Dataset Information

Minimal Interspecies Interaction Adjustment (MIIA): Inference of Neighbor-Dependent Interactions in Microbial Communities.

ABSTRACT: An intriguing aspect in microbial communities is that pairwise interactions can be influenced by neighboring species. This creates context dependencies for microbial interactions that are based on the functional composition of the community. Context dependent interactions are ecologically important and clearly present in nature, yet firmly established theoretical methods are lacking from many modern computational investigations. Here, we propose a novel network inference method that enables predictions for interspecies interactions affected by shifts in community composition and species populations. Our approach first identifies interspecies interactions in binary communities, which is subsequently used as a basis to infer modulation in more complex multi-species communities based on the assumption that microbes minimize adjustments of pairwise interactions in response to neighbor species. We termed this rule-based inference minimal interspecies interaction adjustment (MIIA). Our critical assessment of MIIA has produced reliable predictions of shifting interspecies interactions that are dependent on the functional role of neighbor organisms. We also show how MIIA has been applied to a microbial community composed of competing soil bacteria to elucidate a new finding that - in many cases - adding fewer competitors could impose more significant impact on binary interactions. The ability to predict membership-dependent community behavior is expected to help deepen our understanding of how microbiomes are organized in nature and how they may be designed and/or controlled in the future.

SUBMITTER: Song HS

PROVIDER: S-EPMC6584816 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Minimal Interspecies Interaction Adjustment (MIIA): Inference of Neighbor-Dependent Interactions in Microbial Communities.

Song Hyun-Seob HS Lee Joon-Yong JY Haruta Shin S Nelson William C WC Lee Dong-Yup DY Lindemann Stephen R SR Fredrickson Jim K JK Bernstein Hans C HC

Frontiers in microbiology 20190611

An intriguing aspect in microbial communities is that pairwise interactions can be influenced by neighboring species. This creates context dependencies for microbial interactions that are based on the functional composition of the community. Context dependent interactions are ecologically important and clearly present in nature, yet firmly established theoretical methods are lacking from many modern computational investigations. Here, we propose a novel network inference method that enables pred ...[more]

PMID: 31263456

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Project description:Modulation of interspecies interactions by the presence of neighbor species is a key ecological factor that governs dynamics and function of microbial communities, yet the development of theoretical frameworks explicit for understanding context-dependent interactions are still nascent. In a recent study, we proposed a novel rule-based inference method termed the Minimal Interspecies Interaction Adjustment (MIIA) that predicts the reorganization of interaction networks in response to the addition of new species such that the modulation in interaction coefficients caused by additional members is minimal. While the theoretical basis of MIIA was established through the previous work by assuming the full availability of species abundance data in axenic, binary, and complex communities, its extension to actual microbial ecology can be highly constrained in cases that species have not been cultured axenically (e.g., due to their inability to grow in the absence of specific partnerships) because binary interaction coefficients - basic parameters required for implementing the MIIA - are inestimable without axenic and binary population data. Thus, here we present an alternative formulation based on the following two central ideas. First, in the case where only data from axenic cultures are unavailable, we remove axenic populations from governing equations through appropriate scaling. This allows us to predict neighbor-dependent interactions in a relative sense (i.e., fractional change of interactions between with versus without neighbors). Second, in the case where both axenic and binary populations are missing, we parameterize binary interaction coefficients to determine their values through a sensitivity analysis. Through the case study of two microbial communities with distinct characteristics and complexity (i.e., a three-member community where all members can grow independently, and a four-member community that contains member species whose growth is dependent on other species), we demonstrated that despite data limitation, the proposed new formulation was able to successfully predict interspecies interactions that are consistent with experimentally derived results. Therefore, this technical advancement enhances our ability to predict context-dependent interspecies interactions in a broad range of microbial systems without being limited to specific growth conditions as a pre-requisite.

Project description:BackgroundThe rumen is an ecosystem with a complex microbial microflora in which microbes initiate biofilm formation by attaching to plant surfaces for plant degradation and are capable of converting feed to nutrients and energy via microbial processes. Quorum sensing (QS) is a cell-to-cell communication mechanism that allows microbes to synchronize the expression of multiple genes in the group to perform social behaviors such as chemotaxis and biofilm formation using self-synthesized QS signaling molecules. Whereas QS has been extensively studied in model microorganisms under pure culture conditions, QS mechanisms are poorly understood in complex bacterial communities, such as the rumen microflora, in which cell-to-cell communication may be common.ResultsHere, we analyzed 981 rumens bacterial and archaeal genomes from the Joint Genome Institute (JGI) and GenBank databases and identified 15 types of known QS signaling molecule-related genes. The analysis of the prevalence and abundance of genes involved in QS showed that 767 microbial genomes appeared to possess QS-related genes, including 680 bacterial genomes containing autoinducer-2 (AI-2) synthase- or receptor-encoding genes. Prevotella, Butyivibrio, Ruminococcus, Oribacterium, Selenomonas, and Treponema, known abundant bacterial genera in the rumen, possessed the greatest numbers of AI-2-related genes; these genes were highly expressed within the metatranscriptome dataset, suggesting that intra- and interspecies communication mediated by AI-2 among rumen microbes was universal in the rumen. The QS processes mediated by the dCache_1-containing AI-2 receptors (CahRs) with various functional modules may be essential for degrading plants, digesting food, and providing energy and nutrients to the host. Additionally, a universal natural network based on QS revealed how rumen microbes coordinate social behaviors via the AI-2-mediated QS system, most of which may potentially function via AI-2 binding to the extracellular sensor dCache_1 domain to activate corresponding receptors involved in different signal transduction pathways, such as methyl-accepting chemotaxis proteins, histidine kinases, serine phosphatases, c-di-GMP synthases and phosphodiesterases, and serine/threonine kinases in the rumen.ConclusionsThe exploration of AI-2-related genes, especially CahR-type AI-2 receptors, greatly increased our insight into AI-2 as a potentially "universal" signal mediating social behaviors and will help us better understand microbial communication networks and the function of QS in plant-microbe interactions in complex microecosystems. Video Abstract.

Dataset Information

Minimal Interspecies Interaction Adjustment (MIIA): Inference of Neighbor-Dependent Interactions in Microbial Communities.

Publications

Minimal Interspecies Interaction Adjustment (MIIA): Inference of Neighbor-Dependent Interactions in Microbial Communities.

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