GENETIC POLYMORPHISM AND FTIR SPECTROSCOPY AS TOOLS IN IDENTIFYING PREDISPOSITION TO MULTIFACTORIAL DISORDERS

• Grant number: 23/16314-6
• Support Opportunities: Regular Research Grants
• Duration: September 01, 2024 - August 31, 2026
• Field of knowledge: Engineering - Biomedical Engineering - Bioengineering
• Principal Investigator: Renata de Azevedo Canevari
• Grantee: Renata de Azevedo Canevari
• Host Institution: Instituto de Pesquisa e Desenvolvimento (IP&D). Universidade do Vale do Paraíba (UNIVAP). São José dos Campos , SP, Brazil
• Associated researchers: Leandro José Raniero ; Tanmoy Tapobrata Bhattacharjee ; Virginia Klausner de Oliveira

Abstract

Multifactorial disorders (MD) belong to the most common class of genetic diseases and are the result of complex interactions between several genetic variants, environmental exposures and possible spontaneous events. In recent years, many studies have linked genetic susceptibility to the development of these disorders with the presence of specific polymorphisms in the genome, such as SNPs (single nucleotide polymorphisms). The presence of certain SNPs in genes related to energy metabolism pathways may be related to the development of several MD such as diabetes, obesity and certain types of cancer. Research for a more precise diagnosis and/or predisposition to these diseases has also used optical spectroscopy techniques such as FTIR analysis, which has many advantages over the traditional histopathological technique, with real-time results and the possibility of obtaining non-invasive or minimally invasive diagnostics. Considering these aspects and that the genotypic evaluation of SNPs is an accurate, safe, non-invasive technique capable of detecting changes related to the predisposition to the development of diseases, this study has the main objective of evaluating the feasibility of the ATR-FTIR technique combined with allogarithmic machine learning as an alternative method when compared to the qPCR SNP genotyping analysis method in the detection of genetic polymorphisms such as SNPs related to predisposition to DM2, obesity and the development of cancer. Blood samples from 200 patients divided into groups, according to the presence/absence of the disease, will be collected. Genomic DNA will be extracted and purified and gene variations will be genotyped in the six genes related to metabolism (ADIPOQ, PLIN, PPARGC1A, UCP1, UCP2 and UCP3) by the TaqMan SNP Genotyping assay (Taqman SNP assays MTO Human SM 10, Thermo Fisher Scientific). The ATR-FTIR technique will be performed on samples obtained from patients' blood serum and ATR-FTIR combined with allogarithmic machine learning will be performed on DNA samples amplified by PCR. The data obtained from these analyzes will be statistically compared with the clinical data and biochemical analyzes (blood glucose tests, glycated hemoglobin (HbA1c), insulin, total cholesterol and triglycerides) obtained from each individual to verify whether there is a significant correlation between genetic variations and the development of the MDs analyzed. The information acquired from this study may add additional information to the prevention and/or diagnosis of these disorders and consequently to the treatment of patients affected by these pathologies. (AU)