Dataset Information

Conservation of Species- and Trait-Based Modeling Network Interactions in Extremely Acidic Microbial Community Assembly.

ABSTRACT: Understanding microbial interactions is essential to decipher the mechanisms of community assembly and their effects on ecosystem functioning, however, the conservation of species- and trait-based network interactions along environmental gradient remains largely unknown. Here, by using the network-based analyses with three paralleled data sets derived from 16S rRNA gene pyrosequencing, functional microarray, and predicted metagenome, we test our hypothesis that the network interactions of traits are more conserved than those of taxonomic measures, with significantly lower variation of network characteristics along the environmental gradient in acid mine drainage. The results showed that although the overall network characteristics remained similar, the structural variation was significantly lower at trait levels. The higher conserved individual node topological properties at trait level rather than at species level indicated that the responses of diverse traits remained relatively consistent even though different species played key roles under different environmental conditions. Additionally, the randomization tests revealed that it could not reject the null hypothesis that species-based correlations were random, while the tests suggested that correlation patterns of traits were non-random. Furthermore, relationships between trait-based network characteristics and environmental properties implied that trait-based networks might be more useful in reflecting the variation of ecosystem function. Taken together, our results suggest that deterministic trait-based community assembly results in greater conservation of network interaction, which may ensure ecosystem function across environmental regimes, emphasizing the potential importance of measuring the complexity and conservation of network interaction in evaluating the ecosystem stability and functioning.

SUBMITTER: Kuang J

PROVIDER: S-EPMC5554326 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Conservation of Species- and Trait-Based Modeling Network Interactions in Extremely Acidic Microbial Community Assembly.

Kuang Jialiang J Cadotte Marc W MW Chen Yongjian Y Shu Haoyue H Liu Jun J Chen Linxing L Hua Zhengshuang Z Shu Wensheng W Zhou Jizhong J Huang Linan L

Frontiers in microbiology 20170810

Understanding microbial interactions is essential to decipher the mechanisms of community assembly and their effects on ecosystem functioning, however, the conservation of species- and trait-based network interactions along environmental gradient remains largely unknown. Here, by using the network-based analyses with three paralleled data sets derived from 16S rRNA gene pyrosequencing, functional microarray, and predicted metagenome, we test our hypothesis that the network interactions of traits ...[more]

PMID: 28848508

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Project description:The microbial community of acid mine drainage (AMD) fascinates researchers by their adaption and roles in shaping the environment. Molecular surveys have recently helped to enhance the understanding of the distribution, adaption strategy, and ecological function of microbial communities in extreme AMD environments. However, the interactions between the environment and microbial community of extremely acidic AMD (pH <3) from different mining areas kept unanswered questions. Here, we measured physicochemical parameters and profiled the microbial community of AMD collected from four mining areas with different mineral types to provide a better understanding of biogeochemical processes within the extremely acidic water environment. The prominent physicochemical differences across the four mining areas were in SO4 2-, metal ions, and temperature, and distinct microbial diversity and community assemblages were also discovered in these areas. Mg2+ and SO4 2- were the predominant factors determining the microbial structure and prevalence of dominant taxa in AMD. Leptospirillum, Ferroplasma, and Acidithiobacillus were abundant but showed different occurrence patterns in AMD from different mining areas. More diverse communities and functional redundancy were identified in AMD of polymetallic mining areas compared with AMD of copper mining areas. Functional prediction revealed iron, sulfur, nitrogen, and carbon metabolisms driven by microorganisms were significantly correlated with Mg2+ and SO4 2-, Ca2+, temperature, and Fe2+, which distinguish microbial communities of copper mine AMD from that of polymetallic mine AMD. In summary, microbial diversity, composition, and metabolic potential were mainly shaped by Mg2+ and SO4 2- concentrations of AMD, suggesting that the substrate concentrations may contribute to the distinct microbiological profiles of AMD from different mining areas. These findings highlight the microbial community structure in extremely acidic AMD forming by types of minerals and the interactions of physicochemical parameters and microbiology, providing more clues of the microbial ecological function and adaptation mechanisms in the extremely acidic environment.

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.

Dataset Information

Conservation of Species- and Trait-Based Modeling Network Interactions in Extremely Acidic Microbial Community Assembly.

Publications

Conservation of Species- and Trait-Based Modeling Network Interactions in Extremely Acidic Microbial Community Assembly.

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