VALIDATION OF THE ATR-FTIR MACHINE LEARNING TECHNIQUE IN THE IDENTIFICATION OF SNPs OF THE UCPs GENE FAMILY ASSOCIATED WITH PREDISPOSITION TO MULTIFACTORIAL DISORDERS

• Grant number: 24/19969-6
• Support Opportunities: Scholarships in Brazil - Master
• Start date: May 01, 2025
• End date: June 30, 2026
• Field of knowledge: Engineering - Biomedical Engineering - Bioengineering
• Principal Investigator: Renata de Azevedo Canevari
• Grantee: Ramon Varella Diniz
• Host Institution: Instituto de Pesquisa e Desenvolvimento (IP&D). Universidade do Vale do Paraíba (UNIVAP). São José dos Campos , SP, Brazil
• Associated research grant: 23/16314-6 - GENETIC POLYMORPHISM AND FTIR SPECTROSCOPY AS TOOLS IN IDENTIFYING PREDISPOSITION TO MULTIFACTORIAL DISORDERS, AP.R

Abstract

The presence of certain single nucleotide polymorphisms (SNPs) in genes related to metabolic pathways has been linked to the development of multifactorial disorders, such as type 2 diabetes mellitus (T2DM), obesity and cancer. Multifactorial disorders have high etiological complexity due to the interaction between environmental factors and multiple genetic variants, whose effects modulate susceptibility to disease development. Molecular techniques such as DNA sequencing, PCR-RFLP and qPCR SNP genotyping are essential in identifying SNPs associated with these diseases, allowing the identification of genetic predisposition and early diagnosis. Spectroscopy techniques, such as ATR-FTIR, have some advantages over molecular techniques, with real-time results, being fast and requiring little sample processing. Considering these aspects and that qPCR SNP genotyping is an accurate technique capable of detecting alterations related to the predisposition to disease development, this study aims to evaluate the feasibility of the ATR-FTIR technique combined with the machine learning (ML) algorithm as an alternative method for detecting SNP-type genetic polymorphisms related to the predisposition and development of multifactorial disorders. Genomic DNA obtained from blood samples of 100 participants will be extracted and purified, and SNPs of UCP family genes with functions related to energy metabolism will be evaluated. The assay used will be TaqMan SNP Genotyping (Taqman SNP assays MTO Human SM 10, Thermo Fisher Scientific) and the SNPs evaluated will be -3826A/G, -866G/A and -55C/T located in the UCP1, UCP2 and UCP3 genes, respectively. The ATR-FTIR technique will be performed on DNA samples amplified by qualitative PCR of sequences of the UCP1, UCP2 and UCP3 genes that contain the SNPs of interest. The spectra and ML analysis will be performed with the aid of the Matlab 2015 program, using the standard normal variation (SNV) spectrum and spectra associated with reduced dimensionality PCA. The data obtained from these analyses will be statistically compared with the molecular data obtained from each sample. The use of new and effective techniques in determining the predisposition to the development of these DMs will aid in the prevention and, consequently, in the more effective treatment of patients.