Synthetic metabolic pathways for ethylene glycol assimilation outperform natural counterparts

Biomanufacturing can play a pivotal role in the transition away from fossil fuel dependence for the production of chemicals and fuels. There is growing interest in alternative bioproduction feedstocks to conventional sugars that do not compete for land use with food production. Ethylene glycol, a C2 compound that can be recov- ered from plastic waste or derived from CO 2 with increasing efficiency, is gaining attention as a carbon source for microbial processes. Here we review the natural and synthetic metabolic pathways currently available for ethylene glycol assimila- tion. The pathways are compared in terms of their maximum theoretical yields for biomass and value-added products, thermodynamic favourability, minimum en- zyme costs, and orthogonality to central carbon metabolism. We find that synthetic pathways outperform their natural counterparts in terms of higher thermodynamic driving forces, reduced enzyme costs, and higher theoretical yields for the major- ity of bioproducts analyzed as well as for biomass. However, natural assimilation pathways are equally or even more orthogonal to growth-associated reactions than 1 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 10, 2024. ; https://doi.org/10.1101/2024.09.05.611552doi: bioRxiv preprint synthetic pathways. Given these tradeoffs, the optimal EG assimilation pathway may depend on product and process choice.

ethylene glycol; alternative feedstocks; metabolic engineering; synthetic biology 1 Introduction Rising concerns about climate change and anthropogenic environmental issues are mo- tivating efforts to shift away from fossil fuel dependence in the energy and chemical production sectors. While biomanufacturing has the potential to significantly shift chem- ical production away from reliance on fossil fuels and help curb carbon emissions, most industrial applications thus far have been at the relatively small volumes needed for pharmaceutical and specialty chemical markets (Scown, 2022). The widespread adoption of bioproduction for bulk chemicals produced at very large scales is currently hindered by the high cost associated with microbial fermentation, much of which arises from the carbon source, conventionally simple sugars such as glucose derived from biomass (Burg et al., 2016; Chen, 2012). The cultivation of feedstock biomass requires extensive land use, directly competing with the food and feed industries and raising concerns about responsible land and resource management (Muscat et al., 2020; Harvey and Pilgrim, 2011). As a result, there is growing interest in developing bioprocesses based on renew- able and inexpensive alternative carbon sources, ideally those that can be derived from waste streams or industrially emitted CO 2. Ethylene glycol (EG), a C2 compound and the simplest diol, is an industrial chemical readily available at large scales for relatively low cost. Although commonly known for its use as an antifreeze agent, the use of EG is widespread among many industries involved with energy, chemical synthesis, textiles, automotives and manufacturing technologies (Yue et al., 2012). EG is a component and degradation product of polyethylene tereph- thalate (PET), a ubiquitous polymer used for short-lived products such as single-use plastic bottles and packaging, fabrics, and textiles, with a production volume of approx- imately 70 million tons per year (Neves Ricarte et al., 2021). The recycling of PET is not currently considered profitable due to its low cost relative to the recycling process cost, raising environmental concerns regarding PET waste management (Sheldon and Norton, 2020). This has prompted interest in the upcycling of both its EG and tereph- thalic acid (TPA) monomer constituents into value-added products. PET degradation has been demonstrated via various chemical hydrolysis methods (Cao et al., 2022; Gao et al., 2022; Panda et al., 2021), enzymatic depolymerization (Ellis et al., 2021; Tiso et al., 2022; Erickson et al., 2022; Kosiorowska et al., 2022), and in vivo biological degra- dation using microbial hosts (Tiso et al., 2021; Brandenberg et al., 2022; Liu et al., 2022; Yoshida et al., 2016). 2 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 10, 2024. ; https://doi.org/10.1101/2024.09.05.611552doi: bioRxiv preprint Aside from plastic waste, EG can be generated from such renewable sources as CO 2 (Fan et al., 2023; Lum et al., 2020), syngas (Gor et al., 2023; Tremblay, 2011), and lignocellulosic biomass (Enjamuri and Darbha, 2022; Tullo, 2012). While the direct electrochemical reduction of CO2 to EG is possible via copper (Kuhl et al., 2012) or gold- based electrodes (Tamura et al., 2015), the technology is at an early stage of development. More commonly, CO2 is converted to ethylene (Prajapati et al., 2022; Leow et al., 2020; Nam et al., 2022) or ethylene oxide (Y. Li et al., 2022) which can be further oxidized to EG, an area that has seen recent improvements in terms of achievable current densities and product selectivity (Fan et al., 2023; A.-Z. Li et al., 2024; Lum et al., 2020). While other C1 and C2 compounds can be generated from CO 2 via electrochemical reduction, such as methanol, formate, ethanol, and acetate, the use of EG as a microbial feedstock for bioproduction has been comparatively less explored. In terms of physical properties, it is particularly favourable for such use as it is a relatively non-volatile, low viscosity liquid that is completely soluble in aqueous media (Yue et al., 2012). The conversion of EG to value-added products has been demonstrated in both Escherichia coli and Pseudomonas putida with the production of glycolic acid (Pandit et al., 2021), aromatic amino acids (Panda et al., 2023), and 2,4-dihydroxybutyric acid (Fraz˜ ao et al., 2023). Compared to methanol, another alternative (C1) feedstock garnering attention, EG assimilation involves the formation of less toxic intermediates (glycolaldehyde instead of formaldehyde), a lower O 2 demand for its bioconversion to many products, and physico- chemical properties that create safer conditions in the context of industrial fermentation (Wagner, Wen, et al., 2023). Both natural and synthetic pathways for the assimilation of EG into microbial metabolism have been discovered, conceptualized, and/or imple- mented to various degrees. Here, we provide an overview of these pathways and evaluate their amenability for bioproduction based on theoretical metrics. We look at the max- imum biomass and product yields that can be achieved with each pathway in addition to their thermodynamic feasibility, minimum enzyme costs, and orthogonality to central metabolism. 2 Metabolic pathways for ethylene glycol assimilation 2.1 Natural ethylene glycol assimilation pathways Three types of natural EG degradation pathways are known to exist in microbial metabolism. One is primarily found in acetogens such as Clostridium glycolicum and Acetobacterium woodii and involves the the dehydration of EG to acetaldehyde using an extremely O 2- 3 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 10, 2024. ; https://doi.org/10.1101/2024.09.05.611552doi: bioRxiv preprint sensitive diol-dehydratase (Hartmanis and Stadtman, 1986; Trifunovi´ c et al., 2016). An acetaldehyde dehydrogenase enzyme then activates acetaldehyde to acetyl-CoA, a key central carbon metabolite that serves as the entry point of the tricarboxylic acid (TCA) cycle. In aerobic organisms, all known EG assimilation routes involve the initial oxida- tion of EG to glycolaldehyde (GA). In the naturally-occurring ’glycerate pathway’, GA is subsequently oxidized to glyoxylate and assimilated into central carbon metabolism via transformation to 2-phosphoglycerate of lower glycolysis or through the glyoxylate shunt (Pandit et al., 2021; Boronat et al., 1983). In E. coli, the initial oxidation of EG is mediated by an NAD +-dependent propanediol oxidoreductase (FucO) enzyme with promiscuous activity towards EG. In Pseudomonas putida and Ideonella sakaiensis, this oxidation has been found to be facilitated by pyrroloquinoline quinone (PQQ)-dependent alcohol dehydrogenases (M¨ uckschel et al., 2012, Hachisuka et al., 2022). The conversion of EG to value-added products via the glycerate pathway has been demonstrated in E. coli (Pandit et al., 2021; Panda et al., 2023; Fraz˜ ao et al., 2023), P. putida (Franden et al., 2018; Bao et al., 2023) and Rhodococcus jostii (Diao et al., 2023). A major drawback of this EG assimilation route is the carbon loss associated with the condensation of two glyoxylate molecules, during which 25% of the initial carbon is released as CO 2 (Figure 2.1). An alternative route for the assimilation of glyoxylate (an intermediate compound in the glycerate pathway) is the β-hydroxyaspartate cycle (BHAC), which was recently fully characterized in the bacteria Paracoccus denitrificans (Schada von Borzyskowski et al., 2019), although parts of the module were described as early as the 1960s (Ko- rnberg and Morris, 1963; Kornberg and Morris, 1965). In this pathway, glyoxylate is added to the amino acid glycine to form (2R,3S )-β-hydroxyaspartate (BHA), which is subsequently dehydrated to iminosuccinate and reduced to L-aspartate (Figure 2.1). An aminotransferase converts L-aspartate to the TCA cycle intermediate oxaloacetate while regenerating glycine from glyoxylate. This cycle sidesteps the carbon loss and ATP re- quirements associated with glyoxylate assimilation in the glycerate pathway. The BHAC was recently implemented in P. putida KT2440, enabling it to grow on EG more ef- ficiently in terms of growth rate and biomass yield than using the glycerate pathway (Schada von Borzyskowski et al., 2023). 2.2 Synthetic ethylene glycol assimilation pathways Pathways for the assimilation of EG that do not exist in nature have also been conceptual- ized, and implemented in some cases, with the goal of outperforming native pathways. To the best of our knowledge, all synthetic EG assimilation pathways begin with the oxida- tion of EG to GA (Figure 2.1), mediated either through native aldehyde dehydrogenases such as FucO in E. coli or heterologous analogs with demonstrated improved activity 4 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 10, 2024. ; https://doi.org/10.1101/2024.09.05.611552doi: bioRxiv preprint such as Gox0313 from Gluconobacter oxydans (Zhang et al., 2015). In the synthetic acetyl-CoA (SACA) pathway, GA is converted to acetyl-phosphate (AcP), incorporating inorganic phosphate via an acetyl-phosphate synthase enzyme, followed by the produc- tion of acetyl-CoA from AcP by a phosphate acetyltransferase (PTA) enzyme, which is native to the E. coli metabolism (Lu et al., 2019). This pathway has not been shown as of yet to support microbial growth on EG. In the tartonyl-CoA (TaCo) pathway, suggested by Scheffen et al. (Scheffen et al., 2021), GA is converted to glycolyl-CoA through an aldehyde dehydrogenase (PduP) from Rhodopseudomonas palustris BisB18 (Figure 2.1). A new-to-nature enzyme, glycolyl- CoA carboxylase (GCC) can covert glycolyl-CoA to (S)-tartronyl-CoA, which is finally reduced to glycerate (a central carbon metabolite) through a tartronyl-CoA reducatse (TCR). Scheffen et al. was able to demonstrate the in vitro production of glycerate from EG with simultaneous CO2 fixation (Scheffen et al., 2021), however this pathway has not yet been successfully implemented in vivo. The synthetic arabinose-5-phosphate (Ara5P)-dependent glycolaldehyde assimilation (SAGA) pathway (Yang et al., 2019) provides a cyclical alternative to the SACA path- way for the production of acetyl-CoA from glycolaldehyde (Figure 2.1). An arabinose 5-phosphate aldolase (FsaA) enzyme catalyzes the addition of the acceptor molecule glyc- eraldehyde 3-phosphate (GA3P) to GA yielding D-arabinose 5-phsophate (Ara5P), which is rearranged to form D-ribulose 5-phosphate (Ribu5P) via an isomerase (KdsD) followed by xylulose 5-phosphate (Xylu5P) via an epimerase (Rpe). The cleavage of Xylu5P by a phosphoketolase (Pkt) regenerates GA3P while releasing acetyl phosphate (AcP), the precursor to acetyl-CoA in the reaction catalyzed by phopsphate acetyltransferase (Pta). Wagner et al. experimentally validated this pathway in E. coli using glycerol as the co-substrate for growth (Wagner, Bade, et al., 2023). In a similar strategy, the synthetic allose-6-phosphate (All6P)-dependent SAGA path- way has been proposed as an analog to the Ara5P-dependent SAGA pathway, proceed- ing through the formation of All6P from GA and subsequent conversion to allulose-6- phosphate (Au6P), fructose-6-phosphate (F6P), and erythrose-4-phosphate (E4P), yield- ing AcP (Figure 2.1) (Mao et al., 2021). Mao et al. used a combinatorial algorithm combined with parsimonious flux balance analysis (pFBA) to predict eight novel carbon- conserving pathways for formaldehyde assimilation, all of which can also serve as GA (and by extension, EG) assimilation pathways as they begin with the conversion of formalde- hyde to GA. Of these, the All6P-dependent SAGA pathway was demonstrated in vitro (with a carbon yield of 94%) as it met the criteria of consisting of 10 or fewer reac- tions, exhibiting no carbon loss, being independent of ATP and reducing equivalents, and having enzymes identified to catalyze each reaction (Mao et al., 2021). 5 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 10, 2024. ; https://doi.org/10.1101/2024.09.05.611552doi: bioRxiv preprint 3 Methods 3.1 Theoretical yield calculations Flux balance analysis (FBA) was performed using the COBRApy toolbox (Ebrahim et al., 2013) on Python 3.7.9 using the GLPK solver (GNU Project). The E. coli genome-scale model iML1515 (BiGG database) was used for all maximum theoretical yield (MTY) calculations. Reactions and metabolites that are heterologous to the E. coli metabolism or missing from the models (EG assimilation and non-native bioproduction pathways) were manually added, informed by reaction specifications in the literature (BioCyc). The substrate uptake rate was set to 10 mmol/gDW ·hr while O 2 uptake was constrained to a maximum of 20 mmol/gDW·hr to reflect aerobic conditions (Andersen and von Meyenburg, 1980). The lower bound of the ATP maintenance reaction was set to zero to allow for maximal yield determinations. Other parameters were unchanged from their default states based on BiGG model specifications. EG and bioproducts were assumed to passively diffuse into/out of the cell based on their sizes and charges, which was reflected in the constructed models. To calculate the maximum theoretical biomass yield, the objective function of the metabolic model was set to maximize biomass generation (a pseudo-reaction that repre- sents the overall cellular growth in the metabolic network). The FBA-predicted growth rate was divided by the substrate uptake rate (converted from a molar flux value to mass), as detailed in Equation 1 in which Ybiomass is the biomass yield (g of biomass per g of substrate), µ is the growth rate (g/gDW ·hr), ˙vS is the substrate uptake rate (mmol/gDW·hr), and MS is the molecular weight (g/mol) of the substrate. YBiomass = µ ˙vS · MS (1) To calculate the maximum theoretical product yield, the objective function of the metabolic model was set to each bioproduct’s exchange reaction (a pseudo-reaction that represents the exchange of the product between the cytosol and extracellular matrix). The FBA-predicted product exchange fluxes were divided by the substrate uptake rate, both converted from molar flux values to mass yields, as shown in Equation 2. The product yield is denoted as YP roduct (g of product per g of substrate), ˙ vP is the product exchange flux (mmol/gDW·hr), and MP is the molecular weight (g/mol) of the product. YP roduct = ˙vP · MP ˙vS · MS (2) 6 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 10, 2024. ; https://doi.org/10.1101/2024.09.05.611552doi: bioRxiv preprint 3.2 MDF and ECM calculations The Max-min driving force (MDF) framework (Noor et al., 2014) was accessed through the open-source eQuilibrator API (Noor et al., 2013). The MDF provides a quantita- tive measure of how thermodynamically favorable a metabolic pathway is by calculating the maximum value of the minimum driving force among all reactions in the pathway. Metabolite concentrations are optimized within physiologically feasible ranges to maxi- mize the smallest ∆ rG of any reaction in the pathway. A higher value indicates a more thermodynamically favourable pathway without thermodynamic bottlenecks that is gen- erally capable of sustaining higher fluxes (Noor et al., 2014). The MDF framework is formulated as a linear optimization problem, as shown in Equation 3, where B is the MDF value (kJ/mol), ST is the metabolic network represented by a stoichiometric ma- trix, R is the universal gas constant, T is the temperature, and x is a vector of all log values of metabolite concentrations constrained by the lower ( Cmin) and upper ( Cmax) bounds. maximize B subject to − ∆rG ′ ≥ B ∆rG ′ = ∆rG ′◦ + RT · ST · x ln (Cmin) ≤ x ≤ ln (Cmax) (3) The MDF framework employs the Component Contribution method for the estimation of standard Gibbs energies of formation for each compound, from which it calculates the standard Gibbs energy change (-∆ rG ′◦) for each reaction. A standard temperature of 25 °C was used as Gibbs energy variations with temperature are difficult to predict to match E. coli growth conditions (Jankowski et al., 2008). However, cellular conditions in terms of ionic strength (250 mM) (Szatm´ ari et al., 2020) and cytosolic pH (7.5) (Slonczewski et al., 2009) were used in addition to constraining metabolite concentration to physiolog- ically feasible values (1 µM to 10 mM) (Noor et al., 2014; Bennett et al., 2009). In cases where pathways were deemed infeasible with these concentration bounds for bottleneck reactions, the upper bound of metabolite concentrations was increased (SI). The enzyme cost minimization (ECM) framework was also accessed through the eQui- librator API (Noor et al., 2013) and used to quantify the minimal protein cost required to support each EG assimilation pathway. Both thermodynamic and kinetic parameters are incorporated in the calculations; the same physiological conditions were used as for MDF analysis and experimental kinetic data (K M and Kcat) were used wherever possible (SI). 7 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 10, 2024. ; https://doi.org/10.1101/2024.09.05.611552doi: bioRxiv preprint 3.3 Orthogonality calculations Orthogonality is the design principle of creating independent and modular metabolic pathways that minimize interactions with the host organism’s natural cellular metabolism (Pandit et al., 2017). An orthogonality score (OS) quantifies the independence of biomass and chemical production pathways, with a higher score indicating a greater degree of separation. To calculate the orthogonality score of each pathway, E. coli core metabolic models (Orth et al., 2010) were used with the relevant pathway reactions added. Con- straints on substrate and oxygen uptake were set as described above for genome-scale models. Elementary flux modes (EFMs), the simplest sets of reactions that can operate at steady state, were enumerated using efmtool (Terzer and Stelling, 2008) on CellNet- Analyzer for Python (CNApy; Thiele et al., 2022) for each model. EFMs were split into two distinct sets: St, EFMs which contain a non-zero flux through the target product re- action but have zero biomass flux, and SX, EFMs with non-zero flux through the biomass reaction and zero product flux (Pandit et al., 2017). Acetate was selected as the target product as a stand-in for central biomass precursor acetyl-CoA which cannot be readily exported from the cell. The average similarity (AS) between the reactions that are common to the EFMs in sets St and SX is calculated using Equation 4, in which the dot product of the vectors representing these sets ( et i and ex j , respectively) in the metabolic flux space is calculated and normalized to the size of the biomass-supporting node (Pandit et al., 2017). This value is divided by the product of m and n, the total numbers of EFMs in sets St and SX, respectively. The OS is taken as the compliment of the AS (Equation 5). AS = Pm i=1 Pn j=1 et i · ex j ∥ex j ∥ mn (4) OS = 1 − AS (5) 4 Ethylene glycol assimilation pathway evaluation 4.1 Theoretical yield Metabolic pathways for EG assimilation were compared on the basis of theoretical biomass and product yields, thermodynamic favourability, enzyme burden, and orthogonality to native metabolism. Flux balance analysis (FBA) was used to calculate the theoretical biomass yield of E. coli with EG as the sole source of carbon and energy, in addition to 8 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 10, 2024. ; https://doi.org/10.1101/2024.09.05.611552doi: bioRxiv preprint a selection of bioproducts (Figure 4.1D-E) (see 3.1). In terms of theoretical yield, the synthetic pathways (SACA and SAGA) outperform the natural pathways (glycerate and BHAC) for biomass and most bioproducts, which is consistent with findings in previous analyses (Wagner, Wen, et al., 2023). Glycolate is a notable exception, for which the BHAC shows the highest yield and the glycerate pathway outperforms the SACA path- way, which is unsurprising given glycolate is an intermediate in these routes with the potential for accumulation (Pandit et al., 2021). The calculated yields for the Ara5P and All6P-dependent SAGA pathways are indistinguishable as the pathways share a common overall stoichiometry, and are consistently higher than those for the SACA pathway. 4.2 Thermodynamic favourability and enzyme burden The thermodynamic favourability of each pathway up until the production of acetyl-CoA was assessed using the Max-min driving force framework (Noor et al., 2014), a tool that has been employed to assess the thermodynamic feasibility of pathways and rank compet- ing pathways based on their overall driving force under physiological conditions (Khana et al., 2022; Shultz-Mirbach 2024). Acetyl-CoA was selected as the endpoint of each assimilation route, as it serves as the entry point towards the TCA cycle and is a crucial precursor for the production of a large number of industrially relevant compounds such as 1-butanol, fatty acids and lipids, polyketides, isoprenoids, polyhydroxyalkanoates, and and amino acids (Krivoruchko et al., 2015; Sun et al., 2020). Thermodynamically, the BHAC is the least favourable EG assimilation route (Figure 4.1A) with a negative MDF value due to thermodynamic bottlenecks in glycolate conversion to gloyxolate and its subsequent entry into the cycle via conversion to glycine and then BHA, which has a pos- itive ∆rG value at physiological conditions (Figure 4.1; refer to SI). The SACA pathway yields the highest Max-min driving force followed by the Ara5P- and All6P-dependent SAGA pathways, suggesting a tradeoff may exist between stoichiometric yield and ther- modynamic favourability (Du et al., 2018). The glycerate EG assimilation pathway has a low but positive MDF value provided that the concentration of glycolate is maintained sufficiently high in the cell (Figure 4.1A). In order to investigate the enzyme cost to the cells of the EG assimilation pathways, the Enzyme Cost Minimization (ECM) framework (Noor et al., 2016) was used which in- corporates kinetic parameters of pathway enzymes in addition to reaction thermodynam- ics. Unsurprisingly, the natural EG assimilation routes (glycerate pathway and BHAC) have the highest enzyme demand due to the presence of thermodynamic bottlenecks (Fig- ure 4.1B; Figure 4.1). The significant difference in thermodynamic driving forces between the natural and synthetic pathways underlies the disparity in overall enzyme burden such that variations in kinetic parameters are negligible on the whole. The synthetic pathways are also much shorter routes to acetyl-CoA than the natural pathways, which contributes 9 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 10, 2024. ; https://doi.org/10.1101/2024.09.05.611552doi: bioRxiv preprint to this difference. However, as shown in Figure 4.1, kinetic constraints impose high costs of certain pathway enzymes (leading to enzyme saturation) and can limit the successful implementation of substrate assimilation pathways such as the SACA pathway, in which the key ACPS enzyme has a Michaelis-Menten constant (K m) value of 51 mM (Lu et al., 2019). While the two SAGA pathways result in similar bioproduct yields, it is noteworthy that the All6P-dependent pathway may be more thermodynamically favourable with a lower enzyme burden than its Ara5P-dependent analog, a factor that may be considered when choosing which pathway to implement in a host organism. 4.3 Orthogonality The synthesis of a desired value-added compound is often in direct competition with the natural cellular objective of maximizing growth or biomass generation, which can make achieving productivity and high chemical yields challenging. As a result, there is interest in developing bioprocesses with decoupled growth and chemical production stages, with a dynamic control scheme to switch between the two metabolic states (Ni et al., 2021; Hartline et al., 2021; Venayak et al., 2015; Raj et al., 2020). It has been suggested that orthogonal metabolic pathways, as in those that are largely independent of native metabolism with limited common metabolic nodes, may be more amenable to dynamic control compared to conventional, interconnected natural pathways such as glycolysis for glucose assimilation (Pandit et al., 2017). Indeed, our analysis using the orthogonality framework based on acetate production showed that EG assimilation pathways (with the exception of the Ara5P and All6P-dependent SAGA pathways) are more orthogonal than glucose (Figure 4.1C), with the BHAC yielding the highest orthogonality score. 5 Conclusions and recommendations EG is an interesting feedstock garnering attention alongside other next-generation feed- stocks such as methanol, formate, and syngas towards their microbial conversion to value- added products. The use of EG may confer advantages over such C1 compounds as methanol as its physicochemical properties are more amenable for process safety, its as- similation into microbial metabolism involves the less toxic intermediate GA instead of formaldehyde, and it can be more readily utilized as a carbon source by common in- dustrial workhorse organisms (Wagner, Wen, et al., 2023). Recent work has focused on the implementation of EG assimilation pathways, both naturally-occurring and syn- thetic, in organisms such as E. coli, P. putida, and I. sakaiensis to produce desired target compounds. More pathways may yet be elucidated with computational tools such as bioretrosynthesis, which involves the reverse engineering of a biological pathway, start- 10 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 10, 2024. ; https://doi.org/10.1101/2024.09.05.611552doi: bioRxiv preprint ing from the desired end product and working backwards to identify necessary precursor molecules and enzymatic steps (Koch et al., 2020; Lawson et al., 2021). Furthermore, given the number of possible routes that can allow for the conversion of EG to products, the theoretical evaluation of pathways can drive experimental efforts and identify targets for further engineering. Using an E. coli model, synthetic pathways (SACA and SAGA) for EG assimilation were shown to outperform natural pathways (glycerate and BHAC) in terms of higher biomass and product yields, higher thermodynamic driving forces, and lower enzyme bur- dens. The SACA pathway exhibits the highest thermodynamic driving force with a high orthogonality score and higher theoretical product yields than natural pathways (while lower than SAGA pathways); however its successful implementation is currently hindered by the poor kinetics of its key ACPS enzyme that converts GA to acetyl phosphate (Lu et al., 2019). Novel enzyme engineering tools, such as a synthetic orthogonal replication system (Tian et al., 2024) or machine learning-based platforms, can be used to improve enzyme performance and substrate affinities (achieve lower KM values) in such cases. While the glycerate pathway suffers from carbon inefficiency and is not particularly advantageous in terms of thermodynamics, its relative ease of implementation and high orthogonality score may make it well-suited for the production of the pathway interme- diate glycolate, especially in a two-stage system. The theoretical yield of glycolate is also higher using this pathway compared to the use of glucose or the SACA pathway. The favourability of different substrate assimilation pathways is therefore likely to be dependent on the product of interest, as has been previously suggested (Wagner, Wen, et al., 2023). Given their low orthogonality scores, the Ara5P and All6P-dependent SAGA pathways may be more suited for growth-coupled production, particularly of acetyl-CoA- derived products to which they would provide a shorter metabolic route compared to natural EG assimilation pathways. The BHAC was identified as the EG assimilation pathway with the highest orthogo- nality score, suggesting it may be amenable for a two-stage bioproduction system for a product with a high theoretical yield such as glycolate. Strategies to overcome its low thermodynamic driving force could include the removal of product from the system or the selection of gaseous products to drive the pathway forward. The E. coli model used in this work may also not be reflective of metabolic conditions in other organisms with different metabolic networks (Nogales et al., 2020; Mo et al., 2009). As technologies for converting CO2 into useful chemicals improve and the cost of these processes decreases, EG is poised to become a more appealing feedstock for bioproduction. Advances in CO 2 electrochemical reduction may make it feasible to produce EG more efficiently and cost-effectively. It is also important to highlight that bioprocesses do not require the same level of substrate purity as chemical synthetic processes (e.g. water contamination is not a concern), which can further lower feedstock costs (Lad et al., 11 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted September 10, 2024. ; https://doi.org/10.1101/2024.09.05.611552doi: bioRxiv preprint 2022). Additionally, the ongoing development of technologies for the degradation of PET into EG further enhances the economic viability of using EG as a feedstock. As these technologies mature and scale, the reduced production costs will likely make EG a more attractive feedstock for the bioproduction of value-added chemicals. 6 Conflicts of interest The authors declare competing interests as some of the authors have stocks in company based on this technology. 7 Funding statement Authors acknowledge funding from Natural Sciences and Engineering Research Council of Canada through the Industrial Biocatalysis Network.

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