bioRxivPlant Biology
Dark period transcriptomic and metabolic profiling of two diverse Eutrema salsugineum accessions
Abstract
Eutrema salsugineum is a model species for the study of plant adaptation to abiotic stresses. Two accessions of E. salsugineum, Shandong (SH) and Yukon (YK), exhibit contrasting morphology, biotic, and abiotic stress tolerance. Transcriptome and metabolic profiling from tissue samples collected during the dark period were used to investigate the molecular and metabolic bases of these contrasting phenotypes. RNA sequencing identified 17,888 expressed genes, of which 157 were not in the published reference genome and 65 were detected for the first time. Differential expression was detected for only 31 genes. The RNA sequencing data contained 14,808 single nucleotide polymorphisms (SNPs) in transcripts, 3,925 of which are newly identified. Among the differentially expressed genes, there were no obvious candidates for the physiological or morphological differences between SH and YK. Metabolic profiling indicated that YK accumulates free fatty acids and long-chain fatty acid derivatives as compared to SH; whereas sugars are more abundant in SH. Metabolite levels suggest that carbohydrate and respiratory metabolism, including starch degradation, is more active during the first half of the dark period in SH. These metabolic differences may explain the greater biomass accumulation in YK over SH. The accumulation of 56% of the identified metabolites was lower in F1 hybrids than the mid-parent averages and the accumulation of 17% of the metabolites in F1 plants transgressed the level in both parents. Concentrations of several metabolites in F1 hybrids agree with previous studies and suggest a role for primary metabolism in heterosis. The improved annotation of the E. salsugineum genome and newly-identified high-quality SNPs will permit accelerated studies using the standing variation in this species to elucidate the mechanisms of its diverse adaptations to the environment.
- organism: plant (other)
- assay: bulk RNA-seq
- assay: metabolomics
- research focus: differential expression
- research focus: metabolism & bioenergetics
- approach: statistical model
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