Diagnóstico translacional da hanseníase - aplicações de metabolômica e espectroscopia em diferentes matrizes biológicas

Abstract

Introduction: Leprosy remains a major public health problem, marked by clinical heterogeneity and the lack of routine laboratory methods that allow early diagnosis and adequate patient stratification. The identification of Mycobacterium leprae-specific metabolic and spectral biomarkers represents an opportunity to develop innovative diagnostic strategies. Objectives: This thesis, composed of two complementary studies, aimed to integrate metabolomic and spectroscopic (ATR-FTIR) analyses applied to biological samples from patients with different clinical forms of leprosy, aiming at the development of artificial intelligence-assisted diagnostic models. Materials and Methods: In the first study, formalin-fixed, paraffin-embedded (FFPE) tissue samples from skin biopsies were analyzed by liquid chromatography-mass spectrometry (HPLC/MS) for untargeted identification of differentially expressed metabolites according to the clinical Ridley-Jopling spectrum. In the second study, serum and dermal scraping samples from patients and controls were evaluated by attenuated total reflection infrared (ATR-FTIR) spectroscopy, and the spectra were processed using multivariate techniques (PCA, decision trees, and neural networks) to build predictive models. Results: Metabolomic analysis revealed distinct chemical signatures among the clinical forms, particularly oxidized lipids and tryptophan derivatives, associated with bacillary load and inflammatory response. Spectroscopic analysis allowed the discrimination of samples positive and negative for IgM anti-PGL-I and M. leprae DNA by qPCR, with variance explained above 98% in the first two principal components. Machine learning-based models showed accuracy above 90% for differentiating positive and negative samples. Conclusion: The integration of metabolomics and ATR-FTIR spectroscopy demonstrated potential for rapid, noninvasive, and standardizable recognition of biochemical and structural patterns characteristic of leprosy. The results demonstrate the viability of omics tools and artificial intelligence as diagnostic support and contribution to disease surveillance and control.