Collaborative network: modern genetic approaches to bust yeast tolerance to inhibi...
| Grant number: | 16/02506-7 |
| Support type: | Program for Research on Bioenergy (BIOEN) - Regular Program Grants |
| Duration: | September 01, 2016 - August 31, 2018 |
| Field of knowledge: | Biological Sciences - Genetics |
| Principal Investigator: | Gleidson Silva Teixeira |
| Grantee: | Gleidson Silva Teixeira |
| Home Institution: | Instituto de Biologia (IB). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
| Assoc. researchers: | Gonçalo Amarante Guimarães Pereira ; Marcelo Falsarella Carazzolle |
Abstract
The greater awareness of the environmental consequences resulting from fossil fuels usage leads to an increased interest in the development of renewable energy sources. In this scenario, the production of lignocellulosic ethanol stands out due to the possibility of better utilization of natural resources. However, the economic viability of industrial processes is multifactorial and depends, among other things, on the performance of the yeast used. For this reason, yeasts adapted to industrial conditions - industrial yeasts - have been considered ideal platforms for the development of strains, as well as targets for the study of genes which provide tolerance to stresses observed under industrial conditions. Understanding the genetic basis that confer robustness to these yeasts will contribute to the improvement of existing strains and to the development of new ones. However, these characteristics are generally determined by multiple genes - Quantitative Trait Loci (QTL) - whose identification requires the phenotypic analysis of a high number of segregants and validation of candidate alleles related to the studied phenotype. In this context, this project aims to develop tools that make the QTLs study more accurate, less laborious and more cost-effective, accelerating the development of strains applied to industrial processes. In order to accomplish this, we intend to do the following: optimization of flow cytometry-based procedures for analysis and dissection of tetrads; development of automated high-throughput methods for phenotyping of segregants; implementation of bioinformatics tools for QTL mapping and; optimization of CRISPR/Cas9-based approaches for the validation of candidate alleles related to the studied phenotypes. (AU)