Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

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.

CRISPR/Cas9-Based Genome Editing and Its Application in Aspergillus Species

Aspergillus, a genus of filamentous fungi, is extensively distributed in nature and plays crucial roles in the decomposition of organic materials as an important environmental microorganism as well as in the traditional fermentation and food processing industries. Furthermore, due to their strong potential to secrete a large variety of hydrolytic enzymes and other natural products by manipulating gene expression and/or introducing new biosynthetic pathways, several Aspergillus species have been widely exploited as microbial cell factories. In recent years, with the development of next-generation genome sequencing technology and genetic engineering methods, the production and utilization of various homo-/heterologous-proteins and natural products in Aspergillus species have been well studied. As a newly developed genome editing technology, the clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system has been used to edit and modify genes in Aspergilli. So far, the CRISPR/Cas9-based approach has been widely employed to improve the efficiency of gene modification in the strain type Aspergillus nidulans and other industrially important and pathogenic Aspergillus species, including Aspergillus oryzae, Aspergillus niger, and Aspergillus fumigatus. This review highlights the current development of CRISPR/Cas9-based genome editing technology and its application in basic research and the production of recombination proteins and natural products in the Aspergillus species.

Improvement of Free Fatty Acid Secretory Productivity in Aspergillus oryzae by Comprehensive Analysis on Time-Series Gene Expression

Aspergillus oryzae is a filamentous fungus that has historically been utilized in the fermentation of food products. In recent times, it has also been introduced as a component in the industrial biosynthesis of consumable compounds, including free fatty acids (FFAs), which are valuable and versatile products that can be utilized as feedstocks in the production of other commodities, such as pharmaceuticals and dietary supplements. To improve the FFA secretory productivity of A. oryzae in the presence of Triton X-100, we analyzed the gene expression of a wild-type control strain and a disruptant strain of an acyl-CoA synthetase gene, faaA, in a time-series experiment. We employed a comprehensive analysis strategy using the baySeq, DESeq2, and edgeR algorithms to clarify the vital pathways for FFA secretory productivity and select genes for gene modification. We found that the transport and metabolism of inorganic ions are crucial in the initial stages of FFA production and revealed 16 candidate genes to be modified in conjunction with the faaA disruption. These genes were verified through the construction of overexpression strains, and showed that the manipulation of reactions closer to the FFA biosynthesis step led to a higher increase in FFA secretory productivity. This resulted in the most successful overexpression strains to have an FFA secretory productivity more than two folds higher than that of the original faaA disruptant. Our study provides guidance for further gene modification for FFA biosynthesis in A. oryzae and for enhancing the productivity of other metabolites in other microorganisms through metabolic engineering.

Advances in Genetic Engineering Technology and Its Application in the Industrial Fungus Aspergillus oryzae

The filamentous fungus Aspergillus oryzae is an important strain in the traditional fermentation and food processing industries and is often used in the production of soy sauce, soybean paste, and liquor-making. In addition, A. oryzae has a strong capacity to secrete large amounts of hydrolytic enzymes; therefore, it has also been used in the enzyme industry as a cell factory for the production of numerous native and heterologous enzymes. However, the production and secretion of foreign proteins by A. oryzae are often limited by numerous bottlenecks that occur during transcription, translation, protein folding, translocation, degradation, transport, secretion, etc. The existence of these problems makes it difficult to achieve the desired target in the production of foreign proteins by A. oryzae. In recent years, with the decipherment of the whole genome sequence, basic research and genetic engineering technologies related to the production and utilization of A. oryzae have been well developed, such as the improvement of homologous recombination efficiency, application of selectable marker genes, development of large chromosome deletion technology, utilization of hyphal fusion techniques, and application of CRISPR/Cas9 genome editing systems. The development and establishment of these genetic engineering technologies provided a great deal of technical support for the industrial production and application of A. oryzae. This paper reviews the advances in basic research and genetic engineering technologies of the fermentation strain A. oryzae mentioned above to open up more effective ways and research space for the breeding of A. oryzae production strains in the future.

Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to fermentation only in oxygen limiting conditions that renders it as a suitable industrial host. The yeast also produces ethyl acetate as a major overflow metabolite in aerobic conditions. Here, we report the first genome-scale metabolic model, iPN730, of L. kluyveri comprising of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments. The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. We have also demonstrated the effect of switching carbon sources on the production of ethyl acetate under varying oxygen uptake rates. A phenotypic phase plane analysis described the energetic cost penalty of ethyl acetate and ethanol production on the specific growth rate of L. kluyveri. We generated the context specific models of L. kluyveri growing on uracil or ammonium salts as the sole nitrogen source. Differential flux calculated using flux variability analysis helped us in highlighting pathways like purine, histidine, riboflavin and pyrimidine metabolism associated with uracil degradation. The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism behind ethyl acetate production as well as uracil catabolism (pyrimidine degradation) pathway. iPN730 is an addition to genome-scale metabolic models of non-conventional yeasts that will facilitate system-wide omics analysis to understand fungal metabolic diversity.

Citrate exporter enhances both extracellular and intracellular citric acid accumulation in the koji fungi Aspergillus luchuensis mut. kawachii and Aspergillus oryzae

Citrate exporter CexA plays a key role in the production of citric acid in fungi; however, its role in intracellular metabolism has remained unclear. In this study, we comparably characterized homologous cexA genes in the white koji fungus Aspergillus luchuensis mut. kawachii and the yellow koji fungus Aspergillus oryzae, which exhibit high and low abilities, respectively, to produce citric acid. Disruption of cexA caused a significant decline of both extracellular and intracellular citric acid accumulation in Aspergillus kawachii, while overexpression of the A. kawachii cexA gene (AkcexA) into A. oryzae significantly enhanced both extracellular and intracellular citric acid accumulation in A. oryzae to a level comparable to that of A. kawachii. In addition, overexpression of two intrinsic cexA homologs (AocexA and AocexB) in A. oryzae also enhanced its extracellular and intracellular citric acid accumulation. Comprehensive analysis of intracellular metabolites from an AkcexA-overexpressing strain of A. oryzae compared with its control strain identified metabolic changes associated with intracellular citric acid accumulation via the glycolytic pathway, pentose phosphate pathway, and tricarboxylic acid cycle. Our results indicate that citric acid export enhances not only extracellular citric acid accumulation but also intracellular metabolic fluxes to generate citric acid.

Protocol for Genome-Scale Reconstruction and Melanogenesis Analysis of Exophiala dermatitidis

Exophiala dermatitidis is a polyextremotolerant fungus with a small genome, thus suitable as a model system for melanogenesis and carotenogensis. A genome-scale model, iEde2091, is reconstructed to increase metabolic understanding and used in a shadow price analysis of pigments, as detailed here. Important to this reconstruction is OptFill, a recently developed alternative gap-filling method useful in the holistic and conservative reconstruction of genome-scale models of metabolism, particularly for understudied organisms like E. dermatitidis where gaps in metabolic knowledge are abundant. For complete details on the use and execution of this protocol, please refer to Schroeder and Saha (2020) and Schroeder et al. (2020).

Enhancement of the productivity of free dihomo-γ-linolenic acid via co-overexpression of elongase and two desaturase genes in Aspergillus oryzae

Free dihomo-γ-linolenic acid (DGLA), a polyunsaturated free fatty acid (FFA), is a precursor of the eicosanoid prostaglandin E1 and is expected to be a source material for artificial production. We previously constructed the Aspergillus oryzae mutant strain ARA1 that produced free DGLA from the disruptant of faaA, an acyl-CoA synthetase gene, where FFA productivity increased by 9.2-fold compared with that of the wild-type strain. Here, we aimed to achieve enhancement of free DGLA productivity. Because saturated FFAs, such as palmitic acid and stearic acid, accounted for about 45% and 25% of total FFAs produced by ARA1, respectively, we used a strategy to facilitate elongation and desaturation of these FFAs to oleic acid and linoleic acid by overexpressing genes encoding elongase, Δ9-desaturase, and Δ12-desaturase originally expressed in A. oryzae. Ten genes were predicted to encode desaturases, and their overexpression DNA constructs were introduced into ARA1. AO090001000224 and AO090011000488 facilitated Δ12-desaturation and Δ9-desaturation most, respectively, following overexpression. Next, ARA1 strain was modified to DGLA1cre strain for producing free DGLA as a final product, and co-overexpression of these two desaturase genes was then introduced to DGLA1cre. Moreover, single overexpression of two genes predicted to encode elongases was additionally introduced, and only AO090003000572 facilitated elongation. Consequently, in the co-overexpression mutant of AO090001000224, AO090011000488, and AO090003000572, free DGLA content ratio increased by 1.8-fold from ARA1 to 14.5%, and the productivity also increased by 1.8-fold to 0.086 mmol/g-dry-cell-weight. The yield was 284 mg/L. These findings provided insights into strategies for improving microbial production of polyunsaturated FFAs.

Computation-Driven Analysis of Model Polyextremo-tolerant Fungus Exophiala dermatitidis: Defensive Pigment Metabolic Costs and Human Applications

The polyextremotolerant black yeast Exophiala dermatitidis is a tractable model system for investigation of adaptations that support growth under extreme conditions. Foremost among these adaptations are melanogenesis and carotenogenesis. A particularly important question is their metabolic production cost. However, investigation of this issue has been hindered by a relatively poor systems-level understanding of E. dermatitidis metabolism. To address this challenge, a genome-scale model (iEde2091) was developed. Using iEde2091, carotenoids were found to be more expensive to produce than melanins. Given their overlapping protective functions, this suggests that carotenoids have an underexplored yet important role in photo-protection. Furthermore, multiple defensive pigments with overlapping functions might allow E. dermatitidis to minimize cost. Because iEde2091 revealed that E. dermatitidis synthesizes the same melanins as humans and the active sites of the key tyrosinase enzyme are highly conserved this model may enable a broader understanding of melanin production across kingdoms.

Optimizing cultivation of Cordyceps militaris for fast growth and cordycepin overproduction using rational design of synthetic media

Cordyceps militaris is an entomopathogenic fungus which is often used in Asia as a traditional medicine developed from age-old wisdom. Presently, cordycepin from C. militaris is a great interest in medicinal applications. However, cellular growth of C. militaris and the association with cordycepin production remain poorly understood. To explore the metabolism of C. militaris as potential cell factories in medical and biotechnology applications, this study developed a high-quality genome-scale metabolic model of C. militaris, iNR1329, based on its genomic content and physiological data. The model included a total of 1329 genes, 1821 biochemical reactions, and 1171 metabolites among 4 different cellular compartments. Its in silico growth simulation results agreed well with experimental data on different carbon sources. iNR1329 was further used for optimizing the growth and cordycepin overproduction using a novel approach, POPCORN, for rational design of synthetic media. In addition to the high-quality GEM iNR1329, the presented POPCORN approach was successfully used to rationally design an optimal synthetic medium with C:N ratio of 8:1 for enhancing 3.5-fold increase in cordycepin production. This study thus provides a novel insight into C. militaris physiology and highlights a potential GEM-driven method for synthetic media design and metabolic engineering application. The iNR1329 and the POPCORN approach are available at the GitHub repository: https://github.com/sysbiomics/Cordyceps_militaris-GEM.

A new grid- and modularity-based layout algorithm for complex biological networks

The visualization of biological networks is critically important to aid researchers in understanding complex biological systems and arouses interest in designing efficient layout algorithms to draw biological networks according to their topology structures, especially for those networks with potential modules. The algorithms of grid layout series have an advantage in generating compact layouts with overlap-free nodes compared to force-directed; however, extant grid layout algorithms have difficulty in drawing modular networks and often generate layouts of high visual complexity when applied to networks with dense or clustered connectivity structure. To specifically assist the study of modular networks, we propose a grid- and modularity-based layout algorithm (GML) that consists of three stages: network preprocessing, module layout and grid optimization. The algorithm can draw complex biological networks with or without predefined modules based on the grid layout algorithm. It also outperforms other existing grid-based algorithms in the measurement of computation performance, ratio of edge-edge/node-edge crossings, relative edge lengths, and connectivity F-measures. GML helps users to gain insight into the network global characteristics through module layout, as well as to discern network details with grid optimization. GML has been developed as a VisANT plugin (https://hscz.github.io/Biological-Network-Visualization/) and is freely available to the research community.

Aspergillus fumigatus Challenged by Human Dendritic Cells: Metabolic and Regulatory Pathway Responses Testify a Tight Battle

Dendritic cells (DCs) are antigen presenting cells which serve as a passage between the innate and the acquired immunity. Aspergillosis is a major lethal condition in immunocompromised patients caused by the adaptable saprophytic fungus Aspergillus fumigatus. The healthy human immune system is capable to ward off A. fumigatus infections however immune-deficient patients are highly vulnerable to invasive aspergillosis. A. fumigatus can persist during infection due to its ability to survive the immune response of human DCs. Therefore, the study of the metabolism specific to the context of infection may allow us to gain insight into the adaptation strategies of both the pathogen and the immune cells. We established a metabolic model of A. fumigatus central metabolism during infection of DCs and calculated the metabolic pathway (elementary modes; EMs). Transcriptome data were used to identify pathways activated when A. fumigatus is challenged with DCs. In particular, amino acid metabolic pathways, alternative carbon metabolic pathways and stress regulating enzymes were found to be active. Metabolic flux modeling identified further active enzymes such as alcohol dehydrogenase, inositol oxygenase and GTP cyclohydrolase participating in different stress responses in A. fumigatus. These were further validated by qRT-PCR from RNA extracted under these different conditions. For DCs, we outlined the activation of metabolic pathways in response to the confrontation with A. fumigatus. We found the fatty acid metabolism plays a crucial role, along with other metabolic changes. The gene expression data and their analysis illuminate additional regulatory pathways activated in the DCs apart from interleukin regulation. In particular, Toll-like receptor signaling, NOD-like receptor signaling and RIG-I-like receptor signaling were active pathways. Moreover, we identified subnetworks and several novel key regulators such as UBC, EGFR, and CUL3 of DCs to be activated in response to A. fumigatus. In conclusion, we analyze the metabolic and regulatory responses of A. fumigatus and DCs when confronted with each other.

Dissecting metabolic behavior of lipid over-producing strain of Mucor circinelloides through genome-scale metabolic network and multi-level data integration

Lipid accumulation is an important cellular process of oleaginous microorganisms. To dissect metabolic behavior of oleaginous Zygomycetes, the lipid over-producing strain, Mucor circinelloides WJ11, was subjected for omics-scale analysis. The genome annotation was improved and used for construction of genome-scale metabolic network of WJ11 strain. Then, the quality of the metabolic network was enhanced by incorporating gene and protein expression data. In addition to the known oleaginous genes, our results showed a number of newly identified unique genes of WJ11 strain, which involved in central carbon metabolism, lipid, amino acid and nitrogen metabolisms. The systematic compilations indicated the additional metabolic routes with the involvement in supplying precursors (acetyl-CoA, NADPH and fatty acyl substrate) for fatty acid and lipid biosynthesis. Interestingly, amino acid metabolism played a substantial role in responsive mechanism of the fungal cells to nutrient imbalance circumstance through lipogenesis as the finding of reporter metabolites (l-methionine, l-glutamate, l-aspartate, l-asparagine and l-glutamine) at lipid-accumulating stage. The cooperative function of certain lipid-degrading enzymes at the particular growth stage was elucidated by integrating the metabolic networks with gene expression data. The unique feature of carotenoid biosynthetic route in WJ11 strain was also identified by protein domain analysis. Taken together, there were cross-functional metabolisms in regulating lipid biosynthesis and retaining high level of cellular lipids in the representative of lipid over-producing strains.

Current advance in biological production of malic acid using wild type and metabolic engineered strains

Malic acid (2-hydroxybutanedioic acid) is a four-carbon dicarboxylic acid, which has attracted great interest due to its wide usage as a precursor of many industrially important chemicals in the food, chemicals, and pharmaceutical industries. Several mature routes for malic acid production have been developed, such as chemical synthesis, enzymatic conversion and biological fermentation. With depletion of fossil fuels and concerns regarding environmental issues, biological production of malic acid has attracted more attention, which mainly consists of three pathways, namely non-oxidative pathway, oxidative pathway and glyoxylate cycle. In recent decades, metabolic engineering of model strains, and process optimization for malic acid production have been rapidly developed. Hence, this review comprehensively introduces an overview of malic acid producers and highlight some of the successful metabolic engineering approaches.

Lipid Metabolic Versatility in Malassezia spp. Yeasts Studied through Metabolic Modeling

Malassezia species are lipophilic and lipid-dependent yeasts belonging to the human and animal microbiota. Typically, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis, pityriasis versicolor, atopic dermatitis, and folliculitis. The genomes of Malassezia globosa, Malassezia sympodialis, and Malassezia pachydermatis lack the genes related to fatty acid synthesis. Here, the lipid-synthesis pathways of these species, as well as of Malassezia furfur, and of an atypical M. furfur variant were reconstructed using genome data and Constraints Based Reconstruction and Analysis. To this end, the genomes of M. furfur CBS 1878 and the atypical M. furfur 4DS were sequenced and annotated. The resulting Enzyme Commission numbers and predicted reactions were similar to the other Malassezia strains despite the differences in their genome size. Proteomic profiling was utilized to validate flux distributions. Flux differences were observed in the production of steroids in M. furfur and in the metabolism of butanoate in M. pachydermatis. The predictions obtained via these metabolic reconstructions also suggested defects in the assimilation of palmitic acid in M. globosa, M. sympodialis, M. pachydermatis, and the atypical variant of M. furfur, but not in M. furfur. These predictions were validated via physiological characterization, showing the predictive power of metabolic network reconstructions to provide new clues about the metabolic versatility of Malassezia.

Network reconstruction and systems analysis of plant cell wall deconstruction by Neurospora crassa

BACKGROUND

Plant biomass degradation by fungal-derived enzymes is rapidly expanding in economic importance as a clean and efficient source for biofuels. The ability to rationally engineer filamentous fungi would facilitate biotechnological applications for degradation of plant cell wall polysaccharides. However, incomplete knowledge of biomolecular networks responsible for plant cell wall deconstruction impedes experimental efforts in this direction.

RESULTS

To expand this knowledge base, a detailed network of reactions important for deconstruction of plant cell wall polysaccharides into simple sugars was constructed for the filamentous fungus Neurospora crassa. To reconstruct this network, information was integrated from five heterogeneous data types: functional genomics, transcriptomics, proteomics, genetics, and biochemical characterizations. The combined information was encapsulated into a feature matrix and the evidence weighted to assign annotation confidence scores for each gene within the network. Comparative analyses of RNA-seq and ChIP-seq data shed light on the regulation of the plant cell wall degradation network, leading to a novel hypothesis for degradation of the hemicellulose mannan. The transcription factor CLR-2 was subsequently experimentally shown to play a key role in the mannan degradation pathway of N. crassa.

CONCLUSIONS

Here we built a network that serves as a scaffold for integration of diverse experimental datasets. This approach led to the elucidation of regulatory design principles for plant cell wall deconstruction by filamentous fungi and a novel function for the transcription factor CLR-2. This expanding network will aid in efforts to rationally engineer industrially relevant hyper-production strains.

Whole-genome metabolic model of Trichoderma reesei built by comparative reconstruction

BACKGROUND

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.

RESULTS

A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.

CONCLUSIONS

The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes.

Further increased production of free fatty acids by overexpressing a predicted transketolase gene of the pentose phosphate pathway in Aspergillus oryzae faaA disruptant

Free fatty acids are useful as source materials for the production of biodiesel fuel and various chemicals such as pharmaceuticals and dietary supplements. Previously, we attained a 9.2-fold increase in free fatty acid productivity by disrupting a predicted acyl-CoA synthetase gene (faaA, AO090011000642) in Aspergillus oryzae. In this study, we achieved further increase in the productivity by overexpressing a predicted transketolase gene of the pentose phosphate pathway in the faaA disruptant. The A. oryzae genome is predicted to have three transketolase genes and overexpression of AO090023000345, one of the three genes, resulted in phenotypic change and further increase (corresponding to an increased production of 0.38 mmol/g dry cell weight) in free fatty acids at 1.4-fold compared to the faaA disruptant. Additionally, the biomass of hyphae increased at 1.2-fold by the overexpression. As a result, free fatty acid production yield per liter of liquid culture increased at 1.7-fold by the overexpression.

Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach

New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness ("hubs"), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.

Sequence- and Structure-Based Functional Annotation and Assessment of Metabolic Transporters in Aspergillus oryzae: A Representative Case Study

Aspergillus oryzae is widely used for the industrial production of enzymes. In A. oryzae metabolism, transporters appear to play crucial roles in controlling the flux of molecules for energy generation, nutrients delivery, and waste elimination in the cell. While the A. oryzae genome sequence is available, transporter annotation remains limited and thus the connectivity of metabolic networks is incomplete. In this study, we developed a metabolic annotation strategy to understand the relationship between the sequence, structure, and function for annotation of A. oryzae metabolic transporters. Sequence-based analysis with manual curation showed that 58 genes of 12,096 total genes in the A. oryzae genome encoded metabolic transporters. Under consensus integrative databases, 55 unambiguous metabolic transporter genes were distributed into channels and pores (7 genes), electrochemical potential-driven transporters (33 genes), and primary active transporters (15 genes). To reveal the transporter functional role, a combination of homology modeling and molecular dynamics simulation was implemented to assess the relationship between sequence to structure and structure to function. As in the energy metabolism of A. oryzae, the H⁺-ATPase encoded by the AO090005000842 gene was selected as a representative case study of multilevel linkage annotation. Our developed strategy can be used for enhancing metabolic network reconstruction.

Deciphering the signaling mechanisms of the plant cell wall degradation machinery in Aspergillus oryzae

Background

The gene expression and secretion of fungal lignocellulolytic enzymes are tightly controlled at the transcription level using independent mechanisms to respond to distinct inducers from plant biomass. An advanced systems-level understanding of transcriptional regulatory networks is required to rationally engineer filamentous fungi for more efficient bioconversion of different types of biomass.

Results

In this study we focused on ten chemically defined inducers to drive expression of cellulases, hemicellulases and accessory enzymes in the model filamentous fungus Aspergillus oryzae and shed light on the complex network of transcriptional activators required. The chemical diversity analysis of the inducers, based on 186 chemical descriptors calculated from the structure, resulted into three clusters, however, the global, metabolic and extracellular protein transcription of the A. oryzae genome were only partially explained by the chemical similarity of the enzyme inducers. Genes encoding enzymes that have attracted considerable interest such as cellobiose dehydrogenases and copper-dependent polysaccharide mono-oxygenases presented a substrate-specific induction. Several homology-model structures were derived using ab-initio multiple threading alignment in our effort to elucidate the interplay of transcription factors involved in regulating plant-deconstructing enzymes and metabolites. Systematic investigation of metabolite-protein interactions, using the 814 unique reactants involved in 2360 reactions in the genome scale metabolic network of A. oryzae, was performed through a two-step molecular docking against the binding pockets of the transcription factors AoXlnR and AoAmyR. A total of six metabolites viz., sulfite (H₂SO₃), sulfate (SLF), uroporphyrinogen III (UPGIII), ethanolamine phosphate (PETHM), D-glyceraldehyde 3-phosphate (T3P1) and taurine (TAUR) were found as strong binders, whereas the genes involved in the metabolic reactions that these metabolites appear were found to be significantly differentially expressed when comparing the inducers with glucose.

Conclusions

Based on our observations, we believe that specific binding of sulfite to the regulator of the cellulase gene expression, AoXlnR, may be the molecular basis for the connection of sulfur metabolism and cellulase gene expression in filamentous fungi. Further characterization and manipulation of the regulatory network components identified in this study, will enable rational engineering of industrial strains for improved production of the sophisticated set of enzymes necessary to break-down chemically divergent plant biomass.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0224-5) contains supplementary material, which is available to authorized users.

Survey of protein-DNA interactions in Aspergillus oryzae on a genomic scale

The genome-scale delineation of in vivo protein–DNA interactions is key to understanding genome function. Only ∼5% of transcription factors (TFs) in the Aspergillus genus have been identified using traditional methods. Although the Aspergillus oryzae genome contains >600 TFs, knowledge of the in vivo genome-wide TF-binding sites (TFBSs) in aspergilli remains limited because of the lack of high-quality antibodies. We investigated the landscape of in vivo protein–DNA interactions across the A. oryzae genome through coupling the DNase I digestion of intact nuclei with massively parallel sequencing and the analysis of cleavage patterns in protein–DNA interactions at single-nucleotide resolution. The resulting map identified overrepresented de novo TF-binding motifs from genomic footprints, and provided the detailed chromatin remodeling patterns and the distribution of digital footprints near transcription start sites. The TFBSs of 19 known Aspergillus TFs were also identified based on DNase I digestion data surrounding potential binding sites in conjunction with TF binding specificity information. We observed that the cleavage patterns of TFBSs were dependent on the orientation of TF motifs and independent of strand orientation, consistent with the DNA shape features of binding motifs with flanking sequences.

Current state of genome-scale modeling in filamentous fungi

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

The Cell Factory Aspergillus Enters the Big Data Era: Opportunities and Challenges for Optimising Product Formation

Living with limits. Getting more from less. Producing commodities and high-value products from renewable resources including waste. What is the driving force and quintessence of bioeconomy outlines the lifestyle and product portfolio of Aspergillus, a saprophytic genus, to which some of the top-performing microbial cell factories belong: Aspergillus niger, Aspergillus oryzae and Aspergillus terreus. What makes them so interesting for exploitation in biotechnology and how can they help us to address key challenges of the twenty-first century? How can these strains become trimmed for better growth on second-generation feedstocks and how can we enlarge their product portfolio by genetic and metabolic engineering to get more from less? On the other hand, what makes it so challenging to deduce biological meaning from the wealth of Aspergillus -omics data? And which hurdles hinder us to model and engineer industrial strains for higher productivity and better rheological performance under industrial cultivation conditions? In this review, we will address these issues by highlighting most recent findings from the Aspergillus research with a focus on fungal growth, physiology, morphology and product formation. Indeed, the last years brought us many surprising insights into model and industrial strains. They clearly told us that similar is not the same: there are different ways to make a hypha, there are more protein secretion routes than anticipated and there are different molecular and physical mechanisms which control polar growth and the development of hyphal networks. We will discuss new conceptual frameworks derived from these insights and the future scientific advances necessary to create value from Aspergillus Big Data.

Elucidation of primary metabolic pathways in Aspergillus species: orphaned research in characterizing orphan genes

Primary metabolism affects all phenotypical traits of filamentous fungi. Particular examples include reacting to extracellular stimuli, producing precursor molecules required for cell division and morphological changes as well as providing monomer building blocks for production of secondary metabolites and extracellular enzymes. In this review, all annotated genes from four Aspergillus species have been examined. In this process, it becomes evident that 80–96% of the genes (depending on the species) are still without verified function. A significant proportion of the genes with verified metabolic functions are assigned to secondary or extracellular metabolism, leaving only 2–4% of the annotated genes within primary metabolism. It is clear that primary metabolism has not received the same attention in the post-genomic area as many other research areas—despite its role at the very centre of cellular function. However, several methods can be employed to use the metabolic networks in tandem with comparative genomics to accelerate functional assignment of genes in primary metabolism. In particular, gaps in metabolic pathways can be used to assign functions to orphan genes. In this review, applications of this from the Aspergillus genes will be examined, and it is proposed that, where feasible, this should be a standard part of functional annotation of fungal genomes.

(Post-)genomics approaches in fungal research

To date, hundreds of fungal genomes have been sequenced and many more are in progress. This wealth of genomic information has provided new directions to study fungal biodiversity. However, to further dissect and understand the complicated biological mechanisms involved in fungal life styles, functional studies beyond genomes are required. Thanks to the developments of current -omics techniques, it is possible to produce large amounts of fungal functional data in a high-throughput fashion (e.g. transcriptome, proteome, etc.). The increasing ease of creating -omics data has also created a major challenge for downstream data handling and analysis. Numerous databases, tools and software have been created to meet this challenge. Facing such a richness of techniques and information, hereby we provide a brief roadmap on current wet-lab and bioinformatics approaches to study functional genomics in fungi.

MEMOSys 2.0: an update of the bioinformatics database for genome-scale models and genomic data

The MEtabolic MOdel research and development System (MEMOSys) is a versatile database for the management, storage and development of genome-scale models (GEMs). Since its initial release, the database has undergone major improvements, and the new version introduces several new features. First, the novel concept of derived models allows users to create model hierarchies that automatically propagate modifications along their order. Second, all stored components can now be easily enhanced with additional annotations that can be directly extracted from a supplied Systems Biology Markup Language (SBML) file. Third, the web application has been substantially revised and now features new query mechanisms, an easy search system for reactions and new link-out services to publicly available databases. Fourth, the updated database now contains 20 publicly available models, which can be easily exported into standardized formats for further analysis. Fifth, MEMOSys 2.0 is now also available as a fully configured virtual image and can be found online at http://www.icbi.at/memosys and http://memoys.i-med.ac.at. Database URL: http://memosys.i-med.ac.at.

Investigation of malic acid production in Aspergillus oryzae under nitrogen starvation conditions

Malic acid has great potential for replacing petrochemical building blocks in the future. For this application, high yields, rates, and titers are essential in order to sustain a viable biotechnological production process. Natural high-capacity malic acid producers like the malic acid producer Aspergillus flavus have so far been disqualified because of special growth requirements or the production of mycotoxins. As A. oryzae is a very close relative or even an ecotype of A. flavus, it is likely that its high malic acid production capabilities with a generally regarded as safe (GRAS) status may be combined with already existing large-scale fermentation experience. In order to verify the malic acid production potential, two wild-type strains, NRRL3485 and NRRL3488, were compared in shake flasks. As NRRL3488 showed a volumetric production rate twice as high as that of NRRL3485, this strain was selected for further investigation of the influence of two different nitrogen sources on malic acid secretion. The cultivation in lab-scale fermentors resulted in a higher final titer, 30.27 ± 1.05 g liter(-1), using peptone than the one of 22.27 ± 0.46 g liter(-1) obtained when ammonium was used. Through transcriptome analysis, a binding site similar to the one of the Saccharomyces cerevisiae yeast transcription factor Msn2/4 was identified in the upstream regions of glycolytic genes and the cytosolic malic acid production pathway from pyruvate via oxaloacetate to malate, which suggests that malic acid production is a stress response. Furthermore, the pyruvate carboxylase reaction was identified as a target for metabolic engineering, after it was confirmed to be transcriptionally regulated through the correlation of intracellular fluxes and transcriptional changes.

Characterization of a (D)-stereoselective aminopeptidase (DamA) exhibiting aminolytic activity and halophilicity from Aspergillus oryzae

β-Aminopeptidases exhibit both hydrolytic and aminolytic (peptide bond formation) activities and have only been reported in bacteria. We identified a gene encoding the β-aminopeptidase homolog from a genome database of the filamentous fungus Aspergillus oryzae. The gene was overexpressed in A. oryzae, and the resulting recombinant enzyme was purified. Apart from bacterial homologs [β-Ala-para-nitroanilide (pNA)], the enzyme preferred D-Leu-pNA and D-Phe-pNA as substrates. Therefore, we designated this gene as d-stereoselective aminopeptidase A (damA). The purified recombinant DamA was estimated to be a hexamer and was composed of two subunits with molecular masses of 29.5 and 11.5 kDa, respectively. Optimal hydrolytic activity of DamA toward D-Leu-pNA was observed at 50 °C and pH 8.0. The enzyme was stable up to 60 °C and from pH 4.0-11.0. DamA also exhibited aminolytic activity, producing D-Leu-D-Leu-NH2 from D-Leu-NH2 as a substrate. In the presence of 3.0 M NaCl, the amount of pNA liberated from D-Leu-pNA by DamA was 3.1-fold higher than that in the absence of NaCl. Thus, DamA is a halophilic enzyme. The enzyme was utilized to synthesize several hetero-dipeptides containing a D-amino acid at the N-terminus as well as physiologically active peptides.

Genome-scale analysis of the metabolic networks of oleaginous Zygomycete fungi

Microbial lipids are becoming an attractive option for the industrial production of foods and oleochemicals. To investigate the lipid physiology of the oleaginous microorganisms, at the system level, genome-scale metabolic networks of Mortierella alpina and Mucor circinelloides were constructed using bioinformatics and systems biology. As scaffolds for integrated data analysis focusing on lipid production, consensus metabolic routes governing fatty acid synthesis, and lipid storage and mobilisation were identified by comparative analysis of developed metabolic networks. Unique metabolic features were identified in individual fungi, particularly in NADPH metabolism and sterol biosynthesis, which might be related to differences in fungal lipid phenotypes. The frameworks detailing the metabolic relationship between M. alpina and M. circinelloides generated in this study is useful for further elucidation of the microbial oleaginicity, which might lead to the production improvement of microbial oils as alternative feedstocks for oleochemical industry.

The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production.

Genome-scale models (GEMs) are large stoichiometric models of cell metabolism, where the goal is to incorporate every metabolic transformation that an organism can perform. Such models have been extensively used for the study of bacterial metabolism, in particular for metabolic engineering purposes. More recently, the use of GEMs for eukaryotic organisms has become increasingly widespread. Since these models typically involve thousands of metabolic reactions, the reconstruction and validation of them can be a very complex task. We have developed a software suite, RAVEN Toolbox, which aims at automating parts of the reconstruction process in order to allow for faster reconstruction of high-quality GEMs. The software is particularly well suited for reconstruction of models for eukaryotic organisms, due to how it deals with sub-cellular localization of reactions. We used the software for reconstructing a model of the filamentous fungi Penicillium chrysogenum, the organism used in penicillin production and an important microbial cell factory. The resulting model was validated through an extensive literature survey and by comparison with published fermentation data. The model was used for the identification of transcriptionally regulated metabolic bottlenecks in order to increase the yield in penicillin fermentations. In this paper we present the RAVEN Toolbox and the GEM for P. chrysogenum.

Systematic characterization of hypothetical proteins in Synechocystis sp. PCC 6803 reveals proteins functionally relevant to stress responses

We described here a global detection and functional inference of hypothetical proteins involved in stress response in Synechocystis sp. PCC 6803. In the study, we first applied an iTRAQ-LC-MS/MS based quantitative proteomics to the Synechocystis cells grown under five stress conditions. The analysis detected a total of 807 hypothetical proteins with high confidence. Among them, 480 were differentially regulated. We then applied a Weighted Gene Co-expression Network Analysis approach to construct transcriptional networks for Synechocystis under nutrient limitation and osmotic stress conditions using transcriptome datasets. The analysis showed that 305 and 467 coding genes of hypothetical proteins were functionally relevant to nutrient limitation and osmotic stress, respectively. A comparison of responsive hypothetical proteins to all stress conditions allowed identification of 22 hypothetical proteins commonly responsive to all stresses, suggesting they may be part of the core stress responses in Synechocystis. Finally, functional inference of these core stress responsive proteins using both sequence similarity and non-similarity approaches was conducted. The study provided new insights into the stress response networks in Synechocystis, and also demonstrated that a combination of experimental "OMICS" and bioinformatics methodologies could improve functional annotation for hypothetical proteins.

Enhancement of crystallization with nucleotide ligands identified by dye-ligand affinity chromatography

Ligands interacting with Mycobacterium tuberculosis recombinant proteins were identified through use of the ability of Cibacron Blue F3GA dye to interact with nucleoside/nucleotide binding proteins, and the effects of these ligands on crystallization were examined. Co-crystallization with ligands enhanced crystallization and enabled X-ray diffraction data to be collected to a resolution of atleast 2.7 Å for 5 of 10 proteins tested. Additionally, clues about individual proteins’ functions were obtained from their interactions with each of a panel of ligands.

Synergistic use of plant-prokaryote comparative genomics for functional annotations

BACKGROUND

Identifying functions for all gene products in all sequenced organisms is a central challenge of the post-genomic era. However, at least 30-50% of the proteins encoded by any given genome are of unknown or vaguely known function, and a large number are wrongly annotated. Many of these 'unknown' proteins are common to prokaryotes and plants. We set out to predict and experimentally test the functions of such proteins. Our approach to functional prediction integrates comparative genomics based mainly on microbial genomes with functional genomic data from model microorganisms and post-genomic data from plants. This approach bridges the gap between automated homology-based annotations and the classical gene discovery efforts of experimentalists, and is more powerful than purely computational approaches to identifying gene-function associations.

RESULTS

Among Arabidopsis genes, we focused on those (2,325 in total) that (i) are unique or belong to families with no more than three members, (ii) occur in prokaryotes, and (iii) have unknown or poorly known functions. Computer-assisted selection of promising targets for deeper analysis was based on homology-independent characteristics associated in the SEED database with the prokaryotic members of each family. In-depth comparative genomic analysis was performed for 360 top candidate families. From this pool, 78 families were connected to general areas of metabolism and, of these families, specific functional predictions were made for 41. Twenty-one predicted functions have been experimentally tested or are currently under investigation by our group in at least one prokaryotic organism (nine of them have been validated, four invalidated, and eight are in progress). Ten additional predictions have been independently validated by other groups. Discovering the function of very widespread but hitherto enigmatic proteins such as the YrdC or YgfZ families illustrates the power of our approach.

CONCLUSIONS

Our approach correctly predicted functions for 19 uncharacterized protein families from plants and prokaryotes; none of these functions had previously been correctly predicted by computational methods. The resulting annotations could be propagated with confidence to over six thousand homologous proteins encoded in over 900 bacterial, archaeal, and eukaryotic genomes currently available in public databases.

Aspergillus as a multi-purpose cell factory: current status and perspectives

Aspergilli have a long history in biotechnology as expression platforms for the production of food ingredients, pharmaceuticals and enzymes. The achievements made during the last years, however, have the potential to revolutionize Aspergillus biotechnology and to assure Aspergillus a dominant place among microbial cell factories. This mini-review will highlight most recent breakthroughs in fundamental and applied Aspergillus research with a focus on new molecular tools, techniques and products. New trends and concepts related to Aspergillus genomics and systems biology will be discussed as well as the challenges that have to be met to integrate omics data with metabolic engineering attempts.

MEMOSys: Bioinformatics platform for genome-scale metabolic models

Background

Recent advances in genomic sequencing have enabled the use of genome sequencing in standard biological and biotechnological research projects. The challenge is how to integrate the large amount of data in order to gain novel biological insights. One way to leverage sequence data is to use genome-scale metabolic models. We have therefore designed and implemented a bioinformatics platform which supports the development of such metabolic models.

Results

MEMOSys (MEtabolic MOdel research and development System) is a versatile platform for the management, storage, and development of genome-scale metabolic models. It supports the development of new models by providing a built-in version control system which offers access to the complete developmental history. Moreover, the integrated web board, the authorization system, and the definition of user roles allow collaborations across departments and institutions. Research on existing models is facilitated by a search system, references to external databases, and a feature-rich comparison mechanism. MEMOSys provides customizable data exchange mechanisms using the SBML format to enable analysis in external tools. The web application is based on the Java EE framework and offers an intuitive user interface. It currently contains six annotated microbial metabolic models.

Conclusions

We have developed a web-based system designed to provide researchers a novel application facilitating the management and development of metabolic models. The system is freely available at http://www.icbi.at/MEMOSys.

Integrated analysis of the global transcriptional response to α-amylase over-production by Aspergillus oryzae

The filamentous fungus Aspergillus oryzae is an important microbial cell factory for industrial production of many enzymes, such as α-amylase. In order to optimize the industrial enzyme production process, there is a need to understand fundamental processes underlying enzyme production, here under how enzyme production links to metabolism through global regulation. Through a genome-scale metabolic network for integrated analysis of transcriptome data and flux calculation, we identified key players (genes, enzymes, proteins, and metabolites) involved in the processes of enzyme synthesis and secretion, nucleotide metabolism, and amino acid metabolism that can be the potential targets for improving industrial enzyme production.