Contribution of subgenomes to the transcriptome and their intertwined regulation in the allopolyploid Coffea arabica grown at contrasted temperatures

Abstract

SummaryPolyploidy has occurred throughout the evolutionary history of plants and led to diversification and plant ecological adaptation. Functional plasticity of duplicate genes is believed to play a major role in the environmental adaptation of polyploids. In this context, we characterized genome-wide homoeologous gene expression in Coffea arabica, a recent allopolyploid combining two subgenomes that derive from two closely related diploid species, and investigated its variation in response to changing environment.The transcriptome of leaves of C. arabica cultivated at different growing temperatures suitable for one or the other parental species was examined using RNA-sequencing. The relative contribution of homoeologs to gene expression was estimated for 9959 and 10 628 genes in warm and cold conditions, respectively.Whatever the growing conditions, 65% of the genes showed equivalent levels of homoeologous gene expression. In 92% of the genes, relative homoeologous gene expression varied < 10% between growing temperatures.The subgenome contributions to the transcriptome appeared to be only marginally altered by the different conditions (involving intertwined regulations of homeologs) suggesting that C. arabica's ability to tolerate a broader range of growing temperatures than its diploid parents does not result from differential use of homoeologs.

Polyploidy and novelty: Gottlieb's legacy

“…The factors contributing to these patterns-and what the patterns themselves represent-are complex, as is the terminology [23,47]. However, it is clear that biased expression in allopolyploids, when summed over [48,49] and coffee [50] focused not only on global gene expression patterns in allopolyploids relative to their parental species in a genome-wide manner, but also on how homeologue expression bias is linked to expression-level dominance. Such studies will be particularly effective at determining the extent of novel gene expression in polyploids.…”

The legacy of diploid progenitors in allopolyploid gene expression patterns

“…Another is quantitative SNP assays, such as the Sequenom MassARRAY, as used in Tragopogon [46,47] and Gossypium [48]. A third is high-throughput RNA sequencing, as used in Arabidopsis [49], Gossypium [50], Nicotiana [51], Glycine [52], Brassica [53,54] and Coffea [55] allopolyploids. These methods provide the most comprehensive data that we have so far on allopolyploid gene expression, though in general they measure gene expression relative to other genes rather than in an absolute sense (but see [56]), and post-transcriptional regulation can result in levels of protein expression that may not reflect RNA levels in the transcriptome [57,58].…”

CoffeebEST: an integrated resource for Coffea spp expressed sequence tags

“…One example is the standardization of studies of molecular markers in coffee (Plechakova et al, 2009). Most of the large-scale transcriptomic studies in Coffea are focused on stress responses (Bardil et al, 2011;Carazzolle et al, 2011;Marraccini et al, 2012;Combes et al, 2013), and seed development studies are mostly focused on selected candidate genes (Lepelley et al, 2007;Joët et al, 2009;Budzinski et al, 2011). Understanding these large-scale studies would help to identify genes involved in the chemical composition of the bean and the sensorial quality of the coffee beverage.…”

“…C. arabica (CA) is a tetraploid species (2n = 4x = 44) that is probably derived from a recent (< 1 million years) hybridization between C. canephora and C. eugenioides (Vidal et al, 2010;Yu et al, 2011), and C. canephora (CC) is an allogamous diploid species (2n = 2x = 22). Although, there have been efforts to study the Coffea species genome composition and transcriptional patterns (Lin et al, 2005;Vidal et al, 2010;Mondego et al, 2011;Combes et al, 2013;Dereeper et al, 2013), few research groups have exploited the potential knowledge contained in these data, especially with regard to fruit and grain development. The use of bioinformatics through the development of programs and databases can help in this task not only to analyze the available data but also to generate new knowledge.…”