1. Introduction
Almost all processes in microorganisms are dependent on pH, which is why intracellular pH in all cellular systems is a tightly regulated physiological parameter. pH can affect molecules of living organisms such as metabolites, protein folding and their functions, enzyme activity, and interactions of lipids (Orij, Brul, & Smits, 2011).Saccharomyces cerevisiae , Escherichia coli, andZymomonas mobilis are the most widely used microorganism in bio-based productions such as ethanol (Joshi, Joshi, Bhattarai, & Sreerama, 2019; Monk et al., 2016; Valgepea et al., 2017; Yang et al., 2010). The desirable characteristics of these microorganisms needed for efficient production are to overcome many harsh conditions used in industrial ethanol production; for instance, low pH levels to reduce contamination risks (Benjaphokee et al., 2012; Kuroda et al., 2019). Hence, changes in pH levels may cause stressful conditions that affect cellular metabolism and ethanol production. These stress conditions change metabolite charges, redox balances and the gene expression profile by activating or repressing specific genes involved in different reaction pathways, such as the central metabolic pathway, transcription regulation, protein folding, and cell cycle (Dong, Hu, Fan, & Chen, 2017). The efficiency of the metabolism pathways to produce ethanol depends on the redox control. Alteration in intracellular pH can change NAD: NADH ratio, which must be redox balanced through an electron shunt. This electron transfer has enabled the production of ethanol (Contador et al., 2015; Williams-Rhaesa et al., 2018). Z. mobilis andS. cerevisiae are ethanologenic microorganisms and are also sensitive to pH, while E. coli is a neutralophilic bacteria that maintain its cytoplasmic pH within a restricted range (Jones & Doelle, 1991; Krulwich, Sachs, & Padan, 2011; Shioi, Matsuura, & Imae, 1980; J. L. Slonczewski, Rosen, Alger, & Macnab, 1981; Joan L. Slonczewski, Fujisawa, Dopson, & Krulwich, 2009). Thus, it is vital to investigate different behavior and effect of charge balance due to pH change on the metabolism of these microorganisms.
In order to develop a productive and robust desirable bio-based producer, some researchers have recently used a constraint-based modeling approach to model pH variability in bio-systems, based on the fact that cell activity is restricted by controlling physiochemical constraints (Edwards, Covert, & Palsson, 2002; Reed, Vo, Schilling, & Palsson, 2003). There have been many clarifications in different ways, including the effects of pH on metabolic flux distributions (Çalik & Ileri, 2007), a shift in carbon flux by pH alteration (Jo, Lee, & Park, 2008), thermodynamic feasibility of metabolic pathways due to pH (Vojinović & Von Stockar, 2009). Metabolic models were used to predict the impact of pH upon variation in cell activity. Anderson et al. (Andersen, Lehmann, & Nielsen, 2009) used constant acid disassociation to model acid production at pH 1.5 ~ 6.5. Effects of pH on the capability of R. eutropha to produce poly[R-(-)-3hydroxybutyrate] (PHB) was investigated under pH 6, 7, and 8 values by Park et al. (Park, Kim, & Lee, 2011). In these studies, a metabolic network involving varied biochemical reactions was used to construct a metabolic model based on constraints using a pseudo-steady-state assumption with mass and charge balance on each metabolite and reaction (Swayambhu, Moscatello, Atilla-Gokcumen, & Pfeifer, 2020). Therefore, the proton exchange rate can indicate the charge balance of the network in genome-scale metabolic models. Examining the interaction of proton exchange flux and ethanol production on growth can lead to a better understanding of metabolism associated with the charge balancing and metabolite charge alteration as a result of pH variations. The impact of pH on growth and ethanol production has not yet been evaluated with genome-scale metabolic models of microorganisms.
In this research, using genome-scale models of S. cerevisiae, E. coli, and Z. mobilis that their charge balance has been modified at various pH values (5, 6, and 7), the impact of metabolite charge alteration because of the pH change on the proton exchange rate and ethanol production was investigated to evaluate the metabolic differences of these microorganisms under different pH levels. Furthermore, a systemic approach has been proposed to identify reactions affecting ethanol production rates at various pH levels. Moreover, comparing the flux distributions of S. cerevisiae at optimal pH=5 (Papapetridis et al., 2016) with other pH levels can indirectly lead to the identification of the optimum flux distributions for ethanol production. Hence, this approach offers strategies for improving ethanol production that was experimentally evaluated on S. cerevisiae by adding inhibitors or activators of enzymes as regulators to the medium (Ehsan Motamedian, Sarmadi, & Derakhshan, 2019). Finally, the design of experiment (DOE) was implemented to maximize each selected compound’s concentration.