Yeast Metabolic Engineering Methods And Protocols methods In Molecular Biology

by Valeria Mapelli

Author Valeria Mapelli Isbn 978 1493905621 File size 5 8 MB Year 2014 Pages 316 Language English File format PDF Category Biology Yeast Metabolic Engineering Methods and Protocols provides the widely established basic tools used in yeast metabolic engineering while describing in deeper detail novel and innovative methods that have valuable potential to improve metabolic engineering strategies in industrial biotechnology applications Beginning with an extensive section on molecular tools and

Publisher :

Author : Valeria Mapelli

ISBN : 978 1493905621

Year : 2014

Language: English

File Size : 5.8 MB

Category : Biology

METHODS

IN

M O L E C U L A R B I O LO G Y

Series Editor
John M. Walker
School of Life Sciences
University of Hertfordshire
Hatfield, Hertfordshire, AL10 9AB, UK

For further volumes:
http://www.springer.com/series/7651

Yeast Metabolic Engineering
Methods and Protocols

Edited by

Valeria Mapelli
Industrial Biotechnology, Chalmers University of Technology, Gothenburg, Sweden

Editor
Valeria Mapelli
Industrial Biotechnology
Chalmers University of Technology
Gothenburg, Sweden

ISSN 1064-3745
ISSN 1940-6029 (electronic)
ISBN 978-1-4939-0562-1
ISBN 978-1-4939-0563-8 (eBook)
DOI 10.1007/978-1-4939-0563-8
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Preface
The incidental use of yeast for fermented products can be traced back to about 6,000 years
ago. However, the definition of yeast as living organism and as responsible for sugar fermentation became clear only in the 1800s, when considerable attention was paid—especially for economic reasons—to the study of fermentation, aiming at preventing spoilage of
wines and other alcoholic beverages. Those studies have been seminal for a better understanding of the fermentation process and of the role of yeast, and further steps forward have
been made with the discovery and isolation at the beginning of the 1900s of different yeast
species and strains with peculiar properties. From those years and on, the science of yeast
has never stopped. Thanks to the development of novel molecular biology techniques and
the availability of the complete genome sequence of Saccharomyces cerevisiae, yeast has been
used both as a model organism for higher eukaryotes and as a work horse microorganism
for diverse industrial productions, ranging from proteins to metabolites with diverse applications. The branch of technologies and techniques that have brought the use of yeast to
several production processes goes under the name of Metabolic Engineering, whose aim is
to modify and tune yeast metabolism according to the production target.
Several publications already exist on this topic, but technologies and methods are continuously being developed and improved. Therefore, this volume is intended to provide an
overview of the widely established basic tools used in yeast metabolic engineering; while
describing in deeper detail novel and innovative methods and protocols that have a valuable
potential to improve metabolic engineering strategies aiming at industrial biotechnology
applications.
With this perspective, the first part of the volume tries to give an overview of the basic
tools existing for S. cerevisiae metabolic engineering, such as selection markers and engineered promoters, aiming to give the reader a sort of compendium that collects such tools
which will always remain fundamental in the field. On the other hand, novel metabolic
engineering techniques and technologies, such as the use of RNA switches and the generation of arming yeasts, are described in the form of detailed protocols, as they are not commonly established yet and their potential might be great for certain applications.
Although S. cerevisiae is the species to which the word “yeast” is commonly referred to,
other yeast genera and species are receiving increasing interest thanks to their peculiar features conferring them high potential for specific biotechnological applications. Therefore,
particular focus is given to protocols that can be used when dealing with metabolic engineering of Komagataella spp. (formerly known as Pichia spp.), Hansenula polymorpha, and
Zygosaccharomyces bailii.
The reader familiar with laboratory practices is also aware of the fact that often the
protocols developed for the so-called laboratory yeast strains are not easily transferable to
wild or industrial yeasts, which are known to be genetically more complex. For this reason,
a few chapters provide protocols for the engineering of industrial strains also presenting an
innovative protocol for the optimization of fed-batch fermentations with Pichia pastoris.
While the first section provides the tools for engineering yeasts, the second section
(Tools and technologies for investigation and determination of yeast metabolic features)

v

vi

Preface

provides detailed protocols established to identify and evaluate the actual metabolic
changes generated through genetic engineering. In particular, a protocol for metabolic
flux analysis is described using the yeast P. pastoris as a case study, and a specific metabolite profiling method is reported also providing a summary of existing methodologies for
yeast metabolome analysis. Since one of the most challenging steps in metabolome studies is the analysis of the resulting huge amount of data, it has been considered worthwhile
to dedicate one full chapter to a novel bioinformatics tool for processing and understanding metabolome data.
Along the bioinformatics line, the third section of the volume deals with Metabolic
models for yeast metabolic engineering, which are more and more popular for the initial definition and the improvement of metabolic engineering strategies. The two chapters focusing
on this topic provide an overview on how genome-scale metabolic models are constructed
and show a metabolic engineering application that has been developed exploiting yeast
metabolic models and the related bioinformatics tools.
Since the topics in this volume have been treated giving considerable relevance to the
industrial application of the metabolically engineered yeasts, the editor thought that some
space, though little, could be given to the patenting practice as conclusion of the volume.
It might not look a proper conclusion in a book of methods and protocols, but the editor’s
personal opinion is that knowing the fundamental principles of patenting the products
resulting from laboratory investigation can be extremely useful also in guiding the choice
of the methods that the researchers intend to use in their research.
In conclusion, I would like to thank all the researchers and authors who contributed
with enthusiasm, patience, and professionalism to this volume, willing to share the protocols they developed and the knowledge they hold with the scientific community. It has been
a real pleasure dealing with such people. Furthermore, last but not least, I would like to
thank Dr. John Walker, the Editor-in-Chief of the Methods in Molecular Biology series, for
his continued trust and support.
Gothenburg, Sweden

Valeria Mapelli

Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PART I

v
ix

MOLECULAR TOOLS AND TECHNOLOGY
YEAST ENGINEERING

FOR

1 An Overview on Selection Marker Genes for Transformation
of Saccharomyces cerevisiae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Verena Siewers
2 Natural and Modified Promoters for Tailored Metabolic Engineering
of the Yeast Saccharomyces cerevisiae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Georg Hubmann, Johan M. Thevelein, and Elke Nevoigt
3 Tools for Genetic Engineering of the Yeast Hansenula polymorpha. . . . . . . . . .
Ruchi Saraya, Loknath Gidijala, Marten Veenhuis,
and Ida J. van der Klei
4 Molecular Tools and Protocols for Engineering the Acid-Tolerant
Yeast Zygosaccharomyces bailii as a Potential Cell Factory . . . . . . . . . . . . . . . . .
Paola Branduardi, Laura Dato, and Danilo Porro
5 Strains and Molecular Tools for Recombinant Protein Production
in Pichia pastoris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Michael Felber, Harald Pichler, and Claudia Ruth
6 Methods for Efficient High-Throughput Screening
of Protein Expression in Recombinant Pichia pastoris Strains . . . . . . . . . . . . . .
Andrea Camattari, Katrin Weinhandl, and Rama K. Gudiminchi
7 Synthetic RNA Switches for Yeast Metabolic Engineering:
Screening Recombinant Enzyme Libraries. . . . . . . . . . . . . . . . . . . . . . . . . . . .
Joshua K. Michener and Christina D. Smolke
8 Generation of Arming Yeasts with Active Proteins
and Peptides via Cell Surface Display System: Cell Surface Engineering,
Bio-arming Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kouichi Kuroda and Mitsuyoshi Ueda
9 Genetic Engineering of Industrial Saccharomyces cerevisiae Strains
Using a Selection/Counter-selection Approach . . . . . . . . . . . . . . . . . . . . . . . .
Dariusz R. Kutyna, Antonio G. Cordente, and Cristian Varela
10 Evolutionary Engineering of Yeast. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ceren Alkım, Burcu Turanlı-Yıldız, and Z. Petek Çakar
11 Determination of a Dynamic Feeding Strategy
for Recombinant Pichia pastoris Strains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Oliver Spadiut, Christian Dietzsch, and Christoph Herwig

vii

3

17
43

63

87

113

125

137

157
169

185

viii

Contents

PART II TOOLS AND TECHNOLOGIES FOR INVESTIGATION
AND DETERMINATION OF YEAST METABOLIC FEATURES
12 Yeast Metabolomics: Sample Preparation for a GC/MS-Based Analysis . . . . . .
Sónia Carneiro, Rui Pereira, and Isabel Rocha
13
13 C-Based Metabolic Flux Analysis in Yeast: The Pichia pastoris Case . . . . . . . .
Pau Ferrer and Joan Albiol
14 Pathway Activity Profiling (PAPi): A Tool for Metabolic Pathway Analysis . . . .
Raphael B.M. Aggio
15 QTL Mapping by Pooled-Segregant Whole-Genome Sequencing in Yeast . . . .
Thiago M. Pais, María R. Foulquié-Moreno, and Johan M. Thevelein

PART III

209
233
251

METABOLIC MODELS FOR YEAST METABOLIC ENGINEERING

16 Genome-Scale Metabolic Models of Yeast, Methods
for Their Reconstruction, and Other Applications . . . . . . . . . . . . . . . . . . . . . .
Sergio Bordel
17 Model-Guided Identification of Gene Deletion Targets
for Metabolic Engineering in Saccharomyces cerevisiae . . . . . . . . . . . . . . . . . . .
Ana Rita Brochado and Kiran Raosaheb Patil

PART IV

197

269

281

PATENTING AND REGULATIONS

18 Patents: A Tool to Bring Innovation from the Lab Bench
to the Marketplace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Z. Ying Li and Wolfram Meyer

297

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

311

Contributors
RAPHAEL B.M. AGGIO • Department of Gastroenterology, Institute of Translational
Medicine, University of Liverpool, Liverpool, UK
JOAN ALBIOL • Department of Chemical Engineering, Escola d’Enginyeria,
Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
CEREN ALKIM • Department of Molecular Biology and Genetics, Faculty of Science
and Letters, Dr. Orhan Öcalgiray Molecular Biology, Biotechnology and Genetics Research
Center (ITU-MOBGAM), Istanbul Technical University, Istanbul, Turkey
SERGIO BORDEL • Department of Chemical and Biological Engineering,
Chalmers University of Technology, Gothenburg, Sweden
PAOLA BRANDUARDI • Department of Biotechnology and Biosciences, University of Milano-Bicocca,
Milan, Italy
ANA RITA BROCHADO • Genome Biology Unit, European Molecular Biology Laboratory,
Heidelberg, Germany
Z. PETEK ÇAKAR • Department of Molecular Biology and Genetics, Faculty of Science
and Letters, Dr. Orhan Öcalgiray Molecular Biology, Biotechnology and Genetics Research
Center (ITU-MOBGAM), Istanbul Technical University, Istanbul, Turkey
ANDREA CAMATTARI • Graz University of Technology, Graz, Austria
SÓNIA CARNEIRO • Center of Biological Engineering, IBB Institute for Biotechnology
and Bioengineering, University of Minho, Braga, Portugal
ANTONIO G. CORDENTE • The Australian Wine Research Institute, Adelaide, SA, Australia
LAURA DATO • Department of Biotechnology and Biosciences, University of Milano-Bicocca,
Milan, Italy
CHRISTIAN DIETZSCH • Research Area Biochemical Engineering, Institute of Chemical
Engineering, Vienna University of Technology, Vienna, Austria
MICHAEL FELBER • Austrian Centre of Industrial Biotechnology, Graz, Austria
PAU FERRER • Department of Chemical Engineering, Escola d’Enginyeria, Universitat
Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
MARÍA R. FOULQUIÉ-MORENO • Laboratory of Molecular Cell Biology, Institute of Botany
and Microbiology, KU Leuven, Flanders, Belgium; Department of Molecular
Microbiology, VIB, Flanders, Belgium
LOKNATH GIDIJALA • Molecular Cell Biology, Kluyver Centre for Genomics of Industrial
Fermentation, Groningen Biomolecular Sciences and Biotechnology Institute,
University of Groningen, Groningen, The Netherlands
RAMA K. GUDIMINCHI • Austrian Centre of Industrial Biotechnology (ACIB), Graz, Austria
CHRISTOPH HERWIG • Research Area Biochemical Engineering, Institute of Chemical
Engineering, Vienna University of Technology, Vienna, Austria
GEORG HUBMANN • Molecular Systems Biology, Groningen Biomolecular Sciences
and Biotechnology Institute, University of Groningen, Groningen, The Netherlands;
Laboratory of Molecular Cell Biology, Institute of Botany and Microbiology, KU Leuven,
Flanders, Belgium; Department of Molecular Microbiology, VIB, Flanders, Belgium

ix

x

Contributors

IDA J. VAN DER KLEI • Molecular Cell Biology, Kluyver Centre for Genomics of Industrial
Fermentation, Groningen Biomolecular Sciences and Biotechnology Institute,
University of Groningen, Groningen, The Netherlands
KOUICHI KURODA • Division of Applied Life Sciences, Graduate School of Agriculture,
Kyoto University, Kyoto, Japan
DARIUSZ R. KUTYNA • The Australian Wine Research Institute, Adelaide, SA, Australia
Z. YING LI • Ropes & Gray LLP, New York, NY, USA
WOLFRAM MEYER • European Patent Office, Munich, Germany
JOSHUA K. MICHENER • Department of Organismic and Evolutionary Biology,
Harvard University, Cambridge, MA, USA
ELKE NEVOIGT • School of Engineering and Science, Jacobs University gGmbH,
Bremen, Germany
THIAGO M. PAIS • Laboratory of Molecular Cell Biology, Institute of Botany
and Microbiology, KU Leuven, Flanders, Belgium; Department of Molecular
Microbiology, VIB, Flanders, Belgium; Instituto de Ciências da Saúde, Universidade
Federal de Mato Grosso – UFMT, Sinop, MT, Brazil
KIRAN RAOSAHEB PATIL • Structural and Computational Biology, European Molecular
Biology Laboratory, Heidelberg, Germany
RUI PEREIRA • Center of Biological Engineering, IBB Institute for Biotechnology
and Bioengineering, University of Minho, Braga, Portugal
HARALD PICHLER • Institute of Molecular Biotechnology, Graz University of Technology,
Graz, Austria; Austrian Centre of Industrial Biotechnology, Graz, Austria
DANILO PORRO • Department of Biotechnology and Biosciences, University of Milano-Bicocca,
Milan, Italy
ISABEL ROCHA • Center of Biological Engineering, IBB Institute for Biotechnology
and Bioengineering, University of Minho, Braga, Portugal
CLAUDIA RUTH • Austrian Centre of Industrial Biotechnology, Graz, Austria
RUCHI SARAYA • Molecular Cell Biology, Kluyver Centre for Genomics of Industrial
Fermentation, Groningen Biomolecular Sciences and Biotechnology Institute, University
of Groningen, Groningen, The Netherlands
VERENA SIEWERS • Department of Chemical and Biological Engineering,
Chalmers University of Technology, Gothenburg, Sweden
CHRISTINA D. SMOLKE • Department of Bioengineering, Stanford University, Stanford,
CA, USA
OLIVER SPADIUT • Research Area Biochemical Engineering, Institute of Chemical
Engineering, Vienna University of Technology, Vienna, Austria
JOHAN M. THEVELEIN • Laboratory of Molecular Cell Biology, Institute of Botany
and Microbiology, KU Leuven, Flanders, Belgium; Department of Molecular
Microbiology, VIB, Flanders, Belgium
BURCU TURANLI-YILDIZ • Department of Molecular Biology and Genetics, Faculty of Science
and Letters, Dr. Orhan Öcalgiray Molecular Biology, Biotechnology and Genetics Research
Center (ITU-MOBGAM), Istanbul Technical University, Istanbul, Turkey
MITSUYOSHI UEDA • Division of Applied Life Sciences, Graduate School of Agriculture,
Kyoto University, Kyoto, Japan
CRISTIAN VARELA • The Australian Wine Research Institute, Adelaide, SA, Australia
MARTEN VEENHUIS • Molecular Cell Biology, Kluyver Centre for Genomics of Industrial
Fermentation, Groningen Biomolecular Sciences and Biotechnology Institute,
University of Groningen, Groningen, The Netherlands
KATRIN WEINHANDL • Austrian Centre of Industrial Biotechnology (ACIB), Graz, Austria

Part I
Molecular Tools and Technology for Yeast Engineering

Chapter 1
An Overview on Selection Marker Genes
for Transformation of Saccharomyces cerevisiae
Verena Siewers
Abstract
For genetic manipulation of yeast, numerous selection marker genes have been employed. These include
prototrophic markers, markers conferring drug resistance, autoselection markers, and counterselectable
markers. This chapter describes the different classes of selection markers and provides a number of examples
for different applications.
Key words Auxotrophy, Autoselection, Drug resistance, Counterselection, Marker loop-out

1

Introduction
Deletion of endogenous genes, introduction of new features into
the yeast genome, as well as transformation with centromeric or
episomal plasmids require the use of marker genes in order to be
able to select for transformation events. While after genomic integration the new properties are usually stably inherited and the
strain can be cultivated under nonselective conditions, selective
conditions will in most cases have to be maintained after transformation with a non-integrative plasmid in order to avoid plasmid
loss. The first marker genes used for yeast transformation were
endogenous prototrophic markers, which were later complemented by dominant (mainly drug-resistance) markers and autoselection systems. In the following subchapters, different types of
marker genes with their potential applications, advantages, and
disadvantages are introduced.

Valeria Mapelli (ed.), Yeast Metabolic Engineering: Methods and Protocols, Methods in Molecular Biology,
vol. 1152, DOI 10.1007/978-1-4939-0563-8_1, © Springer Science+Business Media, LLC 2014

3

4

Verena Siewers

2

Prototrophic Markers
Prototrophic marker genes are probably the most commonly used
selection markers. They are usually derived from either amino acid
(e.g., LEU2, TRP1) or nucleotide base (e.g., URA3, ADE2)
biosynthesis pathways and require the availability of an auxotrophic
host strain carrying a nonfunctional version or a deletion of the
respective gene. Further examples are listed in Table 1.
Apart from using endogenous genes, it is also possible to
complement auxotrophies in S. cerevisiae with heterologous
genes. Examples that have shown sufficient activity to be used as
selection markers are the URA3 gene of Kluyveromyces lactis [1]
and the Schizosaccharomyces pombe his5+ gene [2], equivalents of
S. cerevisiae HIS3.
Some prototrophic markers allow for additional genotype
screenings based on colony color. Strains carrying an inactive ade1
or ade2 allele result in red colonies due to the vacuolar accumulation of purine biosynthetic pathway precursors; adenine prototrophic colonies in contrast appear white [3]. Another example are
methionine-auxotrophic met15 strains, which become black when
grown in the presence of divalent lead ions (Pb2+), while their prototrophic counterparts stay white [4, 5].

3

C/N Source-Related Markers
Several genes that confer the ability to grow on certain carbon or
nitrogen sources have been used as selection markers (Table 2).
S. cerevisiae cells expressing FCY1 encoding cytosine deaminase
and GAP1 encoding a general amino acid permease can be selected
on medium containing cytosine and L-citrulline, respectively, as
sole nitrogen source [16, 17]. Since both genes are present in a
wild-type strain, in analogy to auxotrophic markers, the availability
of a background strain carrying the respective deletion is required.
On the other hand, the LAC4/LAC12 and LSD1 genes, which
allow for growth on lactose and dextran as sole carbon sources,
respectively, are derived from different species and do not have any
equivalents in the S. cerevisiae genome [18, 19]; i.e., they represent
dominant marker genes, and this feature makes them very attractive
markers for the transformation of industrial strains.
All marker genes discussed so far rely on the use of chemically
defined media for selection. When selective conditions are required
for stable maintenance of centromeric or episomal plasmids, chemically defined media might not represent an obstacle for small-scale
fermentations. They are however not practical for long-term
plasmid maintenance in industrial processes that are normally
based on complex media. Here, autoselection systems can serve as
an alternative.

w/o: without

a

Sp his5+
Sp ura4+

Heterologous genes
AURA3
CaLYS5
CaURA3
KlLEU2
KlURA3
MET2-CA

HIS2
HIS3
LEU2
LYS2
LYS5
MET15
(=MET17)
TRP1
URA3

ADE2
ADE8
ECM31

Endogenous genes
ADE1

Gene name

Table 1
Prototrophic markers

Arxula adeninivorans orotidine-5′-phosphate decarboxylase
Candida albicans phosphopantetheinyl transferase
C. albicans orotidine-5′-phosphate decarboxylase
Kluyveromyces lactis β-isopropylmalate dehydrogenase
K. lactis orotidine-5′-phosphate decarboxylase
Saccharomyces carlsbergensis L-homoserine-O-acetyltransferase involved
in methionine biosynthesis
Schizosaccharomyces pombe imidazoleglycerol-phosphate dehydratase
Schizosaccharomyces pombe orotidine-5′-phosphate decarboxylase

N-succinyl-5-aminoimidazole-4-carboxamide ribotide synthetase involved
in purine biosynthesis
Phosphoribosylaminoimidazole carboxylase involved in purine biosynthesis
Phosphoribosyl-glycinamide transformylase involved in purine biosynthesis
Ketopantoate hydroxymethyltransferase involved in pantothenic acid
biosynthesis
Histidinolphosphatase involved in histidine biosynthesis
Imidazoleglycerol-phosphate dehydratase involved in histidine biosynthesis
β-Isopropylmalate dehydrogenase involved in leucine biosysthesis
α-Aminoadipate reductase involved in lysine biosynthesis
Phosphopantetheinyl transferase involved in lysine biosynthesis
O-acetyl homoserine-O-acetyl serine sulfhydrylase involved in sulfur
amino acid biosynthesis
Phosphoribosylanthranilate isomerase involved in tryptophan biosynthesis
Orotidine-5′-phosphate decarboxylase involved in pyrimidine biosynthesis

Gene product

w/o histidine
w/o uracil

w/o uracil
w/o lysine
w/o uracil
w/o leucine
w/o uracil
w/o methionine

w/o histidine
w/o histidine
w/o leucine
w/o lysine
w/o lysine
w/o methionine
w/o cysteine
w/o tryptophan
w/o uracil

w/o adenine
w/o adenine
w/o pantothenic acid

w/oa adenine

Selection conditions

[ 2]
[15]

[11]
[10]
[12]
[13]
[1]
[14]

[9]
[9]

[6]
[9]
[9]
[7]
[10]
([4, 5])

[5]
[7]
[8]

[6]

Reference

Selection Markers
5

6

Verena Siewers

Table 2
Carbon/nitrogen source-specific markers
Gene name

Gene product

Selection conditions

Reference

amdS

Aspergillus nidulans acetamidase

Acetamide as sole nitrogen
source

[65]

FCY1

S. cerevisiae cytosine deaminase

Cytosine as sole nitrogen source

[16]

FCA1

Candida albicans cytosine deaminase

Cytosine as sole nitrogen source

[16]

GAP1

S. cerevisiae general amino acid
permease

L-citrulline

[17]

LAC4 +
LAC12

K. lactis β-galactosidase and lactose
permease

Lactose as sole carbon source

[18]

LSD1

Lipomyces starkeyi dextranase

Dextran as sole carbon source

[19]

4

as sole nitrogen

source

Autoselection Systems
In an autoselection system (Table 3), the marker gene is essential
for the viability of the cell under any (or almost any) growth condition. Thus, selection pressure can be maintained even in complex media. Furthermore, there is little risk of cross-feeding,
which when using prototrophic markers even under selective
conditions can lead to subpopulations of cells that have lost the
marker gene while living on metabolites provided by the marker
gene-carrying cells [20].
The URA3 system (see above) was modified by using a background strain, in which not only pyrimidine biosynthesis is inhibited by a ura3 mutation, but even the pyrimidine salvage pathway
is inactivated through a fur1 urk1 double mutation. External supplementation with uracil, uridine, cytosine, or cytidine does therefore not enable growth in the absence of the URA3 gene, and
URA3-bearing plasmids are stably maintained [21].
In several examples, glycolytic pathway genes such as FBA1,
TPI1 (derived from either S. cerevisiae or a heterologous host), and
PGI1 were used as marker genes and shown to provide stable plasmid maintenance in complex media [22–24]. A second group of
genes used as autoselection markers are essential cell division cycle
genes such as CDC4, CDC9, and CDC28 [23, 25, 26].
The construction and maintenance of the host strain used in an
autoselection system can however require a special procedure,
since an essential gene needs to be deleted. One possibility is the
use of a strain that is still viable under specific conditions. For
example, a strain carrying the srb1-1 allele, a mutation in PSA1
encoding GDP-mannose pyrophosphorylase involved in cell wall
synthesis, is nonviable in the absence of osmotic stabilizers but can

Selection Markers

7

Table 3
Autoselection systems
Gene name

Gene product

Reference

URA3 fur1 urk1

Orotidine-5′-phosphate decarboxylase;
uracil phosphoribosyltransferase; uridine/cytidine kinase

[21]

FBA1

Fructose 1,6-bisphosphate aldolase

[22]

POT

Schizosaccharomyces pombe triose phosphate isomerase

[24]

TPI

A. nidulans triose phosphate isomerase

[23]

PGI1

Phosphoglucose isomerase

[23]

CDC4

F-box protein

[23]

CDC9

DNA ligase

[25]

CDC28

Catalytic subunit of the main cell cycle cyclin-dependent kinase

[26]

MOB1

Component of the mitotic exit network

[26]

PSA1 (SRB1)

GDP-mannose pyrophosphorylase

[27]

be maintained by the addition of sorbitol to the medium [27].
A second option is the use of a maintenance plasmid carrying the
essential gene that can be exchanged against the target plasmid in
a plasmid-shuffling procedure [26].

5

Resistance Markers
If the host strain does not contain the appropriate mutant allele
required for the use of a prototrophic or an autoselection marker—
as it is often the case for industrial strains—a (semi)dominant
marker needs to be employed. Two examples for dominant markers (LAC4/LAC12 and LSD1) based on carbon source utilization
have already been mentioned above. Most (semi)dominant markers however confer resistance to various growth-inhibitory or toxic
compounds (Table 4). These can be divided into three groups:
1. Endogenous genes, which confer resistance to specific agents
when overexpressed either by introduction of multiple copies
or by expression from a strong promoter: There are many
examples of such genes in the literature, but only those specifically tested as marker genes are listed in Table 4. For instance,
expression of formaldehyde dehydrogenase encoding SFA1
from the strong GPD1 promoter allowed cells to grow at up to
7 mM formaldehyde [28].
2. Mutant alleles of endogenous genes: These may encode proteins
with a lower affinity for an inhibitory drug such as a ribosomal

8

Verena Siewers

Table 4
Resistance markers
Gene name

Gene product

Endogenous genes
CUP1
Metallothionein conferring resistance
to copper and cadmium
ERG11
Lanosterol 14α-demethylase conferring
resistance to azole antifungals
MPR1
N-acetyltransferase conferring resistance
to L-azetidine-2-carboxylic acid (AZC)
SSU1
Plasma membrane sulfite pump
conferring sulfite resistance
SFA1
Formaldehyde dehydrogenase
conferring resistance to formaldehyde
YAP1
Transcription factor conferring resistance
to cerulenin and cycloheximide
Mutant alleles of endogenous genes
ARO4-OFP Mutated DAHP synthase conferring
resistance to fluorophenylalanine
AUR1-C
Mutated inositol-phosphoceramide
synthase conferring resistance
to aureobasidin A
cyh2
Mutated ribosomal protein conferring
resistance to cycloheximide
FZF1-4
Mutated transcription factor
conferring sulfite resistance
LEU4-1
Mutated α-isopropylmalate synthase
conferring resistance to trifluoroleucine
pdr3-9
Mutated transcriptional activator
conferring multidrug resistance
SMR1-410/ Mutated acetolactate synthases (Ilv2)
SMR1B
conferring resistance
to sulfometuron methyl
Heterologous genes
aroA
E. coli 5-enolpyruvylshikimate3-phosphate synthase conferring
resistance to glyphosate
ble
Tn5 phleomycin-binding protein
conferring resistance to phleomycin
cat
Tn9 acetyltransferase conferring
resistance to chloramphenicol
dehH1
Moraxella sp. dehalogenase
conferring resistance
to fluoroacetate
dsdA
E. coli deaminase conferring
resistance to D-serine
hph
Klebsiella pneumoniae
phosphotransferase conferring
resistance to hygromycin B
kan
Tn 903 phosphotransferase
conferring resistance to G418

Selection conditions

Reference

1–14 mM CuSO4

[34, 35]

1–3 mg/l flusilazole

[36]

0.5–2.0 mg/ml AZC

[37]

3.5 mM Na2SO3

[30]

4 mM formaldehyde

[28]

0.5–1.0 μg/ml cycloheximide
1.0–4.0 μg/ml cerulenin

[38]

2 mg/ml
fluorophenylalanine
0.5–2.0 μg/ml
aureobasidin A

[39]

0.3–10 μg/ml
cycloheximide
3.5 mM Na2SO3

[29]

200 μg/ml trifluoroleucine

[41]

For example 1 μg/ml
cycloheximide
20 μg/ml sulfometuron
methyl

[31]

0.5–6 mg/ml glyphosate

[43]

7.5 μg/ml phleomycin

[13]

1–3 mg/ml chloramphenicol
(glycerol/ethanol medium)
1 mM fluoroacetate
(acetate/ethanol medium)

[44]

2 mg/ml D-serine
5 mg/ml L-proline
300 μg/ml hygromycin B

[45]

200 mg/l G418

[33]

[40]

[30]

[42]

[28]

[46]

(continued)

METHODS

IN

M O L E C U L A R B I O LO G Y

Series Editor
John M. Walker
School of Life Sciences
University of Hertfordshire
Hatfield, Hertfordshire, AL10 9AB, UK

For further volumes:
http://www.springer.com/series/7651

Yeast Metabolic Engineering
Methods and Protocols

Edited by

Valeria Mapelli
Industrial Biotechnology, Chalmers University of Technology, Gothenburg, Sweden

Editor
Valeria Mapelli
Industrial Biotechnology
Chalmers University of Technology
Gothenburg, Sweden

ISSN 1064-3745
ISSN 1940-6029 (electronic)
ISBN 978-1-4939-0562-1
ISBN 978-1-4939-0563-8 (eBook)
DOI 10.1007/978-1-4939-0563-8
Springer New York Heidelberg Dordrecht London
Library of Congress Control Number: 2014936204
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Springer is part of Springer Science+Business Media (www.springer.com)

Preface
The incidental use of yeast for fermented products can be traced back to about 6,000 years
ago. However, the definition of yeast as living organism and as responsible for sugar fermentation became clear only in the 1800s, when considerable attention was paid—especially for economic reasons—to the study of fermentation, aiming at preventing spoilage of
wines and other alcoholic beverages. Those studies have been seminal for a better understanding of the fermentation process and of the role of yeast, and further steps forward have
been made with the discovery and isolation at the beginning of the 1900s of different yeast
species and strains with peculiar properties. From those years and on, the science of yeast
has never stopped. Thanks to the development of novel molecular biology techniques and
the availability of the complete genome sequence of Saccharomyces cerevisiae, yeast has been
used both as a model organism for higher eukaryotes and as a work horse microorganism
for diverse industrial productions, ranging from proteins to metabolites with diverse applications. The branch of technologies and techniques that have brought the use of yeast to
several production processes goes under the name of Metabolic Engineering, whose aim is
to modify and tune yeast metabolism according to the production target.
Several publications already exist on this topic, but technologies and methods are continuously being developed and improved. Therefore, this volume is intended to provide an
overview of the widely established basic tools used in yeast metabolic engineering; while
describing in deeper detail novel and innovative methods and protocols that have a valuable
potential to improve metabolic engineering strategies aiming at industrial biotechnology
applications.
With this perspective, the first part of the volume tries to give an overview of the basic
tools existing for S. cerevisiae metabolic engineering, such as selection markers and engineered promoters, aiming to give the reader a sort of compendium that collects such tools
which will always remain fundamental in the field. On the other hand, novel metabolic
engineering techniques and technologies, such as the use of RNA switches and the generation of arming yeasts, are described in the form of detailed protocols, as they are not commonly established yet and their potential might be great for certain applications.
Although S. cerevisiae is the species to which the word “yeast” is commonly referred to,
other yeast genera and species are receiving increasing interest thanks to their peculiar features conferring them high potential for specific biotechnological applications. Therefore,
particular focus is given to protocols that can be used when dealing with metabolic engineering of Komagataella spp. (formerly known as Pichia spp.), Hansenula polymorpha, and
Zygosaccharomyces bailii.
The reader familiar with laboratory practices is also aware of the fact that often the
protocols developed for the so-called laboratory yeast strains are not easily transferable to
wild or industrial yeasts, which are known to be genetically more complex. For this reason,
a few chapters provide protocols for the engineering of industrial strains also presenting an
innovative protocol for the optimization of fed-batch fermentations with Pichia pastoris.
While the first section provides the tools for engineering yeasts, the second section
(Tools and technologies for investigation and determination of yeast metabolic features)

v

vi

Preface

provides detailed protocols established to identify and evaluate the actual metabolic
changes generated through genetic engineering. In particular, a protocol for metabolic
flux analysis is described using the yeast P. pastoris as a case study, and a specific metabolite profiling method is reported also providing a summary of existing methodologies for
yeast metabolome analysis. Since one of the most challenging steps in metabolome studies is the analysis of the resulting huge amount of data, it has been considered worthwhile
to dedicate one full chapter to a novel bioinformatics tool for processing and understanding metabolome data.
Along the bioinformatics line, the third section of the volume deals with Metabolic
models for yeast metabolic engineering, which are more and more popular for the initial definition and the improvement of metabolic engineering strategies. The two chapters focusing
on this topic provide an overview on how genome-scale metabolic models are constructed
and show a metabolic engineering application that has been developed exploiting yeast
metabolic models and the related bioinformatics tools.
Since the topics in this volume have been treated giving considerable relevance to the
industrial application of the metabolically engineered yeasts, the editor thought that some
space, though little, could be given to the patenting practice as conclusion of the volume.
It might not look a proper conclusion in a book of methods and protocols, but the editor’s
personal opinion is that knowing the fundamental principles of patenting the products
resulting from laboratory investigation can be extremely useful also in guiding the choice
of the methods that the researchers intend to use in their research.
In conclusion, I would like to thank all the researchers and authors who contributed
with enthusiasm, patience, and professionalism to this volume, willing to share the protocols they developed and the knowledge they hold with the scientific community. It has been
a real pleasure dealing with such people. Furthermore, last but not least, I would like to
thank Dr. John Walker, the Editor-in-Chief of the Methods in Molecular Biology series, for
his continued trust and support.
Gothenburg, Sweden

Valeria Mapelli

Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PART I

v
ix

MOLECULAR TOOLS AND TECHNOLOGY
YEAST ENGINEERING

FOR

1 An Overview on Selection Marker Genes for Transformation
of Saccharomyces cerevisiae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Verena Siewers
2 Natural and Modified Promoters for Tailored Metabolic Engineering
of the Yeast Saccharomyces cerevisiae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Georg Hubmann, Johan M. Thevelein, and Elke Nevoigt
3 Tools for Genetic Engineering of the Yeast Hansenula polymorpha. . . . . . . . . .
Ruchi Saraya, Loknath Gidijala, Marten Veenhuis,
and Ida J. van der Klei
4 Molecular Tools and Protocols for Engineering the Acid-Tolerant
Yeast Zygosaccharomyces bailii as a Potential Cell Factory . . . . . . . . . . . . . . . . .
Paola Branduardi, Laura Dato, and Danilo Porro
5 Strains and Molecular Tools for Recombinant Protein Production
in Pichia pastoris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Michael Felber, Harald Pichler, and Claudia Ruth
6 Methods for Efficient High-Throughput Screening
of Protein Expression in Recombinant Pichia pastoris Strains . . . . . . . . . . . . . .
Andrea Camattari, Katrin Weinhandl, and Rama K. Gudiminchi
7 Synthetic RNA Switches for Yeast Metabolic Engineering:
Screening Recombinant Enzyme Libraries. . . . . . . . . . . . . . . . . . . . . . . . . . . .
Joshua K. Michener and Christina D. Smolke
8 Generation of Arming Yeasts with Active Proteins
and Peptides via Cell Surface Display System: Cell Surface Engineering,
Bio-arming Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kouichi Kuroda and Mitsuyoshi Ueda
9 Genetic Engineering of Industrial Saccharomyces cerevisiae Strains
Using a Selection/Counter-selection Approach . . . . . . . . . . . . . . . . . . . . . . . .
Dariusz R. Kutyna, Antonio G. Cordente, and Cristian Varela
10 Evolutionary Engineering of Yeast. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ceren Alkım, Burcu Turanlı-Yıldız, and Z. Petek Çakar
11 Determination of a Dynamic Feeding Strategy
for Recombinant Pichia pastoris Strains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Oliver Spadiut, Christian Dietzsch, and Christoph Herwig

vii

3

17
43

63

87

113

125

137

157
169

185

viii

Contents

PART II TOOLS AND TECHNOLOGIES FOR INVESTIGATION
AND DETERMINATION OF YEAST METABOLIC FEATURES
12 Yeast Metabolomics: Sample Preparation for a GC/MS-Based Analysis . . . . . .
Sónia Carneiro, Rui Pereira, and Isabel Rocha
13
13 C-Based Metabolic Flux Analysis in Yeast: The Pichia pastoris Case . . . . . . . .
Pau Ferrer and Joan Albiol
14 Pathway Activity Profiling (PAPi): A Tool for Metabolic Pathway Analysis . . . .
Raphael B.M. Aggio
15 QTL Mapping by Pooled-Segregant Whole-Genome Sequencing in Yeast . . . .
Thiago M. Pais, María R. Foulquié-Moreno, and Johan M. Thevelein

PART III

209
233
251

METABOLIC MODELS FOR YEAST METABOLIC ENGINEERING

16 Genome-Scale Metabolic Models of Yeast, Methods
for Their Reconstruction, and Other Applications . . . . . . . . . . . . . . . . . . . . . .
Sergio Bordel
17 Model-Guided Identification of Gene Deletion Targets
for Metabolic Engineering in Saccharomyces cerevisiae . . . . . . . . . . . . . . . . . . .
Ana Rita Brochado and Kiran Raosaheb Patil

PART IV

197

269

281

PATENTING AND REGULATIONS

18 Patents: A Tool to Bring Innovation from the Lab Bench
to the Marketplace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Z. Ying Li and Wolfram Meyer

297

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

311

Contributors
RAPHAEL B.M. AGGIO • Department of Gastroenterology, Institute of Translational
Medicine, University of Liverpool, Liverpool, UK
JOAN ALBIOL • Department of Chemical Engineering, Escola d’Enginyeria,
Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
CEREN ALKIM • Department of Molecular Biology and Genetics, Faculty of Science
and Letters, Dr. Orhan Öcalgiray Molecular Biology, Biotechnology and Genetics Research
Center (ITU-MOBGAM), Istanbul Technical University, Istanbul, Turkey
SERGIO BORDEL • Department of Chemical and Biological Engineering,
Chalmers University of Technology, Gothenburg, Sweden
PAOLA BRANDUARDI • Department of Biotechnology and Biosciences, University of Milano-Bicocca,
Milan, Italy
ANA RITA BROCHADO • Genome Biology Unit, European Molecular Biology Laboratory,
Heidelberg, Germany
Z. PETEK ÇAKAR • Department of Molecular Biology and Genetics, Faculty of Science
and Letters, Dr. Orhan Öcalgiray Molecular Biology, Biotechnology and Genetics Research
Center (ITU-MOBGAM), Istanbul Technical University, Istanbul, Turkey
ANDREA CAMATTARI • Graz University of Technology, Graz, Austria
SÓNIA CARNEIRO • Center of Biological Engineering, IBB Institute for Biotechnology
and Bioengineering, University of Minho, Braga, Portugal
ANTONIO G. CORDENTE • The Australian Wine Research Institute, Adelaide, SA, Australia
LAURA DATO • Department of Biotechnology and Biosciences, University of Milano-Bicocca,
Milan, Italy
CHRISTIAN DIETZSCH • Research Area Biochemical Engineering, Institute of Chemical
Engineering, Vienna University of Technology, Vienna, Austria
MICHAEL FELBER • Austrian Centre of Industrial Biotechnology, Graz, Austria
PAU FERRER • Department of Chemical Engineering, Escola d’Enginyeria, Universitat
Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
MARÍA R. FOULQUIÉ-MORENO • Laboratory of Molecular Cell Biology, Institute of Botany
and Microbiology, KU Leuven, Flanders, Belgium; Department of Molecular
Microbiology, VIB, Flanders, Belgium
LOKNATH GIDIJALA • Molecular Cell Biology, Kluyver Centre for Genomics of Industrial
Fermentation, Groningen Biomolecular Sciences and Biotechnology Institute,
University of Groningen, Groningen, The Netherlands
RAMA K. GUDIMINCHI • Austrian Centre of Industrial Biotechnology (ACIB), Graz, Austria
CHRISTOPH HERWIG • Research Area Biochemical Engineering, Institute of Chemical
Engineering, Vienna University of Technology, Vienna, Austria
GEORG HUBMANN • Molecular Systems Biology, Groningen Biomolecular Sciences
and Biotechnology Institute, University of Groningen, Groningen, The Netherlands;
Laboratory of Molecular Cell Biology, Institute of Botany and Microbiology, KU Leuven,
Flanders, Belgium; Department of Molecular Microbiology, VIB, Flanders, Belgium

ix

x

Contributors

IDA J. VAN DER KLEI • Molecular Cell Biology, Kluyver Centre for Genomics of Industrial
Fermentation, Groningen Biomolecular Sciences and Biotechnology Institute,
University of Groningen, Groningen, The Netherlands
KOUICHI KURODA • Division of Applied Life Sciences, Graduate School of Agriculture,
Kyoto University, Kyoto, Japan
DARIUSZ R. KUTYNA • The Australian Wine Research Institute, Adelaide, SA, Australia
Z. YING LI • Ropes & Gray LLP, New York, NY, USA
WOLFRAM MEYER • European Patent Office, Munich, Germany
JOSHUA K. MICHENER • Department of Organismic and Evolutionary Biology,
Harvard University, Cambridge, MA, USA
ELKE NEVOIGT • School of Engineering and Science, Jacobs University gGmbH,
Bremen, Germany
THIAGO M. PAIS • Laboratory of Molecular Cell Biology, Institute of Botany
and Microbiology, KU Leuven, Flanders, Belgium; Department of Molecular
Microbiology, VIB, Flanders, Belgium; Instituto de Ciências da Saúde, Universidade
Federal de Mato Grosso – UFMT, Sinop, MT, Brazil
KIRAN RAOSAHEB PATIL • Structural and Computational Biology, European Molecular
Biology Laboratory, Heidelberg, Germany
RUI PEREIRA • Center of Biological Engineering, IBB Institute for Biotechnology
and Bioengineering, University of Minho, Braga, Portugal
HARALD PICHLER • Institute of Molecular Biotechnology, Graz University of Technology,
Graz, Austria; Austrian Centre of Industrial Biotechnology, Graz, Austria
DANILO PORRO • Department of Biotechnology and Biosciences, University of Milano-Bicocca,
Milan, Italy
ISABEL ROCHA • Center of Biological Engineering, IBB Institute for Biotechnology
and Bioengineering, University of Minho, Braga, Portugal
CLAUDIA RUTH • Austrian Centre of Industrial Biotechnology, Graz, Austria
RUCHI SARAYA • Molecular Cell Biology, Kluyver Centre for Genomics of Industrial
Fermentation, Groningen Biomolecular Sciences and Biotechnology Institute, University
of Groningen, Groningen, The Netherlands
VERENA SIEWERS • Department of Chemical and Biological Engineering,
Chalmers University of Technology, Gothenburg, Sweden
CHRISTINA D. SMOLKE • Department of Bioengineering, Stanford University, Stanford,
CA, USA
OLIVER SPADIUT • Research Area Biochemical Engineering, Institute of Chemical
Engineering, Vienna University of Technology, Vienna, Austria
JOHAN M. THEVELEIN • Laboratory of Molecular Cell Biology, Institute of Botany
and Microbiology, KU Leuven, Flanders, Belgium; Department of Molecular
Microbiology, VIB, Flanders, Belgium
BURCU TURANLI-YILDIZ • Department of Molecular Biology and Genetics, Faculty of Science
and Letters, Dr. Orhan Öcalgiray Molecular Biology, Biotechnology and Genetics Research
Center (ITU-MOBGAM), Istanbul Technical University, Istanbul, Turkey
MITSUYOSHI UEDA • Division of Applied Life Sciences, Graduate School of Agriculture,
Kyoto University, Kyoto, Japan
CRISTIAN VARELA • The Australian Wine Research Institute, Adelaide, SA, Australia
MARTEN VEENHUIS • Molecular Cell Biology, Kluyver Centre for Genomics of Industrial
Fermentation, Groningen Biomolecular Sciences and Biotechnology Institute,
University of Groningen, Groningen, The Netherlands
KATRIN WEINHANDL • Austrian Centre of Industrial Biotechnology (ACIB), Graz, Austria

Part I
Molecular Tools and Technology for Yeast Engineering

Chapter 1
An Overview on Selection Marker Genes
for Transformation of Saccharomyces cerevisiae
Verena Siewers
Abstract
For genetic manipulation of yeast, numerous selection marker genes have been employed. These include
prototrophic markers, markers conferring drug resistance, autoselection markers, and counterselectable
markers. This chapter describes the different classes of selection markers and provides a number of examples
for different applications.
Key words Auxotrophy, Autoselection, Drug resistance, Counterselection, Marker loop-out

1

Introduction
Deletion of endogenous genes, introduction of new features into
the yeast genome, as well as transformation with centromeric or
episomal plasmids require the use of marker genes in order to be
able to select for transformation events. While after genomic integration the new properties are usually stably inherited and the
strain can be cultivated under nonselective conditions, selective
conditions will in most cases have to be maintained after transformation with a non-integrative plasmid in order to avoid plasmid
loss. The first marker genes used for yeast transformation were
endogenous prototrophic markers, which were later complemented by dominant (mainly drug-resistance) markers and autoselection systems. In the following subchapters, different types of
marker genes with their potential applications, advantages, and
disadvantages are introduced.

Valeria Mapelli (ed.), Yeast Metabolic Engineering: Methods and Protocols, Methods in Molecular Biology,
vol. 1152, DOI 10.1007/978-1-4939-0563-8_1, © Springer Science+Business Media, LLC 2014

3

4

Verena Siewers

2

Prototrophic Markers
Prototrophic marker genes are probably the most commonly used
selection markers. They are usually derived from either amino acid
(e.g., LEU2, TRP1) or nucleotide base (e.g., URA3, ADE2)
biosynthesis pathways and require the availability of an auxotrophic
host strain carrying a nonfunctional version or a deletion of the
respective gene. Further examples are listed in Table 1.
Apart from using endogenous genes, it is also possible to
complement auxotrophies in S. cerevisiae with heterologous
genes. Examples that have shown sufficient activity to be used as
selection markers are the URA3 gene of Kluyveromyces lactis [1]
and the Schizosaccharomyces pombe his5+ gene [2], equivalents of
S. cerevisiae HIS3.
Some prototrophic markers allow for additional genotype
screenings based on colony color. Strains carrying an inactive ade1
or ade2 allele result in red colonies due to the vacuolar accumulation of purine biosynthetic pathway precursors; adenine prototrophic colonies in contrast appear white [3]. Another example are
methionine-auxotrophic met15 strains, which become black when
grown in the presence of divalent lead ions (Pb2+), while their prototrophic counterparts stay white [4, 5].

3

C/N Source-Related Markers
Several genes that confer the ability to grow on certain carbon or
nitrogen sources have been used as selection markers (Table 2).
S. cerevisiae cells expressing FCY1 encoding cytosine deaminase
and GAP1 encoding a general amino acid permease can be selected
on medium containing cytosine and L-citrulline, respectively, as
sole nitrogen source [16, 17]. Since both genes are present in a
wild-type strain, in analogy to auxotrophic markers, the availability
of a background strain carrying the respective deletion is required.
On the other hand, the LAC4/LAC12 and LSD1 genes, which
allow for growth on lactose and dextran as sole carbon sources,
respectively, are derived from different species and do not have any
equivalents in the S. cerevisiae genome [18, 19]; i.e., they represent
dominant marker genes, and this feature makes them very attractive
markers for the transformation of industrial strains.
All marker genes discussed so far rely on the use of chemically
defined media for selection. When selective conditions are required
for stable maintenance of centromeric or episomal plasmids, chemically defined media might not represent an obstacle for small-scale
fermentations. They are however not practical for long-term
plasmid maintenance in industrial processes that are normally
based on complex media. Here, autoselection systems can serve as
an alternative.

w/o: without

a

Sp his5+
Sp ura4+

Heterologous genes
AURA3
CaLYS5
CaURA3
KlLEU2
KlURA3
MET2-CA

HIS2
HIS3
LEU2
LYS2
LYS5
MET15
(=MET17)
TRP1
URA3

ADE2
ADE8
ECM31

Endogenous genes
ADE1

Gene name

Table 1
Prototrophic markers

Arxula adeninivorans orotidine-5′-phosphate decarboxylase
Candida albicans phosphopantetheinyl transferase
C. albicans orotidine-5′-phosphate decarboxylase
Kluyveromyces lactis β-isopropylmalate dehydrogenase
K. lactis orotidine-5′-phosphate decarboxylase
Saccharomyces carlsbergensis L-homoserine-O-acetyltransferase involved
in methionine biosynthesis
Schizosaccharomyces pombe imidazoleglycerol-phosphate dehydratase
Schizosaccharomyces pombe orotidine-5′-phosphate decarboxylase

N-succinyl-5-aminoimidazole-4-carboxamide ribotide synthetase involved
in purine biosynthesis
Phosphoribosylaminoimidazole carboxylase involved in purine biosynthesis
Phosphoribosyl-glycinamide transformylase involved in purine biosynthesis
Ketopantoate hydroxymethyltransferase involved in pantothenic acid
biosynthesis
Histidinolphosphatase involved in histidine biosynthesis
Imidazoleglycerol-phosphate dehydratase involved in histidine biosynthesis
β-Isopropylmalate dehydrogenase involved in leucine biosysthesis
α-Aminoadipate reductase involved in lysine biosynthesis
Phosphopantetheinyl transferase involved in lysine biosynthesis
O-acetyl homoserine-O-acetyl serine sulfhydrylase involved in sulfur
amino acid biosynthesis
Phosphoribosylanthranilate isomerase involved in tryptophan biosynthesis
Orotidine-5′-phosphate decarboxylase involved in pyrimidine biosynthesis

Gene product

w/o histidine
w/o uracil

w/o uracil
w/o lysine
w/o uracil
w/o leucine
w/o uracil
w/o methionine

w/o histidine
w/o histidine
w/o leucine
w/o lysine
w/o lysine
w/o methionine
w/o cysteine
w/o tryptophan
w/o uracil

w/o adenine
w/o adenine
w/o pantothenic acid

w/oa adenine

Selection conditions

[ 2]
[15]

[11]
[10]
[12]
[13]
[1]
[14]

[9]
[9]

[6]
[9]
[9]
[7]
[10]
([4, 5])

[5]
[7]
[8]

[6]

Reference

Selection Markers
5

6

Verena Siewers

Table 2
Carbon/nitrogen source-specific markers
Gene name

Gene product

Selection conditions

Reference

amdS

Aspergillus nidulans acetamidase

Acetamide as sole nitrogen
source

[65]

FCY1

S. cerevisiae cytosine deaminase

Cytosine as sole nitrogen source

[16]

FCA1

Candida albicans cytosine deaminase

Cytosine as sole nitrogen source

[16]

GAP1

S. cerevisiae general amino acid
permease

L-citrulline

[17]

LAC4 +
LAC12

K. lactis β-galactosidase and lactose
permease

Lactose as sole carbon source

[18]

LSD1

Lipomyces starkeyi dextranase

Dextran as sole carbon source

[19]

4

as sole nitrogen

source

Autoselection Systems
In an autoselection system (Table 3), the marker gene is essential
for the viability of the cell under any (or almost any) growth condition. Thus, selection pressure can be maintained even in complex media. Furthermore, there is little risk of cross-feeding,
which when using prototrophic markers even under selective
conditions can lead to subpopulations of cells that have lost the
marker gene while living on metabolites provided by the marker
gene-carrying cells [20].
The URA3 system (see above) was modified by using a background strain, in which not only pyrimidine biosynthesis is inhibited by a ura3 mutation, but even the pyrimidine salvage pathway
is inactivated through a fur1 urk1 double mutation. External supplementation with uracil, uridine, cytosine, or cytidine does therefore not enable growth in the absence of the URA3 gene, and
URA3-bearing plasmids are stably maintained [21].
In several examples, glycolytic pathway genes such as FBA1,
TPI1 (derived from either S. cerevisiae or a heterologous host), and
PGI1 were used as marker genes and shown to provide stable plasmid maintenance in complex media [22–24]. A second group of
genes used as autoselection markers are essential cell division cycle
genes such as CDC4, CDC9, and CDC28 [23, 25, 26].
The construction and maintenance of the host strain used in an
autoselection system can however require a special procedure,
since an essential gene needs to be deleted. One possibility is the
use of a strain that is still viable under specific conditions. For
example, a strain carrying the srb1-1 allele, a mutation in PSA1
encoding GDP-mannose pyrophosphorylase involved in cell wall
synthesis, is nonviable in the absence of osmotic stabilizers but can

Selection Markers

7

Table 3
Autoselection systems
Gene name

Gene product

Reference

URA3 fur1 urk1

Orotidine-5′-phosphate decarboxylase;
uracil phosphoribosyltransferase; uridine/cytidine kinase

[21]

FBA1

Fructose 1,6-bisphosphate aldolase

[22]

POT

Schizosaccharomyces pombe triose phosphate isomerase

[24]

TPI

A. nidulans triose phosphate isomerase

[23]

PGI1

Phosphoglucose isomerase

[23]

CDC4

F-box protein

[23]

CDC9

DNA ligase

[25]

CDC28

Catalytic subunit of the main cell cycle cyclin-dependent kinase

[26]

MOB1

Component of the mitotic exit network

[26]

PSA1 (SRB1)

GDP-mannose pyrophosphorylase

[27]

be maintained by the addition of sorbitol to the medium [27].
A second option is the use of a maintenance plasmid carrying the
essential gene that can be exchanged against the target plasmid in
a plasmid-shuffling procedure [26].

5

Resistance Markers
If the host strain does not contain the appropriate mutant allele
required for the use of a prototrophic or an autoselection marker—
as it is often the case for industrial strains—a (semi)dominant
marker needs to be employed. Two examples for dominant markers (LAC4/LAC12 and LSD1) based on carbon source utilization
have already been mentioned above. Most (semi)dominant markers however confer resistance to various growth-inhibitory or toxic
compounds (Table 4). These can be divided into three groups:
1. Endogenous genes, which confer resistance to specific agents
when overexpressed either by introduction of multiple copies
or by expression from a strong promoter: There are many
examples of such genes in the literature, but only those specifically tested as marker genes are listed in Table 4. For instance,
expression of formaldehyde dehydrogenase encoding SFA1
from the strong GPD1 promoter allowed cells to grow at up to
7 mM formaldehyde [28].
2. Mutant alleles of endogenous genes: These may encode proteins
with a lower affinity for an inhibitory drug such as a ribosomal

8

Verena Siewers

Table 4
Resistance markers
Gene name

Gene product

Endogenous genes
CUP1
Metallothionein conferring resistance
to copper and cadmium
ERG11
Lanosterol 14α-demethylase conferring
resistance to azole antifungals
MPR1
N-acetyltransferase conferring resistance
to L-azetidine-2-carboxylic acid (AZC)
SSU1
Plasma membrane sulfite pump
conferring sulfite resistance
SFA1
Formaldehyde dehydrogenase
conferring resistance to formaldehyde
YAP1
Transcription factor conferring resistance
to cerulenin and cycloheximide
Mutant alleles of endogenous genes
ARO4-OFP Mutated DAHP synthase conferring
resistance to fluorophenylalanine
AUR1-C
Mutated inositol-phosphoceramide
synthase conferring resistance
to aureobasidin A
cyh2
Mutated ribosomal protein conferring
resistance to cycloheximide
FZF1-4
Mutated transcription factor
conferring sulfite resistance
LEU4-1
Mutated α-isopropylmalate synthase
conferring resistance to trifluoroleucine
pdr3-9
Mutated transcriptional activator
conferring multidrug resistance
SMR1-410/ Mutated acetolactate synthases (Ilv2)
SMR1B
conferring resistance
to sulfometuron methyl
Heterologous genes
aroA
E. coli 5-enolpyruvylshikimate3-phosphate synthase conferring
resistance to glyphosate
ble
Tn5 phleomycin-binding protein
conferring resistance to phleomycin
cat
Tn9 acetyltransferase conferring
resistance to chloramphenicol
dehH1
Moraxella sp. dehalogenase
conferring resistance
to fluoroacetate
dsdA
E. coli deaminase conferring
resistance to D-serine
hph
Klebsiella pneumoniae
phosphotransferase conferring
resistance to hygromycin B
kan
Tn 903 phosphotransferase
conferring resistance to G418

Selection conditions

Reference

1–14 mM CuSO4

[34, 35]

1–3 mg/l flusilazole

[36]

0.5–2.0 mg/ml AZC

[37]

3.5 mM Na2SO3

[30]

4 mM formaldehyde

[28]

0.5–1.0 μg/ml cycloheximide
1.0–4.0 μg/ml cerulenin

[38]

2 mg/ml
fluorophenylalanine
0.5–2.0 μg/ml
aureobasidin A

[39]

0.3–10 μg/ml
cycloheximide
3.5 mM Na2SO3

[29]

200 μg/ml trifluoroleucine

[41]

For example 1 μg/ml
cycloheximide
20 μg/ml sulfometuron
methyl

[31]

0.5–6 mg/ml glyphosate

[43]

7.5 μg/ml phleomycin

[13]

1–3 mg/ml chloramphenicol
(glycerol/ethanol medium)
1 mM fluoroacetate
(acetate/ethanol medium)

[44]

2 mg/ml D-serine
5 mg/ml L-proline
300 μg/ml hygromycin B

[45]

200 mg/l G418

[33]

[40]

[30]

[42]

[28]

[46]

(continued)

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