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An ancient Chinese wisdom for metabolic engineering: Yin-Yang.

Wu SG, He L, Wang Q, Tang YJ - Microb. Cell Fact. (2015)

Bottom Line: Current biotechnology can effectively edit the microbial genome or introduce novel enzymes to redirect carbon fluxes.Pursue biosynthesis relying only on pathways or genetic parts without significant ATP burden. 3.Combine microbial production with chemical conversions (semi-biosynthesis) to reduce biosynthesis steps. 4.

View Article: PubMed Central - PubMed

Affiliation: Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, 63130, USA. wug@seas.wustl.edu.

ABSTRACT
In ancient Chinese philosophy, Yin-Yang describes two contrary forces that are interconnected and interdependent. This concept also holds true in microbial cell factories, where Yin represents energy metabolism in the form of ATP, and Yang represents carbon metabolism. Current biotechnology can effectively edit the microbial genome or introduce novel enzymes to redirect carbon fluxes. On the other hand, microbial metabolism loses significant free energy as heat when converting sugar into ATP; while maintenance energy expenditures further aggravate ATP shortage. The limitation of cell "powerhouse" prevents hosts from achieving high carbon yields and rates. Via an Escherichia coli flux balance analysis model, we further demonstrate the penalty of ATP cost on biofuel synthesis. To ensure cell powerhouse being sufficient in microbial cell factories, we propose five principles: 1. Take advantage of native pathways for product synthesis. 2. Pursue biosynthesis relying only on pathways or genetic parts without significant ATP burden. 3. Combine microbial production with chemical conversions (semi-biosynthesis) to reduce biosynthesis steps. 4. Create "minimal cells" or use non-model microbial hosts with higher energy fitness. 5. Develop a photosynthesis chassis that can utilize light energy and cheap carbon feedstocks. Meanwhile, metabolic flux analysis can be used to quantify both carbon and energy metabolisms. The fluxomics results are essential to evaluate the industrial potential of laboratory strains, avoiding false starts and dead ends during metabolic engineering.

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Genome-scale FBA models for predicting microbial biofuel yields from glucose. a. E. coli strains produce ethanol (growth rate = 0.05 h−1). b.E. coli strains produce isobutanol (growth rate = 0.05 h−1). c. E. coli strains produce fatty acid (growth rate = 0.05 h−1). d. E. coli strains produce fatty acid (growth rate = 0.20 h−1). We use an E. coli FBA model (iJO1366) to predict production of different biofuels from glucose. Alcohol production is simulated under the microaerobic condition (O2 influx ≤ 1.85 mmol/(gDW∙hr)), while fatty acid is under aerobic condition (O2 influx ≤ 12 mmol/ (gDW∙hr)). The medium conditions and glucose uptake rate (8 mmol/ (gDW∙hr)) are same for all FBAs. Extra metabolic burden is simulated by the costs of both protein overexpression and maintenance energy increase (e.g., 10% extra metabolic burden is equivalent to 10% overexpression of total biomass protein plus proportional increase of non-growth associated ATP loss). For each case, the objective function is set as to maximize the biofuel production. Abbreviations: DW (Dry Weight); FA (Fatty acid); Glc (Glucose); IB (Isobutanol).
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Fig2: Genome-scale FBA models for predicting microbial biofuel yields from glucose. a. E. coli strains produce ethanol (growth rate = 0.05 h−1). b.E. coli strains produce isobutanol (growth rate = 0.05 h−1). c. E. coli strains produce fatty acid (growth rate = 0.05 h−1). d. E. coli strains produce fatty acid (growth rate = 0.20 h−1). We use an E. coli FBA model (iJO1366) to predict production of different biofuels from glucose. Alcohol production is simulated under the microaerobic condition (O2 influx ≤ 1.85 mmol/(gDW∙hr)), while fatty acid is under aerobic condition (O2 influx ≤ 12 mmol/ (gDW∙hr)). The medium conditions and glucose uptake rate (8 mmol/ (gDW∙hr)) are same for all FBAs. Extra metabolic burden is simulated by the costs of both protein overexpression and maintenance energy increase (e.g., 10% extra metabolic burden is equivalent to 10% overexpression of total biomass protein plus proportional increase of non-growth associated ATP loss). For each case, the objective function is set as to maximize the biofuel production. Abbreviations: DW (Dry Weight); FA (Fatty acid); Glc (Glucose); IB (Isobutanol).

Mentions: We employ a genome-scale flux balance model (iJO1366) to simulate the adverse impacts of E. coli energy metabolism on biofuel product yields (Figure 2) [32]. Apart from the intracellular stress caused by enzyme overexpression, the release of large amounts of biofuel molecules (alcohol or fatty acid) will interfere enzymatic reactions in vivo and disrupt the cellular membrane’s integrity, which results in reduced efficiencies of oxidative respiration [25,33]. Thereby, metabolic engineering approaches are effective in redirecting carbon fluxes to biosynthesis only in these low-productivity strains whose energy metabolism are not overloaded. We use FBA to test the penalty of metabolic burdens (such as maintenance cost) and the decrease of P/O ratio on biofuel yields. The simulations show that microbial energy metabolism is usually abundant so that they can support certain amount of metabolic burdens without having apparent biosynthesis deficiency (e.g., without showing a slower growth after mutations). However, cell burden may increase during the routine genetic modifications. When cell powerhouse is unable to afford the increasing ATP expenditure, the biosynthesis yield will have a sudden drop (i.e., “the straw that broke the camel’s back”), forming a “cliff” in Figure 2.Figure 2


An ancient Chinese wisdom for metabolic engineering: Yin-Yang.

Wu SG, He L, Wang Q, Tang YJ - Microb. Cell Fact. (2015)

Genome-scale FBA models for predicting microbial biofuel yields from glucose. a. E. coli strains produce ethanol (growth rate = 0.05 h−1). b.E. coli strains produce isobutanol (growth rate = 0.05 h−1). c. E. coli strains produce fatty acid (growth rate = 0.05 h−1). d. E. coli strains produce fatty acid (growth rate = 0.20 h−1). We use an E. coli FBA model (iJO1366) to predict production of different biofuels from glucose. Alcohol production is simulated under the microaerobic condition (O2 influx ≤ 1.85 mmol/(gDW∙hr)), while fatty acid is under aerobic condition (O2 influx ≤ 12 mmol/ (gDW∙hr)). The medium conditions and glucose uptake rate (8 mmol/ (gDW∙hr)) are same for all FBAs. Extra metabolic burden is simulated by the costs of both protein overexpression and maintenance energy increase (e.g., 10% extra metabolic burden is equivalent to 10% overexpression of total biomass protein plus proportional increase of non-growth associated ATP loss). For each case, the objective function is set as to maximize the biofuel production. Abbreviations: DW (Dry Weight); FA (Fatty acid); Glc (Glucose); IB (Isobutanol).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
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getmorefigures.php?uid=PMC4374363&req=5

Fig2: Genome-scale FBA models for predicting microbial biofuel yields from glucose. a. E. coli strains produce ethanol (growth rate = 0.05 h−1). b.E. coli strains produce isobutanol (growth rate = 0.05 h−1). c. E. coli strains produce fatty acid (growth rate = 0.05 h−1). d. E. coli strains produce fatty acid (growth rate = 0.20 h−1). We use an E. coli FBA model (iJO1366) to predict production of different biofuels from glucose. Alcohol production is simulated under the microaerobic condition (O2 influx ≤ 1.85 mmol/(gDW∙hr)), while fatty acid is under aerobic condition (O2 influx ≤ 12 mmol/ (gDW∙hr)). The medium conditions and glucose uptake rate (8 mmol/ (gDW∙hr)) are same for all FBAs. Extra metabolic burden is simulated by the costs of both protein overexpression and maintenance energy increase (e.g., 10% extra metabolic burden is equivalent to 10% overexpression of total biomass protein plus proportional increase of non-growth associated ATP loss). For each case, the objective function is set as to maximize the biofuel production. Abbreviations: DW (Dry Weight); FA (Fatty acid); Glc (Glucose); IB (Isobutanol).
Mentions: We employ a genome-scale flux balance model (iJO1366) to simulate the adverse impacts of E. coli energy metabolism on biofuel product yields (Figure 2) [32]. Apart from the intracellular stress caused by enzyme overexpression, the release of large amounts of biofuel molecules (alcohol or fatty acid) will interfere enzymatic reactions in vivo and disrupt the cellular membrane’s integrity, which results in reduced efficiencies of oxidative respiration [25,33]. Thereby, metabolic engineering approaches are effective in redirecting carbon fluxes to biosynthesis only in these low-productivity strains whose energy metabolism are not overloaded. We use FBA to test the penalty of metabolic burdens (such as maintenance cost) and the decrease of P/O ratio on biofuel yields. The simulations show that microbial energy metabolism is usually abundant so that they can support certain amount of metabolic burdens without having apparent biosynthesis deficiency (e.g., without showing a slower growth after mutations). However, cell burden may increase during the routine genetic modifications. When cell powerhouse is unable to afford the increasing ATP expenditure, the biosynthesis yield will have a sudden drop (i.e., “the straw that broke the camel’s back”), forming a “cliff” in Figure 2.Figure 2

Bottom Line: Current biotechnology can effectively edit the microbial genome or introduce novel enzymes to redirect carbon fluxes.Pursue biosynthesis relying only on pathways or genetic parts without significant ATP burden. 3.Combine microbial production with chemical conversions (semi-biosynthesis) to reduce biosynthesis steps. 4.

View Article: PubMed Central - PubMed

Affiliation: Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, 63130, USA. wug@seas.wustl.edu.

ABSTRACT
In ancient Chinese philosophy, Yin-Yang describes two contrary forces that are interconnected and interdependent. This concept also holds true in microbial cell factories, where Yin represents energy metabolism in the form of ATP, and Yang represents carbon metabolism. Current biotechnology can effectively edit the microbial genome or introduce novel enzymes to redirect carbon fluxes. On the other hand, microbial metabolism loses significant free energy as heat when converting sugar into ATP; while maintenance energy expenditures further aggravate ATP shortage. The limitation of cell "powerhouse" prevents hosts from achieving high carbon yields and rates. Via an Escherichia coli flux balance analysis model, we further demonstrate the penalty of ATP cost on biofuel synthesis. To ensure cell powerhouse being sufficient in microbial cell factories, we propose five principles: 1. Take advantage of native pathways for product synthesis. 2. Pursue biosynthesis relying only on pathways or genetic parts without significant ATP burden. 3. Combine microbial production with chemical conversions (semi-biosynthesis) to reduce biosynthesis steps. 4. Create "minimal cells" or use non-model microbial hosts with higher energy fitness. 5. Develop a photosynthesis chassis that can utilize light energy and cheap carbon feedstocks. Meanwhile, metabolic flux analysis can be used to quantify both carbon and energy metabolisms. The fluxomics results are essential to evaluate the industrial potential of laboratory strains, avoiding false starts and dead ends during metabolic engineering.

Show MeSH
Related in: MedlinePlus