Identification of oral bacterial species that harbor resistance genes and/or that have developed tolerance strategies to antibiotics and antimicrobial drugs

• Grant number: 24/13692-2
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: April 01, 2025
• End date: March 31, 2026
• Field of knowledge: Health Sciences - Dentistry
• Principal Investigator: Marlise Inêz Klein Furlan
• Grantee: Thiago Pereira Hirata
• Host Institution: Faculdade de Odontologia de Piracicaba (FOP). Universidade Estadual de Campinas (UNICAMP). Piracicaba , SP, Brazil
• Associated research grant: 21/06801-1 - Extracellular matrix: from biology to strategies for controlling cariogenic biofilms, AP.JP2

Abstract

Bacteria may possess genes that confer resistance to antibiotics and antimicrobial drugs. The set of resistance genes is called the resistome. The oral cavity is home to hundreds of bacterial species, which can migrate to extraoral sites (bacteremia, swallowing, aspiration) and cause damage. Therefore, it is necessary to know the oral resistome and its implications for the host. Thus, the main aim of the current project is to characterize the oral resistome. Hence, the specific aims of this project are: (i) to identify oral bacterial species (detected in the mouth) that harbor resistance genes and/or that have developed tolerance strategies to antibiotics and antimicrobial drugs in silico, (ii) to determine the number of genes per species (or genus) in silico, and (iii) to validate the in silico data via the detection of a resistance genes panel using in vivo dental biofilm samples from humans. The approach to achieving the specific aims (i) and (ii) will be in silico analysis via bioinformatics tools from the expanded Oral Microbiome Database or eHOMD (https://www.homd.org/). For specific aim III, the clinical samples will be subjected to bacterial DNA isolation, followed by detection and quantification of resistance genes via quantitative PCR, using specific primers for each target gene. Quantitative and qualitative data will be organized, classified, analyzed, and discussed based on scientific literature. Quantitative data also will be analyzed via descriptive and inferential statistics (¿=0.05).