Integration of in vivo and in silico metabolic fluxes for improvement of recombinant protein production

Metabolic impact of heterologous protein production in Pseudomonas putida: Insights into carbon and energy flux control

For engineered microorganisms, the production of heterologous proteins that are often useless to host cells represents a burden on resources, which have to be shared with normal cellular processes. Within a certain metabolic leeway, this competitive process has no impact on growth. However, once this leeway, or free capacity, is fully utilized, the extra load becomes a metabolic burden that inhibits cellular processes and triggers a broad cellular response, reducing cell growth and often hindering the production of heterologous proteins. In this study, we sought to characterize the metabolic rearrangements occurring in the central metabolism of Pseudomonas putida at different levels of metabolic load. To this end, we constructed a P. putida KT2440 strain that expressed two genes encoding fluorescent proteins, one in the genome under constitutive expression to monitor the free capacity, and the other on an inducible plasmid to probe heterologous protein production. We found that metabolic fluxes are considerably reshuffled, especially at the level of periplasmic pathways, as soon as the metabolic load exceeds the free capacity. Heterologous protein production leads to the decoupling of anabolism and catabolism, resulting in large excess energy production relative to the requirements of protein biosynthesis. Finally, heterologous protein production was found to exert a stronger control on carbon fluxes than on energy fluxes, indicating that the flexible nature of P. putida's central metabolic network is solicited to sustain energy production.

Intelligent Biological Networks: Improving Anti-Microbial Resistance Resilience through Nutritional Interventions to Understand Protozoal Gut Infections

Enteric protozoan pathogenic infections significantly contribute to the global burden of gastrointestinal illnesses. Their occurrence is considerable within remote and indigenous communities and regions due to reduced access to clean water and adequate sanitation. The robustness of these pathogens leads to a requirement of harsh treatment methods, such as medicinal drugs or antibiotics. However, in addition to protozoal infection itself, these treatments impact the gut microbiome and create dysbiosis. This often leads to opportunistic pathogen invasion, anti-microbial resistance, or functional gastrointestinal disorders, such as irritable bowel syndrome. Moreover, these impacts do not remain confined to the gut and are reflected across the gut-brain, gut-liver, and gut-lung axes, among others. Therefore, apart from medicinal treatment, nutritional supplementation is also a key aspect of providing recovery from this dysbiosis. Future proteins, prebiotics, probiotics, synbiotics, and food formulations offer a good solution to remedy this dysbiosis. Furthermore, nutritional supplementation also helps to build resilience against opportunistic pathogens and potential future infections and disorders that may arise due to the dysbiosis. Systems biology techniques have shown to be highly effective tools to understand the biochemistry of these processes. Systems biology techniques characterize the fundamental host-pathogen interaction biochemical pathways at various infection and recovery stages. This same mechanism also allows the impact of the abovementioned treatment methods of gut microbiome remediation to be tracked. This manuscript discusses system biology approaches, analytical techniques, and interaction and association networks, to understand (1) infection mechanisms and current global status; (2) cross-organ impacts of dysbiosis, particularly within the gut-liver and gut-lung axes; and (3) nutritional interventions. This study highlights the impact of anti-microbial resistance and multi-drug resistance from the perspective of protozoal infections. It also highlights the role of nutritional interventions to add resilience against the chronic problems caused by these phenomena.

Ammonium sulfate supplementation enhances erythromycin biosynthesis by augmenting intracellular metabolism and precursor supply in Saccharopolyspora erythraea

In this study, the cellular metabolic mechanisms regarding ammonium sulfate supplementation on erythromycin production were investigated by employing targeted metabolomics and metabolic flux analysis. The results suggested that the addition of ammonium sulfate stimulates erythromycin biosynthesis. Targeted metabolomics analysis uncovered that the addition of ammonium sulfate during the late stage of fermentation resulted in an augmented intracellular amino acid metabolism pool, guaranteeing an ample supply of precursors for organic acids and coenzyme A-related compounds. Therefore, adequate precursors facilitated cellular maintenance and erythromycin biosynthesis. Subsequently, an optimal supplementation rate of 0.02 g/L/h was determined. The results exhibited that erythromycin titer (1311.1 μg/mL) and specific production rate (0.008 mmol/gDCW/h) were 101.3% and 41.0% higher than those of the process without ammonium sulfate supplementation, respectively. Moreover, the erythromycin A component proportion increased from 83.2% to 99.5%. Metabolic flux analysis revealed increased metabolic fluxes with the supplementation of three ammonium sulfate rates.

Simultaneous manipulation of multiple genes within a same regulatory stage for iterative evolution of Trichoderma reesei

Background

While there is growing interest in developing non-canonical filamentous fungi as hosts for producing secretory proteins, genetic engineering of filamentous fungi for improved expression often relies heavily on the understanding of regulatory mechanisms.

Results

In this study, using the cellulase-producing filamentous fungus Trichoderma reesei as a model system, we designed a semi-rational strategy by arbitrarily dividing the regulation of cellulase production into three main stages-transcription, secretion, and cell metabolism. Selected regulatory or functional genes that had been experimentally verified or predicted to enhance cellulase production were overexpressed using strong inducible or constitutive promoters, while those that would inhibit cellulase production were repressed via RNAi-mediated gene silencing. A T. reesei strain expressing the surface-displayed DsRed fluorescent protein was used as the recipient strain. After three consecutive rounds of engineering, the cellulase activity increased to up to 4.35-fold and the protein concentration increased to up to 2.97-fold in the genetically modified strain.

Conclusions

We demonstrated that, as a proof-of-concept, selected regulatory or functional genes within an arbitrarily defined stage could be pooled to stimulate secretory cellulase production, and moreover, this method could be iteratively used for further improvement. This method is semi-rational and can essentially be used in filamentous fungi with little regulatory information.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-022-02122-0.

Assessing the intracellular primary metabolic profile of Trichoderma reesei and Aspergillus niger grown on different carbon sources

Trichoderma reesei and Aspergillus niger are efficient biological platforms for the production of various industrial products, including cellulases and organic acids. Nevertheless, despite the extensive research on these fungi, integrated analyses of omics-driven approaches are still missing. In this study, the intracellular metabolic profile of T. reesei RUT-C30 and A. niger N402 strains grown on glucose, lactose, carboxymethylcellulose (CMC), and steam-exploded sugarcane bagasse (SEB) as carbon sources for 48 h was analysed by proton nuclear magnetic resonance. The aim was to verify the changes in the primary metabolism triggered by these substrates and use transcriptomics data from the literature to better understand the dynamics of the observed alterations. Glucose and CMC induced higher fungal growth whereas fungi grown on lactose showed the lowest dry weight. Metabolic profile analysis revealed that mannitol, trehalose, glutamate, glutamine, and alanine were the most abundant metabolites in both fungi regardless of the carbon source. These metabolites are of particular interest for the mobilization of carbon and nitrogen, and stress tolerance inside the cell. Their concomitant presence indicates conserved mechanisms adopted by both fungi to assimilate carbon sources of different levels of recalcitrance. Moreover, the higher levels of galactose intermediates in T. reesei suggest its better adaptation in lactose, whereas glycolate and malate in CMC might indicate activation of the glyoxylate shunt. Glycerol and 4-aminobutyrate accumulated in A. niger grown on CMC and lactose, suggesting their relevant role in these carbon sources. In SEB, a lower quantity and diversity of metabolites were identified compared to the other carbon sources, and the metabolic changes and higher xylanase and pNPGase activities indicated a better utilization of bagasse by A. niger. Transcriptomic analysis supported the observed metabolic changes and pathways identified in this work. Taken together, we have advanced the knowledge about how fungal primary metabolism is affected by different carbon sources, and have drawn attention to metabolites still unexplored. These findings might ultimately be considered for developing more robust and efficient microbial factories.

Impact of overexpressing NADH kinase on glucoamylase production in Aspergillus niger

Glucoamylase has a wide range of applications in the production of glucose, antibiotics, amino acids, and other fermentation industries. Fungal glucoamylase, in particular, has attracted much attention because of its wide application in different industries, among which Aspergillus niger is the most popular strain producing glucoamylase. The low availability of NADPH was found to be one of the limiting factors for the overproduction of glucoamylase. In this study, 3 NADH kinases (AN03, AN14, and AN17) and malic enzyme (maeA) were overexpressed in aconidial A. niger by CRISPR/Cas9 technology, significantly increasing the size of the NADPH pool, resulting in the activity of glucoamylase was improved by about 70%, 50%, 90%, and 70%, respectively; the total secreted protein was increased by about 25%, 22%, 52%, and 26%, respectively. Furthermore, the combination of the mitochondrial NADH kinase (AN17) and the malic enzyme (maeA) increased glucoamylase activity by a further 19%. This study provided an effective strategy for enhancing glucoamylase production of A. niger.

Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints

Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several of the current top-selling drugs. Due to the essential role and complexity of the secretory pathway, improvement for recombinant protein production through metabolic engineering has traditionally been relatively ad-hoc; and a more systematic approach is required to generate novel design principles. Here, we present the proteome-constrained genome-scale protein secretory model of yeast Saccharomyces cerevisiae (pcSecYeast), which enables us to simulate and explain phenotypes caused by limited secretory capacity. We further apply the pcSecYeast model to predict overexpression targets for the production of several recombinant proteins. We experimentally validate many of the predicted targets for α-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production.

Due to the complexity of the protein secretory pathway, strategy suitable for the production of a certain recombination protein cannot be generalized. Here, the authors construct a proteome-constrained genome-scale protein secretory model for yeast and show its application in the production of different misfolded or recombinant proteins.

Genome-Scale Metabolic Model's multi-objective solving algorithm based on the inflexion point of Pareto front including maximum energy utilization and its application in A.niger DS03043

The solution of genome-scale metabolic model (GSMM) directly affects the simulation accuracy of the metabolic process in digital cells. Single-objective optimization methods, such as Flux Balance Analysis (FBA) which is widely used in solving GSMM, have limitations when simulating actual biological processes, which leads to unrealistic results due to other biological constraints being ignored. A novel multi-objective Differential Evolution algorithm based on general FBA (i.e., DEFBA) is hence proposed to solve GSMM. First, in accordance with to the assumption that cells minimize resource consumption and maximize resource utilization, the maximum specific growth rate and the minimum cellular production rate of ATP, NADPH, and NADH are defined as the multi-objective functions of DEFBA. Second, FBA is used to produce the initial individuals of DEFBA by changing the upper bound of biomass reaction in GSMM. Third, mutation and selection operations help in generating new individuals in the solution space to search the Pareto front. Finally, the optimal solution is selected by analyzing the inflexion point of the Pareto front. In DEFBA, multi-objective technology and optimal solution judging technology can introduce the biological constraints into the GSMM solving method, such that the solution can be more consistent with the essential biological mechanism. DEFBA is applied to solve Aspergillus niger's GSMM. The improved results show that DEFBA can be an effective general solving algorithm for GSMM. This article is protected by copyright. All rights reserved.

Modelling hCDKL5 Heterologous Expression in Bacteria

hCDKL5 refers to the human cyclin-dependent kinase like 5 that is primarily expressed in the brain. Mutations in its coding sequence are often causative of hCDKL5 deficiency disorder, a devastating neurodevelopmental disorder currently lacking a cure. The large-scale recombinant production of hCDKL5 is desirable to boost the translation of preclinical therapeutic approaches into the clinic. However, this is hampered by the intrinsically disordered nature of almost two-thirds of the hCDKL5 sequence, making this region more susceptible to proteolytic attack, and the observed toxicity when the enzyme is accumulated in the cytoplasm of eukaryotic host cells. The bacterium Pseudoalteromonas haloplanktis TAC125 (PhTAC125) is the only prokaryotic host in which the full-length production of hCDKL5 has been demonstrated. To date, a system-level understanding of the metabolic burden imposed by hCDKL5 production is missing, although it would be crucial for upscaling of the production process. Here, we combined experimental data on protein production and nutrients assimilation with metabolic modelling to infer the global consequences of hCDKL5 production in PhTAC125 and to identify potential overproduction targets. Our analyses showed a remarkable accuracy of the model in simulating the recombinant strain phenotype and also identified priority targets for optimised protein production.

Engineering cofactor metabolism for improved protein and glucoamylase production in Aspergillus niger

Background

Nicotinamide adenine dinucleotide phosphate (NADPH) is an important cofactor ensuring intracellular redox balance, anabolism and cell growth in all living systems. Our recent multi-omics analyses of glucoamylase (GlaA) biosynthesis in the filamentous fungal cell factory Aspergillus niger indicated that low availability of NADPH might be a limiting factor for GlaA overproduction.

Results

We thus employed the Design-Build-Test-Learn cycle for metabolic engineering to identify and prioritize effective cofactor engineering strategies for GlaA overproduction. Based on available metabolomics and ¹³C metabolic flux analysis data, we individually overexpressed seven predicted genes encoding NADPH generation enzymes under the control of the Tet-on gene switch in two A. niger recipient strains, one carrying a single and one carrying seven glaA gene copies, respectively, to test their individual effects on GlaA and total protein overproduction. Both strains were selected to understand if a strong pull towards glaA biosynthesis (seven gene copies) mandates a higher NADPH supply compared to the native condition (one gene copy). Detailed analysis of all 14 strains cultivated in shake flask cultures uncovered that overexpression of the gsdA gene (glucose 6-phosphate dehydrogenase), gndA gene (6-phosphogluconate dehydrogenase) and maeA gene (NADP-dependent malic enzyme) supported GlaA production on a subtle (10%) but significant level in the background strain carrying seven glaA gene copies. We thus performed maltose-limited chemostat cultures combining metabolome analysis for these three isolates to characterize metabolic-level fluctuations caused by cofactor engineering. In these cultures, overexpression of either the gndA or maeA gene increased the intracellular NADPH pool by 45% and 66%, and the yield of GlaA by 65% and 30%, respectively. In contrast, overexpression of the gsdA gene had a negative effect on both total protein and glucoamylase production.

Conclusions

This data suggests for the first time that increased NADPH availability can indeed underpin protein and especially GlaA production in strains where a strong pull towards GlaA biosynthesis exists. This data also indicates that the highest impact on GlaA production can be engineered on a genetic level by increasing the flux through the pentose phosphate pathway (gndA gene) followed by engineering the flux through the reverse TCA cycle (maeA gene). We thus propose that NADPH cofactor engineering is indeed a valid strategy for metabolic engineering of A. niger to improve GlaA production, a strategy which is certainly also applicable to the rational design of other microbial cell factories.

Redox Engineering by Ectopic Overexpression of NADH Kinase in Recombinant Pichia pastoris (Komagataella phaffii): Impact on Cell Physiology and Recombinant Production of Secreted Proteins

High-level expression and secretion of heterologous proteins in yeast cause an increased energy demand, which may result in altered metabolic flux distributions. Moreover, recombinant protein overproduction often results in endoplasmic reticulum (ER) stress and oxidative stress, causing deviations from the optimal NAD(P)H regeneration balance. In this context, overexpression of genes encoding enzymes catalyzing endogenous NADPH-producing reactions, such as the oxidative branch of the pentose phosphate pathway, has been previously shown to improve protein production in Pichia pastoris (syn. Komagataella spp.). In this study, we evaluate the overexpression of the Saccharomyces cerevisiaePOS5-encoded NADH kinase in a recombinant P. pastoris strain as an alternative approach to overcome such redox constraints. Specifically, POS5 was cooverexpressed in a strain secreting an antibody fragment, either by directing Pos5 to the cytosol or to the mitochondria. The physiology of the resulting strains was evaluated in continuous cultivations with glycerol or glucose as the sole carbon source, as well as under hypoxia (on glucose). Cytosolic targeting of Pos5 NADH kinase resulted in lower biomass-substrate yields but allowed for a 2-fold increase in product specific productivity. In contrast, Pos5 NADH kinase targeting to the mitochondria did not affect growth physiology and recombinant protein production significantly. Growth physiological parameters were in silico evaluated using the recent upgraded version (v3.0) of the P. pastoris consensus genome-scale metabolic model iMT1026, providing insights on the impact of POS5 overexpression on metabolic flux distributions.IMPORTANCE Recombinant protein overproduction often results in oxidative stress, causing deviations from the optimal redox cofactor regeneration balance. This becomes one of the limiting factors in obtaining high levels of heterologous protein production. Overexpression of redox-affecting enzymes has been explored in other organisms, such as Saccharomyces cerevisiae, as a means to fine tune the cofactor regeneration balance in order to obtain higher protein titers. In the present work, this strategy is explored in P. pastoris In particular, one NADH kinase enzyme from S. cerevisiae (Pos5) is used, either in the cytosol or in mitochondria of P. pastoris, and its impact on the production of a model protein (antibody fragment) is evaluated. A significant improvement in the production of the model protein is observed when the kinase is directed to the cytosol. These results are significant in the field of heterologous protein production in general and in particular in the development of improved metabolic engineering strategies for P. pastoris.

A novel expression vector for Corynebacterium glutamicum with an auxotrophy complementation system

Corynebacterium glutamicum is an important industrial strain used for the production of amino acids and vitamins. Most tools developed for overexpression of genes in C. glutamicum are based on the inducible promoter regulated by the lacI^q gene or contain an antibiotic resistance gene as a selection marker. These vectors are essential for rapid identification of recombinant strains and detailed study of gene functions, but, as a considerable disadvantage, these vectors are not suitable for large-scale industrial production due to the need for the addition of isopropyl-β-D-thiogalactopyranoside (IPTG) and antibiotics. In this study, the novel Escherichia coli-C. glutamicum shuttle expression vector pLY-4, derived from the expression vector pXMJ19, was constructed. The constitutive vector pLY-4 contains a large multiple cloning site, the strong promoter tacM and two selective markers: the original chloramphenicol resistance gene cat is used for molecular cloning operations, and the auxotrophy complementation marker alr, which can be stably replicated in the auxotrophic host strain without antibiotic selection pressure, is used for industrial fermentation. Heterologous expression of the gapC gene using the vector pLY-4 in C. glutamicum for L-methionine production indicated the potential application of pLY-4 in the development of C. glutamicum strain engineering and industrial fermentation.

Systems metabolic engineering for citric acid production by Aspergillus niger in the post-genomic era

Citric acid is the world’s largest consumed organic acid and is widely used in beverage, food and pharmaceutical industries. Aspergillus niger is the main industrial workhorse for citric acid production. Since the release of the genome sequence, extensive multi-omic data are being rapidly obtained, which greatly boost our understanding of the citric acid accumulation mechanism in A. niger to a molecular and system level. Most recently, the rapid development of CRISPR/Cas9 system facilitates highly efficient genome-scale genetic perturbation in A. niger. In this review, we summarize the impact of systems biology on the citric acid molecular regulatory mechanisms, the advances in metabolic engineering strategies for enhancing citric acid production and discuss the development and application of CRISPR/Cas9 systems for genome editing in A. niger. We believe that future systems metabolic engineering efforts will redesign and engineer A. niger as a highly optimized cell factory for industrial citric acid production.

Detection and Elimination of Cellular Bottlenecks in Protein-Producing Yeasts

Yeasts are efficient cell factories and are commonly used for the production of recombinant proteins for biopharmaceutical and industrial purposes. For such products high levels of correctly folded proteins are needed, which sometimes requires improvement and engineering of the expression system. The article summarizes major breakthroughs that led to the efficient use of yeasts as production platforms and reviews bottlenecks occurring during protein production. Special focus is given to the metabolic impact of protein production. Furthermore, strategies that were shown to enhance secretion of recombinant proteins in different yeast species are presented.

Transcriptomic and fluxomic changes in Streptomyces lividans producing heterologous protein

BACKGROUND

The Gram-positive Streptomyces lividans TK24 is an attractive host for heterologous protein production because of its high capability to secrete proteins-which favors correct folding and facilitates downstream processing-as well as its acceptance of methylated DNA and its low endogeneous protease activity. However, current inconsistencies in protein yields urge for a deeper understanding of the burden of heterologous protein production on the cell. In the current study, transcriptomics and [Formula: see text]-based fluxomics were exploited to uncover gene expression and metabolic flux changes associated with heterologous protein production. The Rhodothermus marinus thermostable cellulase A (CelA)-previously shown to be successfully overexpressed in S. lividans-was taken as an example protein.

RESULTS

RNA-seq and [Formula: see text]-based metabolic flux analysis were performed on a CelA-producing and an empty-plasmid strain under the same conditions. Differential gene expression, followed by cluster analysis based on co-expression and co-localization, identified transcriptomic responses related to secretion-induced stress and DNA damage. Furthermore, the OsdR regulon (previously associated with hypoxia, oxidative stress, intercellular signaling, and morphological development) was consistently upregulated in the CelA-producing strain and exhibited co-expression with isoenzymes from the pentose phosphate pathway linked to secondary metabolism. Increased expression of these isoenzymes matches to increased fluxes in the pentose phosphate pathway. Additionally, flux maps of the central carbon metabolism show increased flux through the tricarboxylic acid cycle in the CelA-producing strain. Redirection of fluxes in the CelA-producing strain leads to higher production of NADPH, which can only partly be attributed to increased secretion.

CONCLUSIONS

Transcriptomic and fluxomic changes uncover potential new leads for targeted strain improvement strategies which may ease the secretion stress and metabolic burden associated with heterologous protein synthesis and secretion, and may help create a more consistently performing S. lividans strain. Yet, links to secondary metabolism and redox balancing should be further investigated to fully understand the S. lividans metabolome under heterologous protein production.

Multi-omics integrative analysis with genome-scale metabolic model simulation reveals global cellular adaptation of Aspergillus niger under industrial enzyme production condition

Oxygen limitation is regarded as a useful strategy to improve enzyme production by mycelial fungus like Aspergillus niger. However, the intracellular metabolic response of A. niger to oxygen limitation is still obscure. To address this, the metabolism of A. niger was studied using multi-omics integrated analysis based on the latest GEMs (genome-scale metabolic model), including metabolomics, fluxomics and transcriptomics. Upon sharp reduction of the oxygen supply, A. niger metabolism shifted to higher redox level status, as well as lower energy supply, down-regulation of genes for fatty acid synthesis and a rapid decrease of the specific growth rate. The gene expression of the glyoxylate bypass was activated, which was consistent with flux analysis using the A. niger GEMs iHL1210. The increasing flux of the glyoxylate bypass was assumed to reduce the NADH formation from TCA cycle and benefit maintenance of the cellular redox balance under hypoxic conditions. In addition, the relative fluxes of the EMP pathway were increased, which possibly relieved the energy demand for cell metabolism. The above multi-omics integrative analysis provided new insights on metabolic regulatory mechanisms of A. niger associated with enzyme production under oxygen-limited condition, which will benefit systematic design and optimization of the A. niger microbial cell factory.

Dynamic metabolic response of Aspergillus niger to glucose perturbation: evidence of regulatory mechanism for reduced glucoamylase production

Environmental gradient is an important common issue during scale-up process for protein production. To address the dynamic regulatory mechanism of Aspergillus niger being exposed to inhomogeneous glucose concentrations, glucose perturbation were experimented on the steady state of A. niger chemostat culture, and dynamic profiles of the intracellular metabolites in central carbon metabolism were tracked in a time scale of seconds. The upper glycolysis and pentose phosphate pathway showed sharp variations after glucose perturbation, while the lower glycolysis, TCA cycle and amino acid pools represented a moderate and prolonged response due to the allosteric regulation of enzymes and buffering function of metabolites with large pool sizes. Improved glucose-6-phosphate enhanced the metabolic flux to PP pathway remarkably, which provided not only more redox cofactors (NADPH) for protein synthesis but also more precursors (phosphoribosyl pyrophosphate and ribose-5-phosphate) for cell growth. Moreover, reduction of the total adenine nucleotides and major precursor amino acids indicated the upregulated RNA synthesis was required to produce stress proteins, and partially explained the drop of glucoamylase production when A. niger experienced a fluctuated glucose concentration environment. These findings would be valuable for improving bioreactor operation, design, and scale-up from engineering or genetic aspects.

Protein profile in Aspergillus nidulans recombinant strains overproducing heterologous enzymes

Filamentous fungi are robust cell factories and have been used for the production of large quantities of industrially relevant enzymes. However, the production levels of heterologous proteins still need to be improved. Therefore, this article aimed to investigate the global proteome profiling of Aspergillus nidulans recombinant strains in order to understand the bottlenecks of heterologous enzymes production. About 250, 441 and 424 intracellular proteins were identified in the control strain Anid_pEXPYR and in the recombinant strains Anid_AbfA and Anid_Cbhl respectively. In this context, the most enriched processes in recombinant strains were energy pathway, amino acid metabolism, ribosome biogenesis, translation, endoplasmic reticulum and oxidative stress, and repression under secretion stress (RESS). The global protein profile of the recombinant strains Anid_AbfA and Anid_Cbhl was similar, although the latter strain secreted more recombinant enzyme than the former. These findings provide insights into the bottlenecks involved in the secretion of recombinant proteins in A. nidulans, as well as in regard to the rational manipulation of target genes for engineering fungal strains as microbial cell factories.

Central carbon metabolism influences cellulase production in Bacillus licheniformis

Bacillus licheniformis that can produce cellulase including endo glucanase and glucosidase is an important industrial microbe for cellulose degradation. The purpose of this research was to assess the effect of endo glucanase gene bglC and glucosidase gene bglH on the central metabolic flux in B. licheniformis. bglC and bglH were knocked out using homologous recombination method, respectively, and the corresponding knockout strains were obtained for ¹³ C metabolic flux analysis. A significant change was observed in metabolic fluxes after ¹³ C metabolic flux ratio analysis. In both of the knockout strains, the increased fluxes of the pentose phosphate pathway and malic enzyme reaction enabled an elevated supply of NADPH which provided enough reducing power for the in vivo synthesis reactions. The fluxes through tricarboxylic acid cycle and anaplerotic reactions increased fast in the two knockout strains, which meant more energy generated. The changed fluxes in central carbon metabolism provided a holistic view of the physiological status in B. licheniformis and possible targets for further strain engineering.

SIGNIFICANCE AND IMPACT OF THE STUDY

Cellulase is very important in the field of agriculture and bioenergy because of its degrading effect on cellulosic biomass. This study presented the effect of central carbon metabolism on cellulase production in Bacillus licheniformis. The study also provided a holistic view of the physiological status in B. licheniformis. The shifted metabolism provided a quantitative evaluation of the biosynthesis of cellulase and a priority ranked target list for further strain engineering.

Combined 13C-assisted metabolomics and metabolic flux analysis reveals the impacts of glutamate on the central metabolism of high β-galactosidase-producing Pichia pastoris

Background

Pichia pastoris is a popular recombinant protein expression system for its accessibility of efficient gene manipulation and high protein production. Sufficient supply of precursors, energy, and redox cofactors is crucial for high recombinant protein production. In our present work, we found that the addition of glutamate improved the recombinant β-galactosidase (β-gal) production by P. pastoris G1HL.

Methods

To elucidate the impacts of glutamate on the central metabolism in detail, a combined ¹³C-assisted metabolomics and ¹³C metabolic flux analysis was conducted based on LC–MS/MS and GC–MS data.

Results

The pool sizes of intracellular amino acids were obviously higher on glucose/glutamate (Glc/Glu). The fluxes in EMP entry reaction and in downstream TCA cycle were 50 and 67% higher on Glc/Glu than on Glc, respectively. While the fluxes in upstream TCA cycle kept almost unaltered, the fluxes in PPP oxidative branch decreased.

Conclusion

The addition of glutamate leads to a remarkable change on the central metabolism of high β-galactosidase-producing P. pastoris G1HL. To meet the increased demands of redox cofactors and energy for higher β-galactosidase production on Glc/Glu, P. pastoris G1HL redistributes the fluxes in central metabolism through the inhibitions and/or activation of the enzymes in key nodes together with the energy and redox status.

Electronic supplementary material

The online version of this article (doi:10.1186/s40643-016-0124-6) contains supplementary material, which is available to authorized users.

In silico metabolic network analysis of Arabidopsis leaves

Background

During the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Presently, our limited understanding of their metabolism and its regulation hampers the targeted development of desired plant phenotypes. In this regard, systems biology, in particular the integration of metabolic and regulatory networks, is promising to broaden our knowledge and to further explore the biotechnological potential of plants.

Results

The thale cress Arabidopsis thaliana provides an ideal model to understand plant primary metabolism. To obtain insight into its functional properties, we constructed a large-scale metabolic network of the leaf of A. thaliana. It represented 511 reactions with spatial separation into compartments. Systematic analysis of this network, utilizing elementary flux modes, investigates metabolic capabilities of the plant and predicts relevant properties on the systems level: optimum pathway use for maximum growth and flux re-arrangement in response to environmental perturbation. Our computational model indicates that the A. thaliana leaf operates near its theoretical optimum flux state in the light, however, only in a narrow range of photon usage. The simulations further demonstrate that the natural day-night shift requires substantial re-arrangement of pathway flux between compartments: 89 reactions, involving redox and energy metabolism, substantially change the extent of flux, whereas 19 reactions even invert flux direction. The optimum set of anabolic pathways differs between day and night and is partly shifted between compartments. The integration with experimental transcriptome data pinpoints selected transcriptional changes that mediate the diurnal adaptation of the plant and superimpose the flux response.

Conclusions

The successful application of predictive modelling in Arabidopsis thaliana can bring systems-biological interpretation of plant systems forward. Using the gained knowledge, metabolic engineering strategies to engage plants as biotechnological factories can be developed.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0347-3) contains supplementary material, which is available to authorized users.

Comprehensive reconstruction and in silico analysis of Aspergillus niger genome-scale metabolic network model that accounts for 1210 ORFs

Aspergillus niger is one of the most important cell factories for industrial enzymes and organic acids production. A comprehensive genome-scale metabolic network model (GSMM) with high quality is crucial for efficient strain improvement and process optimization. The lack of accurate reaction equations and gene-protein-reaction associations (GPRs) in the current best model of A. niger named GSMM iMA871, however, limits its application scope. To overcome these limitations, we updated the A. niger GSMM by combining the latest genome annotation and literature mining technology. Compared with iMA871, the number of reactions in iHL1210 was increased from 1,380 to 1,764, and the number of unique ORFs from 871 to 1,210. With the aid of our transcriptomics analysis, the existence of 63% ORFs and 68% reactions in iHL1210 can be verified when glucose was used as the only carbon source. Physiological data from chemostat cultivations, ¹³ C-labeled and molecular experiments from the published literature were further used to check the performance of iHL1210. The average correlation coefficients between the predicted fluxes and estimated fluxes from ¹³ C-labeling data were sufficiently high (above 0.89) and the prediction of cell growth on most of the reported carbon and nitrogen sources was consistent. Using the updated genome-scale model, we evaluated gene essentiality on synthetic and yeast extract medium, as well as the effects of NADPH supply on glucoamylase production in A. niger. In summary, the new A. niger GSMM iHL1210 contains significant improvements with respect to the metabolic coverage and prediction performance, which paves the way for systematic metabolic engineering of A. niger. Biotechnol. Bioeng. 2017;114: 685-695. © 2016 Wiley Periodicals, Inc.

Metabolic engineering of Corynebacterium glutamicum for methionine production by removing feedback inhibition and increasing NADPH level

Relieving the feedback inhibition of key enzymes in a metabolic pathway is frequently the first step of producer-strain construction by genetic engineering. However, the strict feedback regulation exercised by microorganisms in methionine biosynthesis often makes it difficult to produce methionine at a high level. In this study, Corynebacterium glutamicum ATCC 13032 was metabolically engineered for methionine production. First, the metD gene encoding the methionine uptake system was deleted to achieve extracellular accumulation of methionine. Then, random mutagenesis was performed to remove feedback inhibition by metabolic end-products. The resulting strain C. glutamicum ENM-16 was further engineered to block or decrease competitive branch pathways by deleting the thrB gene and changing the start codon of the dapA gene, followed by point mutations of lysC (C932T) and pyc (G1A, C1372T) to increase methionine precursor supply. To enrich the NADPH pool, glucose-6-phosphate dehydrogenase and 6-phosphogluconate dehydrogenase in the pentose phosphate pathway were mutated to reduce their sensitivity to inhibition by intracellular metabolites. The resultant strain C. glutamicum LY-5 produced 6.85 ± 0.23 g methionine l(-1) with substrate-specific yield (Y P/S) of 0.08 mol per mol of glucose after 72 h fed-batch fermentation. The strategies described here will be useful for construction of methionine engineering strains.

Genome wide analysis of protein production load in Trichoderma reesei

BACKGROUND

The filamentous fungus Trichoderma reesei (teleomorph Hypocrea jecorina) is a widely used industrial host organism for protein production. In industrial cultivations, it can produce over 100 g/l of extracellular protein, mostly constituting of cellulases and hemicellulases. In order to improve protein production of T. reesei the transcriptional regulation of cellulases and secretory pathway factors have been extensively studied. However, the metabolism of T. reesei under protein production conditions has not received much attention.

RESULTS

To understand the physiology and metabolism of T. reesei under protein production conditions we carried out a well-controlled bioreactor experiment with extensive analysis. We used minimal media to make the data amenable for modelling and three strain pairs to cover different protein production levels. With RNA-sequencing transcriptomics we detected the concentration of the carbon source as the most important determinant of the transcriptome. As the major transcriptional response concomitant to protein production we detected the induction of selected genes that were putatively regulated by xyr1 and were related to protein transport, amino acid metabolism and transcriptional regulation. We found novel metabolic responses such as production of glycerol and a cellotriose-like compound. We then used this cultivation data for flux balance analysis of T. reesei metabolism and demonstrate for the first time the use of genome wide stoichiometric metabolic modelling for T. reesei. We show that our model can predict protein production rate and provides novel insight into the metabolism of protein production. We also provide this unprecedented cultivation and transcriptomics data set for future modelling efforts.

CONCLUSIONS

The use of stoichiometric modelling can open a novel path for the improvement of protein production in T. reesei. Based on this we propose sulphur assimilation as a major limiting factor of protein production. As an organism with exceptional protein production capabilities modelling of T. reesei can provide novel insight also to other less productive organisms.

13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production

Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many ¹³C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms

Integrated isotope-assisted metabolomics and (13)C metabolic flux analysis reveals metabolic flux redistribution for high glucoamylase production by Aspergillus niger

Background

Aspergillus niger is widely used for enzyme production and achievement of high enzyme production depends on the comprehensive understanding of cell’s metabolic regulation mechanisms.

Results

In this paper, we investigate the metabolic differences and regulation mechanisms between a high glucoamylase-producing strain A. niger DS03043 and its wild-type parent strain A. niger CBS513.88 via an integrated isotope-assisted metabolomics and ¹³C metabolic flux analysis approach. We found that A. niger DS03043 had higher cell growth, glucose uptake, and glucoamylase production rates but lower oxalic acid and citric acid secretion rates. In response to above phenotype changes, A. niger DS03043 was characterized by an increased carbon flux directed to the oxidative pentose phosphate pathway in contrast to reduced flux through TCA cycle, which were confirmed by consistent changes in pool sizes of metabolites. A higher ratio of ATP over AMP in the high producing strain might contribute to the increase in the PP pathway flux as glucosephosphate isomerase was inhibited at higher ATP concentrations. A. niger CBS513.88, however, was in a higher redox state due to the imbalance of NADH regeneration and consumption, resulting in the secretion of oxalic acid and citric acid, as well as the accumulation of intracellular OAA and PEP, which may in turn result in the decrease in the glucose uptake rate.

Conclusions

The application of integrated metabolomics and ¹³C metabolic flux analysis highlights the regulation mechanisms of energy and redox metabolism on flux redistribution in A. niger. Graphical abstract

Current state of genome-scale modeling in filamentous fungi

The group of filamentous fungi contains important species used in industrial biotechnology for acid, antibiotics and enzyme production. Their unique lifestyle turns these organisms into a valuable genetic reservoir of new natural products and biomass degrading enzymes that has not been used to full capacity. One of the major bottlenecks in the development of new strains into viable industrial hosts is the alteration of the metabolism towards optimal production. Genome-scale models promise a reduction in the time needed for metabolic engineering by predicting the most potent targets in silico before testing them in vivo. The increasing availability of high quality models and molecular biological tools for manipulating filamentous fungi renders the model-guided engineering of these fungal factories possible with comprehensive metabolic networks. A typical fungal model contains on average 1138 unique metabolic reactions and 1050 ORFs, making them a vast knowledge-base of fungal metabolism. In the present review we focus on the current state as well as potential future applications of genome-scale models in filamentous fungi.

Engineering the supply chain for protein production/secretion in yeasts and mammalian cells

Metabolic bottlenecks play an increasing role in yeasts and mammalian cells applied for high-performance production of proteins, particularly of pharmaceutical ones that require complex posttranslational modifications. We review the present status and developments focusing on the rational metabolic engineering of such cells to optimize the supply chain for building blocks and energy. Methods comprise selection of beneficial genetic modifications, rational design of media and feeding strategies. Design of better producer cells based on whole genome-wide metabolic network analysis becomes increasingly possible. High-resolution methods of metabolic flux analysis for the complex networks in these compartmented cells are increasingly available. We discuss phenomena that are common to both types of organisms but also those that are different with respect to the supply chain for the production and secretion of pharmaceutical proteins.

Computational prediction of essential metabolic genes using constraint-based approaches

In this chapter, we describe the application of constraint-based modeling to predict the impact of gene deletions on a metabolic phenotype. The metabolic reactions taking place inside cells form large networks, which have been reconstructed at a genome-scale for several organisms at increasing levels of detail. By integrating mathematical modeling techniques with biochemical principles, constraint-based approaches enable predictions of metabolite fluxes and growth under specific environmental conditions or for genetically modified microorganisms. Similar to the experimental knockout of a gene, predicting the essentiality of a metabolic gene for a phenotype further allows to generate hypotheses on its biological function and design of genetic engineering strategies for biotechnological applications. Here, we summarize the principles of constraint-based approaches and provide a detailed description of the procedure to predict the essentiality of metabolic genes with respect to a specific metabolic function. We exemplify the approach by predicting the essentiality of reactions in the citric acid cycle for the production of glucose from fatty acids.

(13)C metabolic flux analysis of recombinant expression hosts

Identifying host cell metabolic phenotypes that promote high recombinant protein titer is a major goal of the biotech industry. (13)C metabolic flux analysis (MFA) provides a rigorous approach to quantify these metabolic phenotypes by applying isotope tracers to map the flow of carbon through intracellular metabolic pathways. Recent advances in tracer theory and measurements are enabling more information to be extracted from (13)C labeling experiments. Sustained development of publicly available software tools and standardization of experimental workflows is simultaneously encouraging increased adoption of (13)C MFA within the biotech research community. A number of recent (13)C MFA studies have identified increased citric acid cycle and pentose phosphate pathway fluxes as consistent markers of high recombinant protein expression, both in mammalian and microbial hosts. Further work is needed to determine whether redirecting flux into these pathways can effectively enhance protein titers while maintaining acceptable glycan profiles.

Impacts of high β-galactosidase expression on central metabolism of recombinant Pichia pastoris GS115 using glucose as sole carbon source via (13)C metabolic flux analysis

The yeast Pichia pastoris GS115 is a widely used microbial cell factory for the production of heterologous protein. In order to reveal the impacts of high heterologous protein expression on the central metabolism of Pichia pastoris GS115 using glucose as sole carbon source, we engineered a high β-galactosidase expression strain P. pastoris G1HL and a low expression control strain P. pastoris GHL through controlling the initiation strength of constitutive promoter pGAP. The carbon flux distributions in these two strains were quantified via (13)C metabolic flux analysis. Compared to the control strain, G1HL showed a lower growth rate, a higher flux through glycolysis pathway, a higher flux through pentose phosphate pathway, and a lower flux through by-products secretion pathway. The metabolic flux redistribution in G1HL was thought to compensate the increased redox cofactors and energy demands caused by the high protein expression. Although the fluxes through Krebs cycle in two engineered strains were almost the same, they were significantly lower than those in wild strain. The enhanced expression of β-galactosidase by glutamate supplementation demonstrated the potential of P. pastoris GS115 to catabolize more carbon through the Krebs cycle for even higher protein expression. In conclusion, our work indicates that P. pastoris GS115 can readjusts the central metabolism for higher heterologous protein expression and provides strategies for strain development or process optimization for enhancing production of heterologous protein.

Model based engineering of Pichia pastoris central metabolism enhances recombinant protein production

The production of recombinant proteins is frequently enhanced at the levels of transcription, codon usage, protein folding and secretion. Overproduction of heterologous proteins, however, also directly affects the primary metabolism of the producing cells. By incorporation of the production of a heterologous protein into a genome scale metabolic model of the yeast Pichia pastoris, the effects of overproduction were simulated and gene targets for deletion or overexpression for enhanced productivity were predicted. Overexpression targets were localized in the pentose phosphate pathway and the TCA cycle, while knockout targets were found in several branch points of glycolysis. Five out of 9 tested targets led to an enhanced production of cytosolic human superoxide dismutase (hSOD). Expression of bacterial β-glucuronidase could be enhanced as well by most of the same genetic modifications. Beneficial mutations were mainly related to reduction of the NADP/H pool and the deletion of fermentative pathways. Overexpression of the hSOD gene itself had a strong impact on intracellular fluxes, most of which changed in the same direction as predicted by the model. In vivo fluxes changed in the same direction as predicted to improve hSOD production. Genome scale metabolic modeling is shown to predict overexpression and deletion mutants which enhance recombinant protein production with high accuracy.

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Recombinant protein production in P. pastoris affects the central metabolism.

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A genome scale metabolic model can predict these metabolic flux changes.

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Mutations in central metabolic genes enhanced recombinant protein yield up to 40%.

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These beneficial mutations were predicted by the metabolic model with high accuracy.

Quantitative Metabolomics and Instationary 13C-Metabolic Flux Analysis Reveals Impact of Recombinant Protein Production on Trehalose and Energy Metabolism in Pichia pastoris

Pichia pastoris has been recognized as an effective host for recombinant protein production. In this work, we combine metabolomics and instationary 13C metabolic flux analysis (INST 13C-MFA) using GC-MS and LC-MS/MS to evaluate the potential impact of the production of a Rhizopus oryzae lipase (Rol) on P. pastoris central carbon metabolism. Higher oxygen uptake and CO2 production rates and slightly reduced biomass yield suggest an increased energy demand for the producing strain. This observation is further confirmed by 13C-based metabolic flux analysis. In particular, the flux through the methanol oxidation pathway and the TCA cycle was increased in the Rol-producing strain compared to the reference strain. Next to changes in the flux distribution, significant variations in intracellular metabolite concentrations were observed. Most notably, the pools of trehalose, which is related to cellular stress response, and xylose, which is linked to methanol assimilation, were significantly increased in the recombinant strain.

Protein secretion biotechnology in Gram-positive bacteria with special emphasis on Streptomyces lividans

Proteins secreted by Gram-positive bacteria are released into the culture medium with the obvious benefit that they usually retain their native conformation. This property makes these host cells potentially interesting for the production of recombinant proteins, as one can take full profit of established protocols for the purification of active proteins. Several state-of-the-art strategies to increase the yield of the secreted proteins will be discussed, using Streptomyces lividans as an example and compared with approaches used in some other host cells. It will be shown that approaches such as increasing expression and translation levels, choice of secretion pathway and modulation of proteins thereof, avoiding stress responses by changing expression levels of specific (stress) proteins, can be helpful to boost production yield. In addition, the potential of multi-omics approaches as a tool to understand the genetic background and metabolic fluxes in the host cell and to seek for new targets for strain and protein secretion improvement is discussed. It will be shown that S. lividans, along with other Gram-positive host cells, certainly plays a role as a production host for recombinant proteins in an economically viable way. This article is part of a Special Issue entitled: Protein trafficking and secretion in bacteria. Guest Editors: Anastassios Economou and Ross Dalbey.

¹³C-based metabolic flux analysis of recombinant Pichia pastoris

Overexpression of a foreign protein may negatively affect several cell growth parameters, as well as cause cellular stress. Central (or core) metabolism plays a crucial role since it supplies energy, reduction equivalents, and precursor molecules for the recombinant product, cell's maintenance, and growth needs. However, the number of quantitative physiology studies of the impact of recombinant protein production on the central metabolic pathways of yeast cell factories has been traditionally rather limited, thereby hampering the application of rational strain engineering strategies targeting central metabolism.The development and application of quantitative physiology and modelling tools and methodologies is allowing for a systems-level understanding of the effect of bioprocess parameters such as growth rate, temperature, oxygen availability, and substrate(s) choice on metabolism, and its subsequent interactions with recombinant protein synthesis, folding, and secretion.Here, we review the recent developments and applications of (13)C-based metabolic flux analysis ((13)C-MFA) of Pichia pastoris and the gained understanding of the metabolic behavior of this yeast in recombinant protein production bioprocesses. We also discuss the potential of multilevel studies integrating (13)C-MFA with other omics analyses, as well as future perspectives on the metabolic modelling approaches to study and design metabolic engineering strategies for improved protein production.

Comparison and improvement of algorithms for computing minimal cut sets

Background

Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been proposed to compute cMCSs from elementary modes. However, in their original formulation both algorithms are not fully comparable.

Results

Here we show that by a small extension to the integer program both methods become equivalent. Furthermore, based on well-known preprocessing procedures for integer programming we present efficient preprocessing steps which can be used for both algorithms. We then benchmark the numerical performance of the algorithms in several realistic medium-scale metabolic models. The benchmark calculations reveal (i) that these preprocessing steps can lead to an enormous speed-up under both algorithms, and (ii) that the adapted Berge algorithm outperforms the binary integer approach.

Conclusions

Generally, both of our new implementations are by at least one order of magnitude faster than other currently available implementations.

Systems-wide analysis and engineering of metabolic pathway fluxes in bio-succinate producing Basfia succiniciproducens

Basfia succiniciproducens has been recently isolated as novel producer for succinate, an important platform chemical. In batch culture, the wild type exhibited a high natural yield of 0.75 mol succinate (mol glucose)⁻¹. Systems-wide ¹³C metabolic flux analysis identified undesired fluxes through pyruvate-formate lyase (PflD) and lactate dehydrogenase (LdhA). The double deletion strain B. succiniciproducens ΔldhA ΔpflD revealed a 45% improved product yield of 1.08 mol mol⁻¹. In addition, metabolic flux analysis unraveled the parallel in vivo activity of the oxidative and reductive branch of the TCA cycle in B. succiniciproducens, whereby the oxidative part mainly served for anabolism. The wild type re-directed surplus NADH via a cycle involving malic enzyme or via transhydrogenase, respectively, to supply NADPH for anabolism, because the fluxes through the oxidative PPP and isocitrate dehydrogenase, that also provide this cofactor, were not sufficient. This was not observed for the deletion mutants, B. succiniciproducens ΔpflD and ΔldhA ΔpflD, where PPP and isocitrate dehydrogenase flux alone matched with the reduced anabolic NADPH demand. The integration of the production performance into the theoretical flux space, computed by elementary flux mode analysis, revealed that B. succiniciproducens ΔldhA ΔpflD reached 62% of the theoretical maximum yield.

Probabilistic strain optimization under constraint uncertainty

Background

An important step in strain optimization is to identify reactions whose activities should be modified to achieve the desired cellular objective. Preferably, these reactions are identified systematically, as the number of possible combinations of reaction modifications could be very large. Over the last several years, a number of computational methods have been described for identifying combinations of reaction modifications. However, none of these methods explicitly address uncertainties in implementing the reaction activity modifications. In this work, we model the uncertainties as probability distributions in the flux carrying capacities of reactions. Based on this model, we develop an optimization method that identifies reactions for flux capacity modifications to predict outcomes with high statistical likelihood.

Results

We compare three optimization methods that select an intervention set comprising up- or down-regulation of reaction flux capacity: CCOpt (Chance constrained optimization), DetOpt (Deterministic optimization), and MCOpt (Monte Carlo-based optimization). We evaluate the methods using a Monte Carlo simulation-based method, MCEval (Monte Carlo Evaluations). We present two case studies analyzing a CHO cell and an adipocyte model. The flux capacity distributions required for our methods were estimated from maximal reaction velocities or elementary mode analysis. The intervention set selected by CCOpt consistently outperforms the intervention set selected by DetOpt in terms of tolerance to flux capacity variations. MCEval shows that the optimal flux predicted based on the CCOpt intervention set is more likely to be obtained, in a probabilistic sense, than the flux predicted by DetOpt. The intervention sets identified by CCOpt and MCOpt were similar; however, the exhaustive sampling required by MCOpt incurred significantly greater computational cost.

Conclusions

Maximizing tolerance to variable engineering outcomes (in modifying enzyme activities) can identify intervention sets that statistically improve the desired cellular objective.

Glucose-methanol co-utilization in Pichia pastoris studied by metabolomics and instationary ¹³C flux analysis

Background

Several studies have shown that the utilization of mixed carbon feeds instead of methanol as sole carbon source is beneficial for protein production with the methylotrophic yeast Pichia pastoris. In particular, growth under mixed feed conditions appears to alleviate the metabolic burden related to stress responses triggered by protein overproduction and secretion. Yet, detailed analysis of the metabolome and fluxome under mixed carbon source metabolizing conditions are missing. To obtain a detailed flux distribution of central carbon metabolism, including the pentose phosphate pathway under methanol-glucose conditions, we have applied metabolomics and instationary ¹³C flux analysis in chemostat cultivations.

Results

Instationary ¹³C-based metabolic flux analysis using GC-MS and LC-MS measurements in time allowed for an accurate mapping of metabolic fluxes of glycolysis, pentose phosphate and methanol assimilation pathways. Compared to previous results from NMR-derived stationary state labelling data (proteinogenic amino acids, METAFoR) more fluxes could be determined with higher accuracy. Furthermore, using a thermodynamic metabolic network analysis the metabolite measurements and metabolic flux directions were validated. Notably, the concentration of several metabolites of the upper glycolysis and pentose phosphate pathway increased under glucose-methanol feeding compared to the reference glucose conditions, indicating a shift in the thermodynamic driving forces. Conversely, the extracellular concentrations of all measured metabolites were lower compared with the corresponding exometabolome of glucose-grown P. pastoris cells.

The instationary ¹³C flux analysis resulted in fluxes comparable to previously obtained from NMR datasets of proteinogenic amino acids, but allowed several additional insights. Specifically, i) in vivo metabolic flux estimations were expanded to a larger metabolic network e.g. by including trehalose recycling, which accounted for about 1.5% of the glucose uptake rate; ii) the reversibility of glycolytic/gluconeogenesis, TCA cycle and pentose phosphate pathways reactions was estimated, revealing a significant gluconeogenic flux from the dihydroxyacetone phosphate/glyceraldehydes phosphate pool to glucose-6P. The origin of this finding could be carbon recycling from the methanol assimilatory pathway to the pentose phosphate pool. Additionally, high exchange fluxes of oxaloacetate with aspartate as well as malate indicated amino acid pool buffering and the activity of the malate/Asp shuttle; iii) the ratio of methanol oxidation vs utilization appeared to be lower (54 vs 79% assimilated methanol directly oxidized to CO₂).

Conclusions

In summary, the application of instationary ¹³C-based metabolic flux analysis to P. pastoris provides an experimental framework with improved capabilities to explore the regulation of the carbon and energy metabolism of this yeast, particularly for the case of methanol and multicarbon source metabolism.

Fast thermodynamically constrained flux variability analysis

MOTIVATION

Flux variability analysis (FVA) is an important tool to further analyse the results obtained by flux balance analysis (FBA) on genome-scale metabolic networks. For many constraint-based models, FVA identifies unboundedness of the optimal flux space. This reveals that optimal flux solutions with net flux through internal biochemical loops are feasible, which violates the second law of thermodynamics. Such unbounded fluxes may be eliminated by extending FVA with thermodynamic constraints.

RESULTS

We present a new algorithm for efficient flux variability (and flux balance) analysis with thermodynamic constraints, suitable for analysing genome-scale metabolic networks. We first show that FBA with thermodynamic constraints is NP-hard. Then we derive a theoretical tractability result, which can be applied to metabolic networks in practice. We use this result to develop a new constraint programming algorithm Fast-tFVA for fast FVA with thermodynamic constraints (tFVA). Computational comparisons with previous methods demonstrate the efficiency of the new method. For tFVA, a speed-up of factor 30-300 is achieved. In an analysis of genome-scale metabolic networks in the BioModels database, we found that in 485 of 716 networks, additional irreversible or fixed reactions could be detected.

AVAILABILITY AND IMPLEMENTATION

Fast-tFVA is written in C++ and published under GPL. It uses the open source software SCIP and libSBML. There also exists a Matlab interface for easy integration into Matlab. Fast-tFVA is available from page.mi.fu-berlin.de/arnem/fast-tfva.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.