Genome-Wide Analysis and Functional Characterization of <i>LACS</i> Gene Family Associated with Lipid Synthesis in Cotton (<i>Gossypium</i> spp.)

Abstract

Cotton (Gossypium spp.) is the fifth largest oil crop in the world, and cottonseed provides abundant vegetable oil resources and industrial bioenergy fuels for people; therefore, it is of practical significance to increase the oil content of cotton seeds for improving the oil yield and economic benefits of planting cotton. Long-chain acyl-coenzyme A (CoA) synthetase (LACS) capable of catalyzing the formation of acyl-CoAs from free fatty acids has been proven to significantly participate in lipid metabolism, of which whole-genome identification and functional characterization of the gene family have not yet been comprehensively analyzed in cotton. In this study, a total of sixty-five LACS genes were confirmed in two diploid and two tetraploid Gossypium species, which were divided into six subgroups based on phylogenetic relationships with twenty-one other plants. An analysis of protein motif and genomic organizations displayed structural and functional conservation within the same group but diverged among the different group. Gene duplication relationship analysis illustrates the LACS gene family in large scale expansion through WGDs/segmental duplications. The overall Ka/Ks ratio indicated the intense purifying selection of LACS genes in four cotton species during evolution. The LACS genes promoter elements contain numerous light response cis-elements associated with fatty acids synthesis and catabolism. In addition, the expression of almost all GhLACS genes in high seed oil were higher compared to those in low seed oil. We proposed LACS gene models and shed light on their functional roles in lipid metabolism, demonstrating their engineering potential for modulating TAG synthesis in cotton, and the genetic engineering of cottonseed oil provides a theoretical basis.

Keywords

• cotton
• long-chain acyl-coenzyme A (CoA) synthetase (<i>LACS</i>)
• fatty acids
• expression analysis