Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes
Abstract
:1. Introduction
2. Major R&D Advancements on SA and A. succinogenes
3. Succinic Acid Production Pathways
4. Metabolic Models of SA Producers
5. Attempts at Metabolic Modeling of A. succinogenes
6. Perspectives and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AcAld | acetaldehyde |
ATP | adenosine triphosphate |
C4 | 4 carbon |
e.g., | example |
F-6-P | fructose-6-phosphate |
G-3-P | glyceraldehyde-3-phosphate |
GEM | genome-scale metabolic model |
HPC | high-performance computing |
IDH | isocitrate dehydrogenase |
NADH | nicotinamide adenine dinucleotide |
OAA | oxaloacetate |
PEP | phosphoenolpyruvate |
PPP | pentose phosphate pathway |
pyc | pyruvate carboxylase gene |
R&D | research and development |
Ru-5-P | ribulose-5-phosphate |
SA | succinic acid |
TCA | tricarboxylic acid |
AcP | acetylphosphate |
C3 | 3 carbon |
CO2 | carbon dioxide |
F-1,6-P | fructose-1,6-bisphosphate |
FBA | flux balance analysis |
G-6-P | glucose-6-phosphate |
GS | glyoxylate shunt |
HT | high throughput |
MFA | metabolic flux analysis |
NADPH | nicotinamide adenine dinucleotide phosphate |
OPR | open reading frame |
Pi | inorganic phosphate |
ptsG | PEP-dependent phosphotransferase system glucose-specific gene |
Pyr | pyruvate |
Rbo-5-P | ribose-5-phosphate |
S-7-P | sedoheptulose-7-phosphate |
SDH | succinate dehydrogenase |
X-5-P | xylulose-5-phosphate |
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Production Route | Pathway Construction | Microorganism | Reference |
---|---|---|---|
Reductive TCA cycle (RT) | Inactivate lactate and acetate formation pathways and overexpress pyruvate carboxylase gene (pyc) | Corynebacterium acetoacidophilum | [44] |
Activate enzymes of RT | Lactobacillus plantarum | [45] | |
Overexpress pyc | C. glutamicum | [46] | |
Oxidative TCA cycle (OT) | Delete succinate dehydrogenase (SDH) gene (sdh) | Yarrowia lipolytica | [47] |
Overexpress genes in the PPP for xylose utilization and delete sdh | Y. lipolytica | [48] | |
Glyoxylate shunt (GS) | Reverse PEP carboxylase via deletion of sdh and overexpress genes involved in GS | E. coli | [49] |
Activate GS via inactivation of SA biosynthetic byproduct (lactate, acetate, formate and ethanol) formation-encoding genes | E. coli | [50] | |
RT-OT (TCA) | Disrupt genes of aconitase, fumarate reductase, alpha ketoglutarate dehydrogenase, SDH, fumarase, isocitrate lyase and fumarate reductase | S. cerevisiae | [51] |
Delete ptsG and genes of SA biosynthetic byproducts and overexpress PEP carboxykinase | E. coli | [52] | |
RT-GS | Overexpress genes in the PPP for xylose utilization, pyc, citrate synthase and succinate exporter | C. glutamicum | [53] |
Delete genes of SDH (sdh1 and sdh2) and isocitrate dehydrogenase (idh1 and idh2) | S. cerevisiae | [54] | |
OT-GS | Delete genes of SDH, IDH and acetate-producing pathway | E. coli | [55] |
Delete genes of SDH and acetate-producing pathway and overexpression of pyc and PEP carboxylase | C. glutamicum | [56] | |
TCA-GS (RT-OT-GS) | Delete genes of SDH and acetyl-CoA transferase and overexpression of key enzymes of RT, OT and GS | Y. lipolytica | [41] |
Kinetic study including RT, OT, GS and other pathways | E. coli | [57] |
Organism | In Silico Operation and Purpose | Year | Reference |
---|---|---|---|
E. coli | Metabolic flux analysis (301 reactions and 294 metabolites) to attain the highest in silico SA yield | 2002 | [70] |
M. succiniciproducens | Genome-scale flux analysis (373 reactions and 352 metabolites) to determine the general genome-scale metabolic characteristics | 2004 | [71] |
E. coli | Metabolic flux analysis (310 reactions and 295 metabolites) to predict volumetric rates of intracellular metabolites | 2004 | [72] |
E. coli | Comparative genomic analysis to estimate the relationship between the maximum biomass and SA production in metabolically modified strains | 2005 | [73] |
E. coli | Genome-scale in silico aided metabolic analysis and flux comparisons to determine the in silico optimal SA production pathway | 2006 | [74] |
M. succiniciproducens | Genome-scale metabolic analysis (686 reactions and 519 metabolites) for genome-scale analysis and designing efficient metabolic engineering studies | 2007 | [75] |
M. succiniciproducens | Constraints-based flux analysis of genome-scale metabolic model to evaluate the production patterns of various organic acids against variable rates of glucose, CO2 and H2 | 2009 | [76] |
A. niger | Genome-scale stoichiometric metabolic model to identify target genes for metabolic manipulation and redirect the pathway towards SA production route | 2009 | [77] |
E. coli | Genome-scale thermodynamics-based flux balance analysis to predict the maximum biomass and SA flux | 2011 | [52] |
S. cerevisiae | Genome-scale metabolic model and flux balance analysis to establish SA overproduction strategies | 2013 | [78] |
S. cerevisiae | Genome-scale metabolic network reconstruction to predict gene deletions that can couple enhanced biomass and SA production | 2013 | [79] |
Basfia succiniciproducens | Metabolic flux analysis to identify undesired fluxes and improve SA yield | 2013 | [80] |
E. coli | Metabolic network construction (65 reactions and 44 metabolites) to evaluate the effect of the carboxylation reactions on SA production | 2014 | [39] |
A. succinogenes | Metabolic model (27 reactions and 28 metabolites) for SA production using a mixture of glucose and xylose substrates | 2014 | [81] |
E. coli | Kinetic model prediction to predict response to multiple environmental perturbations and overproduction of SA | 2015 | [57] |
E. coli | Optimization algorithm and flux balance analysis to identify a set of genes for deletion to improve SA and lactic acid productions | 2015 | [82] |
E. coli | Genome-scale metabolic core model to reconstruct the metabolic fluxes and evaluate the characteristics so as to improve SA production and reduce byproduct formation | 2016 | [83] |
E. coli | Genome-scale metabolic model and Minimization of Metabolic Adjustment algorithm to improve the strain and increase SA production using glucose and glycerol substrates | 2016 | [84] |
E. coli | Genome-scale metabolic model to evaluate the effect of gene deletion for enhanced SA production | 2016 | [85] |
M. succiniciproducens | Genome-scale metabolic simulations to identify gene targets to be engineered for enhanced nearly homo-SA production | 2016 | [86] |
M. succiniciproducens | Genome-scale metabolic flux analysis, omics analyses and metabolic reconstruction to develop a high-yield homo-SA-producing strain by metabolic engineering and carbon source optimization | 2016 | [87] |
A. succinogenes | Thermodynamically constrained metabolic flux analysis to demonstrate the effect of environmental conditions on metabolic fluxes | 2016 | [88] |
E. coli | Simulation and reaction expression analysis to identify genetic strategies for overproduction of SA | 2017 | [89] |
E. coli and A. succinogenes | Dynamic flux balance analysis to estimate the maximum theoretical productivity of a batch culture system | 2017 | [90] |
E. coli | Metabolism–downstream coupled model for metabolic engineering of the strain to produce SA using glycerol substate | 2018 | [91] |
E. coli and Z. mobilis | Hybrid of differential search algorithm and flux balance analysis to identify knockout relations for enhanced SA production | 2018 | [92] |
E. coli | Genome-scale metabolic model to predict gene deletion for enhanced SA production using glycerol substrate | 2018 | [93] |
E. coli and S. cerevisiae | Hybrid of optimization algorithm and genome-scale metabolic models to predict the near-optimal set of gene deletions for overproduction of SA | 2018 | [94] |
A. succinogenes | Comprehensive carbon metabolism model (375 reactions) to analyze the metabolism and predict knockout strategies for maximum SA production with maintaining the cell growth | 2018 | [95] |
A. succinogenes | Genome-scale metabolic model to evaluate the metabolic capability of the strain to produce SA under various conditions | 2018 | [30] |
Zymomonas mobilis | Genome-scale metabolic model to characterize SA-producing capability and comparatively identify gene deletions for enhanced SA production | 2018 | [96] |
E. coli | Optimization modeling to identify near-optimal knockout genes for the maximum production of SA | 2020 | [97] |
Aspergillus niger | Integration of genome-scale metabolic model with dynamic modeling and genetic algorithm to provide simpified gene deletion strategies for the complex evolutionary goals containing multiple targets | 2020 | [98] |
M. succiniciproducens | Flux variability scanning using genome-scale metabolic model to identify amplification target genes for improved SA production | 2020 | [99] |
Genome Size (bp) | Total # Genes | Model Version | Year | # Genes in the Model | # Metabolites | # Reactions | ORF Coverage (%) | Reference | |
---|---|---|---|---|---|---|---|---|---|
A. succinogenes | 2,319,663 | 2210 | iBP722 | 2018 | 722 | 713 | 1072 | 35.00 | [30] |
M. succiniciproducens | 2,314,078 | 2384 | - | 2004 | 335 | 352 | 373 | 14.05 | [71] |
- | 2007 | 425 | 519 | 686 | 17.83 | [75] | |||
C. glutamicum | 3,282,708 | 3002 | ModelCg1 | 2008 | 247 | 411 | 446 | 8.23 | [102] |
3,282,708 | 3002 | ModelCg2 | 2009 | 277 | 423 | 502 | 9.23 | [103] | |
3,292,392 | 3015 | iJM658 | 2015 | 658 | 984 | 1065 | 21.82 | [104] | |
3,282,708 | 3002 | iC773 | 2017 | 773 | 950 | 1207 | 25.57 | [32] | |
E. coli | 4,641,652 | 4453 | iJR904 | 2003 | 904 | 625 | 931 | 20.30 | [105] |
4,641,652 | 4453 | iAF1260 | 2007 | 1260 | 1039 | 2077 | 28.30 | [106] | |
4,639,675 | 4325 | iJO1366 | 2011 | 1366 | 1136 | 2251 | 31.58 | [107] | |
4,639,675 | 4420 | iOL1650-ME | 2013 | 1541 | 6563 | 12,009 | 34.86 | [108] | |
S. cerevisiae | 12,261,038 | 6183 | iFF708 | 2003 | 708 | 733 | 1175 | 16.00 | [101] |
iIN800 | 2008 | 800 | 1013 | 1446 | 17.20 | [109] | |||
iMM904 | 2009 | 904 | 1228 | 1412 | 19.65 | [110] | |||
iTO977 | 2013 | 977 | 1353 | 1566 | 21.24 | [111] |
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Dessie, W.; Wang, Z.; Luo, X.; Wang, M.; Qin, Z. Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes. Fermentation 2021, 7, 220. https://doi.org/10.3390/fermentation7040220
Dessie W, Wang Z, Luo X, Wang M, Qin Z. Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes. Fermentation. 2021; 7(4):220. https://doi.org/10.3390/fermentation7040220
Chicago/Turabian StyleDessie, Wubliker, Zongcheng Wang, Xiaofang Luo, Meifeng Wang, and Zuodong Qin. 2021. "Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes" Fermentation 7, no. 4: 220. https://doi.org/10.3390/fermentation7040220