Development of a Genome-Scale Metabolic Model of Clostridium thermocellum and Its Applications for Integration of Multi-Omics Datasets and Computational Strain Design

Adaptive evolution of Clostridium thermocellum ATCC 27405 on alternate carbon sources leads to altered fermentation profiles

Consolidated bioprocessing candidate, Clostridium thermocellum, is a cellulose hydrolysis specialist, with the ability to ferment the released sugars to produce bioethanol. C. thermocellum is generally studied with model substrates Avicel and cellobiose to understand the metabolic pathway leading to ethanol. In the present study, adaptive laboratory evolution, allowing C. thermocellum DSM 1237 to adapt to growth on glucose, fructose, and sorbitol, with the prospect that some strains will adapt their metabolism to yield more ethanol. Adaptive growth on glucose and sorbitol resulted in an approximately 1 mM and 2 mM increase in ethanol yield per millimolar glucose equivalent, respectively, accompanied by a shift in the production of the other expected fermentation end products. The increase in ethanol yield observed for sorbitol adapted cells was due to the carbon source being more reduced compared to cellobiose. Glucose and cellobiose have similar oxidation states thus the increase in ethanol yield is due to the rerouting of electrons from other reduced metabolic products excluding H2 which did not decrease in yield. There was no increase in ethanol yield observed for fructose adapted cells, but there was an unanticipated elimination of formate production, also observed in sorbitol adapted cells suggesting that fructose has regulatory implications on formate production either at the transcription or protein level.

Engineering agricultural soil microbiomes and predicting plant phenotypes

Plant growth-promoting rhizobacteria (PGPR) can improve crop yields, nutrient use efficiency, plant tolerance to stressors, and confer benefits to future generations of crops grown in the same soil. Unlocking the potential of microbial communities in the rhizosphere and endosphere is therefore of great interest for sustainable agriculture advancements. Before plant microbiomes can be engineered to confer desirable phenotypic effects on their plant hosts, a deeper understanding of the interacting factors influencing rhizosphere community structure and function is needed. Dealing with this complexity is becoming more feasible using computational approaches. In this review, we discuss recent advances at the intersection of experimental and computational strategies for the investigation of plant-microbiome interactions and the engineering of desirable soil microbiomes.

Toward implementation of carbon-conservation networks in nonmodel organisms

Decarboxylation - the release of carbon dioxide (CO₂) from a substrate - reduces the carbon yield of bioproduced chemicals. When overlaid onto central carbon metabolism, carbon-conservation networks (CCNs) that reroute flux around CO₂ release can theoretically achieve higher carbon yields for products derived from intermediates that traditionally require CO₂ release, such as acetyl-CoA. Recently, CCNs have started to be implemented in model organisms to produce compounds at higher carbon yields. However, implementation of CCNs in nonmodel hosts may have the greatest impact given their ability to assimilate a larger array of feedstocks, greater environmental tolerance, and unique biosynthetic pathways, ultimately enabling access to a wider range of products. Here, we review recent advances in CCNs with a focus on their application to nonmodel organisms. The differences in central carbon metabolism among different nonmodel hosts reveal opportunities to engineer and apply new CCNs.

The Roles of Nicotinamide Adenine Dinucleotide Phosphate Reoxidation and Ammonium Assimilation in the Secretion of Amino Acids as Byproducts of Clostridium thermocellum

Clostridium thermocellum is a cellulolytic thermophile that is considered for the consolidated bioprocessing of lignocellulose to ethanol. Improvements in ethanol yield are required for industrial implementation, but the incompletely understood causes of amino acid secretion impede progress. In this study, amino acid secretion was investigated via gene deletions in ammonium-regulated, nicotinamide adenine dinucleotide phosphate (NADPH)-supplying and NADPH-consuming pathways as well as via physiological characterization in cellobiose-limited or ammonium-limited chemostats. First, the contribution of the NADPH-supplying malate shunt was studied with strains using either the NADPH-yielding malate shunt (Δppdk) or a redox-independent conversion of PEP to pyruvate (Δppdk ΔmalE::P_eno-pyk). In the latter, branched-chain amino acids, especially valine, were significantly reduced, whereas the ethanol yield increased from 46 to 60%, suggesting that the secretion of these amino acids balances the NADPH surplus from the malate shunt. The unchanged amino acid secretion in Δppdk falsified a previous hypothesis on an ammonium-regulated PEP-to-pyruvate flux redistribution. The possible involvement of another NADPH-supplier, namely, NADH-dependent reduced ferredoxin:NADP⁺ oxidoreductase (nfnAB), was also excluded. Finally, the deletion of glutamate synthase (gogat) in ammonium assimilation resulted in the upregulation of NADPH-linked glutamate dehydrogenase activity and decreased amino acid yields. Since gogat in C. thermocellum is putatively annotated as ferredoxin-linked, a claim which is supported by the product redistribution observed in this study, this deletion likely replaced ferredoxin with NADPH in ammonium assimilation. Overall, these findings indicate that a need to reoxidize NADPH is driving the observed amino acid secretion, likely at the expense of the NADH needed for ethanol formation. This suggests that metabolic engineering strategies that simplify the redox metabolism and ammonium assimilation can contribute to increased ethanol yields. IMPORTANCE Improving the ethanol yield of C. thermocellum is important for the industrial implementation of this microorganism in consolidated bioprocessing. A central role of NADPH in driving amino acid byproduct formation was demonstrated by eliminating the NADPH-supplying malate shunt and separately by changing the cofactor specificity in ammonium assimilation. With amino acid secretion diverting carbon and electrons away from ethanol, these insights are important for further metabolic engineering to reach industrial requirements on ethanol yield. This study also provides chemostat data that are relevant for training genome-scale metabolic models and for improving the validity of their predictions, especially considering the reduced degree-of-freedom in the redox metabolism of the strains generated here. In addition, this study advances the fundamental understanding on the mechanisms underlying amino acid secretion in cellulolytic Clostridia as well as on the regulation and cofactor specificity in ammonium assimilation. Together, these efforts aid in the development of C. thermocellum for the sustainable consolidated bioprocessing of lignocellulose to ethanol with minimal pretreatment.

Bottom-up synthetic ecology study of microbial consortia to enhance lignocellulose bioconversion

Lignocellulose is the most abundant organic carbon polymer on the earth. Its decomposition and conversion greatly impact the global carbon cycle. Furthermore, it provides feedstock for sustainable fuel and other value-added products. However, it continues to be underutilized, due to its highly recalcitrant and heterogeneric structure. Microorganisms, which have evolved versatile pathways to convert lignocellulose, undoubtedly are at the heart of lignocellulose conversion. Numerous studies that have reported successful metabolic engineering of individual strains to improve biological lignin valorization. Meanwhile, the bottleneck of single strain modification is becoming increasingly urgent in the conversion of complex substrates. Alternatively, increased attention has been paid to microbial consortia, as they show advantages over pure cultures, e.g., high efficiency and robustness. Here, we first review recent developments in microbial communities for lignocellulose bioconversion. Furthermore, the emerging area of synthetic ecology, which is an integration of synthetic biology, ecology, and computational biology, provides an opportunity for the bottom-up construction of microbial consortia. Then, we review different modes of microbial interaction and their molecular mechanisms, and discuss considerations of how to employ these interactions to construct synthetic consortia via synthetic ecology, as well as highlight emerging trends in engineering microbial communities for lignocellulose bioconversion.

Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model

Clostridium thermocellum is a promising candidate for consolidated bioprocessing because it can directly ferment cellulose to ethanol. Despite significant efforts, achieved yields and titers fall below industrially relevant targets. This implies that there still exist unknown enzymatic, regulatory, and/or possibly thermodynamic bottlenecks that can throttle back metabolic flow. By (i) elucidating internal metabolic fluxes in wild-type C. thermocellum grown on cellobiose via ¹³C-metabolic flux analysis (¹³C-MFA), (ii) parameterizing a core kinetic model, and (iii) subsequently deploying an ensemble-docking workflow for discovering substrate-level regulations, this paper aims to reveal some of these factors and expand our knowledgebase governing C. thermocellum metabolism. Generated ¹³C labeling data were used with ¹³C-MFA to generate a wild-type flux distribution for the metabolic network. Notably, flux elucidation through MFA alluded to serine generation via the mercaptopyruvate pathway. Using the elucidated flux distributions in conjunction with batch fermentation process yield data for various mutant strains, we constructed a kinetic model of C. thermocellum core metabolism (i.e. k-ctherm138). Subsequently, we used the parameterized kinetic model to explore the effect of removing substrate-level regulations on ethanol yield and titer. Upon exploring all possible simultaneous (up to four) regulation removals we identified combinations that lead to many-fold model predicted improvement in ethanol titer. In addition, by coupling a systematic method for identifying putative competitive inhibitory mechanisms using K-FIT kinetic parameterization with the ensemble-docking workflow, we flagged 67 putative substrate-level inhibition mechanisms across central carbon metabolism supported by both kinetic formalism and docking analysis.

Inspecting the Solution Space of Genome-Scale Metabolic Models

Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models by using, e.g., Flux Balance Analysis (FBA) poses a problem, since it is hard to thoroughly investigate and often only an arbitrarily selected individual flux distribution is discussed as an outcome of FBA. Here, we introduce a new approach to inspect the solution space and we compare it with other approaches, namely Flux Variability Analysis (FVA) and CoPE-FBA, using several different genome-scale models of lactic acid bacteria. We examine the extent to which different types of experimental data limit the solution space and how the robustness of the system increases as a result. We find that our new approach to inspect the solution space is a good complementary method that offers additional insights into the variance of biological phenotypes and can help to prevent wrong conclusions in the analysis of FBA results.

Functional analysis of H+-pumping membrane-bound pyrophosphatase, ADP-glucose synthase, and pyruvate phosphate dikinase as pyrophosphate sources in Clostridium thermocellum

The atypical glycolysis of Clostridium thermocellum is characterized by the use of pyrophosphate (PP_i) as phosphoryl donor for phosphofructokinase (Pfk) and pyruvate phosphate dikinase (Ppdk) reactions. Previously, biosynthetic PP_i was calculated to be stoichiometrically insufficient to drive glycolysis. This study investigates the role of a H⁺-pumping membrane-bound pyrophosphatase, glycogen cycling, a predicted Ppdk-malate shunt cycle and acetate cycling in generating PP_i. Knockout studies and enzyme assays confirmed that clo1313_0823 encodes a membrane-bound pyrophosphatase. Additionally, clo1313_0717-0718 was confirmed to encode ADP-glucose synthase by knockouts, glycogen measurements in C. thermocellum and heterologous expression in E. coli. Unexpectedly, individually-targeted gene deletions of the four putative PP_i sources did not have a significant phenotypic effect. Although combinatorial deletion of all four putative PP_i sources reduced the growth rate by 22% (0.30±0.01 h^-1) and the biomass yield by 38% (0.18±0.00 g_biomass g_substrate^-1), this change was much smaller than what would be expected for stoichiometrically essential PP_i-supplying mechanisms. Growth-arrested cells of the quadruple knockout readily fermented cellobiose indicating that the unknown PP_i-supplying mechanisms are independent of biosynthesis. An alternative hypothesis that ATP-dependent Pfk activity circumvents a need for PP_i altogether, was falsified by enzyme assays, heterologous expression of candidate genes and whole-genome sequencing. As a secondary outcome, enzymatic assays confirmed functional annotation of clo1313_1832 as ATP- and GTP-dependent fructokinase. These results indicate that the four investigated PP_i sources individually and combined play no significant PP_i-supplying role and the true source(s) of PP_i, or alternative phosphorylating mechanisms, that drive glycolysis in C. thermocellum remain(s) elusive. IMPORTANCE Increased understanding of the central metabolism of C. thermocellum is important from a fundamental as well as from a sustainability and industrial perspective. In addition to showing that H⁺-pumping membrane-bound PPase, glycogen cycling, a Ppdk-malate shunt cycle, and acetate cycling are not significant sources of PP_i supply, this study adds functional annotation of four genes and availability of an updated PP_i stoichiometry from biosynthesis to the scientific domain. Together, this aids future metabolic engineering attempts aimed to improve C. thermocellum as a cell factory for sustainable and efficient production of ethanol from lignocellulosic material through consolidated bioprocessing with minimal pretreatment. Getting closer to elucidating the elusive source of PP_i, or alternative phosphorylating mechanisms, for the atypical glycolysis is itself of fundamental importance. Additionally, the findings of this study directly contribute to investigations into trade-offs between thermodynamic driving force versus energy yield of PP_i- and ATP-dependent glycolysis.

Genome-Wide Transcription Factor DNA Binding Sites and Gene Regulatory Networks in Clostridium thermocellum

Clostridium thermocellum is a thermophilic bacterium recognized for its natural ability to effectively deconstruct cellulosic biomass. While there is a large body of studies on the genetic engineering of this bacterium and its physiology to-date, there is limited knowledge in the transcriptional regulation in this organism and thermophilic bacteria in general. The study herein is the first report of a large-scale application of DNA-affinity purification sequencing (DAP-seq) to transcription factors (TFs) from a bacterium. We applied DAP-seq to > 90 TFs in C. thermocellum and detected genome-wide binding sites for 11 of them. We then compiled and aligned DNA binding sequences from these TFs to deduce the primary DNA-binding sequence motifs for each TF. These binding motifs are further validated with electrophoretic mobility shift assay (EMSA) and are used to identify individual TFs’ regulatory targets in C. thermocellum. Our results led to the discovery of novel, uncharacterized TFs as well as homologues of previously studied TFs including RexA-, LexA-, and LacI-type TFs. We then used these data to reconstruct gene regulatory networks for the 11 TFs individually, which resulted in a global network encompassing the TFs with some interconnections. As gene regulation governs and constrains how bacteria behave, our findings shed light on the roles of TFs delineated by their regulons, and potentially provides a means to enable rational, advanced genetic engineering of C. thermocellum and other organisms alike toward a desired phenotype.

Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with all of 161 production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Overall, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.

Integration of fluxome and transcriptome data in Saccharomyces cerevisiae offers unique features of doxorubicin and imatinib

Improving the efficacy of drugs and developing new drugs are required to compensate for drug resistance. Therefore, it is critical to unveil the mode of action, which can be studied through the cellular response at genome-scale, of the existing drugs. Here, system-level response of Saccharomyces cerevisiae, a eukaryotic model microorganism, to two chemotherapy drugs doxorubicin and imatinib used against cancer are analysed. While doxorubicin is mainly known to interact with DNA through intercalation and imatinib is known to inhibit the activity of the tyrosine kinase enzyme, the exact mechanisms of action for both drugs have not been determined. The response of S. cerevisiae cells to long-term stress by these drugs under controlled aerobic conditions was investigated and analyzed by the genome-wide transcriptome and genome-wide fluxes. The classification of adverse and similar responses of a certain gene at a transcriptional versus flux level indicated the possible regulatory mechanisms under these different stress conditions. Most of the biochemical reactions were found to be regulated at a post-transcriptional or metabolic level, whereas fewer were regulated at a transcriptional level for both stress cases. Furthermore, disparately induced and repressed pathways in the metabolic network under doxorubicin and imatinib stress were identified. The glycolytic and pentose phosphate pathways responded similarly, whereas the purine-histidine metabolic pathways responded differently. Then, a comparison of differential fluxes and differentially co-expressed genes under doxorubicin and imatinib stress provided the potential common and unique features of these drugs. Analyzing such regulatory differences helps in resolving drug mechanisms and suggesting new drug targets.

Genome-Scale Metabolic Model of Caldicellulosiruptor bescii Reveals Optimal Metabolic Engineering Strategies for Bio-based Chemical Production

Metabolic modeling was used to examine potential bottlenecks that could be encountered for metabolic engineering of the cellulolytic extreme thermophile Caldicellulosiruptor bescii to produce bio-based chemicals from plant biomass. The model utilizes subsystems-based genome annotation, targeted reconstruction of carbohydrate utilization pathways, and biochemical and physiological experimental validations. Specifically, carbohydrate transport and utilization pathways involving 160 genes and their corresponding functions were incorporated, representing the utilization of C5/C6 monosaccharides, disaccharides, and polysaccharides such as cellulose and xylan. To illustrate its utility, the model predicted that optimal production from biomass-based sugars of the model product, ethanol, was driven by ATP production, redox balancing, and proton translocation, mediated through the interplay of an ATP synthase, a membrane-bound hydrogenase, a bifurcating hydrogenase, and a bifurcating NAD- and NADP-dependent oxidoreductase. These mechanistic insights guided the design and optimization of new engineering strategies for product optimization, which were subsequently tested in the C. bescii model, showing a nearly 2-fold increase in ethanol yields. The C. bescii model provides a useful platform for investigating the potential redox controls that mediate the carbon and energy flows in metabolism and sets the stage for future design of engineering strategies aiming at optimizing the production of ethanol and other bio-based chemicals. IMPORTANCE The extremely thermophilic cellulolytic bacterium, Caldicellulosiruptor bescii, degrades plant biomass at high temperatures without any pretreatments and can serve as a strategic platform for industrial applications. The metabolic engineering of C. bescii, however, faces potential bottlenecks in bio-based chemical productions. By simulating the optimal ethanol production, a complex interplay between redox balancing and the carbon and energy flow was revealed using a C. bescii genome-scale metabolic model. New engineering strategies were designed based on an improved mechanistic understanding of the C. bescii metabolism, and the new designs were modeled under different genetic backgrounds to identify optimal strategies. The C. bescii model provided useful insights into the metabolic controls of this organism thereby opening up prospects for optimizing production of a wide range of bio-based chemicals.

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.