The function of genes in Sulfurimonas denitrificans (Sievert et al., 2008) was first pre- dicted using a consensus annotation approach (Zhang and Sievert, 2014) and subsequently mapped to the modelSEED database (Devoid et al., 2013) to produce equations represent- ing metabolic reactions. These reactions formed the basis of an initial draft model. An objective biomass function was then defined based on the Helicobacter pylori iIT341 model (Thiele et al., 2005). Several additions were made based on pathways predicted by the modelSEED database. Siroheme was added with the same coefficient as haem, as well as pyridoxine phosphate (vitamin B6). For the synthesis of each type of membrane lipid, the modelSEED database predicted several separate pathways producing different lipid chain lengths; in contrast, the iIT341 model biomass function uses only one chain length per lipid type. In our model, separate reactions predicted by modelSEED were retained, and the coefficient provided in iIT341 biomass function for pooled lipid classes was divided by the number of different chain lengths predicted by modelSEED.
At this point, flux balance analysis (FBA) was attempted to determine the objective flux for the biomass function. However, biomass could not be produced, so a custom script was developed to identify reactions that contained dead-end metabolites; these reactions were then manually curated. Following this procedure, biomass flux remained at zero. Therefore, the FBA model objective was set to each biomass constituent to determine which could be produced, and broken biomass constituents were resolved by manual curation. For both the biomass function and dead-end metabolites, curation was accomplished by removing unneeded reactions, adding custom reactions (denoted with the prefix "jm"), or by using already-existing modelSEED reactions. In some cases, cofactors and/or directionality were changed from the modelSEED annotations to allow flux through reactions. Both have been noted in reaction prefixes by changing the case of either the ’r’ or ’x’ for reversibility and
changes, respectively. For example, RXn00001 would differ from the default modelSEED reaction (rxn00001) in that it is reversed (’R’) and one or more cofactor was changed (’X’). At this stage of curation, the model was able to produce biomass with at least some energy sources during FBA.
Table 4.1: Model summary statistics.
Parameter Number
Original predicted reactions (modelSEED) 768
Remaining predicted reactions 503
Custom reactions 12
Custom transfer reactions 19
Reactions added from modelSEED database 32
Custom source and sink reactions 8
Transfer reactions from modelSEED database 9
Total number of non-biomass reactions 583
The model was further curated by constraining the directionality and reversibility of metabolic reactions. Initially, the annotations from the modelSEED database were applied to the model. However, this rendered the model unable to produce biomass. Therefore, directionality of reactions was manually curated, revealing that several irreversible reactions in the modelSEED (rxn00053, rxn00550, rxn00518-00524, rxn00559-00565 and rxn00629) needed to be reversible in order for the model to produce biomass. Reactions were then manually inspected to constrain those for which directionality was not specified in the mod- elSEED database but are known to be irreversible (e.g. denitrification enzymes). Similar logic was applied to reactions that involving soluble electron carriers such as NAD(P)H and ferredoxin or thermodynamically-irreversible reactions involving ATP or other nucle- oside phosphates. Additionally, FBA output was inspected for unrealistically high fluxes which indicate thermodynamically-impossible loops producing electron donors or ATP for biosynthesis. Summary statistics describing this curation process are presented in Table 4.1.
After biomass flux was non-zero and directionality was constrained, more specific curation of core metabolic reactions was carried out in order to reproduce the metabolic potential of this organism (Timmer-Ten Hoor, 1981; Sievert et al., 2008; Han and Perner, 2014). These reactions were based on Fig. 2 from Sievert et al. (2008) with the following modifications. The cytochrome cbb3 uses only oxygen (not nitric oxide), NorBC is the only
nitric oxide reductase and NosZ accepts electrons from cytochrome c only. Both thiosulfate and sulfide oxidation are modeled as complete oxidation to sulfate, transferring electrons to menaquinone instead of cytochrome c as previously described for the SOx complex (Dahl et al., 2008). Neither polysulfide reductase or formate oxidation were included, and reverse electron transport was modeled as discussed below.
Since it is uncertain which electron donor other than ferredoxin (i.e. NADH, NADPH or both) are used for biosynthetic reactions in the cell, a reversible interconversion reaction between NADPH and NADH was introduced (rxn00083) to reflect this uncertainty.
Energy-generating metabolic reactions of S. denitrificans likely take place in the periplasm (Sievert et al., 2008); compounds in the model were therefore localized into ex- ternal (e), periplasmic (p) and cytoplasmic compartments. In order to distinguish between protons produced during redox reactions and those contributing to the proton-motive force (pmf), periplasmic protons were used as a unique tracer of proton translocation. For pro- tons produced/consumed in periplasmic reactions not contributing to the pmf, protons in the external compartment were the source/sink for these reactions. Whether periplasmic or cytoplasmic protons were used for menaquinone reduction or vectorial proton transport was determined according to Simon et al. (2008) or via logic detailed below.
Several functions posited are not supported by direct biochemical evidence. Even for those complexes whose function has been identified in other organisms (e.g. cytochrome cbb3 oxidase), the stoichiometry of of proton translocation in vivo is unknown. For these
complexes, several hypothetical mechanisms and/or proton pumping stoichiometry were considered. To identify which was most likely to be correct, in silico growth efficiency was compared with chemostat growth yields of Sulfurimonas denitrificans. If predicted growth efficiency was close to experimental data, the mechanism was considered plausible and vice- versa.
Once the model reproduced experimental observations, it was subjected to consistency checks using the PSAMM software (Steffensen et al., 2016) which verified reaction stoichiom-
etry and identified reactions that have zero flux under all conditions. In order for PSAMM to run the model, the metabolite "no" was changed to "nox" since "no" is interpreted as a boolean "false" by PSAMM. The exact solver QSoptEX was used due to limitations in the CPLEX solver for identifying reactions with very low fluxes (e.g. heme biosynthesis). Of the reactions that can carry no flux (according to the fluxcheck tool), only two were kept in the model, both related to nitrate transport into the cytoplasm, which may provide a source for posited assimilatory nitrate reduction (Sievert et al., 2008). For additional annotation of the Sulfurimonas denitrificans genome, operons were predicted using OperonPredicter (Taboada et al., 2010) and transmembrane domains were predicted using the TMHMM program (Sonnhammer et al., 1998).