The common gene sets for both locations represented almost 20% of the genes expressed. The processes involved included the increase of catabolism and the decline of translation and photosynthesis. It is clear that these processes play important roles in berry ripening. Most transcript abundances varied with increasing sugar levels and berry maturation and most of these varied with the vineyard site. Many of the DEGs could be associated with environmental or hormonal stimuli.
DEG expression profiles of grape berry skins were associated with environmental factors and seed development
Plants are exposed to a multitude of factors that influence their physiology even in controlled agricultural fields such as vineyards. The vineyards in BOD and RNO are exposed to very different environments and some of the environmental influences could be detected in our data. Our data support the hypothesis that transcriptomic dynamics in the late stages of berry ripening are sensitive to the local environmental influences on the grapevine.
While most transcript abundances in berries are largely influenced by genetics or genotype, environment also plays a large role [2].
In this study we compared two different clones of Cabernet Sauvignon, one on rootstock and the other on its own roots. There are likely to be some effects on the transcript abundance in the berries between these grapevines as a result of the genetic differences between their roots and their clonal shoots/scions. These and other factors most certainly affect the berries to some degree. In this study, our data indicate that grape berry skins also were responsive to a multitude of potential environmental factors in the two vineyard locations and possibly from signals coming from the maturing seed. We say potential environmental factors because we did not control for these factors; we associated transcript abundance with the factors that were different in the two locations. The transcript abundance profiles along with functional annotation of the genes gave us clues to factors that were influencing the berries and then associations were made with the known environmental variables. Further experiments are required to follow up on these observations.
The environmental factors that we were able to associate with differences in transcript abundance (DEGs) between the two locations included air temperatures, light, moisture (rainfall and relative humidity) and biotic stress. These factors in turn were associated with transcript abundance involved with physiological responses and berry traits such as seed and embryo development, hormone signaling (ABA, ethylene and auxin), phenylpropanoid metabolism, and the circadian clock.
Light effects on transcript abundance
Light regulates the transcript abundance of many genes in plants. It has been estimated that 20% of the plant transcriptome is regulated by white light and this includes genes from most metabolic pathways [53]. Light is sensed by a variety of photoreceptors in plants [34]; there are red/far red, blue and UV light receptors. PHYB is a key light sensor, regulating most of the light sensitive genes [54] and sensing the environment through red light to far-red light ratios and temperature [34, 55]. PHYB entrains the circadian clock affecting the rate of the daily cycle [56] and the expression of many the circadian clock genes [54]; PHYB induces morning phase genes and represses evening phase genes. Other photoreceptors can entrain the circadian clock as well [34].
PHYB and the circadian clock are central regulators of many aspects of plant development including seed germination, seedling growth, and flowering [34, 56, 57]. The circadian clock influences the daily transcript abundance of genes involved in photosynthesis, sugar transport and metabolism, biotic and abiotic stress, even iron homeostasis [56].
Light signaling was very dynamic in the berry skin transcriptome in the late stages of berry ripening with a higher transcript abundance of many light signaling genes in BOD berries. Many photoreceptors that interact with the circadian clock had a higher gene expression in BOD berries. In the circadian clock model, Circadian Clock Associated 1 (CCA1) is an early morning gene and has its highest expression at the beginning of the day. It is at the start of the circadian core clock progression through the day, whereas the transcript abundance of Timing Of CAB Expression 1 (TOC1) is highest at the end of the day and finishes the core clock progression (Fig. 6). In both of these cases, there is a higher transcript abundance of these genes in BOD than in RNO.
The evening complex is a multi-protein complex composed of Early Flowering 3 (ELF3), Early Flowering 4 (ELF4) and Phytoclock 1 (PCL1 also known as LUX) that peaks at dusk. Of the three of these proteins, ELF4 had a small difference in transcript abundance between locations around the 20°Brix level (Fig. 6), whereas there was no distinction between locations or sugar levels for ELF3 (Fig. 6) or PCL1 (Additional file 12). The transcript abundance of ELF3 increased with sugar level and shortening of the day length (the higher sugar level comes later in the season and thus is at a shorter day length). ELF3, as part of the evening complex (EC), has direct physical interactions with PHYB, COP1 (Constitutive Photomorphogenic 1) and TOC1 [58] linking light and temperature signaling pathways directly with the circadian clock. It is interesting that most of the components of the clock showed significant differences in transcript abundance between BOD and RNO, except for the three proteins that make up the evening clock.
The transcript abundance profile of PHYB was similar in both BOD and RNO berries (Fig. 6), however the changes in transcript abundance with sugar level occurred in BOD berries at a lower sugar level. There was a gradual decline of PHYB transcript abundance with increasing sugar level until the last measurement at the fully mature stage, where there was a large increase in transcript abundance. A very similar profile is observed for Reveille 1 (RVE1). RVE1 promotes seed dormancy in arabidopsis and PHYB interacts with RVE1 by inhibiting its expression [59]. PIF7 (Phytochrome Interacting Factor 7), interacts directly with PHYB to suppress PHYB protein levels [60]. Likewise, PIF7 activity is regulated by the circadian clock [61]. PIF7 had different transcript abundance profiles in the BOD and RNO berries. It was somewhat similar to the profiles in PHYB in RNO berries and the opposite in BOD berries, indicating a more complicated role of the transcript abundance for this protein in the two different locations. Note that transcript abundance does not necessarily infer protein abundance and so making functional associations here is not necessarily accurate. The transcript abundance of two of the other grape phytochromes (PHYA and PHYE) did not vary significantly between the two locations or at different sugar levels. PHYC had a higher transcript abundance in RNO berries and did not change much with different sugar levels. Many other light receptors (e.g. CRY3, FAR1, FRS5, etc.) had higher transcript abundance in BOD berries (Additional file 13). Thus, light sensing through the circadian clock is a complicated process with multiple inputs.
RVE1 follows a circadian rhythm [62]. It behaves like a morning-phased transcription factor and binds to the EE element, but it is not clear if it is affected directly from the core clock (e.g. TOC1 or EC which repress other morning gene paralogs like CCA1 and LHY) or through effects of PHYB or both. PHYB downregulates RVE1; RVE1 promotes auxin concentrations and decreases gibberellin (GA) concentrations [59]. Warmer night temperatures (as in BOD) cause more rapid reversion of the active form of PHYB to the inactive form [34] and thus may promote a higher expression/activity of RVE1. Pr (phytochrome in the red form, which is the physiologically inactive form) appears to accelerate the pace of the clock [56]. It is unclear what role phytochromes might have in seed and fruit development in grapes.
Very little is known about the effect of PHY on fruit development in general. In one tomato study, the fruit development of phy mutants was accelerated [63], suggesting that PHYB as a temperature/light sensor and a regulator of the circadian clock may influence fruit development. Carotenoid concentrations, but not sugar concentrations, also were affected in these mutants.
Photoperiod affects the transcript abundance of PHYA and PHYB in grape leaves [64]. In the present study, the transcript abundance of the majority of the photoreceptor genes in berry skins, including red, blue and UV light photoreceptors, had a higher transcript abundance in BOD berries (Additional file 13). It is unclear what the effect of PHYB and the circadian clock have on grape berry development. However, there are clear differences between the two locations and it seems likely that PHYB and the circadian clock are key grape berry sensors of the environment, affecting fruit development and composition.
Temperature effects on transcript abundance
The berry skin transcriptome was sensitive to temperature. The RNO berries were exposed to a much larger temperature differential between day and night than BOD berries and were also exposed to chilling temperatures in the early morning hours during the late stages of berry ripening (Table 1). The transcript abundance of some cold-responsive genes was higher in RNO berry skins than in BOD berry skins, including CBF1 and ERF6L1.
CBF1 transcript abundance is very sensitive to chilling temperatures; it is a master regulator of the cold regulon and improves plant cold tolerance [36, 65, 66]. PIF7 binds to the promoter of CBF1, inhibiting CBF1 transcript abundance, linking phytochrome, the circadian clock and CBF1 expression [61]. Our data are consistent with this model; transcript abundance of PIF7 was higher and CBF1 transcript abundance was lower in BOD berry skins than RNO berry skins (Fig. 6 and 7).
The transcript abundance of ERF6L1 was identified in another study to peak at a TSS where optimum berry flavor and ripeness occur [67]. It was hypothesized that this gene may be associated with berry ripening and the transcript abundance involved with aroma-related compounds. However, in a later study, the transcript abundance of ERF6L1 did not peak, but steadily declined with increasing sugar levels [4]. A major difference between these two studies is that in the earlier study the berries were shipped on ice 24 h before freezing in liquid nitrogen, whereas in the latter study, berries were harvested and flash frozen immediately. Thus, the increased ERF6L1 transcript abundance may have been responding to the chilling temperatures rather than to the sugar concentration or there was an interaction between the two factors. This chilling response may also occur during some commercial harvests, when berries are chilled down before starting fermentation. The cold-responsive transcripts identified in this study (Fig. 7) may be good markers for chilling stress since their abundance was increased by chilling temperatures in shoot tips [35], leaves [37] and berry skins (this study). We do not have an explanation for the large decrease in transcript abundance for many of these genes at 26°Brix for the RNO berries (Fig. 7).
Dehydration and seed development effects on transcript abundance
There appear to be at least two kinds of dehydration signals in the berry skin: 1) an internal skin signal based on the water status of the skin cells and 2) signals received from the dehydrating embryo in the seed. ABA concentrations in plants increase in response to dehydration and ABA triggers a major signaling pathway involved in osmotic stress responses and seed development [68]. ABA concentrations only increase in the seed embryo near the end of seed development when the embryo dehydrates and goes into dormancy. ABA concentrations remain high to inhibit seed germination. The transcript abundance of ABA signaling genes such as ABF2 and SnRK2 kinases increase after application of ABA to cell culture [69] and in response to dehydration [46] in leaves of Cabernet Sauvignon.
The data in this study are consistent with the hypothesis that BOD berries are riper at lower sugar levels. The ABA signaling genes in the berry skins had higher transcript abundance in BOD berries indicating that ABA concentrations were higher in BOD than RNO berries even though RNO berries were exposed to drier conditions (Table 1). ABA concentrations appeared to be higher in the BOD berry skins based upon the higher transcript abundance of important ABA signaling and biosynthesis genes encoding for ABF2, SnRK2 kinases and NCED6. We hypothesize that this is seed derived ABA since water deficit was not apparent in BOD with the recent rainfall and high humidity. In contrast, NCED3 genes had higher transcript abundance in RNO berry skins, which might occur as the result of the very low humidity and large vapor pressure deficit (the vines were irrigated). The lower expression of NCED6 in RNO berry skins may indicate that the seeds in the berry were more immature than the BOD berries. The higher expression of other seed development and dormancy genes (e.g. RVE1, ARF2, ARF10, etc.) in the berry skins support the argument that BOD berries (and seeds) matured at a lower sugar level than the RNO berries.
The ABA concentrations in the berry skins are a function of biosynthesis, catabolism, conjugation and transport. ABA in seeds increase as the seed matures and some of this ABA may be transported to the skin. In fact, ABCG40 genes, which encode ABA transporters, had higher transcript abundance in BOD berry skins than that in RNO (Additional file 3). Part of the ABA in skins may be transported from the seed and part of it might be derived from biosynthesis in the skins. NCED6 transcript abundance in the skins was higher in BOD berries. Perhaps the transcript abundance of NCED6 in the skin is regulated by the same signals as the embryo and reflects an increase in seed maturity. AtNCED6 transcript abundance is not responsive to water deficit in arabidopsis, but AtNCED3 and AtNCED5 are [44]. This is consistent with the higher NCED3 and BAM1 transcript abundance in RNO berries. Thus, there are complex responses of ABA metabolism and signaling. It would appear that there may be two different ABA pathways affecting ABA concentrations and signaling: one involved with embryo development and one involved with the water status in the skins.
Auxin is also involved with ABA signaling during the late stages of embryo development in the seeds. Auxin signaling responses are complex. ABF5 is an auxin receptor that degrades Aux/IAA proteins, which are repressors of ARF transcriptional activity [70]. Thus, a rise in auxin concentration releases Aux/IAA repression of ARF transcription factors, activating auxin signaling. In the berry skins, there was a diversity of transcriptional responses of Aux/IAA and ARF genes in the two locations, some with increased transcript abundance and others with decreased transcript abundance. As with ABA signaling, there may be multiple auxin signaling pathways operating simultaneously.
One pathway appears to involve seed dormancy. ARF2 and ARF10 genes had a higher transcript abundance in BOD berries. ARF10 upregulates ABI3 expression and promotes seed dormancy [71]; ARF2 also promotes dormancy through the ABA signaling pathway [72]. This is consistent with the hypothesis that BOD berries reach maturity at a lower sugar level than RNO berries. Auxin may play a role in promoting seed dormancy through ARF2 and ARF10 effects on ABA signaling in the late stages of berry ripening.
Biotic stress effects on transcript abundance
The top 150 DEGs for BOD berries were highly enriched with biotic stress genes. The higher rainfall and high relative humidity in BOD would make moist conditions suitable for pathogenic fungi to grow. We detected a much higher transcript abundance of powdery mildew-responsive genes in BOD berries and this may be connected to a higher transcript abundance of ethylene and phenylpropanoid genes as part of a defense response. The transcript abundance profiles of some of these genes (e.g. PR10, PAL4, STS10, ACS6, and ERF2; see Figs. 4, 5, 8 and 9) are remarkably similar.
Increased ethylene signaling in grapevines has been associated with powdery mildew infection and phenylpropanoid metabolism and appears to provide plant protection against the fungus [73, 74]. Genes involved with phenylpropanoid metabolism, especially PAL and STS genes, appear to be quite sensitive to multiple stresses in the environment [75]. In arabidopsis there are four PAL genes [76]. PAL1 and PAL2 appear to be involved with flavonoid biosynthesis; PAL4 provides pathogen resistance. Ten different PAL4 orthologs had a higher transcript abundance in BOD berry skins; many STS genes also had a higher transcript abundance in BOD berry skins (Additional file 11). Stilbenes are phytoalexins and provide pathogen resistance in grapes and STS genes are strongly induced by pathogens [77]. Thus, the higher transcript abundance of powdery mildew genes is likely to be associated with the higher transcript abundance of genes in the ethylene and phenylpropanoid pathways.
Transcript abundance of iron homeostasis genes
The transcript abundance of a number of iron homeostasis genes were significantly different in the two locations (Fig. 10) and there was a difference in soil available iron concentrations in the two locations. However, iron uptake and transport in plants is complicated depending on multiple factors, such as pH, soil redox state, organic matter composition, solubility in the phloem, etc. Thus, it is impossible to predict iron concentrations in the berry without direct measurements. The roles of these genes in iron homeostasis and plant physiological functions are diverse. Iron supply can affect anthocyanin concentrations and the transcript abundance of genes in the phenylpropanoid pathway in Cabernet Sauvignon berry skins [78]. One of the DEGs, SIA1, is located in the chloroplast in arabidopsis and appears to function in plastoglobule formation and iron homeostasis signaling in concert with ATH13 (also known as OSA1) [79]. Another DEG, YSL3, is involved in Fe transport [80]. It acts in the SA signaling pathway and appears to be involved in defense responses to pathogens. It also functions in iron transport into seeds [81]. FER4 is one of a family of ferritins (iron-binding proteins) found in arabidopsis [82]. FER2 is a specific ferritin that is associated with the dry seed during fruit development [83]. It is a storage form of iron and also is involved in protection against oxidative stresses. The transcript abundance of FER2, FER3 and FER4 is induced by chilling temperatures in the light [84], but not FER1. VIT1 and NRAMP3 are vacuolar iron transporters [83] and are also involved in iron storage in seeds. Interestingly, increased VIT1 transcript abundance leads to decreased FER2 transcript abundance [83]. This response is consistent within our berries as VIT1 transcript abundance increased with sugar level and had a higher transcript abundance in BOD berries than that of RNO berries and the transcript abundance of FER2 declined with sugar levels in BOD berries to a lower amount than that of RNO berries.
Other DEGs are also responsive to iron supply. IREG3 (also known as MAR1) appears to be involved in iron transport in plastids; its transcript abundance increases with increasing iron concentrations [85]. ABS4 is a MATE (multidrug and toxic compound extrusion) efflux transporter that is located in the Golgi complex and its transcript abundance is induced by iron and osmotic stress [86].
It is unclear what specific roles these iron homeostasis genes are playing in grape berry skins, but they appear to be involved in iron storage in seeds and protection against oxidative stress responses [82, 83]. One possible explanation for the transcript abundance profiles in the BOD and RNO berry skins is that ferritins are known to bind iron and are thought to reduce the free iron concentrations in the chloroplast, thus, reducing ROS production that is caused by the Fenton reaction [82]. As chloroplasts senesce during berry ripening, iron concentrations may rise as a result of the catabolism of iron-containing proteins in the thylakoid membranes; thus, berry skins may need higher concentrations of ferritins to keep free iron concentrations low. This might explain the increase in ferritin transcript abundance with increasing sugar levels. The transcript abundance differences between BOD and RNO in iron homeostasis genes also may be related to an earlier berry ripening (seed development and chloroplast degradation) in BOD berries (earlier decline at lower sugar levels) of transcript abundance of FER2 and higher transcript abundance of VIT1, and the chilling temperatures (higher transcript abundance of FER2 and FER4) in RNO. Most soils contain 2 to 5% iron including available and unavailable iron; soils with 15 and 25 µg g-1 of available iron are considered moderate for grapevines [87], but soils with higher concentrations are not considered toxic. Therefore, for both soils in this study, iron concentrations can be considered to be very high but not toxic. The higher available iron concentrations in the BOD vineyard may be associated with the wetter conditions (more reductive conditions) and the lower soil pH.
Environmental influences on transcript abundance in other studies
Other researchers using Omics approaches have identified environmental factors that influence grape berry transcript abundance and metabolites. One study investigated the differences in transcript abundance in berries of Corvina (a black-skinned grape cultivar that makes red wine) in 11 different vineyards over three years [88]. They determined that approximately 18% of the berry transcript abundance was affected by the environment. Climate had an overwhelming effect but viticultural practices were also significant. Phenylpropanoid metabolism was very sensitive to the environment and PAL transcript abundance was associated with STS transcript abundance.
In another study of a white grape cultivar, Garganega, berries were analyzed by transcriptomic and metabolomic approaches [89]. Berries were selected from vineyards at different altitudes and soil types. Again, phenylpropanoid metabolism was strongly influenced by the environment. Carotenoid and terpenoid metabolism were influenced as well.
Two studies investigated the grape berry transcriptomes during the ripening phase in two different regions of China, a dry region in Western China and a wet region in Eastern China [90, 91]. These two locations mirror some of the differences in our conditions in our study, namely moisture, light and elevation, although the China western region has higher night temperatures and more rainfall than the RNO location. In the Cabernet Sauvignon study [90], they compared the berry transcriptomes (with seeds removed) from the two regions at three different stages: pea size, veraison and maturity. The TSS at maturity was slightly below 20°Brix. Similar to our study, the response to stimulus, phenylpropanoid and diterpenoid metabolism GO categories were highly enriched in mature berries between the two locations. Differences in the transcript abundance of NCED and PR proteins were also noted. Like in our study, the authors associated the transcript abundance of these proteins to the dry (drought response) and wet (pathogen defense) locations, respectively.
In the second study comparing these two regions in China [91], the effects of the environment on the metabolome and transcriptome of Muscat Blanc à Petits Grains berries were investigated over two seasons; specifically, terpenoid metabolism was targeted. Like in our study, the transcripts in terpenoid were in higher abundance in the wetter location. The transcript abundances were correlated with terpenoid concentrations and a coexpression network was constructed. A specific set of candidate regulatory genes were identified including some terpene synthases (TPS14), glycosyl transferases and 1-hydroxy-2-methyl-2-butenyl 4-diphosphate reductase (HDR). We examined the transcript abundance of some of these candidate genes in our own data but did not find significant differences between our two locations. The contrasting results between our study and Wen et al. (2015) could be for a variety of reasons such as different cultivar responses, berry versus skin samples, or different environmental conditions that affect terpenoid production.
Terpenoid metabolism is influenced by the microclimate [92] and is involved in plant defense responses to pathogens and insects [30, 93]. Light exposure to Sauvignon Blanc grapes (a white grape cultivar) was manipulated by removing adjacent leaves without any detectable differences in berry temperatures [92]. Increased light exposure increased specific carotenoid and terpene concentrations in the berry. The responses of carotenoid and terpenoid production to temperature are less clear. Some effect of temperature was associated with carotenoid and terpenoid production, but to a lesser extent than light [92]. Higher concentrations of rotundone, a sesquiterpene, have been associated with cooler temperatures [94]. Water deficit can also alter carotenoid and terpenoid metabolism in grapes [11, 95]. Terpenes can act as signals for insect attacks and attract insect predators [93]. Thus, terpenoid metabolism is highly sensitive to the environment and influenced by many factors.
In contrast to these studies, excess light and heat can affect transcript abundance and damage berry quality. In addition to a higher rate of malate catabolism, anthocyanin concentrations and some of the transcript abundances associated with them are decreased as well [96, 97].
Temperature effects on berry maturity and total soluble solids
BOD berries reached maturity at a lower °Brix level than RNO berries; the cause is likely to be the warmer days and cooler nights in RNO. Higher day temperature may increase photosynthesis and sugar transport and cooler night temperatures may reduce fruit respiration. °Brix or TSS approximates the % sugar in a berry and is a reliable marker of berry maturity in any given location [98]; however, TSS is an unreliable marker of berry maturity when comparing grapes from very different climates. The differences in TSS between BOD and RNO are consistent with other studies on the temperature effects on berry development. Indirect studies have associated gradual warming over the last century to accelerated phenology and increased sugar concentrations in the grape berries [99-102]. Increasing temperature can accelerate metabolism, including sugar biosynthesis and transport, but the increase in metabolism is not uniform. For example, the increase in anthocyanin concentration during the ripening phase is not affected as much as the increase in sugar concentration [103]. These responses vary with the cultivar [100], complicating this kind of analysis even further.
Direct studies of temperature effects on Cabernet Sauvignon berry composition also are consistent with our data. In one study, the composition of Cabernet Sauvignon berries was altered substantially for vines grown in phytotrons at 20 or 30°C temperatures (temperatures that are very similar to the BOD and RNO temperatures occurring in the present study) [104]. Cooler temperatures promoted anthocyanin development and malate concentrations (inhibited malate catabolism) and higher temperatures promoted TSS (°Brix) and proline concentrations [104]. In a second study, vines were grown at 20 or 30°C day temperatures with night temperatures 5°C cooler than the day [105]. In this study, higher temperatures increased berry volume and veraison started earlier by about 3 to 4 weeks [105]. The authors concluded that warmer temperatures hastened berry development. In a third study, Cabernet Sauvignon berry composition was affected in a similar manner by soil temperatures that differed by 13°C [106].
TSS concentrations are also affected by light and the vine water status. Light is generally not a factor because there is usually a large enough leaf area and sufficient light levels to saturate this source to sink relationship [107, 108]. Sun-exposed Cabernet Sauvignon berries in the vineyard had higher TSS than shaded berries [107]. This sunlight effect was attributed largely to an increase in berry temperature rather than an increase in the fluence rate per se. A higher grapevine water status results in larger berry size and lower sugar concentrations [109] and water deficit is known to increase sugar concentrations in Cabernet Sauvignon [11]. However, temperature is thought to have the largest effect on sugar concentrations [16].
Other transcriptomic data in the present study indicated that BOD berries were more mature at a lower sugar level than RNO berries. These included the transcript abundance profiles of genes involved in autophagy, auxin and ABA signaling, iron homeostasis and seed development. Many of these DEGs had an accelerated rate of change in BOD berries. While these transcripts are in the skins, they may be influenced by signals coming from the seed. In addition, there was a higher transcript abundance for most genes involved with the circadian clock in BOD berries. PHYB can regulate the circadian clock [56] and PHYB activity is very sensitive to night temperatures (BOD had higher night temperatures); PHYB reversion is accelerated to the inactive form at warmer temperatures [34]. The inactivity of phytochrome promotes the expression of RVE1, which promotes auxin concentrations and seed dormancy [59]. Thus, all things considered, it is likely that temperature and/or the temperature differentials between day and night were the main environmental factors that was responsible for the differences in the rate of berry development and sugar accumulation in the two locations.
Are there reliable markers to harvest berries at maturity?
Determining maturity of grapes is a difficult and error prone process. Reliable markers could aid in the decision of when to harvest the grapes. “Optimum” maturity is a judgement call and will ultimately depend on the winemaker’s or grower’s specific goals or preferences. A combination of empirical factors can be utilized including °Brix, total acidity, berry tasting in the mouth for aroma and tannins, seed color, etc. TSS by itself may not be the best marker for berry ripening as it appears to be uncoupled from berry maturity by temperature. Phenylpropanoid metabolism, including anthocyanin metabolism, is also highly sensitive to both abiotic and biotic stresses and may not be a good indicator of full maturity. Thus, color may not be a good indicator either. Specific developmental signals from the seed or embryo, such as those involved with auxin and ABA signaling, may provide more reliable markers for berry ripening in diverse environments, but will not be useful in seedless grapes. Aromatic compounds may also be reliable markers but they will need to be generic, developmental markers that are not influenced by the environment. This study revealed many genes that are not reliable markers because they were influenced by the environment. One candidate marker that is noteworthy is ATG18g (Vitvi07g00210). Its transcript abundance increased and was relatively linear with increasing °Brix and these trends were offset at the two locations relative to their level of putative fruit maturity (Fig. 3). ATG18g is required for the autophagy process [110] and maybe important during the fruit ripening phase. It was found to be a hub gene in a gene subnetwork associated with fruit ripening and chloroplast degradation [4]. Further testing will be required to know if it is essential for fruit ripening and whether its transcript abundance is influenced by abiotic and biotic stresses in grape berry skins.