Coexpression network analysis for identify related genes to agronomic traits in Sugarcane

• Grant number: 25/08740-0
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: July 01, 2025
• End date: January 31, 2030
• Field of knowledge: Biological Sciences - Genetics - Plant Genetics
• Principal Investigator: Renato Vicentini dos Santos
• Grantee: Luiza Oliveira Romão
• Host Institution: Instituto de Biologia (IB). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Company: Ministério da Agricultura, Pecuária e Abastecimento (Brasil). Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA). Embrapa Sede
• Associated research grant: 22/04006-2 - Center for Plant Molecular Breeding (CeM²P), AP.PCPE

Abstract

Sugarcane is one of the world's most important crops, ranking first in production quantity and sixth in net production value in 20161. It is by far the most relevant sugar crop, accounting for approximately 80% of the world's sugar production (FAO, 2020; ISO, 2020) and is also a prominent energy crop. However, it has an extremely complex genome; modern cultivars are the product of a few crosses between two autopolyploid species. Saccharum spontaneum (2n = 5x = 40 to 16x = 128; x = 8) (Panje and Babu, 1960), a wild stress-resistant but low-sugar species, was hybridized and backcrossed with Saccharum officinarum (2n = 8x = 80, x = 10) (D'Hont et al., 1998), which has a high sugar content but is sensitive to drought and susceptible to diseases. These procedures gave origin to plants with very large (ca. 10 Gb), highly polyploid, aneuploid and remarkably duplicated genomes (Dhont and Glaszmann, 2001; Sforça et al., 2019). RNA sequencing (RNA-seq) is the most effective method for simultaneously predicting new transcripts and identifying differentially expressed genes among distinct tissues, genotypes, abiotic conditions and developmental stages (Huang et al., 2017). Conversely, considering the large amount of data generated from RNA-seq, new approaches that efficiently extract meaningful associations from highly multivariate datasets are needed (Miao et al., 2016). Transcriptome coexpression studies can show how complex phenotypes depend on the activity of coordinated batteries of genes(Flores-Sandoval et al., 2018). Therefore, the construction of coexpression networks based on gene expression data using correlation metrics provides valuable information regarding alterations in biological systems in response to differential gene expression patterns (Miao et al., 2016; de la Fuente, 2010).