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

Reconstruction of a genome-scale metabolic model and in-silico flux analysis of Aspergillus tubingensis: a non-mycotoxinogenic citric acid-producing fungus

BACKGROUND

Aspergillus tubingensis is a citric acid-producing fungus that can utilize sugars in hydrolysate of lignocellulosic biomass such as sugarcane bagasse and, unlike A. niger, does not produce mycotoxins. To date, no attempt has been made to model its metabolism at genome scale.

RESULTS

Here, we utilized the whole-genome sequence (34.96 Mb length) and the measured biomass composition to reconstruct a genome-scale metabolic model (GSMM) of A. tubingensis DJU120 strain. The model, named iMK1652, consists of 1652 genes, 1657 metabolites and 2039 reactions distributed over four cellular compartments. The model has been extensively curated manually. This included removal of dead-end metabolites and generic reactions, addition of secondary metabolite pathways and several transporters. Several mycotoxin synthesis pathways were either absent or incomplete in the genome, providing a genomic basis for the non-toxinogenic nature of this species. The model was further refined based on the experimental phenotypic microarray (Biolog) data. The model closely captured DJU120 fermentative data on glucose, xylose, and phosphate consumption, as well as citric acid and biomass production, showing its applicability to capture citric acid fermentation of lignocellulosic biomass hydrolysate.

CONCLUSIONS

The model offers a framework to conduct metabolic systems biology investigations and can act as a scaffold for integrative modelling of A. tubingensis.

Study on the mechanism of sodium ion inhibiting citric acid fermentation in Aspergillus niger

Excessive sodium significantly inhibits citric acid fermentation by Aspergillus niger during the recycling of citric acid wastewater. This study aimed to elucidate the inhibition mechanism at the interface of physiology and transcriptomics. The results showed that excessive sodium caused a 22.3 % increase in oxalic acid secretion and a 147.6 % increase in H+-ATPase activity at the 4 h fermentation compared to the control. Meanwhile, a 13.1 % reduction in energy charge level and a 15.2 % decline in NADH content were found, which implied the effects on carbon metabolism and redox balance. In addition, transcriptomic analysis revealed that excessive sodium altered the gene expression profiles related to ATPase, hydrolase, and oxidoreductase, as well as pathways like glyoxylate metabolism, and transmembrane transport. These findings gained insights into the metabolic regulation of A. niger response to environmental stress and provided theoretical guidance for the construction of sodium-tolerant A. niger for industrial application.

Positive effect of inoculation with an Aspergillus strain on phosphorus and iron nutrition plus volatile organic compounds in rice

We explored the potential of a fungal strain Aspergillus costaricensis KS1 for modulating growth and nutrient mobilization in rice. At laboratory conditions, there was a decline in pH of the medium on inoculation with the strain and the production of citric acid was observed under broth conditions. Similarly, there was higher solubilization of tricalcium phosphate and siderophore production in liquid medium on inoculation with the strain. The effect of inoculation of KS1 was studied in rice and higher growth and yield were observed on inoculation compared to control. The content of phosphorus and iron in stem and roots of KS1 inoculated plants was higher in comparison with uninoculated control. There was also increased availability of phosphorus and iron content in soil grown with KS1 inoculated plants. In addition, inoculation with strain resulted in a higher content of volatile organic compounds such as linoleic acid, linolenic acid, and ethyl isoallocholate in stem of rice. A. costaricensis KS1 can be used for improving phosphorus and iron nutrition and impart tolerance against stresses in rice.

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.

Metabolic engineering and fermentation optimization strategies for producing organic acids of the tricarboxylic acid cycle by microbial cell factories

The organic acids of the tricarboxylic acid (TCA) pathway are important platform compounds and are widely used in many areas. The high-productivity strains and high-efficient and low-cost fermentation are required to satisfy a huge market size. The high metabolic flux of the TCA pathway endows microorganisms potential to produce high titers of these organic acids. Coupled with metabolic engineering and fermentation optimization, the titer of the organic acids has been significantly improved in recent years. Herein, we discuss and compare the recent advances in synthetic pathway engineering, cofactor engineering, transporter engineering, and fermentation optimization strategies to maximize the biosynthesis of organic acids. Such engineering strategies were mainly based on the TCA pathway and glyoxylate pathway. Furthermore, organic-acid-secretion enhancement and renewable-substrate-based fermentation are often performed to assist the biosynthesis of organic acids. Further strategies are also discussed to construct high-productivity and acid-resistant strains for industrial large-scale production.

Metabolic Engineering: Methodologies and Applications

Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

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.

Microbial Biosynthesis of L-Malic Acid and Related Metabolic Engineering Strategies: Advances and Prospects

Malic acid, a four-carbon dicarboxylic acid, is widely used in the food, chemical and medical industries. As an intermediate of the TCA cycle, malic acid is one of the most promising building block chemicals that can be produced from renewable sources. To date, chemical synthesis or enzymatic conversion of petrochemical feedstocks are still the dominant mode for malic acid production. However, with increasing concerns surrounding environmental issues in recent years, microbial fermentation for the production of L-malic acid was extensively explored as an eco-friendly production process. The rapid development of genetic engineering has resulted in some promising strains suitable for large-scale bio-based production of malic acid. This review offers a comprehensive overview of the most recent developments, including a spectrum of wild-type, mutant, laboratory-evolved and metabolically engineered microorganisms for malic acid production. The technological progress in the fermentative production of malic acid is presented. Metabolic engineering strategies for malic acid production in various microorganisms are particularly reviewed. Biosynthetic pathways, transport of malic acid, elimination of byproducts and enhancement of metabolic fluxes are discussed and compared as strategies for improving malic acid production, thus providing insights into the current state of malic acid production, as well as further research directions for more efficient and economical microbial malic acid production.

Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes

Succinic acid (SA) is one of the top candidate value-added chemicals that can be produced from biomass via microbial fermentation. A considerable number of cell factories have been proposed in the past two decades as native as well as non-native SA producers. Actinobacillus succinogenes is among the best and earliest known natural SA producers. However, its industrial application has not yet been realized due to various underlying challenges. Previous studies revealed that the optimization of environmental conditions alone could not entirely resolve these critical problems. On the other hand, microbial in silico metabolic modeling approaches have lately been the center of attention and have been applied for the efficient production of valuable commodities including SA. Then again, literature survey results indicated the absence of up-to-date reviews assessing this issue, specifically concerning SA production. Hence, this review was designed to discuss accomplishments and future perspectives of in silico studies on the metabolic capabilities of SA producers. Herein, research progress on SA and A. succinogenes, pathways involved in SA production, metabolic models of SA-producing microorganisms, and status, limitations and prospects on in silico studies of A. succinogenes were elaborated. All in all, this review is believed to provide insights to understand the current scenario and to develop efficient mathematical models for designing robust SA-producing microbial strains.

Biotechnological valorization of cashew apple juice for the production of citric acid by a local strain of Aspergillus niger LCFS 5

Background

This work investigates the production of citric acid from cashew apple juice, an abundant waste in the processing of cashew, using a local strain of Aspergillus niger and the application of the citric acid as a coagulant for the production of soy cheese. Fungal isolates were obtained from a cashew plantation in Ogbomoso, Nigeria, using potato dextrose agar. Further screening was undertaken to determine the qualitative strength of acid production by the fungi on Czapek-Dox agar supplemented with bromocresol green, with the development of yellow zone taken as an indication of citric acid production. Thereafter, the best producing strain was cultivated in a cashew apple juice medium.

Results

Out of 150 fungal isolates generated from the cashew plantation, 92 (61.3%), 44 (29.3%) and 14 (9.3%) were obtained from cashew fruits, soil and cashew tree surfaces, respectively. Different strains of fungi isolated include Aspergillus niger, A. flavus, A. foetidus, A. heteromorphus, A. nidulans and A. viridinutans. The isolates produced yellow zonation of 0.4–5.5 cm on modified Czapek-Dox agar; the highest was observed for a strain of A. niger LCFS 5, which was identified using molecular tools. In the formulated cashew apple juice medium, the citric acid yield of LCFS 5 ranged 16.0–92.8 g/l with the peak obtained on the 10th day of fermentation. The citric acid produced was recovered using the double precipitation method with Ca(OH)₂ and H₂SO₄ having ≈ 70% purity of citric acid on HPLC. The citric acid acted as a coagulant to produce soy cheese with 66.67% acceptability by panelists.

Conclusion

This work has extended the frontiers of valorization of cashew waste by a strain of A. niger to produce citric acid in high yield, with potential application in food industries.

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.