Metabolic modeling of a mutualistic microbial community.
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ABSTRACT:
SUBMITTER: Stolyar S
PROVIDER: S-EPMC1847946 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339-2345, 2016. © 2016 Wiley Periodicals, Inc.
| S-EPMC5132105 | biostudies-literature
Project description:In nature, microbes do not exist in isolation but co-exist in a variety of ecological and biological environments and on various host organisms. Due to their close proximity, these microbes interact among themselves, and also with the hosts in both positive and negative manners. Moreover, these interactions may modulate dynamically upon external stimulus as well as internal community changes. This demands systematic techniques such as mathematical modeling to understand the intrinsic community behavior. Here, we reviewed various approaches for metabolic modeling of microbial communities. If detailed species-specific information is available, segregated models of individual organisms can be constructed and connected via metabolite exchanges; otherwise, the community may be represented as a lumped network of metabolic reactions. The constructed models can then be simulated to help fill knowledge gaps, and generate testable hypotheses for designing new experiments. More importantly, such community models have been developed to study microbial interactions in various niches such as host microbiome, biogeochemical and bioremediation, waste water treatment and synthetic consortia. As such, the metabolic modeling efforts have allowed us to gain new insights into the natural and synthetic microbial communities, and design interventions to achieve specific goals. Finally, potential directions for future development in metabolic modeling of microbial communities were also discussed.
| S-EPMC6225453 | biostudies-literature
Project description:BackgroundThe role of methane in global warming has become paramount to the environment and the human society, especially in the past few decades. Methane cycling microbial communities play an important role in the global methane cycle, which is why the characterization of these communities is critical to understand and manipulate their behavior. Methanotrophs are a major player in these communities and are able to oxidize methane as their primary carbon source.ResultsLake Washington is a freshwater lake characterized by a methane-oxygen countergradient that contains a methane cycling microbial community. Methanotrophs are a major part of this community involved in assimilating methane from lake water. Two significant methanotrophic species in this community are Methylobacter and Methylomonas. In this work, these methanotrophs are computationally studied via developing highly curated genome-scale metabolic models. Each model was then integrated to form a community model with a multi-level optimization framework. The competitive and mutualistic metabolic interactions among Methylobacter and Methylomonas were also characterized. The community model was next tested under carbon, oxygen, and nitrogen limited conditions in addition to a nutrient-rich condition to observe the systematic shifts in the internal metabolic pathways and extracellular metabolite exchanges. Each condition showed variations in the methane oxidation pathway, pyruvate metabolism, and the TCA cycle as well as the excretion of formaldehyde and carbon di-oxide in the community. Finally, the community model was simulated under fixed ratios of these two members to reflect the opposing behavior in the two-member synthetic community and in sediment-incubated communities. The community simulations predicted a noticeable switch in intracellular carbon metabolism and formaldehyde transfer between community members in sediment-incubated vs. synthetic condition.ConclusionIn this work, we attempted to predict the response of a simplified methane cycling microbial community from Lake Washington to varying environments and also provide an insight into the difference of dynamics in sediment-incubated microcosm community and synthetic co-cultures. Overall, this study lays the ground for in silico systems-level studies of freshwater lake ecosystems, which can drive future efforts of understanding, engineering, and modifying these communities for dealing with global warming issues.
| S-EPMC7333651 | biostudies-literature
Project description:Mutualisms can be promoted by pleiotropic win-win mutations which directly benefit self (self-serving) and partner (partner-serving). Intuitively, partner-serving phenotype could be quantified as an individual's benefit supply rate to partners. Here, we demonstrate the inadequacy of this thinking, and propose an alternative. Specifically, we evolved well-mixed mutualistic communities where two engineered yeast strains exchanged essential metabolites lysine and hypoxanthine. Among cells that consumed lysine and released hypoxanthine, a chromosome duplication mutation seemed win-win: it improved cell's affinity for lysine (self-serving), and increased hypoxanthine release rate per cell (partner-serving). However, increased release rate was due to increased cell size accompanied by increased lysine utilization per birth. Consequently, total hypoxanthine release rate per lysine utilization (defined as 'exchange ratio') remained unchanged. Indeed, this mutation did not increase the steady state growth rate of partner, and is thus solely self-serving during long-term growth. By extension, reduced benefit production rate by an individual may not imply cheating.
| S-EPMC6548503 | biostudies-literature
Project description:AIMS:To identify putative mutualistic interactions driving community composition in polymicrobial chronic wound infections using metabolic modelling. METHODS AND RESULTS:We developed a 12 species metabolic model that covered 74% of 16S rDNA pyrosequencing reads of dominant genera from 2963 chronic wound patients. The community model was used to predict species abundances averaged across this large patient population. We found that substantially improved predictions were obtained when the model was constrained with genera prevalence data and predicted abundances were averaged over 5000 ensemble simulations with community participants randomly determined according to the experimentally determined prevalences. Staphylococcus and Pseudomonas were predicted to exhibit a strong mutualistic relationship that resulted in community growth rate and diversity simultaneously increasing, suggesting that these two common chronic wound pathogens establish dominance by cooperating with less harmful commensal species. In communities lacking one or both dominant pathogens, other mutualistic relationship including Staphylococcus/Acinetobacter, Pseudomonas/Serratia and Streptococcus/Enterococcus were predicted consistent with published experimental data. CONCLUSIONS:Mutualistic interactions were predicted to be driven by crossfeeding of organic acids, alcohols and amino acids that could potentially be disrupted to slow chronic wound disease progression. SIGNIFICANCE AND IMPACT OF THE STUDY:Approximately 2% of the US population suffers from nonhealing chronic wounds infected by a combination of commensal and pathogenic bacteria. These polymicrobial infections are often resilient to antibiotic treatment due to the nutrient-rich wound environment and species interactions that promote community stability and robustness. The simulation results from this study were used to identify putative mutualistic interactions between bacteria that could be targeted to enhance treatment efficacy.
| S-EPMC6790277 | biostudies-literature
Project description:Microbial electrosynthesis is a renewable energy and chemical production platform that relies on microbial cells to capture electrons from a cathode and fix carbon. Yet despite the promise of this technology, the metabolic capacity of the microbes that inhabit the electrode surface and catalyze electron transfer in these systems remains largely unknown. We assembled thirteen draft genomes from a microbial electrosynthesis system producing primarily acetate from carbon dioxide, and their transcriptional activity was mapped to genomes from cells on the electrode surface and in the supernatant. This allowed us to create a metabolic model of the predominant community members belonging to Acetobacterium, Sulfurospirillum, and Desulfovibrio. According to the model, the Acetobacterium was the primary carbon fixer, and a keystone member of the community. Transcripts of soluble hydrogenases and ferredoxins from Acetobacterium and hydrogenases, formate dehydrogenase, and cytochromes of Desulfovibrio were found in high abundance near the electrode surface. Cytochrome c oxidases of facultative members of the community were highly expressed in the supernatant despite completely sealed reactors and constant flushing with anaerobic gases. These molecular discoveries and metabolic modeling now serve as a foundation for future examination and development of electrosynthetic microbial communities.
| S-EPMC5566340 | biostudies-literature
Project description:Microbes face intense competition in the natural world, and so need to wisely allocate their resources to multiple functions, in particular to metabolism. Understanding competition among metabolic strategies that are subject to trade-offs is therefore crucial for deeper insight into the competition, cooperation, and community assembly of microorganisms. In this work, we evaluate competing metabolic strategies within an ecological context by considering not only how the environment influences cell growth, but also how microbes shape their chemical environment. Utilizing chemostat-based resource-competition models, we exhibit a set of intuitive and general procedures for assessing metabolic strategies. Using this framework, we are able to relate and unify multiple metabolic models, and to demonstrate how the fitness landscape of strategies becomes intrinsically dynamic due to species-environment feedback. Such dynamic fitness landscapes produce rich behaviors, and prove to be crucial for ecological and evolutionarily stable coexistence in all the models we examined.
| S-EPMC7482850 | biostudies-literature
Project description:To explore the ecological basis for multiple bacteria species coexistence, we set up three bacteria (Ruegeria pomeroyi DSS-3, Vibrio hepatarius HF70, and Thalassospira sp. HF15), either in monoculture or in co-cultures (in all combinations) for a 8 day growth-dilution cycles. At ~15h of day 4 (P4) and day 8 (P8) of growth-dilution cycles, we examined transcriptomic responses of these bacteria. Differential gene expressions were used to generate hypothesis about ecological and physiological responses of one in the presence of another/other bacteria.
2022-08-01 | GSE199797 | GEO
Project description:MotivationSynthetic microbial communities begin to be considered as promising multicellular biocatalysts having a large potential to replace engineered single strains in biotechnology applications, in pharmaceutical, chemical and living architecture sectors. In contrast to single strain engineering, the effective and high-throughput analysis and engineering of microbial consortia face the lack of knowledge, tools and well-defined workflows. This manuscript contributes to fill this important gap with a framework, called FLYCOP (FLexible sYnthetic Consortium OPtimization), which contributes to microbial consortia modeling and engineering, while improving the knowledge about how these communities work. FLYCOP selects the best consortium configuration to optimize a given goal, among multiple and diverse configurations, in a flexible way, taking temporal changes in metabolite concentrations into account.ResultsIn contrast to previous systems optimizing microbial consortia, FLYCOP has novel characteristics to face up to new problems, to represent additional features and to analyze events influencing the consortia behavior. In this manuscript, FLYCOP optimizes a Synechococcus elongatus-Pseudomonas putida consortium to produce the maximum amount of bio-plastic (PHA, polyhydroxyalkanoate), and highlights the influence of metabolites exchange dynamics in a four auxotrophic Escherichia coli consortium with parallel growth. FLYCOP can also provide an explanation about biological evolution driving evolutionary engineering endeavors by describing why and how heterogeneous populations emerge from monoclonal ones.Availability and implementationCode reproducing the study cases described in this manuscript are available on-line: https://github.com/beatrizgj/FLYCOP.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Aeolian soil erosion, exacerbated by anthropogenic perturbations, has become one of the most alarming processes of land degradation and desertification. By contrast, dust deposition might confer a potential fertilization effect. To examine how they affect topsoil microbial community, we conducted a study GeoChip techniques in a semiarid grassland of Inner Mongolia, China. We found that microbial communities were significantly (P<0.039) altered and most of microbial functional genes associated with carbon, nitrogen, phosphorus and potassium cycling were decreased or remained unaltered in relative abundance by both erosion and deposition, which might be attributed to acceleration of organic matter mineralization by the breakdown of aggregates during dust transport and deposition. As a result, there were strong correlations between microbial carbon and nitrogen cycling genes. amyA genes encoding alpha-amylases were significantly (P=0.01) increased by soil deposition, reflecting changes of carbon profiles. Consistently, plant abundance, total nitrogen and total organic carbon were correlated with functional gene composition, revealing the importance of environmental nutrients to soil microbial function potentials. Collectively, our results identified microbial indicator species and functional genes of aeolian soil transfer, and demonstrated that functional genes had higher susceptibility to environmental nutrients than taxonomy. Given the ecological importance of aeolian soil transfer, knowledge gained here are crucial for assessing microbe-mediated nutrient cyclings and human health hazard. The experimental sites comprised of three treatments of control, soil erosion and deposition, with 5 replicates of each treatment.
2015-03-28 | E-GEOD-67347 | biostudies-arrayexpress