Background: Calanthe masuca and C. sinica are two genetically closely related species in Orchidaceae. C. masuca is widely distributed in Asia, whereas C. sinica is restricted to Yunnan and Guangxi Provinces in southwest China. Both play important roles in horticulture and are under the pressure of population decline. Understanding their genetic background can greatly help us develop effective conservation strategies for these species. Simple sequence repeats (SSRs) are useful for genetic diversity analysis, presumably providing key information for the study and preservation of the wild populations of the two species we are interested in.
Results: In this study, we performed RNA-seq analysis on the leaves of C. masuca and C. sinica, obtaining 40,916 and 71,618 unigenes for each species, respectively. In total, 2,019/3,865 primer pairs were successfully designed from 3,764/7,189 putative SSRs, among which 197 polymorphic SSRs were screened out according to orthologous gene pairs. After mononucleotide exclusion, a subset of 129 SSR primers were analysed, and 13 of them were found to have high polymorphism levels. Further analysis demonstrated that they were feasible and effective against C. masuca and C. sinica as well as transferable to another species in Calanthe. Molecular evolutionary analysis revealed functional pathways commonly enriched in unigenes with similar evolutionary rates in the two species, as well as pathways specific to each species, implicating species-specific adaptation. The divergence time between the two closely related species was tentatively determined to be 3.42 ± 1.86 Mya.
Conclusions: We completed and analysed the transcriptomes of C. masuca and C. sinica, assembling large numbers of unigenes and generating effective polymorphic SSR markers. This is the first report of the development of expressed sequence tag (EST)-SSR markers for Calanthe. In addition, our study could enable further genetic diversity analysis and functional and comparative genomic studies on Calanthe.
Keywords: Divergence time; Next-generation sequencing; Polymorphic microsatellite; Population genetics.