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

Wine 2030 Conference

Research to transform the wine industry

+ =

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?

Wine of specified

composition

Complete

compositional

profile of

grapes/juice

Influence of

winemaking

fully defined

Reconcile &

integrate

inputs,

control the

process

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 MetabolomeMetabolites 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)

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

FE

MS

Ye

ast

Res. (2

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8)

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

►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)

Genome-Metabolome Integration, Predictive

Models, Strategies and Strains:

Progress & Challenges

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.