Genome-wide transcriptome analysis reveals the molecular mechanism of high temperature-induced floral abortion in Litchi chinensis

BackgroundWarm winter and hot spring attributed to global warming affected floral development and may induce floral abortion, resulted in poor flowering in litchi. To identify genes potentially involved in litchi floral abortion, six RNA-sequencing (RNA-Seq) libraries of the developing panicles (DPs) under low temperature (LT) conditions and the shrinking panicles (SPs) under high temperature (HT) conditions were constructed.Results3.07-8.97x10(6) clean reads were generated. Digital expression of the DPs with that of the SPs was compared. As a result, 1320 up-regulated and 981 down-regulated differentially expressed genes (DEGs) were identified. From the enriched GO-term, 54 temperature responsive DEGs, 23 hormone homeostasis- or biosynthesis-related DEGs, 137 hormone signal transduction or responsive DEGs, and 18 flowering-related DEGs were identified. Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis indicated that the effects of hormone-related DEGs on NACs, MYBs, WRKYs were stronger than that on flowering-related DEGs. Expression pattern analysis of the inflorescence in Nuomici' and Huaizhi' under LT and HT conditions showed that genes homologous to AIL6 (LcAIL6), LHY (LcLHY), MED16 (LcMED16), SKIP20 (LcSKIP20), POD20 (LcPOD20) in the two cultivars had similar expression trends.ConclusionThis study elucidated the transcriptome in the HT-induced floral abortion and identified key genes involved in the process. NACs, MYBs, WRKYs may act as central players involved in the HT-induced floral abortion underlying hormonal control. Increased transcript in LcLHY, LcMED16, LcSKIP20, LcPOD20 and decreased transcript in LcAIL6 might be related to the inhibition of floral development. Our studies provided potential genes for the future molecular breeding of new cultivars that can reduce floral abortion under warm climates, and a novel clue to reveal the relationship of biological processes based on the RNA-seq data using PLS-SEM.