Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models.

Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis.

COBREXA.jl: constraint-based reconstruction and exascale analysis.

SUMMARY: COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models. AVAILABILITY AND IMPLEMENTATION: https://doi.org/10.17881/ZKCR-BT30. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The view of microbes as energy converters illustrates the trade-off between growth rate and yield.

The application of thermodynamics to microbial growth has a long tradition that originated in the middle of the 20th century. This approach reflects the view that self-replication is a thermodynamic process that is not fundamentally different from mechanical thermodynamics. The key distinction is that a free energy gradient is not converted into mechanical (or any other form of) energy but rather into new biomass. As such, microbes can be viewed as energy converters that convert a part of the energy contained in environmental nutrients into chemical energy that drives self-replication. Before the advent of high-throughput sequencing technologies, only the most central metabolic pathways were known. However, precise measurement techniques allowed for the quantification of exchanged extracellular nutrients and heat of growing microbes with their environment. These data, together with the absence of knowledge of metabolic details, drove the development of so-called black-box models, which only consider the observable interactions of a cell with its environment and neglect all details of how exactly inputs are converted into outputs. Now, genome sequencing and genome-scale metabolic models (GEMs) provide us with unprecedented detail about metabolic processes inside the cell. However, mostly due to computational complexity issues, the derived modelling approaches make surprisingly little use of thermodynamic concepts. Here, we review classical black-box models and modern approaches that integrate thermodynamics into GEMs. We also illustrate how the description of microbial growth as an energy converter can help to understand and quantify the trade-off between microbial growth rate and yield.

Experimentally Validated Reconstruction and Analysis of a Genome-Scale Metabolic Model of an Anaerobic Neocallimastigomycota Fungus.

Anaerobic gut fungi in the phylum Neocallimastigomycota typically inhabit the digestive tracts of large mammalian herbivores, where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. However, quantitative tools to study their physiology are lacking, partially due to their complex and unresolved metabolism that includes the largely uncharacterized fungal hydrogenosome. Modern omics approaches combined with metabolic modeling can be used to establish an understanding of gut fungal metabolism and develop targeted engineering strategies to harness their degradation capabilities for lignocellulosic bioprocessing. Here, we introduce a high-quality genome of the anaerobic fungus Neocallimastix lanati from which we constructed the first genome-scale metabolic model of an anaerobic fungus. Relative to its size (200 Mbp, sequenced at 62× depth), it is the least fragmented publicly available gut fungal genome to date. Of the 1,788 lignocellulolytic enzymes annotated in the genome, 585 are associated with the fungal cellulosome, underscoring the powerful lignocellulolytic potential of N. lanati The genome-scale metabolic model captures the primary metabolism of N. lanati and accurately predicts experimentally validated substrate utilization requirements. Additionally, metabolic flux predictions are verified by 13C metabolic flux analysis, demonstrating that the model faithfully describes the underlying fungal metabolism. Furthermore, the model clarifies key aspects of the hydrogenosomal metabolism and can be used as a platform to quantitatively study these biotechnologically important yet poorly understood early-branching fungi.IMPORTANCE Recent genomic analyses have revealed that anaerobic gut fungi possess both the largest number and highest diversity of lignocellulolytic enzymes of all sequenced fungi, explaining their ability to decompose lignocellulosic substrates, e.g., agricultural waste, into fermentable sugars. Despite their potential, the development of engineering methods for these organisms has been slow due to their complex life cycle, understudied metabolism, and challenging anaerobic culture requirements. Currently, there is no framework that can be used to combine multi-omic data sets to understand their physiology. Here, we introduce a high-quality PacBio-sequenced genome of the anaerobic gut fungus Neocallimastix lanati Beyond identifying a trove of lignocellulolytic enzymes, we use this genome to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model is experimentally validated and sheds light on unresolved metabolic features common to gut fungi. Model-guided analysis will pave the way for deepening our understanding of anaerobic gut fungi and provides a systematic framework to guide strain engineering efforts of these organisms for biotechnological use.

Genomic and functional analyses of fungal and bacterial consortia that enable lignocellulose breakdown in goat gut microbiomes.

The herbivore digestive tract is home to a complex community of anaerobic microbes that work together to break down lignocellulose. These microbiota are an untapped resource of strains, pathways and enzymes that could be applied to convert plant waste into sugar substrates for green biotechnology. We carried out more than 400 parallel enrichment experiments from goat faeces to determine how substrate and antibiotic selection influence membership, activity, stability and chemical productivity of herbivore gut communities. We assembled 719 high-quality metagenome-assembled genomes (MAGs) that are unique at the species level. More than 90% of these MAGs are from previously unidentified herbivore gut microorganisms. Microbial consortia dominated by anaerobic fungi outperformed bacterially dominated consortia in terms of both methane production and extent of cellulose degradation, which indicates that fungi have an important role in methane release. Metabolic pathway reconstructions from MAGs of 737 bacteria, archaea and fungi suggest that cross-domain partnerships between fungi and methanogens enabled production of acetate, formate and methane, whereas bacterially dominated consortia mainly produced short-chain fatty acids, including propionate and butyrate. Analyses of carbohydrate-active enzyme domains present in each anaerobic consortium suggest that anaerobic bacteria and fungi employ mostly complementary hydrolytic strategies. The division of labour among herbivore anaerobes to degrade plant biomass could be harnessed for industrial bioprocessing.

Genomic and proteomic biases inform metabolic engineering strategies for anaerobic fungi.

Anaerobic fungi (Neocallimastigomycota) are emerging non-model hosts for biotechnology due to their wealth of biomass-degrading enzymes, yet tools to engineer these fungi have not yet been established. Here, we show that the anaerobic gut fungi have the most GC depleted genomes among 443 sequenced organisms in the fungal kingdom, which has ramifications for heterologous expression of genes as well as for emerging CRISPR-based genome engineering approaches. Comparative genomic analyses suggest that anaerobic fungi may contain cellular machinery to aid in sexual reproduction, yet a complete mating pathway was not identified. Predicted proteomes of the anaerobic fungi also contain an unusually large fraction of proteins with homopolymeric amino acid runs consisting of five or more identical consecutive amino acids. In particular, threonine runs are especially enriched in anaerobic fungal carbohydrate active enzymes (CAZymes) and this, together with a high abundance of predicted N-glycosylation motifs, suggests that gut fungal CAZymes are heavily glycosylated, which may impact heterologous production of these biotechnologically useful enzymes. Finally, we present a codon optimization strategy to aid in the development of genetic engineering tools tailored to these early-branching anaerobic fungi.

Top-Down Enrichment Guides in Formation of Synthetic Microbial Consortia for Biomass Degradation.

Consortium-based approaches are a promising avenue toward efficient bioprocessing. However, many complex microbial interactions dictate community dynamics and stability that must be replicated in synthetic systems. The rumen and/or hindguts of large mammalian herbivores harbor complex communities of biomass-degrading fungi and bacteria, as well as archaea and protozoa that work collectively to degrade lignocellulose, yet the microbial interactions responsible for stability, resilience, and activity of the community remain largely uncharacterized. In this work, we demonstrate a "top-down" enrichment-based methodology for selecting a minimal but effective lignocellulose-degrading community that produces methane-rich fermentation gas (biogas). The resulting enrichment consortium produced 0.75-1.9-fold more fermentation gas at 1.4-2.1 times the rate compared to a monoculture of fungi from the enrichment. Metagenomic sequencing of the top-down enriched consortium revealed genomes encoding for functional compartmentalization of the community, spread across an anaerobic fungus (Piromyces), a bacterium (Sphaerochaeta), and two methanogenic archaea (Methanosphaera and Methanocorpusculum). Guided by the composition of the top-down enrichment, several synthetic cocultures were formed from the "bottom-up" using previously isolated fungi, Neocallimastix californiae and Anaeromyces robustus paired with the methanogen Methanobacterium bryantii. While cross-feeding occurred in synthetic co-cultures, removal of fungal metabolites by methanogens did not increase the rate of gas production or the rate of substrate deconstruction by the synthetic community relative to fungal monocultures. Metabolomic characterization verified that syntrophy was established within synthetic co-cultures, which generated methane at similar concentrations compared to the enriched consortium but lacked the temporal stability (resilience) seen in the native system. Taken together, deciphering the membership and metabolic potential of an enriched gut consortium enables the design of methanogenic synthetic co-cultures. However, differences in the growth rate and stability of enriched versus synthetic consortia underscore the difficulties in mimicking naturally occurring syntrophy in synthetic systems.

Metabolic characterization of anaerobic fungi provides a path forward for bioprocessing of crude lignocellulose.

The conversion of lignocellulose-rich biomass to bio-based chemicals and higher order fuels remains a grand challenge, as single-microbe approaches often cannot drive both deconstruction and chemical production steps. In contrast, consortia based bioprocessing leverages the strengths of different microbes to distribute metabolic loads and achieve process synergy, product diversity, and bolster yields. Here, we describe a biphasic fermentation scheme that combines the lignocellulolytic action of anaerobic fungi isolated from large herbivores with domesticated microbes for bioproduction. When grown in batch culture, anaerobic fungi release excess sugars from both cellulose and crude biomass due to a wealth of highly expressed carbohydrate active enzymes (CAZymes), converting as much as 49% of cellulose to free glucose. This sugar-rich hydrolysate readily supports growth of Saccharomyces cerevisiae, which can be engineered to produce a range of value-added chemicals. Further, construction of metabolic pathways from transcriptomic data reveals that anaerobic fungi do not catabolize all sugars that their enzymes hydrolyze from biomass, leaving other carbohydrates such as galactose, arabinose, and mannose available as nutritional links to other microbes in their consortium. Although basal expression of CAZymes in anaerobic fungi is high, it is drastically amplified by cellobiose breakout products encountered during biomass hydrolysis. Overall, these results suggest that anaerobic fungi provide a nutritional benefit to the rumen microbiome, which can be harnessed to design synthetic microbial communities that compartmentalize biomass degradation and bioproduct formation.

The importance of sourcing enzymes from non-conventional fungi for metabolic engineering and biomass breakdown.

A wealth of fungal enzymes has been identified from nature, which continue to drive strain engineering and bioprocessing for a range of industries. However, while a number of clades have been investigated, the vast majority of the fungal kingdom remains unexplored for industrial applications. Here, we discuss selected classes of fungal enzymes that are currently in biotechnological use, and explore more basal, non-conventional fungi and their underexploited biomass-degrading mechanisms as promising agents in the transition towards a bio-based society. Of special interest are anaerobic fungi like the Neocallimastigomycota, which were recently found to harbor the largest diversity of biomass-degrading enzymes among the fungal kingdom. Enzymes sourced from these basal fungi have been used to metabolically engineer substrate utilization in yeast, and may offer new paths to lignin breakdown and tunneled biocatalysis. We also contrast classic enzymology approaches with emerging 'omics'-based tools to decipher function within novel fungal isolates and identify new promising enzymes. Recent developments in genome editing are expected to accelerate discovery and metabolic engineering within these systems, yet are still limited by a lack of high-resolution genomes, gene regulatory regions, and even appropriate culture conditions. Finally, we present new opportunities to harness the biomass-degrading potential of undercharacterized fungi via heterologous expression and engineered microbial consortia.