Applying Molecular Phenotyping Tools to Explore Sugarcane Carbon Potential

Abstract

Sugarcane (Saccharum spp.), a C4 grass, has a peculiar feature: it accumulates, gradient-wise, large amounts of carbon (C) as sucrose in its culms through a complex pathway. Apart from being a sustainable crop concerning C efficiency and bioenergetic yield per hectare, sugarcane is used as feedstock for producing ethanol, sugar, high-value compounds, and products (e.g., polymers and succinate), and bioelectricity, earning the title of the world’s leading biomass crop. Commercial cultivars, hybrids bearing high levels of polyploidy, and aneuploidy, are selected from a large number of crosses among suitable parental genotypes followed by the cloning of superior individuals among the progeny. Traditionally, these classical breeding strategies have been favoring the selection of cultivars with high sucrose content and resistance to environmental stresses. A current paradigm change in sugarcane breeding programs aims to alter the balance of C partitioning as a means to provide more plasticity in the sustainable use of this biomass for metabolic engineering and green chemistry. The recently available sugarcane genetic assemblies powered by data science provide exciting perspectives to increase biomass, as the current sugarcane yield is roughly 20% of its predicted potential. Nowadays, several molecular phenotyping tools can be applied to meet the predicted sugarcane C potential, mainly targeting two competing pathways: sucrose production/storage and biomass accumulation. Here we discuss how molecular phenotyping can be a powerful tool to assist breeding programs and which strategies could be adopted depending on the desired final products. We also tackle the advances in genetic markers and mapping as well as how functional genomics and genetic transformation might be able to improve yield and saccharification rates. Finally, we review how “omics” advances are promising to speed up plant breeding and reach the unexplored potential of sugarcane in terms of sucrose and biomass production.

Translational genomics from sorghum (Sorghum bicolor) to sugarcane (Saccharum spp.) for bioenergy breeding

“…They are estimated to be 10 Gb long, typically with 10–13 sets of chromosomes, containing redundant homologous genes that are 99.1% identical within their coding sequences (D'Hont, 2005; Garsmeur et al, 2018; Jannoo et al, 1999; Souza et al, 2011, 2019). This high heterozygosity and the existence of inbreeding depression restricted the development of mapping populations through selfing, hindering quantitative trait locus (QTL) mapping and marker‐assisted selection (Calderan‐Rodrigues et al, 2021). Additionally, the redundant nature of the sugarcane genome further complicated genotyping efforts (Calderan‐Rodrigues et al, 2021).…”

“…This high heterozygosity and the existence of inbreeding depression restricted the development of mapping populations through selfing, hindering quantitative trait locus (QTL) mapping and marker‐assisted selection (Calderan‐Rodrigues et al, 2021). Additionally, the redundant nature of the sugarcane genome further complicated genotyping efforts (Calderan‐Rodrigues et al, 2021). In contrast, the genome of a closely related diploid species sorghum ( Sorghum bicolor ) is comparatively very small (730 Mb, 34,496 genes) and its genome sequence has been available for a decade (Paterson et al, 2009; Xin et al, 2021), with considerable associated genomics resources (Bekele et al, 2013; Cooper et al, 2019; Luo et al, 2016; Mace et al, 2013; McCormick et al, 2018; Shakoor et al, 2014; Zheng et al, 2011).…”

“…of mapping populations through selfing, hindering quantitative trait locus (QTL) mapping and marker-assisted selection (Calderan-Rodrigues et al, 2021). Additionally, the redundant nature of the sugarcane genome further complicated genotyping efforts (Calderan-Rodrigues et al, 2021).…”

Genomic prediction of agronomic traits in sugarcane using machine learning and quantitative genetics

“…In recent years, sugarcane has attracted attention not only as a food source but also as a sustainable source of bioenergy. Research has progressed from first-generation ethanol production using sucrose to second-generation ethanol production through saccharification of cell wall sugars (Kandel et al 2018;Calderan-Rodrigues et al 2021). In addition, sugarcane biomass can be used for the production of high-value compounds, such as hydrogen, cellulose, lignin-derived products, organic acids, and bioplastics (Klein et al 2019).…”

Vias de percepção e sinalização de açúcares durante o crescimento e desenvolvimento da cana-de-açúcar (>i< Saccharum spp>/i<).

“…Incorporating new knowledge about physiological, biochemical, genetic, and phenotyping processes, mainly associated with marker genes, is important to fill the gaps that can contribute to the development of new strategies in sugarcane (Calderan-Rodrigues et al, 2021). Thus, one of the greater biotechnological potentials that aim to increase sucrose in the culm depends mainly on the knowledge of the pathways involved in the partition of photosynthetic C assimilated and the source (mature leaves) and sink (nonphotosynthetic tissues) relationship, which is one of the main limiting factors for storage this disaccharide in sugarcane (Calderan-Rodrigues et al, 2021).…”