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

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Towards consolidated bioprocessing of biomass and plastic substrates for semi-synthetic production of bio-poly(ethylene furanoate) (PEF) polymer using omics-guided construction of artificial microbial consortia

Poly(ethylene furanoate) (PEF) plastic is a 100% renewable polyester that is currently being pursued for commercialization as the next-generation bio-based plastic. This is in line with growing demand for circular bioeconomy and new plastics economy that is aimed at minimizing plastic waste mismanagement and lowering carbon footprint of plastics. However, the current catalytic route for the synthesis of PEF is impeded with technical challenges including high cost of pretreatment and catalyst refurbishment. On the other hand, the semi-biosynthetic route of PEF plastic production is of increased biotechnological interest. In particular, the PEF monomers (Furan dicarboxylic acid and ethylene glycol) can be synthesized via microbial-based biorefinery and purified for subsequent catalyst-mediated polycondensation into PEF. Several bioengineering and bioprocessing issues such as efficient substrate utilization and pathway optimization need to be addressed prior to establishing industrial-scale production of the monomers. This review highlights current advances in semi-biosynthetic production of PEF monomers using consolidated waste biorefinery strategies, with an emphasis on the employment of omics-driven systems biology approaches in enzyme discovery and pathway construction. The roles of microbial protein transporters will be discussed, especially in terms of improving substrate uptake and utilization from lignocellulosic biomass, as well as from depolymerized plastic waste as potential bio-feedstock. The employment of artificial bioengineered microbial consortia will also be highlighted to provide streamlined systems and synthetic biology strategies for bio-based PEF monomer production using both plant biomass and plastic-derived substrates, which are important for circular and new plastics economy advances.

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 microbial bioproduction under low-oxygen conditions

Microbial bioconversion provides access to a wide range of sustainably produced chemicals and commodities. However, industrial-scale bioproduction process operations are preferred to be anaerobic due to the cost associated with oxygen transfer. Anaerobic bioconversion generally offers limited substrate utilization profiles, lower product yields, and reduced final product diversity compared with aerobic processes. Bioproduction under conditions of reduced oxygen can overcome the limitations of fully aerobic and anaerobic bioprocesses, but many microbial hosts are not developed for low-oxygen bioproduction. Here, we describe advances in microbial strain engineering involving the use of redox cofactor engineering, genome-scale metabolic modeling, and functional genomics to enable improved bioproduction processes under low oxygen and provide a viable path for scaling these bioproduction systems to industrial scales.

Deep longitudinal multi-omics analysis of Bordetella pertussis cultivated in bioreactors highlights medium starvations and transitory metabolisms, associated to vaccine antigen biosynthesis variations and global virulence regulation

Bordetella pertussis is the bacterial causative agent of whooping cough, a serious respiratory illness. An extensive knowledge on its virulence regulation and metabolism is a key factor to ensure pertussis vaccine manufacturing process robustness. The aim of this study was to refine our comprehension of B. pertussis physiology during in vitro cultures in bioreactors. A longitudinal multi-omics analysis was carried out over 26 h small-scale cultures of B. pertussis. Cultures were performed in batch mode and under culture conditions intending to mimic industrial processes. Putative cysteine and proline starvations were, respectively, observed at the beginning of the exponential phase (from 4 to 8 h) and during the exponential phase (18 h 45 min). As revealed by multi-omics analyses, the proline starvation induced major molecular changes, including a transient metabolism with internal stock consumption. In the meantime, growth and specific total PT, PRN, and Fim2 antigen productions were negatively affected. Interestingly, the master virulence-regulating two-component system of B. pertussis (BvgASR) was not evidenced as the sole virulence regulator in this in vitro growth condition. Indeed, novel intermediate regulators were identified as putatively involved in the expression of some virulence-activated genes (vags). Such longitudinal multi-omics analysis applied to B. pertussis culture process emerges as a powerful tool for characterization and incremental optimization of vaccine antigen production.

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.

Enhancement of fatty acid degradation pathway promoted glucoamylase synthesis in Aspergillus niger

Background

Our recent multi-omics analyses of glucoamylase biosynthesis in Aspergillus niger (A. niger) suggested that lipid catabolism was significantly up-regulated during high-yield period under oxygen limitation. Since the catabolism of fatty acids can provide energy compounds such as ATP and important precursors such as acetyl-CoA, we speculated that enhancement of this pathway might be beneficial to glucoamylase overproduction.

Results

Based on previous transcriptome data, we selected and individually overexpressed five candidate genes involved in fatty acid degradation under the control of the Tet-on gene switch in A. niger. Overexpression of the fadE, fadA and cyp genes increased the final specific enzyme activity and total secreted protein on shake flask by 21.3 ~ 31.3% and 16.0 ~ 24.2%, respectively. And a better inducible effect by doxycycline was obtained from early logarithmic growth phase (18 h) than stationary phase (42 h). Similar with flask-level results, the glucoamylase content and total extracellular protein in engineered strains OE-fadE (overexpressing fadE) and OE-fadA (overexpressing fadA) on maltose-limited chemostat cultivation were improved by 31.2 ~ 34.1% and 35.1 ~ 38.8% compared to parental strain B36. Meanwhile, intracellular free fatty acids were correspondingly decreased by 41.6 ~ 44.6%. The metabolomic analysis demonstrated intracellular amino acids pools increased 24.86% and 18.49% in two engineered strains OE-fadE and OE-fadA compared to B36. Flux simulation revealed that increased ATP, acetyl-CoA and NADH was supplied into TCA cycle to improve amino acids synthesis for glucoamylase overproduction.

Conclusion

This study suggested for the first time that glucoamylase production was significantly improved in A. niger by overexpression of genes fadE and fadA involved in fatty acids degradation pathway. Harnessing the intracellular fatty acids could be a strategy to improve enzyme production in Aspergillus niger cell factory.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01966-3.

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.

Recent advances in the development of Aspergillus for protein production

Aspergillus had been widely used in the industrial production of recombinant proteins. In addition to the safety and broad substrate utilization spectrum, its efficient post-translational modification and strong protein secretion capacity have significant advantages for developing an excellent protein-producing cell factory in industrial production. However, the difficulties in genetic manipulation of Aspergillus and varying expression levels of different heterologous proteins hampered its further development and application. Recently, the development of CRISPR genome editing and high-throughput screening platforms has facilitated the Aspergillus development of a wide range of modifications and applications. Meanwhile, multi-omics analysis and multiplexed genetic engineering have promoted effective knowledge mining. This paper provides a comprehensive and updated review of these advances, including high-throughput screening, genome editing, protein expression modules, and fermentation optimization. It also highlights and discusses the latest significant progress, aiming to provide a practical guide for implementing Aspergillus as an efficient protein-producing cell factory.

Changes in Oxygen Availability during Glucose-Limited Chemostat Cultivations of Penicillium chrysogenum Lead to Rapid Metabolite, Flux and Productivity Responses

Bioreactor scale-up from the laboratory scale to the industrial scale has always been a pivotal step in bioprocess development. However, the transition of a bioeconomy from innovation to commercialization is often hampered by performance loss in titer, rate and yield. These are often ascribed to temporal variations of substrate and dissolved oxygen (for instance) in the environment, experienced by microorganisms at the industrial scale. Oscillations in dissolved oxygen (DO) concentration are not uncommon. Furthermore, these fluctuations can be exacerbated with poor mixing and mass transfer limitations, especially in fermentations with filamentous fungus as the microbial cell factory. In this work, the response of glucose-limited chemostat cultures of an industrial Penicillium chrysogenum strain to different dissolved oxygen levels was assessed under both DO shift-down (60% → 20%, 10% and 5%) and DO ramp-down (60% → 0% in 24 h) conditions. Collectively, the results revealed that the penicillin productivity decreased as the DO level dropped down below 20%, while the byproducts, e.g., 6-oxopiperidine-2-carboxylic acid (OPC) and 6-aminopenicillanic acid (6APA), accumulated. Following DO ramp-down, penicillin productivity under DO shift-up experiments returned to its maximum value in 60 h when the DO was reset to 60%. The result showed that a higher cytosolic redox status, indicated by NADH/NAD+, was observed in the presence of insufficient oxygen supply. Consistent with this, flux balance analysis indicated that the flux through the glyoxylate shunt was increased by a factor of 50 at a DO value of 5% compared to the reference control, favoring the maintenance of redox status. Interestingly, it was observed that, in comparison with the reference control, the penicillin productivity was reduced by 25% at a DO value of 5% under steady state conditions. Only a 14% reduction in penicillin productivity was observed as the DO level was ramped down to 0. Furthermore, intracellular levels of amino acids were less sensitive to DO levels at DO shift-down relative to DO ramp-down conditions; this difference could be caused by different timescales between turnover rates of amino acid pools (tens of seconds to minutes) and DO switches (hours to days at steady state and minutes to hours at ramp-down). In summary, this study showed that changes in oxygen availability can lead to rapid metabolite, flux and productivity responses, and dynamic DO perturbations could provide insight into understanding of metabolic responses in large-scale bioreactors.

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.

Exploration and characterization of hypoxia-inducible endogenous promoters in Aspergillus niger

Aspergillus niger is widely used for the efficient production of organic acids and enzyme preparations. However, this organism lacks basic genetic elements for dynamic control, especially inducible promoters that can respond to specific environmental signals. Since these are desirable for better adaptation of fermentation to large-scale industrial production, herein, we have identified the two first hypoxia-inducible promoters in A. niger, PsrbB and PfhbA. Their performance under high or low oxygen conditions was monitored using two reporter proteins, green fluorescent protein (EGFP) and β-glucuronidase (GUS). For comparison, basal expression of the general strong promoter PgpdA was lower than PsrbB but higher than PfhbA. However, under hypoxia, both promoters showed higher expression than under hyperoxia, and these values were also higher than those observed for PgpdA. For PsrbB, strength under hypoxia was ~2-3 times higher than under hyperoxia (for PfhbA, 3-9 times higher) and ~2.5-5 times higher than for PgpdA (for PfhbA, 2-3 times higher). Promoter truncation analysis showed that the PsrbB fragment -1024 to -588 bp is the core region that determines hypoxia response. KEY POINTS: The first identification of two hypoxia-inducible promoters in A. niger is a promising tool for modulation of target genes under hypoxia. Two reporter genes revealed a different activity and responsiveness to hypoxia of PfhbA and PsrbB promoters, which is relevant for the development of dynamic metabolic regulation of A. niger fermentation. PsrbB promoter truncation and bioinformatics analysis is the foundation for further research.

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.

Multi-omics Comparative Analysis of Streptomyces Mutants Obtained by Iterative Atmosphere and Room-Temperature Plasma Mutagenesis

Sponges, the most primitive multicellular animals, contain a large number of unique microbial communities. Sponge-associated microorganisms, particularly actinomyces, have the potential to produce diverse active natural products. However, a large number of silent secondary metabolic gene clusters have failed to be revived under laboratory culture conditions. In this study, iterative atmospheric room-temperature plasma. (ARTP) mutagenesis coupled with multi-omics conjoint analysis was adopted to activate the inactive wild Streptomyces strain. The desirable exposure time employed in this study was 75 s to obtain the appropriate lethality rate (94%) and mutation positive rate (40.94%). After three iterations of ARTP mutagenesis, the proportion of mutants exhibiting antibacterial activities significantly increased by 75%. Transcriptome analysis further demonstrated that the differential gene expression levels of encoding type I lasso peptide aborycin had a significant upward trend in active mutants compared with wild-type strains, which was confirmed by LC-MS results with a relative molecular mass of 1082.43 ([M + 2H]²⁺ at m/z = 2164.86). Moreover, metabolome comparative analysis of the mutant and wild-type strains showed that four spectra or mass peaks presented obvious differences in terms of the total ion count or extracting ion current profiles with each peak corresponding to a specific compound exhibiting moderate antibacterial activity against Gram-positive indicators. Taken together, our data suggest that the ARTP treatment method coupled with multi-omics profiling analysis could be used to estimate the valid active molecules of metabolites from microbial crudes without requiring a time-consuming isolation process.

Understanding gradients in industrial bioreactors

Gradients in industrial bioreactors have attracted substantial research attention since exposure to fluctuating environmental conditions has been shown to lead to changes in the metabolome, transcriptome as well as population heterogeneity in industrially relevant microorganisms. Such changes have also been found to impact key process parameters like the yield on substrate and the productivity. Hence, understanding gradients is important from both the academic and industrial perspectives. In this review the causes of gradients are outlined, along with their impact on microbial physiology. Quantifying the impact of gradients requires a detailed understanding of both fluid flow inside industrial equipment and microbial physiology. This review critically examines approaches used to investigate gradients including large-scale experimental work, computational methods and scale-down approaches. Avenues for future work have been highlighted, particularly the need for further coordinated development of both in silico and experimental tools which can be used to further the current understanding of gradients in industrial equipment.

Genetic Manipulation and Transformation Methods for Aspergillus spp

Species of the genus Aspergillus have a variety of effects on humans and have been considered industrial cell factories due to their prominent ability for manufacturing several products such as heterologous proteins, secondary metabolites, and organic acids. Scientists are trying to improve fungal strains and re-design metabolic processes through advanced genetic manipulation techniques and gene delivery systems to enhance their industrial efficiency and utility. In this review, we describe the current status of the genetic manipulation techniques and transformation methods for species of the genus Aspergillus. The host strains, selective markers, and experimental materials required for the genetic manipulation and fungal transformation are described in detail. Furthermore, the advantages and disadvantages of these techniques are described.

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.

Proteomic analysis on Aspergillus strains that are useful for industrial enzyme production

A simple intracellular proteomic study was conducted to investigate the biological activities of Aspergillus niger during industrial enzyme production. A strain actively secreting a heterologous enzyme was compared to a reference strain. In total, 1824 spots on 2-D gels were analyzed using MALDI-TOF MS, yielding 343 proteins. The elevated levels of UPR components, BipA, PDI, and calnexin, and proteins related to ERAD and ROS reduction, were observed in the enzyme-producer. The results suggest the occurrence of these responses in the enzyme-producers. Major glycolytic enzymes, Fba1, EnoA, and GpdA, were abundant but at a reduced level relative to the reference, indicating a potential repression of the glycolytic pathway. Interestingly, it was observed that a portion of over-expressed heterologous enzyme accumulated inside the cells and digested during fermentation, suggesting the secretion capacity of the strain was not enough for completing secretion. Newly identified conserved-proteins, likely in signal transduction, and other proteins were also investigated. Abbreviations: 2-D: two-dimensional; UPR: unfolded protein response; ER: endoplasmic reticulum; ERAD: ER-associated protein degradation; PDI: protein disulfide-isomerase; ROS: reactive oxygen species; RESS: Repression under Secretion Stress; CSAP: Conserved Small Abundant Protein; TCTP: translationally controlled tumor protein.

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.

Systems and synthetic biology tools for advanced bioproduction hosts

The genomic revolution ushered in an era of discovery and characterization of enzymes from novel organisms that fueled engineering of microbes to produce commodity and high-value compounds. Over the past decade advances in synthetic biology tools in recent years contributed to significant progress in metabolic engineering efforts to produce both biofuels and bioproducts resulting in several such related items being brought to market. These successes represent a burgeoning bio-economy; however, significant resources and time are still necessary to progress a system from proof-of-concept to market. In order to fully realize this potential, methods that examine biological systems in a comprehensive, systematic and high-throughput manner are essential. Recent success in synthetic biology has coincided with the development of systems biology and analytical approaches that kept pace and scaled with technology development. Here, we review a selection of systems biology methods and their use in synthetic biology approaches for microbial biotechnology platforms.

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.

Functional exploration of co-expression networks identifies a nexus for modulating protein and citric acid titres in Aspergillus niger submerged culture

BACKGROUND

Filamentous fungal cell factories are used to produce numerous proteins, enzymes, and organic acids. Protein secretion and filamentous growth are tightly coupled at the hyphal tip. Additionally, both these processes require ATP and amino acid precursors derived from the citric acid cycle. Despite this interconnection of organic acid production and protein secretion/filamentous growth, few studies in fungi have identified genes which may concomitantly impact all three processes.

RESULTS

We applied a novel screen of a global co-expression network in the cell factory Aspergillus niger to identify candidate genes which may concomitantly impact macromorphology, and protein/organic acid fermentation. This identified genes predicted to encode the Golgi localized ArfA GTPase activating protein (GAP, AgeB), and ArfA guanine nucleotide exchange factors (GEFs SecG and GeaB) to be co-expressed with citric acid cycle genes. Consequently, we used CRISPR-based genome editing to place the titratable Tet-on expression system upstream of ageB, secG, and geaB in A. niger. Functional analysis revealed that ageB and geaB are essential whereas secG was dispensable for early filamentous growth. Next, gene expression was titrated during submerged cultivations under conditions for either protein or organic acid production. ArfA regulators played varied and culture-dependent roles on pellet formation. Notably, ageB or geaB expression levels had major impacts on protein secretion, whereas secG was dispensable. In contrast, reduced expression of each predicted ArfA regulator resulted in an absence of citric acid in growth media. Finally, titrated expression of either GEFs resulted in an increase in oxaloacetic acid concentrations in supernatants.

CONCLUSION

Our data suggest that the Golgi may play an underappreciated role in modulating organic acid titres during industrial applications, and that this is SecG, GeaB and AgeB dependent in A. niger. These data may lead to novel avenues for strain optimization in filamentous fungi for improved protein and organic acid titres.

Rewiring of Metabolic Network in Mycobacterium tuberculosis During Adaptation to Different Stresses

Metabolic adaptation of Mycobacterium tuberculosis (M. tuberculosis) to microbicidal intracellular environment of host macrophages is fundamental to its pathogenicity. However, an in-depth understanding of metabolic adjustments through key reaction pathways and networks is limited. To understand how such changes occur, we measured the cellular metabolome of M. tuberculosis subjected to four microbicidal stresses using liquid chromatography-mass spectrometric multiple reactions monitoring (LC-MRM/MS). Overall, 87 metabolites were identified. The metabolites best describing the separation between stresses were identified through multivariate analysis. The coupling of the metabolite measurements with existing genome-scale metabolic model, and using constraint-based simulation led to several new concepts and unreported observations in M. tuberculosis; such as (i) the high levels of released ammonia as an adaptive response to acidic stress was due to increased flux through L-asparaginase rather than urease activity; (ii) nutrient starvation-induced anaplerotic pathway for generation of TCA intermediates from phosphoenolpyruvate using phosphoenolpyruvate kinase; (iii) quenching of protons through GABA shunt pathway or sugar alcohols as possible mechanisms of early adaptation to acidic and oxidative stresses; and (iv) usage of alternate cofactors by the same enzyme as a possible mechanism of rewiring metabolic pathways to overcome stresses. Besides providing new leads and important nodes that can be used for designing intervention strategies, the study advocates the strength of applying flux balance analyses coupled with metabolomics to get a global picture of complex metabolic adjustments.

Combinatorial Effect of ARTP Mutagenesis and Ribosome Engineering on an Industrial Strain of Streptomyces albus S12 for Enhanced Biosynthesis of Salinomycin

Salinomycin, an important polyketide, has been widely utilized in agriculture to inhibit growth of pathogenic bacteria. In addition, salinomycin has great potential in treatment of cancer cells. Due to inherited characteristics and beneficial potential, its demand is also inclining. Therefore, there is an urgent need to increase the current high demand of salinomycin. In order to obtain a high-yield mutant strain of salinomycin, the present work has developed an efficient breeding process of Streptomyces albus by using atmospheric and room temperature plasma (ARTP) combined with ribosome engineering. In this study, we investigate the presented method as it has the advantage of significantly shortening mutant screening duration by using an agar block diffusion method, as compared to other traditional strain breeding methods. As a result, the obtained mutant Tet³⁰Chl²⁵ with tetracycline and chloramphenicol resistance provided a salinomycin yield of 34,712 mg/L in shake flask culture, which was over 2.0-fold the parental strain S12. In addition, comparative transcriptome analysis of low and high yield mutants, and a parental strain revealed the mechanistic insight of biosynthesis pathways, in which metabolic pathways including butanoate metabolism, starch and sucrose metabolism and glyoxylate metabolism were closely associated with salinomycin biosynthesis. Moreover, we also confirmed that enhanced flux of glyoxylate metabolism via overexpression gene of isocitrate lyase (icl) promoted salinomycin biosynthesis. Based on these results, it has been successfully verified that the overexpression of crotonyl-CoA reductase gene (crr) and transcriptional regulator genes (orf 3 and orf 15), located in salinomycin synthesis gene cluster, is possibly responsible for the increase in salinomycin production in a typical strain Streptomyces albus DSM41398. Conclusively, a tentative regulatory model of ribosome engineering combined with ARTP in S. ablus is proposed to explore the roles of transcriptional regulators and stringent responses in the biosynthesis regulation of salinomycin.