PLASMA METABOLIC PROFILE BY NMR FROM PATIENTS WITH PULMONARY TUBERCULOSIS

DIAZ ARIANA; VILA ALEJANDRO; BURDISSO PAULA; BAY MARÍA L.; SANTUCCI NATALIA; BOTTASSO OSCAR; D'ATTILIO LUCIANO; BONGIOVANNI BETTINA; RASIA, RODOLFO M.

Rosario

Simposio; 2nd Latin American Metabolic Profiling Symposium 2016; 2016

IBR-CONICET

Tuberculosis (TB) is a major health problem that requires an appropriate cell immune response (IR) to be controlled, however an exacerbated IR could be involved in tissue damage. WHO estimates that one third of the world?s population is infected with the bacillus, with less than 10% of these individuals being likely to develop active tuberculosis. Previously we showed that patients with TB show an important immuno-endocrine-metabolic imbalance. This consisted of elevated plasma concentrations of proinflammatory cytokines likes IFNy, IL-6 compared to healthy controls (HCo) correlating with the severity of lung involvement; along with increased levels of cortisol and lowered amounts of dehydroepiandrosterone also associated with disease severity. At the same time patients have a marked decrease of body mass index (BMI) in presence of decreased levels of leptin and increased adiponectin and ghrelin values. Omics sciences have allowed the use of global data analysis to explain biological processes. Among them, metabolomics deals with the study of metabolites, which represent the last product of cellular processes reflecting the identity and the influence of the environment on an organism. NMR allowed the acquisition of data in an effective, reproducible, and high-throughput manner. To achieve a more comprehensive view of the result of all these biological processes, and elucidating distinctive aspects of this disease for the development of diagnostic and prognostic biomarkers, plasma metabolomic profil from TB patients by acquiring NMR spectra were perform. Forty TB patients, with different degrees of pulmonary involvement (Mild, n = 9; Moderate, n = 14 and Severe n = 17), and 31 HCo similar on sex and age, were incorporated. The workflow, including sample preparation, NMR calibration, data acquisition and processing was set up based on the literature. Samples and quality control samples were analyzed, giving high quality 1H-NMR spectra. Multivariate analysis using an unsupervised methods as Principal Component Analysis, allowed to get a sample of two main components that managed to distinguish outliers and get an overview of the data matrix showing discrimination between the group of patients with TB and HCo, particularly among those with severe pulmonary TB and HCo. The discriminant analysis was performed using the supervised method (OPLS). From the analysis Loadings graph able to identify several regions discriminating among different groups of samples, including signals corresponding to aromatic amino acids (Tyr and Phe) and signals unsaturated lipids were assigned. Using metabolomics through a supervised analysis contributes to discrimination between health and disease in the context of TB. In a disease like TB with a strong metabolic component, this technique would deepen the changes that occur in different metabolic pathways and identify the highest-ranking in relation to the severity of TB.