levels of vitamin D do not play any role in clinical manifestations of any forms of leprosy. ff genotype of Fok1 and tt genotype of Taq1 was found to be associated with leprosy per se. Association of t-F-a haplotype with leprosy was found to be significant and could be used as a genetic marker to identify individuals at high risk for developing leprosy. VDR gene expression was lower in TT/BT and BL/LL groups of leprosy in comparison to that of healthy controls.

Autoimmunity to Tropomyosin-Specific Peptides Induced by Mycobacterium leprae in Leprosy Patients: Identification of Mimicking Proteins.

Background: It has been shown earlier that there is a rise in the levels of autoantibodies and T cell response to cytoskeletal proteins in leprosy. Our group recently demonstrated a rise in both T and B cell responses to keratin and myelin basic protein in all types of leprosy patients and their associations in type 1 reaction (T1R) group of leprosy. Objectives: In this study, we investigated the association of levels of autoantibodies and lymphoproliferation against myosin in leprosy patients across the spectrum and tried to find out the mimicking proteins or epitopes between host protein and protein/s of Mycobacterium leprae. Methodology: One hundred and sixty-nine leprosy patients and 55 healthy controls (HC) were enrolled in the present study. Levels of anti-myosin antibodies and T-cell responses against myosin were measured by ELISA and lymphoproliferation assay, respectively. Using 2-D gel electrophoresis, western blot and MALDI-TOF/TOF antibody-reactive spots were identified. Three-dimensional structure of mimicking proteins was modeled by online server. B cell epitopes of the proteins were predicted by BCPREDS server 1.0 followed by identification of mimicking epitopes. Mice of inbred BALB/c strain were hyperimmunized with M. leprae soluble antigen (MLSA) and splenocytes and lymph node cells of these animals were adoptively transferred to naïve mice. Results: Highest level of anti-myosin antibodies was noted in sera of T1R leprosy patients. We observed significantly higher levels of lymphoproliferative response (p < 0.05) with myosin in all types of leprosy patients compared to HC. Further, hyperimmunization of inbred BALB/c strain of female mice and rabbit with MLSA revealed that both hyperimmunized rabbit and mice evoked heightened levels of antibodies against myosin and this autoimmune response could be adoptively transferred from hyperimmunized to naïve mice. Tropomyosin was found to be mimicking with ATP-dependent Clp protease ATP-binding subunit of M. leprae. We found four mimicking epitopes between these sequences. Conclusion: These data suggest that these mimicking proteins tropomyosin and ATP-dependent Clp protease ATP-binding subunit of M. leprae or more precisely mimicking epitopes (four B cell epitopes) might be responsible for extensive tissue damage during type1 reaction in leprosy.