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

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Improving Gibberellin GA3 Production with the Construction of a Genome-Scale Metabolic Model of Fusarium fujikuroi

Liquid fermentation is the primary method for GA3 production usingFusarium fujikuroi. However, production capacity is limited due to unknown metabolic pathways. To address this, we constructed a genome-scale metabolic model (iCY1235) with 1753 reactions, 1979 metabolites, and 1235 genes to understand the GA3 regulation mechanisms. The model was validated by analyzing growth rates under different glucose uptake rates and identifying essential genes. We used the model to optimize fermentation conditions, including carbon sources and dissolved oxygen. Through the OptForce algorithm, we identified 20 reactions as targets. Overexpressing FFUJ_02053 and FFUJ_14337 resulted in a 37.5 and 75% increase in GA3 titers, respectively. These targets enhance carbon flux toward GA3 production. Our model holds promise for guiding the metabolic engineering of F. fujikuroi to achieve targeted overproduction. In summary, our study utilizes the iCY1235 model to understand GA3 regulation, optimize fermentation conditions, and identify specific targets for enhancing GA3 production through metabolic engineering.

Advances in genome-scale metabolic models of industrially important fungi

Many fungal species have been used industrially for production of biofuels and bioproducts. Developing strains with better performance in biomanufacturing contexts requires a systematic understanding of cellular metabolism. Genome-scale metabolic models (GEMs) offer a comprehensive view of interconnected pathways and a mathematical framework for downstream analysis. Recently, GEMs have been developed or updated for several industrially important fungi. Some of them incorporate enzyme constraints, enabling improved predictions of cell states and proteome allocation. Here, we provide an overview of these newly developed GEMs and computational methods that facilitate construction of enzyme-constrained GEMs and utilize flux predictions from GEMs. Furthermore, we highlight the pivotal roles of these GEMs in iterative design-build-test-learn cycles, ultimately advancing the field of fungal biomanufacturing.

Multi-omics network model reveals key genes associated with p-coumaric acid stress response in an industrial yeast strain

The production of ethanol from lignocellulosic sources presents increasingly difficult issues for the global biofuel scenario, leading to increased production costs of current second-generation (2G) ethanol when compared to first-generation (1G) plants. Among the setbacks encountered in industrial processes, the presence of chemical inhibitors from pre-treatment processes severely hinders the potential of yeasts in producing ethanol at peak efficiency. However, some industrial yeast strains have, either naturally or artificially, higher tolerance levels to these compounds. Such is the case of S. cerevisiae SA-1, a Brazilian fuel ethanol industrial strain that has shown high resistance to inhibitors produced by the pre-treatment of cellulosic complexes. Our study focuses on the characterization of the transcriptomic and physiological impact of an inhibitor of this type, p-coumaric acid (pCA), on this strain under chemostat cultivation via RNAseq and quantitative physiological data. It was found that strain SA-1 tend to increase ethanol yield and production rate while decreasing biomass yield when exposed to pCA, in contrast to pCA-susceptible strains, which tend to decrease their ethanol yield and fermentation efficiency when exposed to this substance. This suggests increased metabolic activity linked to mitochondrial and peroxisomal processes. The transcriptomic analysis also revealed a plethora of differentially expressed genes located in co-expressed clusters that are associated with changes in biological pathways linked to biosynthetic and energetical processes. Furthermore, it was also identified 20 genes that act as interaction hubs for these clusters, while also having association with altered pathways and changes in metabolic outputs, potentially leading to the discovery of novel targets for metabolic engineering toward a more robust industrial yeast strain.

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.

Reconstruction and analysis of genome-scale metabolic model for thermophilic fungus Myceliophthora thermophila

Myceliophthora thermophila, a thermophilic fungus that can degrade and utilize all major polysaccharides in plant biomass, has great potential in biotechnological industries. Here, the first manually curated genome-scale metabolic model iDL1450 for M. thermophila was reconstructed using an auto-generating pipeline with thorough manual curation. The model contains 1450 genes, 2592 reactions and 1784 unique metabolites. High accuracy was shown in predictions related to carbon and nitrogen source utilization based on data obtained from Biolog experiments. Besides, metabolism profiles were analyzed using iDL1450 integrated with transcriptomics data of M. thermophila at various growth temperatures. The refined model provides new insights into thermophilic fungi metabolism and sheds light on model-driven strain design to improve biotechnological applications of this thermophilic lignocellulosic fungus. This article is protected by copyright. All rights reserved.

Integration of Aspergillus niger transcriptomic profile with metabolic model identifies potential targets to optimise citric acid production from lignocellulosic hydrolysate

BACKGROUND

Citric acid is typically produced industrially by Aspergillus niger-mediated fermentation of a sucrose-based feedstock, such as molasses. The fungus Aspergillus niger has the potential to utilise lignocellulosic biomass, such as bagasse, for industrial-scale citric acid production, but realising this potential requires strain optimisation. Systems biology can accelerate strain engineering by systematic target identification, facilitated by methods for the integration of omics data into a high-quality metabolic model. In this work, we perform transcriptomic analysis to determine the temporal expression changes during fermentation of bagasse hydrolysate and develop an evolutionary algorithm to integrate the transcriptomic data with the available metabolic model to identify potential targets for strain engineering.

RESULTS

The novel integrated procedure matures our understanding of suboptimal citric acid production and reveals potential targets for strain engineering, including targets consistent with the literature such as the up-regulation of citrate export and pyruvate carboxylase as well as novel targets such as the down-regulation of inorganic diphosphatase.

CONCLUSIONS

In this study, we demonstrate the production of citric acid from lignocellulosic hydrolysate and show how transcriptomic data across multiple timepoints can be coupled with evolutionary and metabolic modelling to identify potential targets for further engineering to maximise productivity from a chosen feedstock. The in silico strategies employed in this study can be applied to other biotechnological goals, assisting efforts to harness the potential of microorganisms for bio-based production of valuable chemicals.

A new strategy for dynamic metabolic flux estimation by integrating transient metabolome data into genome-scale metabolic models

Metabolic flux analysis (MFA) is a powerful tool for studying microbial cell physiology. Isotope-based MFA is the accepted standard for studying metabolic fluxes under steady-state conditions. However, its application under dynamic extracellular conditions is limited due to lack of proper techniques, such as rapid sampling and quenching, high cost and laborious execution. Here, we propose a new strategy to tackle this through incorporating dynamic metabolite abundance data into genome-scale metabolic models (GEM). First, a dummy extracellular pool concept is proposed for each dynamically changing metabolite, which represents a "sink" or "source", with corresponding dummy reactions coded into the GEM model. The dynamic model (expressed as differential equations) is then transformed into a quasi-steady-state model (expressed as linear equations), which can be easily solved by constraining the GEM model with the dynamic metabolite quantification data. For this, common linear-programming optimization algorithms were utilized to estimate the dynamic fluxes. Dynamic high-accuracy metabolite abundance data were obtained through the Isotope Dilution Mass Spectrometry (IDMS) method and high-speed sampling-quenching, and it was demonstrated that the newly proposed strategy could be successfully applied to obtain intracellular dynamic fluxes of Aspergillus niger under regimes of single and periodic extracellular glucose pulses. The applicability of the new method was also tested on dynamic fluxes estimation in a glucose pulse-response study of Saccharomyces cerevisiae. The proposed method provides a powerful tool to investigate cell physiology under dynamic conditions, especially relevant for bioprocess scale-up to industrial-scale bioreactors.

Aspergillus niger as a Secondary Metabolite Factory

Aspergillus niger, one of the most common and important fungal species, is ubiquitous in various environments. A. niger isolates possess a large number of cryptic biosynthetic gene clusters (BGCs) and produce various biomolecules as secondary metabolites with a broad spectrum of application fields covering agriculture, food, and pharmaceutical industry. By extensive literature search, this review with a comprehensive summary on biological and chemical aspects of A. niger strains including their sources, BGCs, and secondary metabolites as well as biological properties and biosynthetic pathways is presented. Future perspectives on the discovery of more A. niger-derived functional biomolecules are also provided in this review.

Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

Background

Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level.

Results

Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k_{{cat}}$$\end{document}kcat values, predicting the differential expression of enzymes under different growth conditions.

Conclusions

This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01614-2.

Dynamic response of Aspergillus niger to periodical glucose pulse stimuli in chemostat cultures

In industrial large-scale bioreactors, microorganisms encounter heterogeneous substrate concentration conditions, which can impact growth or product formation. Here we carried out an extended (12 h) experiment of repeated glucose pulsing with a 10-min period to simulate fluctuating glucose concentrations with Aspergillus niger producing glucoamylase, and investigated its dynamic response by rapid sampling and quantitative metabolomics. The 10-min period represents worst-case conditions, as in industrial bioreactors the average cycling duration is usually in the order of 1 min. We found that cell growth and the glucoamylase productivity were not significantly affected, despite striking metabolomic dynamics. Periodical dynamic responses were found across all central carbon metabolism pathways, with different time scales, and the frequently reported ATP paradox was confirmed for this A. niger strain under the dynamic conditions. A thermodynamics analysis revealed that several reactions of the central carbon metabolism remained in equilibrium even under periodical dynamic conditions. The dynamic response profiles of the intracellular metabolites did not change during the pulse exposure, showing no significant adaptation of the strain to the more than 60 perturbation cycles applied. The apparent high tolerance of the glucoamylase producing A. niger strain for extreme variations in the glucose availability presents valuable information for the design of robust industrial microbial hosts.

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.

Genome-scale modeling for Bacillus coagulans to understand the metabolic characteristics

Lactic acid is widely used in many industries, especially in the production of poly-lactic acid. Bacillus coagulans is a promising lactic acid producer in industrial fermentation due to its thermophilic property. In this study, we developed the first genome-scale metabolic model (GEM) of B. coagulans iBag597, together with an enzyme-constrained model ec-iBag597. We measured strain-specific biomass composition and integrated the data into a biomass equation. Then, we validated iBag597 against experimental data generated in this study, including amino acid requirements and carbon source utilization, showing that simulations were generally consistent with the experimental results. Subsequently, we carried out chemostats to investigate the effects of specific growth rate and culture pH on metabolism of B. coagulans. Meanwhile, we used iBag597 to estimate the intracellular metabolic fluxes for those conditions. The results showed that B. coagulans was capable of generating ATP via multiple pathways, and switched among them in response to various conditions. With ec-iBag597, we estimated the protein cost and protein efficiency for each ATP-producing pathway to investigate the switches. Our models pave the way for systems biology of B. coagulans, and our findings suggest that maintaining a proper growth rate and selecting an optimal pH are beneficial for lactate fermentation.

Improving cytosolic aspartate biosynthesis increases glucoamylase production in Aspergillus niger under oxygen limitation

Background

Glucoamylase is one of the most industrially applied enzymes, produced by Aspergillus species, like Aspergillus niger. Compared to the traditional ways of process optimization, the metabolic engineering strategies to improve glucoamylase production are relatively scarce.

Results

In the previous study combined multi-omics integrative analysis and amino acid supplementation experiment, we predicted four amino acids (alanine, glutamate, glycine and aspartate) as the limited precursors for glucoamylase production in A. niger. To further verify this, five mutants namely OE-ala, OE-glu, OE-gly, OE-asp1 and OE-asp2, derived from the parental strain A. niger CBS 513.88, were constructed respectively for the overexpression of five genes responsible for the biosynthesis of the four kinds of amino acids (An11g02620, An04g00990, An05g00410, An04g06380 and An16g05570). Real-time quantitative PCR revealed that all these genes were successfully overexpressed at the mRNA level while the five mutants exhibited different performance in glucoamylase production in shake flask cultivation. Notably, the results demonstrated that mutant OE-asp2 which was constructed for reinforcing cytosolic aspartate synthetic pathway, exhibited significantly increased glucoamylase activity by 23.5% and 60.3% compared to CBS 513.88 in the cultivation of shake flask and the 5 L fermentor, respectively. Compared to A. niger CBS 513.88, mutant OE-asp2 has a higher intracellular amino acid pool, in particular, alanine, leucine, glycine and glutamine, while the pool of glutamate was decreased.

Conclusion

Our study combines the target prediction from multi-omics analysis with the experimental validation and proves the possibility of increasing glucoamylase production by enhancing limited amino acid biosynthesis. In short, this systematically conducted study will surely deepen the understanding of resources allocation in cell factory and provide new strategies for the rational design of enzyme production strains.

In silico evolution of Aspergillus niger organic acid production suggests strategies for switching acid output

BACKGROUND

The fungus Aspergillus niger is an important industrial organism for citric acid fermentation; one of the most efficient biotechnological processes. Previously we introduced a dynamic model that captures this process in the industrially relevant batch fermentation setting, providing a more accurate predictive platform to guide targeted engineering. In this article we exploit this dynamic modelling framework, coupled with a robust genetic algorithm for the in silico evolution of A. niger organic acid production, to provide solutions to complex evolutionary goals involving a multiplicity of targets and beyond the reach of simple Boolean gene deletions. We base this work on the latest metabolic models of the parent citric acid producing strain ATCC1015 dedicated to organic acid production with the required exhaustive genomic coverage needed to perform exploratory in silico evolution.

RESULTS

With the use of our informed evolutionary framework, we demonstrate targeted changes that induce a complete switch of acid output from citric to numerous different commercially valuable target organic acids including succinic acid. We highlight the key changes in flux patterns that occur in each case, suggesting potentially valuable targets for engineering. We also show that optimum acid productivity is achieved through a balance of organic acid and biomass production, requiring finely tuned flux constraints that give a growth rate optimal for productivity.

CONCLUSIONS

This study shows how a genome-scale metabolic model can be integrated with dynamic modelling and metaheuristic algorithms to provide solutions to complex metabolic engineering goals of industrial importance. This framework for in silico guided engineering, based on the dynamic batch growth relevant to industrial processes, offers considerable potential for future endeavours focused on the engineering of organisms to produce valuable products.

Advanced strategy for metabolite exploration in filamentous fungi

Filamentous fungi comprise an abundance of gene clusters that encode high-value metabolites, whereas affluent gene clusters remain silent during laboratory conditions. Complex cellular metabolism further limits these metabolite yields. Therefore, diverse strategies such as genetic engineering and chemical mutagenesis have been developed to activate these cryptic pathways and improve metabolite productivity. However, lower efficiencies of gene modifications and screen tools delayed the above processes. To address the above issues, this review describes an alternative design-construction evaluation optimization (DCEO) approach. The DCEO tool provides theoretical and practical principles to identify potential pathways, modify endogenous pathways, integrate exogenous pathways, and exploit novel pathways for their diverse metabolites and desirable productivities. This DCEO method also offers different tactics to balance the cellular metabolisms, facilitate the genetic engineering, and exploit the scalable metabolites in filamentous fungi.

Genomic sequencing, genome-scale metabolic network reconstruction, and in silico flux analysis of the grape endophytic fungus Alternaria sp. MG1

BACKGROUND

Alternaria sp. MG1, an endophytic fungus isolated from grape, is a native producer of resveratrol, which has important application potential. However, the metabolic characteristics and physiological behavior of MG1 still remains mostly unraveled. In addition, the resveratrol production of the strain is low. Thus, the whole-genome sequencing is highly required for elucidating the resveratrol biosynthesis pathway. Furthermore, the metabolic network model of MG1 was constructed to provide a computational guided approach for improving the yield of resveratrol.

RESULTS

Firstly, a draft genomic sequence of MG1 was generated with a size of 34.7 Mbp and a GC content of 50.96%. Genome annotation indicated that MG1 possessed complete biosynthesis pathways for stilbenoids, flavonoids, and lignins. Eight secondary metabolites involved in these pathways were detected by GC-MS analysis, confirming the metabolic diversity of MG1. Furthermore, the first genome-scale metabolic network of Alternaria sp. MG1 (named iYL1539) was reconstructed, accounting for 1539 genes, 2231 metabolites, and 2255 reactions. The model was validated qualitatively and quantitatively by comparing the in silico simulation with experimental data, and the results showed a high consistency. In iYL1539, 56 genes were identified as growth essential in rich medium. According to constraint-based analysis, the importance of cofactors for the resveratrol biosynthesis was successfully demonstrated. Ethanol addition was predicted in silico to be an effective method to improve resveratrol production by strengthening acetyl-CoA synthesis and pentose phosphate pathway, and was verified experimentally with a 26.31% increase of resveratrol. Finally, 6 genes were identified as potential targets for resveratrol over-production by the recently developed methodology. The target-genes were validated using salicylic acid as elicitor, leading to an increase of resveratrol yield by 33.32% and the expression of gene 4CL and CHS by 1.8- and 1.6-fold, respectively.

CONCLUSIONS

This study details the diverse capability and key genes of Alternaria sp. MG1 to produce multiple secondary metabolites. The first model of the species Alternaria was constructed, providing an overall understanding of the physiological behavior and metabolic characteristics of MG1. The model is a highly useful tool for enhancing productivity by rational design of the metabolic pathway for resveratrol and other secondary metabolites.

A community-driven reconstruction of the Aspergillus niger metabolic network

Background

Aspergillus niger is an important fungus used in industrial applications for enzyme and acid production. To enable rational metabolic engineering of the species, available information can be collected and integrated in a genome-scale model to devise strategies for improving its performance as a host organism.

Results

In this paper, we update an existing model of A. niger metabolism to include the information collected from 876 publications, thereby expanding the coverage of the model by 940 reactions, 777 metabolites and 454 genes. In the presented consensus genome-scale model of A. niger iJB1325 , we integrated experimental data from publications and patents, as well as our own experiments, into a consistent network. This information has been included in a standardized way, allowing for automated testing and continuous improvements in the future. This repository of experimental data allowed the definition of 471 individual test cases, of which the model complies with 373 of them. We further re-analyzed existing transcriptomics and quantitative physiology data to gain new insights on metabolism. Additionally, the model contains 3482 checks on the model structure, thereby representing the best validated genome-scale model on A. niger developed until now. Strain-specific model versions for strains ATCC 1015 and CBS 513.88 have been created containing all data used for model building, thereby allowing users to adopt the models and check the updated version against the experimental data. The resulting model is compliant with the SBML standard and therefore enables users to easily simulate it using their preferred software solution.

Conclusion

Experimental data on most organisms are scattered across hundreds of publications and several repositories.To allow for a systems level understanding of metabolism, the data must be integrated in a consistent knowledge network. The A. niger iJB1325 model presented here integrates the available data into a highly curated genome-scale model to facilitate the simulation of flux distributions, as well as the interpretation of other genome-scale data by providing the metabolic context.

Electronic supplementary material

The online version of this article (10.1186/s40694-018-0060-7) contains supplementary material, which is available to authorized users.

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.

Optimal pcr primers for rapid and accurate detection of Aspergillus flavus isolates

Aspergillus flavus is among the most devastating opportunistic pathogens of several food crops including rice, due to its high production of carcinogenic aflatoxins. The presence of these organisms in economically important rice strip farming is a serious food safety concern. Several polymerase chain reaction (PCR) primers have been designed to detect this species; however, a comparative assessment of their accuracy has not been conducted. This study aims to identify the optimal diagnostic PCR primers for the identification of A. flavus, among widely available primers. We isolated 122 A. flavus native isolates from randomly collected rice strips (N = 300). We identified 109 isolates to the genus level using universal fungal PCR primer pairs. Nine pairs of primers were examined for their PCR diagnostic specificity on the 109 isolates. FLA PCR was found to be the optimal PCR primer pair for specific identification of the native isolates, over aflP(1), aflM, aflA, aflD, aflP(3), aflP(2), and aflR. The PEP primer pair was found to be the most unsuitable for A. flavus identification. In conclusion, the present study indicates the powerful specificity of the FLA PCR primer over other commonly available diagnostic primers for accurate, rapid, and large-scale identification of A. flavus native isolates. This study provides the first simple, practical comparative guide to PCR-based screening of A. flavus infection in rice strips.

Alkaline pH shock enhanced production of validamycin A in fermentation of Streptomyces hygroscopicus

Validamycin A (Val-A) is produced by Streptomyces as a secondary metabolite with wide agricultural applications of controlling rice sheath blight, false smut and damping-off diseases. The effect of alkaline pH shock on enhancing Val-A production and its mechanism were investigated. A higher yield of Val-A was achieved by NaOH shock once or several times together with faster protein synthesis and sugar consumption and alkaline pH shock can increase Val-A production by 27.43%. Transcription of genes related to amino acid metabolism, carbon metabolism and electron respiratory chain was significantly up-regulated, accompanied by the substantial increase of respiratory activity and glutamate concentration. Val-A production was promoted by a series of complex mechanisms and made a response to pH stress signal, which led to the enhancement of glutamate metabolism and respiration activity. The obtained information will facilitate future studies for antibiotic yield improvement and the deep revealment of molecular mechanism.

An accurate description of Aspergillus niger organic acid batch fermentation through dynamic metabolic modelling

BACKGROUND

Aspergillus niger fermentation has provided the chief source of industrial citric acid for over 50 years. Traditional strain development of this organism was achieved through random mutagenesis, but advances in genomics have enabled the development of genome-scale metabolic modelling that can be used to make predictive improvements in fermentation performance. The parent citric acid-producing strain of A. niger, ATCC 1015, has been described previously by a genome-scale metabolic model that encapsulates its response to ambient pH. Here, we report the development of a novel double optimisation modelling approach that generates time-dependent citric acid fermentation using dynamic flux balance analysis.

RESULTS

The output from this model shows a good match with empirical fermentation data. Our studies suggest that citric acid production commences upon a switch to phosphate-limited growth and this is validated by fitting to empirical data, which confirms the diauxic growth behaviour and the role of phosphate storage as polyphosphate.

CONCLUSIONS

The calibrated time-course model reflects observed metabolic events and generates reliable in silico data for industrially relevant fermentative time series, and for the behaviour of engineered strains suggesting that our approach can be used as a powerful tool for predictive metabolic engineering.