Characterizing acetogenic metabolism using a genome-scale metabolic reconstruction of Clostridium ljungdahlii

Flux balance analysis of metabolic networks for efficient engineering of microbial cell factories

Metabolic engineering principles have long been applied to explore the metabolic networks of complex microbial cell factories under a variety of environmental constraints for effective deployment of the microorganisms in the optimal production of biochemicals like biofuels, polymers, amino acids, recombinant proteins. One of the methodologies used for analyzing microbial metabolic networks is the Flux Balance Analysis (FBA), which employs applications of optimization techniques for forecasting biomass growth and metabolic flux distribution of industrially important products under specified environmental conditions. The in silico flux simulations are instrumental for designing the production-specific microbial cell factories. However, FBA has some inherent limitations. The present review emphasizes how the incorporation of additional kinetic, thermodynamic, expression and regulatory constraints and integration of omics data into the classical FBA platform improve the prediction accuracy of FBA. A programmed comparison of the simulated data with the experimental observations is presented for supporting the claim. The review further accounts for the successful implementation of classical FBA in biotechnological applications and identifies areas in which classical FBA fails to make correct predictions. The analysis of the predictive capabilities of the different FBA strategies presented here is expected to help researchers in finding new avenues in engineering highly efficient microbial metabolic pathways and identify the key metabolic bottlenecks during the process. Based on the appropriate metabolic network design, fermentation engineers will be able to effectively design the bioreactors and optimize large-scale biochemical production through suitable pathway modifications.

Integration of proteomic data with genome-scale metabolic models: A methodological overview

The integration of proteomics data with constraint-based reconstruction and analysis (COBRA) models plays a pivotal role in understanding the relationship between genotype and phenotype and bridges the gap between genome-level phenomena and functional adaptations. Integrating a generic genome-scale model with information on proteins enables generation of a context-specific metabolic model which improves the accuracy of model prediction. This review explores methodologies for incorporating proteomics data into genome-scale models. Available methods are grouped into four distinct categories based on their approach to integrate proteomics data and their depth of modeling. Within each category section various methods are introduced in chronological order of publication demonstrating the progress of this field. Furthermore, challenges and potential solutions to further progress are outlined, including the limited availability of appropriate in vitro data, experimental enzyme turnover rates, and the trade-off between model accuracy, computational tractability, and data scarcity. In conclusion, methods employing simpler approaches demand fewer kinetic and omics data, consequently leading to a less complex mathematical problem and reduced computational expenses. On the other hand, approaches that delve deeper into cellular mechanisms and aim to create detailed mathematical models necessitate more extensive kinetic and omics data, resulting in a more complex and computationally demanding problem. However, in some cases, this increased cost can be justified by the potential for more precise predictions.

Systems-Level Modeling for CRISPR-Based Metabolic Engineering

The CRISPR-Cas system has enabled the development of sophisticated, multigene metabolic engineering programs through the use of guide RNA-directed activation or repression of target genes. To optimize biosynthetic pathways in microbial systems, we need improved models to inform design and implementation of transcriptional programs. Recent progress has resulted in new modeling approaches for identifying gene targets and predicting the efficacy of guide RNA targeting. Genome-scale and flux balance models have successfully been applied to identify targets for improving biosynthetic production yields using combinatorial CRISPR-interference (CRISPRi) programs. The advent of new approaches for tunable and dynamic CRISPR activation (CRISPRa) promises to further advance these engineering capabilities. Once appropriate targets are identified, guide RNA prediction models can lead to increased efficacy in gene targeting. Developing improved models and incorporating approaches from machine learning may be able to overcome current limitations and greatly expand the capabilities of CRISPR-Cas9 tools for metabolic engineering.

Engineered acetogenic bacteria as microbial cell factory for diversified biochemicals

Acetogenic bacteria (acetogens) are a class of microorganisms with conserved Wood-Ljungdahl pathway that can utilize CO and CO2/H2 as carbon source for autotrophic growth and convert these substrates to acetate and ethanol. Acetogens have great potential for the sustainable production of biofuels and bulk biochemicals using C1 gases (CO and CO2) from industrial syngas and waste gases, which play an important role in achieving carbon neutrality. In recent years, with the development and improvement of gene editing methods, the metabolic engineering of acetogens is making rapid progress. With introduction of heterogeneous metabolic pathways, acetogens can improve the production capacity of native products or obtain the ability to synthesize non-native products. This paper reviews the recent application of metabolic engineering in acetogens. In addition, the challenges of metabolic engineering in acetogens are indicated, and strategies to address these challenges are also discussed.

Probing efficient microbial CO2 utilisation through metabolic and process modelling

Acetogenic gas fermentation is increasingly studied as a promising technology to upcycle carbon-rich waste gasses. Currently the product range is limited, and production yields, rates and titres for a number of interesting products do not allow for economically viable processes. By pairing process modelling and host-agnostic metabolic modelling, we compare fermentation conditions and various products to optimise the processes. The models were then used in a simulation of an industrial-scale bubble column reactor. We find that increased temperatures favour gas transfer rates, particularly for the valuable and limiting H2 , while furthermore predicting an optimal feed composition of 9:1 mol H2 to mol CO2 . Metabolically, the increased non-growth associated maintenance requirements of thermophiles favours the formation of catabolic products. To assess the expansion of the product portfolio beyond acetate, both a product volatility analysis and a metabolic pathway model were implemented. In-situ recovery of volatile products is shown to be within range for acetone but challenging due to the extensive evaporation of water, while the direct production of more valuable compounds by acetogens is metabolically unfavourable compared to acetate and ethanol. We discuss alternative approaches to overcome these challenges to utilise acetogenic CO2 fixation to produce a wider range of carbon negative chemicals.

Microbial electrosynthesis with Clostridium ljungdahlii benefits from hydrogen electron mediation and permits a greater variety of products

Microbial electrosynthesis (MES) is a very promising technology addressing the challenge of carbon dioxide recycling into organic compounds, which might serve as building blocks for the (bio)chemical industry. However, poor process control and understanding of fundamental aspects such as the microbial extracellular electron transfer (EET) currently limit further developments. In the model acetogen Clostridium ljungdahlii, both direct and indirect electron consumption via hydrogen have been proposed. However, without clarification neither targeted development of the microbial catalyst nor process engineering of MES are possible. In this study, cathodic hydrogen is demonstrated to be the dominating electron source for C. ljungdahlii at electroautotrophic MES allowing for superior growth and biosynthesis, compared to previously reported MES using pure cultures. Hydrogen availability distinctly controlled an either planktonic- or biofilm-dominated lifestyle of C. ljungdahlii. The most robust operation yielded higher planktonic cell densities in a hydrogen mediated process, which demonstrated the uncoupling of growth and biofilm formation. This coincided with an increase of metabolic activity, acetate titers, and production rates (up to 6.06 g L^-1 at 0.11 g L^-1 d^-1). For the first time, MES using C. ljungdahlii was also revealed to deliver other products than acetate in significant amounts: here up to 0.39 g L^-1 glycine or 0.14 g L^-1 ethanolamine. Hence, a deeper comprehension of the electrophysiology of C. ljungdahlii was shown to be key for designing and improving bioprocess strategies in MES research.

A detailed genome-scale metabolic model of Clostridium thermocellum investigates sources of pyrophosphate for driving glycolysis

Lignocellulosic biomass is an abundant and renewable source of carbon for chemical manufacturing, yet it is cumbersome in conventional processes. A promising, and increasingly studied, candidate for lignocellulose bioprocessing is the thermophilic anaerobe Clostridium thermocellum given its potential to produce ethanol, organic acids, and hydrogen gas from lignocellulosic biomass under high substrate loading. Possessing an atypical glycolytic pathway which substitutes GTP or pyrophosphate (PP_i) for ATP in some steps, including in the energy-investment phase, identification, and manipulation of PP_i sources are key to engineering its metabolism. Previous efforts to identify the primary pyrophosphate have been unsuccessful. Here, we explore pyrophosphate metabolism through reconstructing, updating, and analyzing a new genome-scale stoichiometric model for C. thermocellum, iCTH669. Hundreds of changes to the former GEM, iCBI655, including correcting cofactor usages, addressing charge and elemental balance, standardizing biomass composition, and incorporating the latest experimental evidence led to a MEMOTE score improvement to 94%. We found agreement of iCTH669 model predictions across all available fermentation and biomass yield datasets. The feasibility of hundreds of PP_i synthesis routes, newly identified and previously proposed, were assessed through the lens of the iCTH669 model including biomass synthesis, tRNA synthesis, newly identified sources, and previously proposed PP_i-generating cycles. In all cases, the metabolic cost of PP_i synthesis is at best equivalent to investment of one ATP suggesting no direct energetic advantage for the cofactor substitution in C. thermocellum. Even though no unique source of PP_i could be gleaned by the model, by combining with gene expression data two most likely scenarios emerge. First, previously investigated PP_i sources likely account for most PP_i production in wild-type strains. Second, alternate metabolic routes as encoded by iCTH669 can collectively maintain PP_i levels even when previously investigated synthesis cycles are disrupted. Model iCTH669 is available at github.com/maranasgroup/iCTH669.

Embracing a low-carbon future by the production and marketing of C1 gas protein

Food scarcity and environmental deterioration are two major problems that human populations currently face. Fortunately, the disruptive innovation of raw food materials has been stimulated by the rapid evolution of biomanufacturing. Therefore, it is expected that the new trends in technology will not only alter the natural resource-dependent food production systems and the traditional way of life but also reduce and assimilate the greenhouse gases released into the atmosphere. This review article summarizes the metabolic pathways associated with C1 gas conversion and the production of single-cell protein for animal feed. Moreover, the protein function, worldwide authorization, market access, and methods to overcome challenges in C1 gas assimilation microbial cell factory construction are also provided. With widespread attention and increasing policy support, the production of C1 gas protein will bring more opportunities and make tremendous contributions to our sustainable future.

Model-based driving mechanism analysis for butyric acid production in Clostridium tyrobutyricum

Background

Butyric acid, an essential C4 platform chemical, is widely used in food, pharmaceutical, and animal feed industries. Clostridium tyrobutyricum is the most promising microorganism for industrial bio-butyrate production. However, the metabolic driving mechanism for butyrate synthesis was still not profoundly studied.

Results

This study reports a first-generation genome-scale model (GEM) for C. tyrobutyricum, which provides a comprehensive and systematic analysis for the butyrate synthesis driving mechanisms. Based on the analysis in silico, an energy conversion system, which couples the proton efflux with butyryl-CoA transformation by two redox loops of ferredoxin, could be the main driving force for butyrate synthesis. For verifying the driving mechanism, a hydrogenase (HydA) expression was perturbed by inducible regulation and knockout. The results showed that HydA deficiency significantly improved the intracellular NADH/NAD+ rate, decreased acetate accumulation (63.6% in serum bottle and 58.1% in bioreactor), and improved the yield of butyrate (26.3% in serum bottle and 34.5% in bioreactor). It was in line with the expectation based on the energy conversion coupling driving mechanism.

Conclusions

This work show that the first-generation GEM and coupling metabolic analysis effectively promoted in-depth understanding of the metabolic driving mechanism in C. tyrobutyricum and provided a new insight for tuning metabolic flux direction in Clostridium chassis cells.

Acetogenic production of 3-Hydroxybutyrate using a native 3-Hydroxybutyryl-CoA Dehydrogenase

3-Hydroxybutyrate (3HB) is a product of interest as it is a precursor to the commercially produced bioplastic polyhydroxybutyrate. It can also serve as a platform for fine chemicals, medicines, and biofuels, making it a value-added product and feedstock. Acetogens non-photosynthetically fix CO₂ into acetyl-CoA and have been previously engineered to convert acetyl-CoA into 3HB. However, as acetogen metabolism is poorly understood, those engineering efforts have had varying levels of success. 3HB, using acetyl-CoA as a precursor, can be synthesized by a variety of different pathways. Here we systematically compare various pathways to produce 3HB in acetogens and discover a native (S)-3-hydroxybutyryl-CoA dehydrogenase, hbd2, responsible for endogenous 3HB production. In conjunction with the heterologous thiolase atoB and CoA transferase ctfAB, hbd2 overexpression improves yields of 3HB on both sugar and syngas (CO/H₂/CO₂), outperforming the other tested pathways. These results uncovered a previously unknown 3HB production pathway, inform data from prior metabolic engineering efforts, and have implications for future physiological and biotechnological anaerobic research.

Oxidoreduction potential controlling for increasing the fermentability of enzymatically hydrolyzed steam-exploded corn stover for butanol production

Background

Lignocellulosic biomass is recognized as an effective potential substrate for biobutanol production. Though many pretreatment and detoxification methods have been set up, the fermentability of detoxicated lignocellulosic substrate is still far lower than that of starchy feedstocks. On the other hand, the number of recent efforts on rational metabolic engineering approaches to increase butanol production in Clostridium strains is also quite limited, demonstrating the physiological complexity of solventogenic clostridia. In fact, the strain performance is greatly impacted by process control. developing efficient process control strategies could be a feasible solution to this problem.

Results

In this study, oxidoreduction potential (ORP) controlling was applied to increase the fermentability of enzymatically hydrolyzed steam-exploded corn stover (SECS) for butanol production. When ORP of detoxicated SECS was controlled at − 350 mV, the period of fermentation was shortened by 6 h with an increase of 27.5% in the total solvent (to 18.1 g/L) and 34.2% in butanol (to 10.2 g/L) respectively. Silico modeling revealed that the fluxes of NADPH, NADH and ATP strongly differed between the different scenarios. Quantitative analysis showed that intracellular concentrations of ATP, NADPH/NADP⁺, and NADH/NAD⁺ were increased by 25.1%, 81.8%, and 62.5%. ORP controlling also resulted in a 2.1-fold increase in butyraldehyde dehydrogenase, a 1.2-fold increase in butanol dehydrogenase and 29% increase in the cell integrity.

Conclusion

ORP control strategy effectively changed the intracellular metabolic spectrum and significantly improved Clostridium cell growth and butanol production. The working mechanism can be summarized into three aspects: First, Glycolysis and TCA circulation pathways were strengthened through key nodes such as pyruvate carboxylase [EC: 6.4.1.1], which provided sufficient NADH and NADPH for the cell. Second, sufficient ATP was provided to avoid “acid crash”. Third, the key enzymes activities regulating butanol biosynthesis and cell membrane integrity were improved.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01824-2.

Genome-Scale Mining of Acetogens of the Genus Clostridium Unveils Distinctive Traits in [FeFe]- and [NiFe]-Hydrogenase Content and Maturation

Knowledge of the organizational and functional properties of hydrogen metabolism is pivotal to the construction of a framework supportive of a hydrogen-fueled low-carbon economy. Hydrogen metabolism relies on the mechanism of action of hydrogenases. In this study, we investigated the genomes of several industrially relevant acetogens of the genus Clostridium (C. autoethanogenum, C. ljungdahlii, C. carboxidivorans, C. drakei, C. scatologenes, C. coskatii, C. ragsdalei, C. sp. AWRP) to systematically identify their intriguingly diversified hydrogenases’ repertoire. An entirely computational annotation pipeline unveiled common and strain-specific traits in the functional content of [NiFe]- and [FeFe]-hydrogenases. Hydrogenases were identified and categorized into functionally distinct classes by the combination of sequence homology, with respect to a database of curated nonredundant hydrogenases, with the analysis of sequence patterns characteristic of the mode of action of [FeFe]- and [NiFe]-hydrogenases. The inspection of the genes in the neighborhood of the catalytic subunits unveiled a wide agreement between their genomic arrangement and the gene organization templates previously developed for the predicted hydrogenase classes. Subunits’ characterization of the identified hydrogenases allowed us to glean some insights on the redox cofactor-binding determinants in the diaphorase subunits of the electron-bifurcating [FeFe]-hydrogenases. Finally, the reliability of the inferred hydrogenases was corroborated by the punctual analysis of the maturation proteins necessary for the biosynthesis of [NiFe]- and [FeFe]-hydrogenases.

IMPORTANCE Mastering hydrogen metabolism can support a sustainable carbon-neutral economy. Of the many microorganisms metabolizing hydrogen, acetogens of the genus Clostridium are appealing, with some of them already in usage as industrial workhorses. Having provided detailed information on the hydrogenase content of an unprecedented number of clostridial acetogens at the gene level, our study represents a valuable knowledge base to deepen our understanding of hydrogenases’ functional specificity and/or redundancy and to develop a large array of biotechnological processes. We also believe our study could serve as a basis for future strain-engineering approaches, acting at the hydrogenases’ level or at the level of their maturation proteins. On the other side, the wealth of functional elements discussed in relation to the identified hydrogenases is worthy of further investigation by biochemical and structural studies to ultimately lead to the usage of these enzymes as valuable catalysts.

Genome-scale metabolic modelling enables deciphering ethanol metabolism via the acrylate pathway in the propionate-producer Anaerotignum neopropionicum

BACKGROUND

Microbial production of propionate from diluted streams of ethanol (e.g., deriving from syngas fermentation) is a sustainable alternative to the petrochemical production route. Yet, few ethanol-fermenting propionigenic bacteria are known, and understanding of their metabolism is limited. Anaerotignum neopropionicum is a propionate-producing bacterium that uses the acrylate pathway to ferment ethanol and CO₂ to propionate and acetate. In this work, we used computational and experimental methods to study the metabolism of A. neopropionicum and, in particular, the pathway for conversion of ethanol into propionate.

RESULTS

Our work describes iANEO_SB607, the first genome-scale metabolic model (GEM) of A. neopropionicum. The model was built combining the use of automatic tools with an extensive manual curation process, and it was validated with experimental data from this and published studies. The model predicted growth of A. neopropionicum on ethanol, lactate, sugars and amino acids, matching observed phenotypes. In addition, the model was used to implement a dynamic flux balance analysis (dFBA) approach that accurately predicted the fermentation profile of A. neopropionicum during batch growth on ethanol. A systematic analysis of the metabolism of A. neopropionicum combined with model simulations shed light into the mechanism of ethanol fermentation via the acrylate pathway, and revealed the presence of the electron-transferring complexes NADH-dependent reduced ferredoxin:NADP⁺ oxidoreductase (Nfn) and acryloyl-CoA reductase-EtfAB, identified for the first time in this bacterium.

CONCLUSIONS

The realisation of the GEM iANEO_SB607 is a stepping stone towards the understanding of the metabolism of the propionate-producer A. neopropionicum. With it, we have gained insight into the functioning of the acrylate pathway and energetic aspects of the cell, with focus on the fermentation of ethanol. Overall, this study provides a basis to further exploit the potential of propionigenic bacteria as microbial cell factories.

Genetic Evidence Reveals the Indispensable Role of the rseC Gene for Autotrophy and the Importance of a Functional Electron Balance for Nitrate Reduction in Clostridium ljungdahlii

For Clostridium ljungdahlii, the RNF complex plays a key role for energy conversion from gaseous substrates such as hydrogen and carbon dioxide. In a previous study, a disruption of RNF-complex genes led to the loss of autotrophy, while heterotrophy was still possible via glycolysis. Furthermore, it was shown that the energy limitation during autotrophy could be lifted by nitrate supplementation, which resulted in an elevated cellular growth and ATP yield. Here, we used CRISPR-Cas12a to delete: (1) the RNF complex-encoding gene cluster rnfCDGEAB; (2) the putative RNF regulator gene rseC; and (3) a gene cluster that encodes for a putative nitrate reductase. The deletion of either rnfCDGEAB or rseC resulted in a complete loss of autotrophy, which could be restored by plasmid-based complementation of the deleted genes. We observed a transcriptional repression of the RNF-gene cluster in the rseC-deletion strain during autotrophy and investigated the distribution of the rseC gene among acetogenic bacteria. To examine nitrate reduction and its connection to the RNF complex, we compared autotrophic and heterotrophic growth of our three deletion strains with either ammonium or nitrate. The rnfCDGEAB- and rseC-deletion strains failed to reduce nitrate as a metabolic activity in non-growing cultures during autotrophy but not during heterotrophy. In contrast, the nitrate reductase deletion strain was able to grow in all tested conditions but lost the ability to reduce nitrate. Our findings highlight the important role of the rseC gene for autotrophy, and in addition, contribute to understand the connection of nitrate reduction to energy metabolism.

Analytical tools for unravelling the metabolism of gas-fermenting Clostridia

Acetogens harness the Wood-Ljungdahl Pathway, a unique metabolic pathway for C1 capture close to the thermodynamic limit. Gas fermentation using acetogens is already used for CO-to-ethanol conversion at industrial-scale and has the potential to valorise a range of C1 and waste substrates to short-chain and medium-chain carboxylic acids and alcohols. Advances in analytical quantification and metabolic modelling have helped guide industrial gas fermentation designs. Further advances in the measurements of difficult to measure metabolites are required to improve kinetic modelling and understand the regulation of acetogen metabolism. This will help guide future synthetic biology designs needed to realise the full potential of gas fermentation in stimulating a circular bioeconomy.

Flux balance analysis of the ammonia-oxidizing bacterium Nitrosomonas europaea ATCC19718 unravels specific metabolic activities while degrading toxic compounds

The ammonia-oxidizing bacterium Nitrosomonas europaea has been widely recognized as an important player in the nitrogen cycle as well as one of the most abundant members in microbial communities for the treatment of industrial or sewage wastewater. Its natural metabolic versatility and extraordinary ability to degrade environmental pollutants (e.g., aromatic hydrocarbons such as benzene and toluene) enable it to thrive under various harsh environmental conditions. Constraint-based metabolic models constructed from genome sequences enable quantitative insight into the central and specialized metabolism within a target organism. These genome-scale models have been utilized to understand, optimize, and design new strategies for improved bioprocesses. Reduced modeling approaches have been used to elucidate Nitrosomonas europaea metabolism at a pathway level. However, genome-scale knowledge about the simultaneous oxidation of ammonia and pollutant metabolism of N. europaea remains limited. Here, we describe the reconstruction, manual curation, and validation of the genome-scale metabolic model for N. europaea, iGC535. This reconstruction is the most accurate metabolic model for a nitrifying organism to date, reaching an average prediction accuracy of over 90% under several growth conditions. The manually curated model can predict phenotypes under chemolithotrophic and chemolithoorganotrophic conditions while oxidating methane and wastewater pollutants. Calculated flux distributions under different trophic conditions show that several key pathways are affected by the type of carbon source available, including central carbon metabolism and energy production.

Nitrosomonas europaea catalyzes the first step of the nitrification process (ammonia to nitrite). It has been recognized as one of the most important members of microbial communities of wastewater treatment processes. Genome-scale models are powerful tools in process optimization since they can predict the organism’s behavior under different growth conditions. The final genome-scale model of N. europaea ATCC19718, iGC535, can predict growth and oxygen uptake rates with 90.52% accuracy under chemolithotrophic and chemolitoorganotrophic conditions. Moreover, iGC535 can predict the simultaneous oxidation of ammonia and wastewater pollutants, such as benzene, toluene, phenol and, chlorobenzene. iGC535 represents the most comprehensive knowledge-base for a nitrifying organism available to date. The genome-scale model reconstructed in this work brings us closer to understanding N. europaea’s role in a community with other nitrifying bacteria.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Biological conversion of carbon monoxide and hydrogen by anaerobic culture: Prospect of anaerobic digestion and thermochemical processes combination

Waste biomass is considered a promising renewable energy feedstock that can be converted by anaerobic digestion. However, anaerobic digestion application can be challenging due to the structural complexity of several waste biomass kinds. Therefore, coupling anaerobic digestion with thermochemical processes can offset the limitations and convert the hardly biodegradable waste biomass, including digestate residue, into value-added products: syngas and pyrogas (gaseous mixtures consisting mainly of H₂, CO, CO₂), bio-oil, and biochar for further valorisation. In this review, the utilisation boundaries and benefits of the aforementioned products by anaerobic culture are discussed. First, thermochemical process parameters for an enhanced yield of desired products are summarised. Particularly, the microbiology of CO and H₂ mixture biomethanation and fermentation in anaerobic digestion is presented. Finally, the state-of-the-art biological conversion of syngas and pyrogas to CH₄ mediated by anaerobic culture is adequately described. Extensive research shows the successful selective biological conversion of CO and H₂ to CH₄, acetic acid, and alcohols. The main bottleneck is the gas-liquid mass transfer which can be enhanced appropriately by bioreactors' configurations. A few research groups focus on bio-oil and biochar addition into anaerobic digesters. However, according to the literature review, there has been no research for utilising all value-added products at once in anaerobic digestion published so far. Although synergic effects of such can be expected. In summary, the combination of anaerobic digestion and thermochemical processes is a promising alternative for wide-scale waste biomass utilisation in practice.

Identifying the essential nutritional requirements of the probiotic bacteria Bifidobacterium animalis and Bifidobacterium longum through genome-scale modeling

Although bifidobacteria are widely used as probiotics, their metabolism and physiology remain to be explored in depth. In this work, strain-specific genome-scale metabolic models were developed for two industrially and clinically relevant bifidobacteria, Bifidobacterium animalis subsp. lactis BB-12® and B. longum subsp. longum BB-46, and subjected to iterative cycles of manual curation and experimental validation. A constraint-based modeling framework was used to probe the metabolic landscape of the strains and identify their essential nutritional requirements. Both strains showed an absolute requirement for pantethine as a precursor for coenzyme A biosynthesis. Menaquinone-4 was found to be essential only for BB-46 growth, whereas nicotinic acid was only required by BB-12®. The model-generated insights were used to formulate a chemically defined medium that supports the growth of both strains to the same extent as a complex culture medium. Carbohydrate utilization profiles predicted by the models were experimentally validated. Furthermore, model predictions were quantitatively validated in the newly formulated medium in lab-scale batch fermentations. The models and the formulated medium represent valuable tools to further explore the metabolism and physiology of the two species, investigate the mechanisms underlying their health-promoting effects and guide the optimization of their industrial production processes.

Global connectivity in genome-scale metabolic networks revealed by comprehensive FBA-based pathway analysis

Background

Graph-based analysis (GBA) of genome-scale metabolic networks has revealed system-level structures such as the bow-tie connectivity that describes the overall mass flow in a network. However, many pathways obtained by GBA are biologically impossible, making it difficult to study how the global structures affect the biological functions of a network. New method that can calculate the biologically relevant pathways is desirable for structural analysis of metabolic networks.

Results

Here, we present a new method to determine the bow-tie connectivity structure by calculating possible pathways between any pairs of metabolites in the metabolic network using a flux balance analysis (FBA) approach to ensure that the obtained pathways are biologically relevant. We tested this method with 15 selected high-quality genome-scale metabolic models from BiGG database. The results confirmed the key roles of central metabolites in network connectivity, locating in the core part of the bow-tie structure, the giant strongly connected component (GSC). However, the sizes of GSCs revealed by GBA are significantly larger than those by FBA approach. A great number of metabolites in the GSC from GBA actually cannot be produced from or converted to other metabolites through a mass balanced pathway and thus should not be in GSC but in other subsets of the bow-tie structure. In contrast, the bow-tie structural classification of metabolites obtained by FBA is more biologically relevant and suitable for the study of the structure-function relationships of genome scale metabolic networks.

Conclusions

The FBA based pathway calculation improve the biologically relevant classification of metabolites in the bow-tie connectivity structure of the metabolic network, taking us one step further toward understanding how such system-level structures impact the biological functions of an organism.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02357-1.

Electron carriers involved in autotrophic and heterotrophic acetogenesis in the thermophilic bacterium Thermoanaerobacter kivui

Thermoanaerobacter kivui is an acetogenic model organism that reduces CO₂ with electrons derived from H₂ or CO, or from organic substrates in the Wood–Ljugdahl pathway (WLP). For the calculation of ATP yields, it is necessary to know the electron carriers involved in coupling of the oxidative and reductive parts of metabolism. Analyses of key catabolic oxidoreductases in cell-free extract (CFE) or with purified enzymes revealed the physiological electron carriers involved. The glyceraldehyde-3-phosphate dehydrogenase (GA3P-DH) assayed in CFE was NAD⁺-specific, NADP⁺ was used with less than 4% and ferredoxin (Fd) was not used. The methylene-THF dehydrogenase was NADP⁺-specific, NAD⁺ or Fd were not used. A Nfn-type transhydrogenase that catalyzes reduced Fd-dependent reduction of NADP⁺ with NADH as electron donor was also identified in CFE. The electron carriers used by the potential electron-bifurcating hydrogenase (HydABC) could not be unambiguously determined in CFE for technical reasons. Therefore, the enzyme was produced homologously in T. kivui and purified by affinity chromatography. HydABC contained 33.9 ± 4.5 mol Fe/mol of protein and FMN; it reduced NADP⁺ but not NAD⁺. The methylene-THF reductase (MetFV) was also produced homologously in T. kivui and purified by affinity chromatography. MetFV contained 7.2 ± 0.4 mol Fe/mol of protein and FMN; the complex did neither use NADPH nor NADH as reductant but only reduced Fd. In sum, these analysis allowed us to propose a scheme for entire electron flow and bioenergetics in T. kivui.

A General Model for Biofilm-Driven Microbial Electrosynthesis of Carboxylates From CO2

Up to now, computational modeling of microbial electrosynthesis (MES) has been underexplored, but is necessary to achieve breakthrough understanding of the process-limiting steps. Here, a general framework for modeling microbial kinetics in a MES reactor is presented. A thermodynamic approach is used to link microbial metabolism to the electrochemical reduction of an intracellular mediator, allowing to predict cellular growth and current consumption. The model accounts for CO₂ reduction to acetate, and further elongation to n-butyrate and n-caproate. Simulation results were compared with experimental data obtained from different sources and proved the model is able to successfully describe microbial kinetics (growth, chain elongation, and product inhibition) and reactor performance (current density, organics titer). The capacity of the model to simulate different system configurations is also shown. Model results suggest CO₂ dissolved concentration might be limiting existing MES systems, and highlight the importance of the delivery method utilized to supply it. Simulation results also indicate that for biofilm-driven reactors, continuous mode significantly enhances microbial growth and might allow denser biofilms to be formed and higher current densities to be achieved.

Dynamic metabolic modelling predicts efficient acetogen-gut bacterium cocultures for CO-to-butyrate conversion

AIMS

While gas-fermenting acetogens have been engineered to secrete non-native metabolites such as butyrate, acetate remains the most thermodynamically favourable product. An alternative to metabolic engineering is to exploit native capabilities for CO-to-acetate conversion by coculturing an acetogen with a second bacterium that provides efficient acetate-butyrate conversion.

METHODS AND RESULTS

We used dynamic metabolic modelling to computationally evaluate the CO-to-butyrate conversion capabilities of candidate coculture systems by exploiting the diversity of human gut bacteria for anaerobic synthesis of butyrate from acetate and ethanol. A preliminary screening procedure based on flux balance analysis was developed to identify 48 gut bacteria which satisfied minimal growth rate and acetate-to-butyrate conversion requirements when cultured on minimal medium containing acetate and a simple sugar not consumed by the paired acetogen. A total of 170 acetogen/gut bacterium/sugar combinations were dynamically simulated for continuous growth using a 70/30 CO/CO₂ feed gas mixture and minimal medium computationally determined for each combination.

CONCLUSIONS

While coculture systems involving the acetogens Eubacterium limosum or Blautia producta yielded low butyrate productivities and CO-to-ethanol conversion had minimal impact on system performance, dynamic simulations predicted a large number of promising coculture designs with Clostridium ljungdahlii or C. autoethanogenum as the CO-to-acetate converter. Pairings with the gut bacterium Clostridium hylemonae or Roseburia hominis were particularly promising due to their ability to generate high butyrate productivities over a range of dilution rates with a variety of sugars. The higher specific acetate secretion rate of C. ljungdahlii proved more beneficial than the elevated growth rate of C. autoethanogenum for coculture butyrate productivity.

SIGNIFICANCE AND IMPACT OF THE STUDY

Our study demonstrated that metabolic modelling could provide useful insights into coculture design that can guide future experimental studies. More specifically, our predictions generated several favourable designs, which could serve as the first coculture systems realized experimentally.

Genetic and metabolic engineering challenges of C1-gas fermenting acetogenic chassis organisms

Unabated mining and utilisation of petroleum and petroleum resources and their conversion to essential fuels and chemicals have drastic environmental consequences, contributing to global warming and climate change. In addition, fossil fuels are finite resources, with a fast-approaching shortage. Accordingly, research efforts are increasingly focusing on developing sustainable alternatives for chemicals and fuels production. In this context, bioprocesses, relying on microorganisms, have gained particular interest. For example, acetogens use the Wood-Ljungdahl pathway to grow on single carbon C1-gases (CO2 and CO) as their sole carbon source and produce valuable products such as acetate or ethanol. These autotrophs can, therefore, be exploited for large-scale fermentation processes to produce industrially relevant chemicals from abundant greenhouse gases. In addition, genetic tools have recently been developed to improve these chassis organisms through synthetic biology approaches. This review will focus on the challenges of genetically and metabolically modifying acetogens. It will first discuss the physical and biochemical obstacles complicating successful DNA transfer in these organisms. Current genetic tools developed for several acetogens, crucial for strain engineering to consolidate and expand their catalogue of products, will then be described. Recent tool applications for metabolic engineering purposes to allow redirection of metabolic fluxes or production of non-native compounds will lastly be covered.

Identifying and Engineering Bottlenecks of Autotrophic Isobutanol Formation in Recombinant C. ljungdahlii by Systemic Analysis

Clostridium ljungdahlii (C. ljungdahlii, CLJU) is natively endowed producing acetic acid, 2,3-butandiol, and ethanol consuming gas mixtures of CO2, CO, and H2 (syngas). Here, we present the syngas-based isobutanol formation using C. ljungdahlii harboring the recombinant amplification of the "Ehrlich" pathway that converts intracellular KIV to isobutanol. Autotrophic isobutanol production was studied analyzing two different strains in 3-L gassed and stirred bioreactors. Physiological characterization was thoroughly applied together with metabolic profiling and flux balance analysis. Thereof, KIV and pyruvate supply were identified as key "bottlenecking" precursors limiting preliminary isobutanol formation in CLJU[KAIA] to 0.02 g L-1. Additional blocking of valine synthesis in CLJU[KAIA]:ilvE increased isobutanol production by factor 6.5 finally reaching 0.13 g L-1. Future metabolic engineering should focus on debottlenecking NADPH availability, whereas NADH supply is already equilibrated in the current generation of strains.

Overcoming Energetic Barriers in Acetogenic C1 Conversion

Currently one of the biggest challenges for society is to combat global warming. A solution to this global threat is the implementation of a CO₂-based bioeconomy and a H₂-based bioenergy economy. Anaerobic lithotrophic bacteria such as the acetogenic bacteria are key players in the global carbon and H₂ cycle and thus prime candidates as driving forces in a H₂- and CO₂-bioeconomy. Naturally, they convert two molecules of CO₂via the Wood-Ljungdahl pathway (WLP) to one molecule of acetyl-CoA which can be converted to different C2-products (acetate or ethanol) or elongated to C4 (butyrate) or C5-products (caproate). Since there is no net ATP generation from acetate formation, an electron-transport phosphorylation (ETP) module is hooked up to the WLP. ETP provides the cell with additional ATP, but the ATP gain is very low, only a fraction of an ATP per mol of acetate. Since acetogens live at the thermodynamic edge of life, metabolic engineering to obtain high-value products is currently limited by the low energy status of the cells that allows for the production of only a few compounds with rather low specificity. To set the stage for acetogens as production platforms for a wide range of bioproducts from CO₂, the energetic barriers have to be overcome. This review summarizes the pathway, the energetics of the pathway and describes ways to overcome energetic barriers in acetogenic C1 conversion.

The Metabolism of Clostridium ljungdahlii in Phosphotransacetylase Negative Strains and Development of an Ethanologenic Strain

The sustainable production of chemicals from non-petrochemical sources is one of the greatest challenges of our time. CO₂ release from industrial activity is not environmentally friendly yet provides an inexpensive feedstock for chemical production. One means of addressing this problem is using acetogenic bacteria to produce chemicals from CO₂, waste streams, or renewable resources. Acetogens are attractive hosts for chemical production for many reasons: they can utilize a variety of feedstocks that are renewable or currently waste streams, can capture waste carbon sources and covert them to products, and can produce a variety of chemicals with greater carbon efficiency over traditional fermentation technologies. Here we investigated the metabolism of Clostridium ljungdahlii, a model acetogen, to probe carbon and electron partitioning and understand what mechanisms drive product formation in this organism. We utilized CRISPR/Cas9 and an inducible riboswitch to target enzymes involved in fermentation product formation. We focused on the genes encoding phosphotransacetylase (pta), aldehyde ferredoxin oxidoreductases (aor1 and aor2), and bifunctional alcohol/aldehyde dehydrogenases (adhE1 and adhE2) and performed growth studies under a variety of conditions to probe the role of those enzymes in the metabolism. Finally, we demonstrated a switch from acetogenic to ethanologenic metabolism by these manipulations, providing an engineered bacterium with greater application potential in biorefinery industry.

Electron availability in CO2 , CO and H2 mixtures constrains flux distribution, energy management and product formation in Clostridium ljungdahlii

Acetogens such as Clostridium ljungdahlii can play a crucial role reducing the human CO2 footprint by converting industrial emissions containing CO2 , CO and H2 into valuable products such as organic acids or alcohols. The quantitative understanding of cellular metabolism is a prerequisite to exploit the bacterial endowments and to fine-tune the cells by applying metabolic engineering tools. Studying the three gas mixtures CO2  + H2 , CO and CO + CO2  + H2 (syngas) by continuously gassed batch cultivation experiments and applying flux balance analysis, we identified CO as the preferred carbon and electron source for growth and producing alcohols. However, the total yield of moles of carbon (mol-C) per electrons consumed was almost identical in all setups which underlines electron availability as the main factor influencing product formation. The Wood-Ljungdahl pathway (WLP) showed high flexibility by serving as the key NAD+ provider for CO2  + H2, whereas this function was strongly compensated by the transhydrogenase-like Nfn complex when CO was metabolized. Availability of reduced ferredoxin (Fdred ) can be considered as a key determinant of metabolic control. Oxidation of CO via carbon monoxide dehydrogenase (CODH) is the main route of Fdred formation when CO is used as substrate, whereas Fdred is mainly regenerated via the methyl branch of WLP and the Nfn complex utilizing CO2  + H2 . Consequently, doubled growth rates, highest ATP formation rates and highest amounts of reduced products (ethanol, 2,3-butanediol) were observed when CO was the sole carbon and electron source.

Synthetic Biology on Acetogenic Bacteria for Highly Efficient Conversion of C1 Gases to Biochemicals

Synthesis gas, which is mainly produced from fossil fuels or biomass gasification, consists of C1 gases such as carbon monoxide, carbon dioxide, and methane as well as hydrogen. Acetogenic bacteria (acetogens) have emerged as an alternative solution to recycle C1 gases by converting them into value-added biochemicals using the Wood-Ljungdahl pathway. Despite the advantage of utilizing acetogens as biocatalysts, it is difficult to develop industrial-scale bioprocesses because of their slow growth rates and low productivities. To solve these problems, conventional approaches to metabolic engineering have been applied; however, there are several limitations owing to the lack of required genetic bioparts for regulating their metabolic pathways. Recently, synthetic biology based on genetic parts, modules, and circuit design has been actively exploited to overcome the limitations in acetogen engineering. This review covers synthetic biology applications to design and build industrial platform acetogens.

Towards continuous industrial bioprocessing with solventogenic and acetogenic clostridia: challenges, progress and perspectives

The sustainable production of solvents from above ground carbon is highly desired. Several clostridia naturally produce solvents and use a variety of renewable and waste-derived substrates such as lignocellulosic biomass and gas mixtures containing H2/CO2 or CO. To enable economically viable production of solvents and biofuels such as ethanol and butanol, the high productivity of continuous bioprocesses is needed. While the first industrial-scale gas fermentation facility operates continuously, the acetone-butanol-ethanol (ABE) fermentation is traditionally operated in batch mode. This review highlights the benefits of continuous bioprocessing for solvent production and underlines the progress made towards its establishment. Based on metabolic capabilities of solvent producing clostridia, we discuss recent advances in systems-level understanding and genome engineering. On the process side, we focus on innovative fermentation methods and integrated product recovery to overcome the limitations of the classical one-stage chemostat and give an overview of the current industrial bioproduction of solvents.

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

Solving environmental and social challenges such as climate change requires a shift from our current non-renewable manufacturing model to a sustainable bioeconomy. To lower carbon emissions in the production of fuels and chemicals, plant biomass feedstocks can replace petroleum using microorganisms as biocatalysts. The anaerobic thermophile Clostridium thermocellum is a promising bacterium for bioconversion due to its capability to efficiently degrade lignocellulosic biomass. However, the complex metabolism of C. thermocellum is not fully understood, hindering metabolic engineering to achieve high titers, rates, and yields of targeted molecules. In this study, we developed an updated genome-scale metabolic model of C. thermocellum that accounts for recent metabolic findings, has improved prediction accuracy, and is standard-conformant to ensure easy reproducibility. We illustrated two applications of the developed model. We first formulated a multi-omics integration protocol and used it to understand redox metabolism and potential bottlenecks in biofuel (e.g., ethanol) production in C. thermocellum. Second, we used the metabolic model to design modular cells for efficient production of alcohols and esters with broad applications as flavors, fragrances, solvents, and fuels. The proposed designs not only feature intuitive push-and-pull metabolic engineering strategies, but also present novel manipulations around important central metabolic branch-points. We anticipate the developed genome-scale metabolic model will provide a useful tool for system analysis of C. thermocellum metabolism to fundamentally understand its physiology and guide metabolic engineering strategies to rapidly generate modular production strains for effective biosynthesis of biofuels and biochemicals from lignocellulosic biomass.

Redox controls metabolic robustness in the gas-fermenting acetogen Clostridium autoethanogenum

Living biological systems display a fascinating ability to self-organize their metabolism. This ability ultimately determines the metabolic robustness that is fundamental to controlling cellular behavior. However, fluctuations in metabolism can affect cellular homeostasis through transient oscillations. For example, yeast cultures exhibit rhythmic oscillatory behavior in high cell-density continuous cultures. Oscillatory behavior provides a unique opportunity for quantitating the robustness of metabolism, as cells respond to changes by inherently compromising metabolic efficiency. Here, we quantify the limits of metabolic robustness in self-oscillating autotrophic continuous cultures of the gas-fermenting acetogen Clostridium autoethanogenum Online gas analysis and high-resolution temporal metabolomics showed oscillations in gas uptake rates and extracellular byproducts synchronized with biomass levels. The data show initial growth on CO, followed by growth on CO and H2 Growth on CO and H2 results in an accelerated growth phase, after which a downcycle is observed in synchrony with a loss in H2 uptake. Intriguingly, oscillations are not linked to translational control, as no differences were observed in protein expression during oscillations. Intracellular metabolomics analysis revealed decreasing levels of redox ratios in synchrony with the cycles. We then developed a thermodynamic metabolic flux analysis model to investigate whether regulation in acetogens is controlled at the thermodynamic level. We used endo- and exo-metabolomics data to show that the thermodynamic driving force of critical reactions collapsed as H2 uptake is lost. The oscillations are coordinated with redox. The data indicate that metabolic oscillations in acetogen gas fermentation are controlled at the thermodynamic level.

Metabolic strategies of marine subseafloor Chloroflexi inferred from genome reconstructions

Uncultured members of the Chloroflexi phylum are highly enriched in numerous subseafloor environments. Their metabolic potential was evaluated by reconstructing 31 Chloroflexi genomes from six different subseafloor habitats. The near ubiquitous presence of enzymes of the Wood-Ljungdahl pathway, electron bifurcation, and ferredoxin-dependent transport-coupled phosphorylation indicated anaerobic acetogenesis was central to their catabolism. Most of the genomes simultaneously contained multiple degradation pathways for complex carbohydrates, detrital protein, aromatic compounds, and hydrogen, indicating the coupling of oxidation of chemically diverse organic substrates to ubiquitous CO₂ reduction. Such pathway combinations may confer a fitness advantage in subseafloor environments by enabling these Chloroflexi to act as primary fermenters and acetogens in one microorganism without the need for syntrophic H₂ consumption. While evidence for catabolic oxygen respiration was limited to two phylogenetic clusters, the presence of genes encoding putative reductive dehalogenases throughout the phylum expanded the phylogenetic boundary for potential organohalide respiration past the Dehalococcoidia class.

Modeling of nitrogen fixation and polymer production in the heterotrophic diazotroph Azotobacter vinelandii DJ

Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH₃). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date.

Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium A. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.

•

Genome-scale metabolic model of Azotobacter vinelandii DJ achives over 90% accuracy.

•

iDT1278 is the most comprehensive model to simulate diazotrophy.

•

Determining the most suitable culture conditions to produce polymers A. vinelandii.

•

Constraint-based modeling unravels links among nitrogen fixation and production of organic nitrogen.

Nitrate Feed Improves Growth and Ethanol Production of Clostridium ljungdahlii With CO2 and H2, but Results in Stochastic Inhibition Events

The pH-value in fermentation broth is a critical factor for the metabolic flux and growth behavior of acetogens. A decreasing pH level throughout time due to undissociated acetic acid accumulation is anticipated under uncontrolled pH conditions such as in bottle experiments. As a result, the impact of changes in the metabolism (e.g., due to a genetic modification) might remain unclear or even unrevealed. In contrast, pH-controlled conditions can be achieved in bioreactors. Here, we present a self-built, comparatively cheap, and user-friendly multiple-bioreactor system (MBS) consisting of six pH-controlled bioreactors at a 1-L scale. We tested the functionality of the MBS by cultivating the acetogen Clostridium ljungdahlii with CO₂ and H₂ at steady-state conditions (=chemostat). The experiments (total of 10 bioreactors) were addressing the two questions: (1) does the MBS provide replicable data for gas-fermentation experiments?; and (2) does feeding nitrate influence the product spectrum under controlled pH conditions with CO₂ and H₂? We applied four different periods in each experiment ranging from pH 6.0 to pH 4.5. On the one hand, our data showed high reproducibility for gas-fermentation experiments with C. ljungdahlii under standard cultivation conditions using the MBS. On the other hand, feeding nitrate as sole N-source improved growth by up to 62% and ethanol production by 2-3-fold. However, we observed differences in growth, and acetate and ethanol production rates between all nitrate bioreactors. We explained the different performances with a pH-buffering effect that resulted from the interplay between undissociated acetic acid production and ammonium production and because of stochastic inhibition events, which led to complete crashes at different operating times.

Modeling ethanol production through gas fermentation: a biothermodynamics and mass transfer-based hybrid model for microbial growth in a large-scale bubble column bioreactor

BACKGROUND

Ethanol production through fermentation of gas mixtures containing CO, CO₂ and H₂ has just started operating at commercial scale. However, quantitative schemes for understanding and predicting productivities, yields, mass transfer rates, gas flow profiles and detailed energy requirements have been lacking in literature; such are invaluable tools for process improvements and better systems design. The present study describes the construction of a hybrid model for simulating ethanol production inside a 700 m³ bubble column bioreactor fed with gas of two possible compositions, i.e., pure CO and a 3:1 mixture of H₂ and CO₂.

RESULTS

Estimations made using the thermodynamics-based black-box model of microbial reactions on substrate threshold concentrations, biomass yields, as well as CO and H₂ maximum specific uptake rates agreed reasonably well with data and observations reported in literature. According to the bioreactor simulation, there is a strong dependency of process performance on mass transfer rates. When mass transfer coefficients were estimated using a model developed from oxygen transfer to water, ethanol productivity reached 5.1 g L^-1 h^-1; when the H₂/CO₂ mixture is fed to the bioreactor, productivity of CO fermentation was 19% lower. Gas utilization reached 23 and 17% for H₂/CO₂ and CO fermentations, respectively. If mass transfer coefficients were 100% higher than those estimated, ethanol productivity and gas utilization may reach 9.4 g L^-1 h^-1 and 38% when feeding the H₂/CO₂ mixture at the same process conditions. The largest energetic requirements for a complete manufacturing plant were identified for gas compression and ethanol distillation, being higher for CO fermentation due to the production of CO₂.

CONCLUSIONS

The thermodynamics-based black-box model of microbial reactions may be used to quantitatively assess and consolidate the diversity of reported data on CO, CO₂ and H₂ threshold concentrations, biomass yields, maximum substrate uptake rates, and half-saturation constants for CO and H₂ for syngas fermentations by acetogenic bacteria. The maximization of ethanol productivity in the bioreactor may come with a cost: low gas utilization. Exploiting the model flexibility, multi-objective optimizations of bioreactor performance might reveal how process conditions and configurations could be adjusted to guide further process development.

A TetR-Family Protein (CAETHG_0459) Activates Transcription From a New Promoter Motif Associated With Essential Genes for Autotrophic Growth in Acetogens

Acetogens can fix carbon (CO or CO2) into acetyl-CoA via the Wood-Ljungdahl pathway (WLP) that also makes them attractive cell factories for the production of fuels and chemicals from waste feedstocks. Although most biochemical details of the WLP are well understood and systems-level characterization of acetogen metabolism has recently improved, key transcriptional features such as promoter motifs and transcriptional regulators are still unknown in acetogens. Here, we use differential RNA-sequencing to identify a previously undescribed promoter motif associated with essential genes for autotrophic growth of the model-acetogen Clostridium autoethanogenum. RNA polymerase was shown to bind to the new promoter motif using a DNA-binding protein assay and proteomics enabled the discovery of four candidates to potentially function directly in control of transcription of the WLP and other key genes of C1 fixation metabolism. Next, in vivo experiments showed that a TetR-family transcriptional regulator (CAETHG_0459) and the housekeeping sigma factor (σA) activate expression of a reporter protein (GFP) in-frame with the new promoter motif from a fusion vector in Escherichia coli. Lastly, a protein-protein interaction assay with the RNA polymerase (RNAP) shows that CAETHG_0459 directly binds to the RNAP. Together, the data presented here advance the fundamental understanding of transcriptional regulation of C1 fixation in acetogens and provide a strategy for improving the performance of gas-fermenting bacteria by genetic engineering.

Basics of genome-scale metabolic modeling and applications on C1-utilization

It is fundamental to understand the relationship between genotype and phenotype in biology. This requires comprehensive knowledge of metabolic pathways, genetic information and well-defined mathematic modeling. Integration of knowledge on metabolism with mathematical modeling results in genome-scale metabolic models which have proven useful to investigate bacterial metabolism and to engineer bacterial strains capable of producing value-added biochemical. Single carbon substrates such as methane and carbon monoxide have drawn interests and they assumed one of next-generation feedstocks because of their high abundance and low price. The methylotroph and acetogen-based biorefineries hold promises for bioconversion of C1 substrates into biofuels and high value compounds. As an effort on expanding our knowledge on C1 utilization approaches, in silico computational framework of C1-metabolism in methylotrophic and acetogenic bacteria has been developed. In this review, genome-scale metabolic models for C1-utilizing bacteria and well-established analysis tools are presented for potential uses for study of C1 metabolism at the genome scale and its application in metabolic engineering.

Metabolic modeling of bacterial co-culture systems predicts enhanced carbon monoxide-to-butyrate conversion compared to monoculture systems

We used metabolic modeling to computationally investigate the potential of bacterial coculture system designs for CO conversion to the platform chemical butyrate. By taking advantage of the native capabilities of wild-type strains, we developed two anaerobic coculture designs by combining Clostridium autoethanogenum for CO-to-acetate conversion with bacterial strains that offer high acetate-to-butyrate conversion capabilities: the environmental bacterium the human gut bacteriumEubacterium rectale. When grown in continuous stirred tank reactor on a 70/0/30 CO/H₂/N₂ gas mixture, the C. autoethanogenum-C Kluyveri co-culture was predicted to offer no mprovement in butyrate volumetric productivity compared to an engineered C. autoethanogenum monoculture despite utilizing vinyl acetate as a secondary carbon source for C. kluyveri growth enhancement. A coculture consisting of C. autoethanogenum and C. kluyveri engineered in silico to eliminate hexanoate synthesis was predicted to enhance both butyrate productivity and titer. The C. autoethanogenum-E. rectale coculture offered similar improvements in butyrate productivity without the need for metabolic engineering when glucose was provided as a secondary carbon source to enhance E. rectale growth. A bubble column model developed to assess the potential for large-scale butyrate production of the C. autoethanogenum-E. rectale design predicted that a 40/30/30 CO/H₂/N₂ gas mixture and a 5 m column length would be preferred to enhance C. autoethanogenum growth and counteract CO inhibitory effects on E. rectale.

Engineering of vitamin prototrophy in Clostridium ljungdahlii and Clostridium autoethanogenum

Clostridium autoethanogenum and Clostridium ljungdahlii are physiologically and genetically very similar strict anaerobic acetogens capable of growth on carbon monoxide as sole carbon source. While exact nutritional requirements have not been reported, we observed that for growth, the addition of vitamins to media already containing yeast extract was required, an indication that these are fastidious microorganisms. Elimination of complex components and individual vitamins from the medium revealed that the only organic compounds required for growth were pantothenate, biotin and thiamine. Analysis of the genome sequences revealed that three genes were missing from pantothenate and thiamine biosynthetic pathways, and five genes were absent from the pathway for biotin biosynthesis. Prototrophy in C. autoethanogenum and C. ljungdahlii for pantothenate was obtained by the introduction of plasmids carrying the heterologous gene clusters panBCD from Clostridium acetobutylicum, and for thiamine by the introduction of the thiC-purF operon from Clostridium ragsdalei. Integration of panBCD into the chromosome through allele-coupled exchange also conveyed prototrophy. C. autoethanogenum was converted to biotin prototrophy with gene sets bioBDF and bioHCA from Desulfotomaculum nigrificans strain CO-1-SRB, on plasmid and integrated in the chromosome. The genes could be used as auxotrophic selection markers in recombinant DNA technology. Additionally, transformation with a subset of the genes for pantothenate biosynthesis extended selection options with the pantothenate precursors pantolactone and/or beta-alanine. Similarly, growth was obtained with the biotin precursor pimelate combined with genes bioYDA from C. acetobutylicum. The work raises questions whether alternative steps exist in biotin and thiamine biosynthesis pathways in these acetogens.