Current state of genome-scale modeling in filamentous fungi

Genome-Scale Model of Rhizopus microsporus: Metabolic integration of a fungal holobiont with its bacterial and viral endosymbionts

Rhizopus microsporus often lives in association with bacterial and viral symbionts that alter its biology. This fungal model represents an example of the complex interactions established among diverse organisms in functional holobionts. We constructed a Genome-Scale Model (GSM) of the fungal-bacterial-viral holobiont (iHol). We employed a constraint-based method to calculate the metabolic fluxes to decipher the metabolic interactions of the symbionts with their host. Our computational analyses of iHol simulate the holobiont's growth and the production of the toxin rhizoxin. Analyses of the calculated fluxes between R. microsporus in symbiotic (iHol) versus asymbiotic conditions suggest that changes in the lipid and nucleotide metabolism of the host are necessary for the functionality of the holobiont. Glycerol plays a pivotal role in the fungal-bacterial metabolic interaction, as its production does not compromise fungal growth, and Mycetohabitans bacteria can efficiently consume it. Narnavirus RmNV-20S and RmNV-23S affected the nucleotide metabolism without impacting the fungal-bacterial symbiosis. Our analyses highlighted the metabolic stability of Mycetohabitans throughout its co-evolution with the fungal host. We also predicted changes in reactions of the bacterial metabolism required for the active production of rhizoxin. This iHol is the first GSM of a fungal holobiont.

Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability

Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.

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.

Towards a Systems Biology Approach to Understanding the Lichen Symbiosis: Opportunities and Challenges of Implementing Network Modelling

Lichen associations, a classic model for successful and sustainable interactions between micro-organisms, have been studied for many years. However, there are significant gaps in our understanding about how the lichen symbiosis operates at the molecular level. This review addresses opportunities for expanding current knowledge on signalling and metabolic interplays in the lichen symbiosis using the tools and approaches of systems biology, particularly network modelling. The largely unexplored nature of symbiont recognition and metabolic interdependency in lichens could benefit from applying a holistic approach to understand underlying molecular mechanisms and processes. Together with ‘omics’ approaches, the application of signalling and metabolic network modelling could provide predictive means to gain insights into lichen signalling and metabolic pathways. First, we review the major signalling and recognition modalities in the lichen symbioses studied to date, and then describe how modelling signalling networks could enhance our understanding of symbiont recognition, particularly leveraging omics techniques. Next, we highlight the current state of knowledge on lichen metabolism. We also discuss metabolic network modelling as a tool to simulate flux distribution in lichen metabolic pathways and to analyse the co-dependence between symbionts. This is especially important given the growing number of lichen genomes now available and improved computational tools for reconstructing such models. We highlight the benefits and possible bottlenecks for implementing different types of network models as applied to the study of lichens.

WY195, a New Inducible Promoter From the Rubber Powdery Mildew Pathogen, Can Be Used as an Excellent Tool for Genetic Engineering

Until now, there are few studies and reports on the use of endogenous promoters of obligate biotrophic fungi. The WY195 promoter in the genome of Oidium heveae, the rubber powdery mildew pathogen, was predicted using PromoterScan and its promoter function was verified by the transient expression of the β-glucuronidase (GUS) gene. WY195 drove high levels of GUS expression in dicotyledons and monocotyledons. qRT-PCR indicated that GUS expression regulated by the WY195 promoter was 17.54-fold greater than that obtained using the CaMV 35S promoter in dicotyledons (Nicotiana tabacum), and 5.09-fold greater than that obtained using the ACT1 promoter in monocotyledons (Oryza sativa). Furthermore, WY195-regulated GUS gene expression was induced under high-temperature and drought conditions. Soluble proteins extracted from WY195-hpaXm transgenic tobacco was bioactive. Defensive micro-HR induced by the transgene expression of hpaXm was observed on transgenic tobacco leaves. Disease resistance bioassays showed that WY195-hpaXm transgenic tobacco enhanced the resistance to tobacco mosaic virus (TMV). WY195 has great potential for development as a new tool for genetic engineering. Further in-depth studies will help to better understand the transcriptional regulation mechanisms and the pathogenic mechanisms of O. heveae.

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.

Extended Utilization of Constraint-Based Metabolic Model in a Long-Growing Crop

The constraint-based rMeCBM-KU50 model of cassava storage root growth was analyzed to evaluate its sensitivity, with respect to reaction flux distribution and storage root growth rate, to changes in model inputted data and constraints, including sucrose uptake rate-related data—photosynthetic rate, total leaf area, total photosynthetic rate, storage root dry weight, and biomass function-related data. These mainly varied within ±90% of the model default values, although exceptions were made for the carbohydrate (−90% to 8%) and starch (−90% to 9%) contents. The results indicated that the predicted storage root growth rate was highly affected by specific sucrose uptake rates through the total photosynthetic rate and storage root dry weight variations; whereas the carbon flux distribution, direction and partitioning inclusive, was more sensitive to the variation in biomass content, particularly the carbohydrate content. This study showed that the specific sucrose uptake rate based on the total photosynthetic rate, storage root dry weight, and carbohydrate content were critical to the constraint-based metabolic modeling and deepened our understanding of the input–output relationship—specifically regarding the rMeCBM-KU50 model—providing a valuable platform for the modeling of plant metabolic systems, especially long-growing crops.

Intercellular cooperation in a fungal plant pathogen facilitates host colonization

Cooperation is associated with major transitions in evolution such as the emergence of multicellularity. It is central to the evolution of many complex traits in nature, including growth and virulence in pathogenic bacteria. Whether cells of multicellular parasites function cooperatively during infection remains, however, largely unknown. Here, we show that hyphal cells of the fungal pathogen Sclerotinia sclerotiorum reprogram toward division of labor to facilitate the colonization of host plants. Using global transcriptome sequencing, we reveal that gene expression patterns diverge markedly in cells at the center and apex of hyphae during Arabidopsis thaliana colonization compared with in vitro growth. We reconstructed a genome-scale metabolic model for S. sclerotiorum and used flux balance analysis to demonstrate metabolic heterogeneity supporting division of labor between hyphal cells. Accordingly, continuity between the central and apical compartments of invasive hyphae was required for optimal growth in planta Using a multicell model of fungal hyphae, we show that this cooperative functioning enhances fungal growth predominantly during host colonization. Our work identifies cooperation in fungal hyphae as a mechanism emerging at the multicellular level to support host colonization and virulence.

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

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

A community-driven reconstruction of the Aspergillus niger metabolic network

Background

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

Results

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

Conclusion

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

Electronic supplementary material

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

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.

Old Yellow Enzyme homologues in Mucor circinelloides: expression profile and biotransformation

The reduction of C=C double bond, a key reaction in organic synthesis, is mostly achieved by traditional chemical methods. Therefore, the search for enzymes capable of performing this reaction is rapidly increasing. Old Yellow Enzymes (OYEs) are flavin-dependent oxidoreductases, initially isolated from Saccharomyces pastorianus. In this study, the presence and activation of putative OYE enzymes was investigated in the filamentous fungus Mucor circinelloides, which was previously found to mediate C=C reduction. Following an in silico approach, using S. pastorianus OYE1 amminoacidic sequence as template, ten putative genes were identified in the genome of M. circinelloides. A phylogenetic analysis revealed a high homology of McOYE1-9 with OYE1-like proteins while McOYE10 showed similarity with thermophilic-like OYEs. The activation of mcoyes was evaluated during the transformation of three different model substrates. Cyclohexenone, α-methylcinnamaldehyde and methyl cinnamate were completely reduced in few hours and the induction of gene expression, assessed by qRT-PCR, was generally fast, suggesting a substrate-dependent activation. Eight genes were activated in the tested conditions suggesting that they may encode for active OYEs. Their expression over time correlated with C=C double bond reduction.

Development of fungal cell factories for the production of secondary metabolites: Linking genomics and metabolism

The genomic era has revolutionized research on secondary metabolites and bioinformatics methods have in recent years revived the antibiotic discovery process after decades with only few new active molecules being identified. New computational tools are driven by genomics and metabolomics analysis, and enables rapid identification of novel secondary metabolites. To translate this increased discovery rate into industrial exploitation, it is necessary to integrate secondary metabolite pathways in the metabolic engineering process. In this review, we will describe the novel advances in discovery of secondary metabolites produced by filamentous fungi, highlight the utilization of genome-scale metabolic models (GEMs) in the design of fungal cell factories for the production of secondary metabolites and review strategies for optimizing secondary metabolite production through the construction of high yielding platform cell factories.

Microarray: gateway to unravel the mystery of abiotic stresses in plants

Environmental factors, such as drought, salinity, extreme temperature, ozone poisoning, metal toxicity etc., significantly affect crops. To study these factors and to design a possible remedy, biological experimental data concerning these crops requires the quantification of gene expression and comparative analyses at high throughput level. Development of microarrays is the platform to study the differential expression profiling of the targeted genes. This technology can be applied to gene expression studies, ranging from individual genes to whole genome level. It is now possible to perform the quantification of the differential expression of genes on a glass slide in a single experiment. This review documents recently published reports on the use of microarrays for the identification of genes in different plant species playing their role in different cellular networks under abiotic stresses. The regulation pattern of differentially-expressed genes, individually or in group form, may help us to study different pathways and functions at the cellular and molecular level. These studies can provide us with a lot of useful information to unravel the mystery of abiotic stresses in important crop plants.