Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments

Deciphering and designing microbial communities by genome-scale metabolic modelling

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

The effect of redox fluctuation on carbon mineralization in riparian soil: An analysis of the hotspot zone of reactive oxygen species production

Riparian zones are important depositional environments at the catchment scale and provide environmental services such as carbon sequestration. This zone is a highly dynamic interface for oxygen and electron exchange, which confers the basis for reactive oxygen species (ROS) production. However, the differences in soil ROS production and their impact on carbon turnover across various redox locations within the riparian zone remain to be fully elucidated. In this study, we investigated the distribution characteristics and generation mechanism of ROS in riparian soil based on soil samples collected in a three-month field monitoring experiment, with additional incubation experiments conducted to examine the effect of hydroxyl radical (•OH) on soil organic carbon (SOC) mineralization. The obtained results demonstrated that the riverine wetland was the hotspot zone for •OH production, with the production flux of 13.05 μmol kg-1 d-1, which was significantly higher than that in floodplain (7.29 μmol kg-1 d-1) and riverbank soils (8.61 μmol kg-1 d-1). Moreover, •OH levels displayed distinct rhythmic fluctuations, with significantly higher concentrations at low water levels compared to those at high water levels, and remained essentially flat over three cycles. The statistic analysis revealed that the ROS production was highly dependent on reduced species and microbial community structure, which function as biogeochemical batteries and electron shuttles under redox fluctuations. Furthermore, the generated •OH involved in the abiotic mineralization of SOC, contributing to 13.1‒21.8 % of total CO2 efflux. Compared to particulate organic carbon (POC), mineral-associated organic carbon (MAOC) fractions of SOC were more susceptible to •OH attacks. The findings provide a novel insight to comprehensively assess the redox process on riparian carbon turnover.

Metabolic cross-feeding interactions modulate the dynamic community structure in microbial fuel cell under variable organic loading wastewaters

The efficiency of microbial fuel cells (MFCs) in industrial wastewater treatment is profoundly influenced by the microbial community, which can be disrupted by variable industrial operations. Although microbial guilds linked to MFC performance under specific conditions have been identified, comprehensive knowledge of the convergent community structure and pathways of adaptation is lacking. Here, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) based on metabolic cross-feeding to study the adaptation of microbial communities in MFCs treating sulfide-containing wastewater from a canned-pineapple factory. The metabolic model encompassed three major microbial guilds: sulfate-reducing bacteria (SRB), methanogens (MET), and sulfide-oxidizing bacteria (SOB). Our findings revealed a shift from an SOB-dominant to MET-dominant community as organic loading rates (OLRs) increased, along with a decline in MFC performance. The mmGEM accurately predicted microbial relative abundance at low OLRs (L-OLRs) and adaptation to high OLRs (H-OLRs). The simulations revealed constraints on SOB growth under H-OLRs due to reduced sulfate-sulfide (S) cycling and acetate cross-feeding with SRB. More cross-fed metabolites from SRB were diverted to MET, facilitating their competitive dominance. Assessing cross-feeding dynamics under varying OLRs enabled the execution of practical scenario-based simulations to explore the potential impact of elevated acidity levels on SOB growth and MFC performance. This work highlights the role of metabolic cross-feeding in shaping microbial community structure in response to high OLRs. The insights gained will inform the development of effective strategies for implementing MFC technology in real-world industrial environments.

Genome-scale models in human metabologenomics

Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.

DyMMM-LEAPS: An ML-based framework for modulating evenness and stability in synthetic microbial communities

There have been a growing number of computational strategies to aid in the design of synthetic microbial consortia. A framework to identify regions in parametric space to maximize two essential properties, evenness and stability, is critical. In this study, we introduce DyMMM-LEAPS (dynamic multispecies metabolic modeling-locating evenness and stability in large parametric space), an extension of the DyMMM framework. Our method explores the large parametric space of genetic circuits in synthetic microbial communities to identify regions of evenness and stability. Due to the high computational costs of exhaustive sampling, we utilize adaptive sampling and surrogate modeling to reduce the number of simulations required to map the vast space. Our framework predicts engineering targets and computes their operating ranges to maximize the probability of the engineered community to have high evenness and stability. We demonstrate our approach by simulating five cocultures and one three-strain culture with different social interactions (cooperation, competition, and predation) employing quorum-sensing-based genetic circuits. In addition to guiding circuit tuning, our pipeline gives an opportunity for a detailed analysis of pockets of evenness and stability for the circuit under investigation, which can further help dissect the relationship between the two properties. DyMMM-LEAPS is easily customizable and can be expanded to a larger community with more complex interactions.

Spatiotemporal dynamics of reactive oxygen species and its effect on beta-blockers' degradation in aquatic plants' rhizosphere

The pathway for pollutant degradation involving reactive oxygen species (ROS) in the rhizosphere is poorly understood. Herein, a rootchip system was developed to pinpoint the ROS hotspot along the root tip of Iris tectorum. Through mass balance analysis and quenching experiment, we revealed that ROS contributed significantly to rhizodegradation for beta-blockers, ranging from 22.18 % for betaxolol to 83.83 % for atenolol. The identification of degradation products implicated ROS as an important agent to degrade atenolol into less toxic transformation products during phytoremediation. Moreover, an active production of ROS in rhizosphere was identified by mesocosm experiment. Across three root-associated regions aquatic plants inhabiting the rhizosphere accumulated the highest •OH of ∼1200 nM after 3 consecutive days, followed by rhizoplane (∼230 nM) and bulk environment (∼60 nM). ROS production patterns were driven by rhizosphere chemistry (Fe and humic substances) and microbiome variations in different rhizocompartments. These findings not only deepen understanding of ROS production in aquatic plants rhizosphere but also shed light on advancing phytoremediation strategies.

Sponge iron enriches autotrophic/aerobic denitrifying bacteria to enhance denitrification in sequencing batch reactor

Sponge iron (SFe) coupled with a sludge system has great potential for improving biological denitrification; however, the underlying mechanism is not yet fully understood. In this study, the denitrification performance and microbial characteristics of ordinary sludge and SFe-sludge systems were investigated. Overall, the SFe-sludge reactor had faster ammonium degradation rate (94.0 %) and less nitrate accumulation (1.5-53.3 times lower) than ordinary reactor during the complete operation cycle of sequencing batch reactors. The addition of SFe increased the activities of nitrate and nitrite reductases. The total relative abundance of autotrophic denitrifying bacteria (Acidovorax, Arenimonas, etc.) in the SFe-sludge system after 38 days of operation was found to be 10.6 % higher than that in the ordinary sludge reactor. The aerobic denitrifying bacteria (Dokdonella, Phaeodactylibacter, etc.) was 5.3 % higher than ordinary sludge. The SFe-sludge system improved denitrification by enriching autotrophic/aerobic denitrifying bacteria in low carbon-to-nitrogen ratio wastewater treatment.

Reconstruction and Analysis of a Genome-Scale Metabolic Model of Acinetobacter lwoffii

Acinetobacter lwoffii is widely considered to be a harmful bacterium that is resistant to medicines and disinfectants. A. lwoffii NL1 degrades phenols efficiently and shows promise as an aromatic compound degrader in antibiotic-contaminated environments. To gain a comprehensive understanding of A. lwoffii, the first genome-scale metabolic model of A. lwoffii was constructed using semi-automated and manual methods. The iNX811 model, which includes 811 genes, 1071 metabolites, and 1155 reactions, was validated using 39 unique carbon and nitrogen sources. Genes and metabolites critical for cell growth were analyzed, and 12 essential metabolites (mainly in the biosynthesis and metabolism of glycan, lysine, and cofactors) were identified as antibacterial drug targets. Moreover, to explore the metabolic response to phenols, metabolic flux was simulated by integrating transcriptomics, and the significantly changed metabolism mainly included central carbon metabolism, along with some transport reactions. In addition, the addition of substances that effectively improved phenol degradation was predicted and validated using the model. Overall, the reconstruction and analysis of model iNX811 helped to study the antimicrobial systems and biodegradation behavior of A. lwoffii.

GEM-based computational modeling for exploring metabolic interactions in a microbial community

Microbial communities play fundamental roles in every complex ecosystem, such as soil, sea and the human body. The stability and diversity of the microbial community depend precisely on the composition of the microbiota. Any change in the composition of these communities affects microbial functions. An important goal of studying the interactions between species is to understand the behavior of microbes and their responses to perturbations. These interactions among species are mediated by the exchange of metabolites within microbial communities. We developed a computational model for the microbial community that has a separate compartment for exchanging metabolites. This model can predict possible metabolites that cause competition, commensalism, and mutual interactions between species within a microbial community. Our constraint-based community metabolic modeling approach provides insights to elucidate the pattern of metabolic interactions for each common metabolite between two microbes. To validate our approach, we used a toy model and a syntrophic co-culture of Desulfovibrio vulgaris and Methanococcus maripaludis, as well as another in co-culture between Geobacter sulfurreducens and Rhodoferax ferrireducens. For a more general evaluation, we applied our algorithm to the honeybee gut microbiome, composed of seven species, and the epiphyte strain Pantoea eucalypti 299R. The epiphyte strain Pe299R has been previously studied and cultured with six different phyllosphere bacteria. Our algorithm successfully predicts metabolites, which imply mutualistic, competitive, or commensal interactions. In contrast to OptCom, MRO, and MICOM algorithms, our COMMA algorithm shows that the potential for competitive interactions between an epiphytic species and Pe299R is not significant. These results are consistent with the experimental measurements of population density and reproductive success of the Pe299R strain.

Probing interspecies metabolic interactions within a synthetic binary microbiome using genome-scale modeling

Aim: Metabolic interactions within a microbial community play a key role in determining the structure, function, and composition of the community. However, due to the complexity and intractability of natural microbiomes, limited knowledge is available on interspecies interactions within a community. In this work, using a binary synthetic microbiome, a methanotroph-photoautotroph (M-P) coculture, as the model system, we examined different genome-scale metabolic modeling (GEM) approaches to gain a better understanding of the metabolic interactions within the coculture, how they contribute to the enhanced growth observed in the coculture, and how they evolve over time. Methods: Using batch growth data of the model M-P coculture, we compared three GEM approaches for microbial communities. Two of the methods are existing approaches: SteadyCom, a steady state GEM, and dynamic flux balance analysis (DFBA) Lab, a dynamic GEM. We also proposed an improved dynamic GEM approach, DynamiCom, for the M-P coculture. Results: SteadyCom can predict the metabolic interactions within the coculture but not their dynamic evolutions; DFBA Lab can predict the dynamics of the coculture but cannot identify interspecies interactions. DynamiCom was able to identify the cross-fed metabolite within the coculture, as well as predict the evolution of the interspecies interactions over time. Conclusion: A new dynamic GEM approach, DynamiCom, was developed for a model M-P coculture. Constrained by the predictions from a validated kinetic model, DynamiCom consistently predicted the top metabolites being exchanged in the M-P coculture, as well as the establishment of the mutualistic N-exchange between the methanotroph and cyanobacteria. The interspecies interactions and their dynamic evolution predicted by DynamiCom are supported by ample evidence in the literature on methanotroph, cyanobacteria, and other cyanobacteria-heterotroph cocultures.

Rational construction of synthetic consortia: Key considerations and model-based methods for guiding the development of a novel biosynthesis platform

The rapid development of synthetic biology has significantly improved the capabilities of mono-culture systems in converting different substrates into various value-added bio-chemicals through metabolic engineering. However, overexpression of biosynthetic pathways in recombinant strains can impose a heavy metabolic burden on the host, resulting in imbalanced energy distribution and negatively affecting both cell growth and biosynthesis capacity. Synthetic consortia, consisting of two or more microbial species or strains with complementary functions, have emerged as a promising and efficient platform to alleviate the metabolic burden and increase product yield. However, research on synthetic consortia is still in its infancy, with numerous challenges regarding the design and construction of stable synthetic consortia. This review provides a comprehensive comparison of the advantages and disadvantages of mono-culture systems and synthetic consortia. Key considerations for engineering synthetic consortia based on recent advances are summarized, and simulation and computational tools for guiding the advancement of synthetic consortia are discussed. Moreover, further development of more efficient and cost-effective synthetic consortia with emerging technologies such as artificial intelligence and machine learning is highlighted.

Community assembly and microbial interactions in an alkaline vanadium tailing pond

Intensive development of vanadium-titanium mines leads to an increasing discharge of vanadium (V) into the environment, imposing potential risks to both environmental system and public health. Microorganisms play a key role in the biogeochemical cycling of V, influencing its transformation and distribution. In addition, the characterization of microbial community patterns serves to assess potential threats imposed by elevated V exposure. However, the impact of V on microbial community remains largely unknown in alkaline V tailing areas with a substantial amounts of V accumulation and nutrient-poor conditions. This study aims to explore the characteristics of microbial community in a wet tailing pond nearby a large-scale V mine. The results reveal V contamination in both water (0.60 mg/L) and sediment tailings (340 mg/kg) in the tailing pond. Microbial community diversity shows distinctive pattern between environmental metrices. Genera with the functional potential of metal reduction\resistance, nitrogen metabolism, and carbon fixation have been identified. In this alkaline V tailing pond, V and pH are major drivers to induce community variation, particularly for functional bacteria. Stochastic processes primarily govern the assemblies of microbial community in the water samples, while deterministic process regulate the community assemblies of sediment tailings. Moreover, the co-occurrence network pattern unveils strong selective pattern for sediment tailings communities, where genera form a complex network structure exhibiting strong competition for limited resource. These findings reveal the patterns of microbial adaptions in wet vanadium tailing ponds, providing insightful guidelines to mitigate the negative impact of V tailing and develop sustainable management for mine-waste reservoir.

Progress on network modeling and analysis of gut microecology: a review

The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota's impact on human health.

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Predicting microbial interactions with approaches based on flux balance analysis: an evaluation

BACKGROUND

Given a genome-scale metabolic model (GEM) of a microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate and its corresponding flux distribution for a specific medium. FBA has been extended to microbial consortia and thus can be used to predict interactions by comparing in-silico growth rates for co- and monocultures. Although FBA-based methods for microbial interaction prediction are becoming popular, a systematic evaluation of their accuracy has not yet been performed.

RESULTS

Here, we evaluate the accuracy of FBA-based predictions of human and mouse gut bacterial interactions using growth data from the literature. For this, we collected 26 GEMs from the semi-curated AGORA database as well as four previously published curated GEMs. We tested the accuracy of three tools (COMETS, Microbiome Modeling Toolbox and MICOM) by comparing growth rates predicted in mono- and co-culture to growth rates extracted from the literature and also investigated the impact of different tool settings and media. We found that except for curated GEMs, predicted growth rates and their ratios (i.e. interaction strengths) do not correlate with growth rates and interaction strengths obtained from in vitro data.

CONCLUSIONS

Prediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.

Computational assessment of lipid production in Rhodosporidium toruloides in two-stage and one-stage batch bioprocesses

Oleaginous yeasts are promising platforms for microbial lipids production as a renewable and sustainable alternative to vegetable oils in biodiesel production. In this paper, a thorough in silico assessment of lipid production in batch cultivation by Rhodosporidium toruloides was developed. By means of dynamic flux balance analysis, the traditional two-stage bioprocess (TSB) performed by the native strain was contrasted with one-stage bioprocess (OSB) using four designed strains obtained by gene knockout strategies. Lipid titer, yield, content, and productivity were analyzed at different initial C/N ratios as relevant performance indicators used in bioprocesses. By weighting these indicators, a global lipid efficiency metric (GLEM) was defined to consider different scenarios. Under simulated conditions, designed strains for lipid overproduction in OSB outperformed the TSB in terms of lipid title (up to threefold), lipid yield (up to 2.4-fold), lipid content (up to 2.8-fold, with a maximum of 76%), and productivity (up to 1.3-fold), depending on C/N ratios. Using these efficiency parameters and the proposed GLEM, the process of selecting the most suitable candidates for lipid production could be carried out before experimental assays. This methodology holds the potential to be extended to other oleaginous microorganisms and diverse strain design techniques.

From microbiome composition to functional engineering, one step at a time

SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.

Potential natural attenuation of petroleum hydrocarbons in fuel contaminated soils: Focusing on anaerobic fuel biodegradation involving microbial Fe(III) reduction

Liquid fossil fuels, collectively known as total petroleum hydrocarbons (TPHs), are highly toxic and frequently leak into subsurface environments due to anthropogenic activities. As an in-situ biological remedial option for TPH contamination, aerobic TPH biodegradation is limited due to oxygen's low solubility in water, and because it is consumed quickly by aerobic bacteria. Thus, we investigated the potential of anaerobic TPH degradation by indigenous fermenting bacteria and Fe(III)-reducing bacteria. Twenty 6-10 m soil cores were collected from a closed military base subject to ongoing TPH contamination since the 1980s. Physicochemical and microbial properties were determined at 0.5-m intervals in each core. To assess the relationship between TPH degradation and microbial Fe(III) reduction, soil samples were grouped into high-TPH (>500 mg kg-1) and high-Fe(II) (>450 mg kg-1), high-TPH and low-Fe(II), low-TPH and high-Fe(II), and low-TPH and low-Fe(II) groups. Alpha diversity was significantly lower in high-TPH groups than in low-TPH groups, suggesting that high TPH concentrations exerted a strong selective pressure on bacterial communities. In the high-TPH and low-Fe(II) group, fermenting bacteria, including Microgenomatia and Chlamydiae, were more abundant, suggesting that TPH biodegradation occurred via fermentation. In the high-TPH and high-Fe(II) group, Fe(III)-reducing bacteria, including Geobacter and Zoogloea, were more abundant, suggesting that microbial Fe(III) reduction enhances TPH biodegradation. In contrast, the fermenting and/or Fe(III)-reducing bacteria were not statistically abundant in the low-TPH groups.

Unveiling metabolic pathways involved in the extreme desiccation tolerance of an Atacama cyanobacterium

Gloeocapsopsis dulcis strain AAB1 is an extremely xerotolerant cyanobacterium isolated from the Atacama Desert (i.e., the driest and oldest desert on Earth) that holds astrobiological significance due to its ability to biosynthesize compatible solutes at ultra-low water activities. We sequenced and assembled the G. dulcis genome de novo using a combination of long- and short-read sequencing, which resulted in high-quality consensus sequences of the chromosome and two plasmids. We leveraged the G. dulcis genome to generate a genome-scale metabolic model (iGd895) to simulate growth in silico. iGd895 represents, to our knowledge, the first genome-scale metabolic reconstruction developed for an extremely xerotolerant cyanobacterium. The model's predictive capability was assessed by comparing the in silico growth rate with in vitro growth rates of G. dulcis, in addition to the synthesis of trehalose. iGd895 allowed us to explore simulations of key metabolic processes such as essential pathways for water-stress tolerance, and significant alterations to reaction flux distribution and metabolic network reorganization resulting from water limitation. Our study provides insights into the potential metabolic strategies employed by G. dulcis, emphasizing the crucial roles of compatible solutes, metabolic water, energy conservation, and the precise regulation of reaction rates in their adaptation to water stress.

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia

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 by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for 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.

Microbial Communities in Ferromanganese Sediments from the Northern Basin of Lake Baikal (Russia)

We analyzed the amplicons of the 16S rRNA genes and assembled metagenome-assembled genomes (MAGs) of the enrichment culture from the Fe-Mn layer to have an insight into the diversity and metabolic potential of microbial communities from sediments of two sites in the northern basin of Lake Baikal. Organotrophic Chloroflexota, Actionobacteriota, and Acidobacteriota, as well as aerobic and anaerobic participants of the methane cycle (Methylococcales and Methylomirabilota, respectively), dominated the communities of the surface layers. With depth, one of the cores showed a decrease in the proportion of the Chloroflexota and Acidobacteriota members and a substantial increase in the sequences of the phylum Firmicutes. The proportion of the Desulfobacteriota and Thermodesulfovibronia (Nitrospirota) increased in another core. The composition of archaeal communities was similar between the investigated sites and differed in depth. Members of ammonia-oxidizing archaea (Nitrososphaeria) predominated in the surface sediments, with an increase in anaerobic methanotrophs (Methanoperedenaceae) and organoheterotrophs (Bathyarchaeia) in deep sediments. Among the 37 MAGs, Gammaproteobacteria, Desulfobacteriota, and Methylomirabilota were the most common in the microbial community. Metagenome sequencing revealed the assembled genomes genes for N, S, and CH₄ metabolism for carbon fixation, and genes encoding Fe and Mn pathways, indicating the likely coexistence of the biogeochemical cycle of various elements and creating certain conditions for the development of taxonomically and functionally diverse microbial communities.

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.

Systems biology and metabolic modeling for cultivated meat: A promising approach for cell culture media optimization and cost reduction

The cultivated meat industry, also known as cell-based meat, cultured meat, lab-grown meat, or meat alternatives, is a growing field that aims to generate animal tissues ex-vivo in a cost-effective manner that achieves price parity with traditional agricultural products. However, cell culture media costs account for 55%-90% of production costs. To address this issue, efforts are aimed at optimizing media composition. Systems biology-driven approaches have been successfully used to improve the biomass and productivity of multiple bioproduction platforms, like Chinese hamster ovary cells, by accelerating the development of cell line-specific media and reducing research and development and production costs related to cell media and its optimization. In this review, we summarize systems biology modeling approaches, methods for cell culture media and bioprocess optimization, and metabolic studies done in animals of interest to the cultivated meat industry. More importantly, we identify current gaps in knowledge that prevent the identification of metabolic bottlenecks. These include the lack of genome-scale metabolic models for some species (pigs and ducks), a lack of accurate biomass composition studies for different growth conditions, and 13 C-metabolic flux analysis (MFA) studies for many of the species of interest for the cultivated meat industry (only shrimp and duck cells have been subjected to 13 C-MFA). We also highlight the importance of characterizing the metabolic requirements of cells at the organism, breed, and cell line-specific levels, and we outline future steps that this nascent field needs to take to achieve price parity and production efficiency similar to those of other bioproduction platforms. Practical Application: Our article summarizes systems biology techniques for cell culture media design and bioprocess optimization, which may be used to significantly reduce cell-based meat production costs. We also present the results of experimental studies done on some of the species of interest to the cultivated meat industry and highlight why modeling approaches are required for multiple species, cell-types, and cell lines.

Assumptions on decision making and environment can yield multiple steady states in microbial community models

BACKGROUND

Microbial community simulations using genome scale metabolic networks (GSMs) are relevant for many application areas, such as the analysis of the human microbiome. Such simulations rely on assumptions about the culturing environment, affecting if the culture may reach a metabolically stationary state with constant microbial concentrations. They also require assumptions on decision making by the microbes: metabolic strategies can be in the interest of individual community members or of the whole community. However, the impact of such common assumptions on community simulation results has not been investigated systematically.

RESULTS

Here, we investigate four combinations of assumptions, elucidate how they are applied in literature, provide novel mathematical formulations for their simulation, and show how the resulting predictions differ qualitatively. Our results stress that different assumption combinations give qualitatively different predictions on microbial coexistence by differential substrate utilization. This fundamental mechanism is critically under explored in the steady state GSM literature with its strong focus on coexistence states due to crossfeeding (division of labor). Furthermore, investigating a realistic synthetic community, where the two involved strains exhibit no growth in isolation, but grow as a community, we predict multiple modes of cooperation, even without an explicit cooperation mechanism.

CONCLUSIONS

Steady state GSM modelling of microbial communities relies both on assumed decision making principles and environmental assumptions. In principle, dynamic flux balance analysis addresses both. In practice, our methods that address the steady state directly may be preferable, especially if the community is expected to display multiple steady states.

More is Different: Metabolic Modeling of Diverse Microbial Communities

Microbial consortia drive essential processes, ranging from nitrogen fixation in soils to providing metabolic breakdown products to animal hosts. However, it is challenging to translate the composition of microbial consortia into their emergent functional capacities. Community-scale metabolic models hold the potential to simulate the outputs of complex microbial communities in a given environmental context, but there is currently no consensus for what the fitness function of an entire community should look like in the presence of ecological interactions and whether community-wide growth operates close to a maximum. Transitioning from single-taxon genome-scale metabolic models to multitaxon models implies a growth cone without a well-specified growth rate solution for individual taxa. Here, we argue that dynamic approaches naturally overcome these limitations, but they come at the cost of being computationally expensive. Furthermore, we show how two nondynamic, steady-state approaches approximate dynamic trajectories and pick ecologically relevant solutions from the community growth cone with improved computational scalability.

New perspectives into the vaginal microbiome with systems biology

The vaginal microbiome (VMB) is critical to female reproductive health; however, the mechanisms associated with optimal and non-optimal states remain poorly understood due to the complex community structure and dynamic nature. Quantitative systems biology techniques applied to the VMB have improved understanding of community composition and function using primarily statistical methods. In contrast, fewer mechanistic models that use a priori knowledge of VMB features to develop predictive models have been implemented despite their use for microbiomes at other sites, including the gastrointestinal tract. Here, we explore systems biology approaches that have been applied in the VMB, highlighting successful techniques and discussing new directions that hold promise for improving understanding of health and disease.

Microbial iron cycling is prevalent in water-logged Alaskan Arctic tundra habitats, but sensitive to disturbance

Water logged habitats in continuous permafrost regions provide extensive oxic-anoxic interface habitats for iron cycling. The iron cycle interacts with the methane and phosphorus cycles, and is an important part of tundra biogeochemistry. Our objective was to characterize microbial communities associated with the iron cycle within natural and disturbed habitats of the Alaskan Arctic tundra. We sampled aquatic habitats within natural, undisturbed and anthropogenically disturbed areas and sequenced the 16S rRNA gene to describe the microbial communities, then supported these results with process rate and geochemical measurements. Undisturbed habitats have microbial communities that are significantly different than disturbed habitats. Microbial taxa known to participate in the iron and methane cycles are significantly associated with natural habitats, whereas they are not significantly associated with disturbed sites. Undisturbed habitats have significantly higher extractable iron and are more acidic than disturbed habitats sampled. Iron reduction is not measurable in disturbed aquatic habitats and is not stimulated by the addition of biogenic iron mats. Our study highlights the prevalence of Fe-cycling in undisturbed water-logged habitats, and demonstrates that anthropogenic disturbance of the tundra, due to legacy gravel mining, alters the microbiology of aquatic habitats and disrupts important biogeochemical cycles in the Arctic tundra.

Metabolic models of human gut microbiota: Advances and challenges

The human gut is a complex ecosystem consisting of hundreds of microbial species interacting with each other and with the human host. Mathematical models of the gut microbiome integrate our knowledge of this system and help to formulate hypotheses to explain observations. The generalized Lotka-Volterra model has been widely used for this purpose, but it does not describe interaction mechanisms and thus does not account for metabolic flexibility. Recently, models that explicitly describe gut microbial metabolite production and consumption have become popular. These models have been used to investigate the factors that shape gut microbial composition and to link specific gut microorganisms to changes in metabolite concentrations found in diseases. Here, we review how such models are built and what we have learned so far from their application to human gut microbiome data. In addition, we discuss current challenges of these models and how these can be addressed in the future.

A Multiscale Spatiotemporal Model Including a Switch from Aerobic to Anaerobic Metabolism Reproduces Succession in the Early Infant Gut Microbiota

The human intestinal microbiota starts to form immediately after birth and is important for the health of the host. During the first days, facultatively anaerobic bacterial species generally dominate, such as Enterobacteriaceae. These are succeeded by strictly anaerobic species, particularly Bifidobacterium species. An early transition to Bifidobacterium species is associated with health benefits; for example, Bifidobacterium species repress growth of pathogenic competitors and modulate the immune response. Succession to Bifidobacterium is thought to be due to consumption of intracolonic oxygen present in newborns by facultative anaerobes, including Enterobacteriaceae. To study if oxygen depletion suffices for the transition to Bifidobacterium species, here we introduced a multiscale mathematical model that considers metabolism, spatial bacterial population dynamics, and cross-feeding. Using publicly available metabolic network data from the AGORA collection, the model simulates ab initio the competition of strictly and facultatively anaerobic species in a gut-like environment under the influence of lactose and oxygen. The model predicts that individual differences in intracolonic oxygen in newborn infants can explain the observed individual variation in succession to anaerobic species, in particular Bifidobacterium species. Bifidobacterium species became dominant in the model by their use of the bifid shunt, which allows Bifidobacterium to switch to suboptimal yield metabolism with fast growth at high lactose concentrations, as predicted here using flux balance analysis. The computational model thus allows us to test the internal plausibility of hypotheses for bacterial colonization and succession in the infant colon. IMPORTANCE The composition of the infant microbiota has a great impact on infant health, but its controlling factors are still incompletely understood. The frequently dominant anaerobic Bifidobacterium species benefit health, e.g., they can keep harmful competitors under control and modulate the intestinal immune response. Controlling factors could include nutritional composition and intestinal mucus composition, as well as environmental factors, such as antibiotics. We introduce a modeling framework of a metabolically realistic intestinal microbial ecology in which hypothetical scenarios can be tested and compared. We present simulations that suggest that greater levels of intraintestinal oxygenation more strongly delay the dominance of Bifidobacterium species, explaining the observed variety of microbial composition and demonstrating the use of the model for hypothesis generation. The framework allowed us to test a variety of controlling factors, including intestinal mixing and transit time. Future versions will also include detailed modeling of oligosaccharide and mucin metabolism.

Unravelling key factors controlling vivianite formation during anaerobic digestion of waste activated sludge

As a product of phosphorous recovery from anaerobic digestion (AD) of waste activated sludge (WAS), vivianite has received increasing attention. However, key factors controlling vivianite formation have not yet been fully addressed. Thus, this study was initiated to ascertain key factors controlling vivianite formation. A simulation of chemical equilibriums indicates that interfering ions such as metallic ions and inorganic compounds may affect vivianite formation, especially at a PO₄^3-concentration lower than 3 mM. The experiments demonstrated that the rate of ferric bio-reduction conducted by dissimilatory metal-reducing bacteria (DMRB) and the competition of methane-producing bacteria (MPB) with DMRB for VFAs (acetate) were not the key factors controlling vivianite formation, and that ferric bio-reduction of DMRB can proceed when a sufficient amount of Fe³⁺ exists in WAS. The determined affinity constants (K_s) of both DMRB and MPB on acetate revealed that the K_HAc constant (4.2 mmol/g VSS) of DMRB was almost 4 times lower than that of MPB (15.67 mmol/g VSS) and thus MPB could not seriously compete for VFAs (acetate) with DMRB. As a result, vivianite formation was controlled mainly by the amount of Fe³⁺ in WAS. In practice, a Fe/P molar ratio of 2:1 should be enough for vivianite formation in AD of WAS. Otherwise, exogenously dosing Fe³⁺ or Fe²⁺ into AD must be applied in AD.

Compositional and Metabolic Responses of Autotrophic Microbial Community to Salinity in Lacustrine Environments

The compositional and physiological responses of autotrophic microbiotas to salinity in lakes remain unclear. In this study, the community composition and carbon fixation pathways of autotrophic microorganisms in lacustrine sediments with a salinity gradient (82.6 g/L to 0.54 g/L) were investigated by using metagenomic analysis. A total of 117 metagenome-assembled genomes (MAGs) with carbon fixation potentially belonging to 20 phyla were obtained. The abundance of these potential autotrophs increased significantly with decreasing salinity, and the variation of sediment autotrophic microbial communities was mainly affected by salinity, pH, and total organic carbon. Notably, along the decreasing salinity gradient, the dominant lineage shifted from Desulfobacterota to Proteobacteria. Meanwhile, the dominant carbon fixation pathway shifted from the Wood-Lungdahl pathway to the less-energy-efficient Calvin-Benson-Bassham cycle, with glycolysis shifting from the Embden-Meyerhof-Parnas pathway to the less-exergonic Entner-Doudoroff pathway. These results suggest that the physiological efficiency of autotrophic microorganisms decreased when the environmental salinity became lower. Metabolic inference of these MAGs revealed that carbon fixation may be coupled to the oxidation of reduced sulfur compounds and ferrous iron, dissimilatory nitrate reduction at low salinity, and dissimilatory sulfate reduction in hypersaline sediments. These results extend our understanding of metabolic versatility and niche diversity of autotrophic microorganisms in saline environments and shed light on the response of autotrophic microbiomes to salinity. These findings are of great significance for understanding the impact of desalination caused by climate warming on the carbon cycle of saline lake ecosystems. IMPORTANCE The Qinghai-Tibetan lakes are experiencing water increase and salinity decrease due to climate warming. However, little is known about how the salinity decrease will affect the composition of autotrophic microbial populations and their carbon fixation pathways. In this study, we used genome-resolved metagenomics to interpret the dynamic changes in the autotrophic microbial community and metabolic pathways along a salinity gradient. The results showed that desalination drove the shift of the dominant microbial lineage from Desulfobacterota to Proteobacteria, enriched autotrophs with lower physiological efficiency pathways, and enhanced coupling between the carbon cycle and other element cycles. These results can predict the future response of microbial communities to lake desalination and improve our understanding of the effect of climate warming on the carbon cycle in saline aquatic ecosystems.

Benthic Microbial Communities in a Seasonally Ice-Covered Sub-Arctic River (Pasvik River, Norway) Are Shaped by Site-Specific Environmental Conditions

The Pasvik River experiences chemical, physical, and biological stressors due to the direct discharges of domestic sewage from settlements located within the catchment and runoff from smelter and mine wastes. Sediments, as a natural repository of organic matter and associated contaminants, are of global concern for the possible release of pollutants in the water column, with detrimental effects on aquatic organisms. The present study was aimed at characterizing the riverine benthic microbial community and evaluating its ecological role in relation to the contamination level. Sediments were sampled along the river during two contrasting environmental periods (i.e., beginning and ongoing phases of ice melting). Microbial enzymatic activities, cell abundance, and morphological traits were evaluated, along with the phylogenetic community composition. Amplified 16S rRNA genes from bacteria were sequenced using a next-generation approach. Sediments were also analyzed for a variety of chemical features, namely particulate material characteristics and concentration of polychlorobiphenyls, polycyclic aromatic hydrocarbons, and pesticides. Riverine and brackish sites did not affect the microbial community in terms of main phylogenetic diversity (at phylum level), morphometry, enzymatic activities, and abundance. Instead, bacterial diversity in the river sediments appeared to be influenced by the micro-niche conditions, with differences in the relative abundance of selected taxa. In particular, our results highlighted the occurrence of bacterial taxa directly involved in the C, Fe, and N cycles, as well as in the degradation of organic pollutants and toxic compounds.

Salinity Impact on Composition and Activity of Nitrate-Reducing Fe(II)-Oxidizing Microorganisms in Saline Lakes

Nitrate-reducing Fe(II)-oxidizing (NRFeOx) microorganisms contribute to nitrogen, carbon, and iron cycling in freshwater and marine ecosystems. However, NRFeOx microorganisms have not been investigated in hypersaline lakes, and their identity, as well as their activity in response to salinity, is unknown. In this study, we combined cultivation-based most probable number (MPN) counts with Illumina MiSeq sequencing to analyze the abundance and community compositions of NRFeOx microorganisms enriched from five lake sediments with different salinities (ranging from 0.67 g/L to 346 g/L). MPN results showed that the abundance of NRFeOx microorganisms significantly (P < 0.05) decreased with increasing lake salinity, from 7.55 × 10³ to 8.09 cells/g dry sediment. The community composition of the NRFeOx enrichment cultures obtained from the MPNs differed distinctly among the five lakes and clustered with lake salinity. Two stable enrichment cultures, named FeN-EHL and FeN-CKL, were obtained from microcosm incubations of sediment from freshwater Lake Erhai and hypersaline Lake Chaka. The culture FeN-EHL was dominated by genus Gallionella (68.4%), while the culture FeN-CKL was dominated by genus Marinobacter (71.2%), with the former growing autotrophically and the latter requiring an additional organic substrate (acetate) and Fe(II) oxidation, caused to a large extent by chemodenitrification [reaction of nitrite with Fe(II)]. Short-range ordered Fe(III) (oxyhydr)oxides were the product of Fe(II) oxidation, and the cells were partially attached to or encrusted by the formed iron minerals in both cultures. In summary, different types of interactions between Fe(II) and nitrate-reducing bacteria may exist in freshwater and hypersaline lakes, i.e., autotrophic NRFeOx and chemodenitrification in freshwater and hypersaline environments, respectively. IMPORTANCE NRFeOx microorganisms are globally distributed in various types of environments and play a vital role in iron transformation and nitrate and heavy metal removal. However, most known NRFeOx microorganisms were isolated from freshwater and marine environments, while their identity and activity under hypersaline conditions remain unknown. Here, we demonstrated that salinity may affect the abundance, identity, and nutrition modes of NRFeOx microorganisms. Autotrophy was only detectable in a freshwater lake but not in the saline lake investigated. We enriched a mixotrophic culture capable of nitrate-reducing Fe(II) oxidation from hypersaline lake sediments. However, Fe(II) oxidation was probably caused by abiotic nitrite reduction (chemodenitrification) rather than by a biologically mediated process. Consequently, our study suggests that in hypersaline environments, Fe(II) oxidation is largely caused by chemodentrification initiated by nitrite formation by chemoheterotrophic bacteria, and additional experiments are needed to demonstrate whether or to what extent Fe(II) is enzymatically oxidized.

A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS)

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are emerging as a valuable avenue for predicting, understanding and designing microbial communities. Computation of microbial ecosystems in time and space (COMETS) extends dynamic flux balance analysis to generate simulations of multiple microbial species in molecularly complex and spatially structured environments. Here we describe how to best use and apply the most recent version of COMETS, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, evolutionary dynamics and extracellular enzyme activity modules. In addition to a command-line option, COMETS includes user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, as well as comprehensive documentation and tutorials. This protocol provides a detailed guideline for installing, testing and applying COMETS to different scenarios, generating simulations that take from a few minutes to several days to run, with broad applicability to microbial communities across biomes and scales.

A gap-filling algorithm for prediction of metabolic interactions in microbial communities

The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.

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.

Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome

A genome-scale metabolic model (GEM) represents metabolic pathways of an organism in a mathematical form and can be built using biochemistry and genome annotation data. GEMs are invaluable for understanding organisms since they analyze the metabolic capabilities and behaviors quantitatively and can predict phenotypes. The development of high-throughput data collection techniques led to an immense increase in omics data such as metagenomics, which expand our knowledge on the human microbiome, but this also created a need for systematic analysis of these data. In recent years, GEMs have also been reconstructed for microbial species, including human gut microbiota, and methods for the analysis of microbial communities have been developed to examine the interaction between the organisms or the host. The purpose of this review is to provide a comprehensive guide for the applications of GEMs in microbial community analysis. Starting with GEM repositories, automatic GEM reconstruction tools, and quality control of models, this review will give insights into microbe-microbe and microbe-host interaction predictions and optimization of microbial community models. Recent studies that utilize microbial GEMs and personalized models to infer the influence of microbiota on human diseases such as inflammatory bowel diseases (IBD) or Parkinson's disease are exemplified. Being powerful system biology tools for both species-level and community-level analysis of microbes, GEMs integrated with omics data and machine learning techniques will be indispensable for studying the microbiome and their effects on human physiology as well as for deciphering the mechanisms behind human diseases.

Knowledge extraction from literature and enzyme sequences complements FBA analysis in metabolic engineering

Flux balance analysis (FBA) using genome-scale metabolic model (GSM) is a useful method for improving the bio-production of useful compounds. However, FBA often does not impose important constraints such as nutrients uptakes, by-products excretions and gases (oxygen and carbon dioxide) transfers. Furthermore, important information on metabolic engineering such as enzyme amounts, activities, and characteristics caused by gene expression and enzyme sequences is basically not included in GSM. Therefore, simple FBA is often not sufficient to search for metabolic manipulation strategies that are useful for improving the production of target compounds. In this study, we proposed a method using literature and enzyme search to complement the FBA-based metabolic manipulation strategies. As a case study, this method was applied to shikimic acid production by Corynebacterium glutamicum to verify its usefulness. As unique strategies in literature-mining, overexpression of the transcriptional regulator SugR and gene disruption related to by-products productions were complemented. In the search for alternative enzyme sequences, it was suggested that those candidates are searched for from various species based on features captured by deep learning, which are not simply homologous to amino acid sequences of the base enzymes.

Bacteria-driven phthalic acid ester biodegradation: Current status and emerging opportunities

The extensive use of phthalic acid esters (PAEs) has led to their widespread distribution across various environments. As PAEs pose significant threats to human health, it is urgent to develop efficient strategies to eliminate them from environments. Bacteria-driven PAE biodegradation has been considered as an inexpensive yet effective strategy to restore the contaminated environments. Despite great advances in bacterial culturing and sequencing, the inherent complexity of indigenous microbial community hinders us to mechanistically understand in situ PAE biodegradation and efficiently harness the degrading power of bacteria. The synthetic microbial ecology provides us a simple and controllable model system to address this problem. In this review, we focus on the current progress of PAE biodegradation mediated by bacterial isolates and indigenous bacterial communities, and discuss the prospective of synthetic PAE-degrading bacterial communities in PAE biodegradation research. It is anticipated that the theories and approaches of synthetic microbial ecology will revolutionize the study of bacteria-driven PAE biodegradation and provide novel insights for developing effective bioremediation solutions.

Investigating the Chemolithoautotrophic and Formate Metabolism of Nitrospira moscoviensis by Constraint-Based Metabolic Modeling and 13C-Tracer Analysis

Nitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used flux balance analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and [¹³C]bicarbonate and [¹³C]formate tracer experiments coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings corroborate that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO₂ fixation, and we also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO₂ followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira’s in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes.

IMPORTANCENitrospira spp. are globally abundant nitrifying bacteria in soil and aquatic ecosystems and in wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of Nitrospira moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and ¹³C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite-oxidizing bacteria.

Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models

Microbial communities and their functions are shaped by complex networks of interactions among microbes and with their environment. While the critical roles microbial communities play in numerous environments have become increasingly appreciated, we have a very limited understanding of their interactions and how these interactions combine to generate community-level behaviors. This knowledge gap hinders our ability to predict community responses to perturbations and to design interventions that manipulate these communities to our benefit. Dynamic models are promising tools to address these questions. We review existing modeling techniques to construct dynamic models of microbial communities at different scales and suggest ways to leverage multiple types of models and data to facilitate our understanding and engineering of microbial communities.

Mechanistic models of microbial community metabolism

Microbial communities affect many facets of human health and well-being. Naturally occurring bacteria, whether in nature or the human body, rarely exist in isolation. A deeper understanding of the metabolic functions of these communities is now possible with emerging computational models. In this review, we summarize frameworks for constructing mechanistic models of microbial community metabolism and discuss available algorithms for model analysis. We highlight essential decision points that greatly influence algorithm selection, as well as model analysis. Polymicrobial metabolic models can be utilized to gain insights into host-pathogen interactions, bacterial engineering, and many more translational applications.

Towards a Systems Biology Approach to Understanding the Lichen Symbiosis: Opportunities and Challenges of Implementing Network Modelling

Lichen associations, a classic model for successful and sustainable interactions between micro-organisms, have been studied for many years. However, there are significant gaps in our understanding about how the lichen symbiosis operates at the molecular level. This review addresses opportunities for expanding current knowledge on signalling and metabolic interplays in the lichen symbiosis using the tools and approaches of systems biology, particularly network modelling. The largely unexplored nature of symbiont recognition and metabolic interdependency in lichens could benefit from applying a holistic approach to understand underlying molecular mechanisms and processes. Together with ‘omics’ approaches, the application of signalling and metabolic network modelling could provide predictive means to gain insights into lichen signalling and metabolic pathways. First, we review the major signalling and recognition modalities in the lichen symbioses studied to date, and then describe how modelling signalling networks could enhance our understanding of symbiont recognition, particularly leveraging omics techniques. Next, we highlight the current state of knowledge on lichen metabolism. We also discuss metabolic network modelling as a tool to simulate flux distribution in lichen metabolic pathways and to analyse the co-dependence between symbionts. This is especially important given the growing number of lichen genomes now available and improved computational tools for reconstructing such models. We highlight the benefits and possible bottlenecks for implementing different types of network models as applied to the study of lichens.

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

Soil nitrogen dynamics and competition during plant invasion: insights from Mikania micrantha invasions in China

Invasive plants often change a/biotic soil conditions to increase their competitiveness. We compared the microbially mediated soil nitrogen (N) cycle of invasive Mikania micrantha and two co-occurring native competitors, Persicaria chinensis and Paederia scandens. We assessed how differences in plant tissue N content, soil nutrients, N cycling rates, microbial biomass and activity, and diversity and abundance of N-cycling microbes associated with these species impact their competitiveness. Mikania micrantha outcompeted both native species by transferring more N to plant tissue (37.9-55.8% more than natives). We found total soil N to be at lowest, and available N highest, in M. micrantha rhizospheres, suggesting higher N cycling rates compared with both natives. Higher microbial biomass and enzyme activities in M. micrantha rhizospheres confirmed this, being positively correlated with soil N mineralization rates and available N. Mikania micrantha rhizospheres harbored highly diverse N-cycling microbes, including N-fixing, ammonia-oxidizing and denitrifying bacteria and ammonia-oxidizing archaea (AOA). Structural equation models indicated that M. micrantha obtained available N via AOA-mediated nitrification mainly. Field data mirrored our experimental findings. Nitrogen availability is elevated under M. micrantha invasion through enrichment of microbes that participate in N cycling, in turn increasing available N for plant growth, facilitating high interspecific competition.

Modeling Growth Kinetics, Interspecies Cell Fusion, and Metabolism of a Clostridium acetobutylicum/Clostridium ljungdahlii Syntrophic Coculture

Clostridium acetobutylicum and Clostridium ljungdahlii grown in a syntrophic culture were recently shown to fuse membranes and exchange cytosolic contents, yielding hybrid cells with significant shifts in gene expression and growth phenotypes. Here, we introduce a dynamic genome-scale metabolic modeling framework to explore how cell fusion alters the growth phenotype and panel of metabolites produced by this binary community. Computational results indicate C. ljungdahlii persists in the coculture through proteome exchange during fusing events, which endow C. ljungdahlii cells with expanded substrate utilization, and access to additional reducing equivalents from C. acetobutylicum-evolved H₂ and through acquisition of C. acetobutylicum-native cofactor-reducing enzymes. Simulations predict maximum theoretical ethanol and isopropanol yields that are increased by 0.64 mmol and 0.39 mmol per mmol hexose sugar consumed, respectively, during exponential growth when cell fusion is active. This modeling effort provides a mechanistic explanation for the metabolic outcome of cellular fusion and altered homeostasis achieved in this syntrophic clostridial community.IMPORTANCE Widespread cell fusion and protein exchange between microbial organisms as observed in synthetic C. acetobutylicum/C. ljungdahlii culture is a novel observation that has not been explored in silico The mechanisms responsible for the observed cell fusion events in this culture are still unknown. In this work, we develop a modeling framework that captures the observed culture composition and metabolic phenotype, use it to offer a mechanistic explanation for how the culture achieves homeostasis, and identify C. ljungdahlii as primary beneficiary of fusion events. The implications for the events described in this study are far reaching, with potential to reshape our understanding of microbial community behavior synthetically and in nature.