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FINAL REPORT (2015)

1. PROGRAMME AND PROJECT LEADER INFORMATION

Research Organisation

Programme leader

Research Team Manager

Project leaders

Title, initials, surname Prof MA Vivier Prof MA Vivier Prof MA Vivier/ Dr PR Young

Present position Professor Professor Professor/ Reseacher

Address Institute of Wine Biotechnology, Department of Viticulture and Oenology, Stellenbosch University, Private Bag X1, Matieland, 7602

Institute of Wine Biotechnology, Department of Viticulture and Oenology, Stellenbosch University, Private Bag X1, Matieland, 7602

Institute of Wine Biotechnology, Department of Viticulture and Oenology, Stellenbosch University, Private Bag X1, Matieland, 7602

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E-mail [email protected] [email protected] [email protected], [email protected],

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2. PROJECT INFORMATION

Research Organisation Project number

IWBT 2012-1

Project title Molecular and metabolite profiling of grapevines in vineyard settings

Short title

Fruit kind(s) Grape

Start date (mm/yyyy) January 2012 End date (mm/yyyy) December 2014

mailto:[email protected]




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Key words Field-omics; leaf-removal; row-orientation; primary berry metabolites; secondary berry metabolites; carotenoids, xanthophylls.

Approved by Research Organisation Programme leader (tick box) 3. EXECUTIVE SUMMARY

1. Rationale

This study applied established molecular and analytical techniques to grapevine within

characterised (model) vineyards in Elgin (cv Sauvignon Blanc, Milestone 1) and Robertson

(cv Shiraz, Milestone 2) in order to understand grapevines adaptation to its environment, and

how these changes affect the grape composition.

2. Methods

Samples have been collected for three vintages. The treatments in the respective vineyards

both affected the microclimate in the bunch zone. For Elgin (Sauvignon Blanc) post-flowering

leaf removal in the bunch zone was performed and samples collected at five developmental

stages. For Robertson (Shiraz) the study utilised an existing study on the effect of row

orientation and samples were obtained from the North-South row orientation at three

developmental stages. The microclimate (temperature and light) was characterised in the

bunch zone and samples were analysed for sugars and organic acids; carotenoids and

chlorophylls; and volatiles. Multivariate data analysis was used to identify metabolites

displaying differential responses to the altered microclimate. Samples were prepared for

comprehensive transcriptomic analysis from the Elgin vineyard.

3. Key results

In Elgin, the leaf removal increased the light exposure of the berries. Ripening-related

metabolites (sugars and organic acids) remained unchanged, but significant changes were

observed in the xanthophylls (specifically zeaxanthin, antheraxanthin and lutein epoxide) in

response to the perceived light stress, as well as a concomitant change in volatile aromatic

profiles of the grapes.

In Robertson, the row orientation differentially affected the berries in the morning versus the

afternoon, and depending on their respective orientation in the vineyard (East-facing versus

the West-facing canopy facet). Analysis was also performed on skins and pulp separately

during the past season.

4. Main conclusion

The respective treatments have affected the bunch microclimate and this has resulted in a

physiological response by the plants. Grapevine responds to the increased exposure (light

and/or temperature) by increasing the concentration of predominantly photoprotective

pigments, which ultimately contributed to changes in the volatile aroma compounds in the

grapes. This study made a clear causal link between light exposure, the physiological

mechanisms used by the plant to acclimatise to the changed microclimate and the metabolic

outcomes on the berry. Moreover, this project confirmed that the field-omics workflows tested

here were instrumental in identifying developmental and treatment-specific responses.

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4. PROBLEM IDENTIFICATION AND OBJECTIVES State the problem being addressed and the ultimate aim of the project.

Problem: One of the major problems facing viticultural (field) studies is that it is difficult to explain the link between an action/impact (biotic and/or abiotic) in the vineyard and the associated impact on the grapes and wine. The objectives of the study are to perform molecular and metabolite profiling of grapevines in vineyard settings to understand how environmental factors impact vine biology and influence quality traits. The proposed work is planned in collaboration with and in support of existing viticultural studies and will focus on the effect of light and temperature on grape development/composition. 5. WORKPLAN (MATERIALS AND METHODS)

Profiling studies have been conducted within existing highly characterised vineyards in collaboration with viticultural teams (see Table 1) and provided baseline data that can be linked to grapevine physiology, berry and wine quality assessments as part of an approach to study wine as an integrated system from the vineyard, through the cellar, to the glass. Table 1. Details of vineyards and viticultural objectives

Characterised vineyard Parameters studied

Shiraz in Robertson (Prof K Hunter) Row orientation and quality parameters; effect of light and temperature on quality impact factors and grape-derived aroma

Sauvignon Blanc in Elgin (initially Prof AD Deloire)

Light and temperature on grape-derived wine aroma

Within the overarching aim of generating molecular and metabolite profiles of grapes in vineyard

settings, the project had two milestones and three associated objectives per milestone as

outlined in the work plan. For both milestones (i.e. vineyards) a plot layout was adopted that

would account for intra-vine and inter-vineyard variability. Grape berry samples were collected

at defined phenological stages to cover the berry growth and ripening period.

Aim: Molecular and metabolite profiling of grapes in vineyard settings to obtain baseline information of berries during development and ripening: Understanding the effect of light and temperature on grape and wine quality.

Milestone 1: Investigating the effect of light and temperature on grape quality of a Sauvignon Blanc vineyard in the Elgin area Characterisation of the model vineyard to optimise and implement suitable sampling strategies Vitis vinifera L. cv. Sauvignon Blanc vines (clone 316 grafted on 101-14 Mgt) were established in 2004. The vines were planted in a northwest (NW)-southeast (SE) row direction with a 2.5 m between-row and 1.8 m in-row spacing. The vines were trellised to a double cordon with a vertical shoot positioning system and pruned in winter to eight two-bud spurs per running meter of cordon. The vineyard has a deep shale with high moisture content, so although irrigation was available, the vineyard was managed under dryland conditions as no water constraints, as determined by stem water potential, were typically experienced by the vines during the growing season. The treatment involved the total leaf and lateral shoot removal in the bunch/fruiting zone (corresponding to removal up to approximately 30-40 cm above the cordon), on the East-facing side of the canopy (i.e. the facet of the vine that received morning sunlight exposure in

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the Southern hemisphere) at Eichhorn-Lorenz system (EL)-stage 29. In the control panels no leaf removal was applied. The treatment was maintained throughout the season, keeping the fruiting zone exposed through lateral shoot removal. The canopy of the control vines were not manipulated and resulted in relatively shaded fruiting zones with reduced irradiation. The leaf removal treatments were alternated down two adjacent vineyard rows (exposed-control alternated) creating a “checkerboard” plot layout with each biological repeat (referred to as a panel) consisting of four consecutive vines (i.e. each row consisted of six panels; each panel consisted of four consecutive vines) – see Figure 1 for the field-omics considerations and workflows adopted for the experiment.

Figure 1. Plot layout of the Sauvignon Blanc in Elgin showing the field-omics workflow. Temperature was monitored at two levels: (1) mesoclimatic (i.e. above the canopy; continuously) and (2) microclimatic (i.e. within the canopy and within the bunch zone) using TinyTag® data loggers (Gemini, Chichester, UK) from pea-size stage (EL31) up until commercial harvest (EL38). Canopy temperatures were monitored with dual channel (temperature and relative humidity) data loggers, whereas bunch temperatures were monitored using thermistor flying lead probes, connected to a dual channel external temperature data logger. The thermistor probes were positioned on the surface of the fruit within representative bunches (for the respective treatments) and within the canopy. Photosynthetic active radiation (PAR) was measured between 09h30 and 10h30 (before and after berry sampling) with an Accupar ceptometer (model LP-80: Decagon Devices Inc., Pullman, WA, USA). PAR was measured by positioning the ceptometer parallel to the ground within the bunch zone. Ambient PAR (i.e. full sunlight) was measured before and after each canopy measurement. Relative PAR values were expressed as a ratio relative to the ambient light measurement on the sampling day (i.e. as a percentage relative to the full sunlight at the time of sampling). Task 2: Sampling and characterisation of samples to confirm a treatment effect. Berry samples were collected (n=48 berries per sample (i.e. per panel); with twelve panels per sampling date (representing six “exposed” and six “control” panels) at five main phenological stages: green stage (pea-sized berries; EL31), pre-véraison (EL33), véraison (EL34), ripening

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(EL35) and ripe berries at harvest (corresponding to the harvest date; EL38) using a supervised sampling method. The sampling is described as supervised due to the fact that samples were not collected randomly. Bunch positioning within the canopy is typically not uniform, and berries were, therefore, only sampled from representative bunches from the bunch facet exposed to the outside (East-facing). All berry samples were collected within an hour (09h00-10h00) on the same day for all five sampling dates. Samples were immediately flash frozen in the field in liquid nitrogen. Seeds were removed and the frozen tissue was homogenised in liquid nitrogen and, if not used immediately, stored at −80°C for further analysis. The experiments were conducted over three consecutive seasons (2010-2011, 2011-2012 and 2012-2013). The weight and diameter for each of the frozen berries sampled per biological repeat (i.e. per panel) was determined before sample processing. The 48 berries per sample were weighed individually using a laboratory balance and the diameters measured with a digital calliper. Task 3: Sample processing, extraction of RNA, cDNA synthesis and transcriptomic analysis with subsequent appropriate data analysis. RNA extraction and preparation of samples for trancriptomic analysis were conducted with methods optimised and tested in our laboratory. The transcriptome analysis was conducted using Nimblegen grape arrays at MoGene (USA) for the 2009-2010 samples and RNAseq for the 2010-2011 samples. Data analysis was performed according to already established workflows and bio-informatic procedures. Milestones 1-Objective 2: Metabolite profiling of green, véraison and ripe berries Task 1: Metabolite profiling of all samples. The major sugars and organic acids present in grape berries were extracted from 100 mg frozen, ground berry tissue from the five developmental stages and analysed by RP-HPLC as described in (Eyéghé-Bickong et al., 2012). Carotenoid and chlorophylls were extracted from 250 mg frozen, ground berry tissue from the five respective developmental stages and analysed by RP-UPLC as described in (Lashbrooke et al., 2010). Flavour and aroma related volatiles (e.g. monoterpenes and norisoprenoids) were extracted from 500 mg of berry tissue using Headspace (HS) Solid Phase Micro Extraction (SPME) and analysed by GC-MS. FTIR-MIR and multivariate data analysis (MVDA) was evaluated to qualitatively and quantitatively profile the major sugars and organic acids in grapevine tissue. The method was evaluated on frozen ground tissue as well as fresh homogenised tissue HPLC-derived data (from the same samples) served as the reference data for calibration as described in Musingarabwi et al (2015). Literature cited for Milestones 1, Objectives 2, Tasks 1: 1. Eyeghe-Bickong, H.A., E.O. Alexandersson, L.M. Gouws, P.R. Young, and M.A. Vivier. 2012.

Optimisation of an HPLC method for the simultaneous quantification of the major sugars and organic acids in grapevine berries. Journal of Chromatography B 885 886:43-49.

2. Lashbrooke, J.G., P.R. Young, A.E. Strever, C. Stander & M.A. Vivier. 2010. The development of a method for the extraction of carotenoids and chlorophylls from grapevine leaves and berries for HPLC profiling. Australian Journal of Grape and Wine Research 16: 349-360.

3. Musingarabwi, D.M., Nieuwoudt, H.H., Young, P.R., Eyéghè-Bickong, H.A., Vivier, M.A., 2015. A rapid qualitative and quantitative evaluation of grape berries at various stages of development using Fourier-transform infrared spectroscopy and multivariate data analysis. Food Chem. doi:10.1016/j.foodchem.2015.05.080

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Milestones 1-Objective 3: 1-2: Integrating the data from the molecular, metabolite and chemical profiles Task 1: Determining differential responses to treatments across all data-sets Task 2: Determining molecular mechanisms and triggers of differential responses Standard statistical analyses were performed using Microsoft Excel. Hierarchical cluster analysis of metabolites was performed using Expander (Sharan et al., 2003). Multivariate data analyses (unsupervised PCA and supervised OPLS discriminant analysis) were performed using SIMCA (version 13.0.3.0 from Umetrics).

Milestone 2: Investigating the effect of light and temperature on grape and wine quality of a Shiraz vineyard in the Robertson area (vineyard managed by Prof K Hunter) Task 1: (completed during preliminary study in 2010-2011) Characterisation of the model vineyard to optimise and implement suitable sampling strategies The Robertson vineyard has been characterised for suitable sampling strategies during the initial project planning phase. Vitis vinifera L. cv. Shiraz vines (clone SH96 grafted on 101-14 Mgt) were established in 2003. The vines were planted in a North (N)-South (S) row direction with a 2.5 m between-row and 1.8 m in-row spacing. The vines were trellised on a 9-wire hedge system with four sets of movable foliage wires with a vertical shoot positioning (VSP) system and spur-pruned in winter. Irrigation was supplied via pressure compensated drip system. A plot layout that would account for intra-vine and inter-vineyard variability was adopted and grape berry samples were collected at defined phenological stages to cover the berry growth and ripening period.

Temperature was monitored at the microclimatic level (within the bunch zone) using TinyTag® data loggers (Gemini, Chichester, UK) from pea-size stage (EL31) up until commercial harvest (~EL38). Bunch temperatures were monitored using thermistor flying lead probes, connected to a dual channel external temperature data logger. The thermistor probes were positioned on the surface of the fruit within representative bunches (one logger with a probe to each side of the canopy). Photosynthetic active radiation (PAR) was measured between 09h30 and 10h30 (AM sampling) and 15h30 and 16h30 (PM sampling) on both sides of the canopy with an Accupar ceptometer (model LP-80: Decagon Devices Inc., Pullman, WA, USA). PAR was measured by positioning the ceptometer parallel to the ground within the bunch zone. Ambient PAR (i.e. full sunlight) was measured before and after each canopy measurement. Relative PAR values were expressed as a ratio relative to the ambient light measurement on the sampling day (i.e. as a percentage relative to the full sunlight at the time of sampling). Task 2: Sampling and characterisation of samples to confirm a treatment effect. Berry samples were collected (n=30 berries per biological sample (i.e. per panel); with five consecutive panels per sampling date at three phenological stages: green stage (pea-sized berries; ~EL31), véraison (~EL34), and ripe berries at harvest (corresponding to the harvest date; ~EL38) using a supervised sampling method. The sampling is described as supervised due to the fact that samples were not collected randomly. Bunch positioning within the canopy is typically not uniform, and berries were therefore only sampled from representative bunches from the outward-facing bunch facet (i.e. East-facing or West-facing). All berry samples were

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collected within an hour (09h00-10h00, AM and 15h30-16h30, PM) from the East- and West-facing facet for all three sampling dates. Samples were immediately flash frozen in the field in liquid nitrogen. Whole berries were homogenised in liquid nitrogen and, if not used immediately, stored at −80°C for further analysis. When required, skin and pulp were separated, and seeds removed before flash-freezing and further processing. Objective 2 of Milestones 1-2: Metabolite profiling of green, véraison and ripe berries Task 1: Metabolite profiling of all samples. The major sugars and organic acids present in grape berries were extracted from 100 mg frozen, ground berry tissue from the five developmental stages and analysed by RP-HPLC as described in (Eyéghé-Bickong et al., 2012). Carotenoid and chlorophylls were extracted from 250 mg frozen, ground berry tissue from the five respective developmental stages and analysed by RP-UPLC as described in (Lashbrooke et al., 2010). Anthocyanins were extracted from 250 mg frozen, ground berry tissue from the five respective developmental stages and analysed by RP-UPLC. Flavour and aroma related volatiles (e.g. monoterpenes and norisoprenoids) were extracted from 500 mg of berry tissue using Headspace (HS) Solid Phase Micro Extraction (SPME) and analysed by GC-MS. FTIR-MIR and multivariate data analysis (MVDA) was evaluated to qualitatively and quantitatively profile the major sugars and organic acids in grapevine tissue. The method was evaluated on frozen ground tissue as well as fresh homogenised tissue. HPLC-derived data (from the same samples) served as the reference data for calibration as described in Musingarabwi et al (2015). Literature cited for Milestones 1-3, Objectives 1, Tasks 2: 4. Eyeghe-Bickong, H.A., E.O. Alexandersson, L.M. Gouws, P.R. Young, and M.A. Vivier. 2012.

Optimisation of an HPLC method for the simultaneous quantification of the major sugars and organic acids in grapevine berries. Journal of Chromatography B 885 886:43-49.

5. Lashbrooke, J.G., P.R. Young, A.E. Strever, C. Stander & M.A. Vivier. 2010. The development of a method for the extraction of carotenoids and chlorophylls from grapevine leaves and berries for HPLC profiling. Australian Journal of Grape and Wine Research 16: 349-360.

6. Musingarabwi, D.M., Nieuwoudt, H.H., Young, P.R., Eyéghè-Bickong, H.A., Vivier, M.A., 2015. A rapid qualitative and quantitative evaluation of grape berries at various stages of development using Fourier-transform infrared spectroscopy and multivariate data analysis. Food Chem. doi:10.1016/j.foodchem.2015.05.080

Objective 3 of Milestones 1-2: Integrating the data from the molecular, metabolite and chemical profiles Task 1: Determining differential responses to treatments across all data-sets. Task 2: Determining molecular mechanisms and triggers of differential responses. Standard statistical analyses were performed using Microsoft Excel. Hierarchical cluster analysis of metabolites was performed using Expander (Sharan et al., 2003). Multivariate data analyses (unsupervised PCA and supervised OPLS discriminant analysis) were performed using SIMCA (version 13.0.3.0 from Umetrics).

6. RESULTS AND DISCUSSION

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The results will be discussed per individual milestone. To limit to essential information, not all year’s data will be reported and/or discussed (when required, relevant results will be highlighted) A number of analytical technologies were developed and optimised in support of this project. Analytical methods were developed to specifically analyse the grape berry matrix from various developmental stages (green through ripe) in both a white (Sauvignon Blanc) and red cultivar (Shiraz). Methods have been developed and optimised for the simultaneous extraction and analysis of sugars and organic acids (RP-HPLC), carotenoids and chlorophylls (RP-UPLC), anthocyanins (RP-HPLC) and volatile flavour- and aroma-related compounds (GC-MS) from limited amount of tissue. In addition IR methods (NIR and MIR) combined with multivariate data analysis were evaluated to rapidly quantitatively and qualitatively analyse grape tissue (frozen and fresh) for sugars and organic acids and anthocyanins. With the exception of the flavour and aroma volatiles and the anthocyanins, all these methods have been published in peer-reviewed journals. MILESTONE 1: INVESTIGATING THE EFFECT OF LIGHT AND TEMPERATURE ON GRAPE AND WINE QUALITY OF A SAUVIGNON BLANC VINEYARD IN THE ELGIN These results were submitted to Plant Physiology (July 2015). It is currently under review. The results are as in the submitted article. TITLE: Grapevine plasticity in response to an altered microclimate: Sauvignon Blanc modulates specific metabolites in response to light AUTHORS: Philip R. Young, Hans A. Eyeghe-Bickong, Erik Alexandersson, Dan A. Jacobson, Zelmari Coetzee, Alain Deloire, Melané A. Vivier SUMMARY OF MOST IMPORTANT FINDINGS: Vineyards are highly variable environments where the vines must respond to changes within and across seasons. Grapevine berry ripening occurs over months and the final berry composition is the expression of the interaction between the specific genotype and the environment (vintage). In this study the effect of an altered microclimate on quality associated primary and secondary metabolites in Vitis vinifera L. cv. Sauvignon Blanc berries was determined. Results show that leaf and lateral shoot removal in the bunch zones altered the microclimate by increasing the light exposure of the berries. The physical parameters (berry diameter and weight), primary metabolites (sugars and organic acids) and leaf water potential were not significantly affected by the treatment. The increased exposure, however, lead to higher levels of specific carotenoids and volatile terpenoids (monoterpenes and norisoprenoids) in exposed berries. Quantitative analytical methods were used to demonstrate that the metabolic responses were specific to developmental stages; with earlier berry stages distinct from the later developmental stages. Plastic and non-plastic metabolite responses could be further classified to identify metabolites that were developmentally controlled or responded to the treatment in a predictable fashion (assessed over two consecutive vintages). The study extends previous work on leaf removal studies by demonstrating that grapevine berries exhibit a degree of plasticity within their secondary metabolites and respond physiologically to the increased light exposure by increasing metabolites with potential antioxidant activity. Different metabolite pools are modulated at specific berry developmental stages: carotenoids predominantly in the early stages and monoterpenes in the later stages. QUANTITATIVE CHARACTERISATION OF THE MICROCLIMATE IN THE BUNCH AND CANOPY ZONES CONFIRMED LIGHT AS THE MAIN ENVIRONMENTAL FACTOR PERTURBED BY THE TREATMENT

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The daily average temperatures in the bunch zones of the respective treatments for the growth period (season) were not significantly different if a daily (24 h) cycle was analysed Figure 2). The temperature range for the entire season was very similar: 13.7⁰C (minimum) to 39.3⁰C

(maximum) with an average of 23.3±6.1⁰C for the bunches in the exposed treatment; and 13.9⁰C (minimum) to 34.5⁰C (maximum) with an average of 22.6±5.3⁰C for the control bunches The average temperatures within the bunch zones were different if the period around sampling was analysed in isolation (i.e. the temperatures two hours before and two hours after the ~10h00 sampling period (i.e. 08h00-12h00)) on the sampling days, but these differences were not statistically significant. As expected, the leaf removal treatment typically resulted in higher temperatures in the bunch zone of exposed bunches (relative to the control bunches) during the

day (<5⁰C higher maximum temperature difference). The average temperature difference (for the 4 hour sampling window) between the exposed bunches and the shaded bunches for the season was 1.9±1.7⁰C (2.7⁰C±1.4⁰C for the five sampling days). The temperature within the bunch zone (microclimate) was therefore only marginally affected by the leaf removal. Interestingly, the temperatures in the canopy above the bunch zone of the exposed treatment were lower than those from the canopy of the control vines at night. Leaf removal is typically used in viticulture to increase the photosynthetic active radiation (PAR) reaching the bunch zone and/or to decrease humidity at the fruit level. The light exposure in the bunch zone was modified by the leaf removal treatment, with average light intensity (PAR) values of 52%±14% (average percentage PAR relative to the ambient, full sunlight (100%) at the date and time of sampling) for all cloudless sampling dates (Figure 3). Conversely, the control bunches intercepted significantly less incoming radiation (PAR) (4%±2%, relative to 100% ambient, full sunlight). Bunches in the exposed panels, therefore, received significantly more (seasonal average of >10-times higher) light than the shaded control bunches and light was therefore regarded as the main treatment effect.




Figure 2: Characterisation of the microclimate: bunch temperature. The daily average (± standard deviation) for each day of the season for the Control bunches (A) and Exposed bunches (C) (sampling days are indicated in red. B, the temperature statistics and ranges for the sampling days. D, hourly bunch temperature and mesoclimatic temperature for the sampling days. E, temperature differential (Exposed – Control bunch temperature) for the sampling days.




Figure 3: Characterisation of the microclimate: light. Photosynthetic active radiation (PAR) in the bunch zone at the time of sampling for the respective sampling days for two consecutive years (vintages): A, 2010-2011, and B, 2011-2012. Only cloudless days are represented. LEAF REMOVAL DID NOT AFFECT THE BERRY PHYSICAL CHARACTERISTICS OR THE RIPENING DYNAMIC OF THE BERRIES Berry weight and diameter were measured for all the berries sampled for metabolite analyses. The relationship between berry weight to diameter showed a positive linear relationship (r2=0.99) across all developmental stages, irrespective of the treatment. There were no significant differences between the control and exposed berries (Figure 4). Major sugars (glucose and fructose) and organic acids (tartaric acid, malic acid and succinic acid) concentrations in berries were measured at five developmental stages. In berries, the changes in major sugars and organic acids are well described with the sugar concentrations accumulating as ripening progresses and the total organic acid concentrations decreasing. Glucose was the most abundant hexose in the earlier stages of development (EL31 and EL33), but from véraison (EL35) until harvest (EL38) glucose and fructose were present in equal amounts. The individual sugars and organic acids were not significantly affected by the leaf removal treatment (Figure 5).

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Figure 4: Berry characterisation: weight and diameter in the five developmental stages (A), and the linear relationship between berry diameter and weight (B).

Figure 5: Berry characterisation: Concentration of the major sugars in grapevine berries. A, glucose; B, fructose; C, total sugars and D, the glucose to fructose ratio in the berries.

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DEVELOPMENTAL AND TREATMENT-SPECIFIC PATTERNS OF METABOLITES WERE EVIDENT Unsupervised PCA plots were used to visualise the metabolite data (Figure 6). Separation were observed for developmental stages (EL31 to EL38; PC1) and treatment (exposed vs control samples; PC2). The increase in glucose and fructose, and inversely the decrease in chlorophylls (chlorophyll a and b) and the majority of the carotenoids (e.g. β-carotene, lutein, neoxanthin) during ripening drove the developmental stage separation (PC2). The compositional differences in specific carotenoids (most notably the xanthophylls zeaxanthin, antheraxanthin and lutein epoxide) and monoterpenes were predominantly responsible for the treatment separation (Figure 6). OPLS discriminant analysis was subsequently used to identify the variables significantly contributing to the models for (1) developmental stage discrimination (Figure 7) and (2), treatment discrimination (Figure 8). OPLS models were generated to visualise the developmental separation respectively. Hierarchical cluster analysis was employed to identify profiles with similar trends between the analysed metabolites (Figure 9). A number of clusters were of particular interest: (1) metabolites showing a predominant developmental trend (Figure 9 clusters 2, 4 and 6); (2) metabolites showing a predominant treatment effect (Figure 9 clusters 1, 3 and 7); and (3) metabolites showing both a developmental and treatment effect (Figure 9 cluster 1, 2, 3, 5 and 6). The response of the measured metabolites typically varied between the different developmental stages, with the early stages (EL31 and EL33) and the later stages (EL35 and EL38) generally responding similarly; with véraison as a transition stage (between the early/green and late/ripe stages). Metabolites showing the developmental trend (clusters 1 and 2) could be further sub-grouped into metabolites that increased with development progression (Figure 9 cluster 6), and metabolites that decreased with development progression (Figure 9 clusters 2 and 4). The major sugars (glucose and fructose), 6-methyl-6-heptan-2-one (MHO) and three monoterpenes (geraniol, linalool and nerol) increased with developmental stage (similar to berry weight and diameter in the same cluster). It is important to note that hierarchical cluster analysis relies on Pearson correlation coefficients to match trends, and does not discriminate similar trends that differ in amplitude. This is evident in the line graphs of geraniol, linalool and nerol (Figure 10 (B)), where both the control and exposed display upward developmental trends, but the absolute values of the respective metabolites in the exposed berries were significantly higher (than the control). Chlorophyll a and chlorophyll b and the major carotenoids (e.g. lutein and ß-carotene representing ~ 80% of the total carotenoids), however, decreased concomitantly throughout development. The major organic acids (i.e. malic acid, succinic acid and tartaric acid), as well as the xanthophyll neoxanthin and the norisoprenoid (apocarotenoid) ß-ionone, displayed a similar (downward) trend (Figure 9 cluster 2 and 4). A cluster of three carotenoid-derived apocarotenoids (i.e. norisoprenoids) (pseudo-ionone, β-damascenone and geranylacetone), shared the same trends, characterised by an early stage (EL31 and EL33) developmental trend followed by a treatment-related trend (from EL34/véraison) with higher levels in the samples from exposed versus the control bunches and positively correlated to the bunch temperature (Figure 9 cluster 5). The monoterpenes α-terpineol and trans-linalool-oxide displayed a biphasic treatment effect, with higher levels in both the exposed berries (versus the control berries) in the early (EL31) and late (EL35 and/or EL38) stages, with insignificant differences in the mid-ripening stages (EL34 and/or EL35) (Figure 9 cluster 8). The xanthophylls antheraxanthin and zeaxanthin showed a clear treatment effect with higher levels in the exposed berries (versus the control) in all developmental stages (EL31-EL38). The treatment effect was significantly greater in the early stages (EL31 and EL33) versus the later stages (EL34, EL35 and EL38) (Figure 9 cluster 7).




Figure 6: Unsupervised PCA of all variables from the study for all the developmental stages. A, the scores plot for the respective samples. Samples are coloured by developmental stage and all Control samples are further indicated by a (), Exposed samples by a (). B, the loadings plot for the measured variables.




Figure 7: Supervised (developmental stage) OPLS of all metabolites from all stages. A, the scores plot for the respective samples. Samples are coloured by developmental stage and all Control samples are further indicated by a (), Exposed samples by a (). B, the loadings plot for the measured variables in green, discriminant classes/categories in blue. .

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Figure 8: Supervised (treatment) OPLS of all metabolites from all stages.

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Figure 9: Hierarchical cluster analysis of all variables from all stages with line graphs of representative clusters.




Figure 10: Bar graphs of selected individual carotenoids (A) and monoterpenes (B) as well as a heatmap (log2-fold change) representation of all analysed metabolites (C).




Sugars and organic acids are predominantly developmentally regulated It is interesting to note that the glucose and fructose concentrations in the berries were present in equal proportions (glucose:fructose ratio ~1) from véraison (EL35) and onwards (Figure 4D) In the earlier stages, however, glucose is the dominant hexose. In the EL31 stage no fructose could be detected. A glucose:fructose ratio of ~1 illustrates that glucose and fructose in the berries are derived from hydrolysis of sucrose (as is expected in a sink organ). Although the absolute concentrations of the individual organic acids were not significantly affected by the leaf removal treatment; interesting trends could, however, be seen in the ratio of tartaric acid to malic acid (Figure 11). This ratio, referred to as the ß-ratio (proposed by (Shiraishi, 1995)) has previously been used to evaluate the organic acids from Vitis germplasm collections. Up until véraison the ß-ratio remained relatively constant (~1) for the exposed and control berries, but from EL35, the ratio increased in both the exposed and control berries. At harvest (EL38), the exposed berries had a ß-ratio of 4, double that of the control berries (with a ß-ratio of 2). This phenomenon is due to a combination of a slight, increase in tartaric acid concentrations, and a concomitant decrease in malic acid concentrations (relative to the control berries). Across all stages the percentage of tartaric acid and malic acid (relative to total organic acids), however, remained relatively constant (~85-90% of total acids) for both the exposed and control berries. Succinic acid levels were similar in the exposed and control berries and fluctuated from 5-15% of total organic acids (Figure 11).

Figure 11: Berry characterisation - Organic acids. The tartrate to malate ratio in developing berries (A) and the relative abundance of the individual organic acids in berries (B).




Figure 12: Pathway analysis of the carotenoid metabolism (Heatmap (log2-fold change) and bar graphs of individual metabolites and ratios) .




Figure 13: The chlorophyll a to chlorophyll b ratio in developing berries Major carotenoids and chlorophylls were predominantly developmentally regulated, but the xanthophylls responded to the treatment Pathway analysis was used to analyse the metabolism of the carotenoids (Figure 12). For carotenoid metabolism (biosynthesis and catabolism), the pathway described in (Young et al., 2012) was used to provide an overview of the relative changes and flux of the related metabolites over time. The regulated catabolism of chlorophylls and the concomitant decrease in total carotenoid concentration is well described for grape berry development (Razungles et al., 1996; Young et al., 2012). The ratio of chlorophyll a to chlorophyll b increased from 2.5 (EL31) to 3.5 (EL38) with no significant differences between the ratio in exposed berries versus that of control berries (Figure 13). Up until véraison, grapevine berries are photosynthetically active, albeit at much lower levels (1-10%) of photosynthetically active leaves (Goodwin, 1980). The decrease in the more abundant carotenoids (i.e. lutein and ß-carotene representing ~80% of the total carotenes in a grape berry) followed the trends of chlorophyll a and b in both the control and exposed berries, and was generally associated with the developmental stages of berries with the earlier stages typically having higher concentrations than the later stages (Figure 6, Figure 9 cluster 2, and Figure 15B). The levels of lutein closely followed the trend of chlorophyll b; whereas ß-carotene followed chlorophyll a degradation (Figure 14). The response of specific carotenoids, the xanthophylls (i.e. lutein, lutein epoxide, zeaxanthin, antheraxanthin and violaxanthin) to light is well described in a host of different photosynthetic organisms (reviewed in (Cunningham and Gantt, 1998; Jahns and Holzwarth, 2012). Of particular importance in this study were the two xanthophyll cycles: (1) the lutein:lutein epoxide (L:LE) cycle and, (2) the zeaxanthin:violaxanthin (Z:V) cycle. These two cycles are functional in plants in response to shade and high light, respectively. The L:LE cycle is considered taxonomically restricted (e.g. found in most woody plant and not formed in Arabidopsis) and it has been proposed that it is involved in the maintenance of photosynthetic performance under limiting light as well as a photo-protective function especially in response to sudden changes in irradiance (Esteban et al., 2009). Lutein epoxide typically accumulates in older leaves that are

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predominantly in the shade, but has been reported in grape berries (Razungles et al., 1996; Young et al., 2012). The levels of lutein epoxide were significantly lower in the berries from exposed vines (relative to the berries from control vines) in the first two stages of development (i.e. EL31 and EL33) (Figure 12B). Lutein epoxide displayed the largest coefficient of variation (135% for exposed versus control) of all the metabolites analysed (data not shown). The ratio of Lx:L was 10% that of the ratio of berries from control vines in EL31 (Figure 12B). The Lx:L ratio stayed relatively low and constant in the exposed berries, but rapidly decreased in the berries from control vines from the initial high at EL31. From stage EL35 onwards, the Lx:L ratio is low (<0.01) and not significantly different in the berries from exposed berries (relative to the control berries). Lutein epoxide, and to a lesser extent violaxanthin; decreased in the berries from exposed vines (Figure 12B), and conversely zeaxanthin and antheraxanthin increased in the berries from exposed vines relative to the control. It is also interesting to note that the ratio of β -carotene:lutein (as an indicator of flux to the β- and α- branches of the carotenoid metabolic pathway) was lower in the exposed berries (relative to the control berries). This was due to lower levels of lutein in the control berries (resulting in a higher β -carotene:lutein ratio). The lutein in the control berries was presumably converted to lutein epoxide in the shaded conditions. Conversely, comparatively low levels of lutein epoxide were found in exposed berries (Figure 12B). Although lower levels of lutein were present in the control berries, it still followed a similar developmental pattern as β-carotene and chlorophyll a and b (Figure 9 cluster 2), but the linear relationship between lutein and chlorophyll b was lower in the control berries than in the exposed berries (Figure 14). As mentioned, in photosynthetic tissues a linear relationship is found for major carotenes (β-carotene and lutein) and chlorophylls (chlorophyll a and chlorophyll b). The ability to modulate the levels of specific carotenoids by a viticultural treatment is of particular interest since the carotenoids have been shown to be precursors for the flavour and aroma compounds, the norisoprenoids (apocarotenoids). It has also been shown that carotenoid cleavage dioxygenases catalyse the cleavage of specific C40-carotenoid substrates to specific C13-apocarotenoid cleavage products (Mathieu et al., 2005; Mathieu et al., 2006; Lashbrooke et al., 2013)

Figure 14: Relationship between chlorophyll and the major carotenes. A, chlorophyll a to b-carotene ratio and, B, chlorophyll b to lutein ratio .




Figure 15: Heatmap (log2-fold change) (A) and bar graphs of summed metabolite pools and selected ratios (per stage) (B).




Figure 16: Changes in the norisoprenoid pool and the V+A+Z pool throughout berry development. Volatile terpenoids are increased in response to leaf removal in the later stages of berry development The volatile terpenoids measured in this study can be grouped into two major classes: the C10-monoterpenes, and the C13-norisoprenoids (or apocarotenoids). The monoterpene content of berries was dominated by the two most abundant monoterpenes: linalool and α-terpineol. The total monoterpene content was affected by the decline in the more abundant linalool in the first three stages (EL31, EL33 and EL34), and then a shift to the increase in α-terpineol in the later developmental stages (EL35 and EL38) (Figure 10B and Figure 15A). A number of monoterpenes were significantly higher in specific stages in the exposed versus the control berries: trans-linalool oxide (>2-fold in EL31), linalool (>2-fold in EL34 and >4-fold in EL35), nerol (>2-fold in EL35), but the majority of monoterpenes were typically higher in the exposed berries (versus the control) at harvest (EL38): t-terpinene, trans-linalool oxide, nerol and α-terpineol (>2-fold) and linalool (>4-fold) (Figure 10B and C). The total volatile norisoprenoids (i.e. ß-ionone, ß-ionone, pseudo-ionone, geranylacetone, MHO, β-damascenone) in berries increased up until EL35 (exposed berries) and EL38 (control berries). MHO and geranylacetone are the two most abundant norisoprenoids, contributing 45-60% and 40-55%, respectively to the total norisoprenoid pool in berries. The treatment resulted in higher norisoprenoid content in the exposed berries (relative to the control berries) at the harvest stage (EL38) (Figure 10C, 15A and Figure 16). CONCLUSIONS AND FUTURE PROSPECTIVES The combined use of targeted metabolite analysis, microclimate characterisation and multivariate data analyses has enabled the (1) identification of metabolites that characterise developmental stage transitions in maturing/ripening Sauvignon Blanc berries, and (2) berry

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metabolites that differentially respond to an altered microclimate. Data analysis of the metabolites at the five developmental stages showed that the total carotenoid pool of the exposed berries increased, and the pool of volatile norisoprenoids/apocarotenoids concomitantly increased in the later ripe/harvest stage (relative to the berries from control canopies) (Figure 16). Volatile monoterpenes were, to varying degrees, also affected by the

altered microclimate with linaool, nerol and -terpineol being the most responsive. The specific changes in the grape metabolite composition in response to the viticultural manipulation (leaf and lateral removal) provided evidence of fruit phenotypic plasticity in Sauvignon Blanc. A single clone of Sauvignon Blanc differentially modulated specific metabolites (or pools of metabolites) in response to the altered microclimate at the bunch zone (predominantly increased exposure to light). The terpenoid compounds formed are known to play a role in photoprotection and various biotic and abiotic stress responses. The increased carotenoid pool in the exposed berries potentially serves as substrates for carotenoid cleavage (via dioxygenases, VvCCDs) to produce volatile norisoprenoid products that are regarded as impact odorants in grapes, wine and port. The field-omics approach employed in this study showed that the early leaf removal in the bunch zone caused quantifiable and stable responses (over years) in the microclimate where the main perturbation was increased exposure to light and to a lesser extent temperature due to the location of the site exposed to sea breeze during the day. We showed the physiological impacts on berries in the different developmental stages by studying affected metabolites, providing for the first time an explanation for how leaf removal leads to the shifts in grape metabolites typically linked to this treatment (over years). We confirm anecdotal evidence and previous reports that leaf removal treatment at an early stage of berry development affects “quality-associated” metabolites (monoterpenes and norisoprenoids). No significant differences were, however, observed for the absolute sugars and organic acids (as reported by other authors). We show that the main physiological response occurs in the early stages of berry development when the berry is still photosynthetically active and therefore responds to changes to the microclimate in the same way as the major photosynthetically active organs (leaves). It also shows that berries in more shaded conditions activate a different protective system involving the conversion of lutein to lutein epoxide. The compositional changes in the carotenoids in the early stages are carried through to the later stages of berry development (e.g. increased norsiporenoids). The observation of phenotypic plasticity (metabolic/compositional plasticity) in Sauvignon Blanc grape berries, however, does not explain how plasticity is primarily regulated. Further study on the transcriptome of the berries will provide insights into the transcriptional regulatory networks controlling the observed phenotypic (metabolic) plasticity. It would be interesting to compare the degree of plasticity observed in the transcriptome with that of the metabolome. The conservation of the identified plastic responses (both occurrence and amplitude) between different grapevine cultivars is also of potential interest. Although the grape composition at harvest, together with the fermentation and ageing process, all contribute to the observed “cultivar x site” expression; phenotypic plasticity potentially contributes to the rather subjective and esoteric wine concepts of “typicity” (the varietal taste and flavour of a wine attributed to a specific cultivar) or “typicality” (the interaction of the cultivar and the site/“terroir”) and “terroir” (the unique taste and flavour of a wine attributed to where it is specifically produced) (Deloire et al., 2008). It is evident that different cultivars (genotypes) modulate their transcriptomes and/or metabolomes uniquely which could result in the “typicity” of cultivars; and similarly that the same cultivar (genotype) in different environments (i.e. different regions, vineyards or even microclimates) will modulate its transcriptome and/or metabolome differently leading to compositional changes in the grapes and subsequent wines resulting in “terroir expression”.

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MILESTONE 2: INVESTIGATING THE EFFECT OF LIGHT AND TEMPERATURE ON GRAPE AND WINE QUALITY OF A SHIRAZ VINEYARD IN THE ROBERTSON AREA Unlike the Elgin Sauvignon Blanc model, the Shiraz vineyard in Robertson did not undergo a viticultural treatment per se (e.g. leaf removal). The North-South row orientation provided the necessary “perturbation” that was investigated since it created two distinct microclimates: East- and West-facing. Samples were taken from both sides of the canopy (separately) at two times per sampling day: AM (in the morning between 09h30 and 10h30 when the East-facing facet was exposed to the sun, and conversely the West-facing facet was shaded) and PM (in the afternoon between 15h30 and 16h30 when the West-facing facet was exposed to the sun and the East-facing facet was shaded). Microclimatic data (light and temperature) and berry compositional (metabolite) data was used to determine how grape berries respond to their immediate environment (using the time of day and the natural canopy exposure on the East- and West-facing facets as proxy for an experimental perturbation). SUMMARY OF MOST IMPORTANT FINDINGS: Similar to the results obtained in Sauvignon Blanc (Elgin), Shiraz responds to increased light and temperature exposure in the bunch zone by upregulating photoprotective carotenoids in the early developmental stages and anthocyanins in the later developmental stages. The most important observation from this study is the specific secondary metabolites that react to the environmental fluctuations and the fact that our data show that grapevine secondary metabolites are not static in the short-term, but highly responsive to their immediate environment and are in flux, even in a day/night cycle. Exposure (light and temperature) are differentially affected by the row orientation and time of sampling The seasonal temperatures between the East and West-facing canopies did not differ significantly (i.e. with regard to the daily minima, -average and -maxima). Differences could, however, be seen in the sampling windows (AM, 09h00-10h00 and PM, 15h30-16h30). These temperature differences are, however, not excessive and the East-facing canopy (AM) is typically as hot as the West-facing canopy (PM), and conversely the East-facing canopy (PM) is typically as cool as the West-facing canopy (AM). As expected the canopy facet facing the sun has higher temperatures than the more shaded facet, with the West-facing canopy typically having slightly higher average temperatures than the East-facing canopy. Similarly, light was significantly higher in the canopy facet facing the sun (East > West in the AM and West > East in the PM). Although intuitive and self-evident, the purpose of these measurements was to provide a quantitative value for both light and temperature per biological sample (panel) that would be required for correlation to the metabolite compositional data. Vineyards are infamously heterogenous and the field-omics approach is to quantify as many variables as possible and not assume all biological samples are equivalent.

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0

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20

25

30

35

40

Daily Average Daily Maximum Daily Minimum

Tem

pe

ratu

re in

de

gre

es

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Temperature statistics

WEST

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Figure 17. Bunch temperature characteristics: Daily maxima, average and minima for the East- and West-facing canopies.

.

Figure 18. Representative spider plots for the average daily bunch temperature (East- in blue, West-facing canopy in red) for a 24-hour cycle.

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Figure 19. Representative line graphs of the temperature ratio (East/West) in five separate 24-hour cycles. The two sampling windows are shaded (AM sampling window in green; and PM in red).

Figure 20. Percentage PAR in the bunch zone relative to ambient full sunlight in the East- and West-facing canopies in the morning (AM) and afternoon (PM) sampling times.

Primary metabolites are not significantly affected Major sugars and organic acids were not significantly affected by the sampling time (AM or PM) or the canopy orientation (East- or West-facing) and were predominantly developmentally regulated. Trends could be seen, (e.g. East-facing canopy having higher organic acids in the PM), but due to the inherent biological variation, these differences are interesting trends, but currently statistically insignificant.

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Figure 21. Organic acid concentrations in the early (green) developmental stage of bunches in the East- and West facing canopies (PM samples).

Developmental early stage specific responses in photoprotective carotenoids The carotenoid composition of the East-facing berries was significantly different from the West-facing samples in the early developmental stages (green stages). Berries from the West-facing canopy typically had higher concentrations of xanthophylls (zeaxanthin, antheraxanthin and lutein), indicative of a photosynthetic stress situation. The most notable difference was in the afternoon (PM) samples of the early green stage berries:

Figure 22. Xanthophyll concentrations in the early (green) developmental stage of bunches in the East- and West facing canopies (PM samples). Only pigments that showed statistically significant differences (p≤0.05) are depicted. Developmental late-stage specific responses in anthocyanins From véraison onwards, anthocyanins were formed and significantly affected, most notably in the East versus West berries (PM). The berries from the East-facing canopy had significantly higher anthocyanins.

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Figure 23. Anthocyanin concentrations in the late (ripe/harvest) developmental stage of bunches in the East- and West facing canopies (PM samples). Grapevine secondary metabolites are not static, but highly responsive to their immediate environment and are in flux, even in a day/night cycle Grapevine metabolites are typically represented within a developmental progression culminating in the final berry composition at harvest. This appears to be true for the primary metabolites (major sugars and organic acids), since they were not significantly affected by the sampling time (AM versus PM) or the canopy side (East versus West-facing canopy) in the samples from various developmental stages. In contrast, secondary metabolites appear to be in constant flux and highly responsive to their immediate environment (microclimate). These responses appear to be stage-specific with the carotenoids responding in the early stages and the anthocyanins in the later stages. The carotenoids were significantly affected by the both the time of sampling and the canopy orientation. The photosynthetic carotenoids (predominantly b-carotene and lutein) were relatively stable, but the photo-responsive xanthophylls were significantly differentially affected. Zeaxanthin was statistically the most differentially affected: the berries in the West-facing canopy had 500% more zeaxanthin than the berries in the East-facing canopy. Similarly berries from the East-facing morning (AM) samples had 4-fold more zeaxanthin than the East-facing afternoon (AM) berries. Zeaxanthin is central to the photo-protective xanthophyll cycle. When light is saturating for photosynthesis, the excess energy can cause photodamage. Zeaxanthin is formed to dissipate this energy as heat. Unlike the Elgin Sauvignon Blanc berries, lutein epoxide was not significantly upregulated in berries from the more shaded canopies (i.e. East-facing canopies in the PM or West-facing in the AM). The IR scans of the berry samples provided exceptional discriminatory power for the separate sampling dates as well as the very clear separation of the AM and PM veraison and ripe berry samples (not East and West). The chemical/molecular basis of this discrimination is not in our analytical data and deserves further investigation to identify the compounds responsible.

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Figure 24. Unsupervised PCA of the samples from Robertson using metabolite data and MIR data.

7. COMPLETE THE FOLLOWING TABLE

Aim: Molecular and metabolite profiling of grapes in vineyard settings to obtain baseline information of berries during development and ripening: Understanding the effect of light and temperature on

grape and wine quality.

Milestone 1: Investigating the effect of light and temperature on grape and wine quality of a Sauvignon Blanc vineyard in the Elgin: COMPLETED

Achievement

Objective 1: Transcriptome analysis of green, véraison and ripe berries (pulp and skin, and pulp/skin separately)

Task 1: Characterisation of the model vineyard to optimise and implement suitable sampling strategies. Task 2: Sampling and characterisation of samples to confirm a treatment effect. Task 3: Due to costs involved, completed only for 2009-2010 (Nimblegen) and 2010-2011 (RNASeq) samples. Samples and protocols available for additional analyses (if required) Sample processing, extraction of RNA, cDNA synthesis and transcriptomic analysis (via Nimblegen platform or RNA sequencing) and subsequent data analysis.

Objective 2: Metabolite profiling of green, pre- véraison, véraison, post- véraison and ripe berries

Task 1: Metabolite profiling of all samples. This included the major sugars and organic acids, and carotenoids and chlorophylls; as well as GC-MS for volatile compounds of samples to generate metabolic profiles. NIR/MIR spectroscopy methods for rapid screening of samples was also optimised and validated for sugars and organic acids and anthocyanins

Objective 3: Integrating the data from the molecular, metabolite and chemical profiles

Task 1: Determining differential responses to treatments across all data-sets Multivariate data analysis was used to analyse the data

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obtained. Task 2: Determining molecular mechanisms and triggers of differential responses.

Milestone 2: Investigating the effect of light and temperature on grape and wine quality of a Shiraz vineyard in the Robertson area: COMPLETED

Objective 1: Characterisation of the model vineyard

Task 1: Characterisation of the model vineyard to optimise and implement suitable sampling strategies. Task 2: Sampling and characterisation of the microclimate.

Objective 2: Metabolite profiling of green, véraison and ripe berries.

Task 1: Metabolite profiling of berry samples. This includes the sugars and organic acids and carotenoids and chlorophylls, as well as HPLC of anthocyanins. NIR/MIR spectroscopy methods for rapid screening of samples was also optimised and validated for sugars and organic acids and anthocyanins.

Objective 3: Integrating the data from the molecular, metabolite and chemical profiles

Task 1: Determining differential responses to treatments across all data-sets Task 2: Determining molecular mechanisms and triggers of differential responses.

8. CONCLUSIONS The data obtained for both these vineyards convincingly shows that the methods were successful to characterise the plant’s response to the treatments, providing insight into which parts of the metabolism was impacted throughout the season and between the treatments. The ability to accurately characterise the response of the plant provides a perspective to interpret the effect of the treatment. 9. ACCUMULATED OUTPUTS

The project spanned three years/vintages (2010-2011, 2011-2012 and 2013-2014) in two vineyards (Elgin, Sauvignon Blanc and Robertson, Shiraz). The accumulated outputs will be discussed per vineyard setting, with the relevant year indicated.

For Elgin:

Characterisation of the microclimate: o Temperature and light were measured in the bunch zones; o Leaf removal in Elgin results in increased light exposure, with insignificant

differences in bunch (microclimate) temperatures.

Characterisation of the berries: o Methods have been optimised for the quantitative analysis of sugars and organic

acids (via RP-HPLC), carotenoids and chlorophylls (RP-UPLC) and volatiles (HS-SPME-GC-MS).

o In addition, MIR and multivariate data analysis (MVDA) has been evaluated to qualitatively and quantitatively profile the major sugars and organic acids in grapevine tissue. The method is suitable for frozen ground tissue as well as fresh homogenised tissue and the data generated compares well with HPLC-derived data (from the same samples). This provides a rapid, reliable and relatively inexpensive method to profile these metabolites in grape berries.

MVDA has been used to analyse the metabolite analytical data (collectively). Results presented in the preceding section highlight how MVDA can be used to identify the metabolites that are significantly altered during (1) development (ripening of the berries), and (2) as a result of the viticultural treatment (in Elgin leaf removal in the bunch zone).

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MVDA of the berry metabolite profiles shows that the data can be clustered into five distinct groupings corresponding to their respective developmental stages. The metabolites contributing most to this developmental stage discrimination were the organic acids (succinic-, citric– and malic acid) and the monoterpenes (citronellol, limonene, and eucalyptol).

MVDA of the berry metabolite profiles shows that the data can also be clustered into two distinct groupings corresponding to the viticultural treatment (Exposed versus Control). The metabolites contributing most to this discrimination were the xanthophylls (zeaxanthin and antheraxanthin), the norisoprenoids (geranylacetone and MHO) and the monoterpenes (nerol, linalool and α-terpineol). This is an interesting and predictable (in a plant physiological/photosynthesis perspective) result: the photo-protective xanthophylls (specifically zeaxanthin and antheraxanthin) are involved in the high light response of plants in the xanthophyll cycle. Under low light conditions violaxanthin is formed; whereas under high light conditions the more photoprotective xanthophylls zeaxanthin is formed (via antheraxanthin as an intermediate). It is also important to note that the ripening-related sugars and organic acids are not significantly affected by the treatment. The altered light in the bunch zone changed specific berry secondary metabolites without altering the ripening/development of the berries (primary metabolites or physical parameters).

For Robertson:

Samples have all been processed (pulverised in liquid N2) and analysed for sugars and organic acids (HPLC) and carotenoids and chlorophylls (UPLC).

MVDA has been used to analyse the metabolite analytical data (collectively). Results presented in the preceding section highlight how the method can be used to identify the metabolites that are significantly altered during (1) development (ripening of the berries), and (2) as a result of the viticultural treatment (in Robertson row orientation and consequent bunch exposure to morning or afternoon sunlight).

MVDA of the berry metabolite profiles shows that the data can be clustered into three distinct groups corresponding to their respective developmental stages. The metabolites contributing most to this discrimination were the organic acids (tartaric-, citric- succinic-, and malic acid) and the sugars (fructose and glucose), and the anthocyanins.

MVDA of the berry metabolite profiles shows that the data can also be clustered into distinct groups corresponding to their position in the canopy (EAST vs WEST) or the time of sampling (AM vs PM). The metabolites contributing most to the discrimination were the xanthophylls (zeaxanthin, neoxanthin and antheraxanthin). As discussed in the preceding section, this is an interesting (and predictable) result in terms of a plants response to altered light exposure.

a) TECHNOLOGY DEVELOPED, PRODUCTS AND PATENTS

Development of a profiling method for carotenoids and chlorophylls from grapevine tissue: Migration of established grapevine HPLC method to a faster more efficient UPLC method.

An HPLC profiling method has been optimised for the simultaneous separation and quantification of sugars and organic acids from berries at different stages of development (method has been published).

An FTIT-MIR method for the rapid qualitative and quantitative analysis of the major sugars and organic acids in grape berries (from fresh and frozen tissue) has been established (method has been published).

A GC-MS profiling method has been developed for the separation and quantification of volatile compounds from grapevine berries at different stages of development (method will be submitted for publication).

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Optimised workflows for the RNA-sequencing of berry samples were obtained; samples were prepared and transcriptomic analysis was performed at Verona University through collaboration with Prof Mario Pezzotti.

b) SUGGESTIONS FOR TECHNOLOGY TRANSFER

Some of the results have already been presented at national industry conferences and technical committees of VinPro. Popular papers will also be prepared and submitted to Winelands.

c) HUMAN RESOURCES DEVELOPMENT/TRAINING

Student Name and Surname

Student Nationality Degree (e.g. MSc Agric, MComm)

Level of studies in

final year of project

Graduation date

Total cost to industry

throughout the project

Honours students

Caitlin McCartney South Africa Hons Hons 2014

Masters Students

Davirai Musingarawbi Zimbabwe MSc MSc 2015

PhD students

Kari du Plessis South Africa PhD MSc 2016 (for the PhD)

Postdocs

Hans Eyeghe-Bickong

Gabon PhD PhD 2012

Jay Belli Kullan India PhD PhD 2013

Support Personnel

Ms Varsha Premsagar (technician)

South Africa MSc MSc N/A

d) PUBLICATIONS (POPULAR, PRESS RELEASES, SEMI-SCIENTIFIC, SCIENTIFIC)

Eyeghe-Bickong, H.A., E.O. Alexandersson, L.M. Gouws, P.R. Young, and M.A. Vivier. 2012. Optimisation of an HPLC method for the simultaneous quantification of the major sugars and organic acids in grapevine berries. Journal of Chromatography B 885 886:43-49.

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Young, P.R., Lashbrooke, J.G. Alexandersson, E. Jacobson, D. Moser, C. Velasco, R. and Vivier, M.A. 2012. The genes and enzymes of the carotenoid metabolic pathway in Vitis vinifera L. BMC Genomics. 13:243. Guedes de Pinho, P., M.A. Vivier, P. R. Young, C.M. Oliveira, R.C. Martins & A.C. Silva Ferreira. 2013. Monitoring carotenoids and derivative compounds in grapes and Port wines: Impact on quality. In: Carotenoid Cleavage Products; Edited by Winterhalter, P and Ebeler, S. American Chemical Society, 139-154. Alexandersson, E., Jacobson, D., Vivier, MA., Weckwerth, W. and Andreasson, E. 2014 "Field-omics" – understanding large-scale molecular data from field crops. Frontiers in Plant Science 5: article 286 (doi: 10.3389/fpls.2014.00286) Musingarabwi, D.M., Nieuwoudt, H.H., Young, P.R., Eyéghè-Bickong, H.A., Vivier, M.A., 2015. A rapid qualitative and quantitative evaluation of grape berries at various stages of development using Fourier-transform infrared spectroscopy and multivariate data analysis. Food Chem. doi:10.1016/j.foodchem.2015.05.080 Young, P.R., Eyéghè-Bickong, H.A., Alexandersson, E. Jacobson, D.A., Coetzee, Z.A., Deloire, A. and M.A. Vivier, M.A. 2015. Grapevine plasticity in response to an altered microclimate: Sauvignon Blanc modulates specific metabolites in response to light. Plant Physiology. (under review)

e) PRESENTATIONS/PAPERS DELIVERED Please list using the format illustrated in the example below.

Musingarabwi, D., Eyéghé-Bickong, H.A., Young, P.R., Nieuwoudt, H.H., and Vivier, M.A. 2014. Rapid techniques for assessment of grapes and wine. Macrowine 2014. Macromolecules and secondary metabolites of grapevine and wine, Stellenbosch, South Africa (7-10 September 2014). Poster Du Plessis, K., Young, P.R., and Vivier, M.A. 2014. A highly characterised model vieyard approach towards effective implementation of “field-omics” in grapevine studies. Macrowine 2014. Macromolecules and secondary metabolites of grapevine and wine, Stellenbosch, South Africa (7-10 September 2014). Poster Eyeghe-Bickong, H., Young, P.R. & Vivier, M.A. 2014. A combination of chromatographic methods to accurately profile and quantify major metabolites in grapevine (Vitis vinifera cv.) berries from characterised vineyards using small sample sizes. Macrowine 2014: Macromolecules and secondary metabolites of grapevine and wine, STIAS Conference venue, Stellenbosch. (7-10 September 2014). Oral presentation Young, P.R., Eyeghe-Bickong, H., Jacobson, D., Alexandersson, E., Coetzee, Z., Deloire, A. & Vivier, M.A. 2014. Grapevine plasticity in response to light: Sauvignon blanc modulates its berry metabolome in response to an altered microclimate. Macrowine 2014: Macromolecules and secondary metabolites of grapevine and wine, STIAS Conference venue, Stellenbosch. (7-10 September 2014). Oral presentation Vivier MA, Young, PR, Du Plessis, K, Eyeghe-Bickong H, Joubert, C. 2014. The molecular and metabolite profiling of grapevine berries in a model vineyard where the microclimate of the developing bunches has been altered. 11th International Conference on Grapevine Breeding and Genetics, Yanjing, China (28 July-2 August)

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Vivier MA. 2014. Systems Biology of Grapevines in Field Settings using Model Vineyards. Plant Gene Discovery & "Omics Technologies" Conference, Vienna, Austria (17-18 February) Vivier, M.A. 2013. From vineyard to grapes – understanding the impact of viticultural treatments. 35th SASEV/WINETECH International Conference, Lord Charles Hotel, Somerset-West, South Africa. (13-15 November 2013) Vivier, M.A. 2013. Trends in Grapevine Biology. 35th SASEV/WINETECH International Conference, Lord Charles Hotel, Somerset-West, South Africa. (13-15 November 2013) Vivier, M.A., P.R. Young, E. Alexandersson, D. Jacobson, J. Lashbrooke, Z.A. Coetzee & A.J. Deloire. 2012. The molecular response of grapevine berries to an altered microclimate: the effect of leaf removal/sunlight exposure in the bunch zone on the carotenoid biosynthetic pathway. SASBMB-FASBMB 2012 congress, Champagne Sports Resort, Drakensberg, KwaZulu-Natal. (29 January – 1 February) Young, P.R and Vivier M.A. 2013. Grape flavours uncorked. 35th SASEV/WINETECH International Conference, Lord Charles Hotel, Somerset-West, South Africa. (13-15 November 2013) Young, P.R., E. Alexandersson, D. Jacobson, J. Lashbrooke, H. Eyeghe-Bickong, Z. Coetzee, C. Coetzee, E. Kritzinger, W.J. du Toit, A.J. Deloire & M.A. Vivier. 2012. Profiling of carotenoid metabolism and quality parameters in grapes and wines from a model vineyard where the microclimate of the developing bunches has been altered. Macrowine 2012 Conference, Bordeaux, France. (18-21 June 2012). Young, P.R., E. Alexandersson, D. Jacobson, J. Lashbrooke, Z.A. Coetzee, A.J. Deloire & M.A Vivier. 2012. The molecular and metabolite profiling of grapevine berries in a model vineyard where the microclimate of the developing bunches has been altered. South African Association of Botany (SAAB 2012), Pretoria University. (15-18 January) Young, P.R., Lashbrooke, J.G., Dockrall, S.J. & Vivier, M.A. 2013. Functional analysis of carotenoid cleavage dioxygenases in grapevine. The Carotenoids Gordon Research Conference, Ventura, California, USA Young, P.R., Lashbrooke, J.G., Dockrall, S.J., Alexandersson, E., Jacobson, D. & Vivier, M.A. 2013. Grapevine carotenoids and carotenoid cleavage dioxygenases: expression profiling and functional analysis. IX International Symposium on Grapevine Physiology and Biotechnology, La Serena, Chile. (21-26 April 2013)

10. BUDGET

a) TOTAL COST SUMMARY OF THE PROJECT

YEAR

CFPA DFTS Deciduous SATI Winetech THRIP OTHER TOTAL

2012

2013

2014

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b) FINAL BUDGET/FINANCIALS OF PROJECT Please ensure that the budget is sufficiently detailed and add notes to explain all significant variations from the budget – you may submit this in an EXCEL document. Please report on the budget for the entire duration of the project. Add additional rows if required.

Project duration Proposed

budget

Actual cost

incurred Variance Notes

TOTAL INCOME

Industry Funding

PHI Funding

Other Funding

TOTAL EXPENDITURE

Running Expenses

General operating costs

(printing, communication, etc.)

Local Travel

Publication costs

Lab Analysis

Lab Consumables

Other

Running expenses SUB-

TOTAL

HR Administration and Project

Management

HR Technical

HR Research

Student Bursaries

HR SUB-TOTAL

OTHER EXPENSES

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Project duration Proposed

budget

Actual cost

incurred Variance Notes

SURPLUS / DEFICIT

EVALUATION BY INDUSTRY

This section is for office use only

Project number

Project name

Name of Sub-Committee*

Comments on project

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Committee’s recommendation

Accepted.

Accepted provisionally if the sub-committee’s comments are also addressed. Resubmit this final report by___________________________________

Unacceptable. Must resubmit final report. Chairperson__________________________________________ Date___________________

*SUB-COMMITTEES Winetech

Viticulture: Cultivation; Soil Science; Plant Biotechnology; Plant Protection; Plant Improvement; Oenology: Vinification Technology; Bottling, Packaging and Distribution; Environmental Impact; Brandy and Distilling; Microbiology Deciduous Fruit

Technical Advisory Committees: Post-Harvest; Crop Production; Crop Protection; Technology Transfer Peer Work Groups: Post-Harvest; Horticulture; Soil Science; Breeding and Evaluation; Pathology; Entomology

Documents

CONCEPT PROJECT PROPOSAL: [Click HERE and … zone and samples were analysed for sugars and organic acids; ... berry and wine quality assessments as ... stages and analysed by RP-HPLC