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Endoplasmic Reticulum Stress Markers and Their Possible Implications in Leprosy's Pathogenesis.

Kelly Emi Hirai | Jorge Rodrigues de Sousa | Luciana Mota Silva | Leônidas Braga Dias Junior | Ismari Perini Furlaneto | Francisca Regina Oliveira Carneiro | Tinara Leila de Souza Aarão | Mirian Nacagami Sotto | Juarez Antonio Simões Quaresma
Disease markers | 2018

Mycobacterium leprae causes leprosy, a dermatoneurological disease which affects the skin and peripheral nerves. One of several cellular structures affected during M. leprae infection is the endoplasmic reticulum (ER). Infection by microorganisms can result in ER stress and lead to the accumulation of unfolded or poorly folded proteins. To restore homeostasis in the cell, the cell induces a series of signaling cascades known as the unfolded protein response called UPR (unfolded protein response). The present work is aimed at investigating the in situ expression of these markers in cutaneous lesions of clinical forms of leprosy and establish possible correlation expression patterns and types of lesion. A total of 43 samples from leprosy patients were analyzed by immunohistochemistry with monoclonal antibodies against GRP78/BiP, PERK, IRE1α, and ATF6. A statistically significant difference between the indeterminate, tuberculoid, and lepromatous clinical forms was detected, with high expression of GRP78/BiP, PERK, IRE1α, and ATF6 in tuberculoid forms (TT) when compared to lepromatous leprosy (LL) and indeterminate (I) leprosy. These results represent the first evidence of ER stress in samples of skin lesions from leprosy patients. We believe that they will provide better understanding of the complex pathogenesis of the disease and facilitate further characterization of the cascade of molecular events elicited during infection.

Pubmed ID: 30647798

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RRID:SCR_002798

Statistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.

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