joão pedro leonor fernandes saraivafrpinto/fr...joão pedro leonor fernandes saraiva reconstruction...
TRANSCRIPT
João Pedro Leonor Fernandes Saraiva
Reconstruction of a generic metabolic model for Streptococcus pneumoniae
Dissertação de Mestrado
Mestrado em Bioinformática
Trabalho realizado sob a orientação de
Doutora Isabel Rocha
Doutor Francisco Pinto
October 2012
É AUTORIZADA A REPRODUÇÃO INTEGRAL DESTA TESE APENAS PARA EFEITOS DE INVESTIGAÇÃO, MEDIANTE AUTORIZAÇÃO ESCRITA DO INTERESSADO, QUE A TAL SE COMPROMETE.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
iii
Acknowledgements
I would like to start by giving thanks to my supervisors Dr. Isabel Rocha and Dr.
Francisco Pinto for the opportunity to work in this project and for all of her assistance and
knowledge when needed.
I would also like to thank Oscar Dias for all of his input, patience and
companionship throughout the course of this study.
To all the people at IBB (Institute for Biotechnology and Bioengineering) namely
but not exclusively, Daniela, Carla, André, Daniel, José Pedro, Paulo Vilaça and Vitor
Costa, I extend my appreciation for all the help and input when trying to solve and
overcome some difficulties during this study.
I would also like to thank my family for all of their support and unconditional love
and understanding.
Last but not least I would like to thank my girlfriend for all of her love, friendship,
support, understanding and patience throughout the countless hours that kept me away
from her.
The present work was achieved under the project PTDC/BIA-MIC/099551/2008 –
Computational search of cellular network motifs associated to Streptococcus pneumoniae
virulence, financed by FEDER funds through the Competitive Factors Operational
Program – COMPETE and by National funds through FCT – Foundation for Science and
Technology.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
iv
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
v
Abstract
Reconstruction of a generic metabolic model for Streptococcus pneumoniae
Streptococcus pneumonia is a gram-positive bacteria with lancet-shaped cells that
survive and thrive in almost any environment. It is usually found in pairs or short chains
of diplococcic and in the decade of 1880 was described as one of the major causes of
several infections such as pneumonia, meningitis, otitis media and endocarditis.
Since antibiotic resistance of S.pneumoniae is pointed out as a challenge in the
treatment of these infections, focus has been greater on disease control, although several
other studies target the discovery of pneumococcal polysaccharide antigens as vaccines.
Methods, such as reconstruction of genome-scale metabolic networks, are essential for
determination of the bacteria´s invasive capability in humans by analysis of their
metabolism.
Evolution of new techniques for data collection and technology platforms allow
researchers to study the cell´s organization and functionality as a whole, as well as the
interactions within the cell. Analysis of genome similarity along with metabolic functions
across strains is crucial in determining if virulence factors and increased invasiveness is
dependent on specific genomic regions or if these are determined by different
environmental conditions.
In this study we aim to reconstruct a generic metabolic model capable of
simulating in vitro experiments of Streptococcus pneumoniae. For such, we rely on draft
metabolic models obtained from SEED, with curation and validation performed on
OptFlux.
As a result, a generic metabolic model for S.pneumoniae was obtained that could
simulate growth, by-product formation and determine amino acid essentiality, serving as
a basis for generating other strain-specific metabolic models. No direct relationship
between genome-metabolic function was ascertained and, therefore, further investigation
is required.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
vi
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
vii
Resumo
Reconstrução de um modelo metabólico genérico para Streptococcus pneumoniae
Streptococcus pneumonia é uma bacteria gram positive com células em forma de
lança com capacidade para sobreviver e crescer em quase qualquer meio ambiente.
Normalmente é encontrado em pares ou cadeias curtas de de diplococcus sendo, na
década de 1880, descrito como uma das maiores causas de numerosas infecções tais como
a pneumonia, meningite, otitis media e endocartite.
O aumento de resistência antimicrobiana de S.pneumoniae é apontado como um
desafio no tratamento destas infecções devido ao aumento de incidência de doenças
causadas por estirpes de pneumococcos invasivas em crianças bem como nos tratamentos
com antibióticos sem sucesso [1].
A maioria dos estudos com S.pneumoniae até à data têm-se prendido com o
control de doenças pneumocócicas embora outros estudos têm como objectivo a
descoberta de antigénios polissacáridos que possam actuar como vacinas [2]. Para
alcançar este ultimo, métodos tais como a reconstrução ao nível do genoma e redes
metabólicas tornam-se essenciais na determinação da capacidade invasiva da bactéria nos
seres humanos.
Actualmente, a evolução de novas técnicas de recolha de dados e plataformas
tecnológicas, permitem aos investigadores o estudo da organização cellular e
funcionalidade como um todo, bem como as interacções intracelulares.
SEED é uma base de dados actualizada de dados microbianos com ênfase em
genes com capacidade para, de forma autómata, reconstruir um modelo através do RAST
(Rapid Annotation Subsystem Technology).
Neste estudo pretende-se reconstruir um modelo metabólico generic para a
Streptococcus pneumonia com capacidade para similar in vitro. Para tal foram utilizados
modelos metabólicos obtidos através do SEED de três estripes de S.pneumoniae,
nomeadamente a G54, TIGR4 e R6.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
viii
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
ix
Table of Contents
Acknowledgements ............................................................................................................ iii
Abstract ................................................................................................................................ v
Resumo .............................................................................................................................. vii
List of Abbreviations ......................................................................................................... xii
List of Figures .................................................................................................................... xv
List of Tables .................................................................................................................... xvi
1. Introduction .................................................................................................................. 2
1.1 - Streptococcus pneumoniae.................................................................................... 2
1.1.1 - Strains ................................................................................................................ 3
1.1.2 - Identification ..................................................................................................... 4
1.1.3 - Virulence Factors .............................................................................................. 4
1.1.4 - Infection and vaccination .................................................................................. 5
1.2 - Systems Biology ................................................................................................... 7
1.3 - Metabolic Networks .............................................................................................. 8
1.4 - Systems Biology Markup Language (SBML) .................................................... 10
1.5 - Reconstruction Process ....................................................................................... 11
2 - Objectives of this Dissertation ...................................................................................... 15
3 - Methods, Databases and Software ................................................................................ 16
3.1 - Medium composition .......................................................................................... 16
3.2 - SEED................................................................................................................... 17
3.3. Optflux ................................................................................................................. 18
3.4 - Flux Calculations ................................................................................................ 19
4 - Results and Discussion ................................................................................................. 22
4.1 - Statistics .............................................................................................................. 22
4.2 - Genome comparison ........................................................................................... 29
4.3 - Individual Models ............................................................................................... 29
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
x
4.4 - Generic Model .................................................................................................... 30
4.4.1 - Biomass Reaction ............................................................................................ 31
4.4.2 - Main Pathway Analysis ................................................................................... 34
4.4.2.1 - Carbohydrates Metabolism ....................................................................... 34
4.4.2.1.1 - Glycolysis .......................................................................................... 34
4.4.2.1.3 - Pentose Phosphate Pathway (PPP)..................................................... 34
4.4.2.1.4 - Fructose and Mannose ....................................................................... 35
4.4.2.1.5 - Galactose ............................................................................................ 35
4.4.2.1.6 - Starch and sucrose ............................................................................. 35
4.4.2.1.7 - Pyruvate ............................................................................................. 36
4.4.2.1.8 - Amino sugar and nucleotide sugar ..................................................... 36
4.4.2.2 - Energy Metabolism ................................................................................... 37
4.4.2.2.1 - Nitrogen ............................................................................................. 37
4.4.2.3 - Lipid Metabolism ..................................................................................... 38
4.4.2.3.1 - Fatty acid Biosynthesis ...................................................................... 38
4.4.2.4 - Nucleotide Metabolism............................................................................. 39
4.4.2.4.1 - Purine and Pyrimidine........................................................................ 39
4.4.2.5 - Amino acid Metabolism ........................................................................... 40
4.4.2.5.1 - Alanine, Aspartate and Glutamate ..................................................... 42
4.4.2.5.2 - Glycine, Serine and Threonine .......................................................... 43
4.4.2.5.3 - Cysteine and Methionine ................................................................... 43
4.4.2.5.4 - Valine, Leucine and Isoleucine biosynthesis ..................................... 44
4.4.2.5.5 - Phenylalanine, Tyrosine and Tryptophan biosynthesis ..................... 44
4.4.2.5.6 - Lysine biosynthesis ............................................................................ 45
4.4.2.6 - Metabolism of Cofactors and Vitamins .................................................... 46
4.4.2.6.2 - Riboflavin .......................................................................................... 46
4.4.2.6.3 - Vitamin B6 ......................................................................................... 47
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
xi
4.4.2.7 - Other metabolisms .................................................................................... 47
4.5 - Simulation results with Hoeprich´s medium....................................................... 47
5 - Conclusions .................................................................................................................. 49
6 - Future Work .................................................................................................................. 52
7 - References .................................................................................................................... 53
Annexes.............................................................................................................................. 60
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
xii
List of Abbreviations
Acessory Regions – AR
Acid Hydrolyzed Casein - AHC
Acyl-Carrier-Protein – ACP
Adenosine – 5’.triphosphate – ATP
ATP Binding Cassette – ABC
ATP-binding cassette – ABC
Autolysin – LytA
Coenzyme A – CoA
Cytosine – C
Deoxyadenosine triphosphate – dATP
Deoxyguanosine triphosphate – dGTP
Deoxyribonucleic acid – DNA
Diaminopimelate – DAP
Dipotassium phosphate – K2HPO4
EFM – Elementary Flux Modes
Enzyme Comission – E.C
Escherichia coli – E.coli
Evolutionary Algorithm – EA
eXtensible Markup Language – XML
Ferrous sulfate – FeSO4
Flavin adenine dinucleotide – FAD
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
xiii
Flux Balance Analysis – FBA
Fructose-6-phosphate – F6P
Guanine – G
Guanosine triphosphate – GTP
Hydrocloric acid - HCl
Kyoto Encyclopedia of Genes and Genomes – KEGG
Magnesium sulfate – MgSO4
Manganese sulfate – MnSO4
Metabolic Engineering – ME
Metabolic Flux Analysis – MFA
Nicotinamide adenine dinucleotide – NADH
Pentose Phosphate Pathway – PPP
Phosphoenolpyruvate – PEP
Phosphostransferase system – PTS
Pneumolysin – Ply
Rapid Annotation Subsystem Technology - RAST
Ribonucleic acid – RNA
Ribose 5-phosphate – R5P
Serine hydroxymethyltransferase – glyA
Simulated Annealing – SA
Sodium bicarbonate – NaHCO3
Sortase A – SrtA
Streptococcus pneumoniae – S.pneumoniae
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
xiv
Systems Biology – SB
Systems Biology Markup Language – SBML
Tricarboxylic acid – TCA
Zinc sulfate – ZnSO4
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
xv
List of Figures
Figure 1 - Streptococcus pneumoniae (A), Group A streptococcus (B), Group B streptococcus (C)
(obtained from Centre for Disease Control – CDC) ......................................................................... 2
Figure 2 – Stoichiometric model. Metabolic network representation composed of metabolites (A,
B, C, D) and fluxes (internal - vi and exchange bi)(2A). Mass balance equations for all reactions
for each species (2B) written in matrix form and considering homeostasis (2C)............................. 9
Figure 3 – Stages of the Reconstruction process ............................................................................ 13
Figure 4 – Functional modules of the OptFlux application, obtained from [33] ............................ 19
Figure 5– Specific growth rate. ...................................................................................................... 20
Figure 6 – Venn diagram for S.pneumoniae strains used in the reconstruction process. ............... 24
Field Code Changed
Field Code Changed
Field Code Changed
Field Code Changed
Field Code Changed
Field Code Changed
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
xvi
List of Tables
Table 1 – Virulence factors and functionality .................................................................................. 5
Table 2 – Serotypes present in Polysaccharide Conjugate Vaccine (PCV) and Pneumococcal
Polysaccharide Vaccine (PPV) ........................................................................................................ 6
Table 3 – Data sources used for metabolic reconstructions. .......................................................... 14
Table 4– Composition of Hoeprich´s medium ............................................................................... 16
Table 5 – Biomass and glucose concentrations during exponential phase of a batch fermentation
with Hoeprich´s medium and Streptococcus pneumoniae serotype 23F strain St 99/95. .............. 20
Table 6 – Reactions initially identified as G54 strain-specific and corresponding genes in other
strains. ............................................................................................................................................ 23
Table 7 – Number of reactions present in the models of strains G54, TIGR4 and R6. ................. 24
Table 8 - List of strain-specific reactions and reactions specific to pairs of strains. ..................... 25
Table 9 – Genome homology comparison (using 90 % threshold). ............................................... 29
Table 10– Data collected from metabolic reconstruction model of strain G54. ............................ 30
Table 11– Data collected from metabolic reconstruction model of strain TIGR4. ........................ 30
Table 12– Data collected from metabolic reconstruction model of strain R6. .............................. 30
Table 13– Data collected from metabolic reconstruction of the generic model. ........................... 30
Table 14 – Biomass composition and compound coefficients ....................................................... 31
Table 15 – Relations Gene/Enzyme in Fatty acid Biosynthesis .................................................... 38
Table 16 – List of models consulted for ATP maintenance limit values ....................................... 40
Table 17 – Essential and non-essential amino acids for in vitro growth of Streptococcus
pneumoniae D39 (serotype 2; NCTC7466) compared to our generic model. ((0) – No effect on
growth; (+) - Essential for growth; (-) - Limiting in case of absence from the medium) .............. 41
Table 18 – List of aminopeptidases ............................................................................................... 50
Table 19 – List of all reactions added/changed in generic model .................................................. 51
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
1
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
2
1. Introduction
1.1 - Streptococcus pneumoniae
Streptococcus pneumonia is an aerotolerant anaerobic gram-positive bacteria
with lancet-shaped cells that survive and thrive in almost any environment (Figure 1A).
It is usually found in pairs or short chains of diplococcic and in the decade of 1880 was
described as one of the major causes of several infections such as pneumonia,
meningitis, otitis media and endocarditis. Streptococci group division can be based on
cell wall composition. The most common and pathogenic groups of streptococcus are A
(Figure 1B) and B (Figure 1C). Diseases from the first group vary from sore throat to
necrotizing fasciitis, while streptococci of Group B cause life-threatening diseases in
young, elderly and adults with a compromised immunity system, such as pneumonia.
Figure 1 - Streptococcus pneumoniae (A), Group A streptococcus (B), Group B
streptococcus (C) (obtained from Centre for Disease Control – CDC)
According to Dopazo and co-workers [3], in 1998, an estimated 3.5 million
deaths worldwide were attributed to S.pneumoniae infections. S. pneumoniae´s main
hosts are humans, and they are mostly found in the nasopharynx [4][5]. However,
common drug resistance has been reported [6][7][8][9]. Sá-Leão [4]states that, because
this bacterium is part of the human nasopharyngeal flora, “any intervention to combat
pneumococcal disease, such as the introduction of antibiotics or vaccines” also has an
B
A
C
B
A
A
B
A
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
3
impact on selecting drug-resistant lineages and strains, thus making humans
“evolutionary partners”. Evolution may occur through re-modulation of penicillin-
sensitive enzymes, acquisition of mobile elements with anti-microbial resistance or
capsular switches to evade action of vaccines that target the capsule [4], or prevention
from complement pathway activity [10].
1.1.1 - Strains
Strain selection for comparative analysis is usually performed to cover the
largest spectrum of representatives for virulent and avirulent serotypes. Genome
sequences for all three strains used in this study are available at NCBI (National Centre
for Biotechnology Information).
Streptococcus pneumoniae strain G54 belongs to serotype 19F and is known to
be more prevalent at younger and older stages in life, being associated to increased
levels of antibiotic resistance and insusceptibility. Its genome is composed of a single
circular chromosome with 2,078,953 base pairs.
S.pneumoniae TIGR4 strain belongs to serotype 4 and has been classified as
highly invasive and virulent in rat models [11]. Its genome is composed of a single
circular chromosome with 2,160,837 base pairs and is the largest of the strains in this
study.
S.pneumoniae R6 strain was originated from a clinical isolate of serotype 2
strain D39 and does not possess a polysaccharide capsule, rendering it avirulent and
safe to work with. Therefore, it is used as a standard strain for laboratory experiments.
All three strains possess a G+C content of 39.7 %, factor linked to DNA stability
[12] where high levels of such confer higher stability and vice versa, which is
understandable due to the fact that all belong to the same species.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
4
1.1.2 - Identification
Identification of S.pneumoniae can be achieved through alfa hemolysis
surrounding colonies obtained on blood agar, negative reaction with catalase,
susceptibility to optodium, and solubility of the bacteria in bile salts. However,
serological typing (serotyping) remains one of the most used methods for
characterization of S.pneumoniae isolates. Serotyping is based on the structure of the
bacteria´s exopolyssacharide capsule [5].
Until 2006, only two strains of S.pneumoniae had had their genome sequence
completely determined: virulent serotype 4 strain TIGR4, and the avirulent,
unencapsulated, laboratory strain R6. These revealed several insights into the
metabolism and organization of S.pneumoniae.
New methodologies have contributed to the discovery of novel strains. An
example of such is that in 2006, 85 serotypes of S.pneumoniae were known, increasing
to 91 in just 3 years (2009) [5][10]. Because S.pneumoniae polysaccharide capsule
provides resistance against phagocytosis, bile was used to dissolve it, leading to the
identification of the first bacterial autolytic enzyme.
1.1.3 - Virulence Factors
The increase of antimicrobial resistance of S.pneumoniae is pointed out as a
challenge in the treatment of these infections and therefore, it is essential to understand
which factors contribute to such [1].
The most common virulence factors of S.pneumoniae are shown in Table 1. The
polyssacharide capsule, the major autolysin (LytA), the intracellular toxin pneumolysin
(Ply) and sortase A (SrtA), which play both defensive as well as aggressive roles in
different stages of a pneumococcal infection [4][13], are some examples of virulence
factors associated to S.pneumoniae. Other interesting virulence factors are the
“accessory regions” (AR) within the genome of S.pneumoniae [14] and pili, essential
for adhesion of the bacterium to epithelial cells of the upper respiratory tract [15].
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
5
Accessory regions, also defined as genomic islands [16], may have an important role in
virulence since, through horizontal gene transfer (the ability to transfer genes among
other strains and species), either through natural genetic transformation, transduction or
conjugation mechanisms, can confer a species with, for instance, drug resistance
attributes. An example of such is β-lactam resistance[17][18]. Harvey and colleagues
[14] state that “comparisons across serotypes risk underestimating the impact of the
serotype itself on virulence, due to serotype-specific structural differences in the capsule
that can affect complement deposition and resistance against phagocytosis”. This means
that, although comparison between different serotypes is useful in determining
invasiveness potential, one must not discard the differences within the same serotype
since virulent and non-virulent strains can belong to the same group[19].
Table 1 – Virulence factors and functionality
Virulence factor Function
Polysaccharide capsule Prevents phagocytosis by host immune
cells
Surface proteins Prevent activation of complement
Pili Adhesion to epithelial cells in upper
respiratory tract
Accessory regions Diverse function depending on which
genes are horizontally transferred (i.e.
drug resistance or fitness)
.
1.1.4 - Infection and vaccination
Streptococcus pneumoniae infections vary from pneumonia (lungs), otitis media
(middle ear cavity) to pneumococcal meningitis, mainly affecting 2 month to 5 year old
children, people with over 65 years old and individuals with compromised immune
systems. Transmission usually occurs via aerosol or by direct and prolonged contact to
respiratory secretions of infected people. Approximately 66% of infections in adults and
80% invasive infections in children are attributed to 8-10 capsular serotypes [20].
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
6
Common treatment for pneumococcal infections relies on antibiotics (penicillin,
cephalosporin or erythromycin) [16][1] but the increase in antibiotic-resistant
pneumococcus has diminished their effectiveness against these bacteria.
In order to overcome this barrier, new methods for immunization have been studied
based on polysaccharide composition. Currently, two types of vaccines are being used
that diminish these infections [21][22][23].
Polysaccharide Conjugate Vaccine (PCV) contains polysaccharide from seven
(7) polysaccharides (Table 2) conjugated with proteins and are applied to young
individuals (up to 5 years old) and risk-groups (individuals with compromised immune
systems).
Pneumococcal Polysaccharide Vaccine (PPV) contains purified capsular
polysaccharide from 23 serotypes (Table 2) and administered to individuals over 65
years old.
Table 2 – Serotypes present in Polysaccharide Conjugate Vaccine (PCV) and
Pneumococcal Polysaccharide Vaccine (PPV)
Vaccine Serotypes
PCV 4,6B,9V,14,18C,19F,23F
PPV 1,2,3,4,5,6B,7F,8,9N,9V,10A,11A,12F,14,15B,17F,18C,19F,19A,20,22F,23
F,33F
The main focus of studies regarding S.pneumoniae to date has been the control
of pneumococcal disease, although several other studies target the discovery of
pneumococcal polysaccharide antigens as vaccines [4]. To perform both, methods such
as reconstruction of genome-scale metabolic networks are essential for determination of
the bacteria´s invasive and virulent capability in humans.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
7
1.2 - Systems Biology
Systems biology (SB) studies the interaction between components of biological
systems and how these interactions influence the behaviour and functions of those
systems, such as enzymes and metabolites in a metabolic network or pathway.
This field of study is able to obtain, integrate and analyse complex data sets from
several experimental origins allowing further study of phenomics, genomics,
transcriptomics, metabolomics or fluxomics. For instance, genome sequences and
protein properties have been identified through molecular biology; however, alone, this
is insufficient for the interpretation of a biological system [24]. The intrinsic complexity
of these systems makes it difficult to determine their functionality, and therefore, the
combination of various approaches, amongst them experimental and computational, are
expected to assist in resolving such issues [24].
Computational biology has two distinct, yet intertwined objects of study: data
mining, which extracts hidden patterns from a large amount of experimental data; and
simulation-based analysis, which tests hypothesis with in silico experiments.
From the first approach, a hypothesis is formed whilst in the second approach
this hypothesis is tested in order to predict certain outcomes for in vitro and in vivo
experiments [24].
An increase in high throughput quantitative data from experimental molecular biology
has favoured advances in simulation-based analysis [24]. Meanwhile, the rapid
evolution of computational tools and software enable the construction and analysis of
more reliable biological models. Despite this progress, the need for researchers to
exchange information remains essential. In order to facilitate collaboration between
researchers, a platform for modelling and analysis of biological systems – Systems
Biology Markup Language (SBML) – was developed, increasing, consequently, the
value of new databases that focus on biological pathways, i.e., KEGG[25].
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
8
1.3 - Metabolic Networks
The most common trades of a biological network are defined by complex
interactions between its various components. Currently, evolution of new techniques for
data collection and technology platforms allow researchers to study the cell´s
organization and functionality as a whole, as well as the interactions within the cell.
Genome annotation along with experimentally determined physiological and
biochemical information of organisms allows the reconstruction of a metabolic network
[26]. The reconstruction of metabolic networks is an important tool in the study of
biological networks; yet, its process is very laborious and time consuming. When all
steps are performed accordingly, predictions on maximal cell growth or production of a
desired metabolite can be achieved using in silico models.
Three distinct groups of models are used in metabolic engineering:
stoichiometric, kinetic and regulatory. Both kinetic and regulatory models are difficult
to obtain due to the lack of information and comprehension of kinetic and complex
cellular regulation processes, respectively, [27] and, therefore, stoichiometric models
are the most commonly used (Figure 2).
In stoichiometric models, biochemical reactions of the network are represented
as stoichiometric equations [27]. The resulting stoichiometric matrix (S) (Figure 2C)
represents the biological network (Figure 2A) in mathematical terms, where the mass
balances for each intercellular metabolite is represented by a set of differential equations
(Figure 2B). The metabolites (m) are placed in the rows of the matrix (in concentration
form) whilst the reactions (n) are placed in the columns (as reaction rate) leading to the
mathematical representation in equation 1.
B3
B1 A B
C D
B2 v1
V3 V2 V5
V4
A
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
9
Figure 2 – Stoichiometric model. Metabolic network representation composed of
metabolites (A, B, C, D) and fluxes (internal - vi and exchange bi)(2A). Mass balance
equations for all reactions for each species (2B) written in matrix form and considering
homeostasis (2C).
Eq. 1
As can be inferred from equation 1, data on stoichiometry (S), biomass
formation (µ) and intracellular reaction fluxes (v) is required. Since the latter is yet
difficult to determine, a steady-state for internal metabolites is considered leading to a
general equation (Eq. 2). This, however, originates an underdetermined steady-state
solution, since the number of reactions is greater than the number of metabolites present
in a metabolic network [27][28][29]. In order to overcome this issue and reach a steady-
state flux distribution, additional constraints, obtained by flux measurements in the
network, are required.
Eq. 2
Stoichiometric models are used by several optimization methods such as
Metabolic Flux Analysis (MFA), which allows the computation of fluxes in space given
a set of measured fluxes, and Flux Balance Analysis (FBA), which simulates phenotype
behaviour under certain environmental conditions [30][31].
Flux balance analysis (FBA) is used to, besides genome-scale network analysis,
analyse how perturbations to the network affect reaction flux distributions, such as gene
deletions, medium composition or in silico drug effectiveness. Stoichiometric matrixes,
associated to an objective function (i.e. ATP production or biomass formation),
𝑑𝐴
𝑑𝑡 𝑣1 + 𝑣3 + 𝑏1
𝑑𝐵
𝑑𝑡 𝑣1 + 𝑣2 𝑏2
𝑑𝐶
𝑑𝑡 𝑣3 𝑣4 + 𝑣5
𝑑𝐷
𝑑𝑡 𝑣2 + 𝑣4 𝑣5 𝑏3
1 1 1 1 1 1 1 1 1 1 1 1 1
𝑣1𝑣2𝑣3𝑣4𝑣5𝑏1𝑏2𝑏3
S
V
B C
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
10
compose the necessary steps to identify optimal reaction flux distributions. [28][32].
This method does not predict exact behaviour of a system, rather uses known
constraints to a function, separating which reactions allow fluxes and which do not. The
decrease in the number of possible solutions/reactions to which an objective function is
achievable improves the study of genotype-phenotype relationships.
Despite all the advances in technology, the reconstruction process is an iterative
one. Limitations derived from organism-specific features prevent the use of
automatically generated networks, requiring manual evaluation [31][10], although
numerous software tools and packages exist to assist in the reconstruction process. In
the present study, we focus on genome and biochemical databases shown in Table 1.
The availability of information on genetics, biochemistry or physiology of our
organism increases the quality and predictive capability of our model [34][31][33].
1.4 - Systems Biology Markup Language (SBML)
In 2000 the SBML, a XML-based language, was developed for representing and
exchanging models between various tools and software [24]. XML´s portability and
overall acceptance in the bioinformatics community were the main reasons for its
selection. Since its development, several levels and versions of SBML have been
introduced in which new features are included that are required by the bioinformatics
community.
Cooperation among researchers is essential for the evaluation and development
of system’s models; therefore, it is necessary that the information is standardized.
SBML aims to establish a common language for the description of biological models,
be it in the fields of computational biology, gene regulation, or others. Although,
initially it was developed for representing dynamic models, today it is also used as a
framework for stoichiometric models [33].
The use of SBML in software packages resolves issues regarding inoperability,
providing a common format for publications and databases [24].
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
11
1.5 - Reconstruction Process
The reconstruction process can be achieved through various methods, although
there are several steps that prove common to all. Here, we focus on the methodology
defined by Thiele and Palsson [33]. This protocol consists, in simple terms, of five
phases comprised of numerous steps within each phase[31] (Figure 3).
The first phase consists on generating a genome-based draft network, which
basically aims to gather and obtain the necessary information for the reconstruction
process: genome annotation, metabolic reactions, and experimental data. This can be
achieved either manually by extracting information from experimental assays and
literature or automatically by genome-based reconstruction tools (see 3.2 – SEED). It
would be optimal to gather information on organism-specific databases, which would
increase the feasibility and quality of the metabolic network; however, generic
databases can be used when there is a lack of organism-specific information
[31][33][34]. The annotated genome provides information on enzyme presence that
leads to the next step in this phase, which is determining the reactions they catalyse.
Presently, there exists a variety of metabolic databases that possess the capability for
inferring information regarding reactions based on enzyme commission number (E.C)
such as KEGG, and their biochemistry, such as BRENDA. RAST from SEED performs
this first phase in an automated manner.
Phase two is defined as a refinement stage. Information regarding substrate and
cofactor usage, determination of charged formula, calculation of stoichiometry,
verification of gene-reaction associations, amongst others, is taken into account. In this
phase, the first goal is to determine if the reaction(s) present, obtained from the draft
reconstruction process, do/does, in fact, occur in our species and whether it is correctly
connected to the rest of the network. Another issue to be addressed in this stage is to
determine the gene-protein-reaction associations, important to gene essentiality
predictions, since removal of one or more genes may affect numerous reactions or if
there are any enzymes that catalyse the same reaction (isoenzymes). Enzyme
identification inferred from homology, therefore not specific to our organism, must be
weighed to prevent erroneous phenotypic behaviours [34]. Another goal at this stage is
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
12
to determine the biomass formation requirements. Literature and experimental assays
usually comprise the information sources for growth requirements [13][33][35].
Phase three consists in the transformation of the obtained model into a
compatible format. Conversion into a mathematical model in order to define objective
functions and simulation constraints are the main steps in this phase. The maximization
of biomass, for instance, is a standard objective function, since it covers growth
prediction. Considering p biomass components, the biomass formation reaction can be
expressed as illustrated in equation 3.
∑ Eq. 3
where the Ck values are determined from the biomass composition for each
metabolite or macromolecule Xk [33]. Constraints are applied to the model in order to
simulate as accurately as possible experimental assays. Boundaries and medium
components and quantities are examples of constraints used to enhance the predictive
quality of the network model.
In phase four, the evaluation of the network model, comprised of several steps,
is performed to determine its predictive capability. The capability of the model to
synthesize biomass precursors, form by-products known to the organism, identify
missing reactions (gaps), detection and correction of dead-end metabolites, model
comparison to known organism incapability (identification of false positives) and a
quantitative evaluation (expected growth rate under the same experimental conditions)
are issues to be addressed and corrected if need be.
The fifth phase was not addressed in the article; however, the authors mention
that it consisted of how the model would be used in a prospective manner.
Being an iterative process, all steps and phases are re-definable in order to refine the
information and predictive capability of the model if such should prove necessary.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
13
Figure 3 – Stages of the Reconstruction process
Table 3 lists some of the databases used to aid in the reconstruction process.
Phase 1
•Draft reconstruction
•Genome annotation
• Identification of metabolic reactions
•Collection of experimental data
Phase 2
•Reconstruction refinement
•Substrate and cofactor usage
•Biomass equation
•Stoichiometry
•Gene-Protein-Reaction associations
Phase 3
•Conversion into computable format (SBML)
•Conversion to mathematical model
•Definition of objective function and constraints
Phase 4
•Model evaluation
• In silico simulations
•Production of biomass precursors
•By-product formation
• Identification of gap filling reactions
•Quantitative evaluation
Phase 5
•Prospective use
Reconstruction
Process
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
14
Table 3 – Data sources used for metabolic reconstructions.
Name
Description Source Reference Link
SEED Database Comparative genomics
tool
Curated and non-curated
data
[36] www.theseed.org/wiki/Home_of_the_SEED
KEGG (Kyoto
Encyclopedia of Genes
and Genomes)
Database with Genomic
and metabolic
information.
Curated and non-curated
data cross-referenced to
other databases
[25] http://www.genome.jp/kegg/
BRENDA – enzyme
database
Enzyme information Manually extracted
from literature
[37] http://www.brenda-enzymes.info/
UNIPROT (Universal
Protein Resource)
Protein sequence and
functional information
Curated and non-curated
data, cross-referenced to
other databases
[38] http://www.uniprot.org
NCBI (National Center
for Biotechnology
Information)
Information of Sequence
data of both microbial
and higher organisms.
Curated and non-curated
data retrieved from
GenBank and literature.
[39] http://www.ncbi.nlm.nih.gov/
CMR (Comprehensive
Microbial Resource)
Display, search and
analysis of sequence and
annotation for complete
archaeal and bacterial
genomes
Curated and non-curated
data retrieved from
Genbank and JCVI
[40] http://cmr.jcvi.org
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
15
2 - Objectives of this Dissertation
In this project our main goal is to reconstruct a generic metabolic network for
S.pneumoniae using metabolic models of strains R6, TIGR4 and G54. The
reconstruction of a generic metabolic network will hopefully provide insights into
metabolic genes that can either be present in all three strains and, therefore, considered
transversal to any given pneumococcus strain, or present itself unique to a given strain
or set of strains, allowing the grouping of strains in accordance to metabolic gene
function. Another objective of this study is to determine if genome similarity has any
correspondence to metabolic function. This type of analysis might present itself useful
for determining if traits, such as virulence, are correlated with specific genes or if they
are present in all strains and only expressed under certain conditions.
This analysis will be performed based on the reactions of each strain using Rapid
Annotation Server Technology (RAST) for model draft reconstruction and Optlux, a
modeling tool for metabolic reconstruction, for growth simulations and constraint
definition.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
16
3 - Methods, Databases and Software
3.1 - Medium composition
Media selection is essential to simulate experimental assays. Among the several
articles that described media composition [41][42][43][9][44][45][46][47][48],
Hoeprich’s medium from Gonçalves´s study was selected due to the fact that most of
the compounds could be accounted for in the model.
Table 4– Composition of Hoeprich´s medium
Hoeprich 1955(as follows per liter)
AHC 20g
Glucose 12,5g
K2HPO4 5g
NaHCO3 1g
L-Cystine 150mg
Tryptophan 20mg
Tyrosine 200mg
L-Glutamine 625mg
L-Asparagine 100mg
Choline 10mg
MgSO4 500mg
FeSO4 5mg
ZnSO4 0,8mg
MnSO4 0,36mg
Thioglycolic acid 1ml (10%)
HCl 0,02ml
Biotin 0,0015mg
Nicotinate 100mg
Pyridoxal 100mg
Calcium pantothenate 500mg
Thiamine 100mg
Riboflavin 100mg
Adenine 100mg
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
17
3.2 - SEED
Increase in data obtained from sequence technology demands innovative new
tools capable of high-throughput of metabolic network models at a genome scale.
As seen in Table 1, various tools have been developed to aid in the
reconstruction of metabolic networks. SEED [35] is an up to date database of all
microbial gene data [35]. Comparative analysis of these data across multiple organisms
in a rich genomic, biochemical and phylogenetic contexts provided by the collection of
annotated subsystems greatly facilitates their interpretation and practical applications,
such as the understanding of cellular networks, gene and pathway discovery,
identification of novel drug targets, and strain engineering. SEED aims to automate the
reconstruction process up to a certain extent, since manual curation is always required.
The draft model reconstruction is obtained through this web-based platform´s
reconstruction pipeline. Integration of genome annotation, Gene-Protein-Reaction
(GPR) associations, biomass reaction, analysis of reaction reversibility and model
optimization are the basis of this process.
Genome sequences are imported into SEED via the RAST server for annotation.
These are used to generate models consisting of the network reactions along with the
GPR associations and an organism-specific biomass reaction. All reactions associated to
a specific enzyme are included in the model, as well as spontaneous non-enzymatic
reactions. GPR associations are genome-based, coupled with the functional roles
assigned to genes during annotation.
The autocompletion step is required to ensure model predictive functionality,
since the draft reconstruction model usually contains gaps that prevent production of
metabolites essential to biomass formation. These gap-filling reactions are selected from
a comprehensive database that integrates biochemistry data from KEGG and 13
published genome-scale metabolic models [36]. This step is important due to the fact
that many pathogens do not possess complete pathways for some metabolites such as
thiamine or lipoteichoic acid, obtaining them from the interaction with the host´s cells.
Uracil 1g
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
18
At the end of the autocompletion step, the model is considered to be functional,
since it can simulate growth. There are several other steps that SEED is capable of
performing in order to optimize the draft model, such as Biolog consistency analysis
and gene essentiality which, in the first case, adds or removes reactions from the model
to fit Biolog phenotyping array data, and in the second case, determines essential genes
to the model. These steps, however, will be performed by Optflux in this work (see
chapter 3.3 Optflux).
This platform will be used for the draft reconstruction of each individual model
on which comparative reaction analysis is to be performed.
3.3. Optflux
As stated in chapter 3.2, Optflux will be used to perform all simulations, gene
essentiality determination and reaction analysis to fit culture conditions.
OptFlux is an open source computational tool for metabolic engineering (ME)
applications developed in the University of Minho [30]. It was developed on top of
AIBench due to its design and architectural principles and uses Java as its core
language. Other characteristics that increase this software´s potential are its user-
friendly interface, modular structure, which allow for addition of new plug-ins, and its
compatibility with standards and other software such as SBML and CellDesigner.
Several tools and operations are at the user’s disposal such as:
- Phenotype simulation, with the use of FBA;
- Calculation of metabolic fluxes, with MFA;
- Elementary Flux Modes, used for pathway analysis (EFM);
- Strain optimization algorithms, such as Simulated Annealing – SA - and
Evolutionary Algorithms – EA;
- And model visualization tools, used for results interpretation.
Figure 1 shows the four main areas of action of this powerful tool.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
19
Flux balance analysis, based on a steady-state approach, for phenotype
simulation uses Linear Programming (LP) to calculate all fluxes over the reactions
enabling wild-type and mutant strain simulation. Usually, but not exclusively, biomass
formation (i.e. growth rate) is the objective function to be maximized [30][49].
For improvement purposes, experimental data can be used to introduce constraints to
the original model in the form of fluxes. Since the model is composed of fluxes
(mmol/gDW.h) and experimental data usually is provided in the form of concentrations
(g/L), conversion of these values must be performed before any simulation with
constraints is run.
Figure 4 – Functional modules of the OptFlux application, obtained from [36]
3.4 - Flux Calculations
In order to simulate growth rate, experimental data, obtained via literature,
required calculations, since the results report concentrations (in g/L and mg/L) and not
fluxes, as required by Optflux (mmol/gDW.h).
These calculations were only carried out for metabolites that were present in the media
solution (see Table 4 in chapter 3.1).
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
20
Experimental assay data from Gonçalves and colleagues [41] described specific growth
rates and glucose consumption yield. By analysis of Fig1A from the same study, the
glucose consumption rate and biomass yield from glucose were retrieved. Table 5
shows the values over time.
Table 5 – Biomass and glucose concentrations during exponential phase of a batch
fermentation with Hoeprich´s medium and Streptococcus pneumoniae serotype 23F
strain St 99/95.
Calculation of specific growth rate (µ) was obtained via linearization of biomass values
during the exponential phase. Figure 4 illustrates the specific growth rate value (0.46h-1
- regression coefficient).
Figure 5 – Specific growth rate.
y = 0,4666x - 1,3852 R² = 0,9795
-2
-1,5
-1
-0,5
0
0,5
1
1,5
0 1 2 3 4 5 6
Bio
mas
s (L
N)
Time (h)
Specific Growth rate
Time Biomass (g/L) Glucose (g/L)
1 0,25 25
2 0,6 23
3 0,8 22
4 1,3 16
5 1,6 12
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
21
In order to obtain values for glucose consumption, the following equation was applied,
although the study from which it was adapted [50] obtained specific substrate
consumption rates by calculating the coefficient of a linear regression of substrate
consumed during the exponential phase :
Eq. 4
Where qS is the substrate consumption rate that equals the total amount of
substrate consumed during exponential phase ( ) divided by the amount of biomass
produced ( ) multiplied by specific growth rate ( ). After applying this equation we
obtained a result of 3.82 h-1
for the glucose consumption rate.
For the remaining constraint fluxes, namely for all amino acids, where
measurements throughout the exponential phase were not performed, we used the same
equation described above assuming complete consumption of the compound and then
converting the value to mmol/gDW.h.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
22
4 - Results and Discussion
4.1 - Statistics
In order to reconstruct a generic metabolic model for Streptococcus pneumoniae
a model of each of the strains selected was needed. A detailed analysis of these was
required to determine which reactions were unique to each strain, which were common
to all three and which were present in pairs.
The models were exported from SEED in spreadsheet format (.xls) to facilitate these
comparisons. Initial analysis demonstrated that the strain G54 had the most specific
reactions (36), followed by R6 (16). The only reaction specific to strain TIGR4 was the
biomass reaction. Further analysis showed that four reactions were common only to
strains G54 and R6, four reactions were present only in R6 and TIGR4, and three
reactions were common solely to G54 and TIGR4. The gross amount of reactions (729)
was present in all three strains, which was expected, since all strains belong to the same
species.
Specific reaction analysis showed that 23 of those that belong to G54 were reactions
added by SEED (AUTOCOMPLETION) in order to obtain a functioning model. Eleven
(11) of these reactions were specific to Group A Streptococci according to SEED. The
auto completion process is necessary due to the existence of gaps that prevent one or
more compounds that exist in the biomass equation to be formed.
When analyzing the specific reactions of strain R6 we observed that almost all of them
– 11 out of 16 - were part of the fatty acid metabolism and were encoded by the same
gene - peg.966.
Some of these results, however, appeared to be incorrect. Curation of the individual
models revealed that, for six (6) reactions assigned only to S.pneumoniae strain G54, no
literature support nor any database, used in this study, confirmed their presence,
therefore, they were removed. Another five (5) reactions were mistakenly assigned only
to G54, since genes of all the strains in this study, were identified to encode the
enzymes required for them. One of the reaction’s (rxn01501) enzyme commission
number and KEGG RID was incorrectly annotated, requiring curation. One (1) reaction
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
23
was incorrectly associated to strains R6 and TIGR4, since evidence of its existence was
found only for R6 strain (spr0852) confirmed by literature [42].
Table 6 shows the list of reactions incorrectly annotated by SEED.
Table 6 – Reactions initially identified as G54 strain-specific and corresponding genes
in other strains.
Reaction E.C KEGG G54 R6 TIGR4 References
Rxn00086 1.8.1.7 R00115 SPG_0714 spr0692 SP_0784 [11]
[51]
[3]
Rxn00205 1.11.1.9 R00274 SPG_1133 spr0285 SP_0313
Rxn01256 5.4.99.5 R01715 SPG_1190 spr1174 SP_1296
Rxn01501 1.1.1.88* R02081* SPG_1631 spr1570 SP_1726
((*) Initial E.C = 1.1.1.34, KEGG RID= 2082)
Table 3 shows the list of reactions, after correction, with data concerning
enzyme commission numbers (E.C), associated gene(s) and metabolism that were
specific to each strain and which were common only to pairs of strains.
Analysis of the reactions showed a substantial increase in the number of
reactions associated to G54 when compared to strains TIGR4 and R6. In regards to R6 a
lesser amount of reactions is expected due to the fact that it does not possess a
polysaccharide capsule and, therefore, reactions associated to its production are not
required.
Results of the comparison of S.pneumoniae strains G54, TIGR4 and R6 are shown in
Table 2 and the respective Venn diagram in Figure 2.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
24
Table 7 – Number of reactions present in the models of strains G54, TIGR4 and R6.
Strain
G54 R6 TIGR4
Total nº Reactions 768 757 739
Strain-specific reactions 22 17 1
% common reactions 96,32 96,70 99,05
% specific reactions 2,90 2,25 0,14
% pair reactions 0,79 1,05 0,81
Figure 6 – Venn diagram for S.pneumoniae strains used in the reconstruction process.
G54
R6 TIGR4
4
22
17
1*
4
732
2
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
25
Table 8 - List of strain-specific reactions and reactions specific to pairs of strains.
Reactions especific to G54
ID Name E.C Gene Metabolism
rxn00048 6,7-Dimethyl-8-(1-D-ribityl)lumazine:6,7-dimethyl-8-(1-D-ribityl) 2.5.1.9 peg.166 Riboflavin
rxn00066 NADH:hydrogen-peroxide oxidoreductase 1.11.1.1 peg.1403 No KEGG map assigned
rxn00105 ATP:nicotinamide-nucleotide adenylyltransferase 2.7.7.1|2.7.7.18 AUTOCOMPLETION Nicotinate and nicotinamide
rxn00178 Acetyl-CoA:acetyl-CoA C-acetyltransferase 2.3.1.9 AUTOCOMPLETION
rxn01207 4-Methyl-2-oxopentanoate:NAD+ oxidoreductase 1.2.1.25 AUTOCOMPLETION No KEGG map assigned
rxn05255 FOLt AUTOCOMPLETION Folate transport
rxn05362 4-methyl-trans-hex-2-enoyl-ACP:NADP+ oxidoreductase (A-specific) 1.3.1.0 AUTOCOMPLETION No KEGG map assigned
rxn05366 6-methyl-trans-oct-2-enoyl-ACP:NADP+ oxidoreductase (A-specific) 1.3.1.0 AUTOCOMPLETION No KEGG map assigned
rxn05370 8-methyl-trans-dec-2-enoyl-ACP:NADP+ oxidoreductase (A-specific) 1.3.1.0 AUTOCOMPLETION No KEGG map assigned
rxn05374 10-methyl-trans-dodec-2-enoyl-ACP:NADP+ oxidoreductase (A-specific) 1.3.1.0 AUTOCOMPLETION No KEGG map assigned
rxn05378 12-methyl-trans-tetra-dec-2-enoyl-ACP:NADP+ oxidoreductase (A-specific) 1.3.1.0 AUTOCOMPLETION No KEGG map assigned
rxn05382 14-methyl-trans-hexa-dec-2-enoyl-ACP:NADP+ oxidoreductase (A-specific) 1.3.1.0 AUTOCOMPLETION No KEGG map assigned
rxn05387 5-methyl-trans-hex-2-enoyl-ACP:NADP+ oxidoreductase (A-specific) 1.3.1.0 AUTOCOMPLETION No KEGG map assigned
rxn05391 7-methyl-trans-oct-2-enoyl-ACP:NADP+ oxidoreductase (A-specific) 1.3.1.0 AUTOCOMPLETION No KEGG map assigned
Comment [J1]: Completar
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
26
rxn05395 9-methyl-trans-dec-2-enoyl-ACP:NADP+ oxidoreductase (A-specific) 1.3.1.0 AUTOCOMPLETION No KEGG map assigned
rxn05399 11-methyl-trans-dodec-2-enoyl-ACP:NADP+ oxidoreductase (A-specific) 1.3.1.0 AUTOCOMPLETION No KEGG map assigned
rxn05403 13-methyl-trans-tetra-dec-2-enoyl-ACP:NADP+ oxidoreductase (A-specific) 1.3.1.0 AUTOCOMPLETION No KEGG map assigned
rxn05407 15-methyl-trans-hexa-dec-2-enoyl-ACP:NADP+ oxidoreductase (A-specific) 1.3.1.0 AUTOCOMPLETION No KEGG map assigned
rxn09345 Undecaprenyl diphosphate synthase AUTOCOMPLETION No KEGG map assigned
rxn10180 Pantothenate sodium symporter AUTOCOMPLETION Pantothenate transporter
rxn10447 calcium transport via ABC system AUTOCOMPLETION Calcium transporter
21
Reactions especific to R6
ID Name E.C Gene Metabolism
rxn05292 FE3t4
peg.1178 Ferrous ion transport
rxn00802 N-(L-Argininosuccinate) arginie-lyase 4.3.2.1 peg.103
Arginine and Proline
Alanine, aspartate and glutamate
rxn00358 UDP-D-galactopyranose furanomutase 5.4.99.9 peg.319 No KEGG map assigned
rxn00947 Palmitate:CoA ligase (AMP-forming) 6.2.1.3 peg.966 No KEGG map assigned
rxn01316 N-Acetylneuraminate pyruvate-lyase (pyruvate-phosphorylating) 2.5.1.56 peg.970 Amino sugars
rxn05247 FACOAL140(ISO) 6.2.1.3 peg.966 No KEGG map assigned
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
27
rxn05248 FACOAL150(anteiso) 6.2.1.3 peg.966 No KEGG map assigned
rxn05249 FACOAL150(ISO) 6.2.1.3 peg.966 No KEGG map assigned
rxn05250 FACOAL160(ISO) 6.2.1.3 peg.966 No KEGG map assigned
rxn05251 FACOAL170(anteiso) 6.2.1.3 peg.966 No KEGG map assigned
rxn05252 FACOAL170(ISO) 6.2.1.3 peg.966 No KEGG map assigned
rxn05736 fatty-acid--CoA ligase (tetradecanoate) 6.2.1.3 peg.966 No KEGG map assigned
rxn09448 fatty-acid--CoA ligase (octadecenoate) 6.2.1.3 peg.966 No KEGG map assigned
rxn09449 fatty-acid--CoA ligase (octadecanoate) 6.2.1.3 peg.966 No KEGG map assigned
rxn09450 fatty-acid--CoA ligase (hexadecenoate), peroxisomal 6.2.1.3 peg.966 No KEGG map assigned
rxn00278 Alanine Dehydrogenase 1.4.1.1 peg.940+peg.941+peg.942 Alanine, aspartate and glutamate
Reactions common to R6 and TIGR4
ID Name E.C Gene Metabolism
rxn05155 L-Glutamine-ABC transport 3.A.1.3 peg.763|peg.1154 Glutamine/aspartate ABC transporter
rxn01352 dGTP triphosphohydrolase 3.1.5.1 peg.1201 Purine
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
28
Reactions common to R6 and G54
ID Name E.C Gene Metabolism
rxn01675 dTTP:alpha-D-glucose-1-phosphate thymidylyltransferase 2.7.7.24 peg.323 Streptomycin
rxn02000 dTDP-4-dehydro-6-deoxy-D-glucose 3,5-epimerase 5.1.3.13 peg.324 Streptomycin
rxn01997 dTDPglucose 4,6-hydro-lyase 4.2.1.46 peg.325 Streptomycin
rxn02003 dTDP-6-deoxy-L-mannose:NADP+ 4-oxidoreductase 1.1.1.13 peg.326 Streptomycin
Reactions common to G54 and TIGR4
ID Name E.C Gene Metabolism
rxn00292 UDP-N-acetyl-D-glucosamine 2-epimerase 5.1.3.14 peg.322 Amino sugar and nucleotide amino sugar
rxn01133 Acetyl-CoA:maltose O-acetyltransferase 2.3.1.79 peg.73 Maltose utilization
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
29
4.2 - Genome comparison
Genomic comparison was also performed using The Comprehensive Microbial
Resource (CMR) [40] in order to demonstrate genome similarities between the strains
G54, R6 and TIGR4. Each DNA molecule was used as the reference and compared to
the other two, originating three sets of genome-homology comparisons. Table 9
illustrates the results obtained.
Table 9 – Genome homology comparison (using 90 % threshold).
Strain Hits (%)
General Specific
TIGR4 63 27
G54 71 20
R6 71 18
Analyzing the results obtained in Table 9, a high level of similarity between
strains is detected (63 - 71 %), suggesting that virulence and increased invasiveness is
determined by strain-specific genes. The latter, however, could not be confirmed by the
reaction analysis described earlier since a low percentage of strain-specific reactions
were identified. An interesting result is that of TIGR4, which was described of having
the largest number of strain-specific genes, suggesting that its high virulence could be
associated to them. However, when comparing these results to the strain-specific
reaction analysis, we do not find a correspondence between them, suggesting that other
genes (most probably those shared with other virulent strains, i.e. G54) are responsible
for virulence and its degree affected by environmental conditions.
Comparing nucleotide the nucleotide sequence of G54 used in CMR to that in
NCBI revealed a mismatch. Since we can´t determine which sequence is correct, for this
analysis, we chose to assume the sequence of G54 provided by CMR.
4.3 - Individual Models
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
30
The data obtained from the reconstruction process for S.pneumoniae strains G54,
TIGR4, R6 and Generic model and loaded onto Optlux are listed in Tables 4, 5, 6 and 7,
respectively. Biomass flux using Hoeprich´s medium was calculated only for the
generic model. Biomass flux for the individual strains of S.pneumoniae was only carried
out in SEED using the complete medium option to ensure growth simulation.
Table 10– Data collected from metabolic reconstruction model of strain G54.
Internal
reactions
Drains External
metabolites
Internal
metabolites
Nº of genes Biomass
flux (h-1
)
768 82 78 727 597 N/A
Table 11– Data collected from metabolic reconstruction model of strain TIGR4.
Internal
reactions
Drains External
metabolites
Internal
metabolites
Nº of genes Biomass
flux (h-1
)
749 97 92 717 563 N/A
Table 12– Data collected from metabolic reconstruction model of strain R6.
Internal
reactions
Drains External
metabolites
Internal
metabolites
Nº of genes Biomass
flux (h-1
)
770 102 97 733 577 N/A
Table 13– Data collected from metabolic reconstruction of the generic model.
4.4 - Generic Model
The generic model was comprised of all the reactions from the three individual
models (Supplementary data). All reactions were copied to a new file and duplicates
were removed. In order to obtain a minimal reliable model, manual curation was
Internal
reactions
Drains External
metabolites
Internal
metabolites
Nº of genes Biomass
flux (h-1
)
836 109 107 738 588 2.134
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
31
performed to this generic model. Pathways chosen for curation were based on media
composition and inspected to ensure correctness or detect gaps for completion.
4.4.1 - Biomass Reaction
The biomass reaction is one of the most important components of a metabolic
model. This reaction determines which compounds are essential to the model in order
to simulate growth. SEED uses gene-based annotation to reconstruct the model and,
therefore, a subsequent biomass reaction is defined in accordance to the organism-
specific requirements integrated with supplementary data shown in annex 2.
SEED´s template for biomass reaction is constructed from a curation of 19 existing
genome-scale metabolic models [35]. This tool assumes that several compounds (39) in
the biomass reaction are universal to all organisms (i.e. nucleotides, amino acids),
whilst others are present depending on organism-specific features and genome
annotation, such as cell wall composition based on Gram-positive or negative, and
cofactors. Stoichiometric coefficient determination is based on a set of rules in order to
approximate these to the individual biomass reaction since most of the times the
experimental data required for these calculations is not available.
Due to the fact that the aim of this study is to reconstruct a “generic” model for
S.pneumoniae, the biomass reaction was assessed by reunion of the three individual
models. Inspection of the biomass equations of each model revealed no differences in
their composition, therefore, only coefficient calculations were performed. These values
were very similar among all three strains, so a coefficient average value was calculated
for each metabolite. The composition and coefficient values are shown n Table 9.
Table 14 – Biomass composition and compound coefficients
Name Stoichiometry
Reactants
CoA 0.005521608
Lysine 0.333545602
Cysteine 0.088980184
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
32
Asparagine 0.234678731
dCTP 0.009329285
Calcium 0.005521608
Phosphatidylglyceroldioctadecanoyl 0.014200641
Manganese 0.005521608
GTP 0.209101254
Undecaprenyldiphosphate 0.092476489
Glutamate 0.256013161
Stearoylcardiolipin(B.subtilis) 0.014200641
Aspartate 0.234678731
Isoleucine 0.282551111
Leucine 0.438656697
Zinc 0.005521608
Fe2+ 0.005521608
CTP 0.129876555
Glycine 0.595802987
DNA_replication 1
Spermidine 0.005521608
Fe3+ 0.005521608
Chloride 0.005521608
Alanine 0.500058228
UTP 0.14026668
Protein_biosynthesis 1
Serine 0.209701837
Valine 0.411598396
Glutamine 0.256013161
Copper2 0.005521608
Potassium 0.005521608
TTP 0.017278415
RNA_transcription 1
dATP 0.017278415
Cobalt 0.005521608
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
33
Riboflavin 0.005521608
dGTP 0.009329285
H2O 3.479.648.046
Thiamindiphosphate 0.005521608
Magnesium 0.005521608
Arginine 0.288274982
Pyridoxalphosphate 0.005521608
Putrescine 0.0053191489
FAD 0.005521608
Sulfate 0.005521608
ATP 4.017.013.829
Threonine 0.246646826
Proline 0.215425709
Acyl-carrierprotein 0.005521608
Tetrahydrofolate 0.005521608
5-Methyltetrahydrofolate 0.005521608
10-Formyltetrahydrofolate 0.005521608
Methionine 0.149341011
S-Adenosyl-L-methionine 0.005521608
Glutathione 0.0053191489
Histidine 0.092622648
NADP+ 0.005521608
NAD+ 0.005521608
Phenylalanine 0.180562128
Tyrosine 0.134250804
Tryptophan 0.055157307
Phosphotodylglycerol_dioctadecanoyl 0.014200641
Peptidoglycan_polymer (n subunits) 0.092476489
Diisoheptadecanoylphosphatidylglycerol 0.014200641
Dianteisoheptadecanoylphosphatidylglycerol 0.014200641
Isoheptadecanoylcardiolipin_(B. subtilis) 0.014200641
Anteisoheptadecanoylcardiolipin_(B. subtilis) 0.014200641
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
34
Products
Apo-[acyl-carrier-protein] 0.005521608
Biomass 1
ADP 40
Pyrophosphate 0.602386901
Orthophosphate 3.999.447.839
Hidrogen 40
Peptidoglycanpolymer(n-1subunits) 0.092476489
4.4.2 - Main Pathway Analysis
4.4.2.1 - Carbohydrates Metabolism
4.4.2.1.1 - Glycolysis
Glycolysis is an essential pathway for all organisms, due to the fact that it is
responsible for the conversion of glucose into pyruvate, generating energy (ATP) and
reducing power (NADH). Analysis of the pathway map on KEGG and confirmed in our
model, showed that all the necessary reactions (and respective protein encoding genes)
for glucose degradation were present, not requiring further in-depth analysis.
4.4.2.1.3 - Pentose Phosphate Pathway (PPP)
The Pentose Phosphate pathway plays an important part in the carbohydrates
metabolism due to its products: NADPH, which is a reducing power required, for
instance, in fatty acid biosynthesis; and ribose-5-phosphate, which is necessary for
nucleotide synthesis [52][53] and also required for FAD formation in the Riboflavin
metabolism .
The reactions present in the generic model fulfilled the main necessary steps for
R5P formation, and therefore the pathway was considered accurate.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
35
4.4.2.1.4 - Fructose and Mannose
Hoskins and colleagues identified all genes necessary for carbohydrate oxidation
into pyruvate via glycolysis [51]. S.pneumoniae can use either the
phosphoenolpyruvate-dependent phosphotransferase system (PTS) or the less energetic-
efficient ATP Binding Cassette Transport system (ABC transporters).
Fructose is a monosaccharide present in human diet and mannose a sugar monomer,
both of which are degraded to Fructose-6-phosphate by a frutokinase (E.C= 2.7.1.4) and
a mannose-6-phosphate (E.C= 5.1.3.8). F6P can either enter the glycolytic pathway for
energy production or the amino sugar and nucleotide metabolism for conversion into
UDP-N-acetylmuramate which will enter the peptidoglycan biosynthesis pathway,
important for cell wall formation[3][51]. Simulations under different experimental
conditions might reveal which pathway is chosen.
Inspection of the generic network model showed that all protein encoding genes
responsible for these reactions were present and considering that both scenarios were
possible, no changes were performed.
4.4.2.1.5 - Galactose
Galactose is another monosaccharide utilized by S.pneumoniae in the
carbohydrate metabolism[51]. Observation on KEGG pathway map in comparison to
the generic model showed that all protein encoding genes responsible for all the multi-
step reactions necessary for conversion of galactose to Glucose-6-phosphate, which
subsequently enters the glycolysis pathway, were accounted for.
4.4.2.1.6 - Starch and sucrose
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
36
Sucrose is a disaccharide composed of fructose and glucose that plays an
important role in S.pneumoniae fitness. Colonization and infection have been linked to
two metabolizing systems, sus and scr, both regulated by the LacI family, which has
been shown to act as a regulator for sucrose transport systems, that have niche-specific
roles in virulence [54]. The first, linked to SusR, primarily affects the lungs, whilst the
latter, linked to ScrR, is primarily associated to the nasopharynx. ScrH, a sucrose-6-
phosphate hydrolase of the PTS is considered to be the main hydrolase involved in this
process. The PTS system transporter subunit IIBCA (E.C = 2.7.1.69), is encoded by the
genes SPG_1627 (G54), SP_1722 (TIGR4) and spr1566 (R6) present in our model,
suggesting that in the case of strain R6 these metabolizing systems are not expressed.
Streptococcus pneumoniae have different possible niches in the human host, therefore,
identifying which system is being expressed might be a form of clustering strains, other
than serotyping.
As occurred in the glycolysis, the starch and sucrose pathway appeared to be
correctly annotated and no re-annotation was required.
4.4.2.1.7 - Pyruvate
Pyruvate or pyruvic acid is an organic acid originated by conversion of PEP and
produced via glycolysis, being able to either, reversibly, regenerate glucose via
glucogenesis pathway or enter the fatty acids metabolism in the form of acetyl-CoA.
Energy production normally follows the fermentation process, obtaining lactic acid and
hydrogen peroxide under anaerobic and aerobic growth conditions, respectively
[11][51].
Analysis of this pathway confirmed the presence of protein encoding genes for
all necessary enzymes that catalyze these reactions.
4.4.2.1.8 - Amino sugar and nucleotide sugar
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
37
Amino sugars are important substrates for glycosyltransferases which are
responsible for polysaccharide synthesis. Carbohydrates such as fructose (fructose and
mannose pathway), glucose (glycolysis pathway), galactose (galactose pathway) and
mannose (extracellular providence, i.e. medium) are degraded to UDP-N-
acetylmuramate which, in turn, plays a key role in peptidoglycan biosynthesis.
Inspection of this pathway revealed that protein encoding genes responsible for
all the enzymes required for UDP-N-acetylmuramate production from the substrates
mentioned above were present.
4.4.2.2 - Energy Metabolism
4.4.2.2.1 - Nitrogen
Nitrogen metabolism is important, not only in bacterial growth, but also for its
contribution to virulence [55] determined by the presence of a gene (glnA) required for
glutamine uptake[56]. Studies [57] have reported that the internal pool-size of
glutamine is key when S.pneumoniae is grown in a nitrogen-limiting environment.
Carbon dioxide (CO2) also plays an important role in several cellular processes,
besides cellular growth. A carbonic hydrolase (E.C=4.2.1.1), encoded by genes
SP_00024 (TIGR4), SPG_0031 (G54) and spr0026 (R6), present in this pathway and
our model, has been associated to growth limiting factors. Several studies [51][42][58]
have concluded that in the absence of CO2 or in CO2-poor environments, growth of
S.pneumoniae is inhibited or limited. Burghout and co-workers [59] classified this
enzyme as essential in preventing the cellular release of CO2, since pneumococci have
an anabolic necessity for either carbon dioxide (CO2) or bicarbonate (HCO3-) during
nucleic, amino and fatty acid biosynthesis.
Analysis of the nitrogen metabolism showed that type I glutamine synthetase,
enzyme encoded by gene glnA was present in the model as was carbamate kinase (E.C=
2.7.2.2), encoded by SPG_2090, required for formation of carbamoyl-P, important to
the arginine and proline pathway.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
38
4.4.2.3 - Lipid Metabolism
4.4.2.3.1 - Fatty acid Biosynthesis
As stated before, the draft reconstruction of the networks obtained from SEED
revealed gaps in several pathways. Analysis of the fatty acid biosynthesis showed that
all last steps of the elongation cycle were missing; however, Marakchi [60] and
colleagues identified the genes sp_0419(TIGR4)[11], spr0379(R6) and spg0385(G54) in
S.pneumoniae that encoded the enoyl(acyl-carrier-protein)(ACP) reductase FabK which
was responsible for the these precise steps. The last cycle was also missing from the
fatty acid biosynthesis which led to the production of octadecanoic and octadecenoic
acids. Studies have demonstrated that enzymes FabF, FabZ and FabG were encoded by
genes sp0422, sp0424 and sp0422 of TIGR4 strain; spr0382, spr0384 and spr0382 of R6
strain; and spg0388, spg0390 and spg0388 of G54 strain[11][61].
Oleoyl-[acyl-carrier-protein] hydrolase (E.C= 3.1.2.14), which catalyzes the production
of hexadecanoic, tetradecanoic, dodecanoic, octadecanoic and octadecenoic acids, was
shown to be encoded by genes sp1408 (TIGR4), spr1265 (R6) and spg1349 (G54).
Table 8 shows the relationships between genes and enzymes in the Fatty acid
biosynthesis added to the model and Annex 1 (obtained through KEGG) illustrates Fatty
acid Biosynthesis pathway with enzymes that have been proven to exist in the strains of
S.pneumoniae used in this study (highlighted in green).
Table 15 – Relations Gene/Enzyme in Fatty acid Biosynthesis
Genes Enzyme
sp0422(TIGR4), spr0381(R6),spg0388(G54) FabG
sp0422(TIGR4), spr0382(R6),spg0388(G54) FabF
sp0419(TIGR4), spr0379(R6),spg0385(G54) FabK
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
39
sp0424(TIGR4), spr0384(R6),spg0390(G54) FabZ
sp_1408(TIGR4), spr1265(R6),spg_1349(G54) 3.1.2.14
4.4.2.4 - Nucleotide Metabolism
4.4.2.4.1 - Purine and Pyrimidine
Purines adenine and guanine and pyrimidines uracil, cytosine and thymine, are
essential in many basic biological processes, mainly providing the metabolites required
for DNA replication and RNA transcription, linked to bacterial growth [62] and energy
storage units in the form of ATP. Gibert´s [43] medium solution had adenine, guanine
and uracil, the latter being important in these pathways, which are vital precursors in
DNA replication and RNA transcription.
Inspection of these pathways in the generic model revealed incomplete reactions
for DNA replication and RNA transcription, neglecting the use of ATP and dATP, and
only attended to these when dGTP and GTP acted as precursors. Through homology, an
enzyme encoding gene (rpoB) responsible for production of DNA-directed RNA
polymerase subunit alpha (E.C = 2.7.7.6), required for RNA transcription, was
identified. This enzyme catalyzes this reaction, either in the use of ATP (R00435) or
GTP (rxn13784) as precursors.
The same methodology was applied to the DNA replication reaction. By
homology, enzyme encoding genes (DPA, dnaE and polA) were identified as
responsible for the production of the DNA polymerase I (E.C = 2.7.7.7). Reactions,
either using dATP (R00375) or dGTP (rxn13783) as precursors, are catalyzed by this
enzyme.
For simulation purposes, since adenine and guanine have been reported to be
present in the media[43], and considering their presence in our model, drains for
adenine and guanine entry or exit were added.
During the reconstruction process, a reaction for ATP maintenance is also
required. This process is key to ensure enough energy for basic biological procedures
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
40
independent of growth. SEED draft reconstruction pipeline created such a reaction
(rxn00062) and established a positive flux (only allowing degradation of ATP to ADP).
Equation 5 illustrates ATP maintenance reaction according to Thiele and Palsson´s
protocol[34].
1 ATP + 1 H2O --> 1 ADP + 1 H + 1 Pi Eq. 5
Several experimental data consulted did not refer these values and investigation
of several other models (Table 11) only presented a positive flux (0 to ∞), however a
study by Oliveira and colleagues [63] on a genome-scale model for Lactococcus lactis,
stated using a flux of 18.15 mmol/ gDW. Growth rate for this species (0.8 h-1
) was very
similar to the maximum obtained for S.pneumoniae (0.92 h-1
) and the proximity
between species suggested we could use this value as standard. This obviously requires
further study but is indicative of the non-growth energy requirements.
Table 16 – List of models consulted for ATP maintenance limit values
Name Source
iYO844 (B.subtilis) SEED
iBsu1103 (B.subtilis) PubMed Central (PMC2718503)
iJR904 (E.coli) Genomebiology.com
Seed370552.3 (S.pyogenes) SEED
Ópt171101.1 (S.pneumoniae R6) SEED
Lactococcus lactis (ssp. Lactis ILI 403) http://www.biomedcentral.com/1471-
2180/5/39
4.4.2.5 - Amino acid Metabolism
Amino acids are structural units of proteins. Different sequences and lengths of
amino acids determine the type of protein that will be formed. Since amino acids can
also be used as nitrogen and carbon sources [11] [51] along with the presence of several
amino acid transporters in the generic model, we decided to deepen our analysis on
these pathways. Additionally, we had to keep in mind that not all amino acids were
produced by S.pneumoniae, requiring them to be introduced via media solution. Some
of them are capable of being produced, cases of valine [51], proline, aspartate (via L-
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
41
Asparagine), Isoleucine (via L-Threonine)[11], but as a safeguard, due to some media
containing all amino acids in the medium [41] it was decided to allow the introduction
of these from an extracellular compartment. Hartel´s isotopologue profiling of
pneumoniae’s central carbon metabolism [42] demonstrated which amino acids were
essential and non-essential for in vitro growth and, therefore, simulations based upon
the same assumptions were performed in order to validate their findings (Table 17).
Table 17 – Essential and non-essential amino acids for in vitro growth of Streptococcus
pneumoniae D39 (serotype 2; NCTC7466) compared to our generic model. ((0) – No
effect on growth; (+) - Essential for growth; (-) - Limiting in case of absence from the
medium)
Amino acid Generic Model Hartel et al
Arginine 0 +
Cysteine + +
Histidine + +
Glycine + +
Glutamine + +
Isoleucine 0 +
Leucine 0 +
Valine + +
Threonine 0 0
Serine 0 0
Asparagine 0 0
Aspartate 0 0
Alanine 0 0
Phenylalanine 0 0
Tyrosine 0 0
Tryptophan 0 0
Lysine 0 0
Proline - -
Methionine 0 -
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
42
Comparison between results obtained by Hartel and our generic model show that almost
all of them coincide, which proves to a certain extent that our model is simulating
correctly phenotypic behavior.
Further analysis is provided, in the following descriptions, for each amino acid pathway.
4.4.2.5.1 - Alanine, Aspartate and Glutamate
Alanine plays a key role in glycolysis, converted into pyruvate by an alanine
synthesizing transaminase (E.C= 2.6.1.66) (gene aspC), and peptidoglycan synthesis, in
the form of D-alanyl-D-alanine by action of a D-alanyl-alanine synthetase S (E.C=
6.3.2.4) (gene ddl).
Aspartate is converted into N-carbamoyl-L-aspartate by action of an aspartate
carbamoyltransferase catalytic subunit (E.C= 2.1.3.2) (gene pyrB), which enters the
pyrimidine pathway, essential in various biological processes (see Nucleotide
pathways).
Glutamate is converted into L-Glutamine by a type I glutamine synthetase (E.C=
6.3.1.2) (gene glnA). The latter can enter the amino sugars pathway, in the form of D-
Glucosamine-6-Phosphate, the Purine pathway, as 5-Phospho-ribosylamine, or the
pyrimidine and arginine and proline pathways, in the form of carbamoyl-phosphate, by
action of glucosamine—fructose-6-phosphate aminotransferase (E.C= 2.6.1.16) (gene
glmS), amidophosphoribosyltransferase (E.C= 2.4.2.14) (gene purF) and carbamoyl
phosphate synthase large subunit (E.C= 6.3.5.5) (genes carB and carA), respectively.
Glutamine also plays a key role in virulence [62].
Out of these three amino acids only glutamate was considered essential for growth by
Hartel and co-workers[42], and in silico simulations, using the same assumptions,
confirmed their findings. Inspection of our model confirmed the presence of all enzyme
encoding genes required for all the reactions of this pathway and, therefore, no
additional curation was performed.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
43
4.4.2.5.2 - Glycine, Serine and Threonine
Glycine, serine and threonine production can be achieved by S.pneumoniae in
accordance to KEGG maps and confirmed in our model. The presence of enzyme
encoding genes for the main reactions (L-serine ammonia-lyase and L-threonine
ammonia-lyase – gene ilvA, for converting glycine to serine and threonine to 2-
oxobutanoate; tryptophan syntethase subunit alpha – gene trpA, responsible for
converting serine to tryptophan; serine hydroxymethyltransferase – gene glyA, used to
produce pyruvate having serine as its main precursor; threonine syntethase – genes
thrC, thrB1, thrB2 and thrH, responsible for production of threonine with homoserine as
the main precursor; and aspartate kinase – genes SP_0413 (TIGR4), SPG_0379 (G54)
and spr0374 (R6), used for production of homoserine having aspartate as the main
precursor, revealed suffice for considering this pathway complete. However, studies
have reported that glycine, unlike serine and threonine, is essential for growth, and only
obtained from the medium [42], therefore, a drain (EX_cpd11580_e) was added to the
model to safeguard this condition.
4.4.2.5.3 - Cysteine and Methionine
Methionine is the universal N-terminal amino acid of proteins [64] and
cysteine´s thiol side chain oxidation leads to formation of cystine, important in protein
structure[65].
Initially, Cysteine and Methionine pathway analysis showed a gap in the
transformation of L-Cystathionine to L-Homocysteine. Further analysis demonstrated
that this cysteine-lyase (E.C= 4.4.1.8) was encoded by genes SP_1524 (TIGR4),
SPG_1449 (G54) and spr1376 (R6), and also involved in other reactions, namely
pyruvate and thiocysteine production. The enzyme that catalyzes the latter is encoded
by the genes SP_1524 (TIGR4), SPG_1449 and spr1376 (R6) but its inclusion would
create a dead-end in the model, since no genes have yet been identified that encode
enzymes for reactions necessary to connect this metabolite with other compounds.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
44
Studies regarding cysteine and methionine metabolism [42] showed that the first was
essential for growth whilst the second had a limiting effect when absent. When
performing simulations in our model, under these same conditions, similar results were
obtained for cysteine but not methionine. A possible explanation for the latter is the de
novo synthesis of methionine. The same studies were incapable of determining if this
could be achieved due to degradation of the amino acid during acidic treatment of
protein hydrolysis.
4.4.2.5.4 - Valine, Leucine and Isoleucine biosynthesis
Valine, Leucine and Isoleucine primarily act as the building blocks of proteins.
Hartel´s study stated that valine, isoleucine and leucine were essential for growth and
inspection of the pneumococcal genome revealed the existence of genes required to
encode all necessary enzymes for their synthesis using threonine and pyruvate as
precursors. Homologous genes in the genome such as ilvB have also been linked to
valine and isoleucine biosynthesis.
Inspection of our model confirmed that the enzyme encoding genes required for
the set of reactions leading to their production were present (SP_0450, SP_0445,
SP_0446 (ilvH), SP_0447, SP_2126 and SP_0856) and therefore, despite obtaining
different results in our simulation when compared to Hartel´s study in the cases of
isoleucine and leucine, the possibility of synthesis via pyruvate and threonine was
considered possible.
Since all the enzymes in this pathway were linked to a protein encoding gene, re-
annotation was not performed.
4.4.2.5.5 - Phenylalanine, Tyrosine and Tryptophan biosynthesis
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
45
As mentioned earlier, S.pneumoniae is capable of utilizing amino acids as
carbon sources. However, aromatic amino acids like phenylalanine, tyrosine and
tryptophan do not appear to affect growth in vitro using glucose as main carbon source
[42], also sustained by simulations performed to the model. Hartel and colleagues stated
that these amino acids were capable of de novo synthesis from erythrose 4-phosphate
along with two molecules of PEP via chorismate pathway.
4.4.2.5.6 - Lysine biosynthesis
Two distinct pathways for synthesis of lysine are known: the diaminopimelate
(DAP), which is found mostly in bacteria; and alfa-aminoadipate, found mostly in fungi
and archeal species [66]. Special interest was taken for this metabolic pathway since
DAP is also associated as a constituent of bacterial cell wall peptidoglycan, although
lysine it is not essential for growth [42], conclusion also sustained by simulations
performed on our model.
The first draft model revealed an incomplete pathway when aspartate was the
key substrate, namely the absence of a diaminopimelate epimerase (E.C= 5.1.1.7)
required for production of meso-2,6-diaminopimelate. Through homology, the gene
dapF (responsible for the production of this enzyme) was identified, allowing the
addition of the missing reaction (R02735).
4.4.2.5.7 - Arginine and Proline
Proline and arginine play an important role in bacterial growth, mainly by
formation of glutamate which, in turn, is used to produce glutamine. Proline, although a
non-essential amino acid for growth[42], unlike arginine[67] , has a limiting effect
when absent from the medium. Simulations in our model where each of these amino
acids were removed individually, confirmed Hartel´s conclusions in the case of proline
but not arginine. This could be caused by an erratic reconstruction of the metabolic
pathway or due to its capability for de novo synthesis. Hartel´s study was inconclusive
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
46
for the latter due to experimental procedural limitations (degradation during acidic
treatment). Analysis of KEGG pathway identified genes responsible for arginine
formation by use of citrulline (SP_2148 – TIGR4, SPG_2088 – G54, and
spr1955/spr1956 – R6) and their presence in our model suggests that de novo synthesis
is the most likely explanation.
4.4.2.6 - Metabolism of Cofactors and Vitamins
4.4.2.6.1 - Pantothenate and CoA biosynthesis
Pantothenate is vital for Co-enzyme A (CoA) production, which is essential in
metabolic pathways such as Fatty acid biosynthesis and energy metabolism (TCA
cycle), mostly by acting as a carbon transporter within the cell, and necessary as an
integral part of acyl-carrier protein (ACP)[68].
Analysis of the reconstructed model showed that one reaction - rxn01790, (R)-
PantoateNADP+ 2-oxidoreductase, was present that had no literature background and
therefor was removed. Further inspection revealed the absence of Pantothenate,
importance stated above, that was added to the model – rxn10180. Pantothenate has
been included in media [41][42] and the identification of a protein - Pantothenate kinase
– (EC=2.7.1.33) encoded by gene coaA present in our genome (obtained through
homology), only enhanced our confidence level when adding this reaction.
ACP is essential in fatty acid biosynthesis, and inspection of the latter revealed
the absence of ACP which prevented fatty acid formation. Addition of the reaction
R01625 corrected this gap.
4.4.2.6.2 - Riboflavin
Riboflavin is a micronutrient vital for flavin mononucleotide and flavin adenine
dinucleotide cofactor production and, as such, plays an important part in the energy and
fatty acids metabolisms. These cofactors are mostly used as prosthetic groups for
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
47
several oxidoreductases [[69], such as glycerol-3-phosphate oxidase (E.C= 1.1.3.21)
identified in the glicerophospholipid pathway of our generic model.
4.4.2.6.3 - Vitamin B6
The presence of pyridoxal phosphate in our model corroborated Hoskins
statement that S.pneumoniae possessed an incomplete pathway for this metabolism.
Pyridoxal was not present in our model which suggested that it had to be added to the
medium. Several studies [41][45][42] confirmed the addition of this compound to the
medium and, therefore, a drain was added to the model – R_pyridoxal - and its
subsequent transport reaction – R00174.
4.4.2.7 - Other metabolisms
Several other metabolisms presented to be incomplete. Over 50 % of the
reactions added during the auto-completion stage in SEED are associated to processes
of cofactor or cell wall biosynthesis [35]. Many pathogens do not possess the capability
to produce certain metabolites, obtaining them extracellularly, for instance the host´s
cells. An example of such is thiamine, a vitamin required for transaminase reactions,
biotin, essential in biosynthetic reactions that require CO2 fixation, or lipoteichoic acids,
a component of the cell wall. For each of these cases, a drain was added to the model to
ensure that the metabolite could be used if need be.
4.5 - Simulation results with Hoeprich´s medium
The medium used for conducting simulations is shown in Table 4 (Hoeprich´s
medium), and simulated a growth (production of biomass) of 2.135 h-1
which is
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
48
approximately more than double of the results obtained in that same experiment (0.92 h-
1 using strain St 99/95). The biomass metabolites of the generic model are listed in
Table 14. In Gonçalves study [41] lactate and polysaccharide capsule production were
also measured. Lactate formation, a by-product of lactic bacteria, was also detected, but
the elevated value (over 1000 h-1
) was obviously incorrect when compared to data
retrieved from literature (1.90 h-1
). The fact that the fluxes that lead to lactate formation
have no constraints might lead to this outcome and therefore, they need to be adjusted.
Simulations for polysaccharide production were not performed.
Differences in the biomass equation and the addition of strain-specific reactions
might explain why the results obtained did not match those in literature. Because the
“generic” biomass equation was comprised of the compounds from all three strains and
its coefficients obtained by average as well as the addition of reactions that were
specific to each strain, differences in biomass flux were expected.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
49
5 - Conclusions
One of the most difficult tasks was the gathering of useful experimental data.
The lack of knowledge on a minimum medium for S.pneumoniae is an obstacle when
trying to simulate growth and therefor requires researchers to contemplate adding
excessive reactions in order obtain a functioning model. Most of the articles refer
complex media complemented with other amino acids, vitamins or buffer solution in
order to obtain growth.
Due to the necessity of complex medium and little information on what is
strictly necessary for the growth of S.pneumoniae, many authors describe using all
amino acids in the media regardless of knowing if they are essential [41][43][45].
However, Hartel and colleagues [42] performed the characterization of the Central
Carbon Metabolism of S.pneumoniae through isotopogue profiling and concluded that
most of the amino acids were essential in different conditions, be it in studying the de
novo synthesis of aspartate, alanine and threonine, be it in the study of growth under
amino acid depravation, only to mention a few. Furthermore, they demonstrated the
dual utilization of carbohydrates and amino acids as well as the de novo synthesis of
several of the latter. These results sustained the changes made to the model where all
fluxes of amino acids were “opened” (Table 9 – List of aminopeptidases). However,
some studies [41] state that not all amino acids are capable of de novo synthesis,
requiring them to be present in the medium. Removal of amino acids such as
phenylalanine, tyrosine and tryptophan, from the biomass equation, confirmed Hartel´s
hypothesis that these had no impact on pneumococci growth. Use of different
experimental conditions might be able to shed some light into amino acid essentiality
and production since enzyme encoding genes for reactions involving de novo synthesis
for some of them (i.e. isoleucine and leucine) are present in the model.
Streptococcus pneumoniae requires carbohydrates to grow and the generic
model demonstrated a large amount of reactions that were linked to transport systems
such as ABC transporters which, in turn, indicated that pneumococci were capable of
utilizing a wide variety of such to improve bacterial fitness and virulence[42].
Additional information regarding protein synthesis, DNA and RNA production is
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
50
essential in order to establish proper boundaries for ATP costs. The lack of information
regarding these precursors is limiting to the quality of the model.
Besides achieving biomass flux proximate to experimental data, the presence of
lactate as a by-product, also known to be present in Lactobacteria, proved to some
extent that the model was indeed simulating correctly. The flux obtained was too high to
be correct and did not match the one obtained through literature, therefore further
inspection and refinement of the model must be carried out.
Table 18 – List of aminopeptidases
Drain (EX_cpd) Name
11580
Aminopeptidases
11581
11582
11583
11584
11585
11586
11587
11588
11589
11590
11591
11592
11593
15603
15604
15605
15606
1017
A list of all reactions added/changed to the generic model, are presented in Table 11.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
51
Table 19 – List of all reactions added/changed in generic model
Reaction E.C Number
R02735 5.1.1.7
R02788 6.2.3.13
R02081 1.1.1.88
R07763 1.1.1.100
R07762 2.3.1.41|2.3.1.179|2.3.1.-
R07765 1.3.1.-
R07764 4.2.1.-
R04968 2.3.1.41|2.3.1.85|2.3.1.86|2.3.1.179|2.3.1.180
R01706 3.1.2.14
R04429 1.3.1.9|2.3.1.86
R04724 1.3.1.9|2.3.1.86
R04955 1.3.1.9|2.3.1.86
R04958 1.3.1.9|2.3.1.86
R04961 1.3.1.9|2.3.1.86
R04966 1.3.1.9|2.3.1.86
R04969 1.3.1.9|2.3.1.86
R03370 1.14.19.2
R08159 3.1.2.14
R01625 2.7.8.7
R01011 2.7.1.29
R00174 2.7.1.35
R01286 4.4.1.8
biogeneric
R00375 2.7.7.7
rxn13783 2.7.7.7
R00435 2.7.7.6
rxn13784 2.7.7.6
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
52
6 - Future Work
Minimum media required for pneumococci growth is essential to obtain a
precise and reliable metabolic model. Therefore, the first step to achieve this would be
to experimentally determine such media composition. Another possibility would be to
reconstruct models for several strains of S.pneumoniae by manual curation and, only
after each of them are validated, would the process of generating a generic model begin.
As the time frame for the presentation of this dissertation is limited, this process was
done by using models with little or no manual curation and performing the necessary
steps to unite them, namely removing duplicate reactions, inconsistencies in compound
presence, incorrect pathways, curation of critical reactions, amongst others.
Quantification of protein, DNA and RNA is also essential to establish minimum
energy requirements for organism sustainability and therefore should be addressed in
future studies. Using different experimental conditions (constraints) might also shed
some light into pathway preferences and, therefore, should be addressed in the future.
Upload of this model onto other modeling software (i.e. Merlin [70]) could also
improve the quality of the annotation.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
53
7 - References
[1] F. Ding, P. Tang, M.-H. Hsu, P. Cui, S. Hu, J. Yu, and C.-H. Chiu, “Genome
evolution driven by host adaptations results in a more virulent and antimicrobial-
resistant Streptococcus pneumoniae serotype 14.,” BMC genomics, vol. 10, p.
158, Jan. 2009.
[2] “Streptococcus pneumoniae,” in The desktop Encyclopedia of Microbiology,
2009, pp. 1061–1074.
[3] J. Dopazo, A. Mendoza, J. Herrero, F. Caldara, Y. Humbert, L. Friedli, M.
Guerrier, E. Grand-schenk, C. Gandin, M. D. E. Francesco, A. Polissi, G. Buell,
G. Feger, and E. García, “Annotated Draft Genomic Sequence from a
Streptococcus pneumoniae Type 19F Clinical Isolate * MATERIALS AND
METHODS,” vol. 7, no. 2, 2001.
[4] A. Sá-Leão, R, Tomasz, “Streptococcus Pneumoniae,” in The desktop
Encyclopedia of Microbiology, 2009, pp. 1061–1074.
[5] J. a Lanie, W.-L. Ng, K. M. Kazmierczak, T. M. Andrzejewski, T. M. Davidsen,
K. J. Wayne, H. Tettelin, J. I. Glass, and M. E. Winkler, “Genome sequence of
Avery’s virulent serotype 2 strain D39 of Streptococcus pneumoniae and
comparison with that of unencapsulated laboratory strain R6.,” Journal of
bacteriology, vol. 189, no. 1, pp. 38–51, Jan. 2007.
[6] M. Gratten, D. Battistutta, P. Torzillo, J. Dixon, and K. Manning, “Comparison
of goat and horse blood as culture medium supplements for isolation and
identification of Haemophilus influenzae and Streptococcus pneumoniae from
upper respiratory tract secretions.,” Journal of clinical microbiology, vol. 32, no.
11, pp. 2871–2, Nov. 1994.
[7] I. Serrano and J. A. Carric, “Characterization of the Genetic Lineages
Responsible for Pneumococcal Invasive Disease in Portugal,” vol. 43, no. 4, pp.
1706–1715, 2005.
[8] M. Carrolo, F. R. Pinto, J. Melo-Cristino, and M. Ramirez, “Pherotypes are
driving genetic differentiation within Streptococcus pneumoniae.,” BMC
microbiology, vol. 9, p. 191, Jan. 2009.
[9] M. D. L. Moura Leal, D. D. S. Gomes Pereira, E. Jessouroun, M. A. Peixoto
Gimenes Couto, and N. Pereira Jr, “Investigation of cultivation conditions for
capsular polysaccharide production by Streptococcus pneumoniae serotype 14,”
Electronic Journal of Biotechnology, vol. 14, no. 5, pp. 0–3, Sep. 2011.
[10] C. Hyams, E. Camberlein, J. M. Cohen, K. Bax, and J. S. Brown, “The
Streptococcus pneumoniae capsule inhibits complement activity and neutrophil
phagocytosis by multiple mechanisms.,” Infection and immunity, vol. 78, no. 2,
pp. 704–15, Feb. 2010.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
54
[11] H. Tettelin, K. E. Nelson, I. T. Paulsen, J. a Eisen, T. D. Read, S. Peterson, J.
Heidelberg, R. T. DeBoy, D. H. Haft, R. J. Dodson, a S. Durkin, M. Gwinn, J. F.
Kolonay, W. C. Nelson, J. D. Peterson, L. a Umayam, O. White, S. L. Salzberg,
M. R. Lewis, D. Radune, E. Holtzapple, H. Khouri, a M. Wolf, T. R. Utterback,
C. L. Hansen, L. a McDonald, T. V. Feldblyum, S. Angiuoli, T. Dickinson, E. K.
Hickey, I. E. Holt, B. J. Loftus, F. Yang, H. O. Smith, J. C. Venter, B. a
Dougherty, D. a Morrison, S. K. Hollingshead, and C. M. Fraser, “Complete
genome sequence of a virulent isolate of Streptococcus pneumoniae.,” Science
(New York, N.Y.), vol. 293, no. 5529, pp. 498–506, Jul. 2001.
[12] P. Yakovchuk, E. Protozanova, and M. D. Frank-Kamenetskii, “Base-stacking
and base-pairing contributions into thermal stability of the DNA double helix.,”
Nucleic acids research, vol. 34, no. 2, pp. 564–74, Jan. 2006.
[13] H.-G. Zimmer, Encyclopedia of Life Sciences. Chichester, UK: John Wiley &
Sons, Ltd, 2001.
[14] R. M. Harvey, U. H. Stroeher, A. D. Ogunniyi, H. C. Smith-Vaughan, A. J.
Leach, and J. C. Paton, “A variable region within the genome of Streptococcus
pneumoniae contributes to strain-strain variation in virulence.,” PloS one, vol. 6,
no. 5, p. e19650, Jan. 2011.
[15] M. A. Barocchi, J. Ries, X. Zogaj, C. Hemsley, B. Albiger, A. Kanth, S.
Dahlberg, J. Fernebro, and M. Moschioni, “A pneumococcal pilus influences
virulence and host,” vol. 103, no. 8, pp. 2857–2862, 2006.
[16] L. J. McAllister, A. D. Ogunniyi, U. H. Stroeher, and J. C. Paton, “Contribution
of a genomic accessory region encoding a putative cellobiose phosphotransferase
system to virulence of Streptococcus pneumoniae.,” PloS one, vol. 7, no. 2, p.
e32385, Jan. 2012.
[17] O. Johnsborg and L. S. HÃ¥varstein, “Regulation of natural genetic
transformation and acquisition of transforming DNA in Streptococcus
pneumoniae,” FEMS Microbiology Reviews, vol. 33, no. 3, pp. 627–642, May
2009.
[18] H. Steinmoen, E. Knutsen, and L. S. Håvarstein, “Induction of natural
competence in Streptococcus pneumoniae triggers lysis and DNA release from a
subfraction of the cell population.,” Proceedings of the National Academy of
Sciences of the United States of America, vol. 99, no. 11, pp. 7681–6, May 2002.
[19] W. P. Hausdorff, D. R. Feikin, and K. P. Klugman, “Epidemiological differences
among pneumococcal serotypes.,” The Lancet infectious diseases, vol. 5, no. 2,
pp. 83–93, Feb. 2005.
[20] D. of Health, “Pneumococcal,” 2012.
[21] J. D. Mooney, A. Weir, J. McMenamin, L. D. Ritchie, T. V. Macfarlane, C. R.
Simpson, S. Ahmed, C. Robertson, and S. C. Clarke, “The impact and
effectiveness of pneumococcal vaccination in Scotland for those aged 65 and
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
55
over during winter 2003/2004.,” BMC infectious diseases, vol. 8, p. 53, Jan.
2008.
[22] E. Alonsodevelasco, A. F. Verheul, J. Verhoef, and H. Snippe, “Streptococcus
pneumoniae : virulence factors , pathogenesis , and vaccines .,” vol. 59, no. 4,
1995.
[23] N. M. Bennett, R. Lynfield, A. Reingold, P. R. Cieslak, T. Pilishvili, D. Jackson,
R. R. Facklam, D. Ph, and J. H. Jorgensen, “new england journal,” pp. 1737–
1746, 2003.
[24] M. Hucka, a. Finney, H. M. Sauro, H. Bolouri, J. C. Doyle, H. Kitano, a. P.
Arkin, B. J. Bornstein, D. Bray, a. Cornish-Bowden, a. a. Cuellar, S. Dronov, E.
D. Gilles, M. Ginkel, V. Gor, I. I. Goryanin, W. J. Hedley, T. C. Hodgman, J.-H.
Hofmeyr, P. J. Hunter, N. S. Juty, J. L. Kasberger, a. Kremling, U. Kummer, N.
Le Novere, L. M. Loew, D. Lucio, P. Mendes, E. Minch, E. D. Mjolsness, Y.
Nakayama, M. R. Nelson, P. F. Nielsen, T. Sakurada, J. C. Schaff, B. E. Shapiro,
T. S. Shimizu, H. D. Spence, J. Stelling, K. Takahashi, M. Tomita, J. Wagner,
and J. Wang, “The systems biology markup language (SBML): a medium for
representation and exchange of biochemical network models,” Bioinformatics,
vol. 19, no. 4, pp. 524–531, Mar. 2003.
[25] M. Kanehisa, S. Goto, S. Kawashima, Y. Okuno, and M. Hattori, “The KEGG
resource for deciphering the genome.,” Nucleic acids research, vol. 32, no.
Database issue, pp. D277–80, Jan. 2004.
[26] M. W. Covert, C. H. Schilling, I. Famili, J. S. Edwards, I. I. Goryanin, E. Selkov,
and B. O. Palsson, “Metabolic modeling of microbial strains in silico.,” Trends in
biochemical sciences, vol. 26, no. 3, pp. 179–86, Mar. 2001.
[27] K. R. Patil, M. Akesson, and J. Nielsen, “Use of genome-scale microbial models
for metabolic engineering.,” Current opinion in biotechnology, vol. 15, no. 1, pp.
64–9, Feb. 2004.
[28] K. J. Kauffman, P. Prakash, and J. S. Edwards, “Advances in flux balance
analysis,” Current Opinion in Biotechnology, vol. 14, no. 5, pp. 491–496, Oct.
2003.
[29] F. Llaneras and J. Picó, “Stoichiometric modelling of cell metabolism.,” Journal
of bioscience and bioengineering, vol. 105, no. 1, pp. 1–11, Jan. 2008.
[30] I. Rocha, P. Maia, P. Evangelista, P. Vilaça, S. Soares, J. P. Pinto, J. Nielsen, K.
R. Patil, E. C. Ferreira, and M. Rocha, “OptFlux: an open-source software
platform for in silico metabolic engineering.,” BMC systems biology, vol. 4, p.
45, Jan. 2010.
[31] A. M. Feist, M. J. Herrgård, I. Thiele, J. L. Reed, and Ø. Bernhard,
“Reconstruction of Biochemical Networks in Microbial Organisms,” vol. 7, no.
2, pp. 129–143, 2011.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
56
[32] K. Raman and N. Chandra, “Flux balance analysis of biological systems:
applications and challenges.,” Briefings in bioinformatics, vol. 10, no. 4, pp. 435–
49, Jul. 2009.
[33] I. Rocha, J. Förster, and J. Nielsen, “Metabolic Models,” vol. 416, pp. 409–433,
2007.
[34] I. Thiele and B. Ø. Palsson, “A protocol for generating a high-quality genome-
scale metabolic reconstruction.,” Nature protocols, vol. 5, no. 1, pp. 93–121, Jan.
2010.
[35] C. S. Henry, M. DeJongh, A. A. Best, P. M. Frybarger, B. Linsay, and R. L.
Stevens, “High-throughput generation, optimization and analysis of genome-
scale metabolic models,” Nat Biotech, vol. 28, no. 9, pp. 977–982, Sep. 2010.
[36] C. S. Henry, M. DeJongh, A. a Best, P. M. Frybarger, B. Linsay, and R. L.
Stevens, “High-throughput generation, optimization and analysis of genome-
scale metabolic models.,” Nature biotechnology, vol. 28, no. 9, pp. 977–82, Sep.
2010.
[37] M. Scheer, A. Grote, A. Chang, I. Schomburg, C. Munaretto, M. Rother, C.
Söhngen, M. Stelzer, J. Thiele, and D. Schomburg, “BRENDA, the enzyme
information system in 2011.,” Nucleic acids research, vol. 39, no. Database
issue, pp. D670–6, Jan. 2011.
[38] T. U. Consortium, “Reorganizing the protein space at the Universal Protein
Resource (UniProt).,” Nucleic acids research, vol. 40, no. Database issue, pp.
D71–5, Jan. 2012.
[39] E. W. Sayers, T. Barrett, D. a Benson, S. H. Bryant, K. Canese, V. Chetvernin,
D. M. Church, M. DiCuccio, R. Edgar, S. Federhen, M. Feolo, L. Y. Geer, W.
Helmberg, Y. Kapustin, D. Landsman, D. J. Lipman, T. L. Madden, D. R.
Maglott, V. Miller, I. Mizrachi, J. Ostell, K. D. Pruitt, G. D. Schuler, E. Sequeira,
S. T. Sherry, M. Shumway, K. Sirotkin, A. Souvorov, G. Starchenko, T. a
Tatusova, L. Wagner, E. Yaschenko, and J. Ye, “Database resources of the
National Center for Biotechnology Information.,” Nucleic acids research, vol.
37, no. Database issue, pp. D5–15, Jan. 2009.
[40] T. Davidsen, E. Beck, A. Ganapathy, R. Montgomery, N. Zafar, Q. Yang, R.
Madupu, P. Goetz, K. Galinsky, O. White, and G. Sutton, “The comprehensive
microbial resource.,” Nucleic acids research, vol. 38, no. Database issue, pp.
D340–5, Jan. 2010.
[41] V. M. Gonçalves, T. C. Zangirolami, R. L. C. Giordano, I. Raw, M. M. Tanizaki,
and R. C. Giordano, “Optimization of medium and cultivation conditions for
capsular polysaccharide production by Streptococcus pneumoniae serotype
23F.,” Applied microbiology and biotechnology, vol. 59, no. 6, pp. 713–7, Sep.
2002.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
57
[42] T. Härtel, E. Eylert, and C. Schulz, “Characterization of Central Carbon
Metabolism of Streptococcus pneumoniae by Isotopologue Profiling,” Journal of
Biological …, 2012.
[43] E. B. Gibert, “Proceedings of the Society for Experimental Biology and
Medicine,” pp. 363–366, 2008.
[44] A. V. Restrepo, B. E. Salazar, M. Agudelo, C. a Rodriguez, A. F. Zuluaga, and
O. Vesga, “Optimization of culture conditions to obtain maximal growth of
penicillin-resistant Streptococcus pneumoniae.,” BMC microbiology, vol. 5, p.
34, Jan. 2005.
[45] L. J. Hathaway, S. D. Brugger, B. Morand, M. Bangert, J. U. Rotzetter, C.
Hauser, W. a Graber, S. Gore, A. Kadioglu, and K. Mühlemann, “Capsule type of
Streptococcus pneumoniae determines growth phenotype.,” PLoS pathogens, vol.
8, no. 3, p. e1002574, Jan. 2012.
[46] K. J. Nye, D. Fallon, B. Gee, S. Messer, R. E. Warren, and N. Andrews, “A
comparison of blood agar supplemented with NAD with plain blood agar and
chocolated blood agar in the isolation of Streptococcus pneumoniae and
Haemophilus influenzae from sputum. Bacterial Methods Evaluation Group.,”
Journal of medical microbiology, vol. 48, no. 12, pp. 1111–4, Dec. 1999.
[47] P. D. Rogers, J. Thornton, K. S. Barker, D. O. Mcdaniel, G. S. Sacks, E. Swiatlo,
L. S. Mcdaniel, R. E. T. Al, and I. N. I. Mmun, “Pneumolysin-Dependent and -
Independent Gene Expression Identified by cDNA Microarray Analysis of THP-
1 Human Mononuclear Cells Stimulated by Streptococcus pneumoniae,” vol. 71,
no. 4, pp. 2087–2094, 2003.
[48] K.-J. Lee, S.-M. Bae, M.-R. Lee, S.-M. Yeon, Y.-H. Lee, and K.-S. Kim,
“Proteomic analysis of growth phase-dependent proteins of Streptococcus
pneumoniae.,” Proteomics, vol. 6, no. 4, pp. 1274–82, Feb. 2006.
[49] M. Rocha, P. Maia, R. Mendes, J. P. Pinto, E. C. Ferreira, J. Nielsen, K. R. Patil,
and I. Rocha, “Natural computation meta-heuristics for the in silico optimization
of microbial strains.,” BMC bioinformatics, vol. 9, p. 499, Jan. 2008.
[50] U. Sauer, D. R. Lasko, J. Fiaux, M. Hochuli, R. Glaser, T. Szyperski, K.
Wüthrich, and J. E. Bailey, “Metabolic flux ratio analysis of genetic and
environmental modulations of Escherichia coli central carbon metabolism.,”
Journal of bacteriology, vol. 181, no. 21, pp. 6679–88, Nov. 1999.
[51] R. Strain, J. Hoskins, W. E. A. Jr, J. Arnold, C. Blaszczak, S. Burgett, B. S.
Dehoff, T. Shawn, L. Fritz, D. Fu, W. Fuller, R. Gilmour, J. S. Glass, A. R. Kraft,
R. E. Lagace, J. Donald, L. N. Lee, E. J. Lefkowitz, J. Lu, S. M. Mcahren, M.
Mchenney, C. W. Mundy, T. I. Nicas, H. Norris, M. O. Gara, R. B. Peery, T.
Gregory, P. Rockey, P. Sun, E. Malcolm, Y. Yang, M. Young-bellido, G. Zhao,
C. A. Zook, R. H. Baltz, S. R. Jaskunas, P. R. R. Jr, P. L. Skatrud, J. I. Glass, W.
E. Alborn, L. C. Blaszczak, B. S. D. E. Hoff, S. T. Estrem, C. Geringer, H.
Khoja, D. J. L. E. Blanc, M. M. C. Henney, and K. M. C. Leaster, “Genome of
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
58
the Bacterium Streptococcus Genome of the Bacterium Streptococcus
pneumoniae Strain R6,” 2001.
[52] M. Igoillo-Esteve, D. Maugeri, A. L. Stern, P. Beluardi, and J. J. Cazzulo, “The
pentose phosphate pathway in Trypanosoma cruzi: a potential target for the
chemotherapy of Chagas disease.,” Anais da Academia Brasileira de Ciências,
vol. 79, no. 4, pp. 649–63, Dec. 2007.
[53] M. Scheer, A. Grote, A. Chang, I. Schomburg, C. Munaretto, M. Rother, C.
Söhngen, M. Stelzer, J. Thiele, and D. Schomburg, “BRENDA, the enzyme
information system in 2011.,” Nucleic acids research, vol. 39, no. Database
issue, pp. D670–6, Jan. 2011.
[54] R. Iyer and A. Camilli, “Sucrose metabolism contributes to in vivo fitness of
Streptococcus pneumoniae.,” Molecular microbiology, vol. 66, no. 1, pp. 1–13,
Oct. 2007.
[55] G. Metabolism, “Chapter 6 Regulation of Glutamine and Glutamate Metabolism
by GlnR and GlnA in Streptococcus pneumoniae,” pp. 25097–25109.
[56] M. V. Tullius, G. Harth, and M. A. Horwitz, “Glutamine Synthetase GlnA1 Is
Essential for Growth of Mycobacterium tuberculosis in Human THP-1
Macrophages and Guinea Pigs Glutamine Synthetase GlnA1 Is Essential for
Growth of Mycobacterium tuberculosis in Human THP-1 Macrophages and
Guinea Pigs,” 2003.
[57] K. E. Klose and J. J. Mekalanos, “Simultaneous prevention of glutamine
synthesis and high-affinity transport attenuates Salmonella typhimurium
virulence . Simultaneous Prevention of Glutamine Synthesis and High-Affinity
Transport Attenuates Salmonella typhimurium Virulence,” vol. 65, no. 2, 1997.
[58] P. G. Flanagan and A. Paull, “Carbon dioxide requirements of,” pp. 669–678,
1998.
[59] P. Burghout, L. E. Cron, H. Gradstedt, B. Quintero, E. Simonetti, J. J. E. Bijlsma,
H. J. Bootsma, and P. W. M. Hermans, “Carbonic anhydrase is essential for
Streptococcus pneumoniae growth in environmental ambient air.,” Journal of
bacteriology, vol. 192, no. 15, pp. 4054–62, Aug. 2010.
[60] H. Marrakchi, W. E. Dewolf, C. Quinn, J. West, B. J. Polizzi, C. Y. So, D. J.
Holmes, S. L. Reed, R. J. Heath, D. J. Payne, C. O. Rock, and N. G. Wallis,
“Characterization of Streptococcus pneumoniae enoyl-(acyl-carrier protein)
reductase (FabK).,” The Biochemical journal, vol. 370, no. Pt 3, pp. 1055–62,
Mar. 2003.
[61] Y. Fujita, H. Matsuoka, and K. Hirooka, “Regulation of fatty acid metabolism in
bacteria.,” Molecular microbiology, vol. 66, no. 4, pp. 829–39, Nov. 2007.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
59
[62] a Polissi, a Pontiggia, G. Feger, M. Altieri, H. Mottl, L. Ferrari, and D. Simon,
“Large-scale identification of virulence genes from Streptococcus pneumoniae.,”
Infection and immunity, vol. 66, no. 12, pp. 5620–9, Dec. 1998.
[63] A. P. Oliveira, J. Nielsen, and J. Förster, “Modeling Lactococcus lactis using a
genome-scale flux model.,” BMC microbiology, vol. 5, p. 39, Jan. 2005.
[64] D. a Rodionov, A. G. Vitreschak, A. a Mironov, and M. S. Gelfand,
“Comparative genomics of the methionine metabolism in Gram-positive bacteria:
a variety of regulatory systems.,” Nucleic acids research, vol. 32, no. 11, pp.
3340–53, Jan. 2004.
[65] G. Bulaj, T. Kortemme, and D. P. Goldenberg, “Ionization-reactivity
relationships for cysteine thiols in polypeptides.,” Biochemistry, vol. 37, no. 25,
pp. 8965–72, Jun. 1998.
[66] D. a. Rodionov, “Regulation of lysine biosynthesis and transport genes in
bacteria: yet another RNA riboswitch?,” Nucleic Acids Research, vol. 31, no. 23,
pp. 6748–6757, Dec. 2003.
[67] D.-S. Lee, H. Burd, J. Liu, E. Almaas, O. Wiest, A.-L. Barabási, Z. N. Oltvai,
and V. Kapatral, “Comparative genome-scale metabolic reconstruction and flux
balance analysis of multiple Staphylococcus aureus genomes identify novel
antimicrobial drug targets.,” Journal of bacteriology, vol. 191, no. 12, pp. 4015–
24, Jun. 2009.
[68] T. U. Consortium, “The universal protein resource (UniProt).,” Nucleic acids
research, vol. 36, no. Database issue, pp. D190–5, Jan. 2008.
[69] A. G. Vitreschak, D. A. Rodionov, and A. A. Mironov, “Regulation of ribo ¯
avin biosynthesis and transport genes in bacteria by transcriptional and
translational attenuation,” vol. 30, no. 14, 2002.
[70] O. Dias and M. Rocha, “Merlin : Metabolic Models Reconstruction using
Genome-Scale Information,” no. 2005, 2007.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
60
Annexes
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
61
Annex 1 – Fatty Acid Biosynthesis of Streptococcus pneumoniae TIGR4, R6 and G54.
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
0
Annex 2 – Supplementary data used by SEED for determination of biomass reaction
Comp
ound
ID
Compound name Rea
cta
nt
Class Coefficient Inclusion criteria
cpd00
001
H2O YE
S
ENE
RGY
40 UNIVERSAL
cpd00
002
ATP YE
S
ENE
RGY
40 UNIVERSAL
cpd00
002
ATP YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
003
NAD+ YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
006
NADP+ YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
008
ADP YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
008
ADP NO ENE
RGY
40 UNIVERSAL
cpd00
009
Orthophosphate YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
009
Orthophosphate NO ENE
RGY
40 UNIVERSAL
cpd00
010
CoA YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
012
Pyrophosphate YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00 FAD YE COF 0.10/(TOTAL MASS OF ALL UNIVERSAL
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
1
015 S ACT
OR
COFACTOR COMPONENTS)
cpd00
016
Pyridoxal phosphate YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
017
S-Adenosyl-L-methionine YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
018
AMP YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
028
Heme YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
AND{SUBSYSTEM:Heme and Siroheme Biosynthesis`A`B`F}
cpd00
030
Manganese YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
031
GDP YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
034
Zinc YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
038
GTP YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
042
Glutathione YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
OR{SUBSYSTEM:Glutathione: Biosynthesis and gamma-glutamyl
cycle`A`B|SUBSYSTEM:Glutathione: Non-redox
reactions`A|SUBSYSTEM:Glutathione: Redox cycle`A`B}
cpd00
046
CMP YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
048
Sulfate YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00 CTP YE COF 0.10/(TOTAL MASS OF ALL UNIVERSAL
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
2
052 S ACT
OR
COFACTOR COMPONENTS)
cpd00
056
Thiamin diphosphate YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
AND{SUBSYSTEM:Thiamin biosynthesis}
cpd00
058
Copper2 YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
063
Calcium YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
067
H+ NO ENE
RGY
40 UNIVERSAL
cpd00
087
Tetrahydrofolate YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
AND{SUBSYSTEM:One-carbon metabolism by
tetrahydropterines|SUBSYSTEM:Folate Biosynthesis|!SUBSYSTEM:One-
carbon metabolism by tetrahydropterines`H}
cpd00
096
CDP YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
099
Chloride YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
118
Putrescine YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
AND{SUBSYSTEM:Polyamine Metabolism`A`B`C`D`E`F`G}
cpd00
126
GMP YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
149
Cobalt YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
166
Cobamide coenzyme YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
AND{SUBSYSTEM:Coenzyme B12 biosynthesis}
cpd00
201
10-Formyltetrahydrofolate YE
S
COF
ACT
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
AND{SUBSYSTEM:One-carbon metabolism by
tetrahydropterines|SUBSYSTEM:Folate Biosynthesis|!SUBSYSTEM:One-
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
3
OR carbon metabolism by tetrahydropterines`H}
cpd00
205
Potassium YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
220
Riboflavin YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
254
Magnesium YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd00
264
Spermidine YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
AND{SUBSYSTEM:Polyamine Metabolism}
cpd00
345
5-Methyltetrahydrofolate YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
AND{SUBSYSTEM:One-carbon metabolism by
tetrahydropterines|SUBSYSTEM:Folate Biosynthesis|!SUBSYSTEM:One-
carbon metabolism by tetrahydropterines`H}
cpd00
557
Siroheme YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
AND{SUBSYSTEM:Heme and Siroheme Biosynthesis`A`F}
cpd01
997
Dimethylbenzimidazole NO COF
ACT
OR
SUM OF COEFICIENTS
FOR(cpd00166)
AND{COMPOUND:cpd00166}
cpd02
229
Undecaprenyl diphosphate YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{!NAME:Mycoplasma|!NAME:Spiroplasma|!NAME:Ureaplasma|!NAM
E:phytoplasma}
cpd03
422
Cobinamide NO COF
ACT
OR
SUM OF COEFICIENTS
FOR(cpd00166)
AND{COMPOUND:cpd00166}
cpd10
515
Fe2+ YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd10
516
Fe3+ YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd11
416
Biomass NO BIO
MAS
1 UNIVERSAL
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
4
S
cpd11
459
tcam YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive}
cpd11
461
DNA YE
S
DNA 0,025 UNIVERSAL
cpd11
462
mRNA YE
S
MRN
A
0,05 UNIVERSAL
cpd11
463
Protein YE
S
PRO
TEIN
0,5 UNIVERSAL
cpd11
493
Acyl-carrier protein YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
UNIVERSAL
cpd12
370
Apo-[acyl-carrier-protein] NO COF
ACT
OR
SUM OF COEFICIENTS
FOR(cpd11493)
UNIVERSAL
cpd15
352
2-Demethylmenaquinone 8 YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
AND{SUBSYSTEM:Menaquinone and Phylloquinone Biosynthesis}
cpd15
432
core oligosaccharide lipid A YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram negative}
cpd15
500
Menaquinone 8 YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
AND{SUBSYSTEM:Menaquinone and Phylloquinone
Biosynthesis|ROLE:Ubiquinone/menaquinone biosynthesis methyltransferase
UbiE/COQ5 (EC 2.1.1.-)}
cpd15
533
phosphatidylethanolamine
dioctadecanoyl
YE
S
LIPI
DS
0.075/(TOTAL MASS OF ALL
LIPID COMPONENTS)
AND{ROLE:Phosphatidylserine decarboxylase (EC
4.1.1.65)|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
540
Phosphatidylglycerol
dioctadecanoyl
YE
S
LIPI
DS
0.075/(TOTAL MASS OF ALL
LIPID COMPONENTS)
AND{OR{ROLE:Phosphatidylglycerophosphatase B (EC
3.1.3.27)|ROLE:Phosphatidylglycerophosphatase A (EC
3.1.3.27)}|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
560
Ubiquinone-8 YE
S
COF
ACT
OR
0.10/(TOTAL MASS OF ALL
COFACTOR COMPONENTS)
AND{SUBSYSTEM:Ubiquinone Biosynthesis}
cpd15
665
Peptidoglycan polymer (n
subunits)
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{!NAME:Mycoplasma|!NAME:Spiroplasma|!NAME:Ureaplasma|!NAM
E:phytoplasma}
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
5
cpd15
666
Peptidoglycan polymer (n-1
subunits)
NO CELL
WAL
L
SUM OF COEFICIENTS
FOR(cpd15665,cpd15667,cpd15
668,cpd15669)
OR{COMPOUND:cpd15665|COMPOUND:cpd15667|COMPOUND:cpd1566
8|COMPOUND:cpd15669}
cpd15
667
glycerol teichoic acid (n=45),
linked, unsubstituted
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
668
glycerol teichoic acid (n=45),
linked, D-ala substituted
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
669
glycerol teichoic acid (n=45),
linked, glucose substituted
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
695
Diisoheptadecanoylphosphatidylet
hanolamine
YE
S
LIPI
DS
0.075/(TOTAL MASS OF ALL
LIPID COMPONENTS)
AND{ROLE:Phosphatidylserine decarboxylase (EC
4.1.1.65)|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
696
Dianteisoheptadecanoylphosphatid
ylethanolamine
YE
S
LIPI
DS
0.075/(TOTAL MASS OF ALL
LIPID COMPONENTS)
AND{ROLE:Phosphatidylserine decarboxylase (EC
4.1.1.65)|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
722
Diisoheptadecanoylphosphatidylgl
ycerol
YE
S
LIPI
DS
0.075/(TOTAL MASS OF ALL
LIPID COMPONENTS)
AND{OR{ROLE:Phosphatidylglycerophosphatase B (EC
3.1.3.27)|ROLE:Phosphatidylglycerophosphatase A (EC
3.1.3.27)}|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
723
Dianteisoheptadecanoylphosphatid
ylglycerol
YE
S
LIPI
DS
0.075/(TOTAL MASS OF ALL
LIPID COMPONENTS)
AND{OR{ROLE:Phosphatidylglycerophosphatase B (EC
3.1.3.27)|ROLE:Phosphatidylglycerophosphatase A (EC
3.1.3.27)}|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
748
Stearoyllipoteichoic acid (n=24),
linked, unsubstituted
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
749
Isoheptadecanoyllipoteichoic acid
(n=24), linked, unsubstituted
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
750
Anteisoheptadecanoyllipoteichoic
acid (n=24), linked, unsubstituted
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
757
Stearoyllipoteichoic acid (n=24),
linked, glucose substituted
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
758
Isoheptadecanoyllipoteichoic acid
(n=24), linked, glucose substituted
YE
S
CELL
WAL
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
University of Minho Reconstruction of a generic metabolic model for Streptococcus pneumoniae
6
L
cpd15
759
Anteisoheptadecanoyllipoteichoic
acid (n=24), linked, glucose
substituted
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
766
Stearoyllipoteichoic acid (n=24),
linked, N-acetyl-D-glucosamine
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
767
Isoheptadecanoyllipoteichoic acid
(n=24), linked, N-acetyl-D-
glucosamine
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
768
Anteisoheptadecanoyllipoteichoic
acid (n=24), linked, N-acetyl-D-
glucosamine
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
775
Stearoyllipoteichoic acid (n=24),
linked, D-alanine substituted
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
776
Isoheptadecanoyllipoteichoic acid
(n=24), linked, D-alanine
substituted
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
777
Anteisoheptadecanoyllipoteichoic
acid (n=24), linked, D-alanine
substituted
YE
S
CELL
WAL
L
0.25/(TOTAL MASS OF ALL
CELL WALL COMPONENTS)
AND{CLASS:Gram positive|SUBSYSTEM:Fatty Acid Biosynthesis FASII}
cpd15
793
Stearoylcardiolipin (B. subtilis) YE
S
LIPI
DS
0.075/(TOTAL MASS OF ALL
LIPID COMPONENTS)
AND{ROLE:Cardiolipin synthetase (EC 2.7.8.-)|SUBSYSTEM:Fatty Acid
Biosynthesis FASII}
cpd15
794
Isoheptadecanoylcardiolipin (B.
subtilis)
YE
S
LIPI
DS
0.075/(TOTAL MASS OF ALL
LIPID COMPONENTS)
AND{ROLE:Cardiolipin synthetase (EC 2.7.8.-)|SUBSYSTEM:Fatty Acid
Biosynthesis FASII}
cpd15
795
Anteisoheptadecanoylcardiolipin
(B. subtilis)
YE
S
LIPI
DS
0.075/(TOTAL MASS OF ALL
LIPID COMPONENTS)
AND{ROLE:Cardiolipin synthetase (EC 2.7.8.-)|SUBSYSTEM:Fatty Acid
Biosynthesis FASII}