A novel TF molecular switch-mechanism found in two contrasting ecotypes of a psammophyte, Agriophyllum squarrosum, in regulating transcriptional drought memory

Abstract

Background: Prior drought stress may change plants response patterns and subsequently increase their tolerance to the same condition, which can be referred to as "drought memory" and proved essential for plants well-being. However, the mechanism of transcriptional drought memory in psammophytes remains unclear. Agriophyllum squarrosum, a pioneer species on mobile dunes, is widely spread in Northern China's vast desert areas with outstanding ability of water use efficiency. Here we conducted dehydration-rehydration treatment on A. squarrosum semi-arid land ecotype AEX and arid land ecotype WW to dissect the drought memory mechanism of A. squarrosum, and to determine the discrepancy in drought memory of two contrasting ecotypes that had long adapted to water heterogeneity.

Result: Physiological traits monitoring unveiled the stronger ability and longer duration in drought memory of WW than that of AEX. A total of 1,642 and 1,339 drought memory genes (DMGs) were identified in ecotype AEX and WW, respectively. Furthermore, shared DMGs among A. squarrosum and the previously studied species depicted that drought memory commonalities in higher plants embraced pathways like primary and secondary metabolisms; while drought memory characteristics in A. squarrosum were mainly related to response to heat, high light intensity, hydrogen peroxide, and dehydration, which might be due to local adaptation to desert circumstances. Heat shock proteins (HSPs) occupied the center of the protein-protein interaction (PPI) network in drought memory transcription factors (TF), thus playing a key regulatory role in A. squarrosum drought memory. Co-expression analysis of drought memory TFs and DMGs uncovered a novel regulating module, whereby pairs of TFs might function as molecular switches in regulating DMG transforming between high and low expression levels, thus promoting drought memory reset.

Conclusion: Based on the co-expression analysis, protein-protein interaction prediction, and drought memory metabolic network construction, a novel regulatory module of transcriptional drought memory in A. squarrosum was hypothesized here, whereby recurrent drought signal is activated by primary TF switches, then amplified by secondary amplifiers, and thus regulates downstream complicated metabolic networks. The present research provided valuable molecular resources on plants' stress-resistance basis and shed light on drought memory in A. squarrosum.

Keywords: Agriophyllum squarrosum; Comparative transcriptomics; Drought memory; Local adaptation; Molecular switch; Psammophytes.

Fig. 1

(a) Experiment design, physiological index in drought memory trial of (b) SMC, (c) leaf RWC, and (d) water loss of isolated leaves, respectively. R0, control; S1-3, the first, the second, and the third round of dehydration treatments, respectively; R1-3, the corresponding water recovery after S1-3, respectively. SMC, soil moisture content; RWC, relative water content. Indexes were shown in mean ± SD, n = 10. Within the same group (ecotype), the same superscripts denoted no significant difference (P > 0.05); different superscripts denoted significant difference (P < 0.05). Asterisks on shoulder lines denote statistically significant differences: *, p < 0.05; **, p < 0.01, between groups (ecotypes)

Fig. 2

Distribution (a), GO (b), and KEGG (c) enrichment of DMG OGs among A. squarrosum and other plants. DMGs in the three previously studied species without homologous in A. squarrosum were combined into one category and labeled as ‘other plants’. The intersection of “other plants”, “AEX”, and “WW” resulted in 257 shared DMG OGs; the intersection of “AEX” and “WW” while “other plants” excluded resulted in 406 OGs that peculiar in A. squarrosum, and named as AS DMG; the 655 and 339 OGs unique in the two ecotypes were designated as AEX DMG and WW DMG, respectively. Figure 2b and c presented the biofunction prediction of the four non-overlapping DMG hierarchies, respectively. Noted that enrichment analyses were performed based on shared, AS, AEX, and WW DMG hierarchies, instead of all the DMG in each species. Thus, “extra” biofunctions compared to the previous hierarchy were presented in this figure

Fig. 3

Four main metabolism networks in which A. squarrosum DMG enriched. (a), glycerophospholipid metabolism; (b), secondary metabolism; (c), signal transduction; (d), carbon metabolism. Heatmap showed the expression profiles of the corresponding DMGs in R0, S1, and S3, where the upper yellow hues and the under blue hues represented AEX and WW, respectively. Modules were framed in dashed boxes of different colors

Fig. 4

PPI network of DMG in AEX (a) and WW (b). PPI networks in AEX and WW were presented in yellow and blue hues, respectively. Hub genes in each network were rated from large (central) to small (edge)

Fig. 5

Co-expression analysis between drought memory TFs and the rest DMGs within the same hierarchy across the two ecotypes. (a), shared DMG expressed in AEX; (b), shared DMG expressed in WW; (c), AS DMG expressed in AEX; (d), AS DMG expressed in WW; (e), ecotype-unique DMG expressed in AEX; (f), ecotype-unique DMG expressed in WW. This figure only presented strong co-expression with Pearson correlation coefficient > 0.9 and p-value < 0.05, where positive correlation and negative correlation were depicted as yellow and blue, respectively

Fig. 6

Q-PCR validation of the transcriptome data. The histogram and the line graph with error bar indicates FPKM generated by RNA-Seq and the relative expression calculated by 2^−ΔΔCt method, while yellow and blue graphs represent gene examined in AEX and WW, respectively

Fig. 7

Possible regulatory module of drought memory in A. squarrosum