Paradigm of engineering recalcitrant non-model microorganism with dominant metabolic pathway as a biorefinery chassis

Integration of enzyme constraints to improve the genome-scale metabolic model

High-quality GEMs play critical roles in the rational design of microbial cell factories in the classical Design-Build-Test-Learn cycle of synthetic biology studies⁴¹. A high-quality GEM iZM516 of Z. mobilis ZM4 was constructed recently with the MEMOTE evaluation score of 91% among all published models, which contains 1389 reactions, 1437 metabolites, 516 genes, and 3 cell compartments⁴¹. Recently, numerous ecModels have been applied to model microorganisms such as E. coli, S. cerevisiae, C. glutamicum, and B. subtilis with superior potential to identify rate-limiting enzymes, simulate overflow metabolism, and elucidate the trade-off between biomass yield and enzyme usage efficiency^44,45.

The previous model of iZM516 built upon the ModelSeed database differs from iZM4_478⁴⁶ in terms of Gene-Protein-Reaction (GPR) relationships by the BiGG database⁴⁷. The unique genes and reactions from iZM4_478 were manually curated and incorporated into iZM516 to obtain the revised iZM547 in this study that contains 2501 reactions, 1455 metabolites, and 547 genes. However, it is important to note that iZM547 only considers stoichiometric constraints and does not reflect the cellular status or provide targets that limit flux through specific product synthesis pathways. With the accumulation of enzyme kinetic data, the predictive accuracy of models can be further enhanced by integrating enzyme constraints that reflect limitations related to protein resources during cell growth. Hence, we applied ECMpy2⁴⁴ and K_cat values provided by AutoPACMEN⁴⁸, which is the closest to the experimental results and more accurate to other methods such as DLk_cat⁴⁹, TurNup⁵⁰, and UniKP⁵¹, for developing an enzyme-constrained model (ecModel) (Supplementary Fig. 1). The resulting eciZM547_AutoPACMEN_mean (subsequently abbreviated as eciZM547) is the enzyme-constrained metabolic network model closest to the experimental results (Supplementary Fig. 1), and ultimately selected for the subsequent analyses.

A relatively comprehensive comparison between iZM516 and iZM4_478 has already been conducted in previous work⁴¹. The results indicate that iZM516 exhibits superior simulation accuracy regarding strain behavior according to the report⁴¹. As shown in Fig. 2, small discrepancies between iZM516 and eciZM547 were observed, mainly manifested in the existence of a maximum in the enzyme-constrained model eciZM547 when the value of glucose uptake exceeds around 71 mmol·gDW⁻¹·h⁻¹. This indicates a shift from glucose-limited growth to proteome-limited growth. The maximum predicted growth rate and ethanol production rate were 0.50 h⁻¹ and 134.76 mmol·gDW⁻¹·h⁻¹, respectively (Fig. 2). Therefore, eciZM547 exhibited better accuracy than the previous model, which highly overestimated the maximum growth rate and ethanol production rate.

a Predictions for specific cell growth rates. b Predictions for ethanol production fluxes. iZM4_478 is a model published recently⁴¹. iZM516 is a revised model version constructed in this study on the basis of the published one. eciZM547 is the enzyme-constrained version on the basis of revised iZM516. The bullet points with different color and shape were experimental data from published literature^52,82,84,85.

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Additionally, iZM516 indicates that most carbon sources are directed towards acetate based on growth criteria when glucose was served as the sole carbon source with an uptake rate of 10 mmol·gDW⁻¹·h⁻¹ under aerobic conditions, whereas eciZM547 calculates that carbon sources flow into both acetate and acetoin, aligning more closely with our previous data³⁶. To further investigate the aforementioned simulation results, we conducted the ¹³C-metabolic flux analysis (MFA) of ZM4 under aerobic condition, and subsequently calculated the distributions of metabolic flux (Supplementary Fig. 2a). Consistent with the predictions made by eciZM547, a greater influx of carbon sources into both acetate and acetoin was observed compared to the anaerobic condition reported before⁵². In addition, in the classical revised model iZM516, the growth rate increased linearly with the substrate uptake. In contrast, the enzyme constraint model of eciZM547 narrowed the solution space, resulting in predictions that are closer to actual experimental conditions in Z. mobilis (Fig. 2).

To investigate the potential of endogenous C2-C5 biochemicals synthesis pathway in Z. mobilis ZM4, we calculated and evaluated the metabolic pathways, flux rates, and theoretical conversion rates of biochemicals through simulation and analyses using the iZM516 and eciZM547 models, respectively (Table 1).

Besides ethanol, lactate, isobutanol, 2,3-BDO, and xylitol also had the same rates under anaerobic or aerobic conditions (Table 1), revealing the advantages of Z. mobilis in producing these biochemicals anaerobically, which is crucial for commodity biochemical production since production anaerobically can save the cost around 30–50%. The yield of 1,4-BDO decreases under enzyme constraints (Table 1). However, as the only microorganism using the ED pathway under anaerobic conditions, the conversion rate of 1,4-BDO remained consistent regardless of anaerobic or aerobic conditions as predicted by either iZM516 or eciZM547 models (Table 1).

Although the rate of succinate production under aerobic condition with the supplementation of CO₂ was higher than that under anaerobic condition, it remains feasible to develop a cell factory of Z. mobilis for anaerobic succinate production at a rate of 0.9 (Table 1). The significant disparity in production rates between anaerobic and aerobic processes for itaconic acid and L-alanine indicates that Z. mobilis is more effective for anaerobic production, as suggested by eciZM547 simulation results (Table 1). Moreover, the low rates of ethylene glycol with glucose and xylose as carbon sources under aerobic or even less under anaerobic conditions imply that Z. mobilis is not an appropriate chassis for ethylene glycerol production (Table 1). In contrast to ethylene glycol, the rate of 1,3-PDO when utilizing glucose as a carbon source is relatively low. However, a higher rate of 1,3-PDO derived from glycerol suggests its feasibility for production using Z. mobilis (Table 1).

Construction of microbial cell factories for C2-C5 biochemical production

A series of recombinant strains were then constructed for biochemical production based on model-guided pathway design, including succinate from glucose, 1,3-PDO from glycerol, xylonic acid, ethylene glycerol, glycolic acid, and 1,4-BDO from xylose (Fig. 3a).

a Model-guided pathway design strategy and metabolic pathway of biochemicals production from glucose, xylose, or glycerol. b Summary of succinic acid production in recombinant Z. mobilis with different strategies. c The titer of ethylene glycerol, xylonic acid, glycolic acid, and 1,4-butanediol in recombinant strains from xylose through Dahms pathway. d The titer of 1,3-PDO from glycerol by overexpressing different dhaB. The solid represents a one-step reaction, while the dotted line indicates a multi-step reaction. Metabolites: 2D3DXA: 2-dehydro-3-deoxy-D-xylonate, 5HKG: 5-hydroxy-α-ketoglutarate, 6PG: 6-phospho-gluconate, AcCoA: acetyl-CoA, EG: ethylene glycol, GA: glycolate, G3P: glyceraldehyde-3-phosphate, G6P: glucose-6-phosphate, HBA: hydroxy-butyraldehyde, KdcA: keto-acid decarboxylase, KDPG: 2-keto-3-dehydro-6-phosphogluconate, KGAS: ketoglutaric semialdehyde, OAA: oxaloacetate, PEP: phosphoenolpyruvate, XA: xylonic acid. Enzymes: Adh: alcohol dehydrogenase, AldA: aldehyde dehydrogenase, DhaB: glycerol dehydratase, Frd: fumarate reductase, FumC, fumarate hydratase, Mdh, malate dehydrogenase, Pdc: pyruvate decarboxylase, Ppc, phosphoenolpyruvate carboxylase, Xdh: D-xylose dehydrogenase, XylX: 2-dehydro3-deoxy-D-xylonate dehydratase, YagE: 2-dehydro-3-deoxy-D-xylonate aldolase, YagF: D-xylonate dehydratase, YqhD: aldehyde reductase. Genes: CbdhaB from Clostridium butyricum, CfdhaB from Citrobacter freundii, Kppdu and kpdhaB were from Klebsiella pneumoniae. Brown font represents carbon source. Blue, green, orange background colors represent C2, C3, C4 compounds, respectively. Data are presented as mean ± s.e.m. (n = 3 biologically independent samples). Statistical analysis was performed using a two-tailed Student t-test. ****p < 0.0001, ***p < 0.001 versus wild-type strain. Source data are provided as a Source Data file.

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Succinic acid has been designated as one of the top 12 building block chemicals by the U.S. Department of Energy for its unique advantages in chemical, food, agricultural, and plastic industries⁵³, and microbial cell factories using E. coli and other microorganisms have been developed for succinic acid production⁵⁴. Previous model simulations employing iZM516 have indicated that succinic acid production under anaerobic condition using Z. mobilis exhibits significant economic advantages⁴¹. The model-guided metabolic pathway was subsequently optimized through the co-expression of genes involved in the reductive TCA cycle and the individual expression of malate dehydrogenase (encoded by mdh) from C. glutamicum to enhance succinic acid production (Supplementary Fig. 3a). Phosphoenolpyruvate carboxylase (encoded by ppc), fumarate hydratase (encoded by fumC), and fumarate reductase (encoded by frd) from different sources were screened and tested using the strain SA3 with mdh overexpression as the parental strain (Supplementary Fig. 3b–d). Although there was no significant increase in succinic acid production in these recombinant strains (Fig. 3b), enhanced CO₂ supply can significantly improve succinic acid production, as predicted by the model (Table 1, Fig. 3b). Further overexpression of mdh and ppc from C. glutamicum, fumC and frd from S. cerevisiae in Z. mobilis with the supplementation of NaHCO₃ can generate 560 mg/L succinic acid (Fig. 3b). Despite attempts to divert carbon from ethanol to succinic acid using the CRISPRi system, no significant improvement was achieved owing to the robust metabolic characteristic of the ethanol pathway and redox imbalance present in Z. mobilis (Supplementary Fig. 4).

Xylose is the second most abundant sugar, accounting for 18–30% of lignocellulose components. Efficient utilization of xylose is crucial for economical biochemical production from renewable biomass⁵⁵. Z. mobilis has been engineered to produce ethanol from xylose by introducing the xylose assimilation and pentose phosphate pathway genes^56,57. Xylose dehydrogenase (Xdh) catalyzes the first step of the Dahms pathway from xylose to xylonic acid (Fig. 3a). The codon-optimized xdh gene from Paraburkholderia xenovorans⁵⁸ driven by the strong promoter Ppdc in pXA1 was constructed to produce 16.78 ± 1.58 g/L xylonic acid with CaCO₃ supplementation in RMG2X2 (Fig. 3c). The introduction of the heterologous Dahms pathway could effectively convert xylonic acid into glycolaldehyde and the biosynthesis of ethylene glycol in Z. mobilis based on model prediction using eciZM547. The strong promoter of Peno was used to control the expression of the genes of yagF, yagE, and yqhD from E. coli (Supplementary Fig. 5). Following CaCO₃ supplementation in the recombinant strain EG2 (Fig. 3c), the final ethylene glycerol titer reached 3.26 ± 0.07 g/L. Furthermore, pyruvate generated from the Dahms pathway can be further converted to ethanol (Fig. 3a). By replacing yqhD with aldA, it is possible to achieve a production of 1.5 g/L glycolic acid in the recombinant strain GA (Fig. 3c). In addition, 1,4-butanediol production from xylose can be achieved to 1.2 g/L in the recombinant strain BDO1 by modifying the Dahms pathway and introducing genes including yagF, xylX, yqhD, and kdcA (Fig. 3c).

Glycerol is a promising renewable waste feedstock in biotechnology for the production of high-value products such as 1,3-PDO, ethanol, lactate, and succinic acid⁵⁹. 1,3-PDO has various applications in food, cosmetics, pharmaceuticals, and biomaterials, as it serves as a monomer for the production of polyesters such as polypropylene terephthalate (PTT)⁶⁰. In this study, we tested the feasibility of producing 1,3-PDO using glycerol in Z. mobilis by introducing glycerol dehydratase based on eciZM547 model prediction. By comparing different sources of dhaB and co-expressing the aldehyde reductase gene yqhD, the optimized recombinant strain could achieve 4.1 g/L 1,3-PDO from glycerol (Fig. 3d). The model prediction results also indicated that the Embden-Meyerhof-Parnas (EMP) pathway has a greater advantage in 1,3-PDO synthesis than the ED pathway of Z. mobilis (Supplementary Fig. 6). Therefore, we attempted to complement the truncated EMP pathway of Z. mobilis by co-expressing phosphofructokinase (encoded by pfk from Borrelia burgdorferi), fructose bisphosphate aldolase (encoded by fba from Z. mobilis), and triosephosphate isomerase (encoded by tpi from Z. mobilis) to increase glycerol biosynthesis for 1,3-PDO production (Supplementary Fig. 7). The results showed that while introducing the EMP pathway resulted in a slight reduction in glucose consumption and ethanol synthesis of the recombinant strain, the ethanol yield remained unchanged. Notably, however, the glycerol yield increased from 0.28 g/L to 0.8 g/L (Supplementary Fig. 7). Therefore, completion of the EMP pathway proves advantageous for enhancing glycerol production and further utilization of carbon source.

Development of dominant-metabolism compromised intermediate-chassis strategy to reconfigure ethanologen Z. mobilis into a D-lactate producer

Despite the successful construction of several cell factories in this study aimed at producing C2-C5 biochemicals using glucose, xylose, or glycerol as carbon sources, the titers of these biochemicals remain insufficient for industrialization. This limitation is primarily attributed to the dominant ethanol pathway, which diverts carbon flux to ethanol production. We were unable to directly replace the ethanol pathway with the D-lactate pathway by replacing the pdc gene with D-ldh. This challenge arises because both pathways utilize pyruvate as the substrate, and are redox and cofactor balanced (Fig. 4a). This result is consistent with a previous report, which indicated that the complete replacement of ethanol with the biosynthesis pathway using inducible promoters to regulate pdc expression led to a maximum conversion rate of only 70%⁴³.

a Construction and application of the 2,3-BDO intermediate-chassis for D-lactate production in Z. mobilis. b Process of constructing D-lactate producer of Z. mobilis using the 2,3-BDO intermediate-chassis. c Flask fermentation results of glucose consumption and D-lactate production of the recombinant D-lactate producer ZMB6 in different concentrations of glucose. d Fermentor results of glucose consumption and D-lactate production of the recombinant strain ZMB6 using corncob residue hydrolysate (CRH). Adh: alcohol dehydrogenase, AldC: acetolactate decarboxylase, Als: acetolactate synthase, Bdh: butanediol dehydrogenase, LmldhA (Ldh): lactate dehydrogenase from L. mesenteroides, Pdc (encoding by ZMO1360): pyruvate decarboxylase. Data are presented as mean ± s.e.m. (n = 2 biologically independent samples). Source data are provided as a Source Data file.

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The heterologous 2,3-BDO pathway was then introduced first into Z. mobilis, which is redox imbalanced with only one NADH comsumption³⁵ (Fig. 4a). Therefore, microaerobic conditions are essential for optimal cell growth to maintain redox balance by oxidizing the extra NADH through respiration with Ndh, which also helps maintain the optimal NADH level for cofactor balancing (Fig. 4a). In addition, the growth of Z. mobilis is not inhibited by elevated concentrations of 2,3-BDO, and Z. mobilis can tolerate to above 100 g/L 2,3-BDO indicating its potential for 2,3-BDO production at high titers³⁵.

Then, the model eciZM547 was used to simulate the result of replacing ethanol pathway with D-lactate pathway in the 2,3-BDO producer background. When glucose was served as the sole carbon source at an uptake rate of 40 mmol·gDW⁻¹·h⁻¹, the quantity of ammonia as the sole nitrogen source was unlimited with biomass as the objective. The results indicated that carbon flux will be shifted toward D-lactate pathway upon knocking out ethanol pathway gene ZMO1360 (pdc). Furthermore, when both ethanol and D-lactate metabolic pathways were knocked out, the enzyme-constrained metabolic model was able to utilize the 2,3-BDO pathway as an alternative for normal cell growth, which is consistent with subsequent experimental results. However, meaningful results cannot be obtained when using eciZM547 to simulate the replacement of ethanol pathway with biochemicals such as isobutanol and 1,3-PDO due to the imbalance in NADH/ NAD⁺ and ATP/ADP ratios.

Hence, the 2,3-BDO producer of Z. mobilis can thus be regarded as the dominant ethanol metabolism-comprised strain under anaerobic condition. This strain was then used in the construction of a D-lactate producer by knocking out pdc gene, using the 2,3-BDO producer as the intermediate chassis (Fig. 4a). As illustrated in Fig. 4b, the wild-type strain ZM4 produces a significant amount of ethanol at a yield of 0.48 g/g. A synthetic operon for high 2,3-BDO production was introduced into the genome of ZM4 to obtain the recombination strain ZMB1, which can produce ethanol, 2,3-BDO, and acetoin at a yield of 0.12, 0.11, and 0.28 (g/g), respectively. To further improve the yield of 2,3-BDO, an additional copy of bdh was integrated at the ZMO0038 chromosomal locus, resulting in the decrease of acetoin and the increase of 2,3-BDO in the recombinant strain ZMB2. Subsequently, the pdc gene was knocked out in ZMB2 to obtain ZMB3 strain, which does not produce ethanol and converts almost all carbon into 2,3-BDO at a yield of 0.42 g/g with only a small amount of acetoin being produced. Therefore, a dominant-metabolism compromised intermediate chassis ZMB3 with 2,3-BDO production was constructed.

To determine the metabolic flux distribution of ZMB3, labeling data from [1,2–¹³C] tracer experiments with biomass, 2,3-BDO, acetoin production rates were simultaneously fitted to a single flux map of eciZM547 including central carbon metabolism as well as the biosynthesis of 2,3-BDO and acetoin (Supplementary Data 1). During exponential growth, over 96% glucose flux was converted to 2,3-BDO synthetic pathway from ethanol via ED pathway, and the remaining central metabolic reactions, including the TCA cycle and amino acid biosynthesis, had low activity with minimal flux to fulfill biosynthetic needs, which was highly consistent with the results predicted by our model. Additionally, ZMB3 exhibited better fermentation characteristics under aerobic condition compared to ZM4 (Supplementary Table 1), which may be attributed to the enhanced redox balance of ZMB3 under aerobic condition. Therefore, the recombinant strain ZMB3 can be regarded as an excellent aerobic dominant-metabolism compromised intermediate chassis aimed at synthesizing other biochemicals.

Consequently, bdh was replaced by LmldhA gene encoding D-lactate dehydrogenase to obtain ZMB4 for the production of D-lactate and 2,3-BDO at yields of 0.74 and 0.15 g/g, respectively. To further enhance D-lactate yield, another copy of LmldhA was integrated into the chromosomal locus ZMO1759 to obtain strain ZMB5. Finally, a recombinant strain ZMB6 was constructed with three copies of D-lactate dehydrogenase engineered into 2,3-BDO intermediate chassis (Fig. 4b). To assess the fermentation performance and D-lactate production of ZMB6, shake flask fermentation was carried out using RM medium with varying glucose concentrations. Nearly all glucose was converted into D-lactate with a titer of 140.92 g/L and a yield of 99% by the recombinant strain ZMB6 (Fig. 4c, Supplementary Table 2).

Approximately 40–70% of total costs are allocated to fermentation substrates such as sugars in microbial lactate production⁶¹. The potential of ZMB6 using non-food feedstock of corncob residue hydrolysate as a substrate was then evaluated. The results demonstrated a high conversion rate of glucose to D-lactate (Fig. 4d). Due to the advantages of abundance, low price, and ability to mitigate conflicts with food supplies, it is an attractive and promising approach for lactate production using the non-food feedstocks. In a scale-up fermenter with corncob residue hydrolysate, ZMB6 also demonstrated excellent glucose conversion capacity with a D-lactate titer of 104.6 g/L at a yield exceeding 0.97 g/g within 45 h (Fig. 4d), along with an optical purity of 99.1% (Supplementary Tables 2 and 3). These findings indicated the significant advantages in commercial applications.

TEA and LCA of D-lactic acid cell factory

The economic feasibility of utilizing lignocellulosic residues from corncob waste for D-lactate production by recombinant Z. mobilis was evaluated (Fig. 5a). The designed production scenario demonstrated the potential to generate over 31,100 tons of D-lactate annually, representing approximately 1.7% of the global lactic acid market, thus establishing a significant market share for the emerging bioeconomy. The comprehensive depiction of assumptions and carbon flow diagram are provided in Supplementary Table 4 and Supplementary Fig. 10. Additionally, the total capital investment (TCI) and total operating cost (TOC) associated with the proposed processes were calculated and listed in Supplementary Tables 5 and 6. Our analysis reveals that with the advantages of lower feedstocks price, higher yield, and scale-up effect in this study, the minimum selling price (MSP) for D-lactate amounted to USD 0.35 kg⁻¹, remarkably lower than the current market price for lactic acid, set at USD 3.15 kg⁻¹, serving as a benchmark for comparison^62,63. When considering the cost of corn stover pretreatment as part of the capital investment (represents approximately 12.89% of the total cost for equipment and installation⁶⁴), the MSP of lactic acid is estimated to be around USD 0.37 kg⁻¹. The competitive advantage of MSP primarily derives from utilizing corncob residues, typically regarded as industrial waste, with a feedstock cost of merely $38.7 per tonne. This is substantially lower than alternative carbon sources like corn dextrose or molasses (Supplementary Table 7), leading to a significant reduction in raw material costs and TOC. The titer, productivity, and yield of D-lactate of this study are 104.6 g/L, 2.32 g/L/h, and 97%, respectively, which are higher than the values reported in previous studies (Supplementary Table 7). The comparisons of the economic feasibility (TCI, TOC, MSP) of lactate from previous literature reports (Supplementary Tables 7 and 8) reveal that the MSP identified in this study aligns with prior findings and falls within the lower range of reported MSP, which fluctuate based on production capacity, feedstock types, and process design, thereby indicating the economic feasibility of D-lactate production using the recombinant strain ZMB6 developed in this study.

a Schematic diagram of the industrial production process of D-lactate. b Single-point sensitivity analysis of the minimum selling price to produce 31,100 tons/year of D-lactate. c Minimum selling price (MSP) of lactic acid in various prospective scenarios. d Contribution analysis of the cradle-to-gate global warming potential to produce 1 kg of D-lactate. e Global warming potential comparison of producing 1 kg of lactic acid and fossil-based organic acid. D-LA: D-lactate.

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As depicted in Fig. 5b, a single-point sensitivity analysis was conducted to assess seven cost-driving forces based on reasonable ranges, which illustrated the maximum and minimum values of the MSP of D-lactate derived from corncob residues. The analysis demonstrated that the MSP fluctuated from USD 0.29 to 0.45 kg⁻¹. Importantly, the high sensitivity to annual plant capacity is aligned with the supply of corncob residue, thereby influencing the TCI, TOC, and MSP. Doubling the corncob residue supply from 5000 to 10,000 kg/h leads to a decrease in MSP to USD 0.29 kg⁻¹. In addition, D-lactate productivity and titer stand out as the most critical production factors influencing overall costs. Reduced productivity and titer can result in a notable increase in MSP, largely attributed to the substantial correlation between TCI and annual plant capacity. However, the impact of productivity and titer on MSP decreases above 1 g/L/h and 50 g/L, respectively (Supplementary Fig. 9), indicating a reduced impact at certain levels of productivity and titer.

A comparison using combined sensitivity analysis can provide further insights into how improving fermentation parameters affects MSP (Fig. 5c). The analysis includes the base case (baseline in the single-point sensitivity analysis), short-term case, and long-term case. Rational predictions for lactic acid productivity, titer, and yield were made based on the optimization of genetic engineering. The selection of long-term factor values was determined by the highest value observed in the single-point sensitivity analysis. The MSP for the short-term and long-term scenario can be USD 0.31 kg⁻¹ and USD 0.28 kg⁻¹, which is reduced by 11% and 20% compared to the base case, respectively. Although the MSP of the base case has shown impressive economic performance, improvements in fermentation-related parameters would further enhance its economic viability.

In addition to economic considerations, a preliminary LCA was also conducted, including results and process simulation using Aspen Plus v14, to demonstrate the global warming potential (GWP) associated with the production of 1 kg D-lactate. The GWP of producing 1 kg D-lactic acid from the corncob residue hydrolysate was determined to be 0.49 kg CO₂-eq (Fig. 5d). A contribution analysis was further performed to identify environmental hotspots throughout the process. The utilization of cellulolytic enzyme (Cellic® CTec3, Novozyme, Denmark) was the most significant driver, accounting for 57.5% of all categories, followed by the conversion process at 39.8%. The proposed pathway for D-lactate production has the potential to further reduce greenhouse gas (GHG) emissions by integrating alternative renewable energy sources, such as solar, wind, and geothermal energy.

Upon comparing production data from various non-food feedstocks, including lignocellulosic biomass, vine shoots, corn stover, sugarcane bagasse, and brown leaves, with the corncob residue hydrolysate used in this study, it was noteworthy that the biorefinery GWP demonstrated a reduction of up to 22.51 tons of GHG emissions per ton of D-lactate produced^65,66,67,68 (Fig. 5e). Moreover, the utilization of this pathway could result in a reduction of GHG emissions by up to 98.16% when compared to fossil-based organic acids (Fig. 5e). Thus, D-lactate production with Z. mobilis from corncob residue hydrolysate processes not only offers a practical method for reusing biomass in an industrial setting but also has the potential to mitigate local GHG emissions and ultimately to reduce the overall carbon footprint.

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• Source: https://www.nature.com/articles/s41467-024-54897-5