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Improving fructose utilization in
wine yeast using adaptive evolution
Tommaso Liccioli
A thesis submitted for the degree of Doctor of
Philosophy in the School of Agriculture, Food and Wine
Faculty of Sciences
The University of Adelaide
August 2010
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SUMMARY
Saccharomyces cerevisiae is the most important micro organism involved in the
production of fermented alcoholic beverages such as wine. Despite its fermentative
capacity and production of desirable metabolites, grape juice represents a hostile
environment for yeasts. Sometimes, adverse conditions reduce yeast biomass
formation or catabolic capacity, which may lead to stuck or sluggish fermentation.
These phenomena represent one of the most common problems during the wine
production process and mean that winery throughput is reduced and residual sugar
adds unwanted sweetness in dry wine styles while offering substrates for microbial
spoilage.
The scientific community has always been alert to the problems linked with
fermentation, considering the vital role of this organism during the production
process. For this reason research has focussed on developing a range of techniques for
strain improvement. With the emergence of modern molecular genetics, the new
methodologies of hybridization and genetic engineering have been used to isolate and
create new yeast strains. However, their application in wine microbiology is not
without complications, as genetically modified yeasts are not universally favoured for
commercial use in the food industry.
A recent development is the notion of using the natural capacity of a population of
single celled organisms to adapt themselves to an environment imposing a specific
stress. The technique is termed “adaptive evolution” or “directed evolution”. In
principle the process is simple: when a species is constricted to live and replicate
under stressful conditions for many generations, some cells will present adaptive
characteristics: i.e. “adaptive mutations” and outgrow the starting population. A key
benefit of this technique is that it does not rely on direct manipulation at the level of
DNA, and can be used to reproduce the stress conditions found in nature or in
fermentation tanks. However, adaptive evolution is a technology that needs to be
more fully explored and developed for its possible use in improving wine yeast
strains.
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A possible improvement for wine yeasts targets their sugar catabolic capacity. The
different affinity of S. cerevisiae for glucose and fructose is thought to be a cause of
stuck or sluggish fermentations in the winemaking process. The possibility of
obtaining a strain with improved fructose utilization using adaptive evolution is
therefore the topic of this investigation.
This thesis describes work that can be divided into four sections. The first part is the
identification of a candidate strain from a selection of commercially available wine
yeasts. The second part is aimed at evolving the candidate strain under a selective
pressure. The third validates new methods for assessing the populations of candidate
evolved yeast in order to isolate clones that can metabolize fructose more efficiently
compared to the parental strain. The last part is focussed on a deeper investigation and
comparison of a number of potentially evolved candidates with the parent.
To identify a candidate strain for use in the adaptive evolution process, it was
necessary to compare fermentative performances of commercially available strains.
Fermentations for 20 strains were conducted in synthetic media, containing fructose
as sole sugar or else an equivalent concentration of glucose and fructose. Particular
attention was focussed on the rate of fructose consumption relative to glucose, and
thus it was necessary to identify a methodology that was independent of sugar
concentration, overall fermentation rate or duration. As such the value of the area
under the fermentation curves determined by the composite trapezoid rule was utilised
to compare glucose and fructose utilisation and hence define the fructophilicity of
each strain screened. This approach allowed the most suitable candidate strain to be
chosen for the application of adaptive evolution. Accordingly, strain AWRI 796 was
cultured under fermentative conditions that elicited an appropriate selective pressure
over some 350 generations. Samples of the population were collected every 50
generations for characterization of individual clones. The next stage of the project
focussed on the identification of clones which showed improved fructose utilization
compared to the parental strain. To define fermentative performance of a high number
of isolates from the adaptive evolution experiment, it was necessary to develop
screening methodologies. For this purpose fermentations in microtiter plates and
automated colorimetric assays for determination of residual sugar were adopted. From
378 clones examined, four were identified to be faster consumers of fructose relative
to the parent. Patterns of glucose utilisation in these clones were unchanged. The last
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stage of the study validated the improved fermentation ability of these novel
phenotypes under winemaking related conditions in fermentations of 20 kg of red
grapes. In these experiments two isolates again showed a significant reduction in the
time required for completion of the fermentation. The results validate the approach
used and the selective pressures applied as a means introducing specific
improvements into wine yeast strains.
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TABLE OF CONTENTS
SUMMARY _____________________________________________________ i STATEMENT OF AUTHORSHIP __________________________________ v ACKNOWLEDGEMENTS ________________________________________ vi CHAPTER 1: INTRODUCTION AND LITERATURE REVIEW ___________________ 1
1.1 – Introduction ______________________________________________ 2 1.2 – Yeast and the winemaking process ____________________________ 3 1.3 – Stuck and sluggish fermentations: one of the biggest problems
during the winemaking process ______________________________ 15 1.4 – Improving wine yeast strains ________________________________ 18 1.5 – Aim of the project _________________________________________ 25
CHAPTER 2: CANDIDATE STRAIN SELECTION ___________________________ 27 2.1 – A novel methodology independent of fermentation rate for assessment
of the fructophilic character of wine yeast strains _______________ 30 2.2 – Basis of candidate strain selection ____________________________ 48
CHAPTER 3: ESTABLISHING AN ADAPTIVE EVOLUTION STRATEGY USING CONTINUOUS CULTURE TO GENERATE FRUCTOPHILIC GENOTYPES _ 51
3.1 – Isolation of a representative single clone of AWRI 796 ___________ 52 3.2 – Induced mutagenesis of AWRI 796 ___________________________ 55 3.3 – Defining experimental conditions for adaptive evolution __________ 55
CHAPTER 4: SCREENING OF ISOLATES FROM CONTINUOUS CULTURE TO IDENTIFY FRUCTOPHILIC GENOTYPES _______________________ 63
4.1 – Screening for isolates showing improved fructophilic ability _______ 68 4.2 – Fermentative performances of 19 isolates identified as candidates
for further characterization _________________________________ 75 4.3 – Conclusions _____________________________________________ 75
CHAPTER 5: PHYSIOLOGICAL AND GENETIC CHARACTERIZATION OF THE IDENTIFIED ISOLATES ___________________________________ 78
5.1 – Fermentation performance of 4 isolates in a higher sugar concentration medium ____________________________________ 79
5.2 – Evaluation of the fermentation performance of isolates 9 and 11, the parental strain and two commercially available strains ________ 82
5.3 – Metabolite production during fermentation ____________________ 84 5.4 – DNA fingerprinting of evolved strains ________________________ 86 5.5 – Phenotype stability ________________________________________ 88 5.6 – Grape juice fermentations __________________________________ 90
CHAPTER 6: CONCLUSIONS, DISCUSSION AND FUTURE DIRECTIONS __________ 97 6.1 – Conclusions ______________________________________________ 98 6.2 – Discussion and future directions ______________________________ 98
APPENDIX 1 ____________________________________________________ 101 APPENDIX 2 ____________________________________________________ 108 REFERENCES ___________________________________________________ 110
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STATEMENT OF AUTHORSHIP
This work contains no material which has been accepted for the award of any other
degree or diploma in any university or other tertiary institution to Tommaso Liccioli
and, to the best of my knowledge and belief, contains no material previously
published or written by another person, except where due reference has been made in
the text.
I give consent to this copy of my thesis, when deposited in the University Library,
being made available for loan and photocopying, subject to the provisions of the
Copyright Act 1968.
I also give permission for the digital version of my thesis to be made available on the
web, via the University’s digital research repository, the Library catalogue, the
Australasian Digital Theses Program (ADTP) and also through web search engines,
unless permission has been granted by the University to restrict access for a period of
time.
Tommaso Liccioli
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ACKNOWLEDGMENTS
I would like to firstly thank my supervisors Assoc. Professor Vladimir Jiranek and Dr. Paul J.
Chambers for their guidance, constant encouragement and patience. They were my reference point
during this study and there is no way to repay what I have learnt from them.
I am also very thankful to Frank Schmid (especially when I did not “listen” to him). Every minute
spent together was an occasion to increase my knowledge, personal experience, scientific
approach and, above all, life. I cannot thank him enough for all the good times I had with him.
Simon Schmidt, with his patience, experience and critical view in this field of study, was always
ready with a solution for solving problems during my experiments and suggesting future
directions. It was great talking with him about life and playing music together.
Thank you to Michelle Walker, Jennie Gardner and Paul Grbin, for their experience, knowledge
and support, indispensable people for my academic life. Cristian Varela for his help with the
continuous culture experiments. Colin McBryde, for the work previously done with the adaptive
evolution, from which this study was a continuation.
A special thought goes to Alana Capaldo and Krista Sumby, who started their PhD in the same
period as me and were a great support during this time: it has been great being in the lab with
them.
Thanks go to Angus Forgan, Jenny Bellon, Darek Kutyna, Maurizio Ugliano, Radka Kolouchova,
Caroline Abrahamse, Mariola Kwiatkowski and all of the other members of AWRI and WIC
building, for their support and making my time enjoyable.
I am also very grateful to Michael Brinkley and his family for their help and lovely moments spent
together. Richard (old mate) Freebairn and Peter (uncle P) Start for their patience of sharing
houses together and “introducing” me to Aussie lifestyle. Medyan Ghareeb, for all of the
incredible experiences shared, reciprocal respect and help: thanks dude, and it is not finished here!
Words can never thank my family enough, that fully understood my problems and supported all
my decisions. I could have not been here writing these words without the knowledge of having my
family. Living so far away from each other was only marginally compensated with the satisfaction
of having lived this experience.
I am grateful to Australia’s grape growers and winemakers through their investment body, the
Grape and Wine Research and Development Corporation, with matching funds from the
Australian Government for the financial support.
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CHAPTER 1
INTRODUCTION AND LITERATURE REVIEW
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1.1 INTRODUCTION Mankind has used yeasts for many millennia in the modification of food products
(Samuel, 1996). In particular, Saccharomyces cerevisiae is the most well know of
these yeast as it is used in the baking, brewing and winemaking processes (Spencer
and Spencer, 1983). It was not until 1863 that its catalytic capacity in wine
fermentation was described by Louis Pasteur, basing his work on previous
observations of other pioneers (Leeuwenhoek, 1680, Cagniard-Latour, Kützing and
Schwann in the late 1830s – for review see Rose and Harrison, 1969). With such
revolutionary knowledge winemakers could start to control the process of
transformation of grape sugars into alcohol and carbon dioxide, and the influence that
yeasts have on the final product. In fact, in this context, in 1890 Müller-Thurgau
introduced the concept of “inoculating fermentation” with pure cultures of superior,
previously selected yeasts (Kunkee and Amerine, 1970). This innovative practice
changed the wine industry, resulting in increased quality and quantity of wine. Pure
culture wine yeasts gave more consistent, better quality and more satisfactory results
than uncontrolled natural fermentations (Mestre and Mestre, 1946) and for this reason
yeast selection has become a tool appreciated among winemakers.
S. cerevisiae strongly influences the composition and structure of the wine, thereby
making yeast one of the principal factors determining wine sensorial profile. In
addition, the yeast in use and the manner in which it interacts with the grape juice
determines the progress of fermentation and whether stuck or sluggish fermentations
are encountered. Such problem fermentations can be linked to issues such as nutritive
limitations or adverse media composition, but also to the capacity of the yeast to take
up sugars per se or specifically fructose compared to glucose (Berthels et al., 2004;
Berthels et al., 2008; Bisson, 1999; Guillaume et al., 2007), the two most important
sugars in grape juice. Possible solutions to these problems come from two directions:
correcting must composition before and during the winemaking process or improving
the resilience and fermentative capacity of the yeast. To change the chemical
composition of the grape juice presents some difficulties and may adversely influence
the quality of the wine. Therefore, improving the fermentative capacity of the yeast in
the presence of adverse physico-chemical conditions remains a primary target for
wine microbiological research.
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Natural selection and recombinant techniques are the two approaches used presently
to isolate, improve or create new yeast strains for the wine industry. In isolating yeast
from nature it is possible to identify representatives of a particularly interesting wine
area and to capture the peculiarities of that “terroir”. But it is difficult to improve
determinate characteristics beyond what is found in the originating environment.
Recombinant techniques can overcome these limitations, but some difficulties are
linked to DNA manipulation (i.e. the need for a high level of knowledge, ethical
debates on GMOs, etc), and thus more research is required.
A technique that seeks to capture the cell’s spontaneous modification of its genome
during exposure to or in response to environmental stress is called adaptive (or
directed) evolution. This technique does not involve DNA manipulation and yet
shows some of the great potential of recombinant techniques, thereby offering much
promise as a means to strain optimisation.
The following literature review provides background to yeast sugar metabolism
during winemaking and reviews recent studies which indicate the potential of adaptive
evolution as a new strategy specifically for improving wine yeast. Particular emphasis
is given to the different ability of strains of S. cerevisiae to ferment glucose and
fructose and this introduction also debates the possibility of the use of adaptive
evolution as strategy for improving fructose metabolism and therefore limiting
problems of stuck or sluggish fermentations.
1.2 YEAST AND THE WINEMAKING PROCESS
During winemaking, the most evident phenomena linked with wine yeast is the
metabolism of the sugar present in grape juice. This process, called alcoholic
fermentation, is complex and involves a high number of chemical and enzymatic
reactions (Boulton et al., 1998). The two principal products of the alcoholic
fermentation are ethanol and carbon dioxide (Brock et al., 1994). However, other
substances present in grape juice are able to penetrate the yeast cell membrane and
participate in biochemical reactions producing numerous volatile end-products. These
include long-chain fatty acids, organic nitrogen-containing compounds and sulphur-
containing compounds and others (Boulton et al., 1998). As such, during the different
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phases of winemaking, the grape matrix is markedly changed and important
influences on the sensory characteristics are detectable in the final product.
This section will be briefly described the main features of the evolution of sensorial
characteristics during winemaking, with particular attention to the influence of yeast.
Then, the metabolic pathways of sugar catabolism by yeast will be discussed.
Fermentation and wine flavours
Wine flavour can be described as the overall sensory impression of wine (Robinson,
1994). However the largest contribution to what is described as flavour is in fact wine
aroma (“associated with odorous, volatile compounds”) or bouquet (”more complex
flavour compounds which evolve as result of fermentation, elevage and ageing” – for
a more extensive review see Lambrechts and Pretorius, 2000). Thus these three terms
are often used interchangeably (Lambrechts and Pretorius, 2000).
It is possible to classify the compounds contributing to wine aroma into four different
classes: varietal, pre-fermentative, fermentative and post-fermentative (Boulton et al.,
1998; Rapp, 1998; Schreier and Jennings, 1979). The first class is based on
compounds which originate from grapes and provides the basis of varietal character.
Those in the second class are formed during extraction and conditioning of must
preceding the fermentation. During the alcoholic and malolactic fermentations,
flavours that belong to the fermentative class are produced by yeast and bacteria. The
last class of compounds appears as a result of enzymatic or physico-chemical
reactions during the process of aging in wood or in the bottle. As already stated, the
factors that most influence the flavour of wine are yeast and fermentation conditions
(Lambrechts and Pretorius, 2000).
During a typical wine fermentation, about 95% of the sugars present in the must are
converted into ethanol and carbon dioxide, 1% into cellular material and 4% into
other end products (Boulton et al., 1998). Ethanol, glycerol and carbon dioxide play
fundamental roles in final aroma perception, but the greatest influence on wine
bouquet comes from organic acids, higher alcohols, esters and, to a lesser extent,
aldehydes. These classes of compounds may also have undesirable effects,
particularly if present at higher concentrations. Other products of microbial activity
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that negatively influence final wine aroma are reduced sulphur compounds, such as
hydrogen sulphide, and at certain concentrations, organic sulphides and thiols. Wine
bouquet characteristics are also influenced by the yeast population. Every yeast genus
and species has a specific metabolic activity, with some strains of the same species
exhibiting large differences in terms of the production of secondary metabolites
(Romano et al., 2003).
The alcoholic fermentation is a process that is carried out by different yeast genera
and species where S. cerevisiae is the most important species, in both inoculated
fermentations and spontaneous fermentations (Fleet et al., 1984; Heard and Fleet,
1985; Lema et al., 1996). The early stages can nonetheless be dominated by the
growth of non-Saccharomyces yeasts, until the ethanol concentration reaches
inhibitory levels (around 5% v/v; Romano et al., 2003). The contribution that the
indigenous micro-flora can make to aroma depends on several factors, related to the
species (taxonomic identity, kinetics of growth, biochemical properties), the condition
and treatment of the grapes in the early stages of the winemaking process and the
operations before and during the alcoholic fermentation. In particular, sensory
differences were shown to exist between spontaneous and inoculated fermentations
(Lambrechts and Pretorius, 2000; Romano et al., 2003), where the influence of wild
yeast on wine quality was minimized in the inoculated wines.
An influence on the composition of wines obtained through the use of different yeast
species and strains of the same species was investigated by Romano and co-workers
(2003) and provided analytical profiles of the wines. The metabolites produced across
the various yeast species were usually the same (with rare exceptions), although they
differed in their concentrations. Thus is seems that only differences in the absolute
amount of specific metabolites will be enough to significantly change the final aroma
of a wine. Therefore an important consideration relates to the differences between
spontaneous or inoculated fermentation. Although the use of commercial starter yeasts
induces reliable and rapid fermentations (favouring good quality wines), it is also
suggested that the product obtained in this way is often nondescript. To carry out a
spontaneous fermentation may result in enhanced aroma complexity and perhaps
terroir identity (Lambrechts and Pretorius, 2000; Romano et al., 2003), but control of
the fermentation may be reduced as a trade off (Rainieri and Pretorius, 2000).
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C6H12O6 2C2H5OH + 2CO2
This last consideration has to be kept in mind during the development and isolation of
new commercial strains. If reliable fermentative capacities and innovative yeast
contributions to the wine are fundamental characteristics of an improved commercial
yeast strain (Fleet, 2008; Rainieri and Pretorius, 2000), it is not possible to create
strains without respecting the main oenological traits of the parental strain (Fleet,
2008). For this reason, techniques that permit the yeast to adapt itself to the
environment where it has to grow and live (i.e. fermentation conditions) may also
allow preservation of the original desirable characteristics of that specific strain.
Adaptive evolution therefore may fit this purpose perfectly (see Section 1.4).
Sugar catabolism by yeast
As previously stated, the two most important products of alcoholic fermentation are
ethanol and carbon dioxide:
For every molecule of hexose (mostly glucose and fructose in the grape juice) two
molecules of ethanol and two of carbon dioxide are produced (Brock et al., 1994).
The molecular weight of the C6 sugars are 180 g/mol, whilst it is 46 g/mol for ethanol
and 44 g/mol for CO2. Thus the production of ethanol corresponds to 51.1% by
weight of the sugars fermented (Boulton et al., 1998). In winemaking, ethanol is not
measured by weigh but by volume (specific gravity at 20°C = 0.78 g/l), thus its
theoretical volumetric yield would be:
However this value is only theoretical and some of the sugar is utilised by the yeasts
as constituents of cell material and other products such as glycerol and succinic acid.
It is assumed that in a wine fermentation the actual percentage of conversion from
sugar to ethanol is around 60% (Boulton et al., 1998).
51.11 / 0.78 = 65.5% v/v
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While the chemical reaction of the alcoholic fermentation can be described by the
equation above, the biochemical mechanisms by which yeasts ferment sugars to
ethanol are complex processes and include many steps, catalyzed by many enzymes
(Boulton et al., 1998). Essentially it is possible to divide this process into three main
parts: the first is the uptake of sugar into the cell through the plasma membrane; the
second is the breakdown of the sugars to pyruvate through glycolysis, which in turn
can have two remain fates: respiration or fermentation. Respiration leads to a more
efficient energy utilization of the sugar, but under winemaking conditions yeast
usually utilise the fermentative pathway.
Another important detail of the alcoholic fermentation is the higher preference of S.
cerevisiae for glucose than for fructose. During the progress of fermentation the
discrepancy between glucose and fructose utilization increases, due to associated
stresses such as increasing ethanol concentration and nutrient limitation (Berthels et
al., 2004; Guillaume et al., 2007). Although more work is required to determine the
precise basis for differences in the rate of glucose and fructose fermentation, two steps
in the utilization of hexoses are implicated: plasma membrane sugar transport systems
and hexose phosphorylation during the first step of glycolysis.
The principal steps of sugar catabolism and the points possibly involved in the
different preference for glucose and fructose are discussed below.
First step of sugar utilization: hexose uptake through the plasma membrane
Hexose transport by S. cerevisiae has been shown by many authors to be a critical
point for the different preference shown by this organism for glucose and fructose. In
this yeast, sugar uptake is mediate by facilitated diffusion, where 34 proteins
constitute the permease family (Figure 1.1; Wieczorke et al., 1999). Of these proteins,
twenty form the subfamily of hexose transporters and glucose sensors (Hxt1-17, Gal2,
Snf3 and Rgt2 – Table 1.1; Reifenberger et al., 1995). However, under fermentation
conditions, Hxt1 to Hxt7 are the most important for glucose and fructose consumption
(Guillaume et al., 2007; Karpel et al., 2008; Perez et al., 2005; Verwaal et al., 2002).
While these transporters have the ability to transport both glucose and fructose with
either low or high affinity, all typically have a higher affinity for glucose than for
fructose.
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Figure 1.1 – The yeast sugar transporter homologues. From Wieczorke et al. (1999)
Table 1.1 – Twenty proteins forming the hexose transporter and glucose sensor family
Protein Affinity/Comment
Hxt1 Low
Hxt2 High (controversial)
Hxt3 Low
Hxt4 Moderately low
Hxt5 Moderate for glucose, low for fructose
Hxt6 High
Hxt7 High
Hxt8-17, Gal2 (Less important during wine fermentation)
Snf3, Rgt2 (Sensing of the available glucose)
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The expression of each HXT gene is regulated by environmental factors, especially the
extracellular hexose concentration. At high hexose concentration in the media, the low
affinity system is principally responsible for sugar uptake into the cell, while the high
affinity is under control of catabolite repression (Bisson, 1988). With the progressive
depletion of hexose from the medium the cell shifts from the low to the high affinity
system. This mechanism, although complex, has in part been explained (Ramos et al.,
1988). The low affinity system is a constitutive, kinase-independent, facilitated
diffusion process. With the de-repression of the high affinity transporter system, the
low affinity system is not exhibited and vice-versa. However this is a dynamic and
progressive process and some of the low and high affinity membrane transporters are
simultaneously expressed during certain stages of fermentation (Perez et al., 2005).
Expression of HXT1-7 is also strain dependent. Various authors have studied the
expression of hexose transporters under oenological conditions, showing some
controversies result depending on which strain is studied. For example, expression of
HXT5 was not found in a study of a wine yeast strain (Perez et al., 2005), while the
same membrane transporter was expressed during mid-fermentation in a different
strain (Karpel et al., 2008). The comparison of these and similar studies (Varela et al.,
2005; Verwaal et al., 2002), also highlights that in different strains HXTs are
expressed at different levels during different phases of fermentation (i.e. different
substrate concentrations).
The high-affinity transporters are induced when the glucose concentration is low (≈ 1
– 4 mM or 0.18 – 0.72 g/l) and are repressed when the concentration of glucose is
higher. Conversely low-affinity carriers are induced by high glucose concentration (≈
50 – 100 mM or 9 – 18 g/l) or in fact are thought to be constitutively express (Ciriacy
and Reifenberger, 1997). Hxt1 and Hxt3 are considered low-affinity transporters, Hxt4
moderately low-affinity and Hxt2, Hxt6 and Hxt7 high-affinity. However, according
to Perez et al. (2005) and Luyten et al. (2002) the Hxt2 transporter is only transiently
expressed during the first hours of the lag phase, when the hexose concentration is
high. This suggests that Hxt2 may be able to bypass glucose repression in the initial
stages of the lag phase, playing an important role in the growth phase. However HXT2
was not expressed at the latter stages of the fermentation, when the concentration of
hexoses drops below repressive levels. Hxt5 has moderate affinity for glucose (Km =
100 mM) and low affinity for fructose (Km = 40 mM; Diderich et al., 2001). During
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fermentation, HXT3 is the only gene of the hexose transport family expressed
throughout the entire process (Luyten et al., 2002; Perez et al., 2005; Varela et al.,
2005). For this reason HXT3 may be the dominant hexose transporter and as such play
a fundamental role in determining glucose and fructose uptake rates (Guillaume et al.,
2007).
More work is required to determine the reasons for the higher affinity of these
transporters for glucose than for fructose. Ethanol is known to have protein denaturing
properties and to disrupt the plasma membrane (Bisson and Block, 2002; Stanley et
al., 2010) and intracellular enzymes and structures (due to the increase in membrane
permeability and passive proton flux). Berthels et al. (2004) observed that a high
ethanol level inhibits sugar utilization, but with a different effect on glucose compared
fructose. This led them to hypothesise that the glucose utilization capability may be
more robust than fructose utilization. In a study of HXT1-7 deletion strains, Karpel et
al. (2008) found that only the strain lacking HXT3 was unable to complete
fermentation in media containing of 5% (v/v) exogenous ethanol. These authors
suggested that HXT3 may have an important role in ethanol tolerance. Ethanol also
has a differing ability to inhibit different Hxt transporters and influence their affinity
for glucose or fructose (Santos et al., 2008). Moreover ethanol is able to shift the
tautomeric equilibrium of fructose from the readily transported pyranose form to the
furanose form. Thus unlike the aldose glucose which is entirely in the transportable
(pyranose) form, the situation for fructose, of which typically only 70% is in the
pyranose form, is made even worse by ethanol. This has been suggested to be one
reason for the generally lower utilisation of fructose by this yeast (Berthels et al.,
2004).
It is important to note that the discrepancy between glucose and fructose is not
constant during the progress of the fermentation. Rather it has been shown to be time
of fermentation and strain dependent (Berthels et al., 2004). Although the
mechanisms are unknown, a possible explanation stems from the different ethanol
tolerance shown by different strains. Another possibility is linked with nitrogen
availability and utilization. Different strains utilise nitrogen at different rates and with
different efficiencies (Jiranek et al., 1995). Moreover, the depletion of nitrogen in
combination with the rapid turnover of sugar transporters in the stationary phase, is
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thought to be responsible for inactivation of sugar transport systems with a resulting
reduction in fermentative capacity. Berthels et al. (2004) showed that in every case of
nitrogen supplementation, fructose consumption was enhanced with respect to
glucose. Another possibility related to glucose and fructose discrepancy could be the
different affinity of the sensor-proteins that are located on the plasma membrane
(Ozcan et al., 1996). Some have been identified as having affinity for glucose and at
least one has a differing affinity for glucose compared to fructose (Rolland et al.,
2001a; 2001b). The presence or location of specific fructose sensors has not be
reported (Berthels et al., 2004).
Second step of sugar utilization: glycolysis
Once sugar has entered the cell, yeast start to break it down liberating energy (as
ATP) and in turn re-oxidising NAD. This first part of the process is called glycolysis.
Two molecules of ATP are required for the production of glyceraldehyde-3-
phosphate, while 4 molecules of ATP are formed with the production of pyruvate
(Figure 1.2). The formation of pyruvate is common for the metabolism of sugar
whether concluding with fermentation or respiration.
Once glucose and fructose have entered into the cell, they are phosphorylated to
fructose-6-phosphate. For fructose this is a direct step, while glucose has to be
phosphorylated to glucose-6-phosphate and then converted in fructose-6-phosphate by
the enzyme phosphoglucose isomerase (PGI). From this point there is no more
differentiation between glucose and fructose. Glucose and fructose are both
phosphorylated by the enzymes hexokinase Hxk1 and Hxk2 (Figure 1.2), albeit at
different rates (glucose faster than fructose). Additionally glucokinase Glk1
phosphorylates glucose (Berthels et al., 2004; Guillaume et al., 2007; Serrano and
Delafuente, 1974). According to these authors, the phosphorylation capacity of these
enzymes exceeds the amount of sugar transported into the cell. This would indicate
that these enzymatic processes do not contribute to the ability of yeast to utilise
glucose and fructose at different rates. However, a recent study (Berthels et al., 2008)
demonstrates that over-expression of hexokinases could in fact alter the rate of
fermentation. Thus, more work is necessary for a better understanding of the topic. A
final consideration relates to the catabolic repression capacity: Hxk2 is required to
maintain catabolic repression by glucose, while fructose catabolic repression
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Figure 1.2 – Principal steps and enzymes of glycolysis (green background) and alcoholic fermentation (blue background). HXT (hexose transporter), HXK (hexokinase), GLK (glucokinase), PGI (phosphoglucose isomerase), PFK (phosphofructokinase), FBA (aldolase), TPI (triosephosphate isomerase), TDH (glyceraldehyde-3-phosphate dehydrogenase), PGK (phosphoglycerate kinase), PGM (phosphoglycerate mutase), ENO (enolase), PYK (pyruvate kinase), PDC (pyruvate decarboxylase), ADH (alcohol dehydrogenase). Glycolysis ends with the formation of pyruvate, while alcoholic fermentation is the transformation of pyruvate in ethanol and CO2. (Adapted from Boulton et al., 1998).
Glycolysis
Alcoholic fermentation
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requires either Hxk1 or Hxk2. Also in this case the mechanism involving glucose and
fructose may be different (Berthels et al., 2004).
Pyruvate is the final product of glycolysis. From this point, pyruvate is further
modified through the Krebs cycle if the respirative route is followed. Alternatively, in
alcoholic fermentation the process ends with the decarboxylation of pyruvate to
acetaldehyde followed by reduction to ethanol and re-oxidation of NAD (Figure 1.2).
Third step of sugar utilization: respiration vs. fermentation.
Wine yeast have the ability to grow under aerobic or anaerobic conditions, as they can
both respire and ferment sugars. The consequences of this dual activity are important
and the production of ATP and biomass differs markedly depending on which
pathway is followed.
In Saccharomyces respiration occurs when the concentration of sugar is below
repressive concentrations (between 1 – 4 g/l and 30 g/l; De Deken, 1966; Hornsey,
2007) and oxygen is available. Respiration allows complete oxidation of one molecule
of sugar to produces 36 molecules of ATP, thus no alcohol is produced and the full
energetic potential of the sugar is achieved. As a result, the yeast has a high growth
rate and produces a large biomass. With respiration, every gram of sugar respired
produces 0.25 g of dry cell weight (i.e. at a ratio of 1:4). This information is important
when the target is the production of biomass, for example in the production of
commercial yeast. However, under winemaking conditions, yeast usually do not
respire. The ideal maximum yield of cells is readily decreased by a poor supply of
oxygen to the cells. Moreover, as stated previously, another phenomena that drives
yeast metabolism through the fermentation pathway, is linked to the sugar
concentration: if sugar exceeds 30 g/l (De Deken, 1966; Postma et al., 1989), even if
in presence of oxygen, a complete suppression of the respiration capacity is observed,
and metabolism switches to an aerobic alcoholic fermentation. This phenomena is
known as the “Crabtree effect”.
Alcoholic fermentation is a process that utilises sugar in the absence of oxygen. In this
case, only part of the energetic potential of sugar is released. For every molecule of
sugar fermented 2 molecules of ATP are produced along with 56 kcal of heat, which
is released to the environment. The remaining energetic potential of the sugar
14
is bound in ethanol. In this case, 176 g of sugar are required to obtain 1 g of dry cells
(a ratio of 1:176). In an industrial setting, the fermentative condition is easier to
achieve than respiration, because the formation of CO2 in tanks and the high level of
sugars inhibit the respirative capacity of yeast from the first hours of contact with
grape juice (conditions typical of winemaking).
Other considerations in sugar utilization
As previously stated, during the alcoholic fermentation yeast produces compounds
apart from CO2 and ethanol. Glycerol, succinic acid, acetic acid, aldehydes, pyruvate
and acetoin are the most common and are produced as a result of secondary
metabolism. The production of glycerol is due to the inability of the cell to re-oxide
NADH2 to NAD via acetaldehyde to ethanol. If a block occurs at this level, the NAD
is formed via reduction of dihydroxyacetone-phosphate to glycerol-phosphate (and
then glycerol). External factors can influence or deviate the alcoholic fermentation
pathway. This is the case for SO2 added to the grape juice, which combines with
acetaldehyde, preventing its reduction and redirecting the pathway to glycerol. If the
addition of SO2 is high, a total arrest of ethanol production is possible, with glycerol,
acetaldehyde and CO2 being the only metabolites formed. It is important to note that
different concentrations of side-products can be strain dependent. For example, some
strains naturally produce different levels of SO2 that can influence the final
concentration of acetaldehyde. Other reasons for differences in ethanol yield relate to
the fact that the fermentation is mediated by variable enzymatic actions and thereby
also influenced by nutrients (especially nitrogen) availability in the media. For a more
extensive review see Alexandre and Charpentier (1998), Bisson (1999) and Boulton et
al. (1998).
To summarize the significance of the discussions so far, sugar catabolism by yeast is a
process that involves several steps. Sometimes a block can occur at the level of one of
these steps and the result is an arrest or a slowdown of sugar catabolism. In the
winemaking process, catabolism of sugar is carried out under fermentative conditions.
Thus, if any problem affects sugar catabolism, it is possible to observe a stuck or
sluggish fermentation. Once a fermentation sticks it can be difficult to restart. This
can have an effect on the logistics of the winery (holding precious tank volume in the
15
busy vintage period) and leave residual sugars in the wine, which can affect sensory
qualities and microbial stability.
Due to the different rates of consumption of glucose and fructose, it is possible to
observe higher concentrations of fructose in the latter and more difficult stages of
fermentation. If the fermentation sticks or becomes sluggish, higher concentrations of
the less preferred hexose (fructose) increase the difficulty of restarting sugar
catabolism. Under this light, generating a wine yeast strain with improved fructophilic
characteristics, might lead to strains showing more robust fermentation capabilities
and a more balanced ratio between glucose and fructose during the progress of
fermentation. Despite hexose uptake and consumption being a mechanism that
depends on numerous environmental and biological parameters, and more work is
needed to clarify the reasons for the glucose and fructose discrepancy, it is clear that
one or more causes will relate to the kinetics of the transport systems and initial
enzymatic processes of the fermentation pathway.
Understanding the causes of slowdown or arrest of fermentation during winemaking is
vital and a necessary step in an improvement program for wine yeast. The main
factors influencing yeast growth and metabolic activity are numerous and a more
extensive discussion of these is given in the following section.
1.3 STUCK AND SLUGGISH FERMENTATIONS: ONE OF THE BIGGEST PROBLEMS DURING THE WINEMAKING PROCESS
Despite yeast being a somewhat robust microorganism, well adapted to winemaking
conditions, fermenting grape juice represents a harsh environment for S. cerevisiae. If
yeast loses its metabolic activity or viability, the most evident outcome is a decrease
of fermentation rate, which sometimes results in a complete arrest of sugar
catabolism. There are many reasons for stuck or sluggish fermentations and thus it can
be difficult to identify the precise cause in a given situation (Bisson and Butzke, 2000;
Malherbe et al., 2007). In fact, the system ‘yeast-must-wine’ presents many
interactions and complicated equilibria which are involved throughout the progression
of fermentation. Total consumption of sugar by yeast can fail even if only one
16
parameter is out of balance. However, most problem fermentations occur as a result of
multiple parameters. For this reason, reduction of the risks of sluggish or stuck
fermentations requires a sufficient understanding of the potential causes of these
problems. Nutrient limitation, physical factors, toxic substances, microbial
incompatibly are arguably the most important (Bisson, 1999). In Table 1.2 are
detailed the main causes implied in problem fermentations. For a more extensive
review on this topic see Alexandre and Charpentier (1998), Bisson (1999), Boulton et
al. (1998) and Fleet (1992).
Many of the factors affecting wine yeast during fermentation can be eliminated pre- or
during winemaking. Modern wineries are usually well equipped to analyse juice
composition and control several parameters of the winemaking. Thus, it is possible to
supply the juice with nutrients necessary for the yeast to complete the fermentation. In
the same way, it is possible to correct and control physical factors, such as
temperature, pH and lack of oxygen, to better suit yeast requirements. Resistance to
toxic substances or competition with other microorganisms can be limited by
inoculating the juice with a strong yeast culture or with more careful vineyard
management. Oenological practices in the winery can be applied to preserve the
viability and the metabolic ability of S. cerevisiae. However at the present it is not
possible to control the differing ability of yeast to utilise fructose compared to
glucose.
As described extensively above, S. cerevisiae shows a higher fermentation rate for
glucose compared to fructose. Thus, in the latter stage of alcoholic fermentation,
fructose is the main sugar remaining. The lower fermentability of fructose, combined
with nutrient depletion and the high alcohol content, contribute to possible
fermentative problems (Berthels et al., 2004; Guillaume et al., 2007). Another
important consideration is linked with the ratio between glucose and fructose
concentrations. In grape juice this ratio is approximately 1.0, progressively decreasing
during fermentation. Schutz and Gafner (1993) in fact linked stuck fermentations with
the value of the glucose and fructose ratio falling below 0.1. These authors also
suggested that it can be stimulatory to restore this ratio to a value above 0.1. Thus a
stuck fermentation can be restarted by artificially adding glucose to the medium. This
finding may suggest that the predominance of fructose in the latter stages of
fermentation is not only a consequence of the higher fermentability of
17
Table 1.2 – Possible factors influencing the catabolic activity and growth of yeast during alcoholic fermentation in wine.
Factors affecting yeast during fermentation
Details Implications and comments
Nutrient limitation
� Macro-nutrients (carbon, nitrogen, phosphate)
� Micro-nutrients (vitamins, minerals)
� Source of energy (carbon) � Proteins synthesis (macro-
nutrients) � Survival factors (micro-
nutrients)
Physical factors � Temperature � Low pH (< 2.8) � Lack of oxygen
Most notable effects of temperature are on the plasma membrane, reducing or increasing its fluidity. Low pH reduces ethanol and fatty acid tolerance. Oxygen is important for biosynthesis of cell membrane components.
Toxic substances
� Ethanol � Medium-chain fatty acids � Fungicides and pesticides
(vineyard residues) � Antifungal agents (plant
response to fungal infections)
Inhibition (to complete arrest) of cell metabolism. Manifold interactions at different levels.
Microbial incompatibly
� Non Saccharomyces yeast � Bacteria
� Nutrient competition � Toxic substances (i.e. yeast
killer factors and mycotoxins)
Other factors Oenological practices Can generate adverse conditions for the growth and metabolism of yeast
Different ability to utilise glucose and fructose
Less efficiency for fructose transport and/or catabolism
Fructose predominates in the latter and more difficult stages of fermentation
18
glucose by wine yeast, but it could also be a cause of the arrest of fermentation when
residual sugar is almost completely composed of fructose.
Many of the possible factors influencing the metabolic activity and growth of yeast
during alcoholic fermentation presented above, can be controlled. In wineries it is
possible to add nutrients, correct pH, regulate temperature, supply oxygen, manage
the micro-flora and control many other parameters. However it is not possible to
change the sugar composition of the juice and thus it is not possible to regulate the
different consumption rate for glucose and fructose, as is characteristic of
Saccharomyces cerevisiae. For this reason, improving wine yeast appears to be the
only solution to achieving a more uniform consumption of glucose and fructose.
1.4 IMPROVING WINE YEAST STRAINS
The importance of improved strains for the modern wine industry
A suitable yeast for winemaking has to satisfy several requirements and show
determinate characteristics. These aspects of yeast can be divided into technological
and qualitative traits (Zambonelli, 1998). Technological traits influence the efficiency
of the fermentation process. Those most often targeted for improvement are
fermentative vigour, ethanol yield and tolerance, growth temperature range, inhibition
by other microbial species, resistance to SO2, type of growth in liquid media
(dispersed or aggregated cells, flocculence, foam and film formation and
sedimentation speed), tolerance of extreme temperatures and resistance to killer factor
(Rainieri and Pretorius, 2000). Qualitative characteristics influence the chemical
composition and sensorial profile of wines: fermentation by-products (glycerol,
succinic acid, acetic acid, acetaldehyde, n-propanol, iso-butanol, isoamyl alcohol, �-
phenylethanol, etc.), production of sulphur compounds (H2S, SO2, etc.) and the action
of enzymatic activities (�-glucosidase, esterase, proteolytic enzymes, autolysis, etc).
An ideal improved strain is obtained with consideration and satisfaction of both
groups of attributes (Rainieri and Pretorius, 2000).
Yeast improvement can be carried out using a broad spectrum of techniques (Table
1.3). It is highly unlikely that one can identify strains possessing an ideal combination
19
Table 1.3 – Possible approaches for the improvement of specific traits in wine yeasts.
Approach Technique Advantages Disadvantages
Non recombinant
Natural selection
Preserves the natural characteristics of a strain
Likely to be limited in the extent of the improvement
� Mutagenesis � Hybridization � Rare-mating � Spheroplast fusion
Increases genetic diversity
Can introduce significant additional non-specific changes to the strains phenotype
Adaptive (directed) evolution
� High and specific potential of improvement
� No direct DNA manipulation
� Difficulties in defining the appropriate selective condition for desired the outcomes
� Potential lost of desirable attributes
Recombinant � Gene cloning and transformation
High and specific potential of improvement
� Requires high knowledge of the relevant biological and genetic mechanisms
� Must consider views on the use of genetically modified microorganisms in the food industry
20
of technological and qualitative traits by only relying on the natural availability of
different phenotypes (Rainieri and Pretorius, 2000). However, selection of clones is
usually carried out from must or wine, where the yeasts are well adapted to the
oenological environment. Typically a high number of yeast strains are considered,
which are then submitted for analysis of their oenological properties. Thus, “clonal
selection” provides a collection of diverse backgrounds that are very useful for
successive genetic improvement programmes (Giudici et al., 2005).
Progress in the field of microbiology has made it possible to obtain strains that are
quite different from the parental strains (Pretorius, 2000): induced mutation and
selection, hybridisation, rare-mating, spheroplast fusion, and gene cloning and
transformation are among the most widely used techniques. For a more extensive
review see Barre et al. (1993), Pretorius (2000), Pretorius and van der Westhuizen
(1991) and Zambonelli (1998). However, generating a strain with many differences
from its parent it is not always desirable. The challenge between tradition and
innovation is the key to describing this phenomenon and the maintenance of the
original identity of the wine has to be considered during strain improvement.
Conversely, the possibility of obtaining genetically engineered strains expressing
novel genes can open doors to some new and exciting winemaking approaches, and
more specifically combine desired oenological traits (Rainieri and Pretorius, 2000).
Recombinant DNA technologies are the most modern approach for yeast
improvement programs. However, in order to manipulate DNA, the complexity of
yeast biology requires a high level of knowledge and a deep understanding of the
intricate interactions between different biological and gene expression and regulation
mechanisms. Moreover, comment needs to be made about the ethical debates existing
around DNA manipulation in the food industry. To some extent, genetic improvement
of plants, animals and micro-organisms is necessary and required by modern
production processes of foods and beverages, but on the other hand a large segment of
the market rejects GM organisms (Pretorius, 2000). Some consumers consider the GM
approach undesirable and thus avoid the consumption of products derived from DNA-
manipulated organisms. In the absence of extensive results about the safety of food
and beverages derived from GM organisms, this ethical aspect has to be considered
and the genetic improvements required by the industry have to be balanced against
consumer expectations and needs.
21
A technique that needs exploration in improving microorganism is adaptive evolution.
Adaptive evolution can selectively retain phenotypes presenting mutations which
permit a better adaptation to a specific stress. Mutation can arise spontaneously from
exposure to or in response to environmental stress. Alternatively mutants can be
artificially induced. This makes adaptive evolution a technique that can be readily
combined with other improvement strategies, using a pre-selected strain from a
natural winemaking environment as a genetic background to isolate phenotypes with
specific improved characteristics. Adaptive evolution doesn’t require a deep
knowledge of the genetics of the process targeted for the improvement and does not
produce GM organisms. Moreover, this technique can preserve the original
oenological traits of the parent, resulting in a perfect tool with which to maintain
oenological traditions and terroir, whilst improving and bringing innovation to the
winemaking process.
The ability of adaptive evolution to yield isolates better suited for a selective
environment has been demonstrated, and several studies have extensively investigated
the genetic mechanisms of the occurrence of adaptive mutations as well as the
dynamics of the evolving populations (see below). What has been less studied, is the
possibility of the practical use of adaptive evolution as an improvement strategy. The
identification of a particular area of interest for improvement appears to be the next
step. Thus in winemaking, the possibility of reducing differences in the consumption
rate of fructose compared to glucose seems to be a perfect case study for this purpose.
The adaptive evolution technique
Historical background
Since the beginning of the last century there have been open debates on evolution
theories. Moreover a better knowledge on unicellular organisms opened new fields of
investigations and two different schools of thought began. The first supported the
“mutation theory”: a random genetic mutation pre-existing in the population prior the
beginning of a stress, giving a specific adaptation to the descending population. The
other notion is the “adaptation theory”: the adaptation to an induced stress is too
22
precise to be explained as a result of a random mutation and thus the adaptation is a
physiological response to a particular stress, i.e. the mutations are not present before
the occurrence of the stress. The first important study that tried to explain these
controversial notions came in 1943 (Luria and Delbruck, 1943). Observing the
distribution of the accumulation of adapted bacteria in culture, these authors
disproved the adaption theory. However they did not completely support the mutation
theory as they believed proof was required.
It was only at the end of the century, armed with a more detailed knowledge of cell
biology, that Cairns and co-workers demonstrated that cells may have mechanisms to
choose which mutations will occur to allow adaptation to a particular environment:
“Bacteria, in stationary phase, have some way of producing (or selectively retaining)
only the most appropriate mutations” (Cairns et al., 1988). Thus, these authors
showed that mutants not only pre-exist in a population as result of random and not-
specific mutation, but they can arise in response to a specific selective pressure.
Several other authors confirmed the Cairnsian theory: Foster (1992), Foster and
Cairns (1992), Hall (1988, 1989, 1992, 1997) and Steele and Jinks-Robertson (1992).
However, the exact mechanisms of how the cell is able to induce a specific mutation
to relieve a stress are not completely understood and more work is necessary.
Pioneering work was carried out on prokaryotic cells. Bacteria have been used to
prove the existence of selection-induced mutations since the beginning of the
evolutionary debate at the end of the nineteenth century. However more recent
experiments proved that selection-induced mutations also occur in yeast (Brown et al,
1998; Hall, 1992; Paquin and Adams, 1983). Thus, adaptive evolution theories are not
limited to prokaryotes and eukaryote cells also have some system to adapt to a
specific stress (Steele and Jinks-Robertson, 1992).
Another field of investigation related with the mechanism of adaptive mutation has
been the frequency of the occurrence of a mutation. This knowledge is fundamental to
understanding the evolution in a population exposed to a selective pressure. One of
the early debates was solved in 1983 (Paquin and Adams, 1983): mutations occur
more frequently in diploid cells than in haploid. These authors support the idea that
diploid cells should generate twice as many adaptive mutations than haploids because
23
the genome is double in size and so too the number of duplications. However they
demonstrated a rate of adaptive mutation only 1.6 times higher in diploids compared
to haploids. This divergence from the theoretical value can be attributed to deleterious
mutations in the recessive heterozygote state. Moreover the size of a haploid
population is usually higher that the isogenic diploid. Population size also affects the
frequency of mutations (Paquin and Adams, 1983; Wahl and Krakauer, 2000; Wick et
al., 2002). Thus, this phenomenon can contribute to a compensation for the lower
frequency of mutation produced by haploid cells. Conversely, for an induced
adaptation to become a permanent and deep-seated characteristic of a population it
requires a mutation at the allele level, its fixation and the replacement of the new
allele in the entire population (Zeyl, 2004). Despite the high frequency of mutation
occurring in diploids (Paquin and Adams, 1983), in larger population experiments the
fixing time of a mutation can become the determinant factor of adaptive evolution
(Chambers et al., 2007). This can be explained in part by the non-dominant mutations,
requiring longer to bring adaptive advantage to a heterozygous population (Zeyl et al.,
2003). According to this point of view, haploids should evolve faster than diploids
(Orr and Otto, 1994). As confirmation, this phenomena has been observed in a 1.0
litre adaptive evolution fermentation experiment (McBryde et al., 2006), where a
diploid population took a longer span of generations to adapt compared to the derivate
haploid.
Another key consideration in adaptive evolution is the relation between the
occurrence of a mutation and the genetic and physiological characteristic of the
studied organism. Different species and environmental conditions drastically affect
the frequency of adaptive mutations. Hall (1988) observed a rate of advantageous
mutations of around 2 x 10-12 per cell divisions. Paquin and Adams (1983) obtained
rates between 5.68 x 10-12 and 3.55 x 10-12 per cell division, while Zeyl (2004)
reported a value of one adaptive mutation every 1011 cell divisions. McBryde and co-
authors (2006) isolated adapt-evolved mutants after 250 and 350 generations, while
Brown and colleagues (1998) observed evolution phenomena after 450 generations.
Since work on adaptive evolution began, new studies have yielded a large amount of
data to demonstrate the validity of this theory and to explain the mechanisms of
adaptive mutation. On the other hand, the exploitation of adaptive mutations as a tool
to induce cell modifications in order to improve specific industrial characteristics of
24
strains is an underdeveloped area of investigation. There are few studies which have
evaluated the adaptive evolution technique in this context. Moreover, only a small
fraction of these investigations have utilised yeast and most work has been performed
on bacteria.
Adaptive evolution
In adaptive evolution, a population of micro-organisms is grown for hundreds or
thousands of generations, while preserving a sample of the population at chosen
intervals so that they can be compared directly with each other and with their
ancestors (Zeyl, 2004). Thus, the critical steps in adaptive evolution are: to create a
specific stress condition; to maintain the population under stress for many
generations; to keep population samples for further comparison with the original
parental strain.
Studies on adaptive evolution have been carried out in either immobilized cells or
populations maintained in liquid media. In the first approach, cells are grown on
plates containing solid media, which impose a specific stress condition. After a period
of time, it is possible to observe growth of mutants, adapted to the specific stress.
Mutants can be easily isolated for characterization or be re-plated for further
adaptation.
For a population maintained in liquid media, two approaches exist (Zeyl, 2004):
chemostats and serial transfers (or batch cultures). In the chemostat, a population is
maintained in a constant environment, adding fresh media to the culture, while
partially exhausted media is removed at the same rate. For serial transfer a small
percentage of the population is transferred into a new vessel containing fresh media.
With this approach it is possible to create a cyclic condition of induced stress,
alternating with periods of non-stress. With both of these methods it is possible to
choose a specific stress (e.g. by changing the chemical composition of the media) to
“force” the population to adapt to that particular environment, by inducing adaptive
mutations. These two approaches of adaptive evolution may seem similar, but a
difference exists that can influence the choice between one approach over the other.
Continuous culture tends to maintain a population in a largely stable environment for
an indefinite time. This is somewhat artificial because in nature (and in almost all
25
industrial food processes) a population is usually subject to a definite growth phase,
depending on the chemical and physical parameters (substrate, temperature, stresses,
etc.) that defines the kinetics of the growth of that population. Continuous culture
represents only a fraction of the life of a culture in the real world. For example, if the
target is to improve a yeast strain under the stressful conditions of the wine
fermentation, continuous culture only reproduces a particular phase of that
fermentation. This can be advantageous because it can drastically reduce the run time
of the experiment since, for example, the population can be maintained in the
exponential growth phase permanently (McBryde et al., 2006). However the
continuous culture is not representative of the dynamic fermentation process and for
this reason it may not be suitable for improving a strain for industrial/commercial use.
This problem can be solved using a sequential batch cultivation system. This approach
ensures the representation of conditions similar to an industrial environment. Thus in
terms of the previous example of an oenological fermentation, cells are inoculated in
a defined medium representing a grape juice (e.g. CDGJM; Henschke and Jiranek,
1993) and allowed to ferment under oenological conditions until the sugar
concentration reaches that of a finished wine (< 2.5 g/l). At this stage sufficient
numbers of cells are re-inoculated into a fresh batch of media and a new fermentation
is initiated. The sequential batch fermentation system has the disadvantage of
requiring an extended culture time, as the cells are not always at the maximum
division rate of the exponential phase. However this system is more representative of
the industrial process and would conceivably eliminate the selection of alterations that
are disadvantageous to general fermentation performance (McBryde et al., 2006).
1.5 AIM OF THE PROJECT As fermentation is one of the most important steps in the winemaking process,
research must focus on all of its resources to make this process much more reliable.
To avoid the risks of stuck and sluggish fermentations, one of the best approaches is
represented by the improvement of yeast strains. Without forgetting the aroma
contribution and other fundamental influences that yeasts have on the final product,
sugar metabolic activity is a key consideration when creating or isolating a new strain
26
for industrial use. In particular, the different affinity of yeasts for glucose and fructose
(present in equal concentration in grape juice) is one of the greatest difficulties for
completion of fermentation under oenological conditions by glucophilic yeast.
To improve wine yeast, research has the possibility of following different approaches
ranging from natural selection of clones to genetic manipulation techniques. However,
some problems linked to inadequate efficiency, application difficulties,
inappropriateness for commercial application and ethical aspects, require alternate
approaches to be used, particularly those that recognise consumer demands. For this
reason, the application of adaptive evolution to improve wine yeast may be an
important methodology to develop. It presents clear evidence of adequacy: adaptive
mutations have been shown to occur; induced mutations arise either in prokaryotes or
eukaryotes; occurrence of mutations has been studied in haploid and diploid states;
frequency of mutation has been reported in several studies; application principles of
the technique have been described along with the advantages and disadvantages of
different approaches. Moreover, the ability of adaptive evolution to generate new
phenotypes in a wine related study has been demonstrated (McBryde et al., 2006).
Thus, the possibility of testing the capacity of adaptive evolution to improve
commercial wine yeast strains for specific characteristics warrants attention. For this
purpose, fructose fermentation efficiency seems to be a good topic for such an
investigation. The use of fructose by yeast is an easy parameter to monitor
analytically, it is regulated by genetic inheritance and it has strong commercial
interest.
The aim of the research proposed here is to choose a S. cerevisiae strain normally
used in the commercial winemaking process and demonstrate the validity of the
adaptive evolution technique to generate improvements specifically in its fructose
utilization.
27
CHAPTER 2
CANDIDATE STRAIN SELECTION
28
The work presented in this chapter was aimed at identifying candidate strains suitable
for improvement of their fructose utilization capabilities. The genetic background to
be used for the adaptive evolution experiment was to be that of a commercially
available strain. To identify the candidate strain, the phenotype of 20 commercially
available strains was characterized in terms of their fermentative properties,
particularly their ability to ferment fructose, either in the presence or absence of
glucose. A description of this screening exercise is given in the following manuscript
(accepted for publication in Journal of Industrial Microbiology and Biotechnology on
17th of August 2010 – In press. Ms. No. JIMB-D-10-00444R1).
29
STATEMENT OF AUTHORSHIP
A NOVEL METHODOLOGY INDEPENDENT OF FERMENTATION RATE FOR
ASSESSMENT OF THE FRUCTOPHILIC CHARACTER OF WINE YEAST
STRAINS
NOTE: Statements of authorship appear in the print copy of the thesis held in the University of Adelaide Library.
30
2.1 A NOVEL METHODOLOGY INDEPENDENT OF
FERMENTATION RATE FOR ASSESSMENT OF
THE FRUCTOPHILIC CHARACTER OF WINE
YEAST STRAINS
T. Liccioli1, P.J. Chambers2, V. Jiranek1
1The University of Adelaide, School of Agriculture, Food and Wine,
PMB 1 Glen Osmond, SA 5064, Australia.
2The Australian Wine Research Institute, PO Box 197, Glen Osmond, SA
5064, Australia.
Corresponding author:
Vladimir Jiranek
The University of Adelaide, School of Agriculture, Food and Wine,
PMB 1 Glen Osmond, SA5064.
Phone: +61 08 8303 6651
Fax: +61 08 8303 7415
e-mail: [email protected]
31
Abstract
The yeast Saccharomyces cerevisiae has a fundamental role in fermenting grape juice
to wine. During alcoholic fermentation its catabolic activity converts sugars (which in
grape juice are a near equal ratio of glucose and fructose) and other grape compounds
into ethanol, carbon dioxide and sensorily important metabolites. However, S.
cerevisiae typically utilises glucose and fructose with different efficiency: glucose is
preferred and is consumed at a higher rate than fructose. This results in an increasing
difference between the concentrations of glucose and fructose during fermentation. In
this study 20 commercially available strains were investigated to determine their
relative abilities to utilise glucose and fructose. Parameters measured included
fermentation duration and the kinetics of utilisation of fructose when supplied as sole
carbon source or in an equimolar mix with glucose. The data were then analysed using
mathematical calculations in an effort to identify fermentation attributes which were
indicative of overall fructose utilisation and fermentation performance. Fermentation
durations ranged from 74.6 to over 150 hours with clear differences in the degree to
which glucose utilisation was preferential. Given this variability we sought to gain a
more holistic indication of strain performance that was independent of fermentation
rate and therefore utilise the Area Under the Curve (AUC) of fermentation of
individual or combined sugars. In this way it was possible to rank the 20 strains for
their ability to consume fructose relatively to glucose. Moreover, it was shown that
fermentations performed in media containing fructose as sole carbon source did not
predict the fructophilicity of strains in wine like conditions (equimolar mixture of
glucose and fructose). This work provides important information for programs which
seek to generate strains that are faster or more reliable fermenters.
Keywords: glucose, fructose, fermentation progress, strain comparison, composite
trapezoid rule. Abbreviation: Area Under the Curve, AUC.
Introduction
When exposed to mixtures of glucose and fructose, as occurs during the fermentation
of grape juice into wine, Saccharomyces cerevisiae utilises these sugars at different
rates (Berthels et al., 2004; Júnior et al., 2008). As a result the ratio between the
32
concentration of glucose and fructose changes, so that late in fermentation fructose
becomes the predominant sugar. This is reported to be one of the causes of arrested or
so-called stuck fermentation (Gafner and Schütz, 1996). Attempts to restart the
fermentation through re-inoculation with fresh but nonetheless glucophilic cultures
are challenging (Cavazza et al., 2004). Regardless of the cause of stuck fermentation,
excess residual sugar is undesirable as it puts the palate of the wine out of balance.
Furthermore, the fact that fructose predominates compounds the problem since
fructose is sweeter than glucose (Lee, 1987).
In seeking the precise basis for differences in the rate of glucose vs fructose
utilisation, steps in the metabolism of hexoses prior to the formation of fructose 1,6
bisphosphate are implicated. In particular this includes the systems for sensing of
extracellular sugars, their transport across the plasma membrane and phosphorylation
as part of the first steps of glycolysis. Plasma membrane sensor-proteins have been
identified which show affinity for glucose, with at least one exhibiting a different
affinity for glucose and fructose (Rolland et al., 2001a; 2001b). The presence of
sensors specific for fructose has not been reported (Berthels et al., 2004).
Phosphorylation of internalised glucose and fructose occurs via the hexokinases Hxk1
and Hxk2, albeit at different efficiencies, with glucose additionally being acted upon
by glucokinase Glk1 (Berthels et al., 2004; Guillaume et al., 2007; Serrano and
Delafuente, 1974). Since the phosphorylation capacity of these enzymes exceeds the
amount of sugar transported into the cell, it would appear that this enzymatic process
is not the basis for differences in the utilisation of glucose compared to fructose.
However, a recent study (Berthels et al., 2008) demonstrate that over-expression of
hexokinases could in fact alter the rate of fermentation. On the other hand, the hexose
transport system has been clearly shown by many authors to be a critical point in
determining fermentation rate.
In S. cerevisiae, hexose uptake is largely mediated by facilitated diffusion (Bisson and
Fraenkel, 1983), where 20 genes (HXT1 to HXT17, GAL2, SFN3 and RGT2) encode
the related proteins. Despite such plurality, only those transporters encoded by HXT1
to HXT7 appear to be of importance for glucose and fructose utilisation under
fermentation conditions (Diderich et al., 1999; 2001; Elbing et al., 2004; Ozcan and
Johnston, 1995, 1999; Reifenberger et al., 1995). Some of these latter HXT
transporters have relatively low-affinity (high Km) for hexose, whilst other are
33
considered high-affinity (low Km), and all have the ability to transport both glucose
and fructose. Importantly, whether they are high- or low-affinity systems, all have a
greater affinity for glucose than for fructose. The expression of each HXT gene is
regulated by environmental factors, especially the extracellular hexose concentration
(Ozcan and Johnston, 1995). The high-affinity transporters are induced when the
glucose concentration is low (~1 – 4 mM or 0.18 – 0.72 g/l) and are repressed when
the concentration of glucose is higher (Ciriacy and Reifenberger, 1997). Conversely
low-affinity carriers are induced by high glucose concentrations (~50 – 100 mM or 9
– 18 g/l) if not constitutively expressed. HXT3, a low-affinity transporter, is
considered key to determining glucose transport and, thereby, could play a
fundamental role in the different rates of glucose and fructose utilization (Guillaume
et al., 2007). Further work is required to define this role.
Beyond affinity differences, other factors such as nitrogen availability and response to
ethanol may be of importance. Ethanol is known to have protein denaturing properties
and disrupts plasma membrane components (Bisson and Block, 2002; Stanley et al.,
2010). Due to increases in membrane permeability and passive proton flux upon
ethanol exposure, damage to intracellular enzymes and structures might also occur.
Berthels and co-authors (2004) observed that a high ethanol concentration inhibited
sugar utilization, but to different extents for glucose and fructose. This led them to
hypothesise that the glucose utilization capability was more robust than fructose
utilization. Moreover ethanol is able to shift the tautomeric equilibrium of fructose
from the readily transported pyranose form to the furanose form. Thus unlike glucose
which is entirely in the transportable pyranose form, typically only 70% of fructose
takes the pyranose form, and less in the presence of ethanol. Thus not only is there a
discrepancy between glucose and fructose transport, it also changes during the
progress of fermentation (Berthels et al., 2004) and may be part cause and part effect
of different ethanol tolerance of various strains.
A further possibility is linked with nitrogen utilisation and availability. Sugar
transporters have been shown to be turned over quickly (t½ ~5 hours) compared to
other proteins (Salmon et al., 1993) thereby creating a demand for active protein
synthesis and assimilable nitrogen. In every case of nitrogen supplementation to
nitrogen starved cultures, fructose consumption was enhanced to a greater extent than
glucose (Berthels et al., 2004). Different strains have been shown to utilise nitrogen to
34
different extents (Jiranek et al., 1995). Perhaps therefore as fermentation progresses
strains with higher nitrogen demands experience greater or earlier restrictions on
assimilable nitrogen availability and thus their ability to maintain fructose transport,
thereby resulting in a higher rate of glucose transport.
Available evidence affirms that hexose utilisation depends on numerous
environmental and biological parameters. Thus determining the precise basis for the
glucose/fructose discrepancy in order to target efforts to reduce such differences and
presumably improve strain performance and wine composition are difficult. In this
study 20 commercially available wine strains were chosen for characterization of their
fructose utilisation proprieties. Such data was sought so as to begin to determine the
relationship between abilities around utilisation of individual sugars and relative
fermentative performance. Fermentations were conducted in a chemically defined
grape juice medium (CDGJM; Henschke and Jiranek, 1993) with two different sugar
compositions. Either an equimolar mix of glucose and fructose or else a medium
containing fructose as the only sugar were used. Various parameters related to total
fermentation duration and kinetics of fructose and/or glucose utilisation were
measured and the resulting values compared though mathematical calculations to
identify relationships.
Materials and methods
Strains and maintenance
A total of 20 commercial strains of S. cerevisiae were selected as representative of
commonly used wine yeast and, where possible, chosen with consideration of
published information about their ability to consume fructose compared to glucose.
(Berthels et al., 2004; Guillaume et al., 2007). The strains used where: B, UCD522,
Cru-Blanc, Primeur, AWRI 350, AWRI 796, AWRI 1503, Elegance (AB Mauri,
Sydney, Australia); EC1118, V1116, D254, W27, BM45, Syrah, Bordeaux Red, S6U,
Uvaferm 43 (Lallemand, Montreal, Canada); VIN13, NT202 (Anchor Yeast, Cape
Town, South Africa), Fermichamp (DSM, Netherland). Strains were collected
aseptically from active dried commercial preparations, re-hydrated in sterile water (20
min) and inoculated into YEPD medium
35
(20 g/l D-glucose, 10 g/l yeast extract, 20 g/l Bacto peptone) in a flask (air/liquid ratio
> 66%) before overnight incubation at 28°C with shaking at 180 rpm. Cultures were
then streaked onto YEPD agar plates and grown overnight at 28°C to check for purity.
Multiple representative colonies were inoculated into 25 ml of YEPD broth and
grown as above. These served as starter cultures for the fermentation experiments
detailed below or, with the combination of 1 ml of culture with 0.5 ml of sterile 80%
(v/v) glycerol, enabled long term storage at -80°C.
Fermentation experiments
Starter cultures were used to generate pre-cultures, which in turn were used to
inoculate fermentation experiments as detailed elsewhere (McBryde et al., 2006). For
each of the 20 strains, fermentations were performed in order to define their sugar
utilisation and fermentation kinetics. To do this, two different formulations of a
CDGJM were used. The first was representative of a typical grape juice in that
glucose and fructose were supplied in equimolar amounts to a combined total of 230
g/L. For the second condition an equivalent amount of sugar was supplied but as
fructose only. In each case 600 mg N/l (as amino acids) were used and the triplicate
fermentations were incubated at 28°C with shaking at 160 rpm.
Fermentation progress was estimated from the Brix value of clarified (14,000 rpm, 2
min) culture samples and fermentation completion (< 2.5 g/l) determined using
Clinitest® tablets (Bayer). Supernatants were stored at -20°C for subsequent
determination of residual sugar content by an enzymatic method (Boehringer-
Mannheim, 1989) adapted for 48 well plates.
Analyses
For every strain, the values obtained from the determination of individual sugars in
each medium where considered separately. Four datasets with regard to sugar
concentration during the progress of fermentation were obtained, i.e. for glucose,
fructose and glucose + fructose in the mixed sugar condition as well as fructose from
the fructose-only media. These datasets were used to plot the respective sugar
utilization curves for each strain. In this way, 20 charts were obtained each containing
four areas delimited by the fermentation curves for the four sugars or combinations
(Figure 2.1).
36
Figure 2.1 – Composite plots showing sugar utilisation profiles of 20 commercial wine strains from two growth media. Yeast were grown in CDGJM containing either 230 g/l of fructose only (top curve in each plot) or 115 g/l each of glucose and fructose (1:1; bottom 3 curves in each plot). Curves are derived from the mean of triplicate fermentations with error bars indicating standard deviation. Moving from top to bottom, the curves correspond to 1) fructose fermentation in a fructose-only medium, 2) combined glucose and fructose fermentation from the mixed sugar medium, 3) fructose fermentation from the mixed sugar medium and 4) glucose fermentation from the mixed sugar medium. The hatched area between the top curve (fructose fermentation in fructose-only medium) and the curve below (combined glucose and fructose fermentation from the mixed sugar medium) highlights the difference in total fermentation profile in the two media. The area corresponding to glucose fermentation from the mixed sugar medium is highlighted in dark grey, while the extent to which fructose utilisation from the mixed sugar condition was delayed compared to glucose is highlighted in light grey.
B
0 50 100 1500
50
100
150
200
250
PRIMEUR
0 50 100 1500
50
100
150
200
250
UCD 522
0 50 100 1500
50
100
150
200
250
AWRI 1503
0 50 100 1500
50
100
150
200
250
AWRI 796
0 50 100 1500
50
100
150
200
250
CRU-BLANC
0 50 100 1500
50
100
150
200
250
EC 1118
0 50 100 1500
50
100
150
200
250
AWRI 350
0 50 100 1500
50
100
150
200
250
V1116
0 50 100 1500
50
100
150
200
250
D 254
0 50 100 1500
50
100
150
200
250
W 27
0 50 100 1500
50
100
150
200
250
BM 45
0 50 100 1500
50
100
150
200
250
SYRAH
0 50 100 1500
50
100
150
200
250
BORDEAUX RED
0 50 100 1500
50
100
150
200
250
S6U
0 50 100 1500
50
100
150
200
250
FERMICHAMP
0 50 100 1500
50
100
150
200
250
UVAFERM 43
0 50 100 1500
50
100
150
200
250
QA23
0 50 100 1500
50
100
150
200
250
VIN 13
0 50 100 1500
50
100
150
200
250
NT 202
0 50 100 1500
50
100
150
200
250
Suga
r con
cent
ratio
n (g
/l)
Time elapsed (hours)
37
The value of these areas was calculated using the composite trapezoid rule (GraphPad
Prism 5 - GraphPad Software Inc., La Jolla, C.A., U.S.A.), a numerical integration
method for approximating an integral or AUC where the function of the curve is
unknown. The value of the AUC is returned in units of the X axis times the units of
the Y axis. Statistical analysis of the data (one-way ANOVA with Dunnet and
Tukey’s multiple comparison post-test analysis) was performed with the same
software.
Results
Fermentation duration
The times required for complete utilisation of glucose and/or fructose were defined
for 20 commercial strains of yeast and are summarised in Table 2.1. For most strains,
fermentation of each medium was completed (i.e. < 2.5 g/l residual sugar) in less than
150 h. The only exception was Maurivin strain B, for which the mixed sugar
fermentation required 151 hrs and the fructose-only condition failed to complete
within this preset maximum duration. In every case the utilisation of fructose lagged
behind that of glucose. Similarly fermentation of fructose took longer than the
fermentation of an equivalent amount of mixed sugars. Glucose depletion from the
mixed sugar condition occurred in between 60 h (UCD522) and 115 h (B) whereas
fructose depletion occurred within 75 h (UCD522) and 151 h (B). Since fructose
depletion always took longer than that of glucose, the time for the former to occur
also equated to the total duration of the mixed sugar fermentations. By comparison,
where complete utilisation of 230 g/l of fructose was seen, a total of between 94 h
(UCD522) and 134 h (BM45) was required for this eventuality. Thus the complete
fermentation of the fructose-only medium took longer than the equivalent mixed sugar
fermentation. These observations confirm the apparent glucophilicity of
Saccharomyces yeast, and suggest that the utilisation of fructose is the rate-limiting
step and that the time taken for this defines the total duration of fermentation.
The inclusion of a medium containing fructose as the only sugar provided an
opportunity to determine whether this condition was predictive of fructose utilisation
capabilities in the mixed sugar medium. The duration of a fructose-only fermentation
38
Table 2.1. Duration of sugar catabolism and fermentation for 20 commercial yeast strains during growth in media containing glucose and fructose or only fructose to a total concentration of 230 g/L. Values are the average of triplicate fermentations ± standard deviation (SD).
Strain
Duration (h) Glucose
(mixed sugars) Fructose or Total sugars
(mixed sugars) Fructose
(fructose only)
Average SD Average SD Average SD B 115.3 12.5 151.3 20.2 DNC1 DNC BORDEAUX RED 73.0 0.0 114.5 5.2 120.3 4.9 AWRI 350 77.2 4.9 101.0 0.0 115.3 3.7 BM45 75.5 4.3 99.2 2.3 134.3 7.2 UVAFERM 43 80.0 0.0 97.0 2.6 106.3 2.3 W27 73.0 0.0 96.5 0.0 104.5 0.0 D254 71.5 0.0 95.0 0.0 109.5 2.6 PRIMEUR 70.0 0.0 94.3 4.6 101.0 0.0 FERMICHAMP 80.0 0.0 94.0 0.0 99.8 1.1 QA23 80.0 0.0 94.0 0.0 102.8 3.7 VIN13 72.0 0.0 94.0 0.0 105.0 0.0 NT202 77.3 4.6 94.0 0.0 112.3 2.9 S6U 73.0 0.0 93.5 5.2 110.0 2.6 SYRAH 64.5 0.0 87.5 0.0 104.5 0.0 V1116 71.5 0.0 86.5 0.0 104.7 3.2 AWRI 796 67.7 4.0 86.0 1.7 102.3 1.1 CRU-BLANC 66.2 4.6 85.2 2.3 95.0 0.0 AWRI 1503 67.7 4.0 84.0 5.2 99.7 2.3 EC1118 71.5 0.0 82.5 0.0 95.0 0.0 UCD522 60.3 4.6 74.6 4.0 94.3 4.6
1 DNC, Fermentations did not complete.
39
was not strongly correlated (R2 = 0.524) with the time required for fructose depletion
from the mixed sugar medium. This indicates that the duration of utilisation of
fructose as sole carbon source is not necessarily a good predictor of the duration of
fructose utilisation in the presence of glucose.
Since fermentation duration alone did not fully describe the performance of individual
strains, other features of the pattern of sugar utilisation were sought by plotting the
utilisation data (Figure 2.1). Several points are evident when the data were presented
graphically. Firstly, all fermentations showed some degree of an initial lag, followed
by an extended period of rapid fermentation before a progressive slowing toward the
complete catabolism of sugars. Thus these periods would collectively and to different
degrees contribute to the overall times required for catabolism of individual sugars.
Secondly, differences existed between strains in terms of the extent to which the
pattern of utilisation of a given sugar(s) reflected that of another sugar(s). Such
differences are highlighted by shading of the area below each curve which
distinguishes it from the adjacent curves (Figure 2.1). Using this approach and
ignoring total fermentation duration, it is evident that strains differ in the extent to
which they preferentially utilised glucose compared to fructose in the mixed sugar
condition. Consequently, Fermichamp has the smallest discrepancy between the two
curves (Figure 2.1, light gray area), whereas strains such as Primeur, AWRI 796 and
Bordeaux Red appear to have the largest discrepancy. The other key observation is
that the difference between the profile for the combined catabolism of glucose and
fructose compared with the pattern of fructose utilisation from the single sugar
medium, also highlights differences between strains (Figure 2.1, hatched area).
Accordingly, strains such as AWRI 796, Syrah and BM45 have a large discrepancy,
while Fermichamp, Cru-Blanc and Uvaferm 43 do not (Figure 2.1).
Application of the composite trapezoid rule
Casual observation suggests that there are no examples of particularly anomalous
behaviour amongst the strains. That is, there are no instances where a given
fermentation duration is in fact not the result of a relatively consistent progression of
fermentation, but instead the result of a protracted commencement followed by a very
rapid completion.
40
However, the validity of this notion was best assessed through a closer, more
quantitative, examination of the data. For this reason much of the subsequent analysis
and strain comparison considers the AUC of utilisation of each sugar as determined
using the composite trapezoid rule (Table 2.2).
If, in fact, the rate of fermentation was consistent, then there should be a relationship
between the AUC of utilisation for that particular sugar and the time taken for the
sugar to be depleted. This was not always the case (N.B. Maurivin strain B was
excluded from this comparison as it was a clear outlier). Thus a comparison of the
AUC of glucose (Table 2.2) was somewhat correlated to the time to glucose depletion
(R2 = 0.715). However, the correlation for the analogous values for fructose in the
mixed sugar condition yielded an R2 value of 0.645. These findings are explained by
the fact that in the case of the latter, there are examples of strains (e.g. Bordeaux Red
and NT202) with similar AUC (i.e. 5833 and 5918) but markedly different times for
fructose catabolism (i.e. 115 and 94 h). The reverse was also evident, that is similar
durations of fructose utilisation (i.e. 101 and 99 h) but different areas under the
corresponding fructose utilisation curve (6016 and 5404) for AWRI 350 and MB45,
respectively. These examples highlight the complexity of fermentation phenotypes
and the difficulty of comparing strains. When considering the combined catabolism of
glucose and fructose from the mixed sugar fermentation, a poor correlation (R2 =
0.461) between the value for the AUC and the total duration of catabolism was again
observed, as was the case for the fructose only condition (R2 = 0.482). Given these
obvious complexities, we therefore suggest that compared to duration alone, the area
under the sugar utilisation curve provides a more complete account of sugar
utilisation, as it incorporates trends occurring during fermentation.
Utilisation of fructose compared with glucose
Of key interest in this study was the manner in which the kinetics of fructose
utilisation influenced the overall fermentation performance of individual strains. For
this reason we sought to identify a basis for comparison of strains which was
independent of overall fermentation duration. As such we compared each strain in
terms of the ratio between the areas under each of the glucose and fructose utilisation
curves (Table 2.2). All strains exhibited a glucose:fructose ratio which was markedly
41
Table 2.2. Area under the glucose and fructose utilisation curves for fermentations by 20 commercial yeast strains of media containing mixed sugars (glucose and fructose) or only fructose to a total concentration of 230 g/L. Values are the average of triplicate fermentations ± standard deviation (SD).
Area under the fermentation curve (trapezoid rule)
Strains Glucose
(mixed sugars) Fructose
(mixed sugars) Glucose area: Fructose area (mixed sugars)
Total sugars (mixed sugars)
Fructose (fructose only)
Total sugars area (mixed sugars): Fructose area (fructose only) Average SD Average SD Average SD Average SD
B 4677 384 8186 828 0.57 12864 1211 DNC1 - - AWRI 350 3861 77 6015 250 0.64 9876 324 11123 252 0.89 NT 202 3743 180 5918 303 0.63 9662 481 11123 572 0.87 UVAFERM 43 3732 77 5731 88 0.65 9463 159 10201 468 0.93 QA 23 3844 111 5548 261 0.69 9392 371 10550 565 0.89 FERMICHAMP 4047 116 5317 91 0.76 9364 184 10108 184 0.93 BORDEAUX RED 3336 65 5833 64 0.57 9169 129 10026 122 0.91 D 254 3404 52 5535 64 0.61 8939 109 9937 352 0.90 VIN 13 3560 77 5363 68 0.66 8923 140 10311 180 0.87 PRIMEUR 3426 45 5476 157 0.63 8902 159 10082 136 0.88 EC 1118 3646 73 5201 216 0.70 8847 146 9456 111 0.94 S6U 3650 16 5180 123 0.70 8830 110 9857 53 0.90 W27 3400 55 5403 84 0.63 8804 139 9709 105 0.91 BM 45 3385 113 5405 76 0.63 8790 127 11065 330 0.79 V1116 3410 95 5201 46 0.66 8611 98 9789 289 0.88 CRU-BLANC 3516 92 5052 107 0.70 8568 178 8946 119 0.96 AWRI 796 3297 7 5135 267 0.64 8432 272 10227 495 0.82 AWRI 1503 3394 8 5017 252 0.68 8411 244 9944 224 0.85 SYRAH 3216 25 4832 20 0.67 8048 25 9915 27 0.81 UCD 522 3001 70 4650 191 0.65 7651 258 9067 179 0.84 1DNC, Fermentations did not complete
42
less than 1, clearly demonstrating the ability of these strains to preferentially utilise
glucose over fructose. The strains ranged in terms of this ratio, such that Bordeaux
Red exhibited a value of 0.57 compared to 0.76 for Fermichamp. The ratio of AUC of
the utilisation of glucose compared with fructose in the mixed sugar condition, is
proposed to give an indication of the relative glucophilicity (increased fructose or
poor glucose consumption) of individual strains. Based on this concept, we averaged
the values of the ratio between glucose and fructose areas for the 19 strains (B
excluded) and used the resulting average as the reference ratio (0.66). Thus, it was
possible to rank all the strains based on their individual glucose:fructose ratio
deviation from the mean of all ratios (Figure 2.2). Two groups of strains can be
identified, the first represents the more fructose efficient strains (negative values), and
the second represents the less fructose efficient strains (positive values). In this way
Fermichamp appears to be either more fructophilic or less efficient at utilising
glucose. Additionally, all strains were compared against each other to determine any
significant differences (Table 2.3). Most strikingly Fermichamp and Bordeaux Red
were the two strains with most significant differences to the other strains.
As was shown for the duration of fermentation, further evidence for the poor
relationship between performances in one condition (medium) compared to another is
provided by comparison of the ratio of the AUC of utilisation of both glucose and
fructose compared to fructose alone (Table 2.2). Such a comparison yielded a
correlation of R2 = 0.449. Further consideration of strain performance was therefore
limited to a comparison of the relative utilisation of glucose and fructose in the mixed
sugar condition.
Discussion
Developing a valid method to assess glucose and fructose utilization during alcoholic
fermentation is not a straightforward matter. Several authors have proposed criteria
for this, but what is clear, and supported by our data, is that the method must be
independent of the overall rate or duration of the fermentation. Also, to simplify its
application, especially for comparison of many strains, the approach must minimise
the need for real-time analysis and, given the dynamic nature of fermentation by
different strains, must incorporate trends from as many stages of the fermentation as
43
Figure 2.2 – Differences from the mean (baseline) of ratios between glucose and fructose areas in the mixed media condition (as reported in Table 2.3). The values were calculated with one-way ANOVA Dunnett’s multiple comparison post-test at 95% confidence interval. FERMICHAMP and BORDEAUX RED (dark bars) were the only two strains showing a significant difference from the average. Table 2.3. One-way ANOVA with Tukey’s multiple comparison post-test analysis of 19 commercial yeast strains (and average of all strains) in terms of their ratio of the area under the curve of glucose utilisation and fructose utilisation from media containing each sugar at 115 g/L (as reported in Table 2.2).
Strains
S6U
EC
1118
C
RU
-BLA
NC
Q
A23
A
WR
I 150
3 SY
RA
H
VIN
13
V11
16
UV
AFE
RM
43
UC
D 5
22
AW
RI 7
96
AW
RI 3
50
NT
202
W 2
7 B
M 4
5 PR
IMEU
R
D 2
54
BO
RD
EUX
RED
A
LL
FERMICHAMP - - • • •• ••• ••• ••• ••• ••• ••• ••• ••• ••• ••• ••• ••• ••• ••• S6U - - - - - - - - - • • •• •• •• •• ••• ••• -
EC1118 - - - - - - - - - • •• •• •• •• ••• ••• - CRU-BLANC - - - - - - - - - • • •• •• •• ••• -
QA 23 - - - - - - - - • • • • •• ••• - AWRI 1503 - - - - - - - - - - - • ••• -
SYRAH - - - - - - - - - - - ••• - VIN 13 - - - - - - - - - - ••• - V1116 - - - - - - - - - ••• -
UVAFERM 43 - - - - - - - - •• - UCD 522 - - - - - - - •• -
AWRI 796 - - - - - - •• - AWRI 350 - - - - - •• -
NT 202 - - - - • - W 27 - - - - -
BM 45 - - - - PRIMEUR - - -
D 254 - - BORDEAUX RED •••
Significant differences were calculated at p value of < 0.05 (•), 0.01 (••) or 0.001 (•••). Non-significant differences also indicated (-).
-0.1
-0.06
-0.02
0.02
0.06
0.1
FER
MIC
HA
MP
S6U
EC11
18
CR
U-B
LAN
C
QA
23
AW
RI 1
503
SYR
AH
VIN
13
V11
16
UV
AFE
RM
43
UC
D 5
22
AW
RI 7
96
AW
RI 3
50
NT
202
W27
BM
45
PRIM
EUR
D 2
54
BO
RD
EAU
X R
ED
Diff
ernc
e fr
om m
ean
44
possible. Thus, even if the second half of the fermentation is the most critical, it is not
appropriate to ignore overall fermentation performance up to this point. The method
suggested from findings of the present study takes into account all these
considerations.
The analysis of the fermentation performance of the 20 strains revealed differences
between some strains to be as much as 2-fold, which equates to about 3 days under
our conditions. Such a difference is likely to have dramatic impacts on winery
throughput and juice processing, particularly at the height of vintage, and no doubt
forms an important criterion in the selection of strains by winemakers. In terms of the
profiles of sugar utilisation, some common trends were seen. Thus, after an initial lag,
sugar utilisation increased markedly before this phase was followed by one of a
slowing of utilisation. In addition an ordered utilisation was evident such that glucose
was removed more rapidly than fructose. Similar reports of glucophilicity in industrial
strains have been made by several authors (Júnior et al., 2008; Meneses et al., 2002;
Meneses and Jiranek, 2002; Tronchoni et al., 2009; Wang et al., 2004). Whilst the
fermentation of a fructose medium was slower compared to an equivalent amount of
mixed sugars, as reported recently (Júnior et al., 2008), it is interesting that the
relative strain performance in the former was not an effective predictor of
performance in the mixed sugar condition.
A recent study of the ability of yeast to grow in a fructose-only medium (Arroyo-
Lopez et al., 2009), compared the area under growth curves to estimate preference or
tolerance of different Saccharomyces yeast for fructose. This method was suggested
as a possible tool for initial screening of yeast. However, with the caveat that our
study examined sugar utilisation rather than growth, the mismatch we observed
between a glucose and fructose mixture and that of fructose alone (R2 = 0.083)
suggests it unlikely that a fructose-only medium will be useful to screen for
performance in an extended mixed sugar fermentation. The presence of glucose is
highly influential on fructose metabolism (Gonçalves et al., 2000; Júnior et al., 2008;
Karpel et al., 2008; Luyten et al., 2002; Perez et al., 2005; Ramos et al., 1988;
Salmon, 1989; Varela et al., 2005; Verwaal et al., 2002). At this point it is not
possible to state how glucose influenced fructose consumption. The effect may be
elicited at the level of the membrane (transport and sensing) or phosphorylation
during the first steps of glycolysis, through a higher Km for fructose compared to
45
glucose. Thus, further work is necessary to better explain the complex interaction
between yeast and the simultaneous presence of two or more sugars. For the moment,
however, it is possible to affirm that this phenomenon is highly strain dependent and
that fermentation performance in presence or absence of glucose can vary
considerably between strains.
In attempting to define fermentation curves of different yeast strains in a mixed sugar
medium, others have developed equations to fit fermentation profiles (Wang et al.,
2004). We chose not to do this, given the high frequency of sampling and good
agreement between replicates in our study. Instead, we used the composite trapezoid
rule to determine the AUC describing the fermentation of each sugar in either
medium. The result is a bi-dimensional measurement of fermentation, which relates
residual sugar concentration and duration of fermentation. This would appear to be the
most inclusive approach to defining fermentation performance used to date. The
subsequent comparison of areas under each of the fructose and glucose curves
therefore provides an overall indication of the sugar affinity of each strain, taking into
account all stages of fermentation. Importantly, the ratio of values derived using this
approach also provides a convenient means for normalising data for strains that
require different times to complete fermentations.
Other approaches to describe different utilisation rates of glucose and fructose whilst
normalising data for different fermentation durations have been described. Berthels
and colleagues (2004) calculated the ratio between glucose and fructose at four points
in a fermentation when 20, 30, 40 and 50% of the glucose had been consumed. Over
this part of the fermentation they observed a linear increase in the glucose:fructose
discrepancy. Moreover they were able to sort strains according to the slope of the
increase in glucose:fructose discrepancy. The reason for selecting four specific
residual glucose concentrations as the points for comparing all strains is not made
clear, but presumably other points within this window would suffice. If not, following
such a prescriptive approach would require a possibly unmanageable high degree of
frequent and real-time glucose quantitation.
As an alternative to such real-time analysis, Guillaume et al. (2007) plotted
fermentation according to g/l of residual sugar normalised against g/l of CO2 released,
the latter being easily determine by weight loss measurements. With this
46
approach they were able to graphically rank two strains based on their different
pattern of fructose and glucose utilization and thereby differentiate the strains. A
shortcoming of the work arises from the fact that the curves were mathematically
fitted and therefore clearly approximations. In a similar approach Dumont and co-
workers (2009) introduced a fructophilic index as a criterion by which to describe the
abilities of yeast to consume fructose compared to glucose. These workers focussed
on the area between the fermentation curves for glucose vs fructose, in the latter ⅔rd of
the fermentation curve (also expressed as g/l of residual sugars vs g/l of CO2
released). Accordingly, strains showing the lowest value, due to smallest area of
differences between the glucose and fructose curves were said to be fructophilic and
were presumed to perform better in situations with high fructose concentrations.
Finally, Tronchoni and colleagues (2009) fitted sugar consumption curves with
various mathematical equations and achieved R2 values of 0.95 and higher. This
enabled them to confidentially estimate the time necessary for different strains to
consume 50% and 100% of glucose and fructose, and, in turn, the residual fructose
concentration at these points. Thus ultimately the comparison between strains was at
one or two time points and did not consider the characteristics of the fermentation
beyond these.
From our results, it was encouraging to observe that Fermichamp showed the highest
ratio between glucose and fructose AUC (0.76). This result agrees with the finding
described by Guillaume and collaborators (2007) about the exceptional ability of this
strain to consume fructose. Similarly, Berthels and colleagues (2004) described the
discrepancy in glucose and fructose for several strains, including EC1118, VIN13 and
Bordeaux Red. Similar results for these three strains have been found in our study
where Bordeaux Red was the slowest fructose fermenter, VIN13 medium and EC1118
one of the fastest. These similarities with other studies increase the confidence of
considering our glucose/fructose comparative approach as a valid method in
describing relative sugar consumption profile during fermentation. Moreover it was
possible to rank 19 commercial wine strains according to their ability to consume
fructose in relation to glucose and identify which strain was significantly different
from the others in terms of fructophilicity (Table 2.3, Figure 2.2).
47
Therefore Fermichamp and Bordeaux red are located at the opposite ends of the chart
and they are the only two strains significantly different from the mean. Other strains
such as S6U and EC1118 are significantly different from most of the strains belonging
to the less fructophilic group, while strains such as D254 and Primeur were
significantly different from most of the strains grouped as the most fructophilic.
In this study, the sampling frequency produced comprehensive fermentation curves
and obviated the need for curve fitting and limitations arising out of this means of
approximation. The post fermentation determination of residual sugars reduced the
analytical burden during the fermentation and the calculation of the AUC is the most
comprehensive encapsulation of all aspects of the fermentation performance of each
strain. Finally, the latter benefit can be said to apply to the ratio of the areas of the
glucose and fructose curves, whilst such ratios also have the advantage of normalising
performance of strains with regard to fermentation duration.
Conclusion
The study proposes a novel approach (AUC) to determine fermentation performances
in wine yeast strains, with particular attention in identifying a methodology to
describe the fructophilicity of the strains. Although one of the goals of the study was
to use a medium containing fructose as only carbon source to predict relative
preference for fructose by strains and to relate this to fermentation performance, the
complexity of glucose and fructose utilization for individual strains remains too great.
Further work is warranted to ascertain the significance of fructose utilising capability
to overall fermentation behaviour and wine quality, and thereby to optimize strain
development programs.
Acknowledgements
This project was supported by Australia’s grape growers and winemakers through
their investment body, the Grape and Wine Research and Development Corporation,
with matching funds from the Australian Government. We would also like to thank
Frank Schmid and Simon Schmidt for their comments during the preparation of this
manuscript. The AWRI and UofA are part of the Wine Innovation Cluster, Adelaide,
South Australia.
48
2.2 BASIS OF CANDIDATE STRAIN SELECTION
In the investigation of 20 commercially available strains (described in the previous
manuscript), use of the area under the fermentation curve (AUC) rather than the
overall fermentation duration was intended to provide a better characterization of the
relative ability of the strains to utilise fructose and glucose. Thus, the most robust
(rapid) fermenter was not necessarily selected for the adaptive evolution experiment,
but rather a strain with a clear difference in its ability to consume fructose in relation
to glucose (lower ratio of the areas under the glucose vs. fructose fermentation
curves). Such a strain should provide a more suitable target for improving fructose
utilization, where the possible margin of improvement is greater and more appreciable
than when using a strain with a smaller difference between glucose and fructose
fermentation. To aid identification of a strain with these attributes, the ratios of AUC
presented in Table 2.2 in the manuscript, were sorted from the smallest to the largest
in Figures 2.3 and 2.4. From Figure 2.3 it appears clear that a strain such as Bordeaux
Red could be a perfect candidate: it shows a very low ratio between the glucose and
fructose AUC in the mixed sugar medium (0.57), thereby making it a very inefficient
fructose fermenter relatively to glucose.
However, other factors have to be considered. A number of approaches could be used
when imposing a fructose related stress during the adaptive evolution experiment. For
example, an improvement in fructose utilization might be induced by forcing a
population to ferment fructose as the only and limited carbon source in the medium. If
this were the intended stress factor to be use, it appears immediately clear that
Bordeaux Red is not longer a suitable candidate. In fact, as shown in Figure 2.4 it
paradoxically ferments sugar (in a fructose only medium) quite fast in relation to
fermentation in presence of glucose (ratio of total sugar AUC in mixed
medium:fructose AUC in fructose only medium = 0.91). Thus a strain that shows a
small difference in fermentation rates between a medium containing glucose
compared to one without, clearly ferments fructose easily in the absence of glucose. If
this is the case, adaptive evolution will further improve the ability of such a strain to
ferment fructose in absence of glucose. Once the strain is returned to a wine-like
medium, its hexose uptake system would again be submitted to glucose regulation.
49
Figure 2.3 – Ratio between areas under fermentation curves for 19 strains. Glucose area : Fructose area (mixed sugars). N.B. Strain Maurivin B was excluded as it was a clear outlier.
Figure 2.4 - Ratio between areas under fermentation curves for 19 strains. Total sugar area (mixed sugars) : Fructose area (fructose only). N.B. Strain Maurivin B was excluded as it was a clear outlier.
0.5
0.6
0.7
0.8B
OR
DEA
UX
RED
D 2
54
BM
45
PRIM
EUR
W27
NT
202
AW
RI 3
50
AW
RI 7
96
UC
D 5
22
UV
AFE
RM
43
V11
16
VIN
13
SYR
AH
AW
RI 1
503
QA
23
CR
U-B
LAN
C
EC 1
118
S6U
FER
MIC
HA
MP
Rat
io o
f are
as
Strains
Glucose area : Fructose area (mixed sugars)
0.7
0.8
0.9
1
BM
45
SYR
AH
AW
RI 7
96
UC
D 5
22
AW
RI 1
503
VIN
13
NT
202
V11
16
PRIM
EUR
AW
RI 3
50
QA
23
S6U
D 2
54
W27
BO
RD
EAU
X R
ED
FER
MIC
HA
MP
UV
AFE
RM
43
EC 1
118
CR
U-B
LAN
C
Rat
io o
f are
as
Strains
Total sugar area (mixed sugars) : Fructose area (fructose only)
50
The strain might again show slow fructose catabolism compared to glucose, as was
the case before the adaptation. On the other hand, in a strain showing a slow fructose
fermentative rate either with or without the presence of glucose, the slow uptake of
fructose can be attributed to factors other than the presence of glucose. Thus, an
improvement arising from the imposition of a fructose-only condition, could
potentially arise in a different step of the hexose uptake system, independent from
glucose regulation. The potential improvement in fructose catabolism arising in such a
strain, may be more easily retained in a fermentation in the presence of glucose.
From these considerations, a suitable candidate has to demonstrate poor fructose
fermentative abilities in media either with or with glucose. BM45 shows a small AUC
ratio in each of such media (0.63 for the mixed sugar condition and 0.79 for the
fructose only medium compared to the mixed sugar medium). However, this strain
required 134.3 hours to complete fermentation in the fructose only media, the slowest
out of all the 19 strains (Table 2.1). Because adaptive evolution requires an extended
running time, a suitable candidate strain would preferably show a reasonably fast
fermentation rate. Otherwise, the total duration of the evolution experiment could be
excessively extended. Thus, BM45 does not satisfy the above criteria and it was not
used further in this study. For the same reason strain B was previously excluded from
the list of candidate strain (see the manuscript), where it was not able to complete
sugar catabolism after 163 hours. Instead, AWRI 796 appears to be the most suitable
strain. It has a medium-low ratio (0.64) between glucose and fructose AUC in the
mixed sugar medium, and a low ratio (0.82) between the total sugar AUC (mixed
sugar media) and the fructose AUC (fructose only media). Moreover, this strain
shows a medium to fast fermentation rate in the fructose only medium, requiring
102.3 hours to catabolise all the sugar.
51
CHAPTER 3
ESTABLISHING AN ADAPTIVE EVOLUTION STRATEGY USING CONTINUOUS CULTURE TO
GENERATE FRUCTOPHILIC GENOTYPES
52
This chapter describes the development of a strategy to generate phenotypes with
improved fructose fermentation rates, applying a selective pressure during continues
culture. The previous chapter dealt with identifying a suitable candidate wine yeast
strain for adaptive evolution to generate fructophilic phenotypes. AWRI 796 was
chosen because it had a large difference between glucose and fructose utilization
rates, but an overall medium-fast fermentation rate, either in media containing an
equimolar mixture of glucose and fructose or fructose alone. This chapter describes
the generation of a variant wine yeast with increased fructose consumption rates,
beginning with the preparation of a starting population for an adaptive evolution
strategy using continuous fermentation.
Despite claims of suppliers, commercial packages of yeast are not pure cultures. Thus,
for the purpose of this study it was important to isolate a single clone of AWRI 796,
physiologically representative of the average of the population. This isolate provided
the genetic background for selective improvement and was a reference for
characterization of adaptively evolved isolates.
Genetic variation is the raw material for adaptive evolution (Chambers et al., 2007).
Thus, the more genetic diversity that exists in a population, the more rapidly a
selection pressure will isolate adaptively evolved novel phenotypes. Starting an
adaptive evolution experiment with a clonal population will therefore greatly reduce
the chance of isolating evolved strains with desirable phenotypes in a reasonable time.
Thus, chemical mutagenesis was used to generate genetic variation in the starting
population, to be used in the adaptive evolution experiment described later in this
chapter.
3.1 ISOLATION OF A REPRESENTATIVE SINGLE CLONE OF AWRI 796
To isolate a representative clone of AWRI 796, the strain was plated onto YEPD agar
(Appendix 1 – Method 2a) from the same glycerol stock as used in Chapter 2 (see also
Appendix 1 – Method 1). From these plates, six individual colonies and a mixed
population derived from a patch from a densely growing region of the plate, were
53
transferred into separate 250 ml conical flasks containing 50 ml YEPD broth. The
seven flasks were incubated overnight at 28°C, shaking at 160 rpm. Each was then
inoculated at a rate of 1 x 106 cells/ml into 50 ml CDGJM starter (50 g/l glucose, 50
g/l fructose – Appendix 1, Method 3) in 250 ml conical flasks (air/liquid ratio > 66%)
and incubated overnight at 28°C, shaking at 180 rpm. Each of these cultures was then
used to inoculate 3 replicates of 100 ml CDGJM (115 g/l glucose, 115 g/l fructose –
Appendix 1, Method 3) at a rate of 5 x 106 cells/ml in 250 ml conical flasks fitted with
water filled air locks and with sampling ports. Fermentation progress was monitored
by CO2 loss, weighing flasks every hour using the Multi-Scale Fermentation Facility
(The University of Adelaide – http://www.sciences.adelaide.edu.au/wine/msff/),
which controls the fermentation temperature (30°C), agitates the culture (with
magnetic stir bars) and monitoring weight loss of the flasks, robotically, on high
sensitivity balances.
CO2 losses (Figure 3.1A) were expressed in g of CO2 lost per 100 g of medium
fermented and fitted with a non-linear, third order polynomial fit using GraphPad
Prism (GraphPad Software Inc., La Jolla, C.A., U.S.A.). For the last third of the
fermentation °Brix of clarified (20,800 rcf, 2 min) culture samples was also
monitored. Fermentation completion (< 2.5 g/l of sugar) was determined using
Clinitest® tablets (Bayer). Supernatants were stored at -20°C for subsequent
determination of residual sugar content (Figure 3.1B and C) as described in
(Boehringer-Mannheim, 1989) with final volumes adjusted to 300 µl for analysis in
48 well microtiter plates.
Fermentation progress of six clonal isolates and the mixed population was monitored.
All fermentation curves show a similar initial profile, but some differences become
evident, especially in the last third of the fermentation (Figure 3.1A). However, clone
5 shows a CO2 weight loss profile almost identical to mixed population. This data was
confirmed in the sugar utilization profile, where clone 5 was able to catabolise all of
the sugar in 113.5 hours, which was the same time as required by the mixed
population (Figure 3.1 B and C).
Due to its similarity in fermentation profile to the mixed population sample of AWRI
796, clone 5 was chosen as the parent strain for subsequent adaptive evolution
experiments.
54
0 25 50 75 100 1250.0
2.5
5.0
7.5
10.0
12.5
0
10
20
30
40
50
60
70
80
Mixed populationClone 5Other clones A
B
C
Time (h)
CO
2 - W
eigh
loss
(g/1
00 g
ram
s of m
ediu
m)
Suga
r con
c (g
/l) Figure 3.1 – A - Non linear (third order polynomial – confidence interval 95%, R2 > 0.98 for the fit of all curves) fit for a mixed population and six clones of AWRI 796 (left axis). B – Glucose concentration (plotted on right axis). C – Fructose concentration (right axis). Each curve is plotted from data from the average of three replicate fermentations. Standard deviations are shown as error bars.
55
3.2 INDUCED MUTAGENESIS OF AWRI 796.
Mutagenesis was induced artificially on clone 5 of AWRI 796 using ethyl
methanesulfonate (EMS) using a method adapted from Fink (1970) and described in
Appendix 1 – Method 4. Thirteen samples were collected at 10 minute intervals
following exposure to 45 µl/ml of EMS and the mutagenesis process arrested with
sodium phosphate buffer. Samples were appropriately diluted and plated onto YEPD
plates, while the remaining of the samples was stored in glycerol at -80°C. From
YEPD plates viable cell counts was determined (see Appendix 1 – Method 4). As can
be seen residual viability decreased progressively with time of exposure to EMS, such
that no viable cells were individuated after 120 minutes (Figure 3.2).
The population treated with EMS for 50 minutes (EMS5), which generated ≈ 40%
death (60% survival), was chosen as the starting population for an adaptive evolution
experiment. Based on past experiences in the laboratory, this population was
anticipated to have a substantial amount of genetic variation.
3.3 DEFINING EXPERIMENTAL CONDITIONS FOR ADAPTIVE EVOLUTION.
As previously mentioned (Chapter 1), there are essentially two different approaches
for adaptive evolution of microorganisms in the laboratory: sequential batch or
continuous culture. Continuous fermentation was the strategy chosen for this work as
it considerably reduced the running time of the experiment. In fact, maintaining a
population in the exponential phase eliminates the time necessary for the complete
depletion of sugar from the media and the sequential inoculation into a fresh batch
ferment. In previous adaptive evolution experiments, McBryde and co-workers (2006)
using similar conditions to those required for the experiment described here,
sequential batch proliferation over 250 generations of a population of wine strain of S.
cerevisiae required 198 days. To achieve the same number of generations in a
chemostat (with a dilution rate of 0.2 h-1) should take approximately 35 days. While it
is recognised that the possibility of affecting the oenological properties of the evolved
56
0 40 80 1200
20
40
60
80
100
Exposure time (min)
Surv
ival
(%)
Figure 3.2 – Survival in a population of clone 5 after varying time lengths of exposure to EMS.
57
strains may exist, a continuous fermentation allows for shorter experiments. This
therefore decrease in running time allows completion of several adaptive evolution
experiments in the same amount of time required for a single sequential batch
approach. This allows trialling of several strategies to generate strains with improved
phenotypes.
Before commencing a long term adaptive evolution experiment it is essential to
establish the optimal conditions for selection of the desired phonotype. This includes:
determining the ‘strength’ of selective pressure, optimal cell density, dilution rate and
composition of feed medium.
Determination of the optimal level of stress for adaptive evolution is critical to drive
selection of the desired phenotype (Butler et al., 1996). It was reasoned that having
fructose as the only sugar in the medium, and maintaining it at a limiting
concentration would be an appropriate selective pressure to generate adaptations to
improve the efficiency of fructose utilization. Using a chemostat which maintains a
continuous low level of fructose provided an effective means of achieving this. An
absence of fructose in the waste medium was taken as an indication that the
concentration of fructose supplied to the bioreactor was limiting. In this environment,
the most efficient fructose utilisers should be fittest.
Maintaining an appropriate cell density is crucial to this approach (Paquin and Adams,
1983; Wahl and Krakauer, 2000; Wick et al., 2002) and this can be achieved by
varying the amount of carbon supplied to the chemostat (Lane et al., 1999). In this
context, an appropriate level of selective pressure (i.e. limiting sugar concentration)
will impose sufficient stress to reduce growth rate without compromising cell density.
For this reason it was important to understand the yeast’s physiological response and
tolerance to the intended stress conditions. The data obtained from previous
experiments (Chapter 2) was not sufficient for this. Thus, information on the
maximum fructose consumption rate and associated growth rate for EMS5 was
sought. Preliminary experiments were performed to estimate the conditions for a
continuous culture experiment in which fructose was limiting (data not shown). From
these initial experiments the following conditions were thought to provide a
reasonable starting point: dilution rate = 0.2 h-1, volume of the fermenting population
58
= 500 ml, cell density = 5 x 107 cells/ml, fructose to be supplied to the population per
hour = 2.13 g/l/h.
In the first experiment using conditions determinate from the above preliminary work,
2.13 g/l/h of fructose was supplied initially at a dilution rate of 0.1 h-1. For this
dilution rate the flow rate was equal to 50 ml/h, and the fructose concentration in the
feed was 20 g/l (Figure 3.3). Under these experimental conditions some fructose
remained unfermented in the exhaust medium (data not shown) indicating that
fructose was not limiting. Additionally, the cell density was around 1.5x108 cells/ml.
Over the first 420 hours of the experiment, the fructose concentration in the feed was
progressively decreased to 15, 10 and then 4 g/l. At the same time the dilution rate
was gradually increased to 0.2 h-1 (corresponding to a flow rate of 100 ml/hour).
Decreasing the amount of fructose to 15 g/l in the feed resulted in a complete
depletion of the fructose from the exhaust (i.e. fructose concentration below
detectable level) indicating that conditions were limiting (data not shown). The
fructose concentration was further decreased and flow rate increased, until the target
value (according to previous experimental experience) of 5 x 107 cell/ml was reached.
To maintain this cell density at a dilution rate of 0.2 h-1 it was necessary to maintain a
fructose concentration in the feed medium of 4 g/l. This preliminary continuous
fermentation defined experimental conditions thought to be appropriate to generate
adaptively evolved phenotypes showing improved fructophilic characteristics.
Adaptive evolution to generate fructophilic wine yeast
An aliquot of frozen cells of EMS5 from a -80°C glycerol stock, was streaked onto
YEPF (see Appendix 1 Method 2b) and grown overnight at 30°C. A patch from a
densely growing region of the plate was then inoculated into liquid YEPF and
incubated overnight at 30°C with agitation on a shaker at 160 rpm. A continuous
culture was then set up by inoculating from the overnight culture 105 cells/ml into
CDGJM (500 ml with 4 g/l fructose – Appendix 1, Method 3). All continuous culture
experiments were conducted in a BIOSTAT® A plus (Sartorius BBI System GmbH)
equipped with a 1.0 litre fermentation vessel and controlled using the MFCS/DA A
plus 2.1 software (Sartorius BBI System GmbH).The medium was supplemented with
59
0 500 1000 15000
5.0�10 7
1.0�10 8
1.5�10 8
0
50
100
50 100 150 200 250 300 350 38520
15 10 420
Time (hours)Number of generations
Cel
l den
sity
(cel
ls/m
l) -�
Flow rate (m
l/hour) -�
Figure 3.3 – Establishing suitable conditions for adaptive evolution to generate fructophilic phenotypes. Flow rate (•) is on the right axis; cell density (▪) is on the left axis, with duration of the experiment (hours) and the relative number of generations on the ‘x’ axis. There is a progressive decrease in fructose concentration in the feedstock (20, 15, 10 and 4 g/l – values reported on the top left corner of the chart). At the same time there was a progressive increment in the flow rate. The combination of these two phenomena resulted in a reduction of total cell number (until it reached a target number at about 5 x 107 cells/ml). Fructose concentration in the exhaust medium was not detectable (except when the feedstock had 20 g/l of fructose).
60
ergosterol and Tween80® (Appendix 1 – Method 3) to supply sterols and fatty acids,
essentially for cell membrane synthesis under anaerobic conditions (Ribereau-Gayon
et al., 2005). CDGJM with the same composition was also used as the feed during the
continuous culture experiments. Fermentation temperature was kept at 30°C and cells
were suspended with agitation at 200 rpm. Head space in the fermentation vessel was
saturated with the CO2 produced during the fermentation and pressure released using
a water filled air lock.
Fermentation was started as a batch culture and after 24 hours (cell density ≈ 5 x 107
cells/ml) the feed pump was started, with an initial dilution rate of 0.08 h-1. At the
same time the waste pump was started at the same rate as the feed pump, ensuring a
constant culture volume at ≈ 500 ml. Over the following 220 hours of the experiment,
the dilution rate was increased progressively from 0.08 h-1 to 0.12, 0.14, 0.15, 0.18 h-1,
until reaching a final value of 0.2 h-1. This dilution rate was kept constant until the end
of the experiment. The duration of the experiment was 1278 hours (≈ 53 days), the
time necessary to allow the multiplication of approximately 350 generations. The
bioreactor was sampled every 48 hours. For each sampling point cell density was
measured (cell count on haemocytometer) and fructose concentration in the exhaust
media was monitored using Clinitest®. Figure 3.4 shows the parameters measured
during the progress of the experiment. It was possible to observe the variation in the
flow rate, maintained constant after 220 hours and the moderate fluctuation of the cell
density (between 4.05 x 107 and 6.75 x 107 cells/ml). Fructose was not detectable in
the fermentation vessel (data not shown). For characterization of mutants, 1 ml
samples of the population were collected every 50 generations and centrifuged 20,800
rcf for 1 min. The supernatant was removed and cells resuspended in 1 ml of fresh
YEPF medium and 0.5 ml of sterile 80% (v/v) glycerol to enabled long term storage
at -80°C.
Conclusions
This chapter has described the development of an adaptive evolution strategy using
continuous culture to generate fructophilic phenotypes. A mutagenised population of
AWRI 796, EMS5, was maintained in continuous culture for 350 generations with
61
0 500 1000 15000
2.0�10 7
4.0�10 7
6.0�10 7
8.0�10 7
0
50
100
50 100 150 200 250 300 350Time (hours)
Number of generations
Cel
l den
sity
(cel
ls/m
l) -�
Flow rate (m
l/hour) -�
Figure 3.4 – Parameters measured during continuous fermentation with limiting sugar (fructose). Flow rate (•) is on the right axis; cell density (▪) is on the left axis.
62
limiting fructose as the only source of sugar. Based on the information reported by
other authors (Paquin and Adams, 1983; Zeyl, 2004) the observed frequency of the
occurrence of a adaptive mutation is somewhere between 1011 and 1012 cell divisions
for non-mutagenised starting populations. Considering that, in the experimental
conditions used in this study the initial population was artificially mutagenised (with
the effect of an increase of the genetic diversity), the population size (around 2.5 x
1010 cells) and the time necessary for a mutation to become fixed, it was decided to
sample the culture every 50 generations. These samples were screened for fructophilic
phenotypes as described in the following chapter.
63
CHAPTER 4
SCREENING OF ISOLATES FROM CONTINUOUS CULTURE TO IDENTIFY FRUCTOPHILIC
GENOTYPES
64
A population of microorganisms forced to live in submerged culture under a specific
stress will eventually acquire isolates that show characteristics of adaptation to that
particular environment. However, the resulting population is not a monoclonal
population derived from the one preceding it, as stipulated in the classical model of
adaptive evolution of an asexual population (Muller, 1932). Other studies have shown
that an evolving population of asexual yeast follow more complex dynamics, resulting
in a mixture of different genotypes, ultimately contributing to different extents to the
total population during the progress of fermentation (Kao and Sherlock, 2008). Thus,
it appears clear that the selection of an adapted clone is not a simple process. As such,
a heterogeneous mixture of different sub-populations will require a reliable
methodology capable of screening several clones to identify those that have adapted
to the selective pressure. The previous chapter described the establishment of a
selective pressure under a continuous fermentation regime, in order to generate
genotypes with improved fructophilicity. In this chapter, the evaluation of isolates
from samples of the populations collected at 50 generation intervals is detailed.
Clearly, the screening of an elevated number of clones would increase the possibility
of identifying superior adapted isolates. In addition, an investigation of many isolates,
would allow broad definition of the dynamics of the evolution of the population.
Particularly, it would help identify when the isolates began to appear and the
percentage of mutants over the total population. Thus, it was fundamental to identify a
methodology suited to the screening of several hundred isolates. Moreover, it was
important to identify a valid parameter on which base the comparison of the
fructophilic ability of the isolates against the parental strain. To better explain these
two concepts, a brief background on the available comparison techniques is given
below.
The methods used traditionally to compare different strains in the laboratory can be
divided into two categories: plates containing solid media and liquid broth culture.
Usually, the use of plates to measure differences between isolates is suitable in
situations where it is possible to observe differences in the morphology of the colonies
(for example differences in colour or shape of the colonies) or other situations where
it is possible to induce limitation, specifically changing the composition of the growth
media. This last situation will determine differences in growth rate of the colonies,
65
leading to difference in colony size, colonies number, days of appearance or even
inhibition of growth of the not-adapted mutants. On the other side, the comparison of
isolates using liquid media is usually adopted when it is important to measure
differences in rate of growth or substrate depletion. The comparison of isolates in
liquid media is usually conduced in small flasks. The maximum number of flasks that
can be managed for each screening exercise limits the number of clones screened. In
addition, a screening experiment usually has to be conducted with at least some
replication for each isolate to increase the statistical significance of the data, and this
represents a further limitation in the number of isolates that can be tested. Moreover,
monitoring parameters in flasks, usually requires more work (according to the
frequency of sampling) than observing events in immobilized colonies: whether
monitoring the evolution of the population or the depletion of substrates, more time
consuming measurements are required.
For the purpose of this study, a screening method based on liquid media appeared to
be the most suitable approach. The intent was to identify isolates showing improved
fructose consumption in wine-like conditions. Thus, the depletion rate of this substrate
from the medium was the parameter thought to be the most pertinent. The use of solid
media was excluded in the early stages of the planning of the screening experiment:
the concern being that parental and mutants would grow well on fructose media and
differences in consumption kinetics of this sugar would not be evident from
observations of the colony growth. Also, because fructose is not an inhibitor or a toxic
substance for S. cerevisiae, it is not possible to create a specific environment that
limits the growth of the not-adapted mutants.
The decision to use laboratory scale fermentations to compare isolates, sought to
resolve other problems also. First, to screen hundreds of isolates in replicate
necessitates more cultures than would be logistically possible. The solution was to
reduce the volume of the population to 200 µl and take advantage of 96 well
microtiter plates as the fermentation vessel. These plates also permit the use of
automated robotic systems to simplify handling and thereby increase the number of
isolates that could be examined.
The use of such small volumes required validation. The relationship between the
performance of strains in 96 microtiter plates compared to large cultures (100 ml) was
66
assessed as part of a collaborative project (manuscript submitted to the Australian
Journal of Grape and Wine Research – see Appendix 2). The key findings were:
1. No difference was observed in the maximum biomass yield and rate of growth
between wine yeast cultures propagated in self-induced anaerobic 100 ml
conical flasks and 96 well microtiter plates (Figure 4.1);
2. Comparable fermentation rates were seen between wine yeast cultures
performed in 100 ml self-induced anaerobic conical flasks and 96 well
microtiter plates. Even if in every case fermentations in microtiter plates were
faster, the relative sugar utilization profiles of the strains examined were
unchanged compared to fermentations in flasks (Figure 4.2).
Thus, the use of microtiter plates became a valid tool to screen large numbers of
isolates during this study. However it was important to identify a parameter that
significantly distinguished between isolates and the parental strain for the ability to
ferment fructose. As described previously in this chapter the principal parameters by
which liquid cultures were compared are the rate of growth of a microorganism and
the rate of substrate consumption. Evaluation of the efficiency of growth on fructose
could conceivably be assessed based on comparisons of rates or extents of biomass
formation or else patterns of fructose removal from the growth medium. Given the
ease by which culture growth can be estimated (i.e. via measurement of optical
density), the validity of this approach was assessed.
Preliminary results showed that no correlation existed between the growth rate and
biomass formation and fermentation rate in a CDGJM containing fructose as sole
carbon source (data not shown). These validation experiments, were conducted
comparing growth rates, yield of biomass and rate of fructose depletion either in 96
well microtiter plates or else 250 ml flasks. A range of sugar content was used
between 4 g/l and 230 g/l of fructose. Moreover, because the final target of the project
was to improve the ability to consume fructose in wine-like conditions (i.e. in the
presence of glucose), the use of a fructose only medium was not thought to be
appropriate. For this reason, other pilot experiments aimed to identify any possible
correlation between the rate of growth and sugar depletion from a medium containing
100 g/l of an equimolar mixture of glucose and fructose were conducted (data not
67
0 50 100 150 2000
2
4
6
SAFMTP
A
time (hours)
dry
cell
weig
ht (g
L-1
)
SAF MTP0.00
0.05
0.10
0.15
0.20
0.25 B
grow
th ra
te (h
-1)
Figure 4.1 – Impact of culture vessel type on culture physiology: Chardonnay juice fermentations using strain AWRI 1493 were performed in self-induced anaerobic flasks (SAF) or microtiter plates (MTP). Growth curves showing biomass accumulation (A) and maximum specific growth rates (B) were estimated from optical density measurements. Error bars show ± standard deviation. Columns show means from two independent experiments. Each treatment was performed in triplicate within each experiment. (From Liccioli et al. – submitted. See Appendix 2 for more details).
Figure 4.2 – Screening of yeast strain fermentation performance and validation using selected strains at a larger scale. The fermentation performances of 15 industrial strains were evaluated in Chardonnay juice using microtiter sacrificial plates (A). One plate was prepared for each time point, each strain was fermented in four wells on each plate. Each time point in (A) is the average sugar concentration from four wells on one plate. Four strains, representing a cross-section of performance profiles, were selected for further characterization using 200 ml air-locked self-induced fermenters (SAF) in the same Chardonnay juice (B). Each time point in B is the average total sugar concentration from 3 fermentations. Error bars indicate ± standard deviation. (From Liccioli et al. – submitted. See Appendix 2 for more details).
0 100 200 3000
50
100
150
200
250
B
time (hours)
tota
l sug
ar (g
/l)
0 50 100 1500
50
100
150
200
250
Strain 1
Strain 4Strain 3Strain 2
Other strains in screen
A
time (hours)
tota
l sug
ar (g
/)
68
shown). Again, the growth curves of the yeast did not reflect the rate of sugar
depletion during these fermentations.
As a result it was concluded that there is no correlation between the growth rate of the
strains used and their ability to ferment sugars. This fact was also confirmed from
Simon Schmidt and collaborators (personal communication), that in different series of
experiments they never founded evidence of this kind of correlation. Thus, despite its
appeal due to the ease with which culture growth can be measured (i.e. as optical
density) the only valid method for comparing the fructophilic nature of isolates from
the adaptive evolution experiment and the parental strain was the direct measurement
of glucose and fructose utilization. The screening of isolates from the sample of the
population collected every 50 generations during the continuous culture experiment is
the topic of the next section.
4.1 SCREENING FOR ISOLATES SHOWING IMPROVED FRUCTOPHILIC ABILITY
As introduced in Chapter 3, clones isolated every 50 generations during the
continuous culture experiment, were investigated to identify the presence of
phenotypes showing improved fructophilic characteristics. Thus, isolates from 7
sequential sampling times were tested. For each point, the fermentative performance
of 54 isolates was compared against the parental strain and a representation of the
mixed population at the same 50 generation sampling point. The screening method
used 96 well microtiter plates with a sacrificial sampling approach (see manuscript –
Appendix 2 and below for a more extensive explanation). Essentially, several copies
of the same plate were prepared such that a fresh plate was used at each time point to
allowing monitoring of sugar depletion profiles, without influencing fermentation
performances in the small volume of the microtiter plates cultures.
Specifically, from -80°C glycerol stocks, cells were streaked onto YEPD plates
(Appendix 1 – Method 2a) and incubated overnight at 30°C. The cells used had been
collected previously from the continuous fermentation experiment at 50 generation
intervals. From the YEPD plates, 54 single colonies were picked and inoculated into
69
500 µl of YEPD broth, incubated without shaking overnight at 30°C. The parental
strain and a mixed population derived from a patch of a densely growing region of the
culture sample streak plate were grown similarily. For each culture, 1 ml of CDGJM
starter medium (25 g/l glucose, 25 g/l fructose) was inoculated with 20 µl of YEDP
culture and incubated at 30°C in 10 ml Falcon tubes without agitation. Once the cells
reached saturation (overnight incubation), each culture was supplemented with 4 ml
of CDGJM containing 50 g/l glucose, 50 g/l fructose (i.e. a 1 in 5 dilution). Ten micro
litres of each suspension were used to inoculate 96 well microtiter plates containing
190 µl of CDGJM (50 g/l glucose, 50 g/l fructose) in each well. The 54 isolates were
distributed over 3 plates and each plate also contained the parental strain and the
mixed population. Each plate was then replicated six times, operation performed with
an automated plate and liquid handling machine (Tecan EVO 150). The dilutions,
gave a dilution factor of 100 for the starter culture. Thus, the average density of the
starter culture was therefore diluted from ≈ 108 cells/ml to around 106 cells/ml. For
each culture, four replicates of the same isolate were inoculated on each 96 well
microtiter plate. Some unutilised wells in the plate contained uninoculated CDGJM as
a control for sterility, while others were used to hold standards when sub-sequentially
performing enzymatic sugar assays described in Boehringer-Mannheim (1989) with
final volumes adjusted to 100 µl for analysis in 96 well microtiter plates. Typical plate
layout is shown in Figure 4.3.
Each of the 6 replicate plates, were sealed with breathable membrane (Breathe-easy,
Diversified Biotech) and placed in an incubator at a controlled temperature (20°C),
humidity (75%) and atmosphere saturated with nitrogen gas (O2 concentration < 1%).
The elevate humidity was intended to limit evaporation from the fermentations, while
the nitrogen atmosphere reduced the influence on fermentation due to oxygen. One of
each of the 6 sacrificial plates was frozen at -20°C (for samples time see Figure 4.4)
every 24 hours, for sub-sequent enzymatic determination of glucose and fructose.
Sugar determination was performed semi-automatically, preparing the enzymatic
reactions using the same liquid handling robot (Tecan EVO 150). An integrated
spectrophotometric plate reader (Tecan M200 Infinite) was used for colorimetric
measurements.
70
Figure 4.3 – Example of plate layout used for screening isolates from continuous culture every 50 generations. Each plate contained 18 isolates in four replicates, as well of the parental strain (PAR), mixed population of the corresponding 50-generational point (MIX), uninoculated wells for checking for contaminations (BLK) and empty wells used subsequently for sugar determinations (boxed area). Each plate was replicated six times for analysis of fermentation performance using a sacrificial sampling approach.
71
Examples of the high throughput analysis of fermentation performance are shown in
Figure 4.4, where the sugars utilization of 18 isolates, the parental strain and the
mixed population are represented. In addition to the curve of total sugar depletion
(Figure 4.4a), utilization of glucose (Figure 4.4b) and fructose (Figure 4.4c) are
shown individually.
A clear differentiation between many of the isolates, the parental strain and the mixed
population is evident (Figure 4.4). Several of the isolates show an improvement in
overall fermentation performance (Figure 4.4a), depleting all sugars in a shorter
period of time compared to the parent. Comparing Figure 4.4a with 4.4b and 4.4c it is
possible to see that the greater contribution to such improvement can principally be
attributed to an improvement of the utilization of fructose. Such an observation
suggested a possible improvement in the fructophilicity of at least some of the
isolates, as per the objective of this study.
The spread in fermentation curves seen across the isolates was typical for each batch
investigated during screening. Thus, in each case, the fermentation kinetics of a few
isolates were faster than the parental strain, while others were slower. However, such
simplistic graphical evaluation of fermentation curves did not provide a detailed
enough comparison of the relative fructophilicity of the isolates. For this reason
differences in fermentation rate were quantified paying particular attention to ability
of isolates to utilize fructose compared to glucose. Once again, determination of the
value of the area under the fermentation curve (described in the manuscript included
in Chapter 2) appeared the most appropriate for this purpose. To eliminate possible
external influence on the fermentative performance due to differences in experimental
or environmental conditions between microtiter plates, the fermentation rate of every
isolate was standardized relative to the parental strain included in each plate. Thus, the
value of the area under the fermentation curve was expressed as a percentage of the
parental strain within every plate. With this approach and in turn the calculation of the
ratio between areas, it was possible to identify and quantify isolates showing
improved overall fermentative performance and to rank them according to
fructophilicity.
The ratio between the area under the fermentation curve of glucose and fructose was
calculated (as described in Chapter 2) for each of 378 isolates and for each of the
72
0 20 40 60 80 1000
50
100
150
A
Isolates
ParentalMixed population
Time elapsed (h)
Tota
l sug
ar c
once
ntra
tion
- GLU
+ F
RU
(g/l)
0 20 40 60 80 1000
20
40
60
80
B
Time elapsed (h)
Glu
cose
con
cent
ratio
n (g
/l)
0 20 40 60 80 1000
20
40
60
80
C
Time elapsed (h)
Glu
cose
con
cent
ratio
n (g
/l)
Figure 4.4 – Example of high throughput analysis of fermentation performance obtained using the sacrificial plate approach. Curves of depletion of total sugar (A), glucose (B) and fructose (C) are shown for 18 isolates, the parental strain and the mixed population. Results are the average of four replicates (standard deviation shown as error bars).
73
parental strain (one for each set of plates = 21 fermentations). The distribution of
these values was examined along the seven groups of isolates of each 50 generation
samples (Figure 4.5). An apparent trend was observed: a progressive increase of the
value of the ratio between glucose and fructose area under curve ratios with the
increasing number of generations of exposure of selective pressure. Moreover, a
comparison with the distribution of the parental strains, which, even if with large
approximation, were mainly distributed within the low values of the ratio, showed a
generally improved fructophilicity of the isolates starting from 200 generations
onwards. However, this parameter alone was not sufficient to assure the identification
of isolates showing improved characteristics. Clones showing improved fructophilic
characteristics but slower overall fermentation performances compared to the parental
strain, were considered not suitable for further studies. To identify candidate isolates
suitable for further characterization, the value of the ratio of the areas under the
fermentation curves of glucose and fructose was combined with the overall
fermentation performance. Thus, it was possible to identify 19 isolates showing
improved fructophilic characteristics and faster overall fermentation performance. For
these 19 clones specifically the ratio of the area under the fermentation curves of
glucose and fructose was seen to have increased to between 0.79 and 0.84, while the
average for the parental strains was 0.72. In every case these clones also showed
values of the area under the overall fermentation curve between 85% and 95% of the
parental value, thereby indicating faster fermentations. These 19 isolates were chosen
for further comparison of fermentation performances (Section 4.2).
A final point to be made in regard to the fermentation rate of the parental strain
related to the proportion of improved isolates. For each of the 50-generational samples
screened, the number of isolates showing a faster fermentation rate compared to the
parental strain was shown to change with the progress of the adaptive evolution
experiment (Figure 4.6). As can be seen, while not statistically significant, there
appeared to be a trend to an increasing proportion of improved isolates toward 250-
300 generations. Beyond this number of generations, the proportion of isolates
improved relative to the parent appeared to decline.
74
0.62
5
0.67
5
0.72
5
0.77
5
0.82
5
0.87
5
0.92
50
5
10
15
20
25
30
50 gen100 gen150 gen200 gen250 gen300 gen350 genParent
Area under the curve of glucose / Area under the curve fructose
Num
ber o
f iso
late
s
Figure 4.5 – Absolute value of the area under the fermentation curve of glucose divided by the value of the area under the curve of fructose as an indication of the fructophilicity of individual isolates. Isolates are grouped (refer to shading) according to the 50-generational sample from which were obtained. The distribution in the performance of replicates of the parental strain is indicated by the red bars.
50 100
150
200
250
300
350
0
20
40
60
80
100TOTGLUFRU
Number of generations elapsed during the AE
Perc
enta
ge o
f iso
late
s sho
win
g a
fast
er ra
te o
fsu
gar u
tiliz
atio
n co
mpa
red
to th
e pa
rent
al st
rain
Figure 4.6 – Number of isolates (expressed as a percentage of the 54 isolates screened from each 50-generational sample) showing faster fermentation rate (smallest area under the fermentation curve) compared to the parental strain for total sugar or glucose and fructose individually. The seven generational points are shown separately. Standard deviation is shown as error bars.
75
4.2 FERMENTATIVE PERFORMANCE OF 19 ISOLATES IDENTIFIED AS CANDIDATES FOR FURTHER CHARACTERISATION
As described in the previous section, 19 isolates were identified as having improved
fructophilicity and overall fermentation rate. A further round of screening was applied
to these strains in the hope of reducing the number of candidates to be asses in the
subsequent large scale evaluation. The experimental approached used was the same as
that used for the initial screen, with the exception that the mixed population was
omitted from the layout of the plate. The data were analysed following the same
procedure described above, that is by using the ratio between the areas under the
fermentation curves of glucose and fructose combined with the overall fermentative
performance.
Although all of the 19 isolates showed a faster fermentation rate compared to the
parental strain in the previous screening, a few of them (6, 10, 15, 16, and 19)
displayed a larger area compared to the parental strain in this experiment (Table 4.1).
For this reason they were excluded from further investigation. On the other side,
isolates 3, 9 and 11 were chosen because they showed an improvement of around 5%
in fermentation performances and a higher ratio between the area under the glucose
and fructose curves. In addition, even if its fermentation performance was almost
equal to the parental strain, isolate 7 was retained for further consideration largely
because of its higher fructophilicity (ratio = 0.682).
4.3 CONCLUSIONS
The identification of a methodology suited to the screening of a large number of
isolates and able to describe their fermentation kinetics, especially the ability to utilise
fructose compared to glucose, enabled identification of four candidates for further
investigation. These candidates, proliferated during continuous fermentation under
selective pressure, showed improved fructophilic characteristic and a faster
fermentation rate compared to the parental strain during the screening experiments.
76
Table 4.1 – Nineteen isolates showing higher fructophilicity compared to the parental strain (ratio between areas under the fermentation curves of glucose and fructose). Strains 6, 10, 15, 16, and 19 (highlighted in gray) showed a slower fermentation rate compared to parent (expressed as a percentage of the area under the curve of total sugar fermentation of the parental strain). Isolates 3, 7, 9 and 11 (highlighted in yellow) were chosen for further characterization.
PAR 16 5 9 13 11 17 1 6 2 3 4 7 8 10 12 14 15 18 19Generations 50 200 200 200 250 250 300 300 350 350 350 350 350 350 350 350 350 350 350Area TOT % 100 126.8 95.2 94.3 95.1 90.0 95.3 96.8 102.2 95.7 94.3 97.4 99.4 98.3 105.6 96.6 98.4 103.5 96.9 101.5Ratio Areas (GLU/FRU) 0.631 0.706 0.650 0.671 0.660 0.661 0.639 0.640 0.658 0.665 0.658 0.662 0.682 0.664 0.638 0.665 0.667 0.631 0.642 0.637
77
However, to confirm and better characterize the improved ability of these clones to
consume fructose, especially in larger scale fermentations and in media including
grape juice, the appropriate strain evaluation was undertaken and is discussed in the
next chapter.
Final comment has to be made about the distributions of isolates showing improved
fructophilicity and the improved fermentation abilities compared to the parental strain.
Even though the data analysed are only derived from a screening experiment and
more work is required before it could be considered a detailed study of evolution in an
evolving population, the trends expressed in Figure 4.5 and 4.6 could reflect a
progressive adaptation of the mutants to the imposed stress.
� The trend shown in Figure 4.5 highlighted the higher frequency of improved
fructophilicity with the progress of the adaptive evolution experiment. The
literature reports that adaptation continues with exposure to selective pressure
over time (Helling et al., 1987; Zeyl, 2005). Even if speculative in this study,
it have been the case that a longer exposure to stress would have allowed
isolation of progressively better adapted strains. Accordingly, 11 of the 19
isolates identified as having improved fructophilicity compared to the parental
strain, have been isolated after 350 generation (longest exposure to selective
pressure in this study).
� Previous studies, although on a much larger scale (Helling et al., 1987; Kao
and Sherlock, 2008), showed that improvement is not a linear trend, but that
adaptation of a population to selective pressure follows oscillatory patterns.
The decline in the number of isolates with improved fermentative ability
compared to the parental around 250-300 generation (Figure 4.6) may
represent such an oscillatory effect.
78
CHAPTER 5
PHYSIOLOGICAL AND GENETIC CHARACTERIZATION OF THE IDENTIFIED
ISOLATES
79
Four isolates showing improved fructophilic and overall fermentation performances
were used in further experiments aimed to more fully evaluate the fermentation of
these. Approaches and techniques with increased sensitivity were therefore used and
represented the initial part of the transition between the laboratory scale to more wine-
like environments. In particular, the screening of the isolates to this point had been
performed in CDGJM containing 50 g/l each of glucose and fructose. In grape juice
the sugar concentration is at least double this and so too the concentration of alcohol
produced.
In addition to fermentation performances it was also important to understand if
isolates differed from the parental strain in terms for other oenological traits and
genetic attributes. The concentrations of organic acids, alcohols and hydrogen
sulphide after the complete catabolism of sugars were measured. Molecular typing of
the strains permitted determination of the extent of the genetic relatedness of isolates
to the parental strain.
The last investigations were aimed at evaluating the fermentative performance of the
mutants in grape juices, to understand if the mutation(s) isolated in artificial
(laboratory) conditions conferred a faster rate of fructose fermentation in a more
wine-like environment.
5.1 FERMENTATION PERFORMANCES OF 4 ISOLATES IN A HIGHER SUGAR CONCENTRATION MEDIUM
Isolates 3, 7, 9 and 11 highlighted from the previous screening experiment (Section
4.2) were streaked on YEPD plates (Appendix 1 – Method 2a) from -80°C glycerol
stock, incubated overnight at 30°C and checked for purity. The parental strain was
prepared similarly. A colony representative of each isolated and multiple colonies of
the parental strain were then inoculated into YEPD broth and grown overnight at 30°C
with agitation at 160 rpm on shaker bench. Cells of each culture were inoculated into
50 ml aliquots of CDGJM starter medium (50 g/l glucose, 50 g/l fructose, Appendix 1
– Method 3) to an inoculation density of 2.5 x 106 cells/ml. The starter cultures were
grown as before and triplicates 250 ml aliquots of CDGJM (115 g/l of glucose, 115 g/l
of fructose) inoculated to 5 x 106 cells/ml. Flasks were sealed with lids provided with
80
water filled air locks, placed into an automatic fermenter (Medicel Explorer) with
continuous flushing of the head space with filtered nitrogen (0.45 µm, 5 ml/min) and
kept at 30°C (in a water bath) with constant agitation (magnetic stir bars, 200 rpm).
Three ml samples were collected automatically and held at -5°C until transferred to
-20°C prior to enzymatic determination of glucose and fructose (Boehringer-
Mannheim, 1989) with final assay volumes adjusted to 100 µl for analysis in 96 well
microtiter plates. The frequency of sampling was generally 6-8 hours at the beginning
and final stages of fermentation, but decreased to 12 hours in the middle of the
fermentation.
The strains showed a clear differentiation in the second half of the fermentation
(Figure 5.1). The parental strain completed the fermentation in 153 hours. By
comparison, isolate 9 showed a marked reduction in fermentation duration depleting
all sugars in 117 hours. Isolate 11 showed an intermediate fermentation duration of
135 hours. Isolates 3 and 7 failed to complete the fermentation and became stuck at
about 30 g/l, which incidentally was composed mainly of fructose. The improvement
in the fermentation kinetic of isolates 9 and 11 was reflected in the area under the
fermentation curves (Table 5.1). These isolates showed a reduction in this value of
≈19% and ≈12% respectively for the total area.
By considering the utilization of the individual sugars, it is possible to see that the
improvement in fermentation was mainly due to the improved fructophilicity of the
two isolates. The increased ability to consume fructose compared to glucose was
confirmed by the calculation of the ratio between the area under the curve of glucose
and fructose, which increased from 0.538 of the parental strain to 0.588 and 0.566 for
isolates 9 and 11 respectively.
As stated, isolates 3 and 7 failed to complete fermentation, leaving mainly residual
fructose. Interestingly the utilisation of glucose was essentially the same as seen for
the parental strain and isolates 9 and 11. The basis of the selective loss of fructose
utilising ability is unknown, but may be linked to the higher ethanol yield of these
high sugar fermentations (230 g/l total) compared to the adaptive experiment (ethanol
concentration ≈ 0 g/l). The experimental conditions of the continuous culture have
lead to an increase in fructophilicity, but they may have contributed to a decrease in
the ethanol tolerance in certain isolates.
81
PAR 9 11
Area TOT % 100 81.37 87.99Area GLU % 100 86.40 92.31Area FRU % 100 79.09 87.77
Ratio Areas (GLU/FRU) 0.538 0.588 0.566
50 100 150
100
200
A
Parent37911
Time elapsed (h)
Tota
l sug
ar c
once
ntra
tion
- GLU
+ F
RU
(g/l)
50 100 150
50
100
B
Time elapsed (h)
Glu
cose
con
cent
ratio
n (g
/l)
Figure 5.1 – Curves of depletion of total sugar (A), glucose (B) and fructose (C) are shown for four isolates and the parental strain. Results are the average of triplicate 250 ml CDGJM fermentations, containing 230 g/l of total sugar (standard deviations are shown as error bars).
Table 5.1 – Percentage of the area under the fermentation curves for isolates 9 and 11 compared to the parental strain for total sugar or glucose and fructose individually. The fructophilicity of the strains is expressed as a ratio between area under the fermentation curves of glucose and fructose. Isolates 3 and 7 were not considered further as they were unable to complete fermentation.
50 100 150
50
100
C
Time elapsed (h)
Fruc
tose
con
cent
ratio
n (g
/l)
82
The increased fermentative performance, mainly attributable to the increased
fructophilicity of mutants 9 and 11 compared to the parental strain, represents the first
confirmation of the ability of adaptive evolution to improve fructose utilization in
wine yeast. Further characterization in more wine-like conditions was required, as was
a comparison of the performance of these isolates with commercial strains. The latter
comparison would allow the magnitude of the improvement of the isolates to be put in
a broader oenological context.
5.2 EVALUATION OF THE FERMENTATION KINETICS OF ISOLATES 9 AND 11, THE PARENTAL STRAIN AND TWO COMMERCIALLY AVAILABLE STRAINS.
EC1118 and Fermichamp, two commercially available strains, already used in this
study (Chapter 2), were included as a reference for the performance of isolates 9 and
11. EC1118 is one of the most widely used strains in winemaking and wine research.
Fermichamp has been described as a strain showing increased fructophilicity, due to a
mutation in the hexose transported encoded by HXT3 (Guillaume et al., 2007). The
preparation and execution of the experiment was the same as that described in Section
5.1. The only difference was the replacement of mutants 3 and 7 with the two
reference strains, EC1118 and Fermichamp.
EC1118 and Fermichamp performed similarly to the parental strain, but they did not
quite complete fermentation in the time frame of the experiment, leaving
approximately 10 g/l of sugar (mainly fructose) in the medium (Figure 5.2). By
comparison, isolate 9 and 11 performed strongly and completed the fermentation in
160 and 168 hours, respectively, while the parental strain required 186 hours. Superior
performance of the parental strain compared to EC1118 and Fermichamp was
attributable to more rapid utilization of glucose. Isolates 9 and 11 exhibit more rapid
utilisation of both glucose and fructose compared to the other strains. Unlike the
previous experiment were improvement of these isolates was largely specific to
fructose utilization, in this case a similar proportional improvement was seen in both
sugars. As results there was no change in the fructophilicity of the isolates as
demonstrated by the fact that the index between the areas under the fermentation
83
PAR 9 11 EC1118 Fermichamp
Area TOT % 100 84.84 80.49 112.82 108.50Area GLU % 100 84.13 78.52 122.77 125.59Area FRU % 100 85.24 81.58 107.34 99.12
Ratio Areas (GLU/FRU) 0.549 0.541 0.528 0.627 0.695
50 100 1500
50
100
Time elapsed (h)
Fruc
tose
con
cent
ratio
n (g
/l)
C
50 100 1500
100
200
Parent911EC1118Fermichamp
A
Time elapsed (h)
Tota
l sug
ar c
once
ntra
tion
- GLU
+ F
RU
(g/l)
50 100 1500
50
100
B
Time elapsed (h)
Glu
cose
con
cent
ratio
n (g
/l)
Figure 5.2 – Curves of depletion of total sugar (A), glucose (B) and fructose (C) are shown for isolates 9 and 11, the parental strain and references EC1118 and Fermichamp. Results are the average of triplicate 250 ml CDGJM fermentations, containing 230 g/l of total sugar (standard deviations are shown as error bars).
Table 5.2 – Percentage of the area under the fermentation curves for isolates 9 and 11 compared to the parental strain, EC1118 and Fermichamp for total sugar or glucose and fructose individually. The fructophilicity of the strains is expressed as a ratio between the areas under the fermentation curves for glucose and fructose.
84
curves of glucose and fructose was unchanged compared to parent (Table 5.2).
Despite this the superior fermentation capability of isolates 9 and 11 warranted further
characterization of their physiological and genetic properties.
5.3 METABOLITE PRODUCTION DURING FERMENTATION
The concentration of the organoleptically relevant organic acids (citric, tartaric, malic,
succinic, lactic, acetic) and alcohols (ethanol and glycerol) was measured by HPLC
(Walker et al., 2003) for isolates 9 and 11 and the parental strain in samples from the
fermentations described in Section 5.2. Frozen ferment samples were thawed and
centrifuged for 5 minutes at 20,800 rcf. Samples were diluted 1:10 and resolved on
either a Benson Carbohydrate SS H+ column (300 mm × 7.8 mm – Alltech, Deerfield,
IL, USA) or an Aminex HPX-87H column (300 mm × 7.8 mm – Bio-Rad, Hercules,
CA, USA). Elution was performed at 60°C with 2.5 mM H2SO4 at a flow rate of 0.5
ml/min. Detection was achieved using a RID-10A refractive index detector
(Shimadzu, Kyoto, Japan). Quantification was achieved by comparison with prepared
standards in CDGJM using Delta integration software (Deltaware, Charlottetown,
Canada – Figure 5.3).
Isolates 9 and 11 showed no large variation in the amount of metabolites produced
during fermentation. The only significative differences observed were the lower
concentration of citric acid and the higher production of acetic acid for the parental
strain compared to the isolates. The amounts of acetic acid produced were of the order
of the legal limit for this acid in commercial wines (Hornsey, 2007) and while
somewhat high, were typical for fermentations of CDGJM (e.g. McBryde et al.,
2006). Determination of the sensory significance of these differences in acetic acids
yield was held over for fermentation of genuine grape juice.
Hydrogen sulphide production of the isolates 9 and 11 compared to the parental strain,
EC1118 and Fermichamp was determined by a different set of fermentations. H2S
production was quantified using lead acetate detector tubes (Katagawa Precision Gas
Detector Tubes, model 120SB-Hydrogen Sulphide, range 0.75-300 ppm, Kawasaki-
City, Japan) as described elsewhere (Ugliano and Henschke, 2010). Starter culture
were prepared as detailed in Section 5.1 and fermentations were conducted in
triplicate in 90 ml of CDGJM (115 g/l glucose, 115 g/l fructose – Appendix 1,
85
Citric A
cid
Tartari
c Acid
Malic A
cid
Succin
ic Acid
Lactic
Acid
Acetic
Acid
Glycero
l
Ethano
l0
2
4
6
8110
120
130
140
Parental911
Metabolites
Con
cent
ratio
n (g
/l)
Figure 5.3 – Metabolite concentration in end of fermentation (< 2.5 g/l sugar) samples for isolates 9 and 11 and the parental strain. The values are the average of single determinations from triplicate 250 ml CDGJM fermentations, containing 230 g/l of total sugar (standard deviations indicated as error bars).
86
Method 3) in 250 ml conical flasks with shaking (160 rpm). Fermentations were
complete (total sugar < 2.5 g/l) after 282 hours at 30°C. The quantity of H2S produced
for each flask (read on the graduate scale of the tubes) was converted into micrograms
per litre using a previously determined calibration curve (data not shown).
Although some strain differences were evident, the quantities of H2S produced during
these fermentations (Figure 5.4) were lower than those found in another study and
deemed sensorially perceptible. Ugliano and co-workers (2009) reported values
between 9 and several hundred micrograms per litre of grape must. The higher values
were mainly associated with fermentations deficient in assimilable nitrogen. In the
present study the CDGJM was prepared with a high nitrogen content (600 µg/litre as
mixed amino acids), thereby likely explaining the limited production of H2S.
5.4 DNA FINGERPRINTING OF EVOLVED STRAINS Any change in the physiological characteristics of a strain, most likely reflects
changes at the DNA level (Giudici et al., 2005). It would ultimately be desirable to
determine the genome sequence of the isolates and the parental strain in order to
define the precise nature of any mutation(s) in the former. However, before
committing to such an undertaking, the use of less detailed genetic comparative
techniques was though appropriate to confirm the close relatedness of isolates and
parental strain and confirm that the isolates were not in fact contaminants. The DNA
fingerprinting method of Ness and co-worker (1993) was chosen since this is a
commonly used protocol for industrial strains of S. cerevisiae.
In brief, the method involves amplification of the repeated elements that flank the Ty1
retrotransposon using the primers listed in Appendix 1 (Method 6). Genomic DNA
was purified from duplicate clonal cell cultures (Appendix 1 – Method 5; Adams et
al., 1997) and its integrity checked by resolution on 2% agarose gel in 1 x TAE buffer
(Tris-acetate-EDTA) and electrophoresis. The Polymerase Chain Reaction (PCR) for
the Ty1 amplification is described in Appendix 1 (Method 6).
87
Figure 5.4 – Quantity of H2S produced per litre of fermentation by mutants 9 and 11, the parental strain and references EC1118 and Fermichamp. Values are the mean of triplicate 90 ml fermentations in CDGJM with 230 g/l of total sugar (standard deviations are indicated as error bars).
0
5
10
15
20
25
30
35
40
Pare
ntal 9 11
EC 1
118
Ferm
icha
mp
H2S
(µg/
l of f
erm
enta
tion)
Strains
88
A distinct banding pattern was observed for each strain set (Figure 5.5). In particular
the genotypic profile of the parent strain (lanes 2 and 3) was discernibly different
from EC1118 (lanes 8 and 9) and Fermichamp (lanes 10 and 11). Importantly, the
genotypic profile of the 2 clonal cultures of isolates 9 (lanes 4 and 5) and 11 (lanes 6
and 7) was identical to the parent, but different from EC1118 and Fermichamp.
These results suggest that it is unlikely that isolates 9 and 11 are contaminants and
instead appear to be mutants derived from the parental strain.
5.5 PHENOTYPE STABILITY In order to observe the long term stability of the phenotype, mutants were propagated
under low stress condition for over 60 generations. This number was considered
sufficient considering that 50 generations was the threshold interval at which it was
possible to observe occurrence of new mutants (Figure 4.5 and 4.6). In this way any
mutation that was not fixed in the population would be lost and the efficient
fermentation phenotype lost with it. Similarly such a passaging exercise would
eliminate the possibility that the observed phenotype was mainly due to adaptation or
pre-conditioning (transient expression of e.g. stress response genes).
Mutants (isolates) 9 and 11 were streaked onto YEPD agar (Appendix 1 – Method 2a)
from a -80°C glycerol stock and grown overnight at 30°C. A single colony from each
plate was transferred into 10 ml of liquid YEPD in a 50 ml Falcon tube and grown
overnight at 30°C. Ten µl of each of these cultures were transferred into 10 ml of
fresh YEPD and growth repeated. This operation was repeated 5 more times. It was
estimated that around 10 generations would elapse for each passage. An aliquot of the
resulting populations was streaked onto YEPD plates and grown as before. Similarly
fresh YEPD plates of the parental strain, a reference strain (Fermichamp) and mutants
9 and 11 prior to passaging (9 original and 11 original), were prepared. Sixteen
colonies from each passaged population as well as a colony from the parental strain,
the reference strain and mutants 9 and 11 (original populations) were transferred into
1 ml of YEPD and grown overnight at 30°C. From this point the experiment was the
same as that described in Section 4.1, utilising duplicate sacrificial plates (200 µl per
well). See Figure 5.6 for the plate layout.
89
Figure 5.5 – Genomic fingerprinting obtained with the Polymerase Chain Reaction (PCR) targeting the Ty1 elements in the genome of the parental strain (lanes 2, 3), mutants 9 (lanes 4, 5) and 11 (lanes 6, 7) and references strains EC1118 (lanes 8, 9) and Fermichamp (lanes 10, 11). Lanes 1, 13 include molecular weight markers (100 base pair ladder) and a PCR product without DNA (blank) is shown in lane 12.
Figure 5.6 – Layout used for the evaluation of sixteen isolates from the passaging of mutants 9 or 11 as well as the mixed population of the passaged cultures (MIX), the parental strain (PAR), a culture of the appropriate original (pre-passaging) mutants 9 and 11 (9 origin or 11 origin) and a reference strain (Fermichamp). Wells in column 11 of the plate were filled with uninoculated media as a sterility check. The wells in column 12 were used during subsequent quantification of sugar concentration.
90
There were no differences between fermentation kinetics of the passaged mutants (16
isolates) 9 and 11 compared to the corresponding mixed population and mutants pre-
passaging (Figures 5.7 panels A to F). Moreover all of the mutants showed faster
fermentation compared to the parent and Fermichamp. In particular and as has been
shown previously for these experimental conditions (Section 4.1), the improvements
in fermentation kinetics were largely attributable to the improvement in fructose
utilization. These data indicate that the changes that have occurred in the mutants and
conferred superior fermentation properties are stable to passaging under low stress
conditions for at least 60 generations.
5.6 GRAPE JUICE FERMENTATIONS The conditions used in this study to isolate improved mutants are ultimately artificial
and do not completely match a grape juice matrix. In addition the continuous
fermentation strategy used does not capture the dynamic compositional phases of
fermentation. In order to begin to evaluate these strains in more industrially relevant
conditions, fermentations were conducted in grape juice and must on a 250 ml and a
20 kg scale, respectively.
Evaluation of fermentation attributes in white grape juice
Fermentations were performed in sterile (0.2 µm) juice obtained from 2007
Chardonnay grapes grown in the Eden Valley (South Australia). Basic compositional
features are shown in Table 5.3. The preparation and execution of the experiment was
largely as described in Section 5.1. Key differences were the incubation temperature
(20°C) and the fact that the starter culture was prepared in white grape juice diluted
1:1 with water. The fermentation performance of mutants 9 and 11 was compared
against the parental strain and reference strains EC1118 and Fermichamp. The
experiment was repeated with similar results, thus only one set of result is shown.
Fermentations of the five strains fell into three different profiles (Figure 5.8).
Fermichamp required 144 hours to deplete all of the sugar from the juice, EC1118 and
mutants 9 and 11 required 235 hours, while the parental strain had not finished
fermentation even after 259 hours (9 g/l residual fructose). Rates of glucose
91
PARAMETER VALUE Glucose + fructose 213.5 g/l Malic acid 2.92 g/l Sulphur dioxide (total) 21 mg/l Sulphur dioxide (free) <5 mg/l pH 3.34 Titratable acid (pH 7.0) 5.4 g/l Titratable acid (pH 8.2) 5.7 g/l Yeast assimilable nitrogen 381 mg/l Ammonia 109 mg/l Alpha amino nitrogen 291 mg/l
20 40 600
50
100
9 or 11 OriginalFermichampMix populationParentalIsolates
A
Time elapsed (h)
Suga
r con
cent
ratio
n (g
luco
se +
fruc
tose
) [g/
l]
20 40 600
20
40
60
B
Time elapsed (h)
Suga
r con
cent
ratio
n (g
luco
se) [
g/l]
20 40 600
20
40
60
Time elapsed (h)
Suga
r con
cent
ratio
n (fr
ucto
se) [
g/l]
C
20 40 600
50
100
D
Time elapsed (h)
Suga
r con
cent
ratio
n (g
luco
se +
fruc
tose
) [g/
l]
20 40 600
20
40
60
E
Time elapsed (h)
Suga
r con
cent
ratio
n (g
luco
se) [
g/l]
20 40 600
20
40
60
F
Time elapsed (h)
Suga
r con
cent
ratio
n (fr
ucto
se) [
g/l]
Figure 5.7 – Curves of depletion of total sugar (A, D), glucose (B, E) and fructose (C, F) are shown for 16 isolates from passaged cultures of mutants 9 (A, B and C) and 11 (D, E and F) along with thecorresponding mixed and pre-passaged (original) population of each and the parental strain. Fermichamp was included as reference. Results are the average of four replicates performed in 200 µl in microtiter plates (standard deviations are shown as error bars).
Table 5.3 – Key compositional features of 2007 Eden Valley Chardonnay juice used to evaluate mutant fermentation performance.
92
consumption were similar across all strains, whereas much of the observed
differentiation was attributable to differences in fructose fermentation rates. As result,
the ratio of the area under the glucose and fructose fermentation curves was highest
for Fermichamp (0.75) followed by EC1118 (0.63) and mutants 9 and 11 (0.56 and
0.53; Table 5.4). Once again this experiment demonstrated the extraordinary ability of
Fermichamp to consume fructose (Guillaume et al., 2007).
Evaluation of fermentation attributes in red grape must
As for the previous experiment, the complete set of fermentations was repeated,
however in this case using two different batches of Merlot grapes (Table 5.5). Given
the similarity of the results across the two must, only data from the Lobethal Merlot
fermentations are reported here.
The grapes were handpicked into 20 kg crates and kept at 4°C until crushed. For each
strain, three replicate fermentations of 20 kg of grape were prepared using fruit
randomly taken from different crates.
Fermentations were conducted in 30 litre plastic containers, provided with a 7 mm
hole for release of CO2. The preparation of the inocula followed the same procedure
as described for the Chardonnay fermentations. In this experiment, due to the
presence of solids, the must was not sterilised. Fermentations were conducted at room
temperature (≈ 25°C) and plunged and sampled at 12 hours intervals. Samples of 1 ml
were stored at -20°C for future enzymatic determination of glucose and fructose
concentration (Boehringer-Mannheim, 1989) with final volumes adjusted to 100 µl
for analysis in 96 well microtiter plates. Other parameters measured at the end of the
fermentations were relevant organic acids (malic, succinic, lactic, acetic) and alcohols
(ethanol and glycerol; HPLC method adopted is described in section 5.3). Colour
measurement (CIELab) of cell free supernatants (20,800 rcf, 1 min) was determined at
the end of fermentation. Calculation of CIELab was obtained measuring the visible
transmission spectrum from 380 nm to 780 nm in 5 nm increments (75 �L in a 96
well microtiter plate, µQuant Microplate Spectrophotometer, Bio-Tek Instruments).
The spectral data was then converted to L* (lightness, 0 – black to 100 – white), a* (-
265, green to 265, magenta) and b* (-265, blue to 265, yellow) using the conversion
equations and tables developed by the Commission Internationale d’Eclairage.
93
100 2000
50
100
150
C
Time elapsed (h)
Fruc
tose
con
cent
ratio
n (g
/l)
VALUES Merlot "Waite"
Merlot "Lobethal" PARAMETER
Glucose + fructose (g/l) 250 265 pH 4.02 3.87
Total acidity (g/l) 3.3 5.0 Alpha Amino Nitrogen (mg/l) 96 93
Ammonia (mg/l) 34 22 Yeast Assimilable Nitrogen (mg/l) 124 111
ADDITIONS SO2 (mg/l) 25 25
Tartaric acid (g/l) 2.3 1 Diammonium phosphate (mg/l) 200 40
PAR 9 11 EC1118 Fermichamp
Area TOT % 100.00 93.06 89.03 98.81 71.43Area GLU % 100.00 100.93 93.31 114.91 92.19Area FRU % 100.00 89.37 87.14 90.90 61.12
Ratio Areas (GLU/FRU) 0.49 0.56 0.53 0.63 0.75
100 2000
50
100
150
B
Time elapsed (h)
Glu
cose
con
cent
ratio
n (g
/l)
100 2000
100
200
300
Parental911EC1118Fermichamp
Time elapsed (h)
Tota
l sug
ar c
once
ntra
tion
- Glu
cose
+ fr
ucto
se (g
/l)
A
Figure 5.8 – Curves of depletion of total sugar (A), glucose (B) and fructose (C) are shown for mutants 9 and 11 compared with the parental strain and commercial strains EC1118 and Fermichamp. Results are the average of triplicate 250 ml Chardonnay juice fermentations (standard deviations are shown as error bars).
Table 5.4 – Percentage of the area under the fermentation curves for isolates 9 and 11 compared to the parental strain, EC1118 and Fermichamp for total sugar or glucose and fructose individually. The fructophilicity of the strains is expressed as a ratio between the areas under the fermentation curves for glucose and fructose. Results are derived from triplicate 250 ml Chardonnay juice fermentations.
Table 5.5 – Key compositional features of 2010 Merlot grapes used to evaluate mutant fermentation performance. Grapes were harvested from the Waite Institute experimental vineyards (Adelaide, South Australia) or at Lobethal (South Australia).
94
Results are expressed using a D65 illuminant at 10° standard observer. Hue (H, 0° -
360°) and chroma (C, 0 - 100) values were calculated using the following equations:
°
As for the Chardonnay juice, fermentation in red must the five strains fell into three
profiles (Figure 5.9). EC1118 and Fermichamp depleted the sugars in 240 hours,
mutants 9 and 11 required 287 hours, while the parent had not finished fermentation
even after 293 hours (≈ 10 g/l residual fructose). Fructose fermentation rate mainly
contributed to differences observed in the overall fermentation performance.
However, in this case, the ratio of the area under the glucose and fructose
fermentation curves did not changed for mutants 9 and 11 compared to the parental
strain (≈ 0.65), while EC1118 and Fermichamp showed higher values (0.77 and 0.84
respectively; Table 5.6). Once again, these references strains showed higher
fructophilicity.
From the analysis of the principal metabolites (Figure 5.10), Fermichamp produced a
larger amount of succinic acid (≈ 5 g/l vs ≈ 3 g/l). Mutants 9 and 11 and Fermichamp
showed a greater concentration of glycerol (≈ 13 g/l) than EC1118 and the parental
strain (≈ 11 g/l).
When expressing red wine colour as a CIELab measurement, differences of 3.0 or
more of the calculated DeltaE (ΔE = (L1 – L2)2 + (a1 – a2)2 + (b1 – b2)2), are considered
to exceed the threshold for detectability by the human eye (Martinez et al., 2001). The
values of ΔE calculated in this experiment, were generally higher than 3.0 CIELab
units (Figure 5.11) suggesting that the differences produced by strains in colour would
be detectable. Unfortunately no sensory panel was available to confirm this.
In conclusions, these last experiments confirmed the improved fitness of two mutants
isolated using an adaptive evolution strategy. Thus mutants 9 and 11 showed faster
rate of utilization of both glucose and fructose compared to the parental strain (AWRI
796). These was observed in CDGJM (250 ml, 230 g/l of sugars), Chardonnay juice
(250 ml, 213 g/l of sugar) and Merlot must (20 kg, 265 g/l of sugar). In most cases
fructose was utilised at faster rate than the parental strain, thereby confirming the
effectiveness of the targeted adaptive evolution strategy adopted in this study.
95
100 200 3000
100
200
300
Parental911EC1118Fermichamp
Time elapsed (h)
Tota
l sug
ar c
once
ntra
tion
- GLU
+ F
RU
(g/l)
100 200 3000
50
100
150
Time elapsed (h)
Glu
cose
con
cent
ratio
n (g
/l)
100 200 3000
50
100
150
Time elapsed (h)
Fruc
tose
con
cent
ratio
n (g
/l)
PAR 9 11 EC 1118 FERM
Area TOT % 100.00 88.01 87.38 86.93 87.93Area GLU % 100.00 88.17 86.27 95.59 101.50Area FRU % 100.00 87.90 88.11 81.26 79.06
Ratio Areas (GLU/FRU) 0.65 0.66 0.64 0.77 0.84
Figure 5.9 – Curves of depletion of total sugar (A), glucose (B) and fructose (C) are shown for mutants 9 and 11 compared with the parental strain and commercial strains EC1118 and Fermichamp. Results are the average of triplicate 20 kg Merlot grape fermentations (standard deviations are shown as error bars).
Table 5.6 – Percentage of the area under the fermentation curves for isolates 9 and 11 compared to the parental strain, EC1118 and Fermichamp for total sugar or glucose and fructose individually. The fructophilicity of the strains is expressed as a ratio between the areas under the fermentation curves for glucose and fructose. Results are derived from triplicate 20 kg Merlot grape fermentations.
96
Malic A
cid
Succin
ic Acid
Lactic
Acid
Acetic
Acid
Glycero
l
Ethano
l0
5
10
15140150160170180190
Parental911EC1118Fermichamp
Metabolites
Con
cent
ratio
n (g
/l)
Figure 5.10 – Metabolite concentration in end of fermentation (< 2.5 g/l sugar) samples for isolates 9 and 11, the parental strain and references EC1118 and Fermichamp. The values are the average of single determinations from triplicate 20 kg Merlot grape fermentations (standard deviations indicated as error bars).
L a b
Chroma
Hue0
20
40
60
80Parental911EC1118Fermichamp
CIELab parameters
Val
ues
Figure 5.11 – CIELab parameters measured at end of fermentation (< 2.5 g/l sugar) samples for isolates 9 and 11, the parental strain and references EC1118 and Fermichamp. The values are the average of single determinations from triplicate 20 kg Merlot grape fermentations (standard deviations indicated as error bars).
97
CHAPTER 6
CONCLUSION, DISCUSSION AND FUTURE DIRECTIONS
98
6.1 CONCLUSIONS This project aimed to generate wine yeast strains with improved fructose utilization,
and the strategy employed was adaptive evolution. The objectives of the study were:
1. to test adaptive evolution as a strategy to generate improved phenotypes in
wine yeast for the wine industry;
2. specifically, to improve the fructophilicity of a commercially available wine
yeast strain.
Both of these objectives were achieved; fructophilic variants of AWRI 796 were
generated using adaptive evolution. Fermentation rates of fructose were greater in the
mutants compared to the parental strain and the fructophilic index* for the mutants
also increased when they were grown in CDGJM. Interestingly, however, whilst
overall fermentation rate was also higher for the mutants than its parent in grape juice,
the fructophilic index was unchanged.
6.2 DISCUSSION AND FUTURE DIRECTIONS
Experimental adaptive evolution has been used several times in the past to generate
yeast with improved phenotypes (see for example Brown and Oliver, 1982; Hall,
1992; McBryde et al., 2006). The work described in this thesis built on this past work
and successfully generated a novel yeast strain with improved fructose utilization.
However the results highlighted an important limitation of adaptive evolution
strategies: evolution is not driven solely by the selective pressure, but it is also shaped
by the matrix of the medium. This may be the reason that fructophilic mutants showed
clear increases in fructophilic index for the medium used in the adaptive evolution
experiment (CDGJM), but for grape juice the index was unchanged from the parent.
Clearly, from a wine industry perspective, it will important in future studies to use a
medium that reflects as closely as possible the matrix of the grape juice where the
mutants will be used in industry.
___________________________________________________________________________________*Defined in this study as the ratio between areas under the fermentation curves of glucose and fructose
99
There are numerous ways in which this project could be developed to improve on the
adaptive evolution strategy that was used. For example:
� EMS mutagenesis increases the genetic variation, however the probability of
generating specific advantageous mutations is limited when done on a single
strain. Other techniques for generating genetic variation that could be used
include mass mating of many wine yeast from culture collections. This would
lead to the generation of many novel gene combinations in a range of wine-
relevant genetic backgrounds and should increase the probability of generating
novel genotypes and phenotypes for adaptive evolution to work on.
� The use of other selective pressures to select for fructophilic mutants should
be trialled. For example, 2-deoxy-D-glucose, a glucose homologue, is toxic to
S. cerevisiae and has been used for a study on hexose uptake (Novak et al.,
1990). If used in a medium with fructose as the principal source of sugar it
should select for mutants with superior fructose utilization at the expense of
glucose uptake.
� Other approaches to adaptive evolution have been described and used in the
past. One strategy is BOICS, (Brown and Oliver Interactive Continuous
Selection) derived from the name of the inventors (Brown and Oliver, 1982).
Briefly, it consists of an interactive chemostat selection: the intensity of the
selective pressure can be automatically adjusted via a feedback control circuit.
Thus, by monitoring parameters such as physiological changes in the
bioreactor (e.g. variation in cell density) or fermentation products (e.g. CO2
release), it is possible to increase the selective pressure without the risk of
completely washing out the population. As the authors stated, “the technique
of continuous selection with feedback should be generally applicable to the
isolation of mutants of any microorganism to improved tolerance to any
inhibitory condition of either its physical or chemical environment” (Brown
and Oliver, 1982). Thus the use of an inhibitor such as 2-deoxy-D-glucose
could be used in this type of approach. Moreover, the BOICS strategy could
also be used in a situation where limited fructose concentration determines the
selective pressure. Fructose could be added to the bioreactor, in ever
decreasing concentrations, in the absence of other carbon sources. When the
cell density drops below a certain level, before wash out, the fructose level
100
would be increased sufficiently to rescue survivors, then decreased again. This
could be done over many cycles to select for mutants that have greater
capacity to utilise limiting fructose.
Further characterization of the physiology of the mutants generated in this study will
be essential. It has been shown that they have a faster fermentation rate compared to
the parent, and, from preliminary experiments (metabolite concentration using HCPL,
colour measurements determined with CIELab), there were no large variations to the
final composition of the wine. However, more detailed investigations (larger scale
winery fermentations, wine evaluation through a sensory panel) are required to
confirm the ability of adaptive evolution to generate specifically improved strains
which maintain other oenological traits of parent.
Time did not permit characterization of the mutation(s) that conferred fructophilicity
in the mutants generated from the work described in this thesis. A deeper
investigation of the genome of the mutants and a sub-sequential comparison with the
parental strain would reveal the identity of the ‘fructophilic genes’ in the mutants.
This would facilitate the use of genetic engineering techniques to produce more
efficient fructophilic strains. Genetic manipulations represent an important approach
in the modern generation of mutants for the wine and food industry. Despite the
ethical debates existing at present on the use of genetic modified organisms, it is
likely that in future they will be accepted for the production of food and beverages.
101
APPENDIX 1
102
METHODS Method 1 – Collection of yeast strains from commercial package for long term storage Strains were collected aseptically from active dried commercial preparations, re-
hydrated in sterile water (20 min) and inoculated into YEPD medium (see Method 2a)
in a flask (air/liquid ratio > 66%) before overnight incubation at 28°C with shaking at
180 rpm. Cultures were then streaked onto YEPD agar plates and grown overnight at
28°C to check for purity. Multiple representative colonies were inoculated into 25 ml
of YEPD broth and grown as above. The combination of 1 ml of culture with 0.5 ml
of sterile 80% (v/v) glycerol (final concentration = 15% v/v), enabled long term
storage at -80°C.
Method 2 – a. YEPD medium b. YEPF medium
10 g/l yeast extract 10 g/l yeast extract
20 g/l bacto-peptone 20 g/l bacto-peptone
20 g/l D-glucose 20 g/l D-fructose
For preparation of plates, 20 g/l agar was added. All media were sterilize by autoclaving at 121°C for 20 min.
103
Method 3 – Chemically Defined Grape Juice Media (CDGJM) (Henschke and Jiranek, 1993) CDGJM is a water based medium. All the chemical components listed below were
dissolved in water. and sterilized by filtration (0.2 µm).
SALTS
L-Malic acid 3 g/l Citric acid 0.2 g/l Potassium phosphate dibasic (K2HPO4) 1.14 g/l Magnesium sulphate heptahydrate (MgSO4.7H2O) 1.23 g/l Potassium sodium tartrate tetrahydrate (KNaC4H4O6.4H2O) 3.12 g/l Calcium cloride dihydrate (CaCl2.2H2O) 0.44 g/l
TRACE MINERALS
Manganese chloride tetrahydrate (MnCl2.4H2O) 198.2 μg/l Zinc chloride (ZnCl2) 135.5 μg/l Iron(II) chloride (FeCl2) 32 μg/l Copper(II) chloride (CuCl2) 13.6 μg/l Boric acid (H3BO3) 5.7 μg/l Cobalt(II) nitrate hexaydrate (Co(NO3)2.6H2O) 29.1 μg/l Sodium molybdate dihydrate (Na2MoO4.2H2O) 24.2 μg/l Potassium iodide (KI) 10.8 μg/l
VITAMINS Myo-insitol 100 mg/l Pyridoxide-HCL 2 mg/l Nicotinic acid 2 mg/l Calcium Pantothenate 1 mg/l Thiamine-HCL 0.5 mg/l ρ-Amino benzoic acid 0.2 mg/l Biotin 0.125 mg/l Folic acid 0.2 mg/l Riboflavin 0.2 mg/l
SUGARS Glucose *Variable Frutose *Variable
#NITROGEN amino acids ^Variable
*Glucose and fructose concentration can vary.
^Nitrogen concentration can vary; in all of the experiments presented in this thesis the
nitrogen concentration was 600 mg/l, added as a 25x amino acid solution.
104
#25x Amino acid solution Alanine 100 mg/l Arginine 750 mg/l Asparagine 150 mg/l Aspartic acid 350 mg/l Glutamic acid 500 mg/l Glutamine 200 mg/l Glycine 50 mg/l Histidine 150 mg/l Isoleucine 200 mg/l Leucine 300 mg/l Lysine 250 mg/l Methionine 150 mg/l Phenylalanine 150 mg/l Proline 500 mg/l Serine 400 mg/l Threonine 350 mg/l Tryptophan 100 mg/l Tyrosine 20 mg/l Valine 200 mg/l Ammonium chloride 100 mg/l
1 ml of 25x amino acid solution to supply 20.27 mg of N For CDGJM starter, was used half of the total sugar concentration plus Tween 80® and ergosterol.
STARTER Tween 80® 0.5 ml/l Erosterol 10 mg/l
105
Method 4 – Ethyl methanesulfonate (EMS) mutagenesis (Fink, 1970)
Material Yeast culture – YEPD (2 x 108 Cells/ml) 15 ml Sodium phosphate 0.1 M buffer pH 7
(2.16 g NaH2PO4 – dibasic 6.54 g NaH2PO4 – monobasic Make up to 200 ml with H2O)
Ethyl methanesulfonate (EMS) 45 µl/ml of YEPD Na thiosulphate 5% sterile water solution YEPD agar plates YEPD Glycerol 80% sterile water solution
The culture was grown over night in 25ml of YEPD at 30°C. Cells were collected
(5000 rpm for 5 min) in a 50 ml sterile plastic tube, washed twice in 15 ml of 0.1 M
sodium phosphate buffer pH 7 and resuspended in 2.5 ml of the same buffer. Cells
were counted and 3 x 109 cells were transferred to a 50 ml sterile plastic tube, brought
to a volume of 15 ml with 0.1 M sodium phosphate buffer pH 7 (final cell
concentration = 2 x 108 cells/ml). One ml of suspension was removed and kept (for
plate inoculation and freeze storage). 630 µl of EMS were added and the culture
incubated at 30°C. 1 ml samples were removed every 10 minutes and wash twice with
one volume of 5% of sodium thiosulphate. Cells were resuspended in 1 ml of YEPD.
For each time point, the appropriate amount of cells was transferred in duplicate onto
agar plates for colony counts. The remaining cells were mixed with 0.5 ml of 80%
(v/v) glycerol for storage at -80°C. The average viable cell count was used to
calculate the relative survival curve.
106
Method 5 – Isolation of genomic DNA (Adams et al., 1997)
Materials:
- YEPD (see Method 2a) - MQ (High purity water – DNAse free) - Phenol : chloroform 5:1 with aqueous phase on top – OR – Phenol:
chloroform: isoamyl alcohol (25:24:1) - Acid washed glass beads - TE: 10 mM Tris-Cl pH 8.0, 1 mM EDTA (in 100 mL 0.12 g tris, 0.037 g
EDTA) - RNAse cocktail - 4 M ammonium acetate - Lysis buffer: 2% Triton X-100, 1% SDS, 100 mM NaCl, 10 mM Tris-Cl pH
8.0, 1 mM Na2EDTA. Autoclaved or filter sterilized.
Method:
1 Grow 10 mL yeast cultures to saturation in YEPD at 30°C overnight from
a streak plate.
2 Collect the cells by centrifugation for 5 minutes. Remove supernatant and
re-suspend in 0.5 ml of water. Transfer to a 1.5 ml microfuge tube. Collect
by centrifugation for 5 seconds.
3 Decant the supernatant
4 Add 0.2 ml lysis buffer and re-suspend. Add 0.2 ml phenol : chloroform
5:1, leaving top aqueous layer. Add 0.3 g acid washed glass beads.
5 Vortex for 3-4 minutes, placing on ice every 30 seconds. Add 0.2 ml TE
6 Centrifuge for 5 minutes. Transfer the aqueous layer to a fresh tube. Add
1.0 ml 100% ethanol. Mix by inversion
7 Centrifuge for 2 minutes. Discard supernatant – pipette. Re-suspend in 0.4
ml TE. Add 1 �l RNAse cocktail. Incubate at 37° for 15 minutes. Add 10
�l 4 M ammonium acetate, plus 1 ml 100% ethanol. Mix by inversion
(denature proteins). Keep in cold room for 15 minutes. Centrifuge for 5
minutes.
8 Discard supernatant. Air-dry pellet. Re-suspend in 50 �l MQ, leaving for
15 minutes, while flicking occasionally.
107
Method 6 – Ty1 transposon (Ness et al., 1993)
Ty1 transposon primers: MLD1 (forward primer) 5’-CAAAATTCACCTATA/TTCTCA-3’ MLD2 (reverse primer) 5’-GTGGATTTTTATTCCAACA-3’ Concentration of the components used for Ty1 transposon PCR reaction
Master MIX Components
Amount used for single PCR
reaction
10x reaction buffer 2.5 µl
MgCl2 (25 mM) 2.5 µl
dNTP’s (10 mM) 1.0 µl
Forward primer (10 µM) 1.0 µl
Reverse primer (10 µM) 1.0 µl
H2O 15.8 µl
Taq polymerase (5 U/µl) 0.2 µl
DNA 1.0 µl
Total volume 25 µl
PCR program 5.0 min 95ºC 0.5 min 95ºC 3 X 0.5 min 42ºC 2.0 min 72ºC 0.5 min 95ºC 29 X 0.5 min 45ºC 2.0 min 72ºC 10 min 72ºC hold at 20ºC
108
APPENDIX 2
109
STATEMENT OF AUTHORSHIP
NOTE: Statements of authorship appear in the print copy of the thesis held in the University of Adelaide Library.
A
Microvinification – How small can we go? 1
2
Tommaso Liccioli1, Tina M.T. Tran2,3, Daniel Cozzolino2, Vladimir Jiranek1, 3
Paul J. Chambers2 and Simon A. Schmidt2* 4
1School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, 5
SA 5064, Australia. 6
2The Australian Wine Research Institute, PO Box 197, Glen Osmond, SA 5064, 7
Australia 8
3School of Molecular Sciences, Victoria University of Technology, Werribee Campus 9
(W008), P.O. Box 14428, Melbourne City, MC, Victoria, Australia, 8001 10
* Corresponding author. Tel.: +61 08 830366600; fax.: +61 08 83036601; e-mail: 11
13
14
B
Abstract 15
Background 16
High throughput methodologies to screen large numbers of micro-organisms 17
necessitate the use of small-scale culture vessels. In this context, an increasing 18
number of researchers are turning to microtiter plate (MTP) formats to conduct 19
experiments. MTPs are now widely used as a culturing vessel for phenotypic 20
screening of aerobic laboratory cultures and their suitability has been assessed for a 21
range of applications. The work presented here extends these previous studies by 22
assessing the metabolic foot-print of MTP fermentation. 23
Results 24
A comparison of Chardonnay grape-juice fermentation in MTPs with fermentations 25
performed in air-locked (self-induced anaerobic) and cotton-plugged (aerobic) flasks 26
was made. Maximum growth rates and biomass accumulation of yeast cultures grown 27
in MTPs were indistinguishable from self-induced anaerobic flask cultures. 28
Metabolic profiles measured using both targeted and non-targeted methods differed 29
depending on the metabolite. While glycerol and acetate accumulation mirrored that 30
of self-induced anaerobic cultures, ethanol accumulation in MTP ferments was limited 31
by the increased propensity of this volatile metabolite for evaporation in microlitre-32
scale culture format. 33
C
Conclusions 34
The data illustrates that microplate cultures can be used as a replacement for self-35
induced anaerobic flasks in some instances and provide a useful and economical 36
platform for the screening of industrial strains and culture media. 37
Keywords 38
Yeast; microplates; grape-juice; fermentation; high-throughput 39
40
D
Background 41
The use of sub-millilitre culture volumes in microtiter plate (MTP) format for the 42
characterization of microbial cultures has become routine for many applications. 43
Extensive evaluation of MTP configuration (2000; Duetz and Witholt 2004; 44
Warringer and Blomberg 2003), sealing membrane performance (Zimmermann et al. 45
2003) and the impact of fluid mixing regimes on oxygen transfer rates (Duetz et al. 46
2004; Hermann et al. 2003; Kensy et al. 2005) has resulted in the use of MTP format 47
in varied applications including phenotypic profiling (Maresova and Sychrova 2007; 48
Stitt et al. 2002; Warringer et al. 2003), optimization of product yield from E. coli and 49
hybridoma cell lines (Ferreira-Torres et al. 2005; Micheletti et al. 2006a), high-50
throughput metabolite (Börner et al. 2007) or metabolic flux analysis (Blank et al. 51
2005a; Blank et al. 2005b), culminating in full automation of MTP fermentation 52
(Zimmermann and Rieth 2007). The breadth and depth of development of MTP-53
based methods was recently reviewed (Duetz 2007; Micheletti and Lye 2006b) with 54
Duetz declaring that MTPs can now be considered a mature alternative to Erlenmeyer 55
shake flasks. 56
57
One area of investigation lacking in this otherwise comprehensively researched field 58
is self-induced anaerobic fermentation. Self-induced anaerobic refers to 59
fermentations in which oxygen is initially present but which quickly tend toward 60
anaerobisis. Self-induced anaerobic fermentation is fundamental to much of industrial 61
microbiology involving, for example, wine, beer and biofuel production. In these 62
environments oxygen is not only limiting but often undesirable. The question 63
associated with culture miniaturization is, therefore, not how to maximize oxygen 64
E
transfer rates, as is the case for aerobic culture, but how to minimize them. As 65
previously noted by Duetz (2007), one of the intrinsic properties associated with 66
culture miniaturization is the high ratio of gas-liquid exchange area relative to the 67
bulk liquid compared with shaken flask cultures. Differences in this ratio inevitably 68
lead to altered oxygen transfer rates (OTRs) and liquid loss due to evaporation. 69
Recent literature addresses the problem of differential OTRs by optimizing fluid 70
mixing regimes (Duetz et al. 2004) however, in the case of self-induced anaerobic 71
Erlenmeyer fermentations, agitation is used to maintain culture homogeneity rather 72
than enhance OTRs. In self-induced anaerobic cultures agitation rates are typically 73
minimized to a level sufficient for the maintenance of biomass suspension, and no 74
more. The purpose of maintaining a suspension is two fold: to enable representative 75
sampling for the estimation of cell biomass and, to ensure that culture reproducibility 76
is not diminished by stochastic effects resulting from the lack of mixing. 77
78
Running self-induced anaerobic cultures in MTP format releases us from the first of 79
these two requirements since culture density is determined non-destructively by 80
measuring optical density in situ, vertically across the whole well; whether or not the 81
cells are in suspension is of little consequence. This leaves us only with the second 82
requirement, culture reproducibility and this raises several questions. Is culture 83
homogeneity a prerequisite for reproducible fermentations when working in small 84
scale culture vessels? Will, for example, settling of biomass to the bottom of a MTP 85
well, limit its access to nutrients and thus diminish or alter the result? What 86
constitutes a set of conditions whereby fermentations conducted in MTP format yield 87
results indistinguishable from self-induced anaerobic Erlenmeyer flasks? We 88
approached these questions by comparing the growth profile and metabolic footprint 89
F
of an industrial yeast during fermentation of grape juice in MTPs, aerobic flasks 90
(AeFs) and self-induced anaerobic flasks (SAFs). In addition, the feasibility of MTP 91
format use in a typical industrial strain development workflow was explored by 92
screening 15 wine yeast strains using fermentation performance as a criterion for 93
selection. The performance profiles of four strains, exhibiting performance diversity 94
in MTP fermentations, were validated using larger scale self-anaerobic fermentation. 95
Using this approach the utility of MTP format for screening of industrial yeast strain 96
properties other than growth and biomass formation is demonstrated. 97
98
Materials and Methods 99
Strain and culture conditions 100
All yeast strains were maintained on 1% w/v yeast extract, 2% w/v bacto peptone and 101
2% w/v glycerol (YPG) agar plates. Overnight cultures were grown in 1% w/v yeast 102
extract, 2% w/v bacto peptone and 2% w/v D-glucose (YPD) liquid broth on a 103
rotating wheel at 28°C. YPD overnight cultures were used to inoculate 50:50 Eden 104
Valley Chardonnay juice : water, which were grown overnight in cotton-plugged 105
Erlenmeyer flasks at 20°C, and used as starter cultures for 100% Chardonnay juice 106
ferments. Inoculation density was 1 � 106 cells ml-1. The Chardonnay juice contained 107
107 gL-1 each of glucose and fructose, 362 mgL-1 yeast assimilable nitrogen and had a 108
pH of 3.32. All juice was 0.2 μm filtered prior to inoculation. Small volume flask 109
fermentations were performed in triplicate in 200 ml Erlenmeyer flasks with side arm 110
sampling ports, stoppered with either water filled airlocks (SAF) or cotton plugs 111
(AeF), incubated at 20°C with shaking at 150 rpm. Immediately following 112
G
inoculation, a sample was drawn from each of the flasks and added to microtiter plates 113
(MTPs) to a final volume of 200 μl per well. Culture from each flask was used to 114
inoculate eight wells on replicate plates, with one plate prepared for each required 115
time point. The MTPs were sealed with Breathe Easy gas permeable membrane 116
(Diversified Biotech, Boston, M.A., U.S.A.) and kept in a humidified box without 117
shaking at 20°C. 118
Analysis of growth data 119
Yeast growth in Erlenmeyer flasks was followed by measuring optical density at 600 120
nm at 1 cm path length corrected for non-linearity at high optical densities (OD600 121
1cmcorr), using a DU530 spectrophotometer (Beckman Coulter Inc., Fullerton, C.A., 122
U.S.A.). Correction for non-linearity was achieved by dilution prior to measurement 123
such that OD600 was always < 0.5. Yeast growth in the MTPs was followed by 124
measuring the optical density at 630 nm using a Multiskan Ascent spectrophotometer 125
(Thermo Fisher Scientific Inc., Waltham, M.A., U.S.A.). A correction function was 126
used to convert observed MTP optical density (OD630MTPobs) to 1 cm path length 127
OD600 corrected for non-linearity (OD600 1 cmcorr). The correction function was 128
applied to MTP absorbance data so that dry cell weight estimates and comparisons 129
with shake flask cultures could be made. This function was derived by measuring the 130
optical density of a yeast culture dilution series in both spectrophotometers, plotting 131
OD600 1 cmcorr (optical density measure at 1 cm pathlength) against OD630 MTPobs 132
(optical density measure from microplate reader) and performing non-linear, least 133
squares regression using GraphPad Prism (GraphPad Software Inc., La Jolla, C.A., 134
U.S.A.). The best fitting equation was selected based on linear regression of 135
OD600 1 cmcorr vs OD600 1 cm predicted. The following equation predicted 136
H
OD600 1 cmcorr from OD630 MTPobs values between 0.007 and 1.46 with RMSE = 137
0.0676; OD600 1 cmcorr= 0.008160 + [2.743 � OD630 MTPobs] + [-0.3495 � OD630 138
MTPobs2] + [0.6282 × OD630 MTPobs
3]. 139
140
OD600 1 cmcorr was used to estimate biomass accumulation as dry cell weight (DCW). 141
This was achieved by transforming OD600 1 cmcorr data to DCW using the formula 142
DCW = OD600 1 cmcorr � 0.64246. The conversion factor was derived from linear 143
regression of OD600 1 cmcorr vs DCW using strain AWRI 1493 generated following 144
direct measurement of DCW using a moisture balance (AMB50, Inscale Measurement 145
Technology Ltd. Sussex, U.K.). 146
147
Maximum growth rates were estimated from natural log transformed DCW data using 148
the Baranyi-model (Baranyi and Roberts 1994) and custom equation curve-fitting in 149
Prism (GraphPad). Final biomass yields were estimated by taking the exponential of 150
the Ymax term (maximum value obtained from the model) following Baranyi-model 151
fitting. Statistical comparisons were made using one-way ANOVA with significance 152
between treatments evaluated using Tukey’s multiple comparison post test. Results 153
are expressed as means ± 95% standard deviation. 154
Ferment analysis 155
Concentrations of glucose, fructose, glycerol, acetate and ethanol were determined by 156
HPLC on an HP 1100 series using the method of Frayne (1986) except that 5 mM 157
H2SO4 was used as the mobile phase. Calibration curves relating concentration to 158
optical density or refractive index measurements were fitted by least squares 159
regression using Chemstation software (Agilent, Santa Clara, C.A., U.S.A.). For high 160
I
throughput microplate fermentations glucose and fructose concentrations were 161
determined using a Randox kit (Randox Laboratories Ltd., Crumlin, Antrim, United 162
Kingdom). 163
Results 164
This work compared fermentation in unshaken MTPs with fermentations typical of 165
those used at laboratory scale in wine research; shaken 200 ml Erlenmeyer flasks 166
fitted with air-locks. In the absence of agitation yeast cells accumulated as a layer on 167
the base of the MTP. Uneven distributions of this layer, as noted by Warringer and 168
Blomberg (2003) when plates were shaken were not evident in the absorbance profiles 169
of these experiments. Furthermore, the observed optical density of settled cells was 170
comparable to that of suspended cells (see additional file 1). The even growth 171
characteristics and similarity of optical density for suspended and settled cell cultures 172
simplified the experimental protocol and permitted the use of plate readers and 173
incubators without built in shakers for the conduct of screening experiments using 174
MTP fermentations. 175
Effect of culture vessel size on maximum growth rate and biomass yield 176
Maximum growth rate and efficiency of growth (biomass yield) have been described 177
as “principally independent variables reflecting strictly different aspects of cell 178
physiology” (Warringer et al. 2003) and are the two parameters most commonly used 179
to characterize performance of a microbial culture. Figure 1A and Figure 1B show a 180
comparison of final biomass yields and maximum growth rates respectively from 181
cultures in non-shaken MTPs, SAFs and AeFs. 182
183
J
Maximum growth rates were indistinguishable, irrespective of culture vessel type 184
(ANOVA and Tukey’s multiple comparison test, alpha = 0.05). In contrast final 185
biomass levels varied depending on the culture vessel used. Aerobic (AeF) cultures 186
produced 1.6 fold (2.4 gL-1 ± 0.2, n = 6) more biomass than those in either SAFs or 187
the MTPs. 188
189
Evaporative volume loss during fermentation in MTPs was minimized by conducting 190
fermentations in humidified chambers. The total volume loss during fermentation 191
was estimated by weighing MTPs. Average weight loss in MTPs was 14.5% of initial 192
weight. A weight loss of 14.5% translates to a volume loss of approximately 12 193
μl/well (6% of initial volume), once weights are adjusted for change in density from 194
juice to wine. No weight loss was observed in un-inoculated MTPs indicating weight 195
loss was not due to evaporation of water during extended incubation. Therefore, 196
while some weight loss can be accounted for by liberation of CO2 some excess 197
volume loss occurs in MTPs that is not evident in larger scale ferments. While the 198
volume loss does not affect the optical density measurement due to reduced path-199
length (see additional file 2) it does lead to altered cell concentration that must be 200
taken into account when calculations of cell concentration are made. 201
Kinetics of sugar utilization 202
The major substrates in grape-juice fermentation are glucose and fructose, present in 203
equimolar concentrations. The kinetics of utilization of these two sugars differed 204
depending on the culture vessel (Figure 2A, B and Figure 3). Sugar utilization in 205
AeFs was both faster and more efficient than in MTPs or SAFs. With access to 206
oxygen provided by the cotton plug, fermentations in AeF flasks completed both 207
K
glucose and fructose utilization within 70 hours and therefore differences between 208
glucose and fructose utilization were minimal in comparison to the other two vessel 209
types. Glucose was exhausted (< 1 gL-1) in within 85 hours in MTPs and 100 hours in 210
SAFs. MTPs required 105 hours to complete fermentation of fructose, a full day 211
longer than was required for completion of glucose fermentation. SAFs still 212
contained 2 gL-1 fructose at 190 hours. These ‘time-to-dryness’ profiles reflected 213
maximum glucose and fructose utilization rates achieved in different vessel types 214
(Figure 3A and B). 215
Ethanol, glycerol and acetic acid accumulation 216
Ethanol, glycerol and acetic acid are metabolites of interest in many industrial 217
fermentation types, particularly in wine and beer production. The maximum ethanol 218
concentrations achieved in AeFs and SAFs did not differ significantly and were 108.7 219
± 1.3 gL-1 and 111.2 ± 1.2 gL-1 respectively. The maximum ethanol concentration 220
achieved in MTPs was significantly less at 92.4 ± 4.2 gL-1 (P < 0.05). Ethanol 221
formation was most rapid in AeFs (Figure 3A and B) which can largely be attributed 222
to the higher amount of biomass in this flask type. This is indicated by the maximum 223
specific ethanol production rates which are not significantly different from that 224
achieved in MTPs. The maximum specific ethanol production rates in both AeFs and 225
MTPs were significantly higher than that achieved in SAFs (P < 0.001) suggesting 226
that the efficiency of ethanol production is assisted by access to oxygen. We attribute 227
the reduced lower absolute concentration of ethanol in MTPs compared with flask 228
cultures to loss through evaporation even though evaporative water loss is not a 229
problem in the humidified incubators used here. 230
231
L
Maximum glycerol concentrations and rates of production did not differ significantly 232
between the SAFs and MTPs, P > 0.05 (Figure 2E and Figure 3). The maximum 233
concentration of glycerol achieved in AeFs was slightly higher than the other two 234
fermentation types (P < 0.05) although its rate of production did not differ 235
significantly. Maximum acetic acid concentrations were highest in AeFs, reaching 236
1.211 ± 0.02 gL-1 at 120 h, lowest in SAFs (0.478 ± 0.08 gL-1) and intermediate in 237
MTPs (0.727 ± 0.02). Rates of acetic acid production did not differ significantly 238
between any of the flask types, although the time during which the maximum rate of 239
acetic acid accumulation occurred was dependent on the flask type. Although acetic 240
acid concentrations in MTP ferments began to increase at the final time point, this 241
occurred well after sugar depletion and never reached the levels observed in AeFs. 242
Application of microplate fermentation to screening of wine yeast for 243
fermentation performance 244
It has previously been shown, and reproduced in this work, that it is possible to 245
reproduce flask type growth and biomass profiles in MTP cultures (Stitt et al. 2002; 246
Warringer et al. 2003), however screening methods that enable other performance 247
parameters to be assessed are also desirable. Sugar utilization is a key parameter of 248
interest and although the exact SAF sugar utilization profiles could not be reproduced 249
in MTP ferments we explored whether sugar utilization trends in MTP fermentations 250
could be used as predictors of performance in larger scales. 251
252
MTP ferments of 15 different wine yeast strains were evaluated using a sacrificial 253
plate format in which a single MTP was set up for each required time point. The 254
sugar utilization curves of these are shown in Figure 4A. A subset comprising 4 255
M
strains (Shown in bold in Figure 4A) whose sugar utilization profiles covered the full 256
range observed in sacrificial MTP assays was chosen for more detailed 257
characterization in 200 ml ferments (Figure 4B). A comparison of Figures 4A and 4B 258
demonstrates that although the total duration of fermentation is reduced in MTP 259
format, the overall profile of sugar utilization for the different strains is consistent 260
irrespective of the fermentation scale. For example, strain 4 continues to ferment 261
strongly until a residual sugar concentration of 50 gL-1 before its fermentation rate 262
slows, whereas the rate of sugar utilization for the other strains slows at > 100 gL-1. 263
When strains are ranked by time taken to reach a target sugar of < 2 gL-1, strain order 264
does not change when ferments are performed on a larger scale (Table 1). 265
Discussion 266
Results reported in this paper indicate that MTPs can be used to follow growth and 267
metabolite production in fermentations of grape juice by wine yeast. This format 268
shows great promise for high-throughput screening of industrial yeast; in many 269
respects an unshaken MTP grape-juice fermentation is indistinguishable from a self-270
induced anaerobic Erlenmeyer flask fermentation. Fermentations performed in these 271
vessels exhibit similar maximum growth rates and biomass formation, utilize glucose 272
and accumulate glycerol and acetic acid to the same degree and with similar 273
maximum rates, many of which are consistent with previous observations (Berthels et 274
al. 2008; Fleet and Heard 1993; Ooi et al. 2008). 275
276
In other respects ferments performed in MTPs were different from those in either 277
SAFs or AeFs and this reflects, at least in some regards, the limitations inherent in the 278
use of MTPs. For example, while the maximum rate of ethanol accumulation in 279
N
MTPs was similar to that in SAFs during the early phase of production, the maximum 280
specific rate of ethanol production was similar to that in AeFs. Later in fermentation 281
evaporation rates of ethanol from MTPs presumably exceeded production and were 282
particularly pronounced during the final stages of fermentation. Thus, the microplate 283
ferments never attained concentrations of ethanol equivalent to those of flask cultures. 284
285
Significantly higher biomass production is the primary distinguishing feature of AeF 286
fermentations. The higher accumulation of biomass in the AeFs is consistent with 287
increased access to oxygen in this vessel type (Henzler and Schedel 1991). Aside 288
from biomass formation, the impact of culture vessel type was most evident in the 289
profiles of glucose and fructose consumption. Total sugar consumption occurred 290
most rapidly in AeFs with both glucose and fructose utilization effectively complete 291
within 75 hours. The faster completion of fermentation is most likely related to 292
higher biomass accumulation (Varela et al. 2004). Sugar consumption was slowest in 293
the SAFs which showed both the slowest total utilization rate and the largest 294
discrepancy between the time required to ferment glucose and fructose. This 295
discrepancy in glucose and fructose utilization is typical of wine ferments with 296
residual fructose representing a frequent difficulty for the wine industry (Berthels et 297
al. 2004; Bisson 1999; Fleet et al. 1993; Reynolds et al. 2001; Schutz and Gafner 298
1995; Varela et al. 2004). Cultures in MTPs were intermediate in their ferment 299
completion times with fructose utilization accelerating only once glucose 300
consumption was complete. Total sugar consumption was complete in 120 hours, a 301
full 70 hours earlier than in SAFs. This accelerated sugar consumption in MTP 302
ferments indicates that agitation is not required either for access to nutrients other than 303
oxygen or for reproducible fermentation. It is likely that the reduced accumulation of 304
O
ethanol in MTP ferments is at least in part responsible for the accelerated use of sugar 305
compared to SAF ferments. The faster fermentation times in MTPs, however, did not 306
change the overall profile of sugar utilization. We were therefore able to use MTP 307
fermentations as a method to screen wine yeast strains for their sugar utilization 308
capacity, a trait that translated well to large fermentation scales despite the difference 309
in overall fermentation duration between MTP and flask fermentations. 310
Conclusions 311
Grape-juice fermentations performed in MTPs imitate standard, SAFs when 312
comparing most of the major criteria used to assess yeast performance and product 313
profiling, with the exception of time to dryness (i.e. completion of sugar consumption) 314
and ethanol concentration. A simple humidified incubator prevented excessive water 315
evaporation however, evaporation of ethanol still occurred suggesting that micro-316
scale ferments of the design described in this paper would be of limited utility in 317
studies focusing on highly volatile metabolites. The data illustrates that microplate 318
cultures can be used as a replacement for SAFs in some instances and provide a useful 319
and economical platform for the screening of industrial strains and culture media. 320
Authors’ contributions 321
SS, TT and TL carried out fermentations and data acquisition. SS and DC performed 322
data analysis. SS, TT, VJ and PC participated in the design of the study and helped to 323
draft the manuscript. All authors read and approved the final manuscript. 324
P
Acknowledgements 325
This project was supported by Australia’s grape growers and winemakers through 326
their investment body, the Grape and Wine Research and Development Corporation, 327
with matching funds from the Australian Government. We would also like to thank 328
Maurizio Ugliano and Richard Gawel for their comments during the preparation of 329
this manuscript. The AWRI and UA are part of the Wine Innovation Cluster, 330
Adelaide, South Australia. 331
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1063 420
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high throughput fermentation. Clinics in Laboratory Medicine 422
27:209-214 423
424
425
Figures 426
Figure 1 Impact of culture vessel type on culture physiology 427
Chardonnay fermentations using strain AWRI 1493 were performed in self-induced 428
anaerobic flasks (SAF), aerobic flasks (AeF) or microtiter plates (MTP). Growth 429
T
curves showing biomass accumulation (A) and maximum specific growth rates (B) 430
were estimated from optical density measurements as described in the materials and 431
methods. Error bars show ± standard deviation. Columns show means from two 432
independent experiments. Each treatment was performed in triplicate within each 433
experiment. 434
Figure 2 Effect of culture vessel type on sugar utilization and metabolite 435
production 436
Chardonnay fermentations using strain AWRI 1493 were performed in SAFs (�), 437
AeFs (�) or microtiter plates (�). Glucose (A), fructose (B), ethanol (C), glycerol 438
(D) and acetic acid (E) concentrations were determined by HPLC as described in 439
materials and methods. Data points show the means from two independent 440
experiments. Each treatment was performed in triplicate in each experiment. Error 441
bars show ± standard deviation. 442
Figure 3 Estimated sugar utilization and major metabolite production rates in 443
different culture vessels 444
Maximal rates (A) and maximum specific rates (B) were estimated from the first 445
derivative of curves fitted to each data set shown in Figure 2. Specific rates were 446
calculated from by dividing maximum rates by the biomass concentration estimated at 447
the time the maximum rate was achieved. Self-induced anaerobic flasks (SAF), 448
aerobic flasks (AeF) and microtiter plates (MTP). 449
U
Figure 4 Screening of yeast strain fermentation performance and validation 450
using selected strains at a larger scale. 451
The fermentation performance of 20 industrial strains was evaluated in Chardonnay 452
juice using MTP sacrificial plates (A). One plate was prepared for each time point, 453
each strain was fermented in four wells on each plate. Each time point in (A) is the 454
average sugar concentration from four wells on one plate. Four strains, representing a 455
cross-section of performance profiles, were selected for further characterization using 456
200 ml air-locked fermenters (SAF) in the same Chardonnay juice (B). Each time 457
point in B is the average total sugar concentration from 3 fermentations. Error bars 458
indicate ± standard deviation. 459
460
Table 1 Strain ranking based on the time taken to reach a target sugar of < 2 gL-1 461
Strain Time (MTP) Rank MTP Time (SAF) Rank (SAF)
1 120 4 280 4
2 110 2,3 265 3
3 110 2,3 209 2
4 95 1 180 1
462
463
464
V
Additional files 465
Additional file 1 466
File Format : PDF 467
Title: Optical density of settled and suspended cells 468
Description: We compared the effect of cell settling on the measurement of optical 469
density in MTPs. The optical density of settled cells was measured at 630 nm. 470
Immediately following measurement, settled cells were suspended by pipetting 471
followed by vortexing. Surface bubbles were removed with a flame and the optical 472
density was again determined. 473
Additional file 2 474
File Format : PDF 475
Title: The effect of MTP well volume on optical density of settled cells 476
Description: Graph 2 shows the effect of reducing well volume on the optical density 477
of settled cells. 200 μl of cell a flask culture was pipetted into a MTP, the cells we 478
allowed to settle and the optical density was determined at 630 nm. 20 μl was 479
removed from each well and the MTP was measured again. This was repeated another 480
3 times. The graph shows the optical density of all wells in the MTP following each 481
subsequent removal of 20 μl. 482
483
484
485
486
487
W
0 50 100 150 2000
2
4
6
8
10
AeFSAF
MTP
time (hours)
dry
cell w
eigh
t (gL
-1)
SAF AeF MTP0.00
0.05
0.10
0.15
0.20
0.25
grow
th ra
te (h
-1)
A
B
488
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Figure 1 490
491
492
493
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496
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498
X
0 50 100 150 2000
20
40
60
80
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120SAFAeFMTP
[Glu
cose
] gL-1
0 50 100 150 2000
20
40
60
80
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[Fru
ctos
e] g
L-1
0 50 100 150 2000
20
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[Eth
anol
] gL-1
0 50 100 150 200012345678
[Gly
cero
l] gL
-1
0 50 100 150 2000.00.20.40.60.81.01.21.41.6
[Ace
tic a
cid]
gL
-1
time (hours)
A
B
D
E
C
499
Figure 2 500
Y
Glucos
e
Fructos
e
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l
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l
Acetat
e0
1
2
3
4
5
0.00
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0.20
0.25SAF AeF MTPG
luco
se, F
ruct
ose,
Eth
anol
gL-1
h-1
Glycerol, Acetic acid
gL-1h
-1
Glucos
e
Fructos
e
Ethano
l
Glycero
l
Acetat
e0.0
0.5
1.0
1.5
2.0
0.00
0.05
0.10
0.15
Glu
cose
, Fru
ctos
e, E
than
olgL
-1h-1
gDC
W-1
Glycerol, Acetic acidgL
-1h-1gD
CW
-1
A
B
501
Figure 3 502
503
504
Z
0 100 200 3000
50
100
150
200
250
time (hours)
tota
l sug
ar [g
L-1]
0 50 100 1500
50
100
150
200
250 Strain 1
Strain 4Strain 3Strain 2
Other strains in screen
time (hours)
tota
l sug
ar [g
L-1]
505
Figure 4 506
507
508
509
110
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