For the moderate and severe long-term WD experiments previously described [8], CS, RA, RI, and SC vines were subjected to one or two week of moderate or severe water deficit, or well-watered daily. Vines were grown in pots under greenhouse conditions. At W1 and W2 of treatment various measurements were taken [8]. Leaves and roots were harvested after W1 and W2 of treatment. The one-week severe water deficit treatment reached a more acute level of stress (lower stem water potentials) than the stress level obtained after two-weeks of moderate water deficit (see Methods).
Grapevine organs and species differ in ABA and ABA metabolite concentrations during one and two-week moderate WD
Leaf and root ABA and ABA metabolite concentrations are different between the four species after a one and two-week moderate WD treatment. ABA, ABA-GE, PA, DPA, NeoPA, and 7’OH ABA were extracted and quantified (Figs. 1 and 2) as previously described [96]. There were significant differences between treatments, organs, species, and weeks for all metabolite concentrations (pmol·mg DW− 1; DW refers to dry weight) except for 7’OH ABA. The term “significant” will be used in this work to mean statistically significant at a p-value of 0.05 or less. To simplify comparisons to WD, metabolite quantities of the Controls of all species were grouped together per organ per week (Figs. 1 and 2) and were referred to as the average of the Controls (Avg Ctrl).
Grapevine organs had different [ABA] under Control and WD treatments. There was a significant difference in [ABA] between W1 WD leaves and roots, between W2 Control leaves and roots, and between W2 WD leaves and roots. Per organ per week only RA WD W1 leaves [ABA] was significantly increased relative to that of Avg Ctrl in (Fig. 1). WD RA W1 had at least 3-fold higher average [ABA] in the leaves and roots than WD RI W1. At W2, the [ABA] for all species was significantly higher in WD leaves relative to Avg Ctrl leaves, and WD RA had a significantly higher [ABA] than WD SC (Fig. 1). W2 roots also had significantly higher [ABA] for all WD species than those of Avg Ctrl (Fig. 1), and again WD RA had an average [ABA] 2-fold and 3-fold higher than WD RI in the leaves and roots, respectively. However, unlike the leaves there was no significant difference between the species in the roots treated with WD at W2 (Fig. 1). As a general rule, [ABA] can be ranked by species during moderate WD as RA > CS > RI > SC in both leaves and roots.
The concentrations of the storage form of ABA, ABA-GE, were significantly higher than [ABA] in leaves and lower than [ABA] in the roots (Fig. 1) and in all combinations per treatment per week. At W2, RA and SC WD leaves had significantly higher [ABA-GE] than Avg Ctrl, and WD RA leaves had significantly higher [ABA-GE] than WD RI leaves (Fig. 1). In the roots, RA was the only species to have significantly higher [ABA-GE] than Avg Ctrl, but WD RA W2 roots were only significantly different from those of CS (Fig. 1).
In general, ABA catabolism was enhanced by WD. At W1, WD [ABA catabolite] were similar to respective Controls (Fig. 2). W1 WD [DPA] in WD RA roots were significantly higher than WD RI roots. By W2, [ABA catabolite] increased by 2- to 10-fold in both leaves and roots relative to W1. Like ABA, ABA catabolites were generally higher in WD W2 leaves than WD W2 roots but were at comparable levels in leaves and roots at W1 (Fig. 2). For example, WD W2 RA had > 10-fold increase in [PA] in leaves relative to roots. In WD W2 leaves, RA was the only species that had significantly different concentrations of all four ABA catabolites relative to Avg Ctrl (Fig. 2). RA and CS WD W2 roots generally had higher concentrations of all four quantified catabolites than those of the other two species (Fig. 2), but there was no significant difference between the WD species for any catabolite in the roots at W2 (Fig. 2). Although not always significant, each species displayed unique responses to moderate WD, particularly in W2, via changes in [ABA catabolite] with RA (and CS) generally having higher [ABA catabolite] than the other species in both organs, consistent with the higher [ABA] observed in these species (Fig. 1).
There were clear differences in the use of the catabolite pathways in grapevines. The major catabolic pathway was through 8’OH ABA with [DPA] and [PA] being much higher than [7’OH ABA] at W1 and W2 (Fig. 2). NeoPA, which also goes through the 8’OH ABA catabolism pathway, had very low concentrations relative to the other catabolites (Fig. 2). Nevertheless, these concentrations also increased with [ABA] induced by moderate WD with RA > CS > RI; SC had very low concentrations and showed no increase due to WD (Fig. 2). PA, an intermediate in the 8’OH ABA catabolic pathway had much higher concentrations than DPA in WD leaves. However, the reverse was true in WD roots, where [DPA] was higher than [PA].
To further understand ABA metabolism in the plant as a whole, the distribution of ABA and ABA metabolites was examined for the whole plant (the sum of leaf and root total ABA and ABA metabolites, to be defined as Total ABA Metabolites). ABA and all ABA metabolites were summed to estimate the total ABA produced by the plant (Additional File 1A) or per organ (Additional File 1B). On a whole plant basis, there were significant increases in [Total ABA Metabolite] by W2 (Additional File 1A) providing evidence for an increase in ABA biosynthesis along with other metabolism pathways. Total ABA Metabolite distribution in W1 was less clear and may indicate that changes in ABA metabolite concentrations were the result of redistribution of ABA (e.g. conjugation, catabolism and transport). [ABA] and each [ABA metabolite] per organ was also divided by the summed Total ABA Metabolite concentration (Additional File 1A) to determine the distribution of each ABA metabolite (Additional File 2). This approach revealed ABA-GE represented the major portion of the leaf ABA metabolites in W1 Controls (0.38 ± 0.05), followed by PA (0.23 ± 0.03), ABA (0.17 ± 0.03), and DPA (0.13 ± 0.02) (Additional File 2). However, RA WD W1 leaves had a significant decrease in the proportion of ABA-GE from 0.48 ± 0.15 to an average 0.20 ± 0.08, indicating ABA metabolism was shifted from ABA-GE to other ABA metabolites such as ABA and DPA. The reduction of ABA-GE pools was specific to W1 RA leaves; there was no significant difference in RA Control vs. WD W2 leaves indicating this shift in ABA metabolism was an early response to WD (Additional File 2). The other species did not show the same shift in ABA-GE distribution in WD leaves. CS ABA-GE (0.2 ± 0.02) remained constant in W1 and W2 and did not change significantly in response to WD in the leaves (Additional File 2). Likewise, the proportion of ABA-GE in WD W2 roots decreased relative to Control in all of the species except for CS, which again was proportionately low relative to the other three species (Additional File 2). No such changes were observed in WD W1 roots. ABA and ABA-GE in the roots in general were a much smaller proportion of Total ABA Metabolites than DPA.
These grapevine species have different relative ABA and ABA metabolite ratios. To further evaluate the range of [ABA] and [ABA metabolite] between the species, the WD:C ratios of [ABA] and [ABA metabolite] were investigated (Fig. 3). ABA and 7’OH ABA had the highest z-scores of all the metabolites quantified (Fig. 3). Such changes were not obvious for 7’OH ABA in Total ABA Metabolites (Additional File 2). RA W1 leaves had the highest score for ABA (Fig. 3). RI W1 leaves had the highest score for WD:C ABA-GE (Fig. 3), indicating ABA-GE may have been accumulating in RI W1 WD leaves. The WD:C ABA-GE score for RA W1 leaves was about two STD lower than the average WD:C ABA metabolite average (Fig. 3), indicating ABA-GE may be deconjugated into active ABA and/or downstream catabolites, supporting similar observations from Additional File 2. ABA-GE also had a negative score for the roots of all species in W1 and W2 (Fig. 3), possibly reflecting ABA activation through this pathway.
The ABA catabolites have different scores in the different species and organs. 7’OH ABA had the highest scores for RA W1 roots and leaves and RA and CS W2 roots and leaves (Fig. 3) despite 7’OH ABA having lower concentrations than metabolites representing catabolism through 8’OH ABA (PA and DPA). The high z-scores for 7’OH ABA in these select species and organs may indicate these species redistribute a higher portion of ABA catabolism through the 7’OH ABA pathway than the other species in response to WD, but this pathway either catabolizes lower [ABA] than that through 8’OH ABA, PA, and DPA (Fig. 2) or catabolites are further degraded into compounds not quantified here. Alternatively, the high z-scores for 7’OH ABA may be attributed to the large difference between W2 root average RA and CS Control [7’OH ABA] (0.14 ± 0.03 pmol·mg DW− 1) and average WD RA and CS (5.31 ± 1.67 pmol·mg DW− 1) that result in a greater fold difference (38-fold difference) than those of PA (6-fold difference) and DPA (3-fold difference); this difference is reflected in the z-score despite differences in [catabolite]. RA W1 roots and RA W2 leaves had the highest score for NeoPA and DPA, which were also about one STD above the average ABA metabolite ratio (Fig. 3). RA was the only species to have a positive score for PA and DPA in W2 leaves (Fig. 3). The differences between the catabolites indicate ABA may be preferentially deactivated by different mechanisms in each species in each organ and with increasing WD stress. For example, at W2, RA was the only species to have positive scores in leaves and roots for PA (~ 0.5 STD from mean), while all other species had negative scores. Combined with the positive scores for RA W2 leaves and roots 7’OH ABA and RA W2 leaves NeoPA scores, RA is clearly catabolizing ABA relative to the other species. WD RA W2 [7’OH ABA] has the greatest change relative to Control [7’OH ABA] and the average [ABA metabolite]. NeoPA, PA, and DPA had the highest z-scores in W1 across various organs and species, but the scores were either lower or negative for W2 indicating these catabolism pathways may be favored at different times during WD. At W1, RA and SC had positive scores for DPA in the roots, but not the leaves, hinting these catabolism pathways may be utilized differently across organs. The different species, organs, and times have different scores for the ABA metabolites.
One and two-week moderate WD significantly increases ABA biosynthesis, conjugation, and catabolism transcripts abundance
ABA metabolism gene transcript abundance significantly increase in response to WD. Differences in many ABA metabolite gene transcript abundances mirrored [ABA metabolite] and may partially explain differences observed between species. Previously, the ABA biosynthesis gene, NCED3, was identified as a potential hub gene in response to WD in this W1 and W2 moderate WD experiment [8]. Additional works indicate NCED3 as a potential WD hub gene [97]. To support this hypothesis, the RNA-Seq series, PRJNA516950, was analyzed with the more accurate CS clone 8 v1.0 reference genome [98] as opposed to the PN40024 reference genome [99] as was done previously [8]. The goal of this analysis was to investigate the transcript abundance of ABA metabolism genes in response to WD. CS ABA metabolism genes were identified with protein basic local alignment search tool (BLAST) using known ABA metabolism PN40024 protein sequences as a query. Orthologs were confirmed based on the highest total score, E-value, and length. When two BLAST hits were highly similar for the same query, the hits were identified as alleles of the same gene. In total, 46 ABA metabolism genes were identified from the primary (alternative (alt)1) and/or secondary (alt2) CS haplotig sequences (Additional File 3). Alleles are referred to as alternatives without a designated haplotype because the phased chromosome-scale assembly was not complete for the CS clone 8 v1.0 genome. Detailed explanation of alt assignment is in Additional File 3. In total, allele(s) for three NCED genes (NCED3, NCED5 alt1 and 2, and NCED6 alt1 and 2) were identified in CS (Additional File 3). Both alleles were identified for BETA-OHASE 2, ABA ALDEHYDE OXIDASE 3 (AAO3), ABA1, ABA2, ABA3, BETA-1, 3-GLUCANASE 1 (BG1), ATP-BINDING CASSETTE G25 (ABCG25), and ATP-BINDING CASSETTE G22-LIKE (ABCG22-like). Several BGLU, BG, UGT, and CYP707A paralogs were also identified. Only one ABCG40 and NPQ1 (Violaxanthin de-epoxidase or NON-PHOTOCHEMICAL QUENCHING 1) allele were annotated in this version of the CS genome.
Organs showed significant differences in the genes expressed and gene expression levels (Additional File 4) [8]. There were significant differences in ABA metabolism genes comparing WD to Control in all four species per organ (Additional File 4). CS (4 and 0 genes in leaves and roots, respectively), RA (1 and 1 genes), and RI (1 and 0 genes) had the most differentially expressed ABA metabolism genes out of all WD vs. Control W1 contrasts (Additional File 4). RA W2 leaves and roots had the most differentially expressed ABA metabolism genes (21 and 17 genes in leaves and roots, respectively) in response to WD followed by CS (10 and 12 genes), SC (10 and 7 genes), and RI (4 and 8 genes) (Additional File 4). The transcripts of NCED3 (Fig. 4) and at least one putative BETA-OHASE 2 allele were significantly increased in response to WD in W2 leaves and roots of all four species (Additional File 4). However, the other differentially expressed ABA metabolism genes varied between species and organs.
RA has distinct ABA metabolism differentially expressed genes (DEGs) compared to the other species. In the previous analysis of this experiment, RA stood out transcriptionally and physiologically by outperforming the other species, having higher photosynthesis and greater and earlier transcriptomic responsiveness to WD [8]. To better understand the role each pathway of ABA metabolism (biosynthesis, conjugation, or catabolism) contributes to the transcriptomic responsiveness of RA and the other species, differential expression analyses (DEAs) contrasting WD to Control per time point per organ were focused upon using the 46 CS ABA metabolism genes. To identify specific genes differentially expressed in WD RA leaves and roots relative to WD organs from the other three species, DEA was performed as previously described [8] using RA WD contrasts (Additional File 5). The majority of WD responsive DEGs related to ABA metabolism in RA and the other species in either leaves or roots were involved in biosynthesis and conjugation (Additional File 5).
In this section, ABA metabolism DEGs that were unique to WD RA (relative to the WD of all three other species) will be discussed in order of ABA metabolism pathway (biosynthesis, (de)conjugation, and catabolism) sequentially in W1 leaves then roots and finally in W2 leaves followed by W2 roots. No gene was significantly differentially expressed in RA relative to all three of the other species in WD W1 leaves. NCED3, NCED5 alt2, and ABCG25 alt1 had significantly higher transcript abundance in RA WD W1 roots than those of the other species (Figs. 4 and 5 and Additional File 5). NCED3 was the only ABA metabolism DEG in RA W1 WD roots relative to Control (Additional File 4), but NCED3 was not a DEG in W1 roots of any other species at W1 in response to WD (Fig. 4 and Additional File 4). BG2 alt1 and UGT71C3 alt1 were also differentially expressed in RA W1 WD roots relative to the other three species, but these genes were not DEGs W1 root WD vs. Control contrast for any species.
There were nine ABA metabolism DEGs in RA leaves after W2 of WD relative to those of the other three species that were also DEGs in RA WD W2 leaves relative to RA Control W2 leaves (Figs. 4 and 6 and Additional Files 5 and 6). Four of these genes were involved in ABA biosynthesis: NCED3 alt1, NPQ1 alt1, ABA1 alt1, AAO3 alt1. NCED3 had significantly higher transcript abundance in RA WD W2 leaves than those of all other species (3.7-, 9.8-, and 6-fold difference for CS, RI, and SC respectively) (Fig. 4 and Additional File 5). NCED3 had the highest expression level of the five annotated NCEDs in both leaves and roots in W1 and W2 of WD in all species with the highest level of expression in RA (Fig. 4). The other NCEDs were lowly expressed (Fig. 4). NCED5 alternatives had higher expression in CS, while both NCED6 alternatives had higher expression levels in RA after W2 of WD compared to the other species (Fig. 4). NCED3 was also the ABA metabolism DEG that had the highest average TPM over all WD species and times compared to the other ABA metabolism DEGs that were significantly differentially expressed in RA WD W2 leaves vs. those of the other species and in RA Control vs. RA WD W2 leaves. Overall, NCED3 appears to be the major NCED contributing transcripts to downstream ABA biosynthesis. NPQ1 alt1 was significantly ~ 4-fold lower in RA WD W2 leaves than those of the other species (Additional Files 5 and 6). ABA1 alt1 was also significantly lower in RA WD W2 leaves than the WD W2 leaves of the three other species (Additional Files 5 and 6). Overall, average ABA1 alt1 transcript abundance over all treatments, times, and species was significantly higher in the leaves than the roots. AAO3 alt1 had significantly lower transcript abundance in RA than CS (16-fold difference), RI, and SC (both about 56-fold difference) (Additional Files 5 and 6) in W2 WD leaves. Although lowly expressed, average AAO3 alt1 transcript abundance was significantly higher in roots than that in leaves across all species, times, and treatments.
There were four ABA metabolism DEGs in RA leaves after W2 of WD that were also DEGs in W2 RA leaves in response to WD involved in deconjugation. The deconjugation DEGs were BG1 alt1, BG1 alt2, BG3 alt1, and BG3 alt2. BG1 alt1 transcript abundance was significantly lower in RA WD W2 leaves than CS (6.5-fold difference), RI, (26-fold difference), and SC (64-fold difference) (Fig. 5 and Additional Files 5). BG1 alt1 and alt2 were DEGs in RA WD leaves and roots relative to respective Controls, but neither BG1 allele was a DEG in the organs of any other species (Additional File 2). BG1 alt1 had higher transcript abundance in roots than in leaves across all species, treatments, and times. BG1 alt2 also had significantly higher average transcript abundance in all Control and WD species roots relative to all Control and WD species leaves. In WD W2 roots, BG1 transcripts alt1 and alt2 were the only significantly differentially expressed ABA metabolism DEGs in RA WD vs. the other three species WD contrasts with ~ 16-40-fold lower abundance in RA than the other species (Fig. 5 and Additional File 5). Control RA and SC had the highest BG3 alt1 expression in the leaves and roots in W2, but RA WD W2 had the lowest BG3 alt1 expression in leaves and roots. Interestingly, per treatment, BG3 alt1 transcript abundance was comparable in the leaves and roots for each species. BG3 alt2 had the lowest transcript abundance in RA for all treatments and organs in W2 relative to the other species. CYP707A1 alt1 was the only ABA catabolism gene significantly differently expressed between RA and all three other species in W2 WD leaf contrasts and Control vs. WD contrasts (Additional Files 4–6). CYP707A1 alt1 had significantly higher transcript abundance in RA WD W2 leaves than those of the other three species (Additional Files 4 and 6), and like AAO3 alt1 and BG1, CYP707A1 alt1 average transcript abundance was higher in roots than leaves as an average over all species, treatments, and times.
One and two-week moderate WD significantly increases ABA transport gene transcript abundance
ABA transport genes were significantly differentially expressed in response to WD. ABA transporter genes ABCG25 alt1 and alt2 and ABCG40 had significantly higher average transcript abundance in roots than leaves in all species, weeks, and treatments. Neither ABCG25 alt2 nor ABCG40 were higher in CS, RA, and RI WD W2 roots or RA WD W2 leaves relative to respective Control (Fig. 6 and Additional File 4). On average ABCG25 alt1 transcript abundance was ~ 4-fold higher in WD than Control W2 leaf and root samples (Additional File 4). ABCG25 alt2 transcripts were significantly higher in RA WD W2 leaves (~ 32-fold increase) and RA WD W2 roots (~ 8-fold increase) relative to respective Control (Fig. 6 and Additional File 5).
RA had significantly increased ABCG25 and ABCG40 transcript abundance relative to other species after W2 of WD in leaves and roots (Additional File 5). ABCG25 alt1 was significantly (~ 3-fold) higher in RA than CS WD W2 leaves, and about ~ 4-fold change higher in RA W1 and W2 roots than those of the other three species. ABCG25 alt2 transcript abundance was also significantly higher in RA WD W2 leaves than those of RI and SC (Additional File 5). After W1 of WD, RA roots had ~ 8-fold change increase in ABCG40 transcripts relative to those of CS, but ABCG40 was not significantly different in Control vs. WD contrasts for either RA or CS in W1 (Additional File 4).
ABA metabolite gene transcript abundance may partially explain ABA metabolite concentrations
Multiple ABA metabolism genes had significantly increased transcript abundance similar to the increases observed in ABA metabolites. To more easily compare species response to WD and link [ABA metabolite] to upstream transcripts, the average ratio of WD:C transformed TPM were expressed as a z-score per ABA metabolism gene group (e.g. NCED3, NCED5 alt1 and 2, and NCED6 alt1 and 2 are included in the NCED gene group) with darker colors indicating greater difference from the mean ratio of the ABA metabolism genes (Fig. 7). RA clearly stands out in this comparison, having the highest score for ß-carotene hydroxylases (roots W1 and W2), zeaxanthin epoxidases (roots W1 and W2), NCEDs (leaves and roots W1 and W2), AAO3s (leaves W1), UDP-glucose glucosyl transferases (leaves and roots W2), ß-d-glucosidases (roots W1), and various ABA hydroxylases (leaves W1 and roots W1 and W2) (Fig. 7) that may partially explain the high [ABA] and [ABA metabolite] observed in RA. RA also had the lowest scores for violaxanthin de-epoxidases (leaves W1), xanthoxin dehydrogenases (roots W2), ß-d-glucosidases (leaves W2), and ABA aldehyde oxidases (leaves and roots week 2) (Fig. 7). The low score (~-2 STDs from the mean) and low expression (Fig. 7) of ß-d-glucosidases as well as the high z-score for UDP-glucose glucosyl transferases (~ 1.75 STD from mean) in RA W2 leaves may explain the high [ABA-GE] observed in RA WD W2 leaves; ABA may be conjugated into ABA-GE but not deconjugated allowing [ABA-GE] to increase. The higher [ABA-GE] in RI W1 WD leaves relative to the other species (Fig. 7) and the higher score for ß-d-glucosidase (~ 2 STD from mean) may indicate ABA-GE is an important source of ABA for RI in earlier WD response. RI and SC had the lowest [catabolite] in the leaves, which is paralleled in the score of the ABA hydroxylases.
ABA metabolism genes were correlated with multiple WGCNA modules
WGCNA was performed on leaves and root samples separately with ABA and the ABA metabolites as additional traits (Figs. 8 and 9 and Additional File 7). Gene association to each module was calculated between each gene and the eigengene of each module [100] (Additional File 7). ABA metabolism genes were spread across multiple gene modules. In the leaves, 30 WGCNA modules were identified (Fig. 8). WD was positively correlated with five modules. These modules included lightyellow, darkgreen, brown, saddlebrown, and green (Fig. 8). Generally, the ABA metabolites were positively correlated with the same five modules as WD (Fig. 8). The 46 ABA metabolism genes were spread across 20 different modules in leaves. However, within these modules only lightyellow overlapped with WD, ABA, and the ABA metabolites. The lightyellow module contained the greatest number of ABA metabolism genes (seven) including NCED3 (0.91), NCED6 alt1 (0.77), BETA-OHASE 2 alt1 (0.74), NCED6 alt2 (0.70), BETA-OHASE 2 alt2 (0.64), CYP707A4 alt1_1 (0.62), and UGT73B4 alt1 (0.61) (Additional File 3).
NCED3 was clearly a hub gene in the lightyellow module, being the 15th most correlated gene to the eigengene representing this module. The top 20 genes of a module are considered hub genes. Hub genes are highly connected to all other genes in the module. Disruption of hub genes disturbs the gene expression of numerous other genes in a module. NCED3 was closely connected to ABA signaling genes in this module including the top 1 gene, HIGHLY ABA INDUCED 1 (HAI1) (VvCabSauv08_P0061F.ver1.0.g440640). Other top genes in the lightyellow module included RAS-RELATED PROTEIN 18 (RAB18) (VvCabSauv08_H0004F_076.ver1.0.g050030; top 9), HOMEOBOX-7 (HB-7 (VvCabSauv08_P0060F.ver1.0.g439740; top 12), and PP2C-8 (VvCabSauv08_P0452F.ver1.0.g610510; top 14) (Additional File 7). The position of NCED3 in the lightyellow module top genes far exceeded that of any other any ABA metabolism gene in any module; the second highest ranking gene was UGT71C4 alt2 (top 90) in the red module (Additional File 7). In the lightyellow module, NCED6 alt1, was the second occurring ABA metabolism gene in the top 259 genes (Additional File 7).
In the roots, 34 WGCNA modules were identified (Fig. 9). In total, ten modules were positively associated with WD (Fig. 9). Among the WD positively correlated modules, six were also positively correlated with 7’OH ABA, ABA, ABA-GE DPA, NeoPA, and PA (Fig. 9 and Additional File 7). Common positively correlated modules between the metabolites included royalblue, lightgreen, midnightblue, pink, and paleturquoise. The ABA metabolism genes were spread across 20 modules in the roots. The midnightblue module contained the greatest number of ABA metabolism genes (ten) including NCED3 (0.95), CYP707A2 alt1 (0.88), BETA-OHASE 2 alt1 (0.85), NCED5 alt2 (0.80), CYP707A3 alt1 (0.80), CYP707A4 alt1_1 (0.78), BETA-OHASE 2 alt2 (0.76), ABCG25 alt1 (0.76), NCED5 alt1 (0.75), and NCED6 alt1 (0.65) (Additional File 3). Of all ABA metabolism genes and all modules, NCED3 was the only hub gene corresponding to the midnightblue module (top 19) (Additional File 7) with BG3 alt1 in the blue module (top 51) being the closest following ABA metabolism gene when considering all modules (Additional File 3). In the midnightblue module, CYP707A2 alt1 (top 191) was the next occurring ABA metabolism gene (Additional File 7). Like in the leaves, NCED3 was closely connected to ABA signaling hub genes in the midnightblue module in the roots; these genes included: RD26 (VvCabSauv08_H0024F_036.ver1.0.g115900; top 1), SEVEN IN ABSENTIA OF ARABIDOPSIS 2 (SINAT2) (VvCabSauv08_P0027F.ver1.0.g381860; top 6), NAC DOMAIN CONTAINING PROTEIN 47 (NAC047) (VvCabSauv08_P0024F.ver1.0.g368680; top 7), GALACTINOL SYNTHASE 1 (GolS1) (VvCabSauv08_P0018F.ver1.0.g349170 and VvCabSauv08_P0018F.ver1.0.g349150; top 9 and 10, respectively), and Hordeum vulgare L. 22 (HVA22E) (VvCabSauv08_P0095F.ver1.0.g483690, top 16) (Additional File 7).
Genes in the ABA metabolite and WD modules have roles in response to stress and stimulus. Gene ontology enrichment was performed using biological process terms for the brown, green, and lightyellow modules in the leaves and the midnightblue and pink modules in the roots (Additional File 8). These modules were selected for high correlation to WD and the ABA metabolites as well as for the number of ABA metabolism genes contained in each module. The gene ontology (GO) term corresponding to response to endogenous stimulus was enriched in all these modules (Additional File 8). All of these modules were enriched for the response to stress and response to stimulus GO terms (Additional File 8). All modules except green leaf included the response to abiotic stimuli GO term (Additional File 8). The lightyellow leaf module was enriched for the biosynthetic process GO term (Additional File 8), and lightyellow had the highest correlation with BETA-OHASE 2, NCED3, and NCED6 in the leaves (Additional File 3) supporting this grouping. The green leaf module was enriched for the catabolic process GO term (Additional File 8) and was most correlated with CYP707A1 alt1 (Additional File 3). The ABA metabolism gene functions were enriched with GO terms assigned to the modules most correlated with WD and the [ABA metabolite] supporting a link between transcript abundance and [metabolite].
One-week severe WD significantly increases ABA biosynthesis transcripts and metabolites, but does not change NCED3 protein
A second experiment was performed with a more severe WD over W1 described previously [8]. Briefly, the same four Vitis species underwent a natural dry-down over the course of a week that achieved a stem water potential lower than that of vines that experienced the two-week moderate WD treatment. RT-qPCR was reported previously, and NCED3 NRQ were significantly increased in response to WD in all species except for RI; RA WD and CS WD had the highest NCED3 NRQ in the leaves and roots [8]. Fold difference (FD) of NCED3 protein abundance was quantified from western blots that were made relative to a CS Control leaf sample run on every gel as an inter-run caliber (IRC) as previously described [101] (Additional File 9). There was no significant difference of relative NCED3 between the treatments or species (Additional File 10). However, CS Control leaves and RA Control and WD leaves had significantly higher relative NCED3 protein than respective roots (Additional File 10).
ABA was quantified, and the [ABA] after W1 of severe WD were comparable to those of W2 moderate WD (Figs. 1 and 10). All WD treated leaves, except those of RI were significantly different from respective Control (Fig. 10), which was paralleled in NCED3 NRQ [8]. RA WD leaves had a significantly higher [ABA] than those of RI and SC, which was also observed in NCED3 NRQ. In the roots, no WD treated species had a significant difference from respective [ABA] Control or to other WD treated species (Fig. 10). The similarity in [ABA] between the W2 moderate and one-week severe WD experiments indicated that ABA metabolism was dependent on severity and duration of WD stress.
Exposing leaves to short-term WD with increasing severity significantly increases NCED3 transcripts and ABA concentration, but it does not impact NCED3 protein abundance
A WD time course experiment was conducted to examine NCED3 transcript abundance, relative NCED3 protein abundance, and [ABA] in response to rapid dehydration WD. As all vines from previous experiments were submitted to the same treatments for the same long-term duration, there may have been a limited range of responses revealed in the previous experiments. To address the limited range of WD and long-term duration in the previous experiments, a third experiment was performed to expose leaves of three of the species (CS, RA, and RI) to a short-term time course of rapid dehydration WD. CS, RA, and RI leaves underwent rapid dehydration or continual petiole irrigation under controlled conditions for 2, 4, 8, and 24 hours.
Over the course of the experiment WD leaves had significantly lower stem water potential and lost significantly more water than Control leaves (Additional File 11). RI WD leaves had significantly lower water content at 2 hours of rapid dehydration relative to respective Control leaves, and CS and RA WD leaves had a significant change in water content by 8 hours of treatment, but there was not a significant difference between WD species at any timepoint. Both stomatal conductance (Gs) and photosynthesis (Ps) were significantly reduced over the course of the WD treatment (Additional File 11). Stem water potential, osmotic potential, and calculated turgor pressure [102] were also significantly reduced in the WD treated leaves (Additional File 11). There were significant differences between treatments and species for most physiological measurements, but there was not a significant difference for time in most cases (Additional File 11). For this reason, Control and WD physiological measurements were shown as an average of all species and time points (Additional File 11). Per time point per measurement there was no significant difference between WD species, but all WD species were significantly different from Control by two hours of treatment.
Each species increased NCED3 transcript abundance in response to the rapid dehydration (Fig. 11). There were no significant differences in NCED3 transcript abundance between the Control leaves of the species for any time point. NCED3 transcript abundance was significantly higher in CS rapid dehydration leaves at all time points relative to CS Control leaves with a general trend of NCED3 transcripts increasing with rapid dehydration time (Fig. 11 and Additional File 12). NCED3 transcript abundance was significantly higher in RA rapid dehydration leaves at all time points except 24 hours relative to RA Control leaves, but NCED3 NRQ was much lower in RA WD leaves than CS WD leaves (Fig. 11 and Additional File 12). Average RA NCED3 transcript abundance was highest after two hours of rapid dehydration and decreased over time, but RA rapid dehydration NCED3 transcripts at two hours of treatment were not significantly different than any other time point of rapid dehydration (Fig. 11 and Additional File 12). Surprisingly, NCED3 transcripts stayed constant in RI rapid dehydration leaves, and RI WD was only significantly different from RI Control after two hours of treatment (Fig. 11 and Additional File 12). Interestingly, there were no significant differences in NCED3 transcript abundance between the rapid dehydration leaves of the species per time point (Fig. 11 and Additional File 12).
[NCED3 protein] was not significantly different for any species in Control or rapid dehydration at any time point (Additional File 10). Western blots were performed as in the one-week severe water deficit experiment using a CS Control two-hour sample as an IRC [101]. RI rapid dehydration two- and four-hour leaves had the greatest variability in NCED3 protein sample between replicates. The NCED3 relative abundance similarity between Control and WD leaves in the rapid dehydration was comparable to those observed in the one-week severe WD.
[ABA] increased in response to short term rapid dehydration WD (Fig. 11). There was no significant difference in Control [ABA] between the species at any timepoint (Fig. 11 and Additional File 12). [ABA] in CS WD 24 hours was significantly different from RA WD 24 hours, but this was the only significant difference between WD species at any time (Fig. 11 and Additional File 12). CS WD [ABA] was significantly different from that of CS Control at two and 24 hours of treatment (Fig. 11 and Additional File 12). CS WD experienced a general increase in ABA with time like CS WD NCED3 transcript abundance (Fig. 11). RA WD had the lowest [ABA] of the WD treated species at all times; RA WD was only significantly different from RA Control after 24 hours of treatment (Fig. 11 and Additional File 12). RA [ABA] paralleled RA NCED3 transcript abundances (Fig. 11), which surprisingly did not increase as much as CS during this short-term WD treatment. RI WD had the highest [ABA] of the WD species at four and eight hours of treatment and steadily increased with time (Fig. 11 and Additional File 12). However, RI WD [ABA] did not follow the same trend as RI WD NCED3 transcript abundance (Fig. 11), which remained relatively constant throughout the stress. This observation may indicate RI is relying on a different source of ABA (like ABA-GE deconjugation) more than the other species under short-term rapid dehydration. Although the WD species were experiencing the same level and duration of stress and having similar physiological responses (Additional File 11), each species displayed unique ABA metabolism responses via NCED3 transcript abundance and [ABA] (Fig. 11 and Additional File 12). NCED3 transcript abundance and [ABA] during short term rapid dehydration did not display the same responses as the longer-term moderate and severe WD, indicating ABA metabolism is highly dependent not only on organ and species but also on stress severity and duration.