Phenolic compounds: tannins and anthocyanins
Phenolic compounds are divided into flavonoid and nonflavonoid compounds. Flavonoids can be further divided into flavonols, flavones, flavan-3-ols, flavanones, and anthocyanins (Figure 5). Nonflavonoids include phenolic acids, hydroxycinnamic acids and their conjugated derivatives, and polyphenolic stilbenes (Monagas et al., 2005; Tsao, 2010; Perestrelo et al., 2012). The most important flavonoid compounds in grapes are anthocyanins and tannins. Tannins are mainly found in the skins and seeds, and anthocyanins are mainly accumulated in skins but also present in pulps in some teinturier cultivars (Montealegre et al., 2006; Guan et al., 2012). They can be partially extracted during winemaking and contribute to wine color, astringency, bitterness, stability, and structure (Revilla and Ryan, 2000). Moreover, tannins can combine with anthocyanins to form pigmented polymers that can improve pigment stabilisation (Waterhouse, 2002).
Precursors of aroma and aroma compounds
Over 1000 volatile compounds have been detected in wine with concentrations ranging from a few ng/L to hundreds of mg/L level (Polášková et al., 2008; Ebeler and Thorngate, 2009). These volatile compounds contribute to olfactory and gustatory quality of wines. The different composition and concentration of volatile compounds helps to construct the characteristics of wine and their concentration differences distinguish one wine from another (Ribéreau-Gayon et al., 2006). Varietal aromas explain the unique flavours of wines made with different types of grapes. They are considered as key factors to recognition of a typicity or sensory identity of wine, such as methoxypyrazines (vegetable like flavour), terpenes and norisoprenoids (floral aromas). Besides their commercial importance, plant volatiles are also associated with defensive and attractive roles (Pichersky et al., 2006)
Methoxypyrazines (MPs) are the main compounds responsible for the “green” flavours found in Cabernet Sauvignon and Sauvignon Blanc grape and wine (Lacey et al., 1991; Noble et al., 1995; Ryona et al., 2008). They have nitrogen-containing heterocyclic structures and probably result as a secondary product of amino acid catabolism in the grape (Figure 6). In grape berries, 7 MPs have been detected in grape and wine (Figure 7), and 2-methoxy-3-isobutylpyrazine (IBMP), 2-methoxy-3-isopropylpyrazine (IPMP) and 2-methoxy-3-sec-butylpyrazine (SBMP) are the predominant MPs (Darriet et al., 2012; Lei et al., 2017). They have been largely studied in grape and wine. The olfactory perception threshold of IBMP in water is very low, less than 1ng/L. The recognition threshold in wine, in range of 2-6 ng/L, is quite different from those determined in water and is variable due to the different composition from one wine to another (Roujou de Boubée et al., 2000; Pickering et al., 2007). An excessive level of IBMP concentration (≥15 ng/L) in wine has a negative effect as an obvious herbaceous off-flavours (Roujou de Boubée et al., 2000), but a concentration near its detection threshold can contribute to pleasant varietal aromas in Sauvignon Blanc wines (Allen et al., 1991). Therefore, controlling IBMP concentration at a suitable level is very important to wine quality. Based on previous studies, IBMP concentration in wine is highly correlated with its concentration in berry at harvest. It is easily extracted during maceration and difficult to reduce by oenological practice (de Boubée et al., 2002; Darriet et al., 2012). It has been found that IBMP accumulates in grape berries before veraison and degrades thereafter (Darriet et al., 2012). In general, high temperature has been reported to reduce its level (Lacey et al., 1991; Allen and Lacey, 1998; Marais et al., 1999; Falcão et al., 2007).
Method for investigation temperature effects
Until recently, three kinds of methods have been used to investigate the influence of temperature on grape berries and vines (Sadras and Soar, 2009). The first one is indirect by comparing seasonal and/or regional differences with the aid of statistical analysis (Chuine et al., 2004; Duchêne and Schneider, 2005; Stoll et al., 2012). This method cannot avoid the bias caused by other environmental factors. Secondly, a direct comparison under artefactual conditions, like greenhouse (Mori et al., 2007b; Rienth et al., 2016; Lecourieux et al., 2017). These studies in controlled conditions were conducted mostly with an abrupt thermal-shock with a temperature increment by more than 5 °C, which is much higher than projected global warming. The last one consists in changing the temperature closely surrounding specific plant parts. To overcome these limitations, Sadras and Soar (2009) created a new and inexpensive open system to increase temperature in realistic vineyard conditions, a passive open-top heating system based on a very local greenhouse effect. This system gently increased maximum bunch temperature with 2.3-3.2°C that was commensurate with the projected global warming. Meanwhile it maintained daily temperature cycle without or with little effect on other microclimate factors (Sadras and Soar, 2009).
Soil and vine water status
Predawn and midday water potentials were measured with a pressure chamber equipped with a digital LCD manometer (SAM Precis 2000, 33175 Gradignan, France), as described in Choné et al. (2000). Five primary leaves per block were measured, two times (MV and MR) in 2015 and five times (BC, MV, MR, R and PR) in 2016.
Extraction and analysis of free amino acid
Amino acids were extracted from 500 mg (fresh weight) finely ground powder of whole berries. The powder was extracted at 80oC successively with 2 mL water/ethanol (1:4, v/v), 2 mL water/ethanol (1:1, v/v) and 2 mL water. The supernatants were combined and then evaporated to dryness in a Speed-Vac concentrator (Savant Instruments, Inc., Hicksville, NY). The dry residue was dissolved in 2 mL deionized water.
Amino acids were determined by using HPLC (Waters, Milford, MA, USA) after derivation with 6-aminoquinolyl-N-hydroxysuccinimidyl-carbamate (AccQ-Fluor Reagent Kit, Waters), as described in Martínez-Lüscher et al. (2014). All the amino acids were identified and quantified with external chemical standards purchased from Sigma (St Louis, MO, USA).
Analysis of soluble sugars and organic acids
Glucose and fructose were measured enzymatically as described in Guan et al. (2016). Tartaric and malic acids were determined using the autoanalyser TRAACS 800 (Bran & Luebbe, Plaisir, France) according to the method of Berdeja et al. (2014).
To get overviews of correlations among amino acids, principal component analysis (PCA) and heatmaps were produced using R software (R development Core Team, 2010). The Student’s t-test was used to test the significance of differences between berries under control and elevated temperature at P<0.05 level. Dynamic profiles of total and individual amino acids were drawn using sigmaplot 11.0 (Systat Software Inc.).
Bunch zone and berry temperature of control and treatment in the field
The seasonal dynamics of maximal, average and minimal bunch zone temperatures in control (Figure 2a and 2b) and the elevation of temperature in treatment (Figure 2c and 2d) were assessed. In 2015, the average elevations of maximal, average and minimal temperatures were ~2.76°C, 1.18°C and 0.97°C respectively and they were 1.13°C, 0.81°C and 0.8°C respectively in 2016 (Figure 3). According to the regression between temperatures of treatment and control (Figure 2c), the system effectively increased the maximal, average and minimal temperatures. The heating effect in 2015 was slightly higher than in 2016 (Figure 3). Because the heating system is passive, the heating efficiency depends on the weather, especially the global solar radiation. In hot clear days, increase of maximal temperature could rise to a maximum of 5.3°C (Figure 2a and 2b). Conversely, in cloudy and rainy days, there was almost no heating effect and even a cooling effect (only three times in 2015 and four times in 2016) (Figure 2c and 2d). The average berry temperature during the treatment period was 22.03 °C in 2015 and 22.6 °C in 2016, increasing by 1.09°C and 0.8°C respectively (Figure 4), which was similar with the increase in bunch zone air temperature (Figure 3).
Total amino acids and amino acid composition
The HPLC method used allowed us to identify and quantify 18 free amino acids in the berries. The total amino acid concentration was calculated by summing all 18 amino acids amounts and dividing by berry volume/weight. The developmental pattern of total amino acid concentration was similar (figure 8). It showed an increasing trend from BC to R/PR. During PR stage in 2016, the total amino acids concentration kept increasing under treatment while it slightly decreased in control. Generally, the total amino acid was present at a low concentration (around 5000 pmol/mg FW) at early stages (BC and MV), and thereafter increased dramatically. For CS, the concentration reached above 17000 pmol/mg FW at harvest and that of SB berries was between 10000 and 17000 pmol/mg FW. The total amino acid only showed a significantly higher concentration in SB control berries at R stage (16095 pmol/mg FW versus 12131pmol/mg FW). Comparing the two vintages, berries in 2015 contained more free amino acids than in 2016 in SB, while similar levels between the two vintages were observed for CS.
Developmental changes of amino acid concentration
The dynamic changes of each amino acid are presented in detail according to their position in the biosynthesis pathway (Figure 10). To further clarify the difference between treatment and control of individual amino acid along berry development, heatmaps (Figure 11) were performed on the normalized ratio between treatment and control (H/C).
In CS, the elevated temperature mainly showed a negative effect during berry maturation (Figure 11), except BC stage in 2015 and PR stage in 2016 which showed a positive influence. Decreases in relative concentrations of most amino acids (except Pro, Tyr, Cys and His) were observed, but only Asp, Glu and Met concentrations exhibited significant differences in both years (Figure 10). Despite these differences, the concentration of total amino acid was not significantly affected by the temperature increase, because proline, the most predominant amino acid, did not significantly differ between heated and control.
In SB, vintage-dependent effects of elevated temperature on individual amino acids were observed between two years (Figure 11). In 2015, 17 out of 18 individual amino acids (except Cys) had a relative higher concentration and 7 of them showed a statistically higher level in control berries at harvest (figure 10). This resulted in a significantly higher concentration of total amino acid in control berries at harvest. In contrast, positive effects at R and PR stage were shown in 2016 (figure11), although only Gly and GABA significantly increased at R stage in heated berries.
Table of contents :
Chapter I: Literature Review
I.1 Global warming and its influences
I.1.1 Climate change and global warming
I.1.2 Impacts of global warming on viticulture
I.2 Grape berry development and quality
I.2.1 Grape berry development
I.2.2 Factors influencing grape quality
I.3 Grape berry metabolites and elevated temperature effects
I.3.1 Primary metabolites
I.3.2 Phenolic compounds: tannins and anthocyanins
I.3.3 Precursors of aroma and aroma compounds
I.4 Method for investigation temperature effects
Chapter II: Effect of moderately elevated temperature on free amino acids content of Cabernet Sauvignon and Sauvignon Blanc berries
II.2 Material and methods
II.2.1 Site and vines
II.2.2 Heating system and measurements
II.2.3 Soil and vine water status
II.2.4 Extraction and analysis of free amino acid
II.2.5 Analysis of soluble sugars and organic acids
II.2.6 Statistical analysis
II.3.1 Heating system performance
II.3.2 Total amino acids and amino acid composition
II.3.3 Developmental changes of amino acid concentration
II.3.4 PCA analysis
II.4.1 Global warming context and heating system performance
II.4.2 Response of technical maturity (sugars and organic acids)
II.4.3 Amino acid response
Chapter III: Moderately elevated temperature affects metabolism and transcriptome of grapevine (Vitis vinifera L.) berries
III.2 Materials and methods
III.2.1 Location, vine material and experimental set up
III.2.2 Metabolites quantification
III.2.3 RNA extraction and gene expression analysis
III.2.4 RNA-Seq library construction and sequencing
III.2.5 Analysis of Illumina reads
III.3 Results and discussion
III.3.1 Heat treatment and berry characterization
III.3.2 Global response of berry transcriptome to elevated temperature
III.3.3 Identification of significantly altered transcripts
III.3.4 Anthocyanins and tannins content in CS berries
III.3.5 3SH precursors content and gene expressions in their pathway in berries .
Chapter IV: Effect of moderately elevated temperature on 2-methoxy-3-isobutylpyrazine in berries of Cabernet Sauvignon and Sauvignon Blanc
IV.2 Materials and methods
IV.2.1 Site and vines
IV.2.2 Heating system
IV.2.3 Extraction and analysis of IBMP
IV.2.4 RNA extraction and gene expression analysis
IV.2.5 Statistical analysis
IV.3.1 Heating system performance and grape berry response
IV.3.2 IBMP content in grape berries and VviOMT expressions
Chapter V: Effect of moderately elevated temperature on free amino acids, carotenoids and C13-Norisoprenoids in berries of Cabernet Sauvignon in Barossa Valley
V.2 Material and methods
V.2.1 Site and vines
V.2.2 Heating system and measurements
V.2.3 Total soluble solids, titratable acidity and pH measurement
V.2.4 Extraction and analysis of free amino acids
V.2.5 Extraction and analysis of carotenoids
V.2.6 Extraction and analysis of total C13-norisoprenoids
V.3.1 Heating system performance
V.3.2 Response of technical maturity to elevated temperature
V.3.3 Developmental changes of amino acid concentration in response to elevated temperature
V.3.4 Developmental changes of carotenoids in response to elevated temperature
V.3.5 Developmental changes of C13-norisoprenoids in response to elevated temperature
V.3.6 PCA analysis
V.4.1 Global warming context and heating system performance
V.4.2 Response of technical maturity
V.4.3 Amino acid response
V.4.4 Carotenoids and β-damascenone response
Chapter VI：General conclusions and perspectives