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.

Crossing and selection of Chlamydomonas reinhardtii strains for biotechnological glycolate production

Abstract

As an alternative to chemical building blocks derived from algal biomass, the excretion of glycolate has been proposed. This process has been observed in green algae such as Chlamydomonas reinhardtii as a product of the photorespiratory pathway. Photorespiration generally occurs at low CO₂ and high O₂ concentrations, through the key enzyme RubisCO initiating the pathway via oxygenation of 1.5-ribulose-bisphosphate. In wild-type strains, photorespiration is usually suppressed in favour of carboxylation due to the cellular carbon concentrating mechanisms (CCMs) controlling the internal CO₂ concentration. Additionally, newly produced glycolate is directly metabolized in the C2 cycle. Therefore, both the CCMs and the C2 cycle are the key elements which limit the glycolate production in wild-type cells. Using conventional crossing techniques, we have developed Chlamydomonas reinhardtii double mutants deficient in these two key pathways to direct carbon flux to glycolate excretion. Under aeration with ambient air, the double mutant D6 showed a significant and stable glycolate production when compared to the non-producing wild type. Interestingly, this mutant can act as a carbon sink by fixing atmospheric CO₂ into glycolate without requiring any additional CO₂ supply. Thus, the double-mutant strain D6 can be used as a photocatalyst to produce chemical building blocks and as a future platform for algal-based biotechnology.

Key Points

• Chlamydomonas reinhardtii cia5 gyd double mutants were developed by sexual crossing

• The double mutation eliminates the need for an inhibitor in glycolate production

• The strain D6 produces significant amounts of glycolate with ambient air only

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-022-11933-y.

You get what you screen for: on the value of fermentation characterization in high-throughput strain improvements in industrial settings

While design and high-throughput build approaches in biotechnology have increasingly gained attention over the past decade, approaches to test strain performance in high-throughput have received less discussion in the literature. Here, we describe how fermentation characterization can be used to improve the overall efficiency of high-throughput DBTAL (design-build-test-analyze-learn) cycles in an industrial context. Fermentation characterization comprises an in-depth study of strain performance in a bioreactor setting and involves semi-frequent sampling and analytical measurement of substrates, cell densities and viabilities, and (by)products. We describe how fermentation characterization can be used to (1) improve (high-throughput) strain design approaches; (2) enable the development of bench-scale fermentation processes compatible with a wide diversity of strains; and (3) inform the development of high-throughput plate-based strain testing procedures for improved performance at larger scales.

Molecular profiling of endophytic Streptomyces cavourensis MH16 inhabiting Millingtonia hortensis Linn. and influence of different culture media on biosynthesis of antimicrobial metabolites

Endophytic actinomycetes, a prolific source of natural products, are well known for their diverse metabolic versatility, and their association with medicinal plants and antimicrobial potential are well worth exploring. We isolated and identified the Streptomyces cavourensis strain MH16 inhabiting the tree Millingtonia hortensis Linn. using phylogenetic analysis based on a 16S rRNA molecular approach. We used the disc diffusion method to evaluate the impact of differences in the compositions of the media on the production of secondary metabolites from strain MH16. The production of antimicrobial metabolites was determined by the observation of inhibition zones on intensive bands when using a TLC-bioautography assay. Biosynthesis of secondary metabolites was optimal when the strain MH16 was cultured in ISP-2 medium as depicted by a zone of inhibition. Strain MH16 effectively inhibited methicillin-resistant Staphylococcus aureus, Escherichia coli, Candida albicans, and other multi drug-resistant pathogens. The minimum inhibitory concentration of the antimicrobial metabolites was 25-100 μg mL^-1. The study manifests the optimization and utilization of different fermentation media which best suits for increased production of the secondary metabolites from Streptomyces cavourensis. This research suggests that the antimicrobial metabolites of strain MH16 found in M. hortensis has great potential for the biodiscovery of new anti-infective drugs against a wide range of multidrug-resistant pathogens.

Gas Chromatography-Mass Spectrometry Microbial Metabolomics for Applications in Strain Optimization

Metabolomics is the systems-scale measurement of biochemical intermediates in biological systems; by virtue of its deep and broad study of metabolism, it has great potential for applications in metabolic engineering. While a number of the analytical techniques used widely in metabolomics are familiar to metabolic engineers performing post hoc analyses of product titers, the requirements for accurately capturing metabolism at a systems scale rather than just measuring a single secreted product are much more complicated. Nonetheless, metabolomics (which is still not widely available as an affordable consumer service like many molecular biology services) is within reach of many properly equipped metabolic engineering groups. To this end, we present a detailed metabolomics protocol with application to strain optimization. Specifically, we focus on characterizing metabolism in the yeast Saccharomyces cerevisiae using gas chromatography coupled to mass spectrometry. The measurement of metabolic intermediates that results from such approaches has the potential to enable more informed and rational efforts towards pathway engineering and strain optimization.

Targeted Mass Spectrometry-Based Proteomics Tools for Strain Optimization

The goal of strain optimization is to create high-performance strains producing compounds of interest at a high titer, yield, and volumetric productivity. The effectiveness of strain optimization relies on methodologies used to aid optimization of native or novel pathways within cells. Many different factors, including mRNA abundance, protein abundance, and enzyme activity/stability, will contribute to the strain performance, which is not often evident by simply monitoring product titers. To this end, targeted proteomics tools utilizing LC-MS-MS (liquid chromatography coupled with tandem mass spectrometry) have recently been developed and can monitor protein levels at great sensitivities. Here, we describe all relevant aspects when developing proteomics tools for strain optimization.

Robust mutant strain design by pessimistic optimization

Background

Flux Balance Analysis (FBA) based mathematical modeling enables in silico prediction of systems behavior for genome-scale metabolic networks. Computational methods have been derived in the FBA framework to solve bi-level optimization for deriving “optimal” mutant microbial strains with targeted biochemical overproduction. The common inherent assumption of these methods is that the surviving mutants will always cooperate with the engineering objective by overproducing the maximum desired biochemicals. However, it has been shown that this optimistic assumption may not be valid in practice.

Methods

We study the validity and robustness of existing bi-level methods for strain optimization under uncertainty and non-cooperative environment. More importantly, we propose new pessimistic optimization formulations: P-ROOM and P-OptKnock, aiming to derive robust mutants with the desired overproduction under two different mutant cell survival models: (1) ROOM assuming mutants have the minimum changes in reaction fluxes from wild-type flux values, and (2) the one considered by OptKnock maximizing the biomass production yield. When optimizing for desired overproduction, our pessimistic formulations derive more robust mutant strains by considering the uncertainty of the cell survival models at the inner level and the cooperation between the outer- and inner-level decision makers. For both P-ROOM and P-OptKnock, by converting multi-level formulations into single-level Mixed Integer Programming (MIP) problems based on the strong duality theorem, we can derive exact optimal solutions that are highly scalable with large networks.

Results

Our robust formulations P-ROOM and P-OptKnock are tested with a small E. coli core metabolic network and a large-scale E. coli iAF1260 network. We demonstrate that the original bi-level formulations (ROOM and OptKnock) derive mutants that may not achieve the predicted overproduction under uncertainty and non-cooperative environment. The knockouts obtained by the proposed pessimistic formulations yield higher chemical production rates than those by the optimistic formulations. Moreover, with higher uncertainty levels, both cellular models under pessimistic approaches produce the same mutant strains.

Conclusions

In this paper, we propose a new pessimistic optimization framework for mutant strain design. Our pessimistic strain optimization methods produce more robust solutions regardless of the inner-level mutant survival models, which is desired as the models for cell survival are often approximate to real-world systems. Such robust and reliable knockout strategies obtained by the pessimistic formulations would provide confidence for in-vivo experimental design of microbial mutants of interest.

An evolutionary optimization of a rhodopsin-based phototrophic metabolism in Escherichia coli

Background

The expression of the Gloeobacter rhodopsin (GR) in a chemotrophic Escherichia coli enables the light-driven phototrophic energy generation. Adaptive laboratory evolution has been used for acquiring desired phenotype of microbial cells and for the elucidation of basic mechanism of molecular evolution. To develop an optimized strain for the artificially acquired phototrophic metabolism, an ancestral E. coli expressing GR was adaptively evolved in a chemostat reactor with constant illumination and limited glucose conditions. This study was emphasized at an unexpected genomic mutation contributed to the improvement of microbial performance.

Results

During the chemostat culture, increase of cell size was observed, which were distinguished from that of the typical rod-shaped ancestral cells. A descendant ET5 strain was randomly isolated from the chemostat culture at 88-days. The phototrophic growth and the light-induced proton pumping of the ET5 strain were twofold and eightfold greater, respectively, than those of the ancestral E. coli strain. Single point mutation of C1082A at dgcQ gene (encoding diguanylate cyclase, also known as the yedQ gene) in the chromosome of ET5 strain was identified from whole genome sequencing analysis. An ancestral E. coli complemented with the same dgcQ mutation from the ET5 was repeated the subsequently enhancements of light-driven phototrophic growth and proton pumping. Intracellular c-di-GMP, the product of the diguanylate cyclase (dgcQ), of the descendant ET5 strain was suddenly increased while that of the ancestral strain was negligible.

Conclusions

Newly acquired phototrophic metabolism of E. coli was further improved via adaptive laboratory evolution by the rise of a point mutation on a transmembrane cell signaling protein followed by increase of signal molecule that eventually led an increase proton pumping and phototrophic growth.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-017-0725-6) contains supplementary material, which is available to authorized users.

Development of a Framework for Metabolic Pathway Analysis-Driven Strain Optimization Methods

Genome-scale metabolic models (GSMMs) have become important assets for rational design of compound overproduction using microbial cell factories. Most computational strain optimization methods (CSOM) using GSMMs, while useful in metabolic engineering, rely on the definition of questionable cell objectives, leading to some bias. Metabolic pathway analysis approaches do not require an objective function. Though their use brings immediate advantages, it has mostly been restricted to small scale models due to computational demands. Additionally, their complex parameterization and lack of intuitive tools pose an important challenge towards making these widely available to the community. Recently, MCSEnumerator has extended the scale of these methods, namely regarding enumeration of minimal cut sets, now able to handle GSMMs. This work proposes a tool implementing this method as a Java library and a plugin within the OptFlux metabolic engineering platform providing a friendly user interface. A standard enumeration problem and pipeline applicable to GSMMs is proposed, making use by the community simpler. To highlight the potential of these approaches, we devised a case study for overproduction of succinate, providing a phenotype analysis of a selected strategy and comparing robustness with a selected solution from a bi-level CSOM.

Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization

BACKGROUND

Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives.

RESULTS

To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock.

CONCLUSIONS

PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.

Designing minimal microbial strains of desired functionality using a genetic algorithm

Background

The rational, in silico prediction of gene-knockouts to turn organisms into efficient cell factories is an essential and computationally challenging task in metabolic engineering. Elementary flux mode analysis in combination with constraint minimal cut sets is a particularly powerful method to identify optimal engineering targets, which will force an organism into the desired metabolic state. Given an engineering objective, it is theoretically possible, although computationally impractical, to find the best minimal intervention strategies.

Results

We developed a genetic algorithm (GA-MCS) to quickly find many (near) optimal intervention strategies while overcoming the above mentioned computational burden. We tested our algorithm on Escherichia coli metabolic networks of three different sizes to find intervention strategies satisfying three different engineering objectives.

Conclusions

We show that GA-MCS finds all practically relevant targets for any (non)-linear engineering objective. Our algorithm also found solutions comparable to previously published results. We show that for large networks optimal solutions are found within a fraction of the time used for a complete enumeration.

Protein secretion biotechnology in Gram-positive bacteria with special emphasis on Streptomyces lividans

Proteins secreted by Gram-positive bacteria are released into the culture medium with the obvious benefit that they usually retain their native conformation. This property makes these host cells potentially interesting for the production of recombinant proteins, as one can take full profit of established protocols for the purification of active proteins. Several state-of-the-art strategies to increase the yield of the secreted proteins will be discussed, using Streptomyces lividans as an example and compared with approaches used in some other host cells. It will be shown that approaches such as increasing expression and translation levels, choice of secretion pathway and modulation of proteins thereof, avoiding stress responses by changing expression levels of specific (stress) proteins, can be helpful to boost production yield. In addition, the potential of multi-omics approaches as a tool to understand the genetic background and metabolic fluxes in the host cell and to seek for new targets for strain and protein secretion improvement is discussed. It will be shown that S. lividans, along with other Gram-positive host cells, certainly plays a role as a production host for recombinant proteins in an economically viable way. This article is part of a Special Issue entitled: Protein trafficking and secretion in bacteria. Guest Editors: Anastassios Economou and Ross Dalbey.

Elementary flux modes in a nutshell: properties, calculation and applications

Elementary flux mode (EFM) analysis allows the unbiased decomposition of a metabolic network into minimal functional units, making it a powerful tool for metabolic engineering. While the use of EFM analysis (EFMA) is still limited by the size of the models it can handle, EFMA has been successfully applied to solve real-world metabolic engineering problems. Here we provide a user-oriented introduction to EFMA, provide examples of recent applications, analyze current research strategies to overcome the computational restrictions and give an overview over current approaches, which aim to identify and calculate only biologically relevant EFMs.

Production of bulk chemicals via novel metabolic pathways in microorganisms

Metabolic engineering has been playing important roles in developing high performance microorganisms capable of producing various chemicals and materials from renewable biomass in a sustainable manner. Synthetic and systems biology are also contributing significantly to the creation of novel pathways and the whole cell-wide optimization of metabolic performance, respectively. In order to expand the spectrum of chemicals that can be produced biotechnologically, it is necessary to broaden the metabolic capacities of microorganisms. Expanding the metabolic pathways for biosynthesizing the target chemicals requires not only the enumeration of a series of known enzymes, but also the identification of biochemical gaps whose corresponding enzymes might not actually exist in nature; this issue is the focus of this paper. First, pathway prediction tools, effectively combining reactions that lead to the production of a target chemical, are analyzed in terms of logics representing chemical information, and designing and ranking the proposed metabolic pathways. Then, several approaches for potentially filling in the gaps of the novel metabolic pathway are suggested along with relevant examples, including the use of promiscuous enzymes that flexibly utilize different substrates, design of novel enzymes for non-natural reactions, and exploration of hypothetical proteins. Finally, strain optimization by systems metabolic engineering in the context of novel metabolic pathways constructed is briefly described. It is hoped that this review paper will provide logical ways of efficiently utilizing 'big' biological data to design and develop novel metabolic pathways for the production of various bulk chemicals that are currently produced from fossil resources.