Microbial interactions from a new perspective: reinforcement learning reveals new insights into microbiome evolution.

MOTIVATION: Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities. RESULTS: Here, we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, which treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulation policies will stabilize in the dynamic ecosystem of interest in the presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions. AVAILABILITY AND IMPLEMENTATION: The source code for this article is available at: https://github.com/chan-csu/SPAM-DFBA.

kb_DRAM: annotation and metabolic profiling of genomes with DRAM in KBase.

Microbial genome annotation is the process of identifying structural and functional elements in DNA sequences and subsequently attaching biological information to those elements. DRAM is a tool developed to annotate bacterial, archaeal, and viral genomes derived from pure cultures or metagenomes. DRAM goes beyond traditional annotation tools by distilling multiple gene annotations to genome level summaries of functional potential. Despite these benefits, a downside of DRAM is the requirement of large computational resources, which limits its accessibility. Further, it did not integrate with downstream metabolic modeling tools that require genome annotation. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated with the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point-and-click interface, as well as generate genome-scale metabolic models from DRAM annotations. AVAILABILITY AND IMPLEMENTATION: For kb_DRAM users, the kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. For kb_DRAM users, a tutorial workflow with all documentation is available at https://narrative.kbase.us/narrative/129480. For kb_DRAM developers, software is available at https://github.com/shafferm/kb_DRAM.

Re-wiring of energy metabolism promotes viability during hyperreplication stress in E. coli.

Chromosome replication in Escherichia coli is initiated by DnaA. DnaA binds ATP which is essential for formation of a DnaA-oriC nucleoprotein complex that promotes strand opening, helicase loading and replisome assembly. Following initiation, DnaAATP is converted to DnaAADP primarily by the Regulatory Inactivation of DnaA process (RIDA). In RIDA deficient cells, DnaAATP accumulates leading to uncontrolled initiation of replication and cell death by accumulation of DNA strand breaks. Mutations that suppress RIDA deficiency either dampen overinitiation or permit growth despite overinitiation. We characterize mutations of the last group that have in common that distinct metabolic routes are rewired resulting in the redirection of electron flow towards the cytochrome bd-1. We propose a model where cytochrome bd-1 lowers the formation of reactive oxygen species and hence oxidative damage to the DNA in general. This increases the processivity of replication forks generated by overinitiation to a level that sustains viability.

SteadyCom: Predicting microbial abundances while ensuring community stability.

Genome-scale metabolic modeling has become widespread for analyzing microbial metabolism. Extending this established paradigm to more complex microbial communities is emerging as a promising way to unravel the interactions and biochemical repertoire of these omnipresent systems. While several modeling techniques have been developed for microbial communities, little emphasis has been placed on the need to impose a time-averaged constant growth rate across all members for a community to ensure co-existence and stability. In the absence of this constraint, the faster growing organism will ultimately displace all other microbes in the community. This is particularly important for predicting steady-state microbiota composition as it imposes significant restrictions on the allowable community membership, composition and phenotypes. In this study, we introduce the SteadyCom optimization framework for predicting metabolic flux distributions consistent with the steady-state requirement. SteadyCom can be rapidly converged by iteratively solving linear programming (LP) problem and the number of iterations is independent of the number of organisms. A significant advantage of SteadyCom is compatibility with flux variability analysis. SteadyCom is first demonstrated for a community of four E. coli double auxotrophic mutants and is then applied to a gut microbiota model consisting of nine species, with representatives from the phyla Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria. In contrast to the direct use of FBA, SteadyCom is able to predict the change in species abundance in response to changes in diets with minimal additional imposed constraints on the model. By randomizing the uptake rates of microbes, an abundance profile with a good agreement to experimental gut microbiota is inferred. SteadyCom provides an important step towards the cross-cutting task of predicting the composition of a microbial community in a given environment.

Combining metabolic engineering and biocompatible chemistry for high-yield production of homo-diacetyl and homo-(S,S)-2,3-butanediol.

Biocompatible chemistry is gaining increasing attention because of its potential within biotechnology for expanding the repertoire of biological transformations carried out by enzymes. Here we demonstrate how biocompatible chemistry can be used for synthesizing valuable compounds as well as for linking metabolic pathways to achieve redox balance and rescued growth. By comprehensive rerouting of metabolism, activation of respiration, and finally metal ion catalysis, we successfully managed to convert the homolactic bacterium Lactococcus lactis into a homo-diacetyl producer with high titer (95mM or 8.2g/L) and high yield (87% of the theoretical maximum). Subsequently, the pathway was extended to (S,S)-2,3-butanediol (S-BDO) through efficiently linking two metabolic pathways via chemical catalysis. This resulted in efficient homo-S-BDO production with a titer of 74mM (6.7g/L) S-BDO and a yield of 82%. The diacetyl and S-BDO production rates and yields obtained are the highest ever reported, demonstrating the promising combination of metabolic engineering and biocompatible chemistry as well as the great potential of L. lactis as a new production platform.

Elucidation of the regulatory role of the fructose operon reveals a novel target for enhancing the NADPH supply in Corynebacterium glutamicum.

The performance of Corynebacterium glutamicum cell factories producing compounds which rely heavily on NADPH has been reported to depend on the sugar being metabolized. While some aspects of this phenomenon have been elucidated, there are still many unresolved questions as to how sugar metabolism is linked to redox and to the general metabolism. We here provide new insights into the regulation of the metabolism of this important platform organism by systematically characterizing mutants carrying various lesions in the fructose operon. Initially, we found that a strain where the dedicated fructose uptake system had been inactivated (KO-ptsF) was hampered in growth on sucrose minimal medium, and suppressor mutants appeared readily. Comparative genomic analysis in conjunction with enzymatic assays revealed that suppression was linked to inactivation of the pfkB gene, encoding a fructose-1-phosphate kinase. Detailed characterization of KO-ptsF, KO-pfkB and double knock-out (DKO) derivatives revealed a strong role for sugar-phosphates, especially fructose-1-phosphate (F1P), in governing sugar as well as redox metabolism due to effects on transcriptional regulation of key genes. These findings allowed us to propose a simple model explaining the correlation between sugar phosphate concentration, gene expression and ultimately the observed phenotype. To guide us in our analysis and help us identify bottlenecks in metabolism we debugged an existing genome-scale model onto which we overlaid the transcriptome data. Based on the results obtained we managed to enhance the NADPH supply and transform the wild-type strain into delivering the highest yield of lysine ever obtained on sucrose and fructose, thus providing a good example of how regulatory mechanisms can be harnessed for bioproduction.

Estimating biological elementary flux modes that decompose a flux distribution by the minimal branching property.

MOTIVATION: Elementary flux mode (EFM) is a useful tool in constraint-based modeling of metabolic networks. The property that every flux distribution can be decomposed as a weighted sum of EFMs allows certain applications of EFMs to studying flux distributions. The existence of biologically infeasible EFMs and the non-uniqueness of the decomposition, however, undermine the applicability of such methods. Efforts have been made to find biologically feasible EFMs by incorporating information from transcriptional regulation and thermodynamics. Yet, no attempt has been made to distinguish biologically feasible EFMs by considering their graphical properties. A previous study on the transcriptional regulation of metabolic genes found that distinct branches at a branch point metabolite usually belong to distinct metabolic pathways. This suggests an intuitive property of biologically feasible EFMs, i.e. minimal branching. RESULTS: We developed the concept of minimal branching EFM and derived the minimal branching decomposition (MBD) to decompose flux distributions. Testing in the core Escherichia coli metabolic network indicated that MBD can distinguish branches at branch points and greatly reduced the solution space in which the decomposition is often unique. An experimental flux distribution from a previous study on mouse cardiomyocyte was decomposed using MBD. Comparison with decomposition by a minimum number of EFMs showed that MBD found EFMs more consistent with established biological knowledge, which facilitates interpretation. Comparison of the methods applied to a complex flux distribution in Lactococcus lactis similarly showed the advantages of MBD. The minimal branching EFM concept underlying MBD should be useful in other applications. CONTACT: sinhu@bio.dtu.dk or p.ji@polyu.edu.hk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables.

Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to identify links between taxonomy and function. This universal network of microbial decomposers is characterized by cross-feeding to metabolize labile decomposition products. The key bacterial and fungal decomposers are rare across non-decomposition environments and appear unique to the breakdown of terrestrial decaying flesh, including humans, swine, mice and cattle, with insects as likely important vectors for dispersal. The observed lockstep of microbial interactions further underlies a robust microbial forensic tool with the potential to aid predictions of the time since death.

Integrated Profiling of Gram-Positive and Gram-Negative Probiotic Genomes, Proteomes and Metabolomes Revealed Small Molecules with Differential Growth Inhibition of Antimicrobial-Resistant Pathogens.

Probiotics produce small molecules that may serve as alternatives to conventional antibiotics by suppressing growth of antimicrobial resistant (AMR) pathogens. The objective of this study was to identify and examine antimicrobials produced and secreted by probiotics using 'omics' profiling with computer-based metabolic flux analyses. The cell-free supernatant of Gram-positive Lacticaseibacillus rhamnosus GG (LGG) and Gram-negative Escherichia coli Nissle (ECN) probiotics inhibited growth of AMR Salmonella Typhimurium, Escherichia coli, and Klebsiella oxytoca ranging between 28.85 - 41.20% (LGG) and 11.48 - 29.45% (ECN). A dose dependent analysis of probiotic supernatants showed LGG was 6.27% to 20.55% more effective at reducing AMR pathogen growth when compared to ECN. Principal component analysis showed clear separation of ECN and LGG cell free supernatant metabolomes. Among 667 metabolites in the supernatant, 304 were differentially abundant between LGG and ECN probiotics. Proteomics identified 87 proteins, whereby 67 (ECN) and 14 (LGG) showed differential expression as enzymes related to carbohydrate and energy metabolic pathways. The whole genomes and metabolomes were next used for in-silico metabolic network analysis. The model predicted the production of 166 metabolites by LGG and ECN probiotics across amino acid, carbohydrate/energy, and nucleotide metabolism with antimicrobial functions. The predictive accuracy of the metabolic flux analysis highlights the novel utility for profiling probiotic supplements as dietary-based antimicrobial alternatives in the control of AMR pathogen growth.

Decomposing flux distributions into elementary flux modes in genome-scale metabolic networks.

MOTIVATION: Elementary flux mode (EFM) is a fundamental concept as well as a useful tool in metabolic pathway analysis. One important role of EFMs is that every flux distribution can be decomposed into a set of EFMs and a number of methods to study flux distributions originated from it. Yet finding such decompositions requires the complete set of EFMs, which is intractable in genome-scale metabolic networks due to combinatorial explosion. RESULTS: In this article, we proposed an algorithm to decompose flux distributions into EFMs in genome-scale networks. It is an iterative scheme of a mixed integer linear program. Unlike previous optimization models to find pathways, any feasible solutions can become EFMs in our algorithm. This advantage enables the algorithm to approximate the EFM of largest contribution to an objective reaction in a flux distribution. Our algorithm is able to find EFMs of flux distributions with complex structures, closer to the realistic case in which a cell is subject to various constraints. A case of Escherichia coli growth in the Lysogeny broth (LB) medium containing various carbon sources was studied. Essential metabolites and their syntheses were located. Information on the contribution of each carbon source not obvious from the apparent flux distribution was also revealed. Our work further confirms the utility of finding EFMs by optimization models in genome-scale metabolic networks. CONTACT: joshua.chan@connect.polyu.hk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolic Engineering Interventions for Sustainable 2,3-Butanediol Production in Gas-Fermenting Clostridium autoethanogenum.

Gas fermentation provides a promising platform to turn low-cost and readily available single-carbon waste gases into commodity chemicals, such as 2,3-butanediol. Clostridium autoethanogenum is usually used as a robust and flexible chassis for gas fermentation. Here, we leveraged constraint-based stoichiometric modeling and kinetic ensemble modeling of the C. autoethanogenum metabolic network to provide a systematic in silico analysis of metabolic engineering interventions for 2,3-butanediol overproduction and low carbon substrate loss in dissipated CO2. Our analysis allowed us to identify and to assess comparatively the expected performances for a wide range of single, double, and triple interventions. Our analysis managed to individuate bottleneck reactions in relevant metabolic pathways when suggesting intervening strategies. Besides recapitulating intuitive and/or previously attempted genetic modifications, our analysis neatly outlined that interventions-at least partially-impinging on by-products branching from acetyl coenzyme A (acetyl-CoA) and pyruvate (acetate, ethanol, amino acids) offer valuable alternatives to the interventions focusing directly on the specific branch from pyruvate to 2,3-butanediol. IMPORTANCE Envisioning value chains inspired by environmental sustainability and circularity in economic models is essential to counteract the alterations in the global natural carbon cycle induced by humans. Recycling carbon-based waste gas streams into chemicals by devising gas fermentation bioprocesses mediated by acetogens of the genus Clostridium is one component of the solution. Carbon monoxide originates from multiple biogenic and abiogenic sources and bears a significant environmental impact. This study aims at identifying metabolic engineering interventions for increasing 2,3-butanediol production and avoiding carbon loss in CO2 dissipation via C. autoethanogenum fermenting a substrate comprising CO and H2. 2,3-Butanediol is a valuable biochemical by-product since, due to its versatility, can be transformed quite easily into chemical compounds such as butadiene, diacetyl, acetoin, and methyl ethyl ketone. These compounds are usable as building blocks to manufacture a vast range of industrially produced chemicals.

Predicting Microbiome Metabolism and Interactions through Integrating Multidisciplinary Principles.

In this Commentary, we will discuss some of the current trends and challenges in modeling microbiome metabolism. A focus will be the state of the art in the integration of metabolic networks, ecological and evolutionary principles, and spatiotemporal considerations, followed by envisioning integrated frameworks incorporating different principles and data to generate predictive models in the future.

Genome-scale metabolic reconstruction of the non-model yeast Issatchenkia orientalis SD108 and its application to organic acids production.

Many platform chemicals can be produced from renewable biomass by microorganisms, with organic acids making up a large fraction. Intolerance to the resulting low pH growth conditions, however, remains a challenge for the industrial production of organic acids by microorganisms. Issatchenkia orientalis SD108 is a promising host for industrial production because it is tolerant to acidic conditions as low as pH 2.0. With the goal to systematically assess the metabolic capabilities of this non-model yeast, we developed a genome-scale metabolic model for I. orientalis SD108 spanning 850 genes, 1826 reactions, and 1702 metabolites. In order to improve the model's quantitative predictions, organism-specific macromolecular composition and ATP maintenance requirements were determined experimentally and implemented. We examined its network topology, including essential genes and flux coupling analysis and drew comparisons with the Yeast 8.3 model for Saccharomyces cerevisiae. We explored the carbon substrate utilization and examined the organism's production potential for the industrially-relevant succinic acid, making use of the OptKnock framework to identify gene knockouts which couple production of the targeted chemical to biomass production. The genome-scale metabolic model iIsor850 is a data-supported curated model which can inform genetic interventions for overproduction.

Systems Biology - A Guide for Understanding and Developing Improved Strains of Lactic Acid Bacteria.

Lactic Acid Bacteria (LAB) are extensively employed in the production of various fermented foods, due to their safe status, ability to affect texture and flavor and finally due to the beneficial effect they have on shelf-life. More recently, LAB have also gained interest as production hosts for various useful compounds, particularly compounds with sensitive applications, such as food ingredients and therapeutics. As for all industrial microorganisms, it is important to have a good understanding of the physiology and metabolism of LAB in order to fully exploit their potential, and for this purpose, many systems biology approaches are available. Systems metabolic engineering, an approach that combines optimization of metabolic enzymes/pathways at the systems level, synthetic biology as well as in silico model simulation, has been used to build microbial cell factories for production of biofuels, food ingredients and biochemicals. When developing LAB for use in foods, genetic engineering is in general not an accepted approach. An alternative is to screen mutant libraries for candidates with desirable traits using high-throughput screening technologies or to use adaptive laboratory evolution to select for mutants with special properties. In both cases, by using omics data and data-driven technologies to scrutinize these, it is possible to find the underlying cause for the desired attributes of such mutants. This review aims to describe how systems biology tools can be used for obtaining both engineered as well as non-engineered LAB with novel and desired properties.

A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data.

Rhodosporidium toruloides is a red, basidiomycetes yeast that can accumulate a large amount of lipids and produce carotenoids. To better assess this non-model yeast's metabolic capabilities, we reconstructed a genome-scale model of R. toruloides IFO0880's metabolic network (iRhto1108) accounting for 2204 reactions, 1985 metabolites and 1108 genes. In this work, we integrated and supplemented the current knowledge with in-house generated biomass composition and experimental measurements pertaining to the organism's metabolic capabilities. Predictions of genotype-phenotype relations were improved through manual curation of gene-protein-reaction rules for 543 reactions leading to correct recapitulations of 84.5% of gene essentiality data (sensitivity of 94.3% and specificity of 53.8%). Organism-specific macromolecular composition and ATP maintenance requirements were experimentally measured for two separate growth conditions: (i) carbon and (ii) nitrogen limitations. Overall, iRhto1108 reproduced R. toruloides's utilization capabilities for 18 alternate substrates, matched measured wild-type growth yield, and recapitulated the viability of 772 out of 819 deletion mutants. As a demonstration to the model's fidelity in guiding engineering interventions, the OptForce procedure was applied on iRhto1108 for triacylglycerol overproduction. Suggested interventions recapitulated many of the previous successful implementations of genetic modifications and put forth a few new ones.

Can microbes compete with cows for sustainable protein production - A feasibility study on high quality protein.

An increasing population and their increased demand for high-protein diets will require dramatic changes in the food industry, as limited resources and environmental issues will make animal derived foods and proteins, gradually more unsustainable to produce. To explore alternatives to animal derived proteins, an economic model was built around the genome-scale metabolic network of E. coli to study the feasibility of recombinant protein production as a food source. Using a novel model, we predicted which microbial production strategies are optimal for economic return, by capturing the tradeoff between the market prices of substrates, product output and the efficiency of microbial production. A case study with the food protein, Bovine Alpha Lactalbumin was made to evaluate the upstream economic feasibilities. Simulations with different substrate profiles at maximum productivity were used to explore the feasibility of recombinant Bovine Alpha Lactalbumin production coupled with market prices of utilized materials. We found that recombinant protein production could be a feasible food source and an alternative to traditional sources.

Acetate kinase isozymes confer robustness in acetate metabolism.

Acetate kinase (ACK) (EC no: 2.7.2.1) interconverts acetyl-phosphate and acetate to either catabolize or synthesize acetyl-CoA dependent on the metabolic requirement. Among all ACK entries available in UniProt, we found that around 45% are multiple ACKs in some organisms including more than 300 species but surprisingly, little work has been done to clarify whether this has any significance. In an attempt to gain further insight we have studied the two ACKs (AckA1, AckA2) encoded by two neighboring genes conserved in Lactococcus lactis (L. lactis) by analyzing protein sequences, characterizing transcription structure, determining enzyme characteristics and effect on growth physiology. The results show that the two ACKs are most likely individually transcribed. AckA1 has a much higher turnover number and AckA2 has a much higher affinity for acetate in vitro. Consistently, growth experiments of mutant strains reveal that AckA1 has a higher capacity for acetate production which allows faster growth in an environment with high acetate concentration. Meanwhile, AckA2 is important for fast acetate-dependent growth at low concentration of acetate. The results demonstrate that the two ACKs have complementary physiological roles in L. lactis to maintain a robust acetate metabolism for fast growth at different extracellular acetate concentrations. The existence of ACK isozymes may reflect a common evolutionary strategy in bacteria in an environment with varying concentrations of acetate.