Metabolic engineering strategies to improve yeast- … engineering strategies to improve yeast-based...

Metabolic engineering strategies to improve yeast-

based production of itaconic acid guided by in

silico metabolic modelling

Ana Isabel de Vila-Santa Braga Campos

Thesis to obtain the Master of Science Degree in

Biotechnology

Supervisors

Prof. Doctor Nuno Gonçalo Pereira Mira

Prof. Doctor Susana de Almeida Mendes Vinga Martins

Examination committee

Chaiperson: Prof. Doctor Isabel Maria de Sá-Correia Leite de Almeida

Supervisor: Prof. Doctor Nuno Gonçalo Pereira Mira

Members of the Commitee: Prof. Doctor César Simões da Fonseca

Prof. Doctor Rafael Sousa Costa

July 2015

i

Acknowledgments

I would like to thank my supervisors, Professor Nuno Mira and Professor Susana Vinga, for the guidance

throughout the work and for the knowledge they’ve passed during this thesis, along with the scientific

enthusiasm. I also would like to thank Professor Isabel Sá-Correia, for providing the conditions to work in

Biological Sciences Research Group and the strains used in this work. I also want to acknowledge Prof

Antonius van Maris, who provided the TAM strain. I would also like to thank financial support of FCT

(UID/BIO/04565/2013). I’m also thankful for the help provided by the BSRG and CSI-IDMEC team members

with the difficulties that have arisen throughout the work. I also want to thank Nicole Rodrigues, who has

taught me the needed lab skills with such patience. I would also like to thank Cláudio Frazão for the

collaboration in the work. A thanks also to my colleagues Laura Luzia, Rui Santos, João Peça, João Silva,

Cátia Pacífico and Raquel Teixeira for making the laboratory experience much more joyfull. I also want to

thank Ana Sofia and Maria, for their friendship, who has meant so much during this thesis. I’m also grateful

to Pedro, for giving me strength and always helping me see the bright side.

Most of all, I’d like to thank my mother and my brother, for their incredible support and for being such

good examples of courage and persistence.

ii

Abstract In the context of microbial production of bulk compounds from renewable feedstocks to replace fuel-

derived chemicals, carboxylic acids are considered of particular interest since these molecules are

precursors of microbial metabolism and their chemical versatility allows their use as catalysts. The work

described in this thesis is focused on the production of itaconic acid, a C5 carboxylic acid, in

Saccharomyces cerevisiae. BY4741 cells transformed with a plasmid having the A. terreus AtCad1 gene

under the control of a galactose-inducible promoter were previously found to produce around 25.12 mg/L

of itaconic acid. In this work it was found that the production of itaconic acid is higher in fermentations

initiated with lower cellular densities. Strikingly, no production of itaconic acid was obtained when a

prototrophic strain was used as a host for the production of itaconic acid, indicating that amino acid

auxotrophies may be necessary to establish yeast-based production of itaconic acid. In the fermentations

undertaken by BY4741, proeminent concentrations of acetic acid were observed in the medium,

suggesting this an important byproduct of the fermentation processes performed. Using FBA-based in

silico metabolic modelling, a set of 21 gene deletions were identified as genetic backgrounds that could

be interesting to increase itaconic acid production in yeast. This beneficial effect was confirmed for at

least six of the mutants tested.

Key-words: itaconic acid, S. cerevisiae, AtCad1, FBA, metabolic modelling

iii

Resumo No contexto da produção microbiana de compostos de valor acrescentado a partir de biomassa com o

objectivo de substituir químicos de origem petrolífera, os ácidos carboxílicos têm um interesse relevante,

visto estas moléculas são precursoras no metabolismo microbiano e a sua versatilidade química permite

o seu uso como catalisadores. O trabalho descrito nesta tese é focado na produção de ácido itacónico,

um ácido carboxílico C5, em Saccharomyces cerevisiae. Foi previamente reportado que células BY4741

transformadas com um plasmídeo que tem o gene AtCad1 de A. terreus sob o controlo de um promotor

indutível por galactose produzem cerca de 25.12 mg/L de ácido itacónico. Neste trabalho foi observado

que a produção de ácido itacónico é aumentada quando as fermentações são iniciadas com uma menor

densidade celular. Surpreendentemente, quando uma estirpe prototrófica foi utilizada como hospedeiro

não se verificou a produção de ácido itacónico, indicando que auxotrofias de aminoácidos podem ser

necessárias para estabelecer uma produção de ácido itacónico baseada em leveduras. Nas fermentações

com a estirpe BY4741 foram observadas concentrações elevadas de ácido acético no meio, sugerindo que

este é um subproduto importante nas fermentações realizadas neste trabalho. Utilizando modelação

metabólica baseada em FBA, foram identificadas 21 delecções genéticas com o potencial de aumentar a

produção de ácido itacónico na levedura. O efeito benéfico foi confirmado para 6 destas estirpes.

Palavras-chave: ácido itacónico, S. cerevisiae, AtCad1, FBA, modelação metabólica

iv

List of Abbreviations

AtCAD1- CAD1 gene from A. terreus

CAD – Cis-aconitate Decarboxylase

CIAP – Calf Intestinal Alkaline Phosphatase

CSM – Complete Supplement Mixture

COBRA – Constraint-based Reconstruction Analysis

DMSO – Dimethyl sulfoxide

dNTP – Deoxynucleotide Triphosphates

EUROSCARF – European Saccharomyces cerevisiae Archive for Functional Analysis

FBA – Flux Balance Analysis

FVA – Flux Variability Analysis

GSMM – Genome-Scale Metabolic Models

MMB – Minimal Growth Medium

MMF – Minimal Fermentation Medium

OD600 – Optical density at a wavelength of 600 nm

pGAL1 – Promoter for the GAL1 gene (Galactokinase)

pTEF1 – Promoter for the TEF1 gene (Transcription Elongation Factor)

PCR – Polymerase Chain Reaction

PEG – Polyethylene glycol

rpm – rotations per minute

SBML – Systems Biology Markup Language

TCA – Tricarboxylic acid

UAS – Upstream Activating Sequences

YPD – Yeast Peptone Dextrose

v

List of Figures Figure 1: Itaconic acid and its compound derivatives. From Werpy, T. & Petersen, G.2004,[3] .. 3

Figure 2: Biosynthetic pathway of itaconic acid in Aspergillus terreus. Itaconic acid biosynthetic

pathway marked in red. From Steiger et al, 2013 [7]. .................................................................. 4

Figure 3: Itaconic acid and lovastatin gene clusters. Adapted from Li et al, 2012 [9]. ................. 4

Figure 4: Experimental design for the generation of high-quality genome-scale metabolic

reconstructions. From Thiele and Palson, 2010 [41]. ................................................................. 12

Figure 5: Mathematical representation of a metabolic model: the glycolytic pathway in E. coli

(a), the stoichiometric matrix (b) and inequality constrains (c). From Becker et al, 2007 [54]. . 14

Figure 6: The solution space in flux-balance analysis. From Orth, Thiele and Palsson, 2010 [57]

..................................................................................................................................................... 16

Figure 7: The bilevel optimization structure of OptKnock. From Burgard, Pharkya and Maranas,

2003 [64]. .................................................................................................................................... 17

Figure 8: Convergence curve of the Optgene algorithm. From Patil et al, 2005 [68]. ................ 18

Figure 9: The central carbon metabolism of S. cerevisiae, and the model-guided metabolic

engineering strategy for succinate over-production. Legend: native reactions (blue solid line),

lumped native reactions (blue dashed line), interrupted reactions (red solid line), up-regulated

reactions (green solid line). From Otero et al, 2013 [53]. ........................................................... 19

Figure 10: Summarized description of the work presented in this thesis. ................................. 21

Figure 11: Genetic engineering strategy used to exchange the Gal1 promoter present in the

engineered pAtCad1 plasmid. As an example it is shown the replacement of GAL1 for TEF1

promoter. .................................................................................................................................... 32

Figure 12: Colony PCR of 24 TEF1-AtCad1 yeast candidates. Lane 0: 1 kb DNA plus ladder; Lane

1: positive control; Lanes 3-27: TEF1-AtCad1 candidates ........................................................... 33

Figure 13: (Left) Concentration of itaconic acid (o, ) and galactose (, ) in supernatant of

cultures of BY4741 cells harboring the pGAL1-AtCad1 plasmid along 7 days of cultivation in

MMF fermentation medium. Two sizes of initial inoculum were tested corresponding to an

initial OD600nm of 0.1 (o,) or 2 (,); (Right) Growth curve of BY4741 cells harboring the

pGAL1-AtCad1 plasmid in MMF fermentation medium along the 7 days of fermentation with

initial OD600 of 0.1() or 2(). .................................................................................................. 34

Figure 14: (Left) Concentration of itaconic acid (o, ) and galactose (, ) in supernatant of

cultures of TAM cells harboring the pGAL1-AtCad1 plasmid along 7 days of cultivation in MMF

fermentation medium. Two sizes of initial inoculum were tested corresponding to an initial

OD600nm of 0.1 (o,) or 2 (,); (Right) Growth curve of TAM cells harboring the pGAL1-

AtCad1 plasmid in MMF fermentation medium along the 7 days of fermentation with initial

OD600 of 0.1() or 2(). ................................................................Error! Bookmark not defined.

Figure 15: Reactions and metabolites added to the Yeast 5 and Yeast 7 models. The

metabolites in red are already present in the models. The reactions associated with the LEU2,

MET15 and HIS3 genes were constrained to zero. ..................................................................... 35

Figure 16: Scenarios used to in silico simulate S. cerevisiae metabolism during production of

itaconic acid. ................................................................................................................................ 36

Figure 17: Fluxes through some aminoacid biosynthesis reactions when the objective function

is the maximization of growth (A) or itaconic acid production (B). Results presented for the

Yeast 7 model. The reactions associated with the genes HIS3, LEU2 and MET15 are highlighted.

.........................................................................................................Error! Bookmark not defined.

Figure 18: (A) Concentration of itaconic acid present in the supernatant of 7-day fermentations

undertaken by BY4741 cells or by the derived deletion mutants transformed with the pGal-

vi

AtCad1 plasmid in MMF medium; (B) Comparison between the growth rate rates of the

BY4741 strain and of the derived deletion mutants in the MMF medium. In both graphs the

dashed lines corresponds to the values obtained for the wild-type strain. ............................... 44

Figure 19: Robustness analysis on the Yeast 7 model. Left) In the standard scenario in the

prototrophic background. Rigth) In the simulated scenario () and in the simulated scenario

without constraints on ammonium availability (o) on the BY4741 background.. ...................... 47

Figure 20: Effect of ammonium concentration in MMF growth medium in the ability of yeast

cells to produce itaconic acid. The concentration of itaconic acid present in the supernatant of

3-day fermentations undertaken by BY4741 cells was measured by HPLC (A). The initial and

final DO600 were measured and the ratio final/initial was calculated (B). The results shown

represent two independent assays that were performed. ......................................................... 48

Figure 21: (Left) Concentration of pyruvic acid in supernatant of cultures of BY4741 (,) and

TAM (o,) cells harboring the pGAL1-AtCad1 plasmid along 7 days of cultivation in MMF

fermentation medium. Two sizes of initial inoculum were tested corresponding to an initial

OD600nm of 0.1 (o,) or 2 (,); (Right) Concentration of pyruvic acid in supernatant of

cultures of BY4741 (,) and TAM (o,) cells harboring the pGAL1-AtCad1 plasmid along 7

days of cultivation in MMF fermentation medium. Two sizes of initial inoculum were tested

corresponding to an initial OD600nm of 0.1 (o,) or 2 (,) .................................................. 61

Figure 22: Concentration of itaconic acid (A) and galactose (B) in the supernatant of

fermentations undertaken by BY4741 or by the derived deletion mutants transformed with the

pGal1-AtCad1 plasmid. ................................................................................................................ 66

vii

List of Tables Table 1: Top added value building blocks from identified by the DOE and BREW reports [3], [4]2

Table 2: Overview of the host used for the production of itaconic acid, other than Aspergillus

species, and the genetic modifications used. ............................................................................... 6

Table 3: Advantages and disadvantages associated with the production of organic acids in

bacteria, filamentous fungi and yeast. .......................................................................................... 7

Table 4: Some applications of the Yeastnet models. Six studies focused on metabolic

engineering are listed, out of 106 reported papers using genome-scale models of

Saccharomyces cerevisiae metabolism (http://sbrg.ucsd.edu/cobra-predictions-app/). .......... 13

Table 5: S. cerevisiae strains used in this study. ......................................................................... 22

Table 6: Final fermentation medium composition in aminoacids, vitamins and trace elements.

..................................................................................................................................................... 23

Table 7: Plasmids used in this study. ........................................................................................... 23

Table 8: Primers used for the amplification of the promoter fragments. .................................. 25

Table 9: Reaction mixture used for the amplification ................................................................. 25

Table 10: Conditions of the PCR cycle for amplification of the promoters ................................. 25

Table 11: Solutions prepared for the microplate fermentations. ............................................... 27

Table 12: Sets of constraints imposed on the lower bounds of the sink reactions corresponding

to the nutrients present in the fermentation medium. .............................................................. 29

Table 13: Reactions where the lower and upper bounds where forced to zero to simulate the

BY4741 genetic background, in the Yeast 5 and Yeast 7 model. ................................................ 30

Table 14: Overview of production titers and yield of itaconic acid produced in 7 day

fermentations undertaken by BY4741 and TAM cells in MMF medium. .................................... 34

Table 15: FBA results for the optimization of cell growth and itaconic acid production when

cells are grown in 10 mmol glucose/gDCW/h (standard conditions).CAD corresponds to cis-

aconitate decarboxylase. * marks the scenarios where no FBA solution was found. ................ 37

Table 16: FBA results for optimization of cell growth and itaconic acid production when cells

have maximal uptake rates based on the fermentation medium. CAD corresponds to cis-

aconitate decarboxylase. ............................................................................................................ 37

Table 17: FBA results for the maximization of biomass/flux. Results presented for both the

Yeast 5 and Yeast 7 models. CAD corresponds to cis-aconitate decarboxylase. * marks the

scenarios where no FBA solution was found .............................................................................. 38

Table 18: FBA results for the maximization of biomass/flux. For each scenario two genetic

backgrounds were tested: prototrophic and the auxotrophic BY4741. Results presented for

both the Yeast 5 and Yeast 7 models. CAD corresponds to cis-aconitate decarboxylase. ......... 39

Table 19: FVA results (allowing a 5% decrease in the growth rate) for both Yeast 5 and Yeast 7

model, using 2 sets of constraints. CAD corresponds to the cis-aconitate decarboxylase

enzyme. ....................................................................................................................................... 40

Table 20: Single deletion mutants identified by OptKnock and OptGene selected to be

experimentally validated. ............................................................................................................ 43

Table 21: Overview of final itaconic acid titers, yield, productivity, galactose consumption rate

and growth rate of the cultures with the BY4741 and mutant strains harboring the pGal1-

AtCad1 plasmid. .......................................................................................................................... 45

Table 22: Deletion strategies predicted by the OptKnock tool for the coupling of itaconic acid

to celular growth. ........................................................................................................................ 46

viii

Contents Acknowledgments .......................................................................................................................... i

Abstract ......................................................................................................................................... ii

Resumo ......................................................................................................................................... iii

List of Abbreviations ......................................................................................................................iv

List of Figures ................................................................................................................................ v

List of Tables ................................................................................................................................. vii

1. Introduction .......................................................................................................................... 1

1.1. Organic acids as added-value building block chemicals: the case of itaconic acid ....... 1

1.1.1. Microbial production of itaconic acid based on Aspergillus terreus ..................... 3

1.1.2. Other microbial hosts used for itaconic acid production ...................................... 5

1.1.3. Advantadges and disadvantadges of using S. cerevisiae as a host system for the

production of carboxylic acids .............................................................................................. 7

1.2. Metabolic engineering strategies used to improve the efficiency of yeast-based

production of carboxylic acids .................................................................................................. 8

1.3. Metabolic modelling methods .................................................................................... 10

1.3.1. Generation of genome-scale metabolic reconstructions .................................... 11

1.3.2. Constraint-based modelling methods ................................................................. 13

1.3.3. Tools used in metabolic modelling based on FBA ............................................... 16

1.3.4. The role of metabolic modelling tools in improving production of carboxylic

acids in S. cerevisiae ............................................................................................................ 18

1.4. Introduction to the theme of the thesis...................................................................... 20

2. Materials and methods ....................................................................................................... 22

2.1. Strains and growth media. ........................................................................................... 22

2.2. Exchange of the GAL1 promoter for other promoters in the pGAL1-AtCad1 plasmid .... 24

2.2.1. In vitro DNA Manipulations ....................................................................................... 24

2.2.2. Yeast transformation ................................................................................................. 25

2.3. Batch fermentations for the production of itaconic acid in yeast cells using strains

derived from BY4741 or 23344c ............................................................................................. 26

2.3. Batch fermentations for the production of itaconic acid in yeast cells using the TAM

strain ...........................................................................................Error! Bookmark not defined.

2.4. Batch fermentations for yeast-based production of itaconic acid using microplates ..... 27

2.5. Simulation of yeast metabolism in itaconic acid-producing cells .................................... 28

3. Results ................................................................................................................................. 32

3.1. Effect of the promoter region used to drive expression of AtCad1 in the ability of

yeast cells to produce itaconic acid ........................................................................................ 32

ix

3.2. Assessment of the size of the inoculum and genetic strain background in yeast ability to

produce itaconic acid from pGal1-Atcad1 .............................................................................. 33

3.3. Effect of the elimination of PDC genes in the ability of yeast cells to produce itaconic

acid ..............................................................................................Error! Bookmark not defined.

3.3. Simulation of yeast metabolism in itaconic acid-producing cells .................................... 35

3.1.3 Flux variability analysis ............................................................................................... 40

3.4. Identification of gene deletions increasing the production of itaconic acid by yeast cells

................................................................................................................................................. 41

3.4.1 In silico identification: OptKnock and OptGene ......................................................... 41

3.4.2. Preliminary results for the proposed mutants in shake-flask fermentations ........... 44

3.5. Role of ammonium availability in itaconic acid production ............................................. 47

3.5.1. In silico robustness analysis ....................................................................................... 47

3.5.2. Microplate fermentations to test different concentrations of ammonium in the

fermentation medium ......................................................................................................... 48

4. Discussion ............................................................................................................................ 49

5. References ........................................................................................................................... 54

2. Annex................................................................................................................................... 61

Annex I) Extracellular concentrations of pyruvic and acetic acid in the BY4741 and TAM

cultures .................................................................................................................................... 61

Annex II) Lists of single mutants proposed by OptGene and OptKnock ................................. 62

Annex III) Extracellular concentrations of itaconic acid and galactose in the batch cultures of

the mutants proposed by OptKnock and OptGene ................................................................ 66

1

1. Introduction

With the decrease in oil reserves and the growing environmental concerns related to the greenhouse

effect caused by petrochemical activity, a worldwide recognition of the need to develop economically and

environmentally sustainable chemical processes is growing. Countries having high energy demands and

limited resources, such as the United States, are now putting a great effort in research focusing this

thematic, being envisaged a replacement of 25% of the fossils fuels used by renewable sources in the next

30 years [1]. To accomplish this ambitious goal, efforts are being put on the development of strategies

aiming to explore microbes as cell factories for the production of biofuels and of industrial chemicals that

are nowadays obtained based on petrochemical industry. The development and implementation of

biorefineries is another strategy that is being used in this context, these being dedicated facilities in which

sugars, oils and proteins obtained from renewable biomass are expected to be converted into biofuels

and, eventually into other added-value industrial chemicals [2]. Although production of biofuels from

biomass has attracted a great deal of interest among the public, the production of renewable chemicals

also represents an important opportunity in the industrial biotechnology field, having lower capital costs

and potentially higher returns. Indeed, the diversification of substrates that can be produced in biomass-

based centrals is considered one of the key steps to assure the economic sustainability of biorefineries

[2].

1.1. Organic acids as added-value building block chemicals: the case of itaconic

acid

In 2004 the U.S. Department of Energy (DOE) has performed an initial screening of the renewable

chemicals that could be co-produced as side streams of biofuels and bioenergy. The analysis resulted in a

list of 30 monomers that can be obtained by fermentation of sugars and that can be used as building block

molecules [3].

Alternatively, these can be novel products that are not yet used in the chemical industry or can serve as

direct replacements of fuel-derived compounds that are already supplied to the chemical industry, such

as maleic anhydride. These building block chemicals can then be chemically converted to intermediate

products that are used in the industry. A compilation of the top value added bulk compounds identified

by the U.S. DOE and the BREW project, as part of the European Comission GROWTH programme is listed

in Table 1 [3], [4]. Since the release of this report, the progress in this biotechnological field has produced

substantial changes in the economics of production of bulk chemicals, such as a decrease in costs.

Consequently, the production of this bulk compounds is an attractive opportunity not only to be used a

co-production strategy in the context of biofuels but also to be produced alone from feedstocks. With the

implementation of increasingly efficient processes and the fluctuations in fuel prices, the potential

2

markets for these building block molecules are expanding and the profit opportunities increasing.

Currently, some of these building blocks, obtained through microbial fermentation, are already available

the market, including 1,4-butanediol, isoprene, isobutanol, lactic, succinic, citric and acetic acids [2].

Table 1: Top added value building blocks from identified by the DOE and BREW reports [3], [4]

Number

of

carbons

Compounds

Commercial

production by

microorganism?

Number

of

carbons

Compound

Commercial

production by

microorganism?

2 Ethanol Y 4 1,4-butanediol y

Acetic acid Y 5 Glutamic acid Y

3 Glycerol N Itaconic acid Y

3-Hydroxy propionic acid N Levulinic acid N

Lactic acid Y Xylitol/arabinitol N

1,3-Propanediol Y Furfural N

Acrylic acid N 6 2,5-Furan dicarboxylic

acid

N

4 1,4- Diacids (succinic,

fumaric and malic)

Y Glucaric acid N

Aspartic acid N Sorbitol N

3-Hydroxybutyrolactone N 5-Hydroxymethylfurfural N

1-butanol y Adipic acid N

Thirteen of the identified add-value molecules are carboxylic acids, as it can be seen in Table 1. The

different functional groups present in carboxylic acids give these molecules the necessary versatility to be

used in different synthetic routes, where they can be subjected to enzymatic and/or chemical catalysis.

Furthermore, most of the carboxylic acids identified as interesting building-block molecules are produced

by most microbes during the course of their normal metabolic activity. Although the market for organic

acids is still limited, the optimization of the production processes in terms of productivity and yield, is

expected to significantly reduce the associated production costs thereby greatly increasing the profits

generated.

Itaconic acid, the carboxylic acid that is focused in this thesis, is a di-carboxylic acid that has one of the

carboxyl groups conjugated to a methylene group (Figure 1). The economic value of itaconic acid is

estimated in 1.5–2.5 US$/kg (1.36-2.2 €/kg) and the total global market is predicted to increase to 398.3

million US$ (352 million €) in 2017. The interest in this compound derives from its application in resins

and as a building block for plastics, acrylate latexes, super-absorbents and anti-scaling agents, among

3

others [5]. Currently itaconic acid occupies a niche market where it is used as a co-monomer in the

production of styrene-butadiene rubber and acrylate latexes with applications in the paper and

architectural coating industry [6]. In 2011 there were four major companies bio-producing itaconic acid,

the global leader being Qingdao Kehai Biochemistry. The market of itaconic acid is currently stagnated

mainly due to the narrow range of applications and the fact that the annual production is currently at half

its maximal capacity. The production capacity for itaconic acid in 2011 was estimated to approximately

80,000 MT per year, nevertheless, the annual production reported was only of 41,400 MT[6].

1.1.1. Microbial production of itaconic acid based on Aspergillus terreus

Itaconic acid is naturally produced by many microorganisms such as Ustilago zeae, Ustilago maydis,

Candida sp and Rhodotorula sp. However, the most relevant natural producer is Aspergillus terreus, where

the titers of itaconic acid produced in fermentation can reach up to 85 g/L [7]. The biochemical pathway

leading to itaconic acid synthesis from sugar in A. terreus has already been characterized: glucose enters

the glycolytic pathway and one of the two pyruvate molecules produced is converted to acetyl-coA by the

cytosolic pyruvate dehydrogenase complex, while the other pyruvate molecule is carboxylated to

oxaloacetate. Both of these products enter the TCA cycle, where they are condensed to citrate. The

isomerization of citrate to isocitrate includes an intermediate dehydration step that leads to the formation

of cis-aconitate, the precursor of itaconic acid (Figure 2). Conversion of cis-aconitic acid into itaconic acid

is mediated by the enzyme cis-aconitate decarboxylase (CadA), encoded by the cad1 gene. Quantification

of enzymatic activity obtained from different cellular fractions together with 14C-labelling experiments

suggests that CadA is a cytosolic enzyme, although the steps leading to the synthesis of cis-aconitic acid

Figure 1: Itaconic acid and its compound derivatives. From Werpy, T. & Petersen, G.2004,[3]

4

occur in the mitochondria. Consequently, the need of having a step involving transport of cis-aconitate

from the mitochondria to the cytosol is required, although no such transporter has yet been identified.

The cad1 gene is included in a genomic region of A. terreus that also includes the ATEG_09960,

ATEG_09970 and ATEG_09972 genes (Figure 3). These genes encode, respectively, a putative regulator

protein which contains a zinc finger, a predicted mitochondrial carrier of tricarboxylic acids and a plasma

membrane transporter of the Major Facilitator Superfamily.

Currently itaconic acid is produced by fermentation using Aspergillus terreus and A. niger, the final titers

obtained ranging 85 g/L and 7 g/L, respectively [7], [8]. However, this is still well below from the titers

1: putative regulator: contains zinc finger domain and fungal specific transcription factor domain

2: (MTT) putative mitochondrial tricarboxylate transporter

3: (cadA) cis-aconitate decarboxylase

4: (MFS) putative Major Facilitator Superfamility transporter (small solutes)

Figure 3: Itaconic acid and lovastatin gene clusters. Adapted from Li et al, 2012 [9].

Figure 2: Biosynthetic pathway of itaconic acid in Aspergillus terreus. Itaconic acid biosynthetic pathway marked in red. From Steiger et al, 2013 [7].

5

obtained in the industrial production of citric acid from Aspergillus niger and the theoretical achievable

yields of 240 g/L [9]. Consequently, efforts have been made to identify the limiting parameters in the

production of this acid and to implement optimization strategies. Tevz et al, 2010 have proposed that the

flux through glycolysis could be limiting the production of itaconic acid in A. terreus, as a result of an

allosteric negative effect imposed by citric acid over the activity of glycolytic enzymes, as observed to

occur in A. niger [10]. In that sense the pfkA gene, encoding phosfrutokinase, has been engineered to

bypass the inhibitory effect exerted by citric acid. Expression of this engineered pfkA gene in A. terreus

increased production of itaconic acid by 2-fold, comparing to the values that were produced by a strain

expressing the wild-type pfkA gene[10]. In a recent study aiming to improve production of itaconic acid in

A. terreus, the effect of over-expressing a set of genes found to be up-regulated in A. terreus in high

itaconic-acid producing conditions was examined [9]. These genes included cadA, mttA , mfsA and

ATEG_09960, the four genes of the itaconic acid-cluster; and also gdpA and acoA, encoding, respectively,

glyceraldehyde 3-phosphate dehydrogenase and aconitase. Additionally, the effect of overexpressing

citA, the gene encoding citrate synthase, was also examined. The overexpression of these genes was

found to impact the production of itaconic acid but also of other organic acids such as citrate. The

individual over-expression of cadA and of mfsA were the most beneficial strategies, highlighting the

importance of a high cis-aconitate decarboxylase activity and of the export of the itaconic acid [11].

Besides genetic manipulation, the improvement of itaconic acid production has also been achieved

through modulation of the composition of the growth medium. More specifically, the amount of

potassium and copper in the growth medium was found to improve the production of itaconic acid. The

concentration of copper was positively correlated with fungal growth, while the amount of copper was

negatively correlated. Thus, the increase in biomass is thought to be the basis of the higher acid

production obtained, with decreasing yields on the carbon source, as the glucose consumption increased.

The beneficial effect obtained upon supplementation of the growth medium with potassium was also

observed in a batch bioreactor setting [12].

1.1.2. Other microbial hosts used for itaconic acid production

The genetic background and the physiology of the Aspergillus terreus species is considered to be an

important factor contributing for the limited yields of itaconic acid that can be produced by this yeast

species. Its poor genetic tractability is another important disadvantage. In that sense, different host

systems have been explored as alternative producers of itaconic acid. One of these hosts is A. niger, a

species that already has a long-track record of utilization in the biotechnological industry, where it is used

for industrial production of citric acid. To enable production of itaconic acid in A. niger, strains expressing

the AtcadA enzyme have been engineered [7]. Although the knowledge previously gathered on

optimization of citric acid production in A. niger may be beneficial for itaconic acid production, the use of

this species is limited by the extensive amounts of oxalic acid that are produced as a by-product.

Therefore, the design of a good strategy to produce itaconic acid in A. niger has been found to include the

6

choice of a strain demonstrated to accumulate citric acid, the use of environmental conditions similar to

those used for citric acid production and the exploration of other genetic modifications considered of

interest. The expression of mfsA and mttA genes from A. terreus, together with CadA, has been found to

increase production of itaconic acid in A. niger, although over-expression of mfsA has also been found to

lead to increased accumulation of oxalic acid in the broth [13]. To circumvent this, a strain devoid of

oxaloacetate hydrolase, the enzyme that leads to synthesis of oxalic acid, has been engineered, this strain

producing around 2-fold more itaconic acid than the non-manipulated strain [13]. Environmental

conditions found to favor itaconic acid production in A. niger include the use of low oxygen dissolved

concentrations (about 20%) [13]. In a recent effort, itaconic acid titer in fermenter cultures with A. niger

were increased up to 7 g/L by heterologous expression of the codon-optimized itaconic acid cluster from

A. terreus, encompassing the cadA, mttA and mfsA genes. The expression of these genes was applied in a

genetic background lacking the oahA and goxC, encoding oxaloacetate hydrolase and glucose oxidase,

with the goal of preventing the production of two major byproducts: oxaloacetate and gluconic acid. It

was observed that the expression of MFS transporter was only relevant when the original strain already

produced high levels of itaconic acid, i.e., when the mttA and cadA had already been introduced in the

strain. Additionally, the transporter encoded by mfsA was proposed to also export citric and oxalic acids

[8].

Table 2: Overview of the host used for the production of itaconic acid, other than Aspergillus species, and the genetic modifications used.

Host Genetic modification Titers

obtained Reference

E. coli Expression of CadA from A. terreus < 10 mg/L [14]

E. coli Expression of CadA from A. terreus

+ expression of altA and acnA from C. glutamicum (citrate

synthase and aconitase)

+ deletion of pta and ldhA (phosphate acetyltransferase and

lactate dehydrogenase)

680 mg/L [14]


+ deletion of icd (isocitrate dehydrogenase)

0.35 g/L [15]


+ deletion of icd (isocitrate dehydrogenase)

+ Oe-acnB (aconitase)

4.34 g/L [15]

Synechocystic sp Expression of CadA from A. terreus 0.9 mg/L [16]

S. cerevisiae Expression of CadA from A. terreus 59 mg/L [17]

S. cerevisiae Expression of CadA from A. terreus

+ deletion of ade3, M5 and tes1

160 mg/L [17]

7

(a cytoplasmic C1-tetrahydrofolate synthase, a tryptophan

2,3-dioxigenase and a peroxisomal acyl-coA thiosterase)

Production of itaconic acid has also been attempted in various hosts other than Aspergilli including E. coli,

Synechocystic sp and S. cerevisiae, although the titers obtained in these systems are well below those

obtained in A. terreus. An overview of the host systems and the required genetic modifications is

presented in Table 2.

1.1.3. Advantadges and disadvantadges of using S. cerevisiae as a host system for the

production of carboxylic acids

S. cerevisiae has emerged as an interesting host for production of carboxylic acids due to its low

nutritional requirements, simple morphology and remarkable tolerance to low pH conditions, which

allows the direct recovery of the acids produced in their undissociated form, bypassing the need of having

to decrease the fermentation broth pH after the fermentation. Other advantages of the use of S.

cerevisiae includes its long history of safe use in biotechnological industry and the central role played by

this species as a model species and an experimental system. A huge panoply of genetic resources are

today available to be used in yeast which significantly boosts all the research performed in this species

including strain optimization [18]–[20]. The availability of several improved and refined genome-scale

models for the yeast metabolism is another advantage for the exploitation of this microbe as a platform

in metabolic engineering [21]. The advantages and disadvantages of S. cerevisiae, compared to other host

systems used for production of carboxylic acids, are summarized in Table 3.

Table 3: Advantages and disadvantages associated with the production of organic acids in bacteria, filamentous fungi and yeast.

Bacteria Filamentous fungi Yeast

Advantages High growth rates Natural producers Low nutritional requirements

Simple morphology

Resistant to low pHs

Easier approval for human

consumption

Highly developed molecular

biology toolbox

Genome-scale metabolic models

available

Disadvantages Complex

nutritional

requirements

Complex

morphology causing

increased aeration

Not a natural producer in some

cases

8

costs and high

variability

Production of toxins

1.2. Metabolic engineering strategies used to improve the efficiency of yeast-

based production of carboxylic acids

An important step for the establishment of microbe-based production of organic acids is the increase in

efficiency, enabling the prices to become more competitive. Strain optimization in yeast, as in any other

host, aims this goal. Classical tools used for strain optimization in yeast include the use of random

mutagenesis and directed evolution; however, in the recent years the design of more rational metabolic

engineering strategies have emerged as powerful tools in strain engineering and optimization allowing a

much more targeted rewiring of the cell’s metabolism and/or physiology. In this sense, the design of yeast

strains optimized for the production of carboxylic acids aims at four major goals:

1. Elimination of alcoholic fermentation;

2. Linking of the glycolytic pathway with the production of the metabolite of interest;

3. Engineering the export of the product;

4. Improving strain robustness, by increasing its tolerance to the products, substrates or culture

environment [19].

Ethanol is the main product of yeast alcoholic fermentation and thus production of this metabolite

represents the most important drain of carbon and co-factors. Several attempts have been made to

eliminate alcoholic fermentation in yeast in order to increase production of carboxylic acids synthesized

along the TCA cycle. The first strategies attempted focused on the elimination of the four genes encoding

alcohol dehydrogenase enzymes (ADH1-4); however, this approach resulted in the toxic accumulation of

acetaldehyde and in the increase in glycerol production, suggesting co-factor imbalance [22]. Subsequent

approaches aimed at the elimination of the PDC1, PDC5 and PDC6 genes, encoding the three isoenzymes

that compose the pyruvate decarboxylase complex [23]. This strain, named Pdc-, was found to be unable

to grow in defined or complex media unless a source of C2 (such as acetate or ethanol) was provided to

the growth medium. The molecular reasons for such phenotype were unclear, although it had been

suggested that it could be attributable to the key role played by pyruvate decarboxylase reaction in

maintaining the pool of cytosolic acetyl-coA, necessary for lipid and lysine synthesis [24]. Consistently, the

C2 requirement was partially circumvented through the overexpression of the GLY1 gene, encoding

threonine aldose, which catalyzes the cleavage of threonine into glycine and acetaldehyde, allowing the

formation of cytosolic acetyl-coA. Upon this genetic modification Δpdc strains were found to be able to

grow only on glucose-limited chemostat cultures; while in batch cultures the cells were found to excrete

9

high amounts of pyruvate but are unable to grow. The molecular basis for this glucose sensitivity remained

unexplained [25]. These two phenotypic traits of the Δpdc strains represent a serious barriers to the use

of these mutants in production of carboxylic acids. To circumvent this, a C2-independent glucose-tolerant

Δpdc strain was obtained by evolutionary engineering, this being designated as TAM [26]. To bypass the

C2 requirement, Δpdc cells were grown in chemostat with decreasing concentrations of acetate. Then,

prolonged shake-flask cultures of the C2 independent strains were cultivated in a media containing 2%

glucose, with 27 serial transfers and increasingly culture growth rates. At the end, the evolved TAM strain

exhibited a maximal growth rate of 0.2 h-1 and, expectedly, produced higher titers of pyruvic acid.

Transcriptomics analysis revealed that the TAM strain has a partial alleviation of the Mig1p-mediated

glucose repression, which results in the up-regulation of genes required for growth in alternative carbon

sources and in down-regulation of the HXT genes, encoding hexose transporters [26]. Further genetic

analysis of the TAM strain revealed an internal deletion on the MTH1 gene, leading to increased stability

of the protein. Mth1 is a transcriptional regulator involved in glucose sensing and its reduced degradation

has been linked to the down-regulation of the expression of the HXT genes, thus accounting for the

decreased glucose uptake rate of the TAM strain. Although these findings don’t fully explain the basis for

the glucose sensitivity of the Δpdc mutants, two hypothesis were proposed:

The Δpdc mutants overproduce pyruvate and this acid does not exit the cell by passive diffusion.

A decrease in glucose uptake results in lower amounts of intracellular pyruvate, preventing

toxicity.

The blockage of the pyruvate decarboxylase reaction prevents the cell from rapidly oxidizing the

cytosolic NADH in the alcohol dehydrogenase enzymatic step. Since the NAD+ regeneration

through the respiratory chain might not occur at a sufficiently fast rate to counter-balance the

surplus of NAD+ utilization in glycolysis, redox imbalance might occur. By reducing intracellular

glucose cells reduce the glycolytic flux, which could then alleviate redox imbalance.

No mechanistic reason was found for the C2 independence of the TAM strain [27]. The obtained TAM

strain has been used with success to setup production of lactic and malic acids in yeast [28], [29].

Despite the general improvement that was obtained in production of carboxylic acids by yeast cells upon

elimination of ethanol formation, other problems have been found, these being very much dependent on

the acid that is being produced. For example, production of malic and succinic acids was found to be

limited by the fact that synthesis of these molecules is not coupled to ATP synthesis. This means that, for

those metabolites to be produced, part of the carbon source has to be directed towards respiration to

obtain energy for growth and maintenance processes. Furthermore, succinate is in a more reduced state

than malate, which means that its production in the glyoxylate cycle implies a net input of two electrons.

This is another source of decrease in the yield, since the carbon source also has to be oxidized through

the TCA cycle. For these reasons, metabolic engineering of succinate production has been found to be

considerably more difficult as all redox reactions found in yeast metabolism have to be taken into account.

10

To solve this, the use of metabolic modelling methods is essential to predict genetic changes that rewire

the metabolism towards the production of the compound of interest.

1.3. Metabolic modelling methods

Rational metabolic engineering approaches have been successfully used to improve strain

performance, however such attempts are limited to the manipulation of only a handful of genes encoding

enzymes and regulatory proteins that were selected based on their anticipated involvement in the

pathway under study, based on available information and research experience [30]. Distant pathways can

play important roles in the synthesis of the compound of interest and genetic alterations can have

unpredicted effects on apparently unrelated parts of the metabolism; therefore, the outcome of these in

the overall metabolism is very difficult to foresee. Such limitation led to the development of the new field

of systems metabolic engineering aiming to develop optimized strain within a systems biology framework,

in which whole cellular networks are optimized [31]. As a result of the work developed in this field,

Genome-Scale Metabolic Models (GSMM) are being built, describing the metabolism of a given organism

through a wide range of mathematical equations and that can be used to guide the engineering of

biological systems. In addition to direct improvements in production capacity of the strains, GSMMs can

also be used to predict relevant phenotypic traits such as growth rate or consumption of a nutrient [32].

A main limitation of these metabolic models is the fact that they ignore all regulatory events that are

underneath the control of microbial metabolism, because much of this knowledge is unknown and also

because some of these regulatory events could not be easily translated through mathematical formalisms.

Thus, the establishment of mechanistic models that describe cellular behavior are usually based on

simplifications [33]. These mathematical models can be divided in two main categories: stoichiometric

and kinetic.

The term stoichiometric modelling applies to all methodologies that represent the cell

metabolism using a metabolic network based on reaction stoichiometry and that assume a pseudo-steady

state for internal metabolites. The stoichiometric framework is only the starting point, since other inputs,

such as constraints, are needed [34]. Constraint-based modelling uses physiochemical constraints (such

as mass balances, energy balances, and flux limitations) to describe the potential behavior of an organism

[35]. In principle, if all constraints operating under a given set of circumstances are known, the actual

state of a metabolic network could be elucidated. Although this scenario does not seem likely to occur in

the near future, the imposition of constraints determines which fluxes can and cannot occur in the cell

[34]. After this selection, a physiologically meaningful steady state is identified by carrying out an

optimization to find the maximum value of a specified objective function with respect to the constraints

imposed in the model. Flux Balance Analysis (FBA) is a constraint-based modelling approach in which the

stoichiometry of the underlying biochemical network constrains the solution [35]. In FBA the solution

11

space is realistically narrowed down by selecting only the flux distribution that can optimize a biologically

relevant objective, such as biomass or ATP production [36]. The main limitation of stoichiometric models

is the lack of regulatory and kinetic input in the models, which limits the accuracy of the predictions.

Another important drawback of constraint-based modelling is the fact that it does not predict metabolite

concentrations, but only fluxes [37]. The model used, which is highly dependent on the database curation,

and on the identification of realistic constraints, based on experimental data, are other limiting factors

that influence greatly the process and that can lead to inaccurate predictions. Every stoichiometric model

available has errors, since correct manual revision of every reaction and metabolite is very hard and time-

consuming. The use of inaccurate constraints is also a reason for incorrect simulation results, where the

obtained flux distributions are significantly different from experimental data.

When detailed information is available about the kinetics of specific cellular processes (e.g.

enzyme-catalyzed reactions, protein–protein interactions, or protein–DNA binding) it is possible to

describe the dynamics of these processes by combining kinetics with the known stoichiometry of

metabolic pathways [33]. This approach has been applied, for example, in modelling of glycolysis and the

pentose phosphate pathway undertaken by S. cerevisiae [38], [39]. However, currently it is difficult to

build accurate dynamic models of cellular metabolism due to a strong dependence on having precise

knowledge of the functional form of all involved enzymatic rate equations and their associated parameter

values. Even when in vivo experimental data is available, the estimation of the parameters is a difficult

task. Additionally, in vitro experimental measures are less reliable in this context [40], which hampers the

correct identification of the complete kinetics of the system.

1.3.1. Generation of genome-scale metabolic reconstructions

The development of Genomics has boosted the development of genome-scale metabolic

reconstruction models for many organisms. These in silico models are created in a bottom-up fashion,

representing a biochemical, genetic and genomic (BiGG) knowledge base. They are then converted into

mathematical models to be used to determine physiological properties and states [41]. This

reconstruction process is usually iterative: the first model is made publicly available and then refined

through a continuous input that is provided by the scientific community. Thus, consecutive releases of

improved versions of the original model are common. The minimum information necessary is the

annotated genome and physiological information [41]. The experimental protocol for the generation of

metabolic reconstruction is schematized in Figure 4.

12

In what concerns the yeast, an ongoing community effort has been responsible for the update of the

consensus network representation of yeast metabolism, yielding five consecutively revised and updated

models for the S288c [42]–[46]. In this work, the Yeast 5 and Yeast 7 models were used

(http://yeast.sourceforge.net/) [44], [47]. The first was the result of a review of gene-reactions

associations, among other improvements, in the Yeast 4 model. One limitation of this model is the fact

that blocked pathways exist in this version and ergosterol metabolism is incompletely represented in the

metabolism. As a result, sterols need to be present in the simulated medium for in silico anaerobic growth

to occur [44]. Meanwhile the model was revised, incorrect reactions were removed and the lipid

metabolism manually curated, yielding the Yeast 7 model. The introduction of these changes enlarged the

lipid metabolism representation in the model and enabled the simulation of growth on fatty acids as the

sole carbon source. However, these changes also created a blockage in the mitochondrial fatty synthesis,

due to the lack of utility of the end products [46]. These models have been used in the past years in several

applications, from guidance of metabolic engineering strategies to phenotype prediction [48]. Six

examples of these applications are listed in Table 4, with a focus on in silico-guided metabolic engineering

strategies.

Figure 4: Experimental design for the generation of high-quality genome-scale metabolic reconstructions. From Thiele and Palson, 2010 [41].

http://yeast.sourceforge.net/

13

Table 4: Some applications of the Yeastnet models. Six studies focused on metabolic engineering are listed, out of 106 reported papers using genome-scale models of Saccharomyces cerevisiae metabolism (http://sbrg.ucsd.edu/cobra-predictions-app/).

Methodology Application Reference

OptKnock Identification of deletion strategies for the

overproduction of 2,3-butanediol [49]

OptGene Identification of deletion strategies for the

overproduction of vanillin [50]

OptGene with MOMA objective function

Identification of deletion strategies for the overproduction of sisquiterpene

[51]

FBA Identification of the necessary steps to rewire the

metabolism towards the production of muconic acid [52]

FBA and robustness analysis

Identification of control steps in fumarate production [32]

OptGene Identification of deletion strategies for the

overproduction of succinic acid [53]

1.3.2. Constraint-based modelling methods

The dissemination of constrain-based modelling methods has created a demand for a computational tool

encompassing many of the FBA related methods for analyzing metabolic networks. In 2007 the COBRA

(COnstraint-Based Reconstruction and Analysis) toolbox for MATLAB and Python was created with this

purpose, allowing predictive computations of steady-state and dynamic optimal growth behavior; assess

the effects in metabolic network of gene deletions; perform comprehensive robustness analyses and

sampling the range of possible cellular metabolic states [54]. This software has been updated to the 2.0

version to include methods that have been developed since its original release: network gap filling, 13C

analysis, metabolic engineering, omics-guided analysis and visualization [55]. These two versions of the

COBRA toolbox have now 346 and 243 citations (by July 2nd) , respectively, according to the information

available at ISI Web of Science, which clearly demonstrates the value that constraint-based methods have

nowadays in the field of metabolic engineering. In the following sections some of these constrain-based

methods are introduced, with an emphasis on Flux Balance Analysis, a commonly used approach in

metabolic engineering.

14

1.3.2.1. Flux balance analysis (FBA): Formulation of the mathematical model, constraints and

optimization

FBA is an approach based on Linear Programming, where the constraint maximization of an objective

function enables the calculation of metabolite fluxes through a given metabolic network. The first step is

the representation of the metabolic reactions in a mathematical form through the stoichiometric matrix

S. It has n rows and m columns, where n is the number of metabolites and m is the number of reactions

included in the model. Each position of the matrix is filled with the stoichiometric coefficient of each

metabolite for each reaction. Since there are many reactions in the model in which only a few metabolites

participate in, the matrix is sparse, for it is mainly composed of zeros [56]. Constraints are applied in the

form of mass balances and inequalities. The mass balances are determined as follows: the change in

metabolite concentration is equal to the product of the reaction fluxes and the stoichiometric coefficients.

This creates a set of ordinary differential equations that can be represented using the S matrix and v

vector, which represent all the fluxes through the reactions. Due to the steady state assumption

(Equation 1), the derivative of the metabolite concentrations is zero and the mathematical problem is

converted into a linear system (Figure 5) [56].

Figure 5: Mathematical representation of a metabolic model: the glycolytic pathway in E. coli (a), the stoichiometric matrix (b) and inequality constrains (c). From Becker et al, 2007 [54].

15

𝑆. 𝑣 = 𝑑𝑋

𝑑𝑡 𝑠𝑡𝑒𝑎𝑑𝑦−𝑠𝑡𝑎𝑡𝑒⇒ 𝑆. 𝑣 = 0

Equation 1: The assumption of steady state: X is metabolite concentration and dX/dt is the derivative of metabolite

concentration over time.

Inequalities can be attributed to each reaction to assign upper and lower bounds of its fluxes. These two

kinds of constraints define the solution space: the set of flux distributions that are allowed given these

conditions [35]. The problem is solved by optimizing the system towards a given biological objective, for

example, the maximization of biomass production. In that case, the goal is to maximize the conversion of

metabolites such as nucleic acids, proteins and lipids in biomass, a process that is represented in the

model as an additional biomass formation reaction/flux. The coefficients for this reaction are based on

experimental measures, meaning that the reaction is scaled, so that the flux through it corresponds to the

exponential growth rate of organism [57].

Although biomass maximization is the most widely used objective function, the appropriate objective

function is dependent on the growth conditions that are being simulated. In fact, there are reports of the

maximization of biomass being the best objective function to describe the growth on limited nutrients in

continuous cultures, while maximization of biomass or ATP yield per flux unit seems to best fit the

behavior of batch cultures [58], [59].

Thus, the matrix and the constraints define a linear system of equations, which is solved by linear

programming. The application of mass balances and reactions bounds constrains the solution space.

However, there are still many possible solutions, that is, many v vectors that satisfy the system Sv=0 (v is

said to be in the null space of S). This is due to the fact that there are more reactions than metabolites

and, consequently, more variables than equations. Within the allowable solution space, FBA is used to

find the optimal solution, the flux distribution that maximizes a given reaction flux. This is performed

through the maximization of an objective function Z=cTv, where v is the flux vector and cT is a vector

containing the weights assigned to each reaction. When cT(x)=1 for some x and zero elsewhere, the

cellular objective is the maximization of reaction x [57].

1.3.2.2. The solution space in FBA The flux solution space of a given in silico genome-scale model gives an idea of the metabolic

capacity of that organism. Since it represents all possible physiological states, the larger it is, the more

interesting the organism is for the production of those compounds. The imposition of realistic constraints

reshapes the solution space and makes it smaller. Removal of useless reactions within the model has the

same effect. By adding heterologous reactions, the solution space increases, as the metabolic capacity of

the organism diversifies [60].

16

Figure 6: The solution space in flux-balance analysis. From Orth, Thiele and Palsson, 2010 [57]

The main limitations of FBA analysis are its inability to calculate metabolite concentrations due

to the lack of kinetic parameters, its assumption of steady state and the fact that it does not account for

regulatory events. In some cases there might be more than one flux distribution that maximize the cellular

objective, i.e., two different pathways might contribute equally to metabolite production. These different

solutions can be identified by Flux Variability Analysis (FVA) [61]

1.3.3. Tools used in metabolic modelling based on FBA

1.3.3.1. Robustness

Robustness analysis is devoted to the exploration of the sensitivity of the cellular objective to changes in

flux levels of essential gene products. It implements an application of FBA to the study of the metabolic

capacities of a given organism. This approach first calculates the wild-type in silico flux, the flux vector

obtained from a regular FBA with only external constraints. Then, the flux through the reaction to be

studied is gradually increased and decreased and the objective function is re-calculated [62].

1.3.3.2. Flux variability analysis

For a given solution space there may more than one flux distribution corresponding to a maximal objective

function value and FBA only identifies one of those distributions. This can happen due to the network’s

topology and the presence of redundancies within the metabolism. The impact of the existence of non-

unique solutions (alternative optima) in the conclusions drawn from the FBA results can be negligible or

highly significant [61].

Flux Variability Analysis (FVA) is focused on the determination of the maximum and minimum flux values

that allow the objective value to still be optimal. It does not identify all possible solutions, only the upper

and lower bounds on every solution. This algorithm first performs a regular FBA to determine the optimal

objective value in standard conditions. Then, both the upper and lower bound of the reaction

corresponding to the objective function are set to said value and, for each reaction in the model, the

maximal and minimal possible fluxes are found through maximization and minimization FBA. This way,

17

the variation that can occur in each single reaction in the model, while the objective function remains

unchanged (i.e., in its maximal value) is identified.

1.3.3.3. Metabolic engineering tools: OptKnock and OptGene

A number of algorithms have been developed (e.g. for the software package COBRA) to identify groups of

gene knockouts that are predicted to change the fermentation profile of a cell when growing at a

maximum growth rate, allowing for simultaneous overproduction of a metabolite of interest, a

characteristic known as growth-coupling [63]. In this section the OptKnock and OptGene tool will be

described in more detail as these are the more commonly used tools in metabolic engineering.

OptKnock

This algorithm suggests gene deletion strategies that may lead to an overproduction of a specific

metabolite. It focuses on network connectivity and finds the metabolic reactions that uncouple the

desired metabolite production from cellular growth. This is a bi-level optimization problem with a nested

framework (Figure 7), since it has two competing objectives: the inner cellular objective, which is growth,

and the bioengineering objective, which is the production of the metabolite [64].

OptGene

OptGene is an evolutionary programming-based method that can be used to identify deletion mutants

with improved substrate-specific productivity. Evolutionary algorithms are well-suited for the solution of

multiobjective optimization problems, a complex task [65]. The objective function is defined as the

product of the growth rate by the productivity, to take this into account. This objective function is non-

linear and its value is termed as the Biomass-Product Coupled Yield.

The scheme of the algorithm can be described in five steps:

1. Initialization of population: The algorithm creates a set of individuals, termed chromosomes, each

one having a set of genes. These genes are randomly assigned as present or absent.

Figure 7: The bilevel optimization structure of OptKnock. From Burgard, Pharkya and Maranas, 2003 [64].

18

2. Scoring fitness of individuals: Using FBA, MOMA or ROOM (Minimization of Metabolic Adjustment

and Regulatory On/Off Minimization, two methodologies used to predict mutant phenotype flux

distribution [66], [67]) to calculate the objective function value, a fitness value is assigned to each

individual, reflecting its ability to produce the compounds of interest.

3. Crossover of chromosomes: The individuals with the best score are selected and crossed, producing

new offspring.

4. Mutation: The offspring is subjected to mutation: in this case, gene deletion.

5. New population and termination: The new population suffers another round of evaluation, crossover

and mutation (steps 2, 3 and 4). When an individual is found with a satisfactory fitness score,

convergence is achieved and the process stops.

Contrasting with most evolutionary algorithms, the convergence curve for OptGene shows step changes,

indicating that the problem is discrete and that a good candidate is found suddenly and its performance

increases rapidly (Figure 8). This convergence can usually be achieved after 500 generations, making this

a quick strategy with low computational cost.

1.3.4. The role of metabolic modelling tools in improving production of carboxylic acids

in S. cerevisiae

There are at least three where constraint-based modelling has aided the design of metabolic engineering

strategies to improve organic acid production in S. cerevisiae [32], [52], [53]. The more paradigmatic case

is the production of succinic acid, this also being one of the first cases where metabolic modelling has

been used to improve yeast-based production of organic acids. Exploring OptGene, Patil et al., 2006 have

identified a set of deletion mutants proposed to have an increased ability to produce succinate [68]. Using

FBA, the maximum predicted theoretical yield of succinic acid production was 0.506 g/g glucose but this

implied the absence of growth. Differently, when biomass production was optimized, no succinic acid

could be produced. OptGene was used to propose a set of multiple knockouts that could have increased

Figure 8: Convergence curve of the Optgene algorithm. From Patil et al, 2005 [68].

19

succinic acid production and a metabolic engineering strategy was based on this, encompassing the

deletion of SDH3, SER3 and SER33. The deletion of one of the cytochrome b subunits of succinate

dehydrogenase (SDH3) minimizes succinate conversion to fumarate, allowing its accumulation in the TCA

cycle. The deletion of 2 isoenzymes of glycerate-3-phosphate dehydrogenase (SER3 and SER33) blocks

serine synthesis from 3-phosphate glycerate, forcing serine and glycine synthesis to occur through

isocitrate lyase, yielding succinate surplus. As glycine and serine are two aminoacids necessary for biomass

production, growth becomes coupled with succinate production (Figure 9). The mutant only grew with

glycine supplementation but a 13-fold increase in succinate production was observed, with only 33%

decrease in growth rate. Although the need for glycine supplementation was not predicted in silico, the

observed decrease in the growth rate is very similar to the predicted by OptGene.

In addition to succinic acid, itaconic acid production in S. cerevisiae has already been improved by in silico

guided metabolic engineering strategies, by iterating between in silico predictions of appropriate mutants

and in vivo cultivation of the mutants to assess itaconic acid production. In the study of Blazeck et al, 2014,

[17] the Yeast 1 model was used and single-deletion scans were performed with FBA to identify knockouts

that could improve itaconic acid production. A total of 11 knockouts were identified and tested in vivo;

while the experimental data presented some discrepancies with the in silico data, four overproducing

mutants were identified, these being Δynk1, Δfat1, Δshm2 and Δade3. The Δade3 was the most promising

candidate, as itaconic acid titers in this strains increased to 90 mg/L, compared to 59 mg/L in the wild-

Figure 9: The central carbon metabolism of S. cerevisiae, and the model-guided metabolic engineering strategy for succinate over-production. Legend: native reactions (blue solid line), lumped native reactions (blue dashed line), interrupted reactions (red solid line), up-regulated reactions (green solid line). From Otero et al, 2013 [53].

20

type. Following deletion of the Ade3p reaction from the model and a 2nd round of single-deletion scans,

six new strategies were proposed and tested in vivo. Of these, only the bna2 deletion in a Δade3

background resulted in increased itaconic titers in shake flask fermentations, of 122 mg/L. For this reason,

this double mutant was selected to continue the work. In the 3rd iteration, the Bna2p and Ade3p reactions

were removed from the model and a new search for relevant gene deletions yielded 17 targets. Upon

experimental validation, the Δade3 Δbna2 Δtes1 background was selected as the optimal one for

expression of the CAD gene, with itaconic acid titers of 126 mg/L [17]. The involvement of the 3 knockouts

on itaconic acid synthesis is non-intuitive: ADE3 encodes for a cytoplasmic trifunctional C1-

tetrahydrofolate synthase, involved in purine synthesis, the bna2p is a putative tryptophan 2,3-

dioxygenase acting on the NAD biosynthetic pathway, while Tes1p is a peroxisomal acyl-coA thioesterase

involved in fatty acid synthesis. Yet, the implementation of this strategy resulted in a significant increase

in itaconic acid production, highlighting the utility of genome-scale models to identify genetic changes in

distant pathways that can affect the production of a given compound of interest.

1.4. Introduction to the theme of the thesis

Heterologous expression of A. terreus AtCad1 enzyme has been successfully performed in previous works

undertaken at the iBB laboratory [69], [70]. For this a plasmid driving the expression of the Cad1 gene

from A. terreus C-terminally fused to a 6-histidine tail and put under the control of the galactose-inducible

promoter Gal1 was engineered. Transformation of yeast cells with this plasmid, named pGal1-AtCad1,

enabled production of itaconic acid in this species in titers achieving 25.12 mg/L [70]. In the first part of

the work that is described in this Master thesis it was examined the effect of several important variables

in the ability of S. cerevisiae to produce itaconic acid, including the initial cellular density of the culture,

the genetic background of the strain used and the promoter used to drive the expression of the AtCad1

gene. In the second part of the work the metabolic modelling strategies that were used in order to identify

gene deletions favoring production of itaconic acid in yeast cells will be described. Besides this general

goal, other aspects relevant for the metabolic modelling were also taken into account, including the

assessment of the cellular objective functions that best describe the production of itaconic acid by yeast

cells in batch cultures; the influence of the genetic background of the host strain in the production of

itaconic acid; how suboptimal states may affect the balance between maximization of the growth rate

and the drain of TCA precursors toward the cis-aconitate decarboxylase reaction; and the role played by

ammonium availability on the production of itaconic acid through the study of the robustness of the

metabolic network. The workflow is presented in Figure 10.

21

1) Initial characterization of the batch production of itaconic acid in S. cerevisiae and assessment of important variables

•The effect of initial cellular culture

•Involvement of strain’s genetic background and auxotrophies

3) Metabolic modelling of yeast metabolism in a model containing the cadA reaction

•Assessment of the best cellular objective function to describe the batch culture of cadA expressing yeast

•Investigation of the influence of aminoacid auxotrophies in itaconic acid production

•Exploration of suboptimal states in a cadA expressing yeast

•Investigation of the role of ammonium availability in itaconic acid production in yeast

4) In silico identification of interesting deletion mutants for the improvement of itaconic acid production in yeast

•Preliminary shake-flask results

Figure 10: Summarized description of the work presented in this thesis.

22

2. Materials and methods

2.1. Strains and growth media.

The strains used in this thesis are listed in Table 5, the mutant strains being derived from the BY4741

background and acquired from the Euroscarf collection [71].

Table 5: S. cerevisiae strains used in this study.

Strain Genotype

BY4741 MATa, his3Δ1, leu2Δ0, met15Δ0, ura3Δ0

23344c MATα ura3Δ0

ΔM2 MATa, his3Δ1, leu2Δ0, met15Δ0, ura3Δ0, M2::KanMx [72]




ΔM3 MATa, his3Δ1, leu2Δ0, met15Δ0, ura3Δ0, M3:: KanMx [72]





Strains were maintained in YPD growth medium which contains 20 g/L glucose (Merck), 20 g/L yeast

extract (Difco) and 10 g/L bactopeptone (Difco). The synthetic MMB medium contains, per liter, 20 g

glucose (Merck), 1.7 g Yeast Nitrogen Base without amino acids and ammonium sulphate (Difco) and 2.65

g ammonium sulphate (Merck) . When needed MMB medium was supplemented with 20 mg/L histidine,

60 mg/L leucine and 20 mg/L methionine to complement the auxotrophies of the BY4741 background or

with 20 mg/L to complement the auxotrophy of the 23344c strain. Solid YPD or MMB was obtained

supplementing the liquid growth medium with 2% agar (Iberagar). Fermentation assays for production of

itaconic acid were performed in MMF which has the following composition: 20 g/L galactose or glucose,

depending on the assay; 2.65g/L ammonium sulfate; 1.7 g/L Yeast Nitrogen Base; 2 g/L of potassium

phosphate and a designated concentration of various amino acids, vitamins and trace elements which is

described in Table 6.

23

Table 6: Final fermentation medium composition in aminoacids, vitamins and trace elements.

CSM U- Vitamins

Aminoacid Concentration in the

medium (mg/L)

Chemical compound Concentration in the

medium (mg/L)

Adenine 2,5 D-biotin 0,05

Arginine HCl 12,5 Panthothenic acid calcium salt 1

L-Aspartic Acid 20 Nicotinic acid 1

L- Histidine HCl 5 Myo-inositol 25

L-Isoleucine 12,5 Thiamine chloride hydrochloride 1

L-Leucine 25 Pyridixol hydrochloride 1

L-Lysine HCl 12,5 4-Aminobenzoic acid 0,2

L-Methionine 5 Trace elements

L-Phenylalanine 12,5 Chemical compound Concentration in the

medium (mg/L)

L-Threonine 25 Na2EDTA 1,5

L-Tryptophane 12,5 ZnSO4·7H2O 0,45

Uracil 5 MnCl2·2H2O 0,10

L-Tyrosine 12,5 CoCl2·6H2O 0,03

L-Valine 35 CuSO4·5H2O 0,03

Total 197,5 Na2MoO4·2H2O 0,03

CaCl2·2H2O 0,45

FeSO4·7H2O 0,30

H3BO3 0,1

KI 0,01

The E. coli XL1-Blue strain was used for a host in the described genetic procedures. This bacterial strain

was maintained and cultivated in LB medium or in this same growth medium supplemented with 150

mg/L of ampicillin. The plasmids used in this study are described in Table 7.

Table 7: Plasmids used in this study.

Plasmid Description References

24

pGreg586 Empty plasmid (URA3 selection marker) [73]

pGal1-AtCad1 With codon-optimized AtCad1 gene from A.

terreus under the control of the GAL1

promoter, with 6-HIS tail (URA3 selection

marker)

[74]

pTEF1-AtCad1 With codon-optimized AtCad1 gene from A.

terreus under the control of the TEF1

promoter, with a 6-HIS tail (URA3 selection

marker)

In this work

2.2. Exchange of the GAL1 promoter for other promoters in the pGAL1-AtCad1

plasmid

A general strategy, based on homologous recombination, was designed to allow the exchange of the GAL1

promoter present in the pGal1-AtCAD1-His6 by the TEF1, ADH1, PMA1 and ICL1 promoters. This strategy

is schematically represented in Figure 11 shown in the Results section.

2.2.1. In vitro DNA Manipulations

The pGal1-AtCAD1-His6 plasmid was digested with 20 U of NotI (ThermoScientific) at 37ºC during 2 hours

after which the mixture was concentrated using the DNA Clean and Concentration Kit TM (Zymo

Research). The digested product was then incubated in the presence of 20 U of AscI (New England Biolabs)

at 37ºC overnight and finally with 15 U of Calf Intestinal Alkaline Phosphatase (CIAP) (Invitrogen) at 37ºC

for 45 minutes to prevent re-circularization. Appropriate restriction of the plasmid was confirmed by

running one sample of the digested DNA in a 0.8% agarose gel and confirming the release of a 500 bp

fragment which corresponded to the expected size of the GAL1 promoter.

The promoter region of TEF1, ADH1, ICL1 and PMA1 genes to be inserted in the digested pAtCad1 plasmid

were obtained by PCR using genomic DNA of S. cerevisiae BY4741 as a template. The promoter region was

considered to be the 1000 nucleotides located upstream of the genes start codon. The PCR reactions were

performed using Phusion® high-fidelity polymerase (Thermo Scientific) and a set of primers that were

designed to specifically hybridize in the selected promoter regions. Besides the region that allow PCR

amplification the primers selected also containing the necessary regions of homology to the plasmid,

essential for the homologous recombination process. The nucleotide sequences of the primers used are

indicated in Table 8, while in Table 9 and Table 10 it is described the reaction mixture used and the

experimental conditions used in the PCR amplification. Amplification of the fragments was confirmed by

running the PCR products in a 0.8% agarose gel. The bands obtained, having a size of approximately 1000

bp, were excised and purified using the JETQUICK Gel Extraction Spin kit (Genomed). The purified DNA

was then concentrated in a speed vaccum and finally resuspended in 10 µl of water.

25

Table 8: Primers used for the amplification of the promoter fragments.

Table 9: Reaction mixture used for the amplification

of the promoters

Table 10: Conditions of the PCR cycle for

amplification of the promoters

2.2.2. Yeast transformation

The amplified promoter regions and the digested pAtCad1 plasmid were used to transform S. cerevisiae

BY4741 cells using the Alkali-Cation TM Yeast Transformation kit (MP Biomedicals). For this, the cells were

cultivated until mid-exponential fase (OD600 nm=0.4) in 50 mL of YPD and then centrifuged for 5 minutes at

6000 rpm and 4ºC. The pellet obtained was resuspended in 4.5 mL TE (pH 7.5) and then centrifuged again

using the same conditions described above. The pellet was resuspended in 2.5 mL of Lithium/Cesium

Acetate Solution and the obtained suspension was then incubated for 30 minutes at 30ºC with 100 rpm

agitation. The cells were centrifuged as described above and finally resuspended in 500 µL of TE (pH 7.5)

to obtain competent cells. Each transformation mixture used 100 µL of competent cells, 10 µL of purified

promoter fragment, 1 µL of digested pGal1-Atcad1-6HIS plasmid, 5 µL of carrier DNA and 5 µL of histamine

ADH1_fw: AAC AAA AGC TGG AGC TCG TTT AAA CGG CGC GCC AAG GTG AGA CGC GCA TAA CC

ADH1_rev: CCC GTG ATG GTG ATG GTG ATG CGA TCC TCT CAT GAG ATA GTT GAT TGT ATG CTT GG

PMA1_fw: AAC AAA AGC TGG AGC TCG TTT AAA CGG CGC GCC CTC AGC TTT GCT AAA GTG CA

PMA1_rev: ATG GTG ATG CGA TCC TCT CAT ACT AGT GCG GCC CTT TCT TAT CTT CTT ATT C

TEF1_fw: AAC AAA AGC TGG AGC TCG TTT AAA CGG CGC GCC CTT CAT CGG TAT CTT CGC TA

TEF1_rev: ATG GTG ATG CGA TCC TCT CAT ACT AGT GCG GCC GAT TAG ATT GCT ATG CTT TC

ICL1_fw: AAC AAA AGC TGG AGC TCG TTT AAA CGG CGC GCC GTA AAGGATAATG AGTGAGC

ICL1_rev: ATG GTG ATG CGA TCC TCT CAT ACT AGT GCG GCC GCT AAG AGT TTT CTC TC

Component Volume or mass per

reaction

HF buffer 10 µl

dNTPs 1 µl

Primer forward 1 µl

Primer reverse 1 µl

Template DNA ~200 ng

MgCl2 2 µl

DMSO 1.5 µl

Phusion 0.4 µl

Water Remaining volume

Total 50 µl

5 min 94ºC

30 sec 94ºC x30

20 sec 54ºC

1 min 72ºC

7 min 72ºC

∞ 10ºC

26

solution. The mixtures were incubated at room temperature for 15 minutes. After this, 0.2 ml of TE/Cation

Mix and 0.8 mL of PEG were added to each reaction and these were incubated for another 10 minutes at

30ºC. Cells were then heat shocked for 10 minutes at 42ºC and subsequently cooled to 30ºC. The reactions

mixtures were centrifuged at 8000 rpms for 3 minutes and cells were resuspended in 100 µL of YPD and

plated on MMB plates lacking uracil.

Confirmation of the integration of the different promoters in the pAtCAd1 plasmid was initially performed

by colony PCR. For this, a loop of cells obtained from colonies obtained in the transformation step was

resuspended in 10 µL of 20mM NaOH, incubated at 100ºC for 10 minutes and then centrifuges for 1

minute in a table-top centrifuge at 13000 rpm. 5 µL of the supernatant were used for the subsequent PCR

reaction which was performed using the same conditions described above in Tables 4 and 5. Those

colonies in which amplification of a 1000 bp product was observed were considered to be positive

candidates. Positive candidates were only found for the TEF1 promoter. Total DNA was extracted from

these positive candidates by resuspending a loop of biomass in 200 L of lysis buffer (50 mM Tris, 50 mM

EDTA, 250 mM NaCl and 0.3% SDS). 100 µL of glass beads were added and then the tube was vortexed for

2 minutes at maximum speed. After this, the mixture was incubation at 65ºC and then put on ice.

Afterwards, the suspension was centrifuged for 15 minutes at 13000 rpms at 4ºC and the supernatant

was recovered. 20 µL of 1/10 NaAc 3M (pH(4.8) and 440 µL of ethanol 100% were added to induce plasmid

DNA precipitation. The sample was incubated for 30 minutes at -20ºC and then centrifuged for 20 minutes

at 13000 rpms at 4ºC. The pelleted DNA was washed with 70% ethanol and finally resuspended in 100 µL

of water.

The plasmid DNA obtained from the positive candidates was inserted in E. coli XL1-Blue cells by adding 10

µL of the purified plasmid DNA to 100 uL of competent cells in an electroporation cuvette. The conditions

used for the electroporation were 2.5 kV, 25 µFaraday and 400 Ω. After the electric shock, 1 mL of liquid

LB was added to the cell suspension and this was then incubated for 1 hour at 37ºC at 250 rpm to allow

recovery of the cells. The culture was then centrifuged at maximum speed in a table-top centrifuge and

the pellet obtained was resuspended in LB and finally plated in selective LBA medium. Plasmid DNA was

recovered from the E. coli transformants using the QIAprep Spin Miniprep Kit (Quiagen). Integration was

confirmed by performing the restriction map of the recovered plasmids with NotI and SacI and then by

Sanger sequencing.

2.3. Batch fermentations for the production of itaconic acid in yeast cells using

strains derived from BY4741 or 23344c

S. cerevisiae cells derived from the BY4741 or from the 23344c background harboring the plasmids

pGal1-AtCad1, pTEF1-AtCad1 or pGREG586 were batch-cultured at 30ºC and 250 rpm over-night in MMF

growth medium having 2% glucose as the carbon source. In the next day an appropriate volume of this

pre-culture was used to inoculate shake-flasks containing fresh MMF medium (having 2% galactose or

glucose as the carbon source) at an initial OD600 nm of 0.1 or 2, depending on the experiment. A ratio of

27

volume medium to air of 1:5 was used. These batch fermentations were performed for 7 days at 30ºC and

250 rpm. 1 mL-aliquots of the cultures were taken every 24 hours. Growth was followed based on the

increase of OD600 nm. At the end of each fermentation the cultures were checked for contaminations by

cultivating samples of the fermentation broths in solid YPD plates. Extracellular metabolites, including

itaconic acid, were detected by HPLC by separating 10 µL of the culture supernatant in an Aminex HPX-

87H column (Biorad) eluted with 0.05% sulfuric acid at a flow rate of 0.6 mL/min. A UV detector set at 210

nm was used for detection of organic acids while detection of sugars, ethanol and glycerol was performed

using an RI detector. Under the experimental conditions used the retention times for the following

compounds were obtained: citric acid- 8.5 min, pyruvic acid- 9.5 min, succinic acid-11.3 min and itaconic

acid-12 min. The RI detector was used to identify and quantify glucose -8.7 min, galactose-9.9 min,

glycerol-13.3 min and ethanol-20.5 min.

2.4. Batch fermentations for yeast-based production of itaconic acid using

microplates

To assess the effect of ammonium availability in production of itaconic acid undertaken by BY4741 cells

an experimental setup based on the use of microplates was implemented. For this a 4x concentrated MMF

without ammonium was prepared. For each microplate column, 4x ammonium sulfate solutions of 1 mL

were prepared from a stock solution. Subsequently, 2x MMF medium solution were obtained by mixture

of the 4x medium and the 4x ammonium solutions prepared previously (Table 11). Cells used to inoculate

the microplates were cultivated in the same manner as described above for the batch fermentations. In

this case the fermentation was started with a cellular suspensions having and OD600 of 0.2. To each well it

was added 100 µL of the prepared cellular suspension and 100 µL of the 2x fermentation medium, giving

a total well volume of 200 µL. The microplates were cultivated for 72h at 30ºC in a microplate orbital

shaker at 900 rpms. At the end of the cultivation the cells were separated from the growth medium by

centrifugation and itaconic acid present in the growth medium was quantified by HPLC, as previously

described.

Table 11: Solutions prepared for the microplate fermentations.

Microplate

column

Final

ammonium

concentration

in the medium

(g/L)

Volume of stock of

ammonium solution

used to obtain 1 mL of

4x concentrated

ammonium solution

Volume of 4x

ammonium

solution added to

obtain 2x MMF

medium

Volume of 4x

medium without

ammonium added

to obtain 2x MMF

medium

1 0,00131 1:2 dilution from

previous eppendorf 500 µL 500 µL

2 0,00262

28

3 0,00524

4 0,01048

5 0,02096

6 0,04192

7 0,08384

8 0,16768

9 0,33536

10 0,67072

11 1,34144 268,28 µL

12 1,64 328 µL

2.5. Simulation of yeast metabolism in itaconic acid-producing cells

All simulation experiments were performed using the COBRA 2.0 toolbox for MATLAB [55] or the OptFlux

workbench [75]. The SBML files for the Yeast 5.1 and Yeast 7.0 models were downloaded from

http://yeast.sourceforge.net/. These are XML files in which the model is defined in the Systems Biology

Markup Language, a standardized and machine-readable language [76]. The reaction of cis-aconitate

transport from the mitochondria to the cytosol was added to both yeast 5.1 and 7.0 models, along with

the cytoplasmic cis-aconitate decarboxylase enzyme and the itaconic acid export reaction. A sink reaction

was also added, to allow the maintenance of a mass balance in the model.

Simulation was performed using two types of conditions: a standard condition in which nutrients do not

have a defined concentration and in a “simulated medium” in which the fluxes were calculated based on

the nutrient concentration profile of the MMF growth medium, which was used for the fermentations. In

the standard scenario cells receive a maximum of 10 mmol/glucose/gDCW/h and the uptake of the

remaining nutrients is unchanged, using the only constraints that are inherent to the model (e.g. no

aminoacid uptake). In the simulated medium scenario cells are exposed to conditions similar to the ones

used in the in vivo fermentations, with constraints on the uptake of galactose, ammonium and amino

acids. The calculations of the lower bounds of the sink reactions for the nutrients were based on two sets

of assumptions:

I. All the nutrients are consumed after 3 days so the maximum fluxes are calculated by

dividing the concentration in the medium by 3 days.

or

II. Only galactose is consumed after 3 days. The remaining nutrients can be consumed in 1

day.

From these assumptions the fluxes were calculated and normalized to the average dry cell weight in the

first 3 days of culture, obtaining the fluxes that were applied to the corresponding sink reactions (Table

http://yeast.sourceforge.net/

29

12). The average dry cell weight was calculated from the OD600 measurements of the culture of BY4741

pGal1-Atcad1 and a using correlation factor between OD600 and dry cell weight of 2.45 OD600/(g/L) [77].

Table 12: Sets of constraints imposed on the lower bounds of the sink reactions corresponding to the nutrients present

in the fermentation medium.

1st set of constraints 2nd set of constraints

Sink reaction name Flux (mmol/d/gDCW) Flux (mmol/d/gDCW)

D-galactose exchange -42,5635 -42,5635

D-glucose exchange 0 0

ammonium exchange -7,7472 -23,2415

adenine exchange -0,0284 -0,0851

L-arginine exchange -0,0910 -0,2730

L-aspartate exchange -0,2305 -0,6914

L-histidine exchange -0,0494 -0,1483

L-leucine exchange -0,2923 -0,8769

L-lysine exchange -0,1311 -0,3934

L-methionine exchange -0,0514 -0,1542

L-phenylalanine exchange -0,1161 -0,3482

L-threonine exchange -0,3219 -0,9656

L-tryptophan exchange -0,0939 -0,2816

L-tyrosine exchange -0,1058 -0,3174

L-valine exchange -0,4582 -1,3746

biotin exchange -8,2E-05 -0,0002

(R)-pantothenate exchange -0,00223 -0,0068

folic acid exchange -1,7E-06 -5,2E-06

1st set of constraints 2nd set of constraints

Sink reaction name Flux (mmol/d/gDCW) Flux (mmol/d/gDCW)

myo-inositol exchange -0,0575 -0,1724

nicotinate exchange -0,0044 -0,0131

4-aminobenzoate exchange -0,0011 -0,0034

pyridoxine exchange -0,0026 -0,0078

riboflavin exchange -0,0002 -0,0006

Thiamine exchange -0,0016 -0,0048

sulphate exchange -1,6014 -4,8042

potassium exchange -8,4622 -25,3867

sodium exchange -0,6619 -1,9856

30

phosphate exchange -8,4612 -25,386

iron (II) exchange -0,0004 -0,0012

The optimization using the objective function of Max (biomass/flux unit) was used in the COBRA toolbox

with the methods described in Costa, Nguyen, Hartmann & Vinga, 2014 [58] and the corresponding scripts

available at https://github.com/rs-costa/ObjComparison. The procedure can be described as follows:

1. Determination of the range of maximal and minimal growth rate; define a vector with

1000 points in this range.

2. For each of these points:

- minimize the L1 Manhattan norm of the fluxes

- the ratio between biomass flux and the L2 norm is calculated

3. Chose the point for which the ratio is the highest. This growth value corresponds to

biomass yield per flux unit

The BY4741 genetic background was simulated in silico by taking into account the histidine, leucine and

methionine auxotrophies and forcing the lower and upper bounds to zero on the reactions corresponding

to the HIS3, LEU2 and MET15 genes (Table 13). The ΔURA3 auxotrophy wasn’t introduced, as it is rescued

by the selection marker of the pGREG586 or of the pAtCad1 plasmid.

Table 13: Reactions where the lower and upper bounds where forced to zero to simulate the BY4741 genetic

background, in the Yeast 5 and Yeast 7 model.

Simulation of the BY4741 genetic background

Gene Reaction name Reaction in Yeast 5 Reaction in Yeast7

HIS3 Imidazoleglycerol-phosphate dehydratase r_0564 r_0564

LEU2 3-isopropylmalate dehydrogenase r_0061 r_0061

MET15

Cysteine synthase r_0312 r_0312

O-acetylhomoserine (thiol)-lyase r_0812 r_0812

O-acetylhomoserine (thiol)-lyase r_0813 r_0813

Whenever needed, the flux distributions were visualized in Cytoscape 2.8. (http://cytoscape.org/) [78], by

importing the corresponding models and flux distributions in .xml format.

The OptKnock algorithm implemented in the COBRA toolbox was applied to the Yeast 5 and Yeast 7

models, using standard constraints and the 2nd set of constraints in the simulated medium scenario. All

reactions were allowed to be deleted in the constrop option, excepting the CAD and the biomass

formation reactions.

https://github.com/rs-costa/ObjComparison

http://cytoscape.org/

31

The OptGene algorithm was implemented in the OptFlux software. The program was downloaded from

http://darwin.di.uminho.pt/optflux/ [75] and the standard constraint of 10 mmol/gDCW/h.

http://darwin.di.uminho.pt/optflux/

32

3. Results

3.1. Effect of the promoter region used to drive expression of AtCad1 in the

ability of yeast cells to produce itaconic acid

The plasmid already available in the laboratory that enabled production of itaconic acid in yeast cells

had the AtCad1 gene under the control of the strong galactose-inducible promoter GAL1 [69], [74]. In the

first part of this work the effect of other promoters having different “strengths” was tested. In particular,

it was aimed to replace the GAL1 promoter for the promoter region of TEF1, encoding a translation factor;

of ADH1, encoding alcohol dehydrogenase 1; and PMA1, encoding the yeast plasma membrane proton

pump. According to the information available in the literature these promoters have the following rank in

terms of strength: GAL1>TEF1>ADH1, during exponential growth [79]. PMA1 is also considered a strong

promoter, however, its activity has not been yet compared with the other promoters mentioned above.

The use of ADH1, TEF1 and PMA1 gene promoters also have the advantage of being constitutive which

means that their activity would be independent of the carbon source used in the growth medium. The

strategy used to replace the GAL1 promote present in pAtCad1 is schematically represented in Figure 11.

Promoter amplificatio

n by PCR

NotI AscI

Sequencing

Cloning the promoter by homologous

recombination in

S. cerevisiae

DNA extraction from S.

cerevisiae and E. coli

electroporation

Extraction of plasmid DNA

from E. Coli and restriction with NotI and AscI

pTEF1

Figure 11: Genetic engineering strategy used to exchange the Gal1 promoter present in the engineered pAtCad1 plasmid. As an example it is shown the replacement of GAL1 for TEF1 promoter.

33

Briefly, the promoters of interest were amplified from S. cerevisiae genomic DNA, recovered from the

agarose gel, concentrated, purified and then transformed into yeast cells together with a NotI-SacI

digested pAtcad1 plasmid. After two days of incubation at 30ºC only the plates obtained from the

transformations performed with the TEF1, ADH1 and PMA1 promoters had colonies. A subsequent

analysis of these colonies by colony PCR only allowed the identification offour candidates having the TEF1

promoter fused with the AtCad1 sequence, as evidenced by the agarose gel shown in Figure 12. Correct

insertion of the TEF1 promoter upstream of the AtCad1 gene in the plasmid was confirmed by Sanger

sequencing. This plasmid was designated pTEF1-AtCad1. BY4741 cells harboring the pTEF1-AtCad1

plasmid were cultivated, for 7 days, in MMF fermentation medium containing 2% glucose. However,

despite various attempts no itaconic acid was found to be present in the culture supernatants (results not

shown). Because of this the remaining parts of the work were performed with the pGAL1-AtCad1 plasmid.

3.2. Assessment of the size of the inoculum and genetic strain background in yeast

ability to produce itaconic acid from pGal1-Atcad1

Previous work performed at the iBB laboratory has demonstrated that transformation of BY4741 cells

with pGAL1-AtCad1 plasmid enabled production of itaconic acid in titers of around 25.12 mg/L [74]. The

effect of the size of the inoculum used in the fermentation in this work was examined by starting the

fermentations with an initial OD600 of 0.1 or 2. In both cases the cultures exhibited growth, evidenced by

the increase in the OD600 of the culture; however, in the case of the more concentrated culture the period

of exponential growth was considerably shorter (Figure 13). Surprisingly, the increase in the size of the

inoculum reduced itaconic acid production by around 2-fold (Figure 13). As expected, cultures harboring

the cloning vector pGreg586 produced no itaconic acid (results not shown). Cultivation of BY4741 cells

harboring the pGal1-AtCad1 plasmid in MMF fermentation medium having 2% glucose as the sole carbon

source also did not led to a detectable production of itaconic acid, this being consistent with the low

activity of the Gal1 promoter under these conditions [80]. In both cultures the production of itaconic acid

was found to occur at higher rate when cells were in stationary phase (Figure 13).

Figure 12: Colony PCR of 24 TEF1-AtCad1 yeast candidates. Lane 0: 1 kb DNA plus ladder; Lane 1: positive control; Lanes 3-27: TEF1-AtCad1 candidates

34

A significant amount of acetic acid (108 and 147 mM) was found in the supernatant of the BY4741

cultures. This differences in the production of acetic acid could not be attributed to the expression of the

AtCad1 gene since the supernatants of BY4741 cells transformed with the pGREG plasmid also had higher

titers of acetic acid (146 and 91 mM for the cultures with initial DO600 of 2 and 0.1, respectively).

Table 14: Overview of production titers and yield of itaconic acid produced in 7 day fermentations undertaken by

BY4741 and TAM cells in MMF medium.

Strain Initial

OD600

Final

itaconic acid

titer (mg/L)

Residual

galactose

(g/L)

Yield itaconic

acid (mg /g)

Final

ethanol

titer (%)

Final

pyruvate

titer

(mM)

Final

acetic

acid

titer

(mM)

By4741 0,1 9,39 0,00 0,47 0,41 0,22 108

2,0 4,46 1,90 0,25 0,38 2,98 145

The genetic background of the yeast strain used is another aspect that might have an important impact

in the ability of yeast cells to produce itaconic acid, as it has been shown that different strains with or

without auxotrophies exhibit remarkable differences in their fermentative capacity [81]. In that sense, the

prototrophic strain 23344c was transformed with the pGAL1-AtCad1 plasmid and the ability of the

transformed cells to produce itaconic acid was assessed and compared with the one exhibited by BY4741

cells. Strikingly, although 23344c cells consumed galactose and were able to grow, only trace amounts of

itaconic acid were detected in the culture supernatants of 23344c fermentations (results not shown). This

0

5

10

15

20

25

0

0,02

0,04

0,06

0,08

0 2 4 6

Gal

acto

se (

g/L)

Itac

on

ic a

cid

(m

M)

Fermentation time (d)

0,01

0,1

1

10

0 2 4 6

OD

60

0


Figure 13: (Left) Concentration of itaconic acid (o, ) and galactose (, ) in supernatant of cultures of BY4741 cells harboring

the pGAL1-AtCad1 plasmid along 7 days of cultivation in MMF fermentation medium. Two sizes of initial inoculum were tested

corresponding to an initial OD600nm of 0.1 (o,) or 2 (,); (Right) Growth curve of BY4741 cells harboring the pGAL1-AtCad1

plasmid in MMF fermentation medium along the 7 days of fermentation with initial OD600 of 0.1() or 2().

35

results clearly demonstrate the importance of taking into account the strain background and the influence

of amino acid biosynthetic processes in the production of itaconic acid.

3.3. Simulation of yeast metabolism in itaconic acid-producing cells

To simulate the production of itaconic acid in yeast cells transformed with the pGAl1-AtCad1 plasmid

the in silico Yeast 5 and Yeast 7 models [44], [47] were altered by adding the transport reaction of cis-

aconitate from the mitochondrion to the cytoplasm, the conversion of cis-aconitate into itaconic acid and

the itaconic export reaction (Figure 14). Additionally, the flux of the three reactions (catalyzed by His3,

Leu2 and Met15) that are known to be absent in the BY4741 strain was constrained to zero, to simulate

the auxotrophies.

Two environmental scenarios were simulated, one considering the use of “standard” conditions and

another in which the nutrient concentrations were matched to the concentrations that are found present

in the MMF growth medium (Figure 15). In the “standard” scenario the only constraint imposed is that

cells uptake the carbons source (in this case glucose) from the growth medium at maximum rate of 10

mmol/gDCW/h. The remaining nutrients were considered to have an unlimited availability, using only the

constraints that are inherent to the model (e.g. no aminoacid uptake). In the simulated medium scenario,

limitations were imposed to the uptake of galactose, ammonium, amino acids and other elements, taking

Itaconate export

Cis-aconitate transport Cis-aconitate

(mitochondrion) Cis-aconitate

(cytosol)

Cis-aconitate decarboxylase Itaconate

(cytosol) CO2

(cytosol)

Itaconate (extracellular)

Itaconate sink

MET15

HIS3

LEU2 Leucine biosynthesis Methionine biosynthesis Histidine biosynthesis

Figure 14: Reactions and metabolites added to the Yeast 5 and Yeast 7 models. The metabolites in red are already present in the models. The reactions associated with the LEU2, MET15 and HIS3 genes were constrained to zero.

36

into account the concentration of these nutrients that was present in the fermentation medium. The

fluxes were normalized to the average dry cell weight present in culture.

For the simulated medium scenario two approaches were used to perform the calculations: in one case

all the nutrients were considered to be depleted from the medium after 3 days of fermentation while in

the second approach, only galactose was considered to be completely consumed after 3 days and the

maximum uptake rate for the remaining nutrients was set at the concentration found in the fermentation

medium. Such constraints were based on the experimental observation that galactose is depleted from

the medium after 3 days, as it can be seen in the results shown in Figure 13. While galactose has a

constant uptake rate and the maximal bound on the sink reaction can be set on this assumption, the

consumption of the remaining nutrients is unknown: it can be faster in the beginning of the fermentation.

To account for this, the second set of constraints was calculated, allowing for a higher consumption of

these nutrients. The sets of constraints used on the simulated medium scenario were meant to

approximate the behavior in the first 3 days of fermentation, where it is more similar to steady-state: the

culture is in exponential phase and the carbon source is still available at an approximately constant rate.

Using two metabolic models available for yeast, 5.1 and 7.0 [44], [47], and simulating the scenarios

described above, the Flux Balance Analysis was performed. The flux distribution in the metabolic network

was calculated maximizing the growth rate or the production of itaconic acid. The first objective function

is the most commonly used and has been applied in numerous studies focused on metabolic engineering,

including those aiming to improve yeast-based production of fumaric and succinic acids [32], [35], [53].

The second objective function used represents the maximal theoretical production. The results obtained

in these calculations are summarized in Table 15 and Table 16.

No glucose uptake

Constrained ammonium

uptake

Constrained aminoacids

uptake

Constrained galactose

uptake

10 mmol/gDCW/

h glucose

Unconstrained ammonium

uptake

No aminoacid

uptake

Standard scenario Simulated medium scenario

1. Constant

consumption of all

the nutrients in 3

days

2. Constant

consumption of

galactose in 3 days.

The remaining

nutrients can be

consumed in 1 day.

Figure 15: Scenarios used to in silico simulate S. cerevisiae metabolism during production of itaconic acid.

37

Table 15: FBA results for the optimization of cell growth and itaconic acid production when cells are grown in 10 mmol

glucose/gDCW/h (standard conditions).CAD corresponds to cis-aconitate decarboxylase. * marks the scenarios where

no FBA solution was found.

Standard scenario

Objective (maximization) Growth rate (h-1) CAD flux (mmol/gDCW/h)

Yeast

5

Growth rate (d-1) * *

CAD flux (mmol/gDCW/d) 0 10,9

Yeast

7

Growth rate (d-1) * *

CAD flux (mmol/gDCW/d) 0 12,0

Table 16: FBA results for optimization of cell growth and itaconic acid production when cells have maximal uptake

rates based on the fermentation medium. CAD corresponds to cis-aconitate decarboxylase.

Simulated medium scenario

Yeast 5 Yeast 7

Objective

function

Growth rate

(d-1)

CAD flux

(mmol/gDCW/d)

Objective

function

Growth

rate (d-

1)

CAD flux

(mmol/gDCW/d)

set

1

Growth rate 0,5532 0 set

1

Growth rate 0,7454 0

CAD flux 0 47,24 CAD flux 0 51,82

set

2

Growth rate 1,6596 0 set

2

Growth rate 2,236 0

CAD flux 0 48,24 CAD flux 0 53,31

As expected, no FBA solutions were found when growth was the objective function in the standard

scenario, since auxotrophic cells can’t grow without the addition of aminoacids to the media. The results

obtained using the two models have minor differences in the predicted values for growth and itaconate

production, regardless the simulation scenario used. Nevertheless, a consistent trend was observed in all

the simulations: maximization of growth impairs itaconic acid production and vice-versa. Such observation

demonstrates that the two objectives are in competition. In fact, this would be expected, since the cis-

aconitate decarboxylase reaction consumes cis-aconitic acid from the TCA cycle, representing a cost for

ATP production and amino acid synthesis, which are necessary for biomass formation. Furthermore,

itaconic acid is an end-point metabolite in yeast metabolism and therefore if all carbon was channeled to

this compound the cell would be depleted of all essential precursors. Differences were also found in the

38

values obtained for the two sets of constrains used in the simulated medium scenario within each yeast

model used, suggesting that not only constraints on the carbon source are determinant and that taking

into account the other nutrients in the medium affect the values predicted by FBA.

The in silico simulation performed with the objective function of growth maximization could not predict

the ability of yeast cells to produce itaconic acid while growing, which is actually what occurs in vivo as it

can be observed by the results shown in Figure 13. Given this, a different objective function was

implemented for the FBA analysis. In specific, it was used the maximization of biomass yield while

minimizing the total metabolic flux (maximization of biomass per flux unit), as this was found to be more

accurate for the prediction of fluxes in batch cultures [58], [59].

Table 17: FBA results for the maximization of biomass/flux. Results presented for both the Yeast 5 and Yeast 7 models.

CAD corresponds to cis-aconitate decarboxylase. * marks the scenarios where no FBA solution was found

The use of this new objective function in the FBA resulted in flux vectors with non-null fluxes for both

growth and itaconic acid production in the simulated medium scenarios (Table 17). Again, no solutions

were found in the standard scenarios. However, the differences between the values obtained for the Yeast

5 and Yeast 7 models were very prominent. The modifications made to the yeast models along time, such

as the removal of blocked and dead-end reactions in the Yeast 6 model and the revision of the lipid

metabolism, may account for the observed differences [45], [47].

To test if the model could predict the different abilities of the BY4741 and 23344c strains to produce

itaconic acid, a new FBA analysis was performed using a complete model that included the reactions

Maximization of biomass and minimization of total metabolic flux

Scenario Growth rate (d-1) CAD flux (mmol/gDCW/d)

Yeast 5

Standard * *

Simulated

medium: set 1 0,0055 3,52e-06

Simulated

medium: set 2 0,0166 1,49e-07

Yeast 7

Standard * *

Simulated

medium: set 1 0,0075 1,53e-05

Simulated

medium: set 2 0,0538 1,07e-06

39

catalyzed by His3, Leu2 and Met15. The FBA problem was solved using as an objective function the

maximization of biomass per flux unit, as described above.

Table 18: FBA results for the maximization of biomass/flux. For each scenario two genetic backgrounds were tested:

prototrophic and the auxotrophic BY4741. Results presented for both the Yeast 5 and Yeast 7 models. CAD corresponds

to cis-aconitate decarboxylase.

Influence of the genetic background

Yeast 5 Yeast 7

Scenario Genetic

background

Growth rate

(d-1)

CAD flux

(mmol/gDCW/d)

Growth

rate (d-1)

CAD flux

(mmol/gDC

W/d)

Standard Prototrophic 0,2194 8,17e-05 8,0928 2,62e-04

BY4741-like - - - -

Simulated

medium:

set 1

Prototrophic 0,0182 3,62e-06 0,0538 1,07e-06

BY4741-like 0,0055 3,52e-06 0,0075 1,52e-05

Simulated

medium:

set 2

Prototrophic 0,0448 2,41e-07 0,0186 7,13e-07

BY4741-like 0,0166 1,49e-07 0,0538 1,07e-06

Now simulating a prototrophic background it was possible to find solutions in the standard scenario of

growth without aminoacids in the medium. The simulated medium solutions were obtained, although

prominent differences were observed in the values results reported by the Yeast 7 and Yeast 5 models. In

the Yeast 5 model, the flux through the CAD reaction is lower in the auxotrophic strain than in the

prototrophic strain, this being observed for the two simulated medium scenarios. Differently, the Yeast 7

model predicted an increase in CAD flux in a genetic background similar to the one of the BY4741 strain,

particularly when the 1st set of constraints was used, where the increase was of about 14-fold. On the

overall, these results of the in silico simulation show that the genetic background of the strain and, in

particular, the auxotrophies of the BY4741 strain have an impact in yeast metabolism and, in particular,

in production of itaconic acid. It is important to notice that the difference in production of itaconic acid

between BY4741 and a prototrophic strain could only be detected when the yeast model 7 was used

40

(Table 18). To further investigate the reasons underlying the increase in CAD flux upon elimination of

the HIS3, LEU2 and MET15 genes, the flux distributions of the Yeast 7 model corresponding to the

maximization of growth or itaconic acid production were imported into Cytoscape. The shortest

undirected path found between the nodes of the reactions associated to HIS3, LEU2 and MET15 genes

and the cis-aconitate decarboxylase node was the release of carbon dioxide, which occurs in all these

reactions (Error! Reference source not found.). This path is most probably not relevant for the observed

henotype, since carbon dioxide concentrations in the cytosol are not expected to be limiting production

of itaconic acid. Notably, when the simulation is performed maximizing production of itaconic acid, the

flux through His3, Leu2 and Met15 reactions is zero, while maximization of growth implies active reactions

catalyzed by Leu2 and His3. This observation is, in part, consistent with the beneficial effect that the

deletion of LEU2 and HIS3 seems to have in improving itaconic acid production, although it is unclear why,

since leucine and histidine biosynthesis are not directly linked with itaconic acid production.

3.1.3 Flux variability analysis

The observation that production of itaconic acid and growth are competing objectives led to the

examination of the extension of this effect. In specific, it was examined how a reduction in the growth

rate would affect the ability of yeast cells to produce itaconic acid. For that, FVA analysis was used,

imposing a 5% reduction in the growth rate. The results obtained are shown in Table 19.

Table 19: FVA results (allowing a 5% decrease in the growth rate) for both Yeast 5 and Yeast 7 model, using 2 sets of

constraints. CAD corresponds to the cis-aconitate decarboxylase enzyme.

Flux variability analysis

Model Scenario CAD flux (mmol/gDCW/d) Growth rate (d-1)

Figure 16: Fluxes through some aminoacid biosynthesis reactions when the objective function is the maximization of growth (A) or itaconic acid production (B). Results presented for the Yeast 7 model. The reactions associated with the genes HIS3, LEU2 and MET15 are highlighted.

Maximization of itaconic acid production B) Maximization of the growth rate

HIS3 HIS3

LEU2 LEU2

MET15 MET15

41

Minimal flux Maximal flux Minimal

flux

Maximal

flux

Itaconic acid yield

(mmol/mmol)

Yeast 5

Standard 0 15,51 20,84 21,94 0,065

Simulated

medium: set 2 0 4,048 2,769 3,856 0,095

Yeast 7

Standard 0 16,00 32,05 33,74 0,067

Simulated

medium: set 2 0 3,042 4,252 4,476 0,715

By allowing a 5% decrease in the growth rate, the maximal achievable flux through the CAD

reaction was now non-null in all the tested scenarios (Table 19). This observation suggests that the

implementation of sub-optimal growth rate in vivo could benefit for the production of itaconic acid, as it

would allow the activation of enzymatic steps that would be normally inactive [61]. The maximal fluxes

are higher in the standard scenario than in the simulated medium. However, the corresponding yields of

itaconic acid per mmol of carbon source used are lower. These differences are attributable to the higher

availability of nutrients that are present in the simulated medium and that allow a better use of the carbon

source towards itaconic acid production.

3.4. Identification of gene deletions increasing the production of itaconic acid by

yeast cells

3.4.1 In silico identification: OptKnock and OptGene

To identify interesting deletion mutants that could increase the production of itaconic acid in yeast cells

the OptKnock and OptGene algorithm were used through the COBRA toolbox and OptFlux program,

respectively. In a first stage, the goal was to obtain a dataset comprised of single-deletion mutants

proposed by the two algorithms. The main advantage of the identification of single knockout is that

individual yeast deletion mutants are commercially available and thus it is possible to test them in a

relatively easy manner. For this the OptGene and OptKnock algorithms were used resulting in a total of

54 single-deletions that are predicted to increase itaconic acid production (Annex II). This dataset of

deletion mutants was fine-tuned by excluding the artificial reactions (e.g. isa reactions or pseudolipid

formation), reactions having no gene associated in the yeast metabolic model and also deletions of

essential genes. After this 21 single-deletion mutants were selected as putative candidates to improve

production of itaconic acid (Table 20). There were only two overlaps between the reactions identified by

the Yeast5 or Yeast7 models: the reactions associated with the M7 and M4 genes, highlighting the

importance of using two models for these approaches. The targeted reactions span through various

metabolic pathways; however, it is interesting to observe that elimination of genes involved in fatty acid

42

beta-oxidation, in cardiolipin synthesis, in synthesis of sphingolipids and in fatty acids, in protein

glycolysation, in nicotinic acid and in utilization of non-fermentable carbon sources appear to be more

prominent in the dataset. The extent to which itaconic acid production could be increased in the multiple

knockouts couldn’t be predicted in silico, since the fluxes in the CAD reaction predicted by FBA of these

mutants was always zero, presumably because the objective function being used is the maximization of

growth.

43

Table 20: Single deletion mutants identified by OptKnock and OptGene selected to be experimentally validated.

Yeast 5 Yeast 7

Gene Enzyme Function Gene Enzyme Function

M1 Homoserine dehydrogenase

(NADH) Methione and threonine

biosynthesis M8 Ap4A hydrolase Purine metabolism

M2 acetyl-coA synthetase Acetate and ethanol

utilization M9

Various reactions of the type inositolphosphotransferase

Sphingolipids biosynthesis

M3 GTP/GDP translocase Mitochondrial transporter M10 acetyl-CoA C-acyltransferase (decanoyl-CoA) Fatty acid beta-oxidation

M4

3-hydroxyacyl-coA dehydrogenase (3-

oxooctadecanoyl-coA) Fatty acid beta-oxidation

M7 nucleoside diphosphatase Protein glycolysation

3-hydroxyacyl-coA dehydratase (3-hydroxydecanoyl-coA)

M11 fatty-acid--CoA ligase (tetradecanoate) Fatty acid beta-oxidation

M5 N-formyl-L-kynurenine

amidohydrolase Biosynthesis of nicotinic acid M12 DAG kinase (1-16:0, 2-18:1), ER membrane

Synthesis of membrane phospholipids and transduction

pathway

M6 aldehyde dehydrogenase

(acetaldehyde, NADP) Acetate biosynthesis, NADPH

regeneration M13

CL synthase (1-18:0, 2-16:1, 3-16:0, 4-16:1), mitochondrial membrane

Cardiolipin biosynthesis

M7 Nucleoside triphosphatase Protein glycolysation M14 'MLCL (1-16:1, 2-16:1, 4-18:1):PC (1-16:0, 2-

18:1) acyltransferase, mitochondrial membrane'

Cardiolipin biosynthesis (required for normal

phospholipid content of mitochondrial membranes)

M15 'B-ketoacyl-CoA reductase (3-hydroxyoctadecanoyl-CoA)'

Sphingolipids and long-chain fatty acid biosyntheis

M16 'glycerol-3-phosphate acyltransferase (18:1),

ER membrane' Lipid biosynthesis

M4 '2-enoyl-CoA hydratase (3-

hydroxytetradecanoyl-CoA)' Beta-oxidation

M17 and M18 'aldehyde dehydrogenase (2-phenylethanol,

NADP)' Alcohol synthesis

TGL3 and TGL4 Associated to 2 reactions of the type

triacylglycerol lipase Lipid metabolism

44

3.4.2. Preliminary results for the proposed mutants in shake-flask fermentations

The production of itaconic acid in nine of the deletion mutants (ΔM2, ΔM17, ΔM18, ΔM11, ΔM3, ΔM15,

ΔM9, ΔM10 and ΔM7) identified by the in silico modelling was tested. For this, the mutant strains were

transformed with the pGAL1-AtCad1 plasmid and fermentations were undertaken in MMF fermentation

medium in the same conditions as those described above for the BY4741 strain. The results obtained are

summarized in Figure 17 and Table 21.

The supernatants of 6 of the 9 mutants tested were found to have higher itaconic acid titers than those

registered for the parental strain BY4741 (Figure 17). The kinetics of production was similar in all strains,

the maximal itaconic acid concentration being achieved in all cases in the last day of fermentation (Annex

III). Galactose was fully consumed in all cultures, although at different rates (Annex III and Table 21). The

growth rate of the different strains was also found to differ from the one of the wild-type strain, with a

particular emphasis for the strains devoid of M10 gene, which exhibited a particularly low growth rate. As

the result of the lower growth rate and higher itaconic acid titers, the mutant strains exhibited significant

higher yields than the BY4741 strain (Table 21). It is interesting to observe that the mutants exhibiting

more robust increases in the production of itaconic acid had slower growth rates than the parental strain,

which is in agreement with the above described FVA analysis. The most promising mutant identified was

the strain devoid of M18 which had a ~ 2.5 fold higher yield than the one obtained for wild-type cells

(Table 21).

Figure 17: (A) Concentration of itaconic acid present in the supernatant of 7-day fermentations undertaken by BY4741 cells or by the derived deletion mutants transformed with the pGal-AtCad1 plasmid in MMF medium; (B) Comparison between the growth rate rates of the BY4741 strain and of the derived deletion mutants in the MMF medium. In both graphs the dashed lines corresponds to the values obtained for the wild-type strain.

0,0

5,0

10,0

15,0

20,0

25,0

Itac

on

ic a

cid

(m

g/L)

0,000

0,020

0,040

0,060

0,080

0,100

0,120

0,140

0,160

0,180

Gro

wth

rat

e (h

-1)

A B

45

Table 21: Overview of final itaconic acid titers, yield, productivity, galactose consumption rate and growth rate of the

cultures with the BY4741 and mutant strains harboring the pGal1-AtCad1 plasmid.

Strain Final Itaconic

titer (mg/L)

Yield (mg

itaconic /g

galactose)

Productivity

(mg/L/d)

Galactose

consumption rate

(g/L/d)

Growth rate

(h-1)

BY4741 9,4 0,469 1,34 4,0 0,161

ΔM2 11,0 0,550 1,57 3,3 0,090

ΔM17 11,1 0,556 1,59 10,4 0,117

ΔM18 23,1 1,155 3,30 5,5 0,118

ΔM11 8,8 0,441 1,26 5,7 0,082

ΔM3 5,0 0,249 0,71 5,0 0,137

ΔM15 16,2 0,812 2,32 5,5 0,086

ΔM9 6,0 0,298 0,85 4,5 0,105

ΔM10 14,1 0,703 2,01 4,6 0,012

ΔM7 18,4 0,920 2,63 4,7 0,116

Subsequently it was attempted to identify mutants with multiple deletions that could also be interesting

for the production of itaconic acid. For this, OptKnock was implemented in Yeast 7 and Yeast 5 model,

setting 5 as the maximal number of knockouts allowed. The results obtained are shown in Table 22. No

overlap between the genes identified by the model 7 or model 5 was obtained, which shows the relevance

of using both models to perform this analysis (Table 22). Notably, a triple mutant devoid of enzymes

involved in conversion of ceramides into phytosphingosine and of PDC genes was identified, this being a

particularly interesting result considering the increase in itaconic acid production that was already

observed upon elimination of alcoholic fermentation. It is also relevant to note that the deletion of the

pyruvate decarboxylase reaction was previously suggested as a single deletion strategy by OptKnock

(Annex II). Some of the strategies proposed include artifacts from the model, such as the lipid

pseudoreaction and exchange reactions, since the first doesn’t correspond to a process that happens in

vivo and the second ones are not associated to genes.

46

Table 22: Deletion strategies predicted by the OptKnock tool for the coupling of itaconic acid to celular growth.

Model

Number

of

knockouts

Enzymes to be deleted Simulated scenario

Yeast 7.0 3

Dihydroceramidase (substrate: ceramide C-24)

Simulated medium Dihydroceramidase (substrate: ceramide C-26)

Pyruvate decarboxylase

Yeast 5.1

2 Adenylate kinase

Simulated medium Sulfate uniport

3

Adenylate kinase

Standard Sulphate exchange

Lipid pseudoreaction

3

Adenylate kinase

Simulated medium L-lysine exchange


4

2-keto-4-methylthiobutyrate transamination

Standard Thioredoxin reductase (NADPH)

Sulfate uniport


47

3.5. Role of ammonium availability in itaconic acid production

3.5.1. In silico robustness analysis

As the MMF medium to produce itaconic acid is complex and includes a wide variety of nutrients a strategy

that could allow an in silico prediction of how the manipulation of the growth medium composition could

affect itaconic acid production was attempted. A first focus was put on the availability of ammonium since

this has been found to modulate, at some extent, production of itaconic acid in A. terreus, presumably by

modulating the activity of amino acid biosynthetic pathways [84]. In that sense, the impact exerted by

ammonium availability on the balance between growth and CAD flux was explored using FBA. The

robustness analysis of the network obtained with the standard scenario simulation reveals an inverse

relation between the growth rate and the CAD flux, which is line with the previously obtained results

(Figure 18).

However, when the 1st set of constraints of the simulated scenario of constraints is imposed, this relation

becomes discontinuous: only states corresponding to high CAD fluxes and low growth rates are allowed

(closed circles in Figure 18 B). This observation can be explained by the presence of a nutritional

constraint that is limiting for growth and not itaconic acid production. If the constraints on ammonium

availability are removed, the robustness line becomes similar to what is found in standard conditions. The

disconituity between growth and itaconic acid production suggests that the limitation of ammonium

availability could modulate itaconic acid production, as initially hypothesized based on results obtained in

A. terreus [84]. It is relevant to note that, when the 2nd set of constraints was imposed, the robustness

line became continuous again and the cells are now allowed to be in states where the CAD flux is close to

zero and the growth rate is nearly maximal. As the 1st implies a more prohibitive interpretation of nutrient

availability in the medium, it is possible that the effects of limiting ammonium are only observed if other

nutrients become limiting as well, in particular, amino acids. The described robustness analysis followed

the same patterns for both Yeast models.

0

2

4

6

8

10

12

0 0,5 1 1,5 2

CA

D f

lux

(mm

ol/

gDC

W/h

)

Growth rate (h-1)

0

10

20

30

40

50

60

0 1 2 3 4 5 6

CA

D f

lux

(mm

ol/

gDC

W/d

)

Growth rate (d-1)

Figure 18: Robustness analysis on the Yeast 7 model. Left) In the standard scenario in the prototrophic background. Rigth) In the

simulated scenario () and in the simulated scenario without constraints on ammonium availability (o) on the BY4741 background..

48

3.5.2. Microplate fermentations to test different concentrations of ammonium in the

fermentation medium

To test the hypothesis of ammonium limitation being a predisposing factor for itaconic acid production,

microplate fermentations with the BY4741 cells harboring the pGal1-Atcad1 plasmid were performed

using the MMF fermentation medium supplemented with a range of concentrations of ammonium

sulfate. The fermentations were carried out for 72 hours and the itaconic acid present in the supernatant

at the end of the fermentation was quantified. The results shown are relative to two independent replicas

that were performed. As observed, although some differences in the absolute values of the two assays

were obtained, the trend is similar in both of them. Under these conditions the amount of itaconic acid

achieved a minimum when the ammonium concentration was equal to ~0.04 g/L. Both the decrease and

the increase in the concentration of ammonium resulted in an increase in itaconic acid production

indicating that the relationship existing between ammonium availability and itaconic acid production is

complex. It is however important to stress that the reduction of ammonium concentration in the medium

didn’t affect the growth rate, which suggests that the cells may also be using the available amino acids in

the growth medium as nitrogen sources.

Figure 19: Effect of ammonium concentration in MMF growth medium in the ability of yeast cells to produce itaconic acid. The concentration of itaconic acid present in the supernatant of 3-day fermentations undertaken by BY4741 cells was measured by HPLC (A). The initial and final DO600 were measured and the ratio final/initial was calculated (B). The results shown represent two independent assays that were performed.

0

0,01

0,02

0,03

0,04

0,05

0,06

0,07

0,001 0,01 0,1 1 10

Itac

on

ic a

cid

(m

M)

Ammonium concentration in the medium (g/L)

0

5

10

15

20

0,001 0,01 0,1 1 10

Gro

wth

rat

io

Ammonium concentration in the medium (g/L)

49

4. Discussion

In the first part of this work the production itaconic acid in S. cerevisiae BY4741 cells in batch was further

characterized, being examined the effect played by the initial size of the inoculum and also the genetic

background of the strain used. The results obtained render clear that lower initial cell densities led to an

increase of around 2-fold of itaconic acid (for cultures starting with an initial OD600 of 0.1 and 2 the titer

of itaconic acid obtained was of 9.4 and 4.5 mg/L, respectively). Despite the improvement, the titers of

itaconic acid obtained were still below those previously described in the study of Blazeck et al., 2014,

which reported a titer between 23 mg/L and 59 mg/L, depending on the promoter used [17]. There are

several differences between the experimental setup used in this work and the one used in the study of

Blazeck et al.,2014, including the fact that in this study the size of the initial inoculum was of 6 while herein

we have worked with much lower initial ODs of the cultures (between 0.1 and 2). Furthermore, the

aeration ratio was also different in the two studies since we have used 50 mL cultures on 250 mL shake-

flask, while Blazeck et al, 2014 have used 15 mL cultures on 250 mL shake-flask bottles [17]. Aeration has

been shown to favor production of itaconic acid in A. terreus and therefore it may also be beneficial when

yeast is used as a host. Another important difference between this study and the study of Blazeck et al.

(2014) is the use of a different promoter to drive the expression of the AtCad1 gene: while in this study

the galactose-inducible plasmid GAL1, in the study of Blazeck et al, (2014) the expression of AtCad1 was

under the control of an artificial promoter that was specifically engineered for this effect. Although GAL1

promoter is known to be very potent upon supplementation of the growth medium with galactose, the

activity of this promoter decreases over time, accompanying the decrease in galactose titers [85]. Thus, it

can be argued that in the overall fermentation the expression of the AtCad1 enzyme could achieve higher

titers in the study of Blazeck et al., (2014) thereby leading to higher production of itaconic acid. When

galactose is the carbon source, the induction of the GAL promoter has been found to be higher during

exponential growth, followed by a decrease [86]. Taking this into account, the lower itaconic acid titers

obtained in fermentations that were started with an initial OD600 of 2 can result from a lower expression

of the AtCad1 gene under such conditions, since growth in this setup is considerably lower than the

obtained when the fermentations were started with an initial OD600nm of 0.1.The pGAL-AtCad1 plasmid

can be used for production of itaconic acid from galactose but also from other raw-materials that also

have high concentrations of this sugar such as those derived from red algae and spent sulfate pulping

liquor from the papermaking industry [87], [88]. Furthermore, it may also be possible to use this plasmid

to produce itaconic acid from substrates enriched in glucose using strains devoid of genes mediating the

glucose-repressive effect of the GAL1 promoter and that lack the ability of using galactose as a carbon

source, a strategy that has been used before with success [85].

Despite various attempts the use of the TEF1 promoter to control AtCad1 expression didn’t enabled

production of itaconic acid in yeast cells. The reasons behind these results are unclear, since the usage of

this promoter has been reported for metabolic engineering purposes [79]. Moreover, it has been

50

previously reported that itaconic acid can be produced in S. cerevisiae using a synthetic enhanced TEF1

promoter to control AtCad1 expression [17]. In our case we have used as the TEF1 promoter the 1000 bp

region upstream of ATG start codon, while the synthetic promoter pTEF1 is composed of three UAS from

the mitotic cyclin CLB2 gene (the enhancer element) and the -401 to -198 upstream region from the ATG

start codon of the TEF1 gene (the core promoter element) [89].

In the batch fermentations undertaken with the BY4741 strain two main C-based by-products were

identified, ethanol and acetic acid, which achieved a final titer of 3.2 g/L and ~7.5 g/L, respectively.

Production of acetic acid occurs in the alcohol fermentation pathway when a shortage of NADH occurs,

which results in accumulation of acetaldehyde which is subsequently converted into acetate by the

acetaldehyde dehydrogenase enzymes [90]. The concentration of acetic acid is considerably higher than

the one that would be expected to have if this product was being produced as a by-pass product of the

yeast alcoholic fermentation. This observation suggested that under the experimental conditions used

here, the yeast cells might be undergoing a ferment-respiratory metabolism. The accumulation of acetate

(and of other derived products such as acetoin or 2,3-butanediol) in the growth medium under conditions

favoring mixed fermentative-respiratory metabolism has been reported before [90]. The high ratio of

aeration used in the fermentations could contribute to increase oxygen availabily which can trigger

ferment-respiratory metabolism, especially in period of low growth-rate. It is also relevant to note that

MMF medium used in this work contains para-aminobenzoic acid, in a formulation defined by Verduyn et

al, 92 and that the presence of this acid has been linked to an increase in the respiratory capacity of S.

cerevisiae [91]. It is also important to stress that a higher titer of acetic acid was present in the supernatant

harvested from the cultures initiated with an OD600 of 2, which also have reduced growth. Furthermore,

this over-accumulation of acetic acid in the medium was also observed in the supernatants collected from

BY4741 cells transformed with the pGREG586 cloning vector. The absence of glycerol in the supernatants

of the different fermentations is also in line with the hypothesis of a fermento-respiratory metabolism,

suggesting that NADH is being mostly re-oxidized in respiration. These results indicate that strategies that

should be pursued to improve production of itaconic acid should involve, for example, channeling of this

exceeding acetate to acetyl-CoA which could then fuel the TCA cycle and boost itaconic acid production.

Production of itaconic acid was also attempted in the prototrophic strain 23344c (only auxotrophic to

ura3, the selection marker) transformed with the pGal-AtCad1 plasmid. However, in the fermentations

undertaken by these cells, only trace amounts of itaconic acid were detected in the fermentation broth,

which was surprising. Within an industrial setting the use of a prototrophic strain would be advisable and

in that sense the herein obtained result is of importance. In silico simulation confirmed, at least in one of

the tested cases, the better performance of the BY4741 strain in producing itaconic acid. According to the

obtained results the key deletions should be Leu2 and His3. It is not easy to understand how the deletion

of these two genes impact itaconic acid production, although it can be argued that the lack of leucine and

histidine biosynthesis that is expected to occur in the BY4741 could contribute to increase the availability

of TCA cycle-precursors which could then benefit itaconic acid production. It can’t also be disregarded the

51

possibility of this phenomenon registered in the 23344c strain resulting from a regulatory mechanism that

can’t be predicted by the metabolic models used. It will be essential to test if other prototrophic strains

are also unable to produce itaconic acid to understand if this could be a strain-dependent effect or if its

associated to prototrophy.

In the second part of this thesis a set of constraint-based methods were implemented to explore the

metabolic network of a yeast strain performing the conversion of cis-aconitate into itaconic acid.

Additionally OptGene and OptKnock were used to identify interesting deletion strategies for the

overproduction of itaconic acid. The optimization of biomass is generally used as a cellular objective

function in most studies performed in yeast-based metabolic engineering [57]. Nevertheless, using these

conditions it was not possible to simulate the occurrence of itaconic acid biosynthesis and growth

simultaneously, which is what is observed to occur in vivo. Modification of the objective function to

maximization of biomass yield per flux unit allowed a better representation of what was found to occur

in vivo, predicting the ability of yeast cells to produce itaconic acid and to grow at the same time. This

objective function is based on the assumption of parsimonious enzyme usage: under exponential growth,

there is a selection for the fastest growing strains that require lowest overall flux through the network

[95]. The application of this principle results in the selection of the flux distribution with minimal total

metabolic flux among the alternate optima that are usually provided by FBA. Upon maximization of

biomass yield per flux unit the CAD fluxes were non null for all the simulated scenarios, indicating that

this objective function is more well-suited for this application in particular. Production of itaconic acid and

growth are expected to be decoupled, since itaconic acid biosynthesis represents a cost for cellular

energetics by diverting cis-aconitic acid from the TCA cycle (which consequently leads to a decrease in the

flux going through the subsequent reactions that reduce NAD+ to NADH and that are necessary for ATP

production in the respiratory chain). Indeed, FVA-based analysis revealed that a decrease of only 5% in

the optimality of the growth rate was enough to trigger production of itaconic acid in yeast cells. Also

consistent with this idea, deletion mutants found to over-produce itaconic acid exhibited decreased

growth rates compared to the parental strain. A similar negative correlation with growth was also

observed when the metabolism of yeast cells producing succinic acid was simulated through FBA [68].

These results reinforce the need of implementing strategies that force the network to balance growth and

itaconic acid production.

Considering the results obtained by FVA and the established competition between growth and itaconic

acid production in the yeast metabolic network, it is possible that the induction of suboptimal states

improves itaconic acid production in yeast. One possible way to achieve this would be utilizing non-

fermentable carbon sources in the fermentation setting, as it has been observed that the utilization of

carbon sources for which the strains haven’t been adapted to can lead to changes in growth characteristics

that could signify parallel or opposing growth objectives, i.e., suboptimal states [96]. Further exploration

of the FVA results to identify the necessary flux changes that allow the achievement of the maximal fluxes

52

through the CAD reaction could help unravel competing pathways and serve as basis for strain

improvement, a methodology that has been used before [97].

Following the common trend in studies aiming optimization of yeast metabolism in the direction of a given

compound of interest, the Optknock and OptGene tools were used to predict non-intuitive deletion

strategies that could lead to overproduction of itaconic acid. A total of 21 deletion mutants were identified

as being those more prone to exhibit higher ability to produce itaconic acid. There were two intersection

between the set of mutants identified in the Yeast 7 and Yeast 5 (M7 and M4 genes), reinforcing the

strength of these predictions. These low overlap might be due to the differences underlying the

construction of these models. Besides the PDC genes, the other genes that were highlighted by the in

silico modelling were not intuitive and their elimination could not be anticipated to improve itaconic acid

production. However, it was noted that the obtained dataset appeared to be enriched in reactions

involved in beta-oxidation, in cardiolipin synthesis, purine biosyntheis, in synthesis of sphingolipids and

fatty acids, in protein glycolysation, in nicotinic acid biosynthesis and in utilization of non-fermentable

carbon sources. In this context, it is relevant to take into account that the Yeast 7 model has reported

blocked reactions in fatty acid synthesis and cardiolipin metabolism, so the deletion strategies in these

pathways may be artifacts [47]. Interestingly, the results from OptKnock suggest that deletion of two

enzymes in the phytosphingosine synthesis pathway could further increase production in the Δpdc

background.

Preliminary results were obtained for 9 of the 21 selected deletion mutants, by shake-flask fermentation

and in 6 of them it was confirmed the existence of higher concentrations of itaconic acid in the

supernatant. The most prominent results were obtained upon deletion of M18, M15 and M7, which led

to increases in yield of itaconic acid production ranging from 1.7 to 2.5-fold, comparing with the values

attained in the wild-type BY4741 strain. It is not easy to fully understand the reasons why the deletion of

these genes results in increased itaconic acid production. The deletion of M18 gene was the one found to

lead to a more prominent increase in itaconic acid production. M18 encodes a cynammyl alcohol

dehydrogenase, acting on the third step of the Ehrlich pathway of fusel alcohol synthesis, a reaction that

consumes NADPH. The M17 gene, encoding an homologue of M18, was also selected as interesting

deletion because it is associated to the same reaction as M18 in the Yeast 7 model [98]. However, the

yield obtained with the M18 mutant was higher than the one obtained with the ΔM17 mutant (1.15 vs

0.56 mg/g). It is possible that by impairing the Ehrlich pathway through M18 deletion the amount of α-

ketoglutarate being diverted from the TCA cycle toward the first reaction in this pathway decreases. The

M15 gene encodes for a microsomal beta-ketoreductase acting on fatty acid elongation. The deletion of

this gene has been reported to cause the accumulation of ceramides and palmitic acid, a precursor of de

novo ceramide and phytosphingosine biosynthesis, a pathway that was already indicated by the OptKnock

tool, by the suggestion of the knockout of two dehydroceramidases in a Δpdc background [99]. The M7

gene encodes for an apyrase with role on protein glycolysation, by acting on the transport nucleotide-

sugars to the Golgi compartment antiporters [100]. It is possible that the knockout of the M7 gene results

53

in a lower transport of these precursors, decreasing the drain of sugars from the first steps of glycolysis,

increasing the flux through the TCA cycle.

Notably, the herein identified set of deletions predicted to increase production of itaconic acid in S.

cerevisiae differs from the one described by Blazeck et al., (2014). Nevertheless, despite the individual

genes identified are different there is some convergence of the metabolic pathways involved. In the work

of Blazeck et al., 2014 the simultaneous knockout of ade3, bna2 and tes1 was found to be optimal for the

production of itaconic acid. Ade3 is a gene involved in purine synthesis, a category that was enriched in

the dataset obtained this work from OptKnock and OptGene. Additionally, tes1 encodes for an acyl-coA

thioesterase, with a role in the fatty acid oxidation. This pathway was also enriched in the dataset and the

deletion of M10, a peroxisoal ketoacyl-coA thiolase was also found to increase final itaconic acid titers in

vivo. Finally, bna2 encodes for a indoleamine 2,3-dioxygenase, an enzyme involded in the biosynthesis of

nicotinic acid from tryptophan via kynurenine. In this study one of the suggested knockouts is M5,

encoding a kynurenine aminotransferase involved in the same pathway. This reinforces the idea that a

decrease in the formation of nicotinic acid might allow a higher production of itaconic acid, either by a

decrease in the use of electron equivalents or by reducing cofactors availability and shifting its balance.

On the overall the results obtained in this work, combining experimental efforts with computational

simulation and optimization, have provided a set of of both rational and non-intuitive strategies that may

help to implement successful processes for the production of itaconic acid in yeast and, eventually, in

other fungal hosts currently used for production of itaconic acid, such as Aspergillii.

54

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2. Annex

Annex I) Extracellular concentrations of pyruvic and acetic acid in the BY4741 and

TAM cultures

Figure 20: (Right) Concentration of pyruvic acid in supernatant of cultures of BY4741 cells harboring the pGAL1-AtCad1 plasmid along 7 days of cultivation in MMF fermentation medium. Two sizes of initial

inoculum were tested corresponding to an initial OD600nm of 0.1 () or 2 ()

0

20

40

60

80

100

120

140

160

180

0 2 4 6 8

Ace

tic

acid

(m

M)

Time of fermentation (d)

62

Annex II) Lists of single mutants proposed by OptGene and OptKnock

Table 1: Single-mutants proposed by OptGene in the Yeast 7 model

Reaction index

Reaction name ORFs associated in the model

Gene

Reactions associated with only one gene in the model: selected to be experimentally validated

Gene associated with various reactions of the type inositolphosphotransferase M9

Acetyl-CoA C-acyltransferase (decanoyl-CoA) M10

Inositolphosphotransferase (PI (1-16:1, 2-18:1) MIPC-D (C24)) M9

Nucleoside diphosphatase M7

Fatty-acid--CoA ligase (tetradecanoate) M11

DAG kinase (1-16:0, 2-18:1), ER membrane M11

CL synthase (1-18:0, 2-16:1, 3-16:0, 4-16:1), mitochondrial membrane CRD!

MLCL (1-16:1, 2-16:1, 4-18:1):PC (1-16:0, 2-18:1) acyltransferase, mitochondrial membrane M14

B-ketoacyl-CoA reductase (3-hydroxyoctadecanoyl-CoA) M15

Glycerol-3-phosphate acyltransferase (18:1), ER membrane M16

2-enoyl-CoA hydratase (3-hydroxytetradecanoyl-CoA) M4

No genes associated in the model

3137 palmitate transport, mitochondrial membrane-cytoplasm

1179 5-nucleotidase (dGMP)

3426 isa 1-phosphatidyl-1D-myo-inositol for PI (1-16:0, 2-18:1)

1035 isa IPC

3061 ADP transport, cytoplasm-cell envelope

1212 alpha-ketoglutarate/malate transporter

3272 phosphatidyl-L-serine (1-16:0, 2-16:1) transport, ER membrane-Golgi membrane

3230 phosphatidyl-L-serine (1-18:0, 2-18:1) transport, ER membrane-mitochondrial membrane

3191 phosphatidylcholine (1-16:1, 2-16:1) transport, ER membrane-lipid particle

3090 1-phosphatidyl-1D-myo-inositol 4-phosphate (1-18:1, 2-18:1) transport, cell envelope-cytoplasm

1182 5-nucleotidase (GMP)

63

3263 phosphatidate (1-18:1, 2-18:1) transport, ER membrane-Golgi membrane

Mutants are non viable

2952

Associated with various reactions of the type inositol phosphorylceramide synthase (YDR367W AND YKL004W)

AUR1 and KEI1

2957

2930

2942

2985

1908 CDP-diacylglycerol synthase (1-18:1, 2-18:1), ER membrane YBR029C CDS1

2007 phosphatidylglycerolphosphate synthase (1-18:0, 2-16:1), mitochondrial membrane YCL004W PGS1

Reactions associated to multiple genes in the model (TGL3, TGL4, M17 and M18 were selected for experimental validation)

151 aldehyde dehydrogenase (2-phenylethanol, NADP) YCR105W OR YMR318C

M17 and M18

2741

Associated with reactions of the type triacylglycerol lipase

YMR313C OR YKR089C OR YOR081C OR YBR204C

TGL3, TGL4, TGL5 and LDH1 2729

846 V-ATPase, Golgi

YBR127C VMA2

YDL185W VMA1

YEL027W VMA3

YEL051W VMA8

YGR020C VMA7

YHR026W VMA13

YHR039C-A VMA10

YKL080W VMA5

YLR447C VMA6

YMR054W STV1

YOR332W VMA4

YPL234C VMA11

YPR036W VMA13

916 glycine transport (extracellular <--> cytoplasm)

YKR039W OR YPL265W OR YOR348C OR YOL020W OR YCL025C

GAP1

919 guanine transport (extracellular <--> cytoplasm) YER056C FCY2

YER060W FCY21

64

YER060W-A FCY22

YGL186C TPN1

957 L-methionine transport (extracellular <--> cytoplasm)

YKR039W GAP1

YDR508C GNP1

YHL036W MUP3

YBR068C BAP2

YDR046C RAD57

YG5055W MUP1

YCL025C AGP1

Table 2: Single-mutants proposed by Optknock in the Yeast 7 model

Reaction index

Reation name ORFs associated in the model

Gene

Selected for experimental validation

- Ap4A hydrolase - M8

Table 3: Single-mutants proposed by OptGene in the Yeast 5 model

65

Reaction Reaction name

ORFs associated in the model

Gene

Selected to be experimentally validated

Homoserine dehydrogenase (NADH) M1

homoserine dehydrogenase NADP

acetyl-coA synthetase M2

GTP/GDP translocase M3

3-hydroxyacyl-coA dehydrogenase (3-oxooctadecanoyl-coA) M4

3-hydroxyacyl-coA dehydratase (3-hydroxydecanoyl-coA)

N-formyl-L-kynurenine amidohydrolase M5

aldehyde dehydrogenase (acetaldehyde, NADP) M6

Nucleoside triphosphatase M7

No gene associated in the mode

r_1803 GDP-alpha-D-mannose transport - -

r_1811 Glycine transport - -

r_1957 NAD transport - -

Mutants are non viable

r_0772 NADH kinase mitochondrial YEL041W or YJR049C

YEF1

UTR1

Necessary to add fatty acids to the culture medium

r_0433 fatty-acyl-coA synthase (C14)

YGR037C and YKL182W and YNR016C and YPL231W

ACB1

FAS1

FAs2

ACC1

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Annex III) Extracellular concentrations of itaconic acid and galactose in the batch

cultures of the mutants proposed by OptKnock and OptGene

0

0,05

0,1

0,15

0,2

0 2 4 6

Itac

on

ic a

cid

(m

M)

Fermentation time(d)

0

5

10

15

20

25

0 2 4 6

Gal

acto

se (

g/L)


Figure 21: Concentration of itaconic acid (A) and galactose (B) in the supernatant of fermentations undertaken by BY4741 or by the derived deletion mutants transformed with the pGal1-AtCad1 plasmid.

A B

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