Genome-Scale Metabolic Reconstruction and Metabolic Versatility of an Obligate Methanotroph Methylococcus Capsulatus Str. Bath

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1 Genome-scale metabolic reconstruction and metabolic versatility of

2 an obligate methanotroph Methylococcus capsulatus str. Bath

3 4 Authors : Ankit Gupta1†, Ahmad Ahmad2†, Dipesh Chothwe1†, Midhun K. Madhu1, 5 Shireesh Srivastava2*, Vineet K. Sharma1* 6 7 Affiliation 8 1 Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh, INDIA 9 2 International Centre For Genetic Engineering And Biotechnology, New Delhi, INDIA 10 †These authors have contributed equally to this work 11 *Corresponding Authors 12 13 Correspondence to be addressed: 14 Dr. Vineet K. Sharma 15 Address: IISER Bhopal, Bhopal By-Pass Road, Bhauri, Bhopal, Madhya Pradesh - 462066, 16 INDIA 17 Telephone: +91-755-6691401 18 Email-id: [email protected] 19 20 Dr. Shireesh Srivastava, Systems Biology for Biofuels Group, ICGEB Campus, Aruna Asaf 21 Ali Marg, New Delhi 110067 22 Telephone: +91-11-26741361 x 450 23 Email: [email protected]

24 Abstract 25 The increase in greenhouse gases with high global warming potential such as methane is a 26 matter of concern and requires multifaceted efforts to reduce its emission and increase its 27 mitigation from the environment. Microbes such as methanotrophs can assist in methane 28 mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome- 29 scale metabolic model of an obligate methanotroph, Methylococcus capsulatus str. Bath was 30 reconstructed. The model contains 535 genes, 898 reactions and 784 unique metabolites and 31 is named iMC535. The predictive potential of the model was validated using previously- 32 reported experimental data. The model predicted the Entner-Duodoroff (ED) pathway to be 33 essential for the growth of this bacterium, whereas the Embden-Meyerhof-Parnas (EMP) 34 pathway was found non-essential. The performance of the model was simulated on various 35 carbon and nitrogen sources and found that M. capsulatus can grow on amino acids. The 36 analysis of network topology of the model identified that six amino acids were in the top- 37 ranked metabolic hubs. Using flux balance analysis (FBA), 29% of the metabolic genes were 38 predicted to be essential, and 76 double knockout combinations involving 92 unique genes 39 were predicted to be lethal. In conclusion, we have reconstructed a genome-scale metabolic 40 model of a unique methanotroph Methylococcus capsulatus str. Bath. The model will serve as 41 a knowledge-base for deeper understanding, as a platform for exploring the metabolic 42 potential, and as a tool to engineer this bacterium for methane mitigation and industrial 43 applications. 44 45 Keywords 46 Methanotrophs, Methylococcus capsulatus, Methane mitigation, Metabolic model, 47 Constraint-based model, Metabolic network, Genome-scale

48 1 Introduction 49 The problem of global warming needs urgent attention and requires a multifaceted approach 50 including the control on the emission of greenhouse gases. Methane is among the most potent 51 greenhouse gases and has 21 times higher global warming potential than CO2 over a 100 year 52 period (MacFarling Meure et al., 2006). Atmospheric methane value has increased 53 significantly from 722 ppb in the preindustrial era (in 1750) to 1,834 ppb in 2015 54 (MacFarling Meure et al., 2006), primarily due to anthropogenic activities such as wastewater 55 treatment plants, manure management, landfills, etc. Efforts are underway to reduce methane 56 emission into the atmosphere and to synthesize other useful chemical commodities from it. 57 However, the conversion of natural methane to desired liquid products has been a 58 technological challenge (Conrado and Gonzalez, 2014;Clomburg et al., 2017). In this 59 scenario, the use of 'methanotrophs', microbes for methane mitigation, seems to be an 60 appealing approach. Methanotrophs are organisms capable of surviving on methane as the 61 sole source of carbon and energy. In addition, methanotrophs can also act as biocatalysts to 62 convert methane to various value-added products such as high-protein feed and liquid 63 biofuels. Furthermore, methanotrophs can also produce industrially important products and 64 biopolymers such as polyhydroxybutarate (PHB), vitamins, carboxylic acids, single cell 65 protein (SCP), and antibiotics using methane as the carbon source (Fei et al., 2014;Strong et 66 al., 2015). Despite these positives, bioconversion through methanotrophs faces multiple 67 challenges of low energy and carbon efficiencies, and cultures with low productivity (Shima 68 et al., 2012;Conrado and Gonzalez, 2014). However, with the advancements in synthetic 69 biology, there is a potential to address these challenges as shown in some recent studies 70 (Shima et al., 2012;Bogorad et al., 2013;de la Torre et al., 2015). 71 72 Methanotrophs perform the task of methane bioconversion by expressing membrane- 73 associated (pMMO) or soluble (sMMO) forms of methane monooxygenase, which can 74 activate methane by oxidizing it to methanol. The methanol is converted to formaldehyde, via 75 the ribulose monophosphate RuMP in type I methanotrophs, or serine or CBB cycle in type II 76 methanotrophs (Shapiro, 2009;Haynes and Gonzalez, 2014). Methylococcus capsulatus Bath 77 (henceforth referred as Mcap), is a gram-negative type-I gammaproteobacterium with ability 78 to grow optimally at high temperatures of around 450C and even in very low oxygen 79 concentrations (Walters et al., 1999). All these properties make this bacterium a unique 80 candidate to study methanotrophs, and to exploit its metabolic potential as cell factories for 81 synthesis of bioproducts. Furthermore, Mcap is among the few known methanotrophs with 82 available genome sequence, which is essential for identifying the metabolic potential of an 83 organism. 84 85 A genome-scale metabolic model (GSMM) is a collection of most of the annotated metabolic 86 reactions in form of a model which makes it possible to simulate cellular metabolic behavior 87 under different conditions. The GSMM are reconstructed and analyzed in Flux Balance 88 Analysis (FBA) using tools such as COBRA (Constraint-Based Reconstruction and Analysis) 89 Toolbox (Becker et al., 2007;Schellenberger et al., 2011). At present, only one validated 90 genome-scale metabolic model for a methanotroph Methylomicrobium buryatense strain 91 5G(B1) is available (de la Torre et al., 2015). For Mcap, the genome-scale biochemical 92 network reconstructions based on automatic annotation pipelines are available in BioCyc and 93 ModelSeed. However, the network information in these automated reconstructions is not 94 curated and contains general reactions which are not specific to Mcap. More importantly, 95 these automated models do not contain information on the reactions utilizing methane as the 96 carbon source, which is one of the most significant properties of methanotrophs. Therefore, 97 these models are insufficient to explore the metabolic landscape of this organism. In this

98 study, we present a detailed genome-scale metabolic reconstruction ‘iMC535’ for the type I 99 methanotroph Methylococcus capsulatus Bath, constructed using a systematic standard 100 approach (Thiele and Palsson, 2010;Ahmad et al., 2017;Shah, 2017) and was validated using 101 flux balance analysis. 102 103 2 Material And Methods 104 2.1 Model Reconstruction 105 The metabolic reconstruction of Methylococcus capsulatus Bath (Mcap) was carried out 106 using its annotated genome sequence obtained from NCBI. The complete reaction network 107 was built using primary and review literature, biochemical databases such as KEGG 108 (http://www.genome.jp/kegg/) and MetaCyc (http://metacyc.org/), and automated GSMM 109 pipelines such as ModelSEED (Henry et al., 2010) and BioModels (Chelliah et al., 2013). 110 The model was manually constructed on a pathway-by-pathway basis, beginning from the 111 core set of reactions (central metabolic pathways) and expanding it manually to include the 112 annotated reactions and pathways associated with Mcap. Annotations for reaction IDs and 113 compound IDs used in the constructed model were obtained from the KEGG database. The 114 neutral formula for the compounds was obtained from KEGG database or EMBL-ChEBI 115 database. The charge on the metabolites and charged formula were calculated at pH 7.2. All 116 of the reactions included in the network were both elementally and charged balanced and 117 were categorized into either reversible or irreversible. If available, the directionality for each 118 of the reactions was determined from primary literature data, or from the KEGG database. 119 The other reactions were assumed to be reversible. However, some reactions were made 120 irreversible to resolve futile cycles in the model. The GPR (gene-protein-reaction) 121 assignments were made from the genome annotation and the KEGG database. Spontaneous 122 reactions were included into the reconstruction if evidence suggested their presence such as 123 the presence of at least the substrate or product in the reconstruction, or on the basis of 124 literature evidence. The final model included a list of metabolic reactions, required enzymes 125 and their respective genes, gene protein reaction association, the major subsystem of the 126 reaction, metabolite name, and compartment [p- periplasm or c- cytoplasm, e- extracellular]. 127 The model was converted into an sbml format recognizable by COBRA Toolbox for flux 128 balance analysis. 129 130 2.2 Biomass Composition 131 Biomass components, their respective percentages in dry cell weight (DCW), references, and 132 corresponding coefficients in biomass equation are summarized in Supplementary Table 1. 133 The relative percentages of deoxynucleotides were calculated using the GC content (63.58 %) 134 reported for Methylococcus capsulatus Bath (Ward et al., 2004). The amino acid composition 135 in proteins, relative percentages of ribonucleotides, and ash were assumed to similar to that in 136 M. buryatense 5GB1 (de la Torre et al., 2015;Gilman et al., 2015). As literature references on 137 the composition of carbohydrates, cell wall components (LPS and peptidoglycan), vitamins, 138 and cofactors in dry cell weight for Methylococcus capsulatus Bath are unavailable, these 139 components were assumed to be in same proportion as reported for Methylomicrobium 140 buryatense 5GB1 (de la Torre et al., 2015;Gilman et al., 2015;Puri et al., 2015), 141 Methylomicrobium alcaliphilum 20Z (Khmelenina et al., 1997;Ivanova et al., 2006;But et al., 142 2013;Kalyuzhnaya et al., 2013), Methylomonas methanica (Trotsenko and Shishkina, 143 1990;Khmelenina et al., 1994), Methylobacterium extorquens AM1 (Peyraud et al., 2011) or 144 Escherichia coli (Wientjes et al., 1991), in proportions as shown in the Supplementary Table 145 1. The lipid content was assumed to be same to that in M. buryatense 5GB1 (de la Torre et 146 al., 2015) (Supplementary Table 1). The fatty acid composition of the phospholipids was

147 compiled from primary literature (Makula, 1978;Trotsenko and Shishkina, 1990). For 148 phosphatidylcholine and cardiolipin, the building blocks (fatty acids) were assumed to be 149 same as phosphatidylglycerol and ethanolamine and each species was supposed to be in equal 150 proportion. The fatty acid composition for other lipid species was considered to be similar to 151 that of phospholipids. Calculations related to the biomass equation are provided in 152 Supplementary Table 1. 153 154 2.3 Model Simulation and Analysis 155 2.3.1 Flux balance analysis 156 A network can be converted into a mathematical form that can be read by software like 157 COBRA toolbox. This mathematical representation is a matrix of order m × n, where m is a 158 number of metabolites (rows), n is the number of reactions (columns), and each element as 159 the coefficient of a metabolite in a reaction. A negative coefficient corresponds to 160 consumption or intake of a metabolite, whereas a positive coefficient corresponds to its 161 production. This stoichiometric matrix (S) is a linear transformation of the flux vector ( = 162 , ,…, ) to the vector of rate of change of concentration vector (X) with respect to 163 time

164

165 Where, is the vector of intracellular metabolite concentrations

166 With the psuedo-steady state assumption, this differential equation changes to a linear 167 equation of the form ( ). Other than these constraints, flux vector for each reaction 168 can be assigned a range that defines the maximum and minimum flux values it can take 169 ( . These constraints were used to define irreversible reactions by 170 setting both the and as non-negative. Similarly, transport reactions for nutrient 171 uptake were set as negative and reactions for by-product secretion were set as positive. 172 173 2.3.2 Flux variability analysis 174 A metabolic network can attain more than one flux distributions at optimal biomass flux. 175 Minimum and maximum flux values for each reaction constituting network can be computed 176 using FVA. In this work, we separately minimized and maximized flux through each reaction 177 while keeping the growth rate constant at 0.37 h-1 (maximum reported specific growth rate 178 (Joergensen and Degn, 1987)) and the methane intake rate fixed at 28.10 mmol/gDCW/h. 179 180 2.3.3 Gene essentiality analysis and knock-out simulations 181 In silico single and double gene knockout studies were performed on the model. For the 182 single gene deletion studies, each reaction was deleted one at a time by constraining the flux 183 bounds to zero, and the metabolic network was tested for growth using FBA. A reaction was 184 defined as essential if the mutant showed (≤ 10-6) growth rate. Essential reactions for Mcap 185 in different conditions like optimal, oxygen stress, different nitrogen sources, and under 186 deleted EMP cycle were determined using these deletion studies. In addition to a genome- 187 scale single reaction deletion study, a genome-scale double-deletion study was also 188 performed using the doubleGeneDeletion function of the COBRA Toolbox. 189 190 2.4 Energy parameters and main energetic assumptions 191 Bacteria require energy in the form of ATP for growth (e.g. macromolecular syntheses such as 192 DNA, RNA and Protein) and cellular maintenance. Depending on the growth rate of cells, 193 growth associated ATP maintenance could vary, hence in this study, two different conditions

194 were tested i) ATP maintenance value of 23 mmol ATP g−1 DCW (same as the low value used 195 for E. coli (Varma et al., 1993;de la Torre et al., 2015)), and was considered as low ATP 196 maintenance as shown in Table 2, ii) ATP maintenance value of 59.81 mmol ATP g−1 DCW, 197 and was mentioned as wild type network in Table 2 (same as high value used for E. coli 198 (Feist et al., 2007)). To account for non-growth-associated maintenance requirement, an ATP 199 hydrolysis reaction was added to the model and the flux of this reaction was fixed to 8.39 200 mmol/gDCW/h (Feist et al., 2007). The ATP is produced by the ATPase complex which 201 involves the translocation of protons between periplasm of cell membrane and cytosol. The 202 net reaction of the electron transport chain (ETC) can be described by the following equation.

+ 203 4 NADH + 9H + 5 ADP + 5 Pi + 2 O2  4 NAD + 9 H2O + 5 ATP

204 The ATP production rate in the ETC depends on the amount of ATP produced per Oxygen 205 atom (P/O ratio) and the oxygen consumption rate. Here, 5 molecules of ATP were produced 206 by the oxidation of 4 NADH by 4 Oxygen atoms. This resulted in the P/O ratio of 1.25, 207 whereas in the case of E. coli, the P/O ratio is 1.33 (Bar-Even et al., 2010). 208 209 2.5 Evaluation on nitrogen and carbon sources 210 To evaluate the metabolic versatility of Mcap, different nitrogen and carbon sources were 211 tested and the growth rate was observed. For testing different carbon sources, the methane 212 uptake was set to zero and the flux for the respective carbon source was set to 28.10 213 mmol/gDCW/h (the methane uptake at the optimal growth of 0.37 h-1) unless mentioned 214 otherwise in the result section. Similarly, in the case of different nitrogen sources, the flux 215 value for the uptake of the default nitrogen source, i.e., nitrate, was set to zero and the lower 216 bound of the reaction flux for the respective nitrogen source was set equal to the uptake rate 217 of nitrate in optimal growth conditions (0.37 h-1). 218 219 2.6 Identification of Co-sets 220 Correlated sets (Co-Sets) are the sets of reactions whose fluxes remains functionally 221 correlated, i.e., remain correlated in all possible states of the network under a condition. The 222 Co-Sets for Mcap model were calculated by the COBRA Toolbox function 223 "identifyCorrelSets". We used uniform random sampling using the Artificial Centering Hit- 224 and-Run(ACHR) sampler while keeping the maximum correlation (R2) to 1.0 for the 225 sampling to be used for "identifyCorrelSets". 226 227 3 Results 228 3.1 Characteristics of the Metabolic Reconstruction 229 The metabolic reconstruction for Methylococcus capsulatus Bath (Mcap) ‘iMC535’ was 230 carried out based on its annotated genome sequence, available literature, information from 231 biochemical databases such as KEGG and BRENDA, and automated GSMM pipelines such 232 as ModelSEED and BioModels (see Methods section for more details). The final constructed 233 model contained 898 reactions corresponding to 535 genes with 784 distinct metabolites 234 distributed over two different cellular compartments: cytoplasm and periplasm (Table 1). The 235 extracellular space was used as a pseudo-compartment. Depending on the cellular 236 localization, each metabolite was placed in one or more of the three compartments, and the 237 flux across the outer and inner membranes was enabled by transport reactions. Out of the 898 238 reactions, 765 were associated with metabolic conversions, while 133 were required for the 239 transport or exchange of nutrients. A total of 46 non-annotated metabolic reactions were 240 included in the model because either they were known to occur in Mcap based on the 241 literature reports, or were required to fill the gaps in the network (Supplementary Table 2).

242 243 All reactions constituting the model were distributed into 14 systems on the basis of major 244 metabolic properties of Mcap (Figure 1). The majority of the reactions were associated with 245 amino acid, exchange, carbohydrate, lipid and nucleotide metabolism. The information for 246 each of the pathways identified downstream of core metabolism was verified manually. 247 Central metabolic pathways including carbohydrate metabolism, energy metabolism, and 248 biosynthetic pathways for fatty acids, amino acids, nucleic acids, lipids, cofactors and 249 vitamins along with other correlated pathways were manually reconstructed. Similarly, the 250 pathways related to nitrogen fixation, utilization of sulfate, ammonia, nitrate, urea and 251 phosphate were also manually reconstructed. Each reaction was tested for mass balance and 252 irreversibility constraints. The complete functional model ‘iMC535’ is provided in 253 Supplementary Table 3 in a Spreadsheet format, and in Supplementary File 1 in SBML 254 format. 255 256 3.2 Metabolic Versatility and Energy Source Determination 257 Flux balance analysis (FBA) is widely used to analyze the reconstructed metabolic networks. 258 The model performance was evaluated by comparing in silico performance with previously 259 reported experimental observations for growth and response to different carbon, nitrogen and 260 energy sources. 261 262 3.2.1 Methane Utilization 263 A summary of methane metabolism and central metabolic pathways is shown in Figure 2. In 264 Mcap, methane can be oxidized by both sMMO and pMMO depending on the copper 265 concentration in the environment. It has been reported that the growth rates for Mcap lie 266 between 0.25 h-1 to 0.37 h-1 (Joergensen and Degn, 1987). However, the experimental values 267 of methane and oxygen consumption rates at the maximum-reported growth rate are not 268 available. When we used the methane consumption rate of 18.5 mmol/gDCW/h, which has 269 been reported for a related bacterium (Methylomicrobium buryatense 5GB1) (de la Torre et 270 al., 2015), the growth rate was simulated to be 0.23 h-1 (similar to Mb5GB1), and the oxygen 271 uptake at this growth was 23.26 mmol/gDCW/h. The methane uptake rate of 28.10 272 mmol/gDCW/h was predicted at the optimal growth rate of 0.37 h-1, and was used for all the 273 downstream analysis. Therefore, the methane intake rates of Mcap are expected to be higher 274 than that for M. buryatense 5GB1. As the FBA maximized the biomass yield on methane, in 275 reality the biomass yield is expected to be lower. That is, the methane intake rate is very 276 likely higher than that suggested by FBA. The model-calculated ratio of uptake values of 277 oxygen and methane corroborates with the available experimental values which suggests that 278 almost an equal or a slightly higher amount of oxygen is required as compared to methane for 279 the optimum growth of Mcap (Islam et al., 2015). 280 281 The growth rates of 0.28 h-1 and 0.37 h-1 were observed in the case of pMMO knockout (low 282 copper) and sMMO knockout (high copper) simulated conditions, respectively (Table 2). 283 These values corroborate well with the experimentally observed growth range of 0.25-0.37 h- 284 1 (Joergensen and Degn, 1987) in these conditions. In terms of carbon conversion efficiency 285 (CCE), a 34.14% increase is observed from low-copper to high-copper simulated conditions, 286 which is very close to the previously reported experimental value of 38% increase (Leak and 287 Dalton, 1986). Since, NADH is a limiting factor for the growth efficiency in Mcap (Leak and 288 Dalton, 1986), the lower growth observed in low-copper conditions when sMMO are active 289 can be attributed to the utilization of NADH by sMMO for the oxidation of methane. On the 290 contrary, in high-copper conditions, the higher growth efficiency is attributed to a reduced 291 NADH requirement for methane oxidation, since pMMO uses quinones for the oxidation of

292 methane (Shiemke et al., 1995;Zahn and DiSpirito, 1996;Larsen and Karlsen, 2016). 293 Additionally, iMC535 also predicts release of 0.37 - 0.39 moles of CO2 per mole of methane 294 consumed at the optimum growth range of 0.25 - 0.37 h-1, which is in agreement with 295 previously-reported experimental value of more than 0.3 moles of CO2 per mole of methane 296 consumed (Whittenbury et al., 1970). These results further validate the reconstructed model. 297 298 After the oxidation of methane into methanol, the next step of formaldehyde formation is 299 catalyzed by ferricytochrome c-dependent methanol dehydrogenase, which can be utilized by 300 one of the following pathways: Tetrahydromethanopterin pathway (THMPT), 301 Tetrahydrofolate (THF)-linked carbon transfer pathways, Ribulose monophosphate (RuMP) 302 cycle, or Pentose Phosphate pathway (PPP). In Mcap, formaldehyde is oxidized to CO2 via 303 either THMPT or THF pathway and is assimilated in biomass through the RuMP. The flux 304 distribution shows that 56.2% of the formaldehyde goes to the RuMP pathway and 43.2% 305 goes to the THMPT cycle. Thus, the RuMP pathway is 1.2 times more active than THMPT 306 pathway (Figure 3). Mcap has three variants for pathways downstream of the RuMP cycle: 307 Entner–Doudoroff (ED) pathway, Embden-Meyerhof-Parnas pathway (EMP), and 308 Bifidobacterial shunt (BS) (Kao et al., 2004). All these downstream pathways are 309 interconnected through the transaldolase reactions of the PPP and result in ribulose-5- 310 phosphate regeneration or pyruvate synthesis. We observed that the flux through PPP and ED 311 pathway is almost 3 times more than that of glycolysis (Figure 3). ED pathway is important 312 as it supports the only known NADP-linked reaction in C-1 oxidation in Mcap via 6- 313 phosphogluconate dehydrogenase, and thus could serve as the NADPH source for 314 biosynthesis (Kao et al., 2004). The model did not show any growth in ED pathway knockout 315 simulations, which is mainly because 6-phospho-D-gluconate (an intermediate of this 316 pathway) is required for the growth of this bacterium, and can only be synthesized by the 317 enzyme phosphogluconolactonase present in the ED pathway. Additionally, the gene for 318 gluconokinase, which provides an alternate source for 6-phospho-D-gluconate, is also absent 319 in the Mcap genome and hence, in the absence of any alternate source the ED becomes 320 essential for Mcap. On shutting down the EMP pathway, the predicted growth rate was only 321 slightly reduced to 98.56% of the optimal growth (Table 2). 322 323 The ratio of the activity of the enzymes involved in the ED (6-Phosphogluconate 324 dehydrase/phospho-2-keto-3-deoxygluconate aldolase) and EMP pathway 325 (Phosphofructokinase and Fructose diphosphate aldolase) has been reported as 3.3:1 (Kao et 326 al., 2004). With methane intake of 28.10 mmol/gDCW/h (at a growth rate of 0.37 h-1), our 327 model showed flux through ED and EMP pathways at a similar ratio of 3.4:1 without any 328 external constraints, and hence corroborates very well with the available literature evidence. 329 Additionally, in the robustness analysis, it was observed that the ED:EMP ratio can go up to a 330 maximum of 10.9:1 for the optimal growth of 0.37 h-1. All these observations are consistent 331 with the biochemical studies of gammaproteobacteria, which point towards the role of ED 332 pathway as a major route for C-1 assimilation even though the energy requirement of this 333 pathway is greater (Strom et al., 1974;Anthony, 1982;Boden et al., 2011). The flux through 334 BS under normal simulation conditions was zero, suggesting that this pathway was non- 335 essential in this organism. 336 337 An intriguing property which makes this bacterium unique is the presence of genes of both 338 RuMP cycle and serine cycle. While the former plays a major role in biomass production, the 339 role of serine cycle is still unclear. It has also been hypothesized in earlier studies that serine 340 cycle cannot operate as the major assimilatory pathway in Mcap unless glyoxylate, which is 341 depleted during the cycle, is regenerated. However, no genes were identified in the Mcap

342 genome which can convert acetyl-CoA to glyoxylate (Chistoserdova et al., 2005). In our 343 unconstrained network, we observed that all the reactions involved in serine cycle had non- 344 zero flux values and hence, were active. However, it is also important to note that all these 345 reactions were also involved in other major pathways, and hence this observation points 346 towards a secondary role of reactions of this cycle. In the case of serine cycle knockout 347 simulations in iMC535, it was observed that only one reaction of this cycle was essential for 348 the growth of Mcap, and had a role in glycerate synthesis and purine metabolism. These 349 observations of flux distribution are consistent with the fact that Methylococcus capsulatus 350 bath is type I methanotroph and a member of the gammaproteobacteria family which utilises 351 RuMP pathway instead of the serine pathway for formaldehyde assimilation. 352 353 3.2.2 Alternate Energy and Nitrogen Sources 354 We simulated the growth of Mcap on different carbon and nitrogen sources to determine its 355 metabolic versatility. The different carbon sources such as methanol, formaldehyde, formate, 356 acetate, and CO2, were tested on iMC535. The model showed growth on reported 357 downstream oxidation products of methane such as methanol and formaldehyde (Eccleston 358 and Kelly, 1972), which could be assimilated in biomass via the RuMP pathway (Table 2). 359 Further, acetate could also be utilized for growth, which is in accordance with a previously- 360 published report (Eccleston and Kelly, 1972). The model showed no growth on CO2. 361 362 The mATP values are not known for Mcap. Therefore, we tested the response of the model to 363 different mATP values. At low growth-associated mATP (GAM) value of 23 mmol/gDCW/h, 364 the growth rate and the CCE were higher while reduced O2 consumption and CO2 production 365 were observed compared to the wild type network (Table 2). 366 367 Similarly, iMC535 was tested on different nitrogen sources such as nitrate, nitrite, and 368 ammonia. It was observed that using ammonia as the nitrogen source, the flux through 369 biomass was higher as compared to nitrate/nitrite, and the CCE of Mcap was also increased 370 (Table 3). The reason for these observations could be that extra NADH is required for nitrate 371 assimilation as compared to ammonia. Another property, which makes Mcap unique as 372 compared to the other known methanotrophs, is its ability to utilize amino acids such as 373 Alanine, Asparagine, Cysteine, Glutamine, Glutamate, Aspartate, and Valine, as a nitrogen 374 source for its growth (Patel and Hoare, 1971). We tested our model for the growth on each of 375 these reported amino acids as the nitrogen source, and methane as the carbon source. The 376 lower bound for amino acid intake was set as equal to the intake of nitrate (3.4 377 mmol/gDCW/h) at optimal growth conditions when nitrate was used as nitrogen source. 378 Growth was predicted in all the cases as shown in Table 3 since all these amino acids could 379 also form pyruvate (Patel and Hoare, 1971;Eccleston and Kelly, 1972), which is a nodal point 380 in the metabolic network for the production of all the necessary biomass components. Among 381 all the amino acids tested, the maximum growth of 0.58 h-1 was observed when glutamine 382 was utilized as the nitrogen source (Table 3). However, the model could not grow on other 383 nutrient sources, such as glucose and fructose, since their transporters were absent in the 384 genome, and hence were not included in the model. 385 386 3.3 Analysis of Essential Genes and Flux Variability in Mcap 387 iMC535 was used as a framework to identify the candidate essential genes in Mcap. To 388 simulate deletion of the reactions associated with each gene, the flux through the reaction was 389 set to zero and the biomass reaction was maximized by FBA. Out of the 535 metabolic genes, 390 the deletion of 161 (30.1%) genes was predicted to be lethal (biomass flux less than 10-6) 391 (Supplementary Table 4, Figure 4). As expected, gene deletions in central metabolic

392 processes had a highly negative impact. However, most of the single gene deletions for 393 methane oxidation were not lethal, since multiple copies of these genes were present in the 394 genome. Interestingly, nitrite reductase gene was found essential for Mcap when it was 395 grown on nitrogen sources such as nitrite and nitrate, whereas this gene was not essential 396 when the model was grown on amino acids as the nitrogen source. The majority of the 397 essential genes belonged to pathways associated with amino acid metabolism, carbohydrate 398 metabolism, nucleotide metabolism, and metabolism of vitamins and cofactors. In addition to 399 single-gene deletion study, double gene deletion study was also carried out. A total of 103 400 unique gene pairs showed multiple lethal/sick interactions, i.e., growth rate less than 0.01 h-1 401 with respect to either of the single gene deletion condition, out of which 76 interactions were 402 lethal (Supplementary Table 5). The genes showing lethal interactions were mostly involved 403 in amino acid metabolism, exchange, carbohydrate metabolism, lipid metabolism, nucleotide 404 metabolism, and metabolism of vitamins (Supplementary Table 5). 405 406 In a metabolic network, optimal flux through objective function can be achieved through a 407 number of alternative flux distributions if the alternate routes for the same function are 408 available in the bacterium. Such variability in the solution space can be explored using FVA 409 which computes the range of allowed flux values for each reaction while keeping the 410 optimum value of growth rate. This analysis revealed that 99 out of 383 active enzymatic 411 reactions could show more than 0.1 mmol/gDCW/h flux change without affecting the growth 412 rate. Other 15 enzymatic reactions showed variability between 10-4 and 10-1 mmol/gDCW/h. 413 A total of 158 enzymatic reactions with zero flux could potentially carry flux in an alternate 414 optimal solution space. On the other hand, 269 reactions had fixed flux (less than 10-4 415 mmol/gDCW/h variation) at optimal growth value and represented 70.23% of the active 416 network. High variability was observed for reactions related to glucose, fructose, respective 417 phosphates and bisphosphates because alpha-D, beta-D, and D compounds of above- 418 mentioned species were considered as different entities. Glycolysis, EMP, EDD, serine cycle, 419 fatty acid synthesis, etc., showed high variability due to alternate reactions or presence of 420 above entities. 421 422 3.4 Network Topology and Reaction Subsets 423 In the metabolic network, only a few metabolites were found to be highly connected, whereas 424 the majority of the metabolites showed limited connections. The highly connected 425 metabolites were referred to as hubs, which are crucial to the network since they connect the 426 other metabolites (with few connections) to the central metabolism. Top 35 highly connected 427 metabolites out of 827 unique metabolites in the iMC535 model are shown in (Figure S1). 428 Ranking of most of the highly connected metabolites in Mcap is similar to that observed in 429 other models (de la Torre et al., 2015). As apparent, Mcap model also had the common 430 metabolic hubs such as water, energy carrying molecules (ATP, NADH, NADPH, etc.), CoA, 431 pyruvate, etc., which were expected due to their common functions in the metabolic network. 432 Interestingly, amino acids such as glutamate, glutamine, aspartate, alanine, and serine also 433 showed high connectivity (Patel and Hoare, 1971). 434 435 In a metabolic model, reactions with linearly correlated fluxes form the "correlated reaction 436 set" (Co-set) (Price et al., 2004;Marashi and Hosseini, 2015). Earlier studies on reaction 437 correlation showed that uniformly sampled fluxes can be used to explore the regulatory 438 properties of reactions in metabolic models (Barrett et al., 2006;Barrett et al., 2009). There 439 are 88 Co-sets present in the Mcap model, out of which the largest Co-set had 146 correlated 440 reactions. Further analysis of this Co-set revealed that these reactions were involved in the 441 synthesis of biomass precursors, and hence showed a correlation in all the conditons

442 considered for this analysis. There were 17 reactions present in Co-set 2 participating in fatty 443 acid biosynthesis, 12 reactions in Co-set 3 participating in lipopolysaccharide biosynthesis, 6 444 reactions in Co-set 4 participating in methane metabolism and 5 reactions in Co-set 5 445 participating in sulfur metabolism. From Co-set 6 onwards, there were 7, 14 and 62 Co-sets 446 having 4, 3 and 2 reactions, respectively. The summary and detailed information of Co-sets 447 and its participating reactions is given in Supplementary Table 6. 448 449 4 Discussion 450 Methylococcus capsulatus is one of the two important experimental model systems to 451 understand the biochemistry of methanotrophy. Thus, the first genome-scale metabolic 452 reconstruction of Methylococcus capsulatus str. Bath, and constraint-based analysis of its 453 metabolic network are the major steps in understanding the potential capabilities of these 454 obligate methanotrophs and understanding methanotrophy in general. Improvements in the 455 carbon assimilation pathways and channelling the metabolic flux of these pathways will be 456 desired to fine tune its metabolic capabilities to various products of interest. Hence, the 457 GSMM constructed in this study would play a pivotal role in designing and accelerating the 458 metabolic engineering approaches to harness the potential of this methanotroph. 459 460 The model analysis revealed several interesting features about this organism. iMC535 could 461 not simulate growth in the absence of ED pathway. It is because, 6-phospho-D-gluconate, an 462 intermediate of ED pathway, is crucial for the growth of Mcap, and the gene (gluconokinase) 463 for its alternate source was absent in the Mcap genome. Similarly, the analysis identified that 464 NH3 could be a better N-source than NO3- and that Mcap could grow on amino acids. 465 466 The properties of iMC535 model were also compared with iMb5G(B1) (Methylomicrobium 467 buryatense strain 5G(B1)) model, which is the only available model for a methanotroph. The 468 iMb5G(B1) network is available in two formats: a network of 841 reactions that cannot be 469 simulated, and a smaller COBRA toolbox-compatible model containing 402 reactions 470 (http://sci.sdsu.edu/kalyuzhlab/computational-models/). Hence, the latter was used for this 471 analysis. The number of reactions in the Mcap model (898 reactions) is more than twice of 472 the working (that can be simulated) 5GB1 model (402 reactions). The models were compared 473 using pathways and the number of reactions in 12 different subsystems (Figure S2). The 474 reactions that do not belong to these 12 categories were categorised as “Others”. As expected, 475 most of the subsystems in iMC535 had about two-fold or more the number of reactions 476 compared to iMb5G(B1). For example, there are 32 reactions belonging to cysteine and 477 methionine pathway in Mcap model while only 5 reactions in the 5GB1 model. The 478 subsystems such as nucleotide metabolism, vitamin metabolism and the “Others” had over 479 three-times the number of reactions in our model compared to the iMb5G(B1) model. This 480 additional information becomes important while identifying the essential genes as well as 481 targets for metabolic engineering. For example, while 56.36% (177 out of 314) genes were 482 found to be essential in the 5GB1 model, only 30% (161 out of the 535) genes were found 483 essential in the Mcap model. 484 485 The availability of more experimental data on Mcap, particularly on methane uptake rate and 486 specific growth maintenance values, will help to improve the accuracy of iMC535 to predict 487 cellular phenotypes. We anticipate that iMC535 model will serve as a framework for 488 improved metabolic reconstructions, and as an analysis platform for the engineering of 489 methanotrophs for methane mitigation and industrial applications.

490 Acknowledgement

491 We thank MHRD, Govt of India, funded Centre for Research on Environment and 492 Sustainable Technologies (CREST) at IISER Bhopal for its support. However, the views 493 expressed in this manuscript are that of the authors alone and no approval of the same, 494 explicit or implicit, by MHRD should be assumed. 495 496 Authors’ contributions

497 VKS conceived the study. AG, DC and AA carried out the reconstruction of iMC535. AG, 498 DC AA, MKM performed the analyses. AG, DC, AA, SS and VKS designed the study and 499 wrote the manuscript. All authors read and approved the final manuscript. 500 501 Funding 502 We thank the intramural funding received from IISER Bhopal for carrying out this work. 503 Ankit Gupta is a recipient of DST-INSPIRE Fellowship and thanks the Department of 504 Science and Technology for the fellowship.

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641

642

643 Figures and Tables

644 Table 1: Properties of metabolic reconstruction of iMC535

Property # Total Reactions 898 Enzymatic Reactions 765 Exchange Reactions 61 Transport Reactions 72 Number of Genes 535 Gene Associated Metabolic Reactions 714 Non-gene Associated Metabolic Reactions 46 Unique Metabolites 784 645

646 Table 2. Computational predictions for different knockout simulations and growth 647 conditions. The intake flux for each of the carbon source was set as 28.10 mmol/ gDCW/h.

O : 2 Nitrate Biomass O Consumption C CO CCE 2 2 Consumption flux (h-1) (mmol/gDCW/h) rati Production (%) (mmol/gDCW/h) o Wild Type 0.37 34.18 1.22 12.54 3.41 55.36 Network Low ATP 0.44 29.85 1.06 9.49 4.07 66.23 Maintenance Knockout Simulations

sMMO 0.37 34.18 1.22 12.54 3.41 55.36 knockout pMMO 0.28 39.77 1.42 16.49 2.54 41.30 knockout EMP 0.36 34.49 1.23 12.77 3.36 54.57 knockout Carbon Source

Acetate 0.59 21.12 0.38 31.42 5.42 44.09

Methanol 0.43 16.40 0.58 9.91 3.98 64.74

Formaldehyd 0.28 11.67 0.42 16.49 2.54 41.30 e 648

649 Table 3. Computational predictions on different nitrogen sources and methane as a carbon 650 source.

Nitrogen Source Biomass flux O2 Consumption Nitrogen CCE (%) (h-1) (mmol/gDCW/h) Consumption (mmol/gDCW/h) Alanine 0.37 49.50 3.41 40.60 Asparagine 0.38 35.79 3.41 78.00 Cysteine 0.37 54.61 3.41 40.60 Glutamine 0.58 41.64 2.68 58.98 Glutamate 0.37 54.61 3.41 34.48 Valine 0.37 59.72 3.41 34.47 Aspartate 0.37 49.50 3.41 37.29 Nitrate 0.36 32.95 3.34 54.25 Nitrite 0.37 34.18 3.41 55.36 Ammonia 0.44 36.21 4.02 65.43 651

652 Figure Titles

653 Figure 1. Distribution of reactions in metabolic subsystems 654 Figure 2. Summary of methane metabolism in Mcap 655 Figure 3. Flux distribution through the central metabolic pathways of Mcap 656 Figure 4. Distribution of total genes and essential genes in metabolic subsystems 657 Figure S1. Detailed information of the genes involved in the 88 co-sets 658 Figure S2. Distribution of all the reactions of both models along various subsystems 659 660 Supplementary File 1: complete functional model ‘iMC535’ in SBML format. 661 Supplementary Table 1. Detailed information of the biomass components and related 662 calculations 663 Supplementary Table 3 : The complete functional model ‘iMC535’ in Excel format 664 Supplementary Table 4 : Detailed list of essential genes in 'iMC535' 665 Supplementary Table 5 : List of genes participating in double gene lethal interactions 666 Supplementary Table 6 : Metabolite hubs in the metabolic model iMC535 667

18 bioRxiv preprint doi: https://doi.org/10.1101/349191; this version posted June 18, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. bioRxiv preprint doi: https://doi.org/10.1101/349191; this version posted June 18, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. bioRxiv preprint doi: https://doi.org/10.1101/349191; this version posted June 18, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. bioRxiv preprint doi: https://doi.org/10.1101/349191; this version posted June 18, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.