[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a...[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a guide,we simulated pyruvate production of E.coli,screened key genes in metabolic pathways,and developed gene editing procedures accordingly.We knocked out the acetate kinase gene ackA,phosphate acetyltransferase gene pta,alcohol dehydrogenase adhE,glycogen synthase gene glgA,glycogen phosphorylase gene glgP,phosphoribosyl pyrophosphate(PRPP)synthase gene prs,ribose 1,5-bisphosphate phosphokinase gene phnN,and transporter encoding gene proP.Furthermore,we knocked in the transporter encoding gene ompC,flavonoid toxin gene fldA,and D-serine ammonia lyase gene dsdA.[Results]A shake flask process with the genetically edited mutant strain MG1655-6-2 under anaerobic conditions produced pyruvate at a titer of 10.46 g/L and a yield of 0.69 g/g.Metabolomic analysis revealed a significant increase in the pyruvate level in the fermentation broth,accompanied by notable decreases in the levels of certain related metabolic byproducts.Through 5 L fed-batch fermentation and an adaptive laboratory evolution,the strain finally achieved a pyruvate titer of 45.86 g/L.[Conclusion]This study illustrated the efficacy of a gene editing strategy predicted by a genome-scale metabolic network model in enhancing pyruvate accumulation in E.coli under anaerobic conditions and provided novel insights for microbial metabolic engineering.展开更多
Saccharomyces cerevisiae,a widely utilized model organism,has seen continuous updates to its genome-scale metabolic model(GEM)to enhance the prediction performance for metabolic engineering and systems biology.This st...Saccharomyces cerevisiae,a widely utilized model organism,has seen continuous updates to its genome-scale metabolic model(GEM)to enhance the prediction performance for metabolic engineering and systems biology.This study presents an auxotrophy-based curation of the yeast GEM,enabling facile upgrades to yeast GEMs in future endeavors.We illustrated that the curation bolstered the predictive capability of the yeast GEM particularly in predicting auxotrophs without compromising accuracy in other simulations,and thus could be an effective manner for GEM refinement.Last,we leveraged the curated yeast GEM to systematically predict auxotrophs,thereby furnishing a valuable reference for the design of nutrient-dependent cell factories and synthetic yeast consortia.展开更多
Owing to the rapid advancement of genome engineering technologies,the scale of genome engineering has expanded dramatically.Genome editing has progressed from one genomic alteration at a time that could only be employ...Owing to the rapid advancement of genome engineering technologies,the scale of genome engineering has expanded dramatically.Genome editing has progressed from one genomic alteration at a time that could only be employed in few species,to the simultaneous generation of multiple modifications across many genomic loci in numerous species.The development and recent advances in multiplex automated genome engineering(MAGE)-associated technologies and clustered regularly interspaced short palindromic repeats and their associated protein(CRISPR-Cas)-based approaches,together with genome-scale synthesis technologies offer unprecedented opportunities for advancing genome-scale engineering in a broader range.These approaches provide new tools to generate strains with desired phenotypes,understand the complexity of biological systems,and directly evolve a genome with novel features.Here,we review the recent major advances in genome-scale engineering tools developed for Escherichia coli,focusing on their applications in identifying essential genes,genome reduction,recoding,and beyond.展开更多
Background:Synthetic microbial communities,with different strains brought together by balancing their nutrition and promoting their interactions,demonstrate great advantages for exploring complex performance of commun...Background:Synthetic microbial communities,with different strains brought together by balancing their nutrition and promoting their interactions,demonstrate great advantages for exploring complex performance of communities and for further biotechnology applications.The potential of such microbial communities has not been explored,due to our limited knowledge of the extremely complex microbial interactions that are involved in designing and controlling effective and stable communities.Results:Genome-scale metabolic models(GEM)have been demonstrated as an effective tool for predicting and guiding the investigation and design of microbial communities,since they can explicitly and efficiently predict the phenotype of organisms from their genotypic data and can be used to explore the molecular mechanisms of microbehabitats and microbe-microbe interactions.In this work,we reviewed two main categories of GEM-based approaches and three uses related to design of synthetic microbial communities:predicting multi-species interactions,exploring environmental impacts on microbial phenotypes,and optimizing community-level performance.Conclusions:Although at the infancy stage,GEM-based approaches exhibit an increasing scope of applications in designing synthetic microbial communities.Compared to other methods,especially the use of laboratory cultures,GEM-based approaches can greatly decrease the trial-and-error cost of various procedures for designing synthetic communities and improving their functionality,such as identifying community members,determining media composition,evaluating microbial interaction potential or selecting the best community configuration.Future efforts should be made to overcome the limitations of the approaches,ranging from quality control of GEM reconstructions to community-level modeling algorithms,so that more applications of GEMs in studying phenotypes of microbial communities can be expected.展开更多
Deciphering gene function is fundamental to engineering of microbiology.The clustered regularly interspaced short palindromic repeats(CRISPR)system has been adapted for gene repression across a range of hosts,creating...Deciphering gene function is fundamental to engineering of microbiology.The clustered regularly interspaced short palindromic repeats(CRISPR)system has been adapted for gene repression across a range of hosts,creating a versatile tool called CRISPR interference(CRISPRi)that enables genome-scale analysis of gene function.This approach has yielded significant advances in the design of genome-scale CRISPRi libraries,as well as in applica-tions of CRISPRi screening in medical and industrial microbiology.This review provides an overview of the recent progress made in pooled and arrayed CRISPRi screening in microorganisms and highlights representative studies that have employed this method.Additionally,the challenges associated with CRISPRi screening are discussed,and potential solutions for optimizing this strategy are proposed.展开更多
High-quality genome-scale metabolic models(GEMs)could play critical roles on rational design of microbial cell factories in the classical Design-Build-Test-Learn cycle of synthetic biology studies.Despite of the const...High-quality genome-scale metabolic models(GEMs)could play critical roles on rational design of microbial cell factories in the classical Design-Build-Test-Learn cycle of synthetic biology studies.Despite of the constant establishment and update of GEMs for model microorganisms such as Escherichia coli and Saccharomyces cerevisiae,high-quality GEMs for non-model industrial microorganisms are still scarce.Zymomonas mobilis subsp.mobilis ZM4 is a non-model ethanologenic microorganism with many excellent industrial characteristics that has been developing as microbial cell factories for biochemical production.Although five GEMs of Z.mobilis have been constructed,these models are either generating ATP incorrectly,or lacking information of plasmid genes,or not providing standard format file.In this study,a high-quality GEM iZM516 of Z.mobilis ZM4 was constructed.The information from the improved genome annotation,literature,datasets of Biolog Phenotype Microarray studies,and recently updated Gene-Protein-Reaction information was combined for the curation of iZM516.Finally,516 genes,1389 reactions,1437 metabolites,and 3 cell compartments are included in iZM516,which also had the highest MEMOTE score of 91%among all published GEMs of Z.mobilis.Cell growth was then predicted by iZM516,which had 79.4%agreement with the experimental results of the substrate utilization.In addition,the potential endogenous succinate synthesis pathway of Z.mobilis ZM4 was proposed through simulation and analysis using iZM516.Furthermore,metabolic engineering strategies to produce succinate and 1,4-butanediol(1,4-BDO)were designed and then simulated under anaerobic condition using iZM516.The results indicated that 1.68 mol/mol succinate and 1.07 mol/mol 1,4-BDO can be achieved through combinational metabolic engineering strategies,which was comparable to that of the model species E.coli.Our study thus not only established a high-quality GEM iZM516 to help understand and design microbial cell factories for economic biochemical production using Z.mobilis as the chassis,but also provided guidance on building accurate GEMs for other non-model industrial microorganisms.展开更多
Over the last 15 years,genome-scale metabolic models(GEMs)have been reconstructed for human and model animals,such as mouse and rat,to systematically understand metabolism,simulate multicellular or multi-tissue interp...Over the last 15 years,genome-scale metabolic models(GEMs)have been reconstructed for human and model animals,such as mouse and rat,to systematically understand metabolism,simulate multicellular or multi-tissue interplay,understand human diseases,and guide cell factory design for biopharmaceutical protein production.Here,we describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints for flux simulation.Then,we review the history of GEM development for quantitative understanding of Homo sapiens and other relevant animals,together with their applications.We describe how model develops from H.sapiens to other animals and from generic purpose to precise context-specific simulation.The progress of GEMs for animals greatly expand our systematic understanding of metabolism in human and related animals.We discuss the difficulties and present perspectives on the GEM development and the quest to integrate more biological processes and omics data for future research and translation.We truly hope that this review can inspire new models developed for other mammalian organisms and generate new algorithms for integrating big data to conduct more in-depth analysis to further make progress on human health and biopharmaceutical engineering.展开更多
The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades.This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of...The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades.This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant micro-bial pathogens.However,the detailed mechanisms of action,resistance,and toxicity of many antimicrobials remain uncertain,significantly hampering the development of novel antimicrobials.Genome-scale metabolic model(GSMM)has been increasingly employed to investigate microbial metabolism.In this review,we discuss the latest progress of GSMM in antimicrobial pharmacology,particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity,resistance,and toxicity.We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities(e.g.,gene expression),identi-fication of potential drug targets,and integration with machine learning and pharmacokinetic/pharmacodynamic modeling.Overall,GSMM has significant potential in elucidating the critical role of metabolic changes in antimi-crobial pharmacology,providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.展开更多
Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the ...Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.展开更多
Corynebacterium glutamicum is a versatile industrial microorganism for producing various amino acids.However,there have been no reports of well-defined C.glutamicum strains capable of hyperproducing L-tryptophan.This ...Corynebacterium glutamicum is a versatile industrial microorganism for producing various amino acids.However,there have been no reports of well-defined C.glutamicum strains capable of hyperproducing L-tryptophan.This study presents a comprehensive metabolic engineering approach to establish robust C.glutamicum strains for Ltryptophan biosynthesis,including:(1)identification of potential targets by enzyme-constrained genome-scale modeling;(2)enhancement of the L-tryptophan biosynthetic pathway;(3)reconfiguration of central metabolic pathways;(4)identification of metabolic bottlenecks through comparative metabolome analysis;(5)engineering of the transport system,shikimate pathway,and precursor supply;and(6)repression of competing pathways and iterative optimization of key targets.The resulting C.glutamicum strain achieved a remarkable L-tryptophan titer of 50.5 g/L in 48h with a yield of 0.17 g/g glucose in fed-batch fermentation.This study highlights the efficacy of integrating computational modeling with systems metabolic engineering for significantly enhancing the production capabilities of industrial microorganisms.展开更多
Background:Human immunodeficiency virus type 1(HIV-1)remains a persistent global health challenge.Therefore,a continuous exploration of novel therapeutic strategies is essential.A comprehensive understanding of how HI...Background:Human immunodeficiency virus type 1(HIV-1)remains a persistent global health challenge.Therefore,a continuous exploration of novel therapeutic strategies is essential.A comprehensive understanding of how HIV-1 utilizes the cellular metabolism machinery for replication can provide insights into new therapeutic approaches.Methods:In this study,we performed a flux balance analysis using a genome-scale metabolic model(GEM)integrated with an HIV-1 viral biomass objective function to identify potential targets for anti–HIV-1 interventions.We generated a GEM by integrating an HIV-1 production reaction into CD4+T cells and optimized for both host and virus optimal states as objective functions to depict metabolic profiles of cells in the status for optimal host biomass maintenance or for optimal HIV-1 virion production.Differential analysis was used to predict biochemical reactions altered optimal for HIV-1 production.In addition,we conducted in silico simulations involving gene and reaction knock-outs to identify potential anti–HIV-1 targets,which were subsequently validated by human phytohemagglutinin(PHA)blasts infected with HIV-1.Results:Differential analysis identified several altered biochemical reactions,including increased lysine uptake and oxidative phosphorylation(OXPHOS)activities in the virus optima compared with the host optima.In silico gene and reaction knock-out simulations revealed de novo pyrimidine synthesis,and OXPHOS could serve as potential anti–HIV-1 metabolic targets.In vitro assay confirmed that targeting OXPHOS using metformin could suppress the replication of HIV-1 by 56.6%(385.4±67.5 pg/mL in the metformintreated group vs.888.4±32.3 pg/mL in the control group,P<0.001).Conclusion:Our integrated host-virus genome-scale metabolic study provides insights on potential targets(OXPHOS)for anti-HIV therapies.展开更多
“In silico organisms”are computational genome-scale metabolic models used in systems and synthetic biology developed by constraint-based metabolic simulations using multi-omics and phenotypic data.The quality of the...“In silico organisms”are computational genome-scale metabolic models used in systems and synthetic biology developed by constraint-based metabolic simulations using multi-omics and phenotypic data.The quality of these models is hidden because of the limited availability of genomic information and genome-scale metabolic reconstruction methods.In this review,237 manually curated genome-scale models for various organisms with industrial and clinical significance were comprehensively reviewed,and their modelling information was tabulated based on literature.This review provides a comprehensive summary of potential applications of systems biology in biotechnology and biomedical research.Their broad applicability has been explored in the process of model improvement and design of experiments in metabolic design and drug development.This review summarizes their recent advances,challenges,and practical applications in Gram-negative bacteria,Gram-positive bacteria,archaea,fungi,algae,plants,and animals.Genome-scale models of microbes have been reviewed to address their various applications in metabolic systems engineering,strain optimization,bioremediation,biomanufacturing,and personalized systems medicine.Several models have been explored to understand the molecular mechanisms underlying pathogenesis,virulence,host-microbe interactions,and metabolic crosstalk.This review provides an overview of the current knowledge on human metabolic reconstructions and their important roles in human,microbiota-related,and complex metabolic disorders.Genome-scale models of human and animal metals offer ethical alternatives to the traditional animal testing methods.Current progress in systems biology research will lead to the development of indispensable databases,computational tools,and analytical platforms.This will strengthen data-driven discovery and facilitate integration of biological information into living systems.展开更多
The development of a cost-competitive bioprocess requires that the cell factory converts the feedstock into the product of interest at high rates and yields.However,microbial cell factories are exposed to a variety of...The development of a cost-competitive bioprocess requires that the cell factory converts the feedstock into the product of interest at high rates and yields.However,microbial cell factories are exposed to a variety of different stresses during the fermentation process.These stresses can be derived from feedstocks,metabolism,or industrial production processes,limiting production capacity and diminishing competitiveness.Improving stress tolerance and robustness allows for more efficient production and ultimately makes a process more economically viable.This review summarises general trends and updates the most recent developments in technologies to improve the stress tolerance of microorganisms.We first look at evolutionary,systems biology and computational methods as examples of non-rational approaches.Then we review the(semi-)rational approaches of membrane and tran-scription factor engineering for improving tolerance phenotypes.We further discuss challenges and perspectives associated with these different approaches.展开更多
Genome-scale metabolic models(GEMs)have been widely used to design cell factories in silico.However,initial flux balance analysis only considers stoichiometry and reaction direction constraints,so it cannot accurately...Genome-scale metabolic models(GEMs)have been widely used to design cell factories in silico.However,initial flux balance analysis only considers stoichiometry and reaction direction constraints,so it cannot accurately describe the distribution of metabolic flux under the control of various regulatory mechanisms.In the recent years,by introducing enzymology,thermodynamics,and other multiomics-based constraints into GEMs,the metabolic state of cells under different conditions was more accurately simulated and a series of algorithms have been presented for microbial phenotypic analysis.Herein,the development of multiconstrained GEMs was reviewed by taking the constraints of enzyme kinetics,thermodynamics,and transcriptional regulatory mechanisms as examples.This review focused on introducing and summarizing GEMs application tools and cases in cell factory design.The challenges and prospects of GEMs development were also discussed.展开更多
Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells.The prediction functions were significantly expanded by integrating c...Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells.The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years.However,if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge,the conflicts between stoichiometric and other constraints,such as thermodynamic feasibility and enzyme resource availability,would lead to distorted predictions.In this work,we investigated a prediction anomaly of EcoETM,a constraints-based metabolic network model,and introduced the idea of enzyme compartmentalization into the analysis process.Through rational combination of reactions,we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites.This allowed us to correct the pathway structures of L-serine and L-tryptophan.A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments.Notably,this work also reveals the trade-off between product yield and thermodynamic feasibility.Our work is of great value for the structural improvement of constraints-based models.展开更多
Due to the increasing demand for microbially manufactured products in various industries,it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its...Due to the increasing demand for microbially manufactured products in various industries,it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its flux size by means of metabolic engineering such as knocking out competing pathways and introducing exogenous pathways to increase the yield of desired products.Recently,with the gradual cross-fertilization between computer science and bioinformatics fields,machine learning and intelligent optimization-based approaches have received much attention in Genome-scale metabolic network models(GSMMs)based on constrained optimization methods,and many high-quality related works have been published.Therefore,this paper focuses on the advances and applications of machine learning and intelligent optimization algorithms in metabolic engineering,with special emphasis on GSMMs.Specifically,the development history of GSMMs is first reviewed.Then,the analysis methods of GSMMs based on constraint optimization are presented.Next,this paper mainly reviews the development and application of machine learning and intelligent optimization algorithms in genome-scale metabolic models.In addition,the research gaps and future research potential in machine learning and intelligent optimization methods applied in GSMMs are discussed.展开更多
基金supported by the Hebei Provincial Key Research and Development Project(21372803D)。
文摘[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a guide,we simulated pyruvate production of E.coli,screened key genes in metabolic pathways,and developed gene editing procedures accordingly.We knocked out the acetate kinase gene ackA,phosphate acetyltransferase gene pta,alcohol dehydrogenase adhE,glycogen synthase gene glgA,glycogen phosphorylase gene glgP,phosphoribosyl pyrophosphate(PRPP)synthase gene prs,ribose 1,5-bisphosphate phosphokinase gene phnN,and transporter encoding gene proP.Furthermore,we knocked in the transporter encoding gene ompC,flavonoid toxin gene fldA,and D-serine ammonia lyase gene dsdA.[Results]A shake flask process with the genetically edited mutant strain MG1655-6-2 under anaerobic conditions produced pyruvate at a titer of 10.46 g/L and a yield of 0.69 g/g.Metabolomic analysis revealed a significant increase in the pyruvate level in the fermentation broth,accompanied by notable decreases in the levels of certain related metabolic byproducts.Through 5 L fed-batch fermentation and an adaptive laboratory evolution,the strain finally achieved a pyruvate titer of 45.86 g/L.[Conclusion]This study illustrated the efficacy of a gene editing strategy predicted by a genome-scale metabolic network model in enhancing pyruvate accumulation in E.coli under anaerobic conditions and provided novel insights for microbial metabolic engineering.
基金supported by the National Key R&D Program of China(2023YFA0913900)the Shenzhen Medical Research Fund(A2303026)the Shenzhen Science and Technology Program(KJZD20230923114415032).
文摘Saccharomyces cerevisiae,a widely utilized model organism,has seen continuous updates to its genome-scale metabolic model(GEM)to enhance the prediction performance for metabolic engineering and systems biology.This study presents an auxotrophy-based curation of the yeast GEM,enabling facile upgrades to yeast GEMs in future endeavors.We illustrated that the curation bolstered the predictive capability of the yeast GEM particularly in predicting auxotrophs without compromising accuracy in other simulations,and thus could be an effective manner for GEM refinement.Last,we leveraged the curated yeast GEM to systematically predict auxotrophs,thereby furnishing a valuable reference for the design of nutrient-dependent cell factories and synthetic yeast consortia.
基金supported by the National Key Research and Development Program of China(2018YFA0903700)the National Natural Science Foundation of China(32030004,32150025,31901020)+3 种基金Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project(TSBICIP-PTJS-002)Guangdong Basic and Applied Basic Research Foundation(2023A1515030285)Shenzhen Science and Technology Program(KQTD20180413181837372)Shenzhen Outstanding Talents Training Fund.Shenzhen Bay Laboratory startup funding.
文摘Owing to the rapid advancement of genome engineering technologies,the scale of genome engineering has expanded dramatically.Genome editing has progressed from one genomic alteration at a time that could only be employed in few species,to the simultaneous generation of multiple modifications across many genomic loci in numerous species.The development and recent advances in multiplex automated genome engineering(MAGE)-associated technologies and clustered regularly interspaced short palindromic repeats and their associated protein(CRISPR-Cas)-based approaches,together with genome-scale synthesis technologies offer unprecedented opportunities for advancing genome-scale engineering in a broader range.These approaches provide new tools to generate strains with desired phenotypes,understand the complexity of biological systems,and directly evolve a genome with novel features.Here,we review the recent major advances in genome-scale engineering tools developed for Escherichia coli,focusing on their applications in identifying essential genes,genome reduction,recoding,and beyond.
基金the National Natural Science Foundation of China(Nos.92051102,32200099,32225003 and 31970105)the Innovation Team Project of Universities in Guangdong Province(No.2020KCXTD023)the Shenzhen Science and Technology Program(JCYJ20200109105010363).
文摘Background:Synthetic microbial communities,with different strains brought together by balancing their nutrition and promoting their interactions,demonstrate great advantages for exploring complex performance of communities and for further biotechnology applications.The potential of such microbial communities has not been explored,due to our limited knowledge of the extremely complex microbial interactions that are involved in designing and controlling effective and stable communities.Results:Genome-scale metabolic models(GEM)have been demonstrated as an effective tool for predicting and guiding the investigation and design of microbial communities,since they can explicitly and efficiently predict the phenotype of organisms from their genotypic data and can be used to explore the molecular mechanisms of microbehabitats and microbe-microbe interactions.In this work,we reviewed two main categories of GEM-based approaches and three uses related to design of synthetic microbial communities:predicting multi-species interactions,exploring environmental impacts on microbial phenotypes,and optimizing community-level performance.Conclusions:Although at the infancy stage,GEM-based approaches exhibit an increasing scope of applications in designing synthetic microbial communities.Compared to other methods,especially the use of laboratory cultures,GEM-based approaches can greatly decrease the trial-and-error cost of various procedures for designing synthetic communities and improving their functionality,such as identifying community members,determining media composition,evaluating microbial interaction potential or selecting the best community configuration.Future efforts should be made to overcome the limitations of the approaches,ranging from quality control of GEM reconstructions to community-level modeling algorithms,so that more applications of GEMs in studying phenotypes of microbial communities can be expected.
基金supported by the National Key R&D Program of China(2018YFA0901500)the National Natural Science Foundation of China(32222004 and 32270101)the Youth Innovation Promotion Association of Chinese Academy of Sciences(2021177).
文摘Deciphering gene function is fundamental to engineering of microbiology.The clustered regularly interspaced short palindromic repeats(CRISPR)system has been adapted for gene repression across a range of hosts,creating a versatile tool called CRISPR interference(CRISPRi)that enables genome-scale analysis of gene function.This approach has yielded significant advances in the design of genome-scale CRISPRi libraries,as well as in applica-tions of CRISPRi screening in medical and industrial microbiology.This review provides an overview of the recent progress made in pooled and arrayed CRISPRi screening in microorganisms and highlights representative studies that have employed this method.Additionally,the challenges associated with CRISPRi screening are discussed,and potential solutions for optimizing this strategy are proposed.
基金the National Key Technology Research and Development Program of China(2018YFA0900300 and 2022YFA0911800)the National Natural Science Foundation of China(21978071 and U1932141)+3 种基金the Key Science and Technology Innovation Project of Hubei Province(2021BAD001)2022 Joint Projects between Chinese and CEEC’s Universities(202004)the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang Province(2018R01014)the Innovation Base for Introducing Talents of Discipline of Hubei Province(2019BJH021)。
文摘High-quality genome-scale metabolic models(GEMs)could play critical roles on rational design of microbial cell factories in the classical Design-Build-Test-Learn cycle of synthetic biology studies.Despite of the constant establishment and update of GEMs for model microorganisms such as Escherichia coli and Saccharomyces cerevisiae,high-quality GEMs for non-model industrial microorganisms are still scarce.Zymomonas mobilis subsp.mobilis ZM4 is a non-model ethanologenic microorganism with many excellent industrial characteristics that has been developing as microbial cell factories for biochemical production.Although five GEMs of Z.mobilis have been constructed,these models are either generating ATP incorrectly,or lacking information of plasmid genes,or not providing standard format file.In this study,a high-quality GEM iZM516 of Z.mobilis ZM4 was constructed.The information from the improved genome annotation,literature,datasets of Biolog Phenotype Microarray studies,and recently updated Gene-Protein-Reaction information was combined for the curation of iZM516.Finally,516 genes,1389 reactions,1437 metabolites,and 3 cell compartments are included in iZM516,which also had the highest MEMOTE score of 91%among all published GEMs of Z.mobilis.Cell growth was then predicted by iZM516,which had 79.4%agreement with the experimental results of the substrate utilization.In addition,the potential endogenous succinate synthesis pathway of Z.mobilis ZM4 was proposed through simulation and analysis using iZM516.Furthermore,metabolic engineering strategies to produce succinate and 1,4-butanediol(1,4-BDO)were designed and then simulated under anaerobic condition using iZM516.The results indicated that 1.68 mol/mol succinate and 1.07 mol/mol 1,4-BDO can be achieved through combinational metabolic engineering strategies,which was comparable to that of the model species E.coli.Our study thus not only established a high-quality GEM iZM516 to help understand and design microbial cell factories for economic biochemical production using Z.mobilis as the chassis,but also provided guidance on building accurate GEMs for other non-model industrial microorganisms.
基金Shenzhen Scienceand Technology Innovation Commission,Grant/Award Number:KCXFZ20201221173207022National Natural Science Foundation of China,key program,Next Generation Corynebacterium Glutamate Cell Factory System Creation Technology,Grant/Award Number:21938004Department of Chemical Engineering-i BHE special cooperation joint fund project,Grant/Award Number:DCE-iBHE-2023-1。
文摘Over the last 15 years,genome-scale metabolic models(GEMs)have been reconstructed for human and model animals,such as mouse and rat,to systematically understand metabolism,simulate multicellular or multi-tissue interplay,understand human diseases,and guide cell factory design for biopharmaceutical protein production.Here,we describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints for flux simulation.Then,we review the history of GEM development for quantitative understanding of Homo sapiens and other relevant animals,together with their applications.We describe how model develops from H.sapiens to other animals and from generic purpose to precise context-specific simulation.The progress of GEMs for animals greatly expand our systematic understanding of metabolism in human and related animals.We discuss the difficulties and present perspectives on the GEM development and the quest to integrate more biological processes and omics data for future research and translation.We truly hope that this review can inspire new models developed for other mammalian organisms and generate new algorithms for integrating big data to conduct more in-depth analysis to further make progress on human health and biopharmaceutical engineering.
文摘The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades.This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant micro-bial pathogens.However,the detailed mechanisms of action,resistance,and toxicity of many antimicrobials remain uncertain,significantly hampering the development of novel antimicrobials.Genome-scale metabolic model(GSMM)has been increasingly employed to investigate microbial metabolism.In this review,we discuss the latest progress of GSMM in antimicrobial pharmacology,particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity,resistance,and toxicity.We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities(e.g.,gene expression),identi-fication of potential drug targets,and integration with machine learning and pharmacokinetic/pharmacodynamic modeling.Overall,GSMM has significant potential in elucidating the critical role of metabolic changes in antimi-crobial pharmacology,providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.
文摘Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.
基金supported by the National Key R&D Program of China(No.2021YFC2100900)the National Natural Science Foundation of China(Grant Nos.21938004,22078172)Tsinghua University Initiative Scientific Research Program(No.20223080016).
文摘Corynebacterium glutamicum is a versatile industrial microorganism for producing various amino acids.However,there have been no reports of well-defined C.glutamicum strains capable of hyperproducing L-tryptophan.This study presents a comprehensive metabolic engineering approach to establish robust C.glutamicum strains for Ltryptophan biosynthesis,including:(1)identification of potential targets by enzyme-constrained genome-scale modeling;(2)enhancement of the L-tryptophan biosynthetic pathway;(3)reconfiguration of central metabolic pathways;(4)identification of metabolic bottlenecks through comparative metabolome analysis;(5)engineering of the transport system,shikimate pathway,and precursor supply;and(6)repression of competing pathways and iterative optimization of key targets.The resulting C.glutamicum strain achieved a remarkable L-tryptophan titer of 50.5 g/L in 48h with a yield of 0.17 g/g glucose in fed-batch fermentation.This study highlights the efficacy of integrating computational modeling with systems metabolic engineering for significantly enhancing the production capabilities of industrial microorganisms.
基金the National Natural Science Foundation of China(82071784)the Fundamental Research Funds for the Central Universities(2042022dx0003 and PTPP2023002)+1 种基金the Key Research and Development Project of Hubei Province(2020BCA069)the Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University(ZNJC202007).
文摘Background:Human immunodeficiency virus type 1(HIV-1)remains a persistent global health challenge.Therefore,a continuous exploration of novel therapeutic strategies is essential.A comprehensive understanding of how HIV-1 utilizes the cellular metabolism machinery for replication can provide insights into new therapeutic approaches.Methods:In this study,we performed a flux balance analysis using a genome-scale metabolic model(GEM)integrated with an HIV-1 viral biomass objective function to identify potential targets for anti–HIV-1 interventions.We generated a GEM by integrating an HIV-1 production reaction into CD4+T cells and optimized for both host and virus optimal states as objective functions to depict metabolic profiles of cells in the status for optimal host biomass maintenance or for optimal HIV-1 virion production.Differential analysis was used to predict biochemical reactions altered optimal for HIV-1 production.In addition,we conducted in silico simulations involving gene and reaction knock-outs to identify potential anti–HIV-1 targets,which were subsequently validated by human phytohemagglutinin(PHA)blasts infected with HIV-1.Results:Differential analysis identified several altered biochemical reactions,including increased lysine uptake and oxidative phosphorylation(OXPHOS)activities in the virus optima compared with the host optima.In silico gene and reaction knock-out simulations revealed de novo pyrimidine synthesis,and OXPHOS could serve as potential anti–HIV-1 metabolic targets.In vitro assay confirmed that targeting OXPHOS using metformin could suppress the replication of HIV-1 by 56.6%(385.4±67.5 pg/mL in the metformintreated group vs.888.4±32.3 pg/mL in the control group,P<0.001).Conclusion:Our integrated host-virus genome-scale metabolic study provides insights on potential targets(OXPHOS)for anti-HIV therapies.
基金the Science and Engineering Research Board(EEQ/2020/000095),Government of India,for their financial assistance.
文摘“In silico organisms”are computational genome-scale metabolic models used in systems and synthetic biology developed by constraint-based metabolic simulations using multi-omics and phenotypic data.The quality of these models is hidden because of the limited availability of genomic information and genome-scale metabolic reconstruction methods.In this review,237 manually curated genome-scale models for various organisms with industrial and clinical significance were comprehensively reviewed,and their modelling information was tabulated based on literature.This review provides a comprehensive summary of potential applications of systems biology in biotechnology and biomedical research.Their broad applicability has been explored in the process of model improvement and design of experiments in metabolic design and drug development.This review summarizes their recent advances,challenges,and practical applications in Gram-negative bacteria,Gram-positive bacteria,archaea,fungi,algae,plants,and animals.Genome-scale models of microbes have been reviewed to address their various applications in metabolic systems engineering,strain optimization,bioremediation,biomanufacturing,and personalized systems medicine.Several models have been explored to understand the molecular mechanisms underlying pathogenesis,virulence,host-microbe interactions,and metabolic crosstalk.This review provides an overview of the current knowledge on human metabolic reconstructions and their important roles in human,microbiota-related,and complex metabolic disorders.Genome-scale models of human and animal metals offer ethical alternatives to the traditional animal testing methods.Current progress in systems biology research will lead to the development of indispensable databases,computational tools,and analytical platforms.This will strengthen data-driven discovery and facilitate integration of biological information into living systems.
基金the Novo Nordisk Foundation(NNF18OC0034844)the Chalmers Foundation and Angpanneforeningens Forskningsstiftelse.
文摘The development of a cost-competitive bioprocess requires that the cell factory converts the feedstock into the product of interest at high rates and yields.However,microbial cell factories are exposed to a variety of different stresses during the fermentation process.These stresses can be derived from feedstocks,metabolism,or industrial production processes,limiting production capacity and diminishing competitiveness.Improving stress tolerance and robustness allows for more efficient production and ultimately makes a process more economically viable.This review summarises general trends and updates the most recent developments in technologies to improve the stress tolerance of microorganisms.We first look at evolutionary,systems biology and computational methods as examples of non-rational approaches.Then we review the(semi-)rational approaches of membrane and tran-scription factor engineering for improving tolerance phenotypes.We further discuss challenges and perspectives associated with these different approaches.
基金This work was financially supported by the Key Research and Development Program of China(2020YFA0908300)the National Natural Science Foundation of China(31870069 and 32021005)the Fundamental Research Funds for the Central Universities(USRP52019A,JUSRP121010,and JUSRP221013).
文摘Genome-scale metabolic models(GEMs)have been widely used to design cell factories in silico.However,initial flux balance analysis only considers stoichiometry and reaction direction constraints,so it cannot accurately describe the distribution of metabolic flux under the control of various regulatory mechanisms.In the recent years,by introducing enzymology,thermodynamics,and other multiomics-based constraints into GEMs,the metabolic state of cells under different conditions was more accurately simulated and a series of algorithms have been presented for microbial phenotypic analysis.Herein,the development of multiconstrained GEMs was reviewed by taking the constraints of enzyme kinetics,thermodynamics,and transcriptional regulatory mechanisms as examples.This review focused on introducing and summarizing GEMs application tools and cases in cell factory design.The challenges and prospects of GEMs development were also discussed.
基金funded by the National Key Research and Development Program of China(2018YFA0900300,2020YFA0908301)the National Natural Science Foundation of China(32201188)+1 种基金the Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project(TSBICIP-CXRC-060,TSBICIP-PTJS-001,and TSBICIP-PTJS-013)the China Postdoctoral Science Foundation(2022M723341).
文摘Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells.The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years.However,if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge,the conflicts between stoichiometric and other constraints,such as thermodynamic feasibility and enzyme resource availability,would lead to distorted predictions.In this work,we investigated a prediction anomaly of EcoETM,a constraints-based metabolic network model,and introduced the idea of enzyme compartmentalization into the analysis process.Through rational combination of reactions,we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites.This allowed us to correct the pathway structures of L-serine and L-tryptophan.A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments.Notably,this work also reveals the trade-off between product yield and thermodynamic feasibility.Our work is of great value for the structural improvement of constraints-based models.
基金supported by the National key research and development program of China(Grant no.2020YFA0908303).
文摘Due to the increasing demand for microbially manufactured products in various industries,it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its flux size by means of metabolic engineering such as knocking out competing pathways and introducing exogenous pathways to increase the yield of desired products.Recently,with the gradual cross-fertilization between computer science and bioinformatics fields,machine learning and intelligent optimization-based approaches have received much attention in Genome-scale metabolic network models(GSMMs)based on constrained optimization methods,and many high-quality related works have been published.Therefore,this paper focuses on the advances and applications of machine learning and intelligent optimization algorithms in metabolic engineering,with special emphasis on GSMMs.Specifically,the development history of GSMMs is first reviewed.Then,the analysis methods of GSMMs based on constraint optimization are presented.Next,this paper mainly reviews the development and application of machine learning and intelligent optimization algorithms in genome-scale metabolic models.In addition,the research gaps and future research potential in machine learning and intelligent optimization methods applied in GSMMs are discussed.
