Genome-scale metabolic reconstruction and constraint-based modelling of the Antarctic bacterium P...

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Genome scale metabolic reconstruction and constraints-

based modelling of the Antarctic bacterium Pseudoalteromonas haloplanktis TAC125

Journal: Environmental Microbiology and Environmental Microbiology Reports

Manuscript ID: EMI-2014-0451.R1

Manuscript Type: EMI - Research article

Journal: Environmental Microbiology

Date Submitted by the Author: 13-May-2014

Complete List of Authors: Fondi, Marco; University of Florence, Dep. of Biology Maida, Isabel; University of Florence, Dep. of Biology Perrin, Elena; University of Florence, Dep. of Biology Mellera, Alessandra; University of Florence, Dep. of Biology Mocali, Stefano; Centro di Ricerca per l’Agrobiologia e la Pedologia (CRA-ABP), Consiglio per la Ricerca e la Sperimentazione in Agricoltura Parrilli, Ermenegilda; University of Naples Federico II, Department of Chemical Sciences Tutino, Maria Luisa; University of Naples Federico II, Department of Chemical Sciences Liò, Pietro; University of Cambridge, Computer Laboratory Fani, Renato; University of Florence, Dep. of Biology; University of Florence, Evolutionary Biology

Keywords:

bioinformatics, environmental signal/stress responses, extremophiles/extremophily, gene expression/regulation, genomics/functional genomics/comparative genomics, metabolic networks, metabolism, modelling

Wiley-Blackwell and Society for Applied Microbiology

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Genome scale metabolic reconstruction and constraints-based 1

modelling of the Antarctic bacterium Pseudoalteromonas haloplanktis 2

TAC125. 3

4

Marco Fondi1,2§, Isabel Maida

1, Elena Perrin

1, Alessandra Mellera

1,2, Stefano Mocali

3, Ermenegilda 5

Parrilli4, Maria Luisa Tutino

4, Pietro Liò

5, Renato Fani

1,2 6

7

1 Laboratory of Microbial and Molecular Evolution, Department of Biology, University of Florence, Via Madonna del 8

Piano 6, 50019 Sesto Fiorentino (Firenze) 9

2 ComBo, Florence Computational Biology group, University of Florence, Via Madonna del Piano 6, 50019 Sesto 10

Fiorentino (Firenze) 11

3 Consiglio per la Ricerca e la Sperimentazione in Agricoltura, Centro di Ricerca per l’Agrobiologia e la Pedologia 12

(CRA-ABP), Piazza d’Azeglio 30, 50121 Firenze, Italy 13

4 Department of Chemical Sciences, University of Naples Federico II, Complesso Universitario M. S. Angelo, Via 14

Cintia, I-80126, Naples, Italy 15

5 Computer Laboratory, Cambridge University, William Gates Building 15, JJ Thomson Avenue, Cambridge, United 16

Kingdom§Corresponding author 17

Dr. Marco Fondi 18

Laboratory of Microbial and Molecular Evolution, Department of Biology, University of Florence, Via Madonna del 19

Piano 6, 50019 Sesto Fiorentino (Firenze) 20

Tel +390554574736 21

Email: [email protected] 22

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Summary 23

The Antarctic strain Pseudoalteromonas haloplanktis TAC125 is one of the model organisms of 24

cold-adapted bacteria and is currently exploited as a new alternative expression host for numerous 25

biotechnological applications. 26

Here, we investigated several metabolic features of this strain through in silico modelling and 27

functional integration of –omics data. A genome-scale metabolic model of P. haloplanktis TAC125 28

was reconstructed, encompassing information on 721 genes, 1133 metabolites and 1322 reactions. 29

The predictive potential of this model was validated against a set of experimentally determined 30

growth rates and a large dataset of growth phenotypic data. Furthermore, evidence synthesis from 31

proteomics, phenomics, physiology and metabolic modeling data revealed possible drawbacks of 32

cold-dependent changes in gene expression on the overall metabolic network of P. haloplanktis 33

TAC125. These included, for example, variations in its central metabolism, amino acids 34

degradation and fatty acids biosynthesis. 35

The genome scale metabolic model described here is the first one reconstructed so far for an 36

Antarctic microbial strain. It allowed a system-level investigation of variations in cellular metabolic 37

fluxes following a temperature downshift. It represents a valuable platform for further 38

investigations on P. haloplanktis TAC125 cellular functional states and for the design of more 39

focused strategies for its possible biotechnological exploitation. 40

41

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Introduction 42

Antarctica is one of the most extreme environments on Earth due to the presence of stably low 43

temperatures. Many (psychrophilic) microorganisms inhabit this environment, being responsible for 44

most of the biomass of this ecological niche and playing a key role in maintaining proper ecosystem 45

functions (Wilkins et al., 2013). Such cold-adapted microorganisms have begun to attract the 46

attention of the scientific community from both fundamental and application viewpoints 47

(Cavicchioli et al., 2002; D'Amico et al., 2006). Indeed, psychrophiles have evolved peculiar 48

features (Feller and Gerday, 2003; Giordano et al., 2012) to face the profound impact of cold shock 49

on several cellular processes (e.g. growth rates, ribosomal synthesis, cytoplasmic membrane 50

composition, etc.) (Phadtare, 2004). Overall, survival at low temperature depends on the ability of 51

microbes to sense changes in temperature and to transduce these signals to the genome, ultimately 52

bringing changes in the regulation of cold-adaptation related genes (Shivaji and Prakash, 2010). 53

These include: i) genes for fatty acid desaturases (Sato and Murata, 1980), ii) genes involved in 54

replication (Jones et al., 1992; Graumann and Marahiel, 1999), transcription (Sledjeski et al., 1996; 55

Inaba et al., 2003) and translation (Jones et al., 1987; Xia et al., 2003), iii) genes encoding cold 56

shock proteins (Brandi et al., 1994; Atlung and Ingmer, 1997), as well as iv) many genes coding for 57

still uncharacterized proteins (Kawamoto et al., 2007; Ting et al., 2010). Moreover, the increase of 58

enzymes concentration (Willem et al., 1999) and the expression of specific cold-adapted enzymatic 59

isoforms (Hoyoux et al., 2004) represent two additional examples of how psychrophilic enzymatic 60

machineries may counteract the growth limiting effects of cold temperatures. 61

Recently, the spreading of –omics technologies has allowed the system-level investigation of the 62

mechanisms involved in microbial cold adaptation (Seo et al., 2004; Goodchild et al., 2005; 63

Bakermans et al., 2007; Zheng et al., 2007; Piette et al., 2010). For example, a transcriptomics 64

analysis on Shewanella oneidensis MR-1 showed that more than 70% of its genes involved in 65

energy metabolism were down-regulated following a temperature downshift (Gao et al., 2006). A 66

proteomic study on Sphingopyxis alaskensis revealed that a large fraction of genes involved in 67

metabolic processes (e.g. energy production/conversion, carbohydrate, amino acids, nucleotides and 68

cofactors transport/metabolism) are less abundant at lower temperatures (Ting et al., 2010). Finally, 69

from a metabolic viewpoint, a large-scale metabolomics analysis on the cold adaptation of 70

Mesorhizobium sp. strain N33 revealed a key role of (unsaturated) fatty acids biosynthetic process. 71

From such examples, it emerges that, despite some strategies may be shared, different 72

microorganisms use different strategies to cope with cold environments (Kawamoto et al., 2007). 73

The Antarctic marine bacterium Pseudoalteromonas haloplanktis TAC125 (PhTAC125) has been 74

isolated from sea water sampled along the Antarctic ice-shell, a permanently cold environment. 75

PhTAC125 is capable of growing in a wide temperature range (4–25°C) and its lowest observed 76

doubling time was detected at 20°C (Medigue et al., 2005). Several exceptional genomic and 77

metabolic features were derived from the genome sequence of this bacterium, showing adaptation to 78

periodic situations of nutrient abundance (Medigue et al., 2005). Indeed PhTAC125 is considered to 79

be one of the model organisms of cold-adapted bacteria and has been suggested as an alternative 80

host for the soluble overproduction of heterologous proteins, given its capability to grow fast at low 81

temperatures (Duilio et al., 2004; Wilmes et al., 2010; Rippa et al., 2012; Corchero et al., 2013). 82

Furthermore, bacteria belonging to the genus Pseudoalteromonas are known to possess an 83

inhibitory activity against human pathogens belonging to the Burkholdeia cepacia complex (Bcc) 84

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and to be able to produce antibiofilm molecules (Papa et al., 2013; Papaleo et al., 2013), revealing 85

the interesting biotechnological potential and metabolic biodiversity of these microorganisms. The 86

increasing interest in PhTAC125 has led to the accumulation of different data types for this 87

bacterium in the last few years, including its complete genome sequence (Medigue et al., 2005), its 88

proteome (Piette et al., 2010; Piette et al., 2011) and detailed growth phenotypes (Wilmes et al., 89

2010; Giuliani, 2011). Thus, it is now possible to integrate such different data sources and perform 90

a system-level investigation of PhTAC125 metabolism. 91

Genome scale metabolic modeling represents a valuable tool in this context. Indeed, this in silico 92

approach can be adopted to quantitatively simulate chemical reactions fluxes within the cell, 93

including metabolic adjustments in response to external perturbations (e.g. temperature downshift). 94

Genome annotations are usually transformed into models by defining the boundaries of the system, 95

a biomass assembly reaction, and exchange fluxes with the environment (Durot et al., 2009; Thiele 96

and Palsson, 2010). Constraint-based modelling methods (e.g. Flux Balance Analysis, FBA) can 97

then be used to compute the resulting balance of all the active cellular reactions in the cell and to 98

simulate the maximal growth of a cell in a given environmental condition (Varma and Palsson, 99

1994; Schilling et al., 2000). In the past decade, this approach has been successfully applied for 100

studying large-scale metabolic networks in microbes, with the aim of guiding rational engineering 101

of biological systems for applications in industrial and medical biotechnology (Milne et al., 2009). 102

Interestingly, different data types can be mapped onto metabolic models in order to elucidate more 103

thoroughly the metabolism of a cell and its response to environmental factors. This is usually done 104

by including functional characterization and accurate quantification of all the main cellular 105

information levels of gene products, mRNA, proteins and metabolites, as well as their interaction 106

(Zhang et al., 2010). In recent years, –omics-derived data have been used to refine, validate and/or 107

integrate metabolic models, including transcriptomics (Colijn et al., 2009; Jensen and Papin, 2011), 108

proteomics (Gille et al., 2010), fluxomics (Chen et al., 2011; Feng and Zhao, 2013) and phenomics 109

(Fang et al., 2011). 110

In at least two study-cases, FBA and expression data have been merged to explore the effect of 111

temperature downshift at the system level (Navid and Almaas, 2012; Tong et al., 2013). In 112

particular, Tong et al. (2013) performed robustness analysis on the core metabolic model of 113

Thermoanaerobacter tengcongensis to study the dynamic changes of the metabolic network 114

following the perturbation of the culture temperature and collecting the bacterial growth rates and 115

differential proteomes. Given the overall agreement between in silico simulations and observed 116

phenotypes, this approach was shown to provide a reliable platform to systematically evaluate the 117

mechanisms of bacterial metabolism and relevant switches in the presence of a temperature 118

downshift. 119

In this study, a genome-scale reconstruction of P. haloplanktis TAC125’s metabolism was 120

performed based on its genome annotation. The predictive capability of the model [named iMF721 121

according to the current naming convention (Reed et al., 2003)] was successfully validated 122

comparing constraints-based modeling outcomes with experimentally determined growth rates and 123

large scale growth phenotype data (Phenotype Microarray). The iMF721 model was then used to 124

globally investigate possible metabolic adjustments of P. haloplanktis TAC125 during growth at 125

low temperature by means of robustness analysis and functional integration of protein abundance 126

data into the reconstructed network. 127

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Finally, the metabolic reconstruction reported herein represents a reliable platform for the future 128

design of experimental strategies aimed at the exploitation of the biotechnological potential of 129

PhTAC125. 130

131

132

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Experimental procedures 133

Metabolic model reconstruction 134

An initial draft metabolic reconstruction of the strain PhTAC125 was constructed using RAST 135

annotation system with default parameters (Overbeek et al., 2014) and then downloaded from the 136

ModelSEED database (Henry et al., 2010). This reconstruction was then thoroughly inspected 137

following the main steps listed in (Thiele and Palsson, 2010) and refined by integrating information 138

from PhTAC125 original genome annotation (Medigue et al., 2005) and from different functional 139

databases, including KEGG (Kanehisa, 2002) BRENDA (Scheer et al., 2011) and MetCyc (Caspi et 140

al., 2006). At this stage, models of related organisms (e.g. Escherichia coli, Shewanella oneidensis 141

MR-1) were used as reference in a comparative genomics workflow for identifying potentially 142

missing reactions. BLAST (Altschul et al., 1997) searches (adopting the Bidirectional Best Hit 143

criterion) on the PhTAC125 genome were carried out to confirm/exclude the inclusion of further 144

reactions to the original model. The list of the PhTAC125’s cellular transporters was obtained 145

probing the Transporter Classification Data Base [TCDB (Saier et al., 2006)]. At the end of this 146

iterative, manual refinement procedure, the model gained information on 60 additional genes and 147

(about) 200 reactions were added to the reconstruction (see Table S1). Also, to properly reconcile 148

the model with experimental data, some of the reactions initially included by the adopted automatic 149

reconstruction method were removed from the model during manual refinement. 150

To date, no detailed information on the biomass composition of PhTAC125 is available in scientific 151

literature, except for specific constituents [e.g. the structure of its lipo-oligosaccharide fraction 152

(Corsaro et al., 2001), RNA and DNA composition (Medigue et al., 2005)]. Accordingly, missing 153

information concerning the biomass assembly reaction of PhTAC125 was derived from the closely 154

related gamma-proteobacterium Shewanella oneidensis MR-1 and E. coli, for which detailed 155

metabolic models were already available (Feist et al., 2007) (Pinchuk et al., 2010) (Orth et al., 156

2011). The description of the biomass composition can be found in Table S2. 157

The final PhTAC125 model was named iMF721 according to the current nomenclature standard 158

(Reed et al., 2003) and was successfully validated with the SBML Validator tool available at 159

http://sbml.org/Facilities/Validator/index.jsp. The SBML-formatted version of iMF721 is available 160

as Document S1. 161

162

Metabolic modeling 163

The Flux Balance Analysis (FBA) method was employed to simulate flux distribution in different 164

conditions. Briefly, FBA is a constraint-based method relying on the representation of the 165

biochemical system under investigation in the form of stoichiometric matrix S (m×n), where m is 166

the number of metabolites and n the number of reactions. FBA is based on the assumption of the 167

cellular pseudo-steady state, according to which the net sum of all the production and consumption 168

rates of each internal metabolite within a cell is considered to be zero. Under this assumption, the 169

system can be described by the set of linear equations: 170

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�� = �� = 0, ∀

�

�� ∈ �, ∀� ∈ �

in which Xi is the concentration of metabolite i, Sij is the stoichiometric coefficient of the ith 171

metabolite in the jth reaction, vj is the flux of the j

th reaction, N the entire set of metabolites and M 172

the entire set of reactions. 173

Upper and lower bounds of flux through each reaction act as further constraints and are expressed 174

as: 175

�� < <�� where lb and ub are the lower and upper limits for reaction j, respectively. Finally, FBA exploits 176

linear programming to determine a feasible steady state flux vector that optimizes a given objective 177

function (e.g. biomass production). 178

The reconstructed model was analysed using COBRAToolbox-2.0 (Schellenberger et al., 2011) in 179

MATLAB® R2009b (Mathworks Inc.). Gurobi 5.6 (www.gurobi.com) and GLPK 4.32 180

(http://www.gnu.org/software/glpk/) solvers were used for computational simulations presented 181

herein. 182

Proteomic data and robustness analysis 183

Up- and down-regulation patterns of protein expression used in this work were obtained from 184

previously published studies (Piette et al., 2010; Piette et al., 2011) in which the proteome of cells 185

of PhTAC125 grown at 4°C and 18°C were analysed using two-dimensional differential in-gel 186

electrophoresis. 187

These data were used to compare (by means of robustness analysis) the experimentally determined 188

changes in protein abundance (following a temperature downshift) with the in silico inferred impact 189

of various reaction flux variations on the cell growth rate. Robustness analysis consists in the 190

calculation of suboptimal cellular growth (using FBA) when the reaction flux of a given reaction is 191

varied around the optimal value. In this context, we perturbed the flux through each reaction whose 192

corresponding genes i) were included in the model and ii) showed a significant change in 193

expression during proteomics experiments. Results of this analysis can be visualized as a plot of the 194

reaction flux (x-axis) versus the cellular growth rates (y-axis). We used the ad hoc implemented 195

function in COBRA Toolbox to perform robustness analysis. This approach overall resembles the 196

one recently used to investigate the response of Thermoanaerobacter tengcongensis adaptation to 197

high temperatures (Tong et al., 2013). 198

Proteomic data integration and fluxes visualization 199

Up- and down-regulation ratios (and corresponding p-values) of protein expression were mapped 200

onto the PhTAC125 metabolic model using MADE (Metabolic Adjustment by Differential 201

Expression) (Jensen and Papin, 2011). The visualization of the changes in reaction fluxes in the two 202

conditions was performed using iPath 2.0 (Yamada et al., 2011). 203

204

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205

206

Phenotype Microarray 207

Cells to be tested were previously plated on TYP medium agar plates and incubated overnight at 208

25°C. Cells were swabbed from the plates after overnight growth and suspended in appropriate 209

medium containing Inoculating fluid (BIOLOG) and Schatz salts (NaCl 10g/l, KH2PO4 1g/l, 210

NH4NO3 1g/l, MgSO4 7H2O 0.2g/l, FeSO4 7H2O 10mg/l, CaCl2 2H2O 10mg/l) as additive solution 211

until the 85% transmittance suspension of cells was obtained on a Biolog turbidimeter. In order to 212

inoculate the microplates PM1 and PM2, 1% tetrazolium violet (vol/vol) (Dye Mix A, Biolog) was 213

added to the suspension and 100 µL of such mixture were then inoculated in each well. Plates were 214

incubated at 15°C for 1 week (167h) with readings taken manually three times a day using a 215

Synergy HT (Biotek) system. The cellular growth was determined on the absorbance (OD 590nm) 216

values of the kinetic curve of dye formation. In particular OD=0.45 was selected as cut-off value for 217

cellular growth within each well of the PM microplates as tetrazolium colour development was 218

visually detected only for OD>0.45. 219

220

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Results and discussion 221

Characteristics of the reconstructed metabolic model of PhTAC125 222

P. haloplanktis TAC125 was firstly isolated from an Antarctic coastal seawater sample. It is 223

considered one of the model organisms of cold-adapted bacteria (Feller and Gerday, 2003) and, 224

given its capability to growth at reduced temperatures in respect to other model organisms (e.g. E. 225

coli), it is being exploited as a new alternative expression host for a number of heterologous 226

proteins. The metabolism of this versatile representative of the marine bacterioplankton was here 227

investigated by means of metabolic network reconstruction and constraints-based modelling. 228

The metabolic network of PhTAC125 was initially obtained from its genome annotation and 229

integrated with additional functional information as described in Experimental procedures. The 230

reconstructed genome-scale metabolic model (named iMF721) encompasses information on 721 231

ORFs (20.7% of the PhTAC125 protein encoding genes), 1133 metabolites and 1322 reactions 232

(Table 1). The model includes non-gene-associated reactions accounting for i) the biomass 233

assembly reaction (which also takes into consideration non-growth-associated ATP costs), ii) 48 234

reactions which filled gaps in the metabolic network (19 added by the “AUTOCOMPLETION” 235

function of the RAST annotation system, and 29 added during the manual evaluation of the model), 236

iii) 85 exchange reactions allowing the simulation of external conditions (e.g. nutrients exchange) 237

and iv) 17 spontaneous reactions. Additionally, during the gap-filling process sink and demand 238

reactions were added to the model when necessary. 239

Importantly, our model embeds almost all (93 out of 96, 97%) of the metabolism-related protein 240

inventory expressed by P. haloplanktis TAC125 with a complex amino acid mixture as the only 241

carbon and nitrogen source, as assessed by proteomics analysis (Wilmes et al., 2011). Also, the 242

model falls well within the range of currently available models of (more or less) closely related 243

microorganisms (Oberhardt et al., 2008; Puchalka et al., 2008; Flynn et al., 2012). On this basis, we 244

conclude that the iMF721 model should, in principle, be able to provide a comprehensive picture of 245

the metabolic features of PhTAC125. 246

iMF721 model validation 247

PhTAC125 seems to be well adapted to grow on rich media and in all media described for this 248

bacterium, amino acids were used as carbon and nitrogen source (Wilmes et al., 2010). Also, the 249

capability of this strain to grow on defined medium containing amino acids as the sole carbon 250

source (including L-leucine, L-alanine, L-aspartate and L-glutamate) has been previously shown 251

(Giuliani, 2011). To quantitatively assess the model’s accuracy in predicting growth rates, we 252

simulated growth phenotypes for minimal medium supplemented with these different amino acids 253

and compared the in vivo growth data with in silico prediction. 254

Accordingly, an in silico minimal growth medium was defined using exchange reactions present in 255

the model and biomass optimization was selected as the model objective function (O.F.). More in 256

detail, lower bounds of exchange reactions accounting for all the salts present in Schatz medium 257

(Papa et al., 2007) were set to -1000 mmol/g*h-1, in order to mimic non-limiting conditions. Each of 258

the aforementioned amino acids was then chosen as the unique carbon source of this in silico 259

medium. The PhTAC125 enzymatic capacity for these compounds was calculated as the ratio of the 260

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growth rate to the biomass yield in batch experiments (Varma and Palsson, 1994) and was set to 261

0.7, 3.6, 2.5 and 3.4 (mmol/g*h-1) for leucine, alanine, aspartate and glutamate, respectively. The 262

predicted growth rates were compared to those experimentally determined for PhTAC125 (Figure 263

1), revealing an overall agreement between experimentally determined growth rates and in silico 264

predictions. 265

Furthermore, we used Biolog Phenotype Microarray (PM) data (obtained at 15°C) to evaluate and 266

iteratively refine the iMF721 model. This is typically achieved by (qualitatively) comparing the 267

estimated flux value across biomass assembly reaction of the model with the activity directly 268

measured during phenotype microarray experiment. Of the 192 carbon sources tested with PM 269

microplates, 64 (~33%) were accounted for by the iMF721 model and thus could be used to directly 270

test model predictions. In silico growth on these substrates was simulated by setting each of them as 271

sole carbon source and its uptake rate to the arbitrary value of 1 mmol/g*h-1 (under aerobic 272

conditions). Simulation results (either “growth” or “no growth”) were compared with in vivo 273

determined phenotypes. Inconsistencies between simulation results and PM data allowed the 274

identification of metabolic gaps in the model and/or missing transport reactions. These included, for 275

example, the gluconate:H+ symporter (encoded by PSHAb0479), the pyruvate transporter 276

(putatively) encoded by PSHAa0587 and the ATP:D-fructose 6-phosphotransferase (encoded by 277

PSHAb0209); these genes were missing in the initial draft reconstruction and thus precluded the 278

model from using some of the tested carbon sources. 279

After this iterative refinement procedure, iMF721 growth phenotypes predictions were compared 280

again with PM results, revealing that in 84% of the cases (54 out of 64) the outcomes of in silico 281

simulations correctly matched growth phenotypes assessed by in vivo experiments (Table 2, Table 282

S3). Discrepancies in experimental and in silico growth phenotypes may be due to several factors, 283

including incomplete/incorrect homology-based gene annotation or regulatory mechanisms not 284

currently accounted for by the iMF721 reconstruction. However, all the observed incongruences 285

derived from the fact that the model and PM outcomes were “growth” and “no growth”, 286

respectively. In these cases, the wrong incorporation of transport reaction(s) in the iMF721 model is 287

the most likely explanation. It must be noted that, for at least two of the tested compounds (i.e. 288

leucine and mannose), the model predictions are probably correct since it has been previously 289

reported that PhTAC125 is able to utilize leucine and mannose as single carbon sources (Papa et al., 290

2006; Giuliani, 2011). However, it is also possible that such reactions were not detectable within the 291

incubation time (167h) at 15°C due to the slow metabolism of PhTAC125 in presence of leucine as 292

single c-source (Figure 1). Moreover the cut-off value considered in this work (“growth” if 293

OD>0.450) could have led to an underestimation of the overall agreement between Biolog 294

outcomes and in silico predictions as, for example, malate or succinate which resulted as “no 295

growth” because of their OD values <0.450 even if close to the cut-off (OD=0.384 and OD=0,372, 296

respectively). 297

The quantitative and qualitative evaluations of the predictive capability of the model reconstructed 298

herein falls within the range of those from most of the metabolic reconstructions available to date 299

(see, for example (Durot et al., 2008; Fang et al., 2011; Schatschneider et al., 2013; Bartell et al., 300

2014)), supporting iMF721 as being a reliable reconstruction of the central metabolism of this 301

bacterium. 302

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303

iMF721 robustness analysis following temperature downshift 304

The growth rates and the corresponding proteomes of PhTAC125 at 4° and 18°C have been recently 305

obtained (Piette et al., 2010; Piette et al., 2011). These results showed that this bacterium maintains 306

a doubling time of 4h at 4°C and that this value decreases to 1h40min when the culture temperature 307

is raised to 18°C. These studies also led to the identification of 123 differentially expressed genes 308

among the two temperatures (83 down- and 40 up-regulated). Importantly, the reconstructed 309

iMF721 model encompasses gene-protein relationship (GPR) for nearly half of them (65) and for 310

most of the metabolic genes showing a differential expression among the two conditions (54 out of 311

60, 90%) (Table S4). Since proteomics experiments were performed on cells grown in a culture 312

broth containing peptone, we simulated an amino acid rich medium by setting exchange reactions of 313

all amino acids to an arbitrary value of 1 mmol/g*h-1. FBA in this condition (with biomass 314

optimization as the O.F.) resulted into a predicted growth rate of 0.68 h-1, a value that resembles the 315

doubling time of PhTAC125 at 18°C (0.62h-1) on such medium (Piette et al., 2011). 316

First, we focused on evaluating the functional response of the PhTAC125’s metabolic model to the 317

temperature shift. To do this we exploited protein abundance information for this set of genes and a 318

recently proposed robustness-based approach (Tong et al., 2013). More in detail, we defined the 319

proteome and the corresponding reaction fluxes of PhTAC125 grown at 18°C as its optimal overall 320

metabolic state. Under the assumption that protein abundance can be proportional to the metabolic 321

reaction fluxes in bacteria (Rossell et al., 2011), we then conducted robustness analysis on iMF721 322

combining the information on up- and down-regulated genes (and corresponding growth rates) 323

obtained at 18°C (optimal temperature) and 4°C (perturbation temperature). According to this 324

general modelling framework, a reaction flux is disturbed from the optimal status by a perturbation 325

factor and the corresponding change in trend of cell growth is derived and compared to 326

experimental results. Specifically, since PhTAC125’s growth rate decreases when the bacterium is 327

facing lower temperatures, increasing or decreasing the flux in up- and down-regulated genes, 328

respectively, should result in slower biomass production. 329

To avoid possible ambiguous results, at this stage of the work we excluded from the analysis those 330

genes involved in multiple reactions showing conflicting results during robustness analysis. 331

Similarly, a set of 14 reactions displaying an optimal flux that remained non-unique throughout the 332

whole perturbation range (Figure S1) was not considered for the following step. Indeed, this set of 333

reactions cannot provide information concerning the capability of the iMF721 model of predicting 334

growth rate variations in response to external perturbations and further work will be necessary to 335

fully reconcile metabolic model predictions and in vivo effects of perturbing these genes. 336

For a set of 16 reactions an optimal flux could be identified (grey dots in Figure 2). Robustness 337

analysis of these reactions revealed that the iMF721 model is able to correctly predict the effect of 338

perturbation (i.e. up- or down-regulation of the corresponding genes) in 13 cases (81.25%, Figure 339

2a-o). For example, Figure 2a shows how, according to the model prediction, an increase in the flux 340

of the enzymatic reaction encoded by PSHAa1317 would be followed by a reduced growth rate. 341

This is in line with the reduced growth of PhTAC125 at 4°C and, in particular, the up-regulation 342

ratio of methionyl-tRNA synthetase has been tentatively related to the requirement of an increased 343

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pool of initiation tRNA to promote protein synthesis (Piette et al., 2010). The same overall pattern 344

applies to other tRNA synthetases (e.g. PSHAa0006 encoding Glycine tRNA ligase, PSHAa0518 345

encoding for Lysine tRNA ligase and so on). Conversely, the model predicts that a decrease in the 346

flux of the reaction encoded by PSHAa0649 (Xanthosine-5'-phosphateL-glutamine amido-ligase, 347

involved in purine metabolism) would be linked to a decrease of PhTAC125 growth rate (Figure 348

2b). Again this is compatible with the reduced biomass production observed for PhTAC125 at 4°C 349

(Piette et al., 2011). 350

Model reactions whose perturbation did not match proteomics outcomes (Figure 2p-r) included, for 351

example, (R)-S-Lactoylglutathione methylglyoxal-lyase (encoded by PSHAa1601); in this case a 352

decrease in the expression level of the corresponding genes was observed at 4°C, parallel to a 353

decrease in biomass production. Our model, instead, predicts a zero flux through this reaction in 354

correspondence to the optimal growth state and that an increase in of the flux through this reaction 355

would result in a decrease of the growth rate. The same pattern was observed PSHAa2168 and 356

PSHAa2935 encoding for phenylpyruvate oxygen oxidoreductase and 5-Aminolevulinate hydro-357

lyase, respectively. 358

Interestingly, also for those reactions for which a non-unique optimal value was identified, the 359

correct overall trend of growth rate following their perturbation (i.e. up- or down-regulation) was 360

identified in about 72.2% of the cases (13 out of 18 reactions, Figure S1). 361

Taken together, these results revealed that the iMF721 model correctly predicts the effect of gene 362

perturbation for 76.4% of the reactions whose perturbation had effect on the predicted growth rate. 363

This value is in line with studies on the effect of temperature downshift on other (core) metabolic 364

models (Tong et al., 2013) and suggests that our reconstruction is overall capable of (qualitatively) 365

capturing the response of PhTAC125’s metabolic system to a decreased growth temperature. 366

Integrating proteomics data with metabolic modelling 367

In order to globally examine changes in PhTAC125’s metabolism resulting from the temperature 368

transition, we integrated protein abundance data with constraints-based modelling of the iMF721 369

model. Up- and down-regulation ratios (and corresponding p-values) of protein expression were 370

combined with the iMF721 metabolic model using MADE (Metabolic Adjustment by Differential 371

Expression) (Jensen and Papin, 2011). Briefly, MADE creates a sequence of binary expression 372

states that matches the most statistically significant changes in the series of gene expression 373

measurements and, as such, it does not require an arbitrarily imposed gene expression threshold. 374

The resulting gene states produce functioning models that simulate the real metabolic functional 375

state of the cell, given the input expression values. Accordingly, this approach allows the 376

identification of two distinct metabolic models (functional metabolic states), i.e. the original 377

(optimal) iMF721 model and the one derived from simulating growth at lower temperature. These 378

two models will differ in that some of their reactions will be (completely) “turned on” or “off” 379

according to the measured levels of their corresponding proteins (and associated p-values). Overall, 380

12 genes (involved in 35 reactions) were turned on in the 4°C model (i.e. their expression showed 381

an increase in the transition between 18° and 4° growth temperature) whereas 33 (involved in 54 382

reactions in the model) were turned off (since they were down-regulated at 4°C). 383

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After mapping information on up- and down-regulated genes onto the iMF721 model, the predicted 384

(adjusted) growth rate decreased to 0.48 h-1; the 31% downshift in respect to the growth rate at 385

18°C (0.69 h-1) is compatible with experimental evidences on the reduced biomass production of 386

PhTAC125 at lower temperature (Piette et al., 2010). Also, this is line with the perturbation analysis 387

performed in the previous paragraph since “turning off” all the reactions whose genes are down-388

regulated basically corresponds to perturbing all of them simultaneously and recording consequent 389

changes in growth rate. 390

Notably, the same overall trend was observed when growth was simulated on Shatz medium 391

supplemented with each of the aforementioned amino acids as the sole carbon sources. In these 392

cases, the decrease of growth rates ranged from 10% (in the case of alanine) to 40% (with glutamate 393

as the sole carbon source). These results are also confirmed by preliminary Biolog data, showing an 394

overall reduced growth rate of PhTAC125 when grown at low temperatures with amino acids as the 395

sole carbon source (Mocali et al. manuscript in preparation). 396

We analyzed changes in fluxes distribution across the PhTAC125’s metabolism among the two 397

temperature conditions (4°C and 18°C); in both cases iMF721 was optimized for biomass 398

production using FBA. Overall, we found 209 reactions whose fluxes varied between the two 399

conditions (Figure 3, 4 and Table S5). In particular, 141 reactions displayed a reduced flux at 4°C in 400

respect to 18°C, whereas 68 reactions increased their flux in the shift between 18°C and 4°C. This 401

is in line with the overall number of induced vs. repressed genes in the two conditions and with the 402

observed decrease of the PhTAC125’s growth rate at 4°C (Piette et al., 2010; Piette et al., 2011). 403

Most of the reactions displaying a reduced metabolic activity belong to pathways involved in the 404

biosynthesis of compounds and the production of energy (Figure 3). These include most of the 405

reactions involved in the biosynthesis of purine and pyrimidine precursors, such as nucleoside 406

diphosphate kinase (E.C. 2.7.4.6, pyrimidine metabolism) or adenylosuccinate lyase (E.C. 4.3.2.2, 407

de novo purine biosynthesis). Similarly, fluxes through reactions belonging to glycolysis and 408

pentose phosphate pathway are strongly reduced at 4°C (Figure 3), thus hampering both energy 409

production and biosynthesis of important intermediates to be used in the assembly of nucleic acids 410

and amino acids. Also, amino acids biosynthetic pathways display a sensibly reduced activity at 411

4°C in respect to 18°C. These reactions include, for example, aspartokinase and aspartate-412

semialdehyde dehydrogenase (EC 2.7.2.4 and EC 1.2.1.11, respectively, involved in the initial steps 413

of the biosynthesis of the branched chains amino acids lysine and threonine), ketol-acid 414

reductoisomerase (EC 1.1.1.86, valine, leucine and isoleucine biosynthesis), histidinol 415

dehydrogenase (EC 1.1.1.23, histidine biosynthesis) and aspartate aminotransferase (EC 2.6.1.1, 416

aspartate biosynthesis). It is interesting to notice that, in the case of histidine biosynthesis, some of 417

the intermediates are also precursors for the biosynthesis of purines (e.g. Phosphoribosyl-ATP and 418

phosphoribulosyl-formimino-AICAR-phosphate); their reduced flux is thus compatible with the 419

decrease in PhTAC125’s growth rate at 4°C. 420

Reactions involved in amino acids degradation revealed an opposite trend (increased flux at 4 in 421

respect to 18°C). This is the case, for example, of cystathionine beta-lyase (EC 4.4.1.8), involved in 422

cysteine and methionine degradation and catalyzing the conversion of cystathionine to 423

homocysteine with the production of pyruvate; similarly fluxes through both threonine dehydratase 424

(EC 4.3.1.19, catalyzing the conversion of L-threonine to 2-oxobutanoate and representing the first 425

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step in threonine degradation, possibly leading to L-isoleucine) and the glycine cleavage system are 426

increased in the “4°C” metabolic model. Reactions devoted to fatty acids biosynthesis/metabolism 427

also increased their flux at 4°C. More precisely, 23 reactions involved in fatty acids metabolism 428

followed this trend (Figure 3), 7 of them being involved in fatty acids biosynthesis and 16 in fatty 429

acids elongation. These included, for example, i) acetyl-CoA carbon-dioxide ligase (EC 6.3.4.15), 430

providing the malonyl-CoA substrate for biosynthesis of fatty acids; ii) malonyl CoA-acyl carrier 431

protein transacylase (EC 2.3.1.39) represents a critical enzyme responsible for the transfer of the 432

malonyl moiety to holo-acyl carrier protein (ACP) forming the malonyl-ACP intermediates in the 433

initiation step of type II fatty acid synthesis (FAS II) in bacteria (Ruch and Vagelos, 1973); iii) (S)-434

Hydroxydecanoyl-CoA hydrolyase (EC:4.2.1.17), involved in fatty acids elongation, catalyzing the 435

formation of n-unsaturated fatty acyl-CoA (trans-dec-2-enoyl-CoA) from (S)-hydroxydecanoyl-436

CoA. The only reaction in fatty acids metabolism that displays a reduced flux at 4°C (Figure 3) is 437

Butanoyl-CoAoxygen 2-oxidoreductase (EC 1.3.3.6), catalyzing the conversion of butanoyl-CoA to 438

crotonoyl-CoA and being involved in fatty acids degradation. 439

Taking all these data together, it appears that the cell primary metabolic adjustment following (cold-440

dependent) changes in gene expression involves the reduction of the activity of its central 441

metabolism. Indeed, the metabolic network of PhTAC125 appears to be rewired towards the 442

reduction of the energetic costs associated to amino acids and nucleotide biosynthesis; conversely, 443

amino acids degradation and fatty acids metabolism are particularly active in this specific cellular 444

functional state. Notably, a similar functional pattern has been observed in other organisms 445

following a temperature downshift. In Bacillus subtilis, for example, genes involved in amino acids 446

and nucleotides biosynthesis were found to be down-regulated after a temperature downshift. 447

Conversely, genes involved in amino acids degradation and fatty acids metabolism showed an 448

increased expression at lower temperatures (Kaan et al., 2002). In this case, it was inferred that the 449

intermediates of amino acids degradation were redirected towards the synthesis of fatty acids which, 450

in turn, may contribute essentially to sufficient fluidity of the membrane under low-temperature 451

conditions. Alternatively, amino acids degradation may entail the reduced activity of glycolytic 452

reactions (as in the case of PhTAC125) and lead to the synthesis of compounds (such as pyruvate) 453

that can more easily feed into the major metabolic pathways (e.g., TCA cycle and fatty acids 454

biosynthesis). Thus, the functional outcome of changes in the expression of metabolic-related genes 455

of PhTAC125 seems to recapitulate common biological trends of cold adaptation. 456

A large body of data supports a link between fatty acids metabolism and adaptation to cold 457

temperature in bacteria. Indeed, a mechanism shared by cold-adapted microorganisms is to 458

manipulate membrane lipid composition (incorporation of lower-melting point unsaturated, short 459

chain and branched chain fatty acids) in order to maintain membrane fluidity for proper membrane 460

permeability and function of membrane protein complexes (Russell and Nichols, 1999). Changes in 461

fatty acids composition, for example, were observed in the lipooligosaccharide (LOS) fraction of 462

PhTAC125 cells grown at different temperatures (Corsaro et al., 2004). Additionally, many –omics 463

oriented experiments have captured an increased activity in fatty acids biosynthesis, at different 464

cellular levels. A quantitative proteomics survey for S. alaskensis, for example, revealed that 465

adaptation to growth at low temperature involves de novo synthesis of fatty acids in this bacterium 466

(Ting et al., 2010). Similarly, a recent metabolomics study on the cold acclimation of 467

Mesorhizobium sp. strain N33 revealed that (unsaturated) fatty acids biosynthesis probably plays a 468

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role in the specific metabolic responses to low temperatures. Finally, genomics-derived data 469

revealed the presence of a relatively large set of CDSs predicted to encode proteins functioning in 470

fatty acid biosynthesis in psychrophilic microorganisms (Methe et al., 2005), as well as in cold 471

environments in general (Simon et al., 2009; Varin et al., 2012). 472

The integration of expression data with functional modelling of PhTAC125 metabolism presented 473

herein has allowed the identification of possible metabolic consequences derived from the re-474

modulation of gene expression in response to cold adaptation. Our findings supports and extends 475

previous observations suggesting that this bacterium depresses its general metabolism when grown 476

at low temperature, in agreement with the reduced biomass produced at 4°C (Piette et al., 2011). 477

Also, despite different cold-adapted microorganisms are supposed to use different strategies to cope 478

with cold environments (Kawamoto et al., 2007), features similar to those observed in this Antarctic 479

strain (i.e. increased amino acids degradation and changes in fatty acids metabolism) have also been 480

observed in the metabolic landscape of other bacteria during cold adaptation (Graumann and 481

Marahiel, 1999; Ting et al., 2010). 482

483

Conclusions 484

In this work, we have reconstructed a genome-scale metabolic model of the strain P. haloplanktis 485

TAC125 (iMF721). To the best of our knowledge, this represents the first metabolic reconstruction 486

of a bacterium isolated from Antarctica. This model encompasses information on (roughly) 20% of 487

the proteins encoded by the PhTAC125 genome; also, it is worth of noticing that, when considering 488

purely metabolic genes, our model embeds information on 97% of such genes expressed at 16°C 489

(Wilmes et al., 2011). The iMF721 model was successfully validated against a set of experimentally 490

determined growth phenotypes (Figure 1 and Table 2), suggesting a reliable predictive potential. 491

Evidence synthesis from multiple data sources (including proteomics, phenomics, modelling and 492

physiology) allowed the study of the metabolic response of PhTAC125 to a temperature downshift 493

at the system level. First, we exploited robustness analysis to test whether the model was able to 494

correctly predict changes in trend of cell growth in response to variations in reaction fluxes under 495

perturbation (i.e. up- or down-regulation of the corresponding genes). Results of this analysis 496

revealed that model’s predictions matched experimental data in 76.4% of the cases. 497

Furthermore, biologically consistent metabolic adjustments caused by changes in gene expression at 498

the two growth temperatures were inferred by mapping protein abundance values onto the iMF721 499

reconstruction. The overall scenario emerging from comparing the reactions fluxes from 500

computational simulations at the two temperatures suggests that PhTAC125 depresses its general 501

metabolism following a switch between 18 and 4°C, compatibly with i) the amino acids enriched 502

nutritional environment of both in silico simulations and proteomics experiments, ii) the number of 503

up- vs. down-regulated genes and iii) the reduced growth rate of this strain at 4°C in respect to 504

18°C. In this context, amino acids degradation and fatty acids metabolism seem to cover an 505

important role. Amino acids could be used as important carbon and energy sources. Indeed, this 506

strategy represents an advantage in protein-rich environments and it is used also in cases in which 507

crucial metabolic processes (e.g. protein biosynthesis) are impaired (Fonknechten et al., 2010). 508

Fatty acids metabolism, in turn, may be linked to the manipulation of membrane lipid composition 509

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in order to maintain membrane fluidity for proper membrane permeability and function of 510

membrane protein complexes at low temperatures. Importantly, similar patterns, although at 511

different cellular levels (e.g. transcription), have been observed in other cold-adapted organisms. 512

Finally, it can be anticipated that the iMF721 model presented here will be a valuable platform for a 513

further understanding of PhTAC125 cellular physiology at the system level, including the design of 514

more focused strategies for its possible biotechnological exploitation. 515

516

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Acknowledgments 517

Marco Fondi was financially supported by a FEMS advanced fellowship (FAF2012). This work is 518

supported by two 2013 MIUR/PNRA grants (Piano Nazionale per la Ricerca in Antartide, PNRA 519

2013/B4.02 and 2013/AZ1.04). 520

521

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721

Figure legends 722

Figure 1: Comparison between model-predicted growth rates and experimentally determined ones 723

on 4 different carbon sources: L-Alanine, L-Aspartate, L-Leucine and L-Glutamate. 724

Figure 2: Prediction of changes in bacterial growth by robustness analysis. Grey dots represent the 725

point of optimal status whereas solid and dashed arrow lines represent changes in reaction flux and 726

growth rate, respectively. “*” refers to genes whose robustness analysis result did not match 727

experimental data 728

Figure 3: Metabolic processes (vertical axis) and number of reactions (horizontal axis) whose 729

fluxes increased (blue bars) or decreased (red bars) of at least a factor 2 in the shift between 18 and 730

4°C. 731

Figure 4: Schematic representation of changes in fluxes distribution after the shift from high 18° to 732

4°C temperature. Red and blue lines indicate a decrease or an increase (of at least a factor 2) in 733

reaction fluxes when shifting between the two conditions, respectively. Grey lines represent 734

reactions for which a significant change in fluxes was not observed. 735

736

Tables 737

Table 1: Main features of the P. haloplanktis TAC125’s metabolic reconstruction 738

P. haloplanktis TAC125 genome

Genome size (Chr1 + Chr2) 3850272

N. of protein encoding genes 3484

P. haloplanktis TAC125 model

N. of genes (% of coding genes) 721 (20.7)

N. of reactions 1322

Gene-associated 1146

Non gene-associated (Exchange reactions) 176 (85)

N. of metabolites 1133

739

740

Table 2: Comparison between model growth predictions and Phenotype Microarray data 741

KEGG code Compound name Biolog Growth Biolog Growth model Agreement

C00022 Pyruvic Acid Yes Yes Yes

C00025 L-Glutamic Acid Yes Yes Yes

C00026 a-Keto-Glutaric Acid No No Yes

C00033 Acetic Acid No No Yes

C00037 Glycine No Yes No

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C00041 L-Alanine Yes Yes Yes

C00047 L-Lysine No No Yes

C00048 Glyoxylic Acid No No Yes

C00049 L-Aspartic Acid No Yes No

C00058 Formic Acid No No Yes

C00062 L-Arginine No No Yes

C00064 L-Glutamine Yes Yes Yes

C00073 L-Methionine No No Yes

C00077 L-Ornithine No No Yes

C00079 L-Phenylalanine No No Yes

C00085 D-Fructose-6-Phosphate No No Yes

C00103 D-Glucose-1-Phosphate No No Yes

C00109 a-Keto-Butyric Acid No No Yes

C00116 Glycerol Yes Yes Yes

C00122 Fumaric Acid Yes Yes Yes

C00123 L-Leucine No Yes No

C00124 D-Galactose Yes Yes Yes

C00133 D-Alanine No No Yes

C00134 Putrescine No No Yes

C00135 L-Histidine No No Yes

C00137 m-Inositol No No Yes

C00140 N-Acetyl-D-Glucosamine No No Yes

C00148 L-Proline Yes Yes Yes

C00152 L-Asparagine Yes Yes Yes

C00156 4-Hydroxy-Benzoic Acid No No Yes

C00158 Citric Acid No No Yes

C00160 Glycolic Acid No No Yes

C00163 Propionic Acid No No Yes

C00164 Acetoacetic Acid No Yes No

C00183 L-Valine No No Yes

C00184 Dihydroxy-Acetone No No Yes

C00188 L-Threonine No No Yes

C00189 Ethanolamine No No Yes

C00208 Maltose Yes Yes Yes

C00209 Oxalic Acid No No Yes

C00212 Adenosine No Yes No

C00214 Thymidine No Yes No

C00246 Butyric Acid No No Yes

C00257 D-Gluconic Acid Yes Yes Yes

C00263 L-Homoserine No No Yes

C00270 N-Acetyl-Neuraminic Acid Yes Yes Yes

C00294 Inosine No Yes No

C00299 Uridine Yes Yes Yes

C00334 g-Amino-Butyric Acid No No Yes

C00392 D-Mannitol Yes Yes Yes

C00407 L-Isoleucine No No Yes

C00559 2`-Deoxy-Adenosine No Yes No

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C00624 N-Acetyl-L-Glutamic Acid No No Yes

C00645 N-Acetyl-b-D-Mannosamine No No Yes

C01089 b-Hydroxy-Butyric Acid Yes Yes Yes

C01835 Maltotriose Yes Yes Yes

C03383 D-Galactonic Acid-g-Lactone No No Yes

C00031 a-D-Glucose No No Yes

C00065 L-Serine Yes Yes Yes

C00623,C00093 L-Glycerol 1-phosphate No No Yes

C00149 L-Malic Acid No Yes No

C00092 D-Glucose-6-Phosphate No No Yes

C00159 D-Mannose No Yes No

C00085 D-Fructose-6-Phosphate No No Yes

742

743

Supporting Information 744

Table S1: Reactions (and corresponding coding genes) added/removed to/from the model during 745

the manual refinement procedure. 746

Table S2: Biomass assembly reaction for the iMF721 reconstructed model 747

Document S1: The SBML formatted iMF721 metabolic model 748

Table S3: Detailed information on the comparison between model growth phenotype predictions 749

vs. Biolog results and full Biolog data on PM1 and PM2. “1” stands for “Growth” and “0” stands 750

for “No growth” 751

Table S4: The whole set of protein abundance data obtained from previously published papers (see 752

text for details). Bold lines refer to those genes whose corresponding reactions are embedded in the 753

iMF721 reconstruction. 754

Figure S1: Robustness analysis for reactions with non-unique optimal flux. In a) an example of a 755

reaction showing a non-unique optimal flux throughout the robustness analysis is shown. b) to u) 756

reactions with non-unique optimal flux but whose perturbation still influences growth rate. “*” 757

refers to genes whose robustness analysis result did not match experimental data. 758

Table S5: Complete list of reactions whose fluxes increased or decreased by at least a factor of 2 in 759

the shift between 18 and 4°C 760

761

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0 100 200 300 400 500 600 7000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9rxn00799

Reaction �ux (mmol/g*h −1)

Gro

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rate

(h− 1)

0 100 200 300 400 500 600 7000

0.1

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0.9rxn00414


Gro

wth

rate

(h− 1)

−100 0 100 200 300 4000

0.1

0.2

0.3

0.4

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0.6

0.7

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0.9rxn00290


Gro

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rate

(h− 1)

−400 −300 −200 −100 00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

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0.9rxn01504


Gro

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rate

(h− 1)

−200 0 200 400 600 8000

0.1

0.2

0.3

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0.6

0.7

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0.9rxn00256


Gro

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rate

(h− 1)

−1000 −500 0 500 10000

0.1

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0.3

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0.6

0.7

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0.9rxn00330


Gro

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rate

(h− 1)

−1000 −800 −600 −400 −200 00

0.1

0.2

0.3

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0.9rxn00336


Gro

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rate

(h− 1)

0 100 200 300 4000

0.1

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0.3

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0.6

0.7

0.8

0.9rxn01241


Gro

wth

rate

(h− 1)

−400 −200 0 200 400 6000

0.1

0.2

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0.9rxn00692


Gro

wth

rate

(h− 1)

−1000 −500 0 500 10000

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0.9rxn00973


Gro

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rate

(h− 1)

−600 −500 −400 −300 −200 −100 0 1000

0.1

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0.9rxn01200


Gro

wth

rate

(h− 1)

−500 −400 −300 −200 −100 0 1000

0.1

0.2

0.3

0.4

0.5

0.6

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0.9rxn00785


Gro

wth

rate

(h− 1)

0 100 200 300 400 500 600 7000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

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0.9rxn06493


Gro

wth

rate

(h− 1)

0 100 200 300 400 500 600 7000

0.1

0.2

0.3

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0.6

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0.9rxn06377


Gro

wth

rate

(h− 1)

−100 0 100 200 300 400 500 6000

0.1

0.2

0.3

0.4

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0.9rxn01333


Gro

wth

rate

(h− 1)

−300 −250 −200 −150 −100 −50 00

0.1

0.2

0.3

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0.9rxn00086


Gro

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rate

(h− 1)

PSHAa1565((S)-MalateNADP+ oxidoreductase)

0 200 400 600 800 10000

0.1

0.2

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0.9rxn00285


Gro

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(h− 1)

−1000 −500 0 500 10000

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0.9rxn01517


Gro

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−1000 −500 0 500 10000

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0.9rxn00119


Gro

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(h− 1)

PSHAa1228 and PSHAa1229Carbon-dioxideL-glutamine amido-ligase

PSHAa1447Succinyl-CoA3-oxo-acid CoA-transferase

PSHAa1653Citrate oxaloacetate-lyase

PSHAa2559Sedoheptulose-7-phosphateD-glyceraldehyde-3-phosphate

PSHAa1450(S)-3-Hydroxy-3-methylglutaryl-CoA hydro-lyase

PSHAb0061L-Malate glyoxylate-lyase

PSHAb0062Isocitrate glyoxylate-lyase

PSHAa0184 and PSHAa0159Citrate hydro-lyase

PSHAa2473Glycinelipoylprotein oxidoreductase

PSHAa0671Sedoheptulose-7-phosphateD-glyceraldehyde-3-phosphate

PSHAa0671D-Fructose 6-phosphateD-glyceraldehyde

3-phosphate glycolaldehyde

PSHAa0048 * or PSHAa1166(S)-Malate hydro-lyase

PSHAa0393*DihydrolipoamideNAD+ oxidoreductase

PSHAa2376*5,10-Methylenetetrahydrofolateglycine hydroxymethyltransferase

PSHAa0360*GlutathioneNADP+ oxidoreductase

PSHAa2473DihydrolipoylproteinNAD+ oxidoreductase

PSHAa2034*ATPdTMP phosphotransferase

PSHAa2034*ATPnucleoside-phosphate phosphotransferase

a b dc e

f g ih l

m n po q

r s ute

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Genome-scale metabolic reconstruction and constraint-based modelling of the Antarctic bacterium P...

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