Integrating Ecological and Engineering Concepts of Resilience in Microbial Communities

Many definitions of resilience have been proffered for natural and engineered ecosystems, but a conceptual consensus on resilience in microbial communities is still lacking. We argue that the disconnect largely results from the wide variance in microbial community complexity, which range from compositionally simple synthetic consortia to complex natural communities, and divergence between the typical practical outcomes emphasized by ecologists and engineers. Viewing microbial communities as elasto-plastic systems that undergo both recoverable and unrecoverable transitions, we argue that this gap between the engineering and ecological definitions of resilience stems from their respective emphases on elastic and plastic deformation, respectively. We propose that the two concepts may be fundamentally united around the resilience of function rather than state in microbial communities and the regularity in the relationship between environmental variation and a community\u27s functional response. Furthermore, we posit that functional resilience is an intrinsic property of microbial communities and suggest that state changes in response to environmental variation may be a key mechanism driving functional resilience in microbial communities

Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data

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

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

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

Characterizing the Catalytic Potential of Deinococcus, Arthrobacter and other Robust Bacteria in Contaminated Subsurface Environments of the Hanford Site

Until recently, there have been no clear physiologic predictors of a cell's ability to recover from ionizing radiation (IR), desiccation, and other DOE-relevant oxidative stress conditions. In general, the most resistant bacteria have been Gram-positive (e.g., Deinococcus, Arthrobacter, Lactobacillus & Enterococcus spp.) and the most sensitive have been Gram-negative (e.g., Pseudomonas, Shewanella & Neisseria spp.). However, there are several reported exceptions to this paradigm, the Gram-negative cyanobacterium Chroococcidiopsis is extremely resistant to IR, whereas the Gram-positive Micrococcus luteus is sensitive. We have identified biomolecular signatures for radiation sensitivity and resistance which are independent of phylogeny, where very high and very low intracellular Mn/Fe concentration ratios correlated with very high and very low resistances, respectively; and restricting Mn(II) in the famously resistant Deinococcus radiodurans sensitized this eubacterium to IR (http://cfyn.ifas.ufl.edu/radiation.pdf)

Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process

In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community’s traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accounted for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators

Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process

In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community’s traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accounted for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators

Systems biology and ecology of microbial mat communities

Microbial mat communities consist of dense populations of microorganisms embedded in exopolymers and/or biomineralized solid phases, and are often found in mm-cm thick assemblages, which can be stratified due to environmental gradients such as light, oxygen or sulfide. Microbial mat communities are commonly observed under extreme environmental conditions, deriving energy primarily from light and/or reduced chemicals to drive autotrophic fixation of carbon dioxide. Microbial mat ecosystems are regarded as living analogues of primordial systems on Earth, and they often form perennial structures with conspicuous stratifications of microbial populations that can be studied in situ under stable conditions for many years. Consequently, microbial mat communities are ideal natural laboratories and represent excellent model systems for studying microbial community structure and function, microbial dynamics and interactions, and discovery of new microorganisms with novel metabolic pathways potentially useful in future industrial and/or medical applications. Due to their relative simplicity and organization, microbial mat communities are often excellent testing grounds for new technologies in microbiology including micro-sensor analysis, stable isotope methodology and modern genomics. Integrative studies of microbial mat communities that combine modern biogeochemical and molecular biological methods with traditional microbiology, macro-ecological approaches, and community network modeling will provide new and detailed insights regarding the systems biology of microbial mats and the complex interplay among individual populations and their physicochemical environment. These processes ultimately control the biogeochemical cycling of energy and/or nutrients in microbial systems. Similarities in microbial community function across different types of communities from highly disparate environments may provide a deeper basis for understanding microbial community dynamics and the ecological role of specific microbial populations. Approaches and concepts developed in highly-constrained, relatively stable natural communities may also provide insights useful for studying and understanding more complex microbial communities

Exploring the biochemistry at the extracellular redox frontier of bacterial mineral Fe(III) respiration

Many species of the bacterial Shewanella genus are notable for their ability to respire in anoxic environments utilizing insoluble minerals of Fe(III) and Mn(IV) as extracellular electron acceptors. In Shewanella oneidensis, the process is dependent on the decahaem electron-transport proteins that lie at the extracellular face of the outer membrane where they can contact the insoluble mineral substrates. These extracellular proteins are charged with electrons provided by an inter-membrane electron-transfer pathway that links the extracellular face of the outer membrane with the inner cytoplasmic membrane and thereby intracellular electron sources. In the present paper, we consider the common structural features of two of these outer-membrane decahaem cytochromes, MtrC and MtrF, and bring this together with biochemical, spectroscopic and voltammetric data to identify common and distinct properties of these prototypical members of different clades of the outer-membrane decahaem cytochrome superfamily

Single-cell Imaging And Spectroscopic Analyses Of Cr(vi) Reduction On The Surface Of Bacterial Cells

We investigate the single-cell reduction of toxic Cr(VI) by the dissimilatory metal-reducing bacterium Shewanella oneidensis MR-1 (MR-1), an important bioremediation process, using Raman spectroscopy and scanning electron microscopy (SEM) combined with energy-dispersive X-ray spectroscopy (EDX). Our experiments indicate that the toxic, highly soluble Cr(VI) can be efficiently reduced to less toxic, nonsoluble Cr2O3 nanoparticles by MR-1. Cr2O3 is observed to emerge as nanoparticles adsorbed on the cell surface and its chemical nature is identified by EDX imaging and Raman spectroscopy. Co-localization of Cr2O3 and cytochromes by EDX imaging and Raman spectroscopy suggests a terminal reductase role for MR-1 surface-exposed cytochromes MtrC and OmcA. Our experiments revealed that the cooperation of surface proteins OmcA and MtrC makes the reduction reaction most efficient, and the sequence of the reducing reactivity of MR-1 is wild type \u3e single mutant Delta mtrC or mutant Delta omcA \u3e double mutant (Delta omcA-Delta mtrC). Moreover, our results also suggest that direct microbial Cr(VI) reduction and Fe(II) (hematite)-mediated Cr(VI) reduction mechanisms may coexist in the reduction processes