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The field of metabolic modelling at the genomescale continues to grow with more models being created and curated. This comes with an increasing demand for adopting common principles regarding transparency and versioning, in addition to standardisation efforts regarding file formats, annotation and testing. Here, we present a standardised template for git-based and GitHub-hosted genome-scale metabolic models (GEMs) supporting both new models and curated ones, following FAIR principles (findability, accessibility, interoperability, and reusability), and incorporating bestpractices.standard-GEMfacilitates the reuse of GEMs across web services and platforms in the metabolic modelling field and enables automatic validation of GEMs. The use of this template for new models, and its adoption for existing ones, paves the way for increasing model quality, openness, and accessibility with minimal effort.Availabilitystandard-GEMis available fromgithub.com/MetabolicAtlas/standard-GEMunder the conditions of the CC BY 4.0 licence along with additional supporting material.

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities.This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation byC. autoethanogenumandC. kluyverifor the static tools, b) glucose/xylose fermentation with engineeredE. coliandS. cerevisiaefor the dynamic tools, and c) a Petri dish ofE. coliandS. entericafor tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.Author summaryConstraint-based modeling employing genome-scale reconstructions of microbial species has become one of the most successful approaches for studying, analyzing, and engineering microbial consortia. Over the past decade, many constraint-based modeling tools have been published to examine an immense variety of microbial consortia spanning from the application areas of bioremediation to food and health biotechnology. However, new potential users lack an overview of the quality and performance of existing metabolic modeling tools that would guide their choice. To tackle this issue, we examined 24 tools for genome-scale metabolic modeling of microbial consortia. After an initial qualitative screening, we quantitatively evaluated 14 adequate tools against published experimental data that included different organisms and conditions. We conducted simulations and evaluated model features such as predictive accuracy, computational time, and tractability in capturing critical physiological properties. We found that, generally, more up-to-date, accessible, and documented tools were superior in many important aspects of model quality and performance. Although, in some cases, we observed tradeoffs in older, less elaborate tools that can be more accurate or flexible. This work has broad implications to help researchers navigate the most suitable tools, and suggests to developers opportunities for improvement of the currently existing capabilities for metabolic modeling of multi-species microbial consortia.

One-carbon (C1) feedstocks derived from CO2and renewable electricity, such as formate, are promising substrates for sustainable production of chemicals, food and fuels. Energetically more efficient, engineered C1-fixation pathways were proposed to increase biomass yields above their natural counterparts, but have so far not been shown to achieve this. Here, we replace the native ‘energy-inefficient’ Calvin-Benson-Bassham (CBB) cycle inCupriavidus necatorby genomic integration of the synthetic reductive glycine pathway for growth on formate. Our final engineered strain reaches a higher biomass yield than the CBB-cycle-utilizing wild type, showing for the first time that efficiencies found in natural metabolism can be exceeded via a synthetic pathway. This yield increase demonstrates the potential of synthetic metabolism and is an important step towards realizing truly sustainable, economically feasible bio-based production.

One-carbon (C1) compounds are promising feedstocks for sustainable production of commodity chemicals. CO2is a particularly advantageous C1-feedstock since it is an unwanted industrial off-gas that can be converted to valuable products while reducing its atmospheric levels. Acetogens are known microorganisms that can grow on CO2/H2and syngas converting these substrates into ethanol and acetate. Co-cultivation of acetogens with microbes that can further process such products can expand the variety of products to, for example, medium chain fatty acids (MCFA) and longer chain alcohols. Solventogens are microorganisms known to produce MCFA and alcohols via the acetone, butanol and ethanol (ABE) fermentation in which acetate is a key metabolite. Thus, co-cultivation of a solventogen and acetogen in a consortium provides a potential platform to produce valuable chemicals from CO2.In this study, metabolic modelling was implemented to design a new co-culture of an acetogen and a solventogen to produce butyrate from CO2/H2mixtures. The model-driven approach suggested the ability of the studied solventogenic species to grow on lactate/glycerol with acetate as co-substrate. This ability was confirmed experimentally by cultivation ofClostridium beijerinckiion these substrates in serum bottles and subsequently in pH-controlled bioreactors.Community modelling also suggested that a novel microbial consortium consisting of the acetogenClostridium autoethanogenum,and the solventogenC. beijerinckiiwould be feasible and stable. On the basis of this prediction, a co-culture was experimentally established.C. autoethanogenumgrew on CO2/H2producing acetate and traces of ethanol. Acetate was in turn, consumed byC. beijerinckiitogether with lactate, producing butyrate. These results show that community modelling of metabolism is a valuable tool to guide the design of microbial consortia for the tailored production of chemicals from renewable resources.

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.

Methanol and formate are attractive microbial feedstocks as they can be sustainably produced from CO2 and renewable energy, are completely miscible, and are easy to store and transport. Here, we provide a biochemical perspective on microbial growth and bioproduction using these compounds. We show that anaerobic growth of acetogens on methanol and formate is more efficient than on H2/CO2 or CO. We analyze the aerobic C1 assimilation pathways and suggest that new-to-nature routes could outperform their natural counterparts. We further discuss practical bioprocessing aspects related to growth on methanol and formate, including feedstock toxicity. While challenges in realizing sustainable production from methanol and formate still exist, the utilization of these feedstocks paves the way towards a truly circular carbon economy.

Microbial fermentation of synthesis gas (syngas) is becoming more attractive for sustainable production of commodity chemicals. To date, syngas fermentation focuses mainly on the use ofClostridiumspecies for the production of small organic molecules such as ethanol and acetate. The cocultivation of syngas-fermenting microorganisms with chain-elongating bacteria can expand the range of possible products, allowing, for instance, the production of medium-chain fatty acids (MCFA) and alcohols from syngas. To explore these possibilities, we report herein a genome-scale, constraint-based metabolic model to describe growth of a co-culture ofClostridium autoethanogenumandClostridium kluyverion syngas for the production of valuable compounds. Community flux balance analysis was used to gain insight into the metabolism of the two strains and their interactions, and to reveal potential strategies enabling production of butyrate and hexanoate. The model suggests that addition of succinate is one strategy to optimize the production of medium-chain fatty-acids from syngas with this co-culture. According to the predictions, addition of succinate increases the pool of crotonyl-CoA and the ethanol/acetate uptake ratio inC. kluyveri, resulting in the flux of up to 60% of electrons into hexanoate. Other potential way to optimize butyrate and hexanoate is to increase ethanol production byC. autoethanogenum. Deletion of either formate transport, acetaldehyde dehydrogenase or formate dehydrogenase (ferredoxin) from the metabolic model ofC. autoethanogenumleads to a (potential) increase in ethanol production up to 150%, which is clearly very attractive.

Microbial biosensors are used to detect the presence of compounds provided externally or produced internally. The latter case is commonly constrained by the need to screen a large library of enzyme or pathway variants to identify those that can efficiently generate the desired compound. To address this limitation, we suggest the use of metabolic sensor strains which can grow only if the relevant compound is present and thus replace screening with direct selection. We used a computational platform to design metabolic sensor strains with varying dependencies on a specific compound. Our method systematically explores combinations of gene deletions and identifies how the growth requirement for a compound changes with the media composition. We demonstrate this approach by constructing a set of E. coli glycerate sensor strains. In each of these strains a different set of enzymes is disrupted such that central metabolism is effectively dissected into multiple segments, each requiring a dedicated carbon source. We find an almost perfect match between the predicted and experimental dependence on glycerate and show that the strains can be used to accurately detect glycerate concentrations across two orders of magnitude. Apart from demonstrating the potential application of metabolic sensor strains, our work reveals key phenomena in central metabolism, including spontaneous degradation of central metabolites and the importance of metabolic sinks for balancing small metabolic networks., Metabolic Engineering, 57, ISSN:1096-7176, ISSN:1096-7184

Microbial fermentation of synthesis gas (syngas) is becoming more attractive for sustainable production of commodity chemicals. To date, syngas fermentation focuses mainly on the use of Clostridium species for the production of small organic molecules such as ethanol and acetate. The co-cultivation of syngas-fermenting microorganisms with chain-elongating bacteria can expand the range of possible products, allowing, for instance, the production of medium-chain fatty acids (MCFA) and alcohols from syngas. To explore these possibilities, we report herein a genome-scale, constraint-based metabolic model to describe growth of a co-culture of Clostridium autoethanogenum and Clostridium kluyveri on syngas for the production of valuable compounds. Community flux balance analysis was used to gain insight into the metabolism of the two strains and their interactions, and to reveal potential strategies enabling production of butyrate and hexanoate. The model suggests that one strategy to optimize the production of medium-chain fatty-acids from syngas would be the addition of succinate. According to the prediction, addition of succinate would increase the pool of crotonyl-CoA and the ethanol/acetate uptake ratio in C. kluyveri, resulting in a flux of up to 60 % of electrons into hexanoate. Another potential way to further optimize butyrate and hexanoate production would be an increase of C. autoethanogenum ethanol production. Blocking either acetaldehyde dehydrogenase or formate dehydrogenase (ferredoxin) activity or formate transport, in the C. autoethanogenum metabolic model could potentially lead to an up to 150 % increase in ethanol production.

This is a repository of the scripts used for a manuscript where we evaluated genome-scale modeling tools for microbial communities.