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Wine 2030 Conference
Research to transform the wine industry


           Yeast Genomics:
      Medium and Long Term Goals

                  Assoc Prof Vladimir Jiranek
              School of Agriculture, Food and Wine
          The University of Adelaide, Waite Campus.


 The University of Adelaide is a partner in the Wine Innovation Cluster
Winemaking is easy
But hard to predict exact outcome (composition)




                     +               =


A particular composition is important to sell wine
(i.e. one specifically sought after by consumers)
Winemaking is easy
But hard to predict exact outcome (composition)




                     +               =


A particular composition is important to sell wine
(i.e. one specifically sought after by consumers)
• Must know what consumers (producers) want
    (from a compositional point of view)

• Need to understand process so that can
  achieve this
   – per se i.e. in a „typical‟ year
           but also
   – against increasing variability
     (of inputs and consumer desires)

Long term goal: To achieve a precise (tailored)
compositional outcome (i.e. „dial-up a wine‟)
What does the outcome of reaching the
      long term goal look like?
    Complete
  compositional                   Influence of
    profile of                    winemaking
   grapes/juice                   fully defined
                  Reconcile &
                   integrate
                    inputs,
                  control the
                    process




              Wine of specified
                composition
Can’t tackle all aspects toward long term goal

What are some key medium term goals?

Defining the Yeast and „Wine‟ Metabolomes

Defining Wine Yeast Genomics

Integrating these datasets

Developing models and thus strategies or strains

Moving to include more winemaking parameters
The Wine Yeast and ‘Wine’ Metabolomes:
         Progress & Challenges
 Development of protocols for exo- & endo-
  metabolomes of yeast in „wine‟ media (merit of each?)

 Definition of the Wine Metabolome
     Metabolites from yeast and bacteria, grapes, additives,
     chemical reactions, enzymatic modification, etc.

  • Definition of above is a large but „pedestrian‟ task
  • Analytical systems exist but not a single platform
  • Can detect but not identify all – need reference libraries
  • Need to integrate multiple datasets – data management
Wine Yeast Genomics: Progress & Challenges
  Lab yeast genome has been sequenced (1996)
  Genomes of wine yeasts recently sequenced (2008)




                                              FEMS Yeast Res. (2008)
            Interpretation – lost, added, modified, new genes

  Functional Genomics to understand function of
      genes in winemaking
            Can look at all genes OR just the vital genes i.e.
            the “(Minimal) Wine Genome”
            Use libraries – deletion, over-expression, etc.
            Need to look at interactions of genes
Genome-Metabolome Integration, Predictive
     Models, Strategies and Strains:
         Progress & Challenges
 Integration of information from two(+) data sets
      leading to a predictive model of fermentation
      and metabolism.

 Development of strategies for tailoring wine
     Juice:strain matching
     Ferment management protocols
Genome-Metabolome Integration, Predictive
     Models, Strategies and Strains:
         Progress & Challenges
►Minimal Genome used to yield a „skeletal‟ yeast
    as a basis for constructing tailored yeasts
     Strains used: as is (i.e. GM)
                   as proof-of-concept
                   for selecting natural variants (non-GM)


 Data + data + data = bioinformatics
          will be essential!
Long Term Goal
Move towards a systems approach by way of:

• Rapid and comprehensive analytical capabilities.

• Completely integrated model(s) of viticultural and
     oenological parameters.

To give protocols for the ready modification of
winemaking (i.e. process and yeast) for a given
outcome even if inputs are variable.

This ability to will need to able to rapidly respond to
changing consumer preferences or market forces.

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Yeast Genomics: Medium and Long Term Goals

  • 1. Wine 2030 Conference Research to transform the wine industry Yeast Genomics: Medium and Long Term Goals Assoc Prof Vladimir Jiranek School of Agriculture, Food and Wine The University of Adelaide, Waite Campus. The University of Adelaide is a partner in the Wine Innovation Cluster
  • 2. Winemaking is easy But hard to predict exact outcome (composition) + = A particular composition is important to sell wine (i.e. one specifically sought after by consumers)
  • 3. Winemaking is easy But hard to predict exact outcome (composition) + = A particular composition is important to sell wine (i.e. one specifically sought after by consumers)
  • 4. • Must know what consumers (producers) want (from a compositional point of view) • Need to understand process so that can achieve this – per se i.e. in a „typical‟ year but also – against increasing variability (of inputs and consumer desires) Long term goal: To achieve a precise (tailored) compositional outcome (i.e. „dial-up a wine‟)
  • 5. What does the outcome of reaching the long term goal look like? Complete compositional Influence of profile of winemaking grapes/juice fully defined Reconcile & integrate inputs, control the process Wine of specified composition
  • 6. Can’t tackle all aspects toward long term goal What are some key medium term goals? Defining the Yeast and „Wine‟ Metabolomes Defining Wine Yeast Genomics Integrating these datasets Developing models and thus strategies or strains Moving to include more winemaking parameters
  • 7. The Wine Yeast and ‘Wine’ Metabolomes: Progress & Challenges  Development of protocols for exo- & endo- metabolomes of yeast in „wine‟ media (merit of each?)  Definition of the Wine Metabolome Metabolites from yeast and bacteria, grapes, additives, chemical reactions, enzymatic modification, etc. • Definition of above is a large but „pedestrian‟ task • Analytical systems exist but not a single platform • Can detect but not identify all – need reference libraries • Need to integrate multiple datasets – data management
  • 8. Wine Yeast Genomics: Progress & Challenges  Lab yeast genome has been sequenced (1996)  Genomes of wine yeasts recently sequenced (2008) FEMS Yeast Res. (2008) Interpretation – lost, added, modified, new genes  Functional Genomics to understand function of genes in winemaking Can look at all genes OR just the vital genes i.e. the “(Minimal) Wine Genome” Use libraries – deletion, over-expression, etc. Need to look at interactions of genes
  • 9. Genome-Metabolome Integration, Predictive Models, Strategies and Strains: Progress & Challenges  Integration of information from two(+) data sets leading to a predictive model of fermentation and metabolism.  Development of strategies for tailoring wine Juice:strain matching Ferment management protocols
  • 10. Genome-Metabolome Integration, Predictive Models, Strategies and Strains: Progress & Challenges ►Minimal Genome used to yield a „skeletal‟ yeast as a basis for constructing tailored yeasts Strains used: as is (i.e. GM) as proof-of-concept for selecting natural variants (non-GM) Data + data + data = bioinformatics will be essential!
  • 11. Long Term Goal Move towards a systems approach by way of: • Rapid and comprehensive analytical capabilities. • Completely integrated model(s) of viticultural and oenological parameters. To give protocols for the ready modification of winemaking (i.e. process and yeast) for a given outcome even if inputs are variable. This ability to will need to able to rapidly respond to changing consumer preferences or market forces.