Closed microbial communities self-organize to persistently cycle carbon

Applications of the Microalgae Chlamydomonas and Its Bacterial Consortia in Detoxification and Bioproduction

The wide metabolic diversity of microalgae, their fast growth rates, and low-cost production make these organisms highly promising resources for a variety of biotechnological applications, addressing critical needs in industry, agriculture, and medicine. The use of microalgae in consortia with bacteria is proving valuable in several areas of biotechnology, including the treatment of various types of wastewater, the production of biofertilizers, and the extraction of various products from their biomass. The monoculture of the microalga Chlamydomonas has been a prominent research model for many years and has been extensively used in the study of photosynthesis, sulphur and phosphorus metabolism, nitrogen metabolism, respiration, and flagellar synthesis, among others. Recent research has increasingly recognised the potential of Chlamydomonas-bacteria consortia as a biotechnological tool for various applications. The detoxification of wastewater using Chlamydomonas and its bacterial consortia offers significant potential for sustainable reduction of contaminants, while facilitating resource recovery and the valorisation of microalgal biomass. The use of Chlamydomonas and its bacterial consortia as biofertilizers can offer several benefits, such as increasing crop yields, protecting crops, maintaining soil fertility and stability, contributing to CO2 mitigation, and contributing to sustainable agricultural practises. Chlamydomonas-bacterial consortia play an important role in the production of high-value products, particularly in the production of biofuels and the enhancement of H2 production. This review aims to provide a comprehensive understanding of the potential of Chlamydomonas monoculture and its bacterial consortia to identify current applications and to propose new research and development directions to maximise their potential.

Challenges in quantifying functional redundancy and selection in microbial communities

Microbiomes can exhibit large variations in species abundances but high reproducibility in abundances of functional units, an observation often considered evidence for functional redundancy. Based on such reduction in functional variability, selection is hypothesized to act on functional units in these ecosystems. However, the link between functional redundancy and selection remains unclear. Here, we show that reduction in functional variability does not always imply selection on functional profiles. We propose empirical null models to account for the confounding effects of statistical averaging and bias toward environment-independent beneficial functions. We apply our models to existing data sets, and find that the abundances of metabolic groups within microbial communities from bromeliad foliage do not exhibit any evidence of the previously hypothesized functional selection. By contrast, communities of soil bacteria or human gut commensals grown in vitro are selected for metabolic capabilities. By separating the effects of averaging and functional bias on functional variability, we find that the appearance of functional selection in gut microbiome samples from the Human Microbiome Project is artifactual, and that there is no evidence of selection for any molecular function represented by KEGG orthology. These concepts articulate a basic framework for quantifying functional redundancy and selection, advancing our understanding of the mapping between microbiome taxonomy and function.

Closed ecosystems extract energy through self-organized nutrient cycles

Our planet is a self-sustaining ecosystem powered by light energy from the sun, but roughly closed to matter. Many ecosystems on Earth are also approximately closed to matter and recycle nutrients by self-organizing stable nutrient cycles, e.g., microbial mats, lakes, open ocean gyres. However, existing ecological models do not exhibit the self-organization and dynamical stability widely observed in such planetary-scale ecosystems. Here, we advance a conceptual model that explains the self-organization, stability, and emergent features of closed microbial ecosystems. Our model incorporates the bioenergetics of metabolism into an ecological framework. By studying this model, we uncover a crucial thermodynamic feedback loop that enables metabolically diverse communities to almost always stabilize nutrient cycles. Surprisingly, highly diverse communities self-organize to extract [Formula: see text]10[Formula: see text] of the maximum extractable energy, or [Formula: see text]100 fold more than randomized communities. Further, with increasing diversity, distinct ecosystems show strongly correlated fluxes through nutrient cycles. However, as the driving force from light increases, the fluxes of nutrient cycles become more variable and species-dependent. Our results highlight that self-organization promotes the efficiency and stability of complex ecosystems at extracting energy from the environment, even in the absence of any centralized coordination.

Statistical prediction of microbial metabolic traits from genomes

The metabolic activity of microbial communities is central to their role in biogeochemical cycles, human health, and biotechnology. Despite the abundance of sequencing data characterizing these consortia, it remains a serious challenge to predict microbial metabolic traits from sequencing data alone. Here we culture 96 bacterial isolates individually and assay their ability to grow on 10 distinct compounds as a sole carbon source. Using these data as well as two existing datasets, we show that statistical approaches can accurately predict bacterial carbon utilization traits from genomes. First, we show that classifiers trained on gene content can accurately predict bacterial carbon utilization phenotypes by encoding phylogenetic information. These models substantially outperform predictions made by constraint-based metabolic models automatically constructed from genomes. This result solidifies our current knowledge about the strong connection between phylogeny and metabolic traits. However, phylogeny-based predictions fail to predict traits for taxa that are phylogenetically distant from any strains in the training set. To overcome this we train improved models on gene presence/absence to predict carbon utilization traits from gene content. We show that models that predict carbon utilization traits from gene presence/absence can generalize to taxa that are phylogenetically distant from the training set either by exploiting biochemical information for feature selection or by having sufficiently large datasets. In the latter case, we provide evidence that a statistical approach can identify putatively mechanistic genes involved in metabolic traits. Our study demonstrates the potential power for predicting microbial phenotypes from genotypes using statistical approaches.

Emergent community architecture despite distinct diversity in the global whale shark (Rhincodon typus) epidermal microbiome

Microbiomes confer beneficial physiological traits to their host, but microbial diversity is inherently variable, challenging the relationship between microbes and their contribution to host health. Here, we compare the diversity and architectural complexity of the epidermal microbiome from 74 individual whale sharks (Rhincodon typus) across five aggregations globally to determine if network properties may be more indicative of the microbiome-host relationship. On the premise that microbes are expected to exhibit biogeographic patterns globally and that distantly related microbial groups can perform similar functions, we hypothesized that microbiome co-occurrence patterns would occur independently of diversity trends and that keystone microbes would vary across locations. We found that whale shark aggregation was the most important factor in discriminating taxonomic diversity patterns. Further, microbiome network architecture was similar across all aggregations, with degree distributions matching Erdos-Renyi-type networks. The microbiome-derived networks, however, display modularity indicating a definitive microbiome structure on the epidermis of whale sharks. In addition, whale sharks hosted 35 high-quality metagenome assembled genomes (MAGs) of which 25 were present from all sample locations, termed the abundant 'core'. Two main MAG groups formed, defined here as Ecogroup 1 and 2, based on the number of genes present in metabolic pathways, suggesting there are at least two important metabolic niches within the whale shark microbiome. Therefore, while variability in microbiome diversity is high, network structure and core taxa are inherent characteristics of the epidermal microbiome in whale sharks. We suggest the host-microbiome and microbe-microbe interactions that drive the self-assembly of the microbiome help support a functionally redundant abundant core and that network characteristics should be considered when linking microbiomes with host health.

Evolutionary repeatability of emergent properties of ecological communities

Most species belong to ecological communities where their interactions give rise to emergent community-level properties, such as diversity and productivity. Understanding and predicting how these properties change over time has been a major goal in ecology, with important practical implications for sustainability and human health. Less attention has been paid to the fact that community-level properties can also change because member species evolve. Yet, our ability to predict long-term eco-evolutionary dynamics hinges on how repeatably community-level properties change as a result of species evolution. Here, we review studies of evolution of both natural and experimental communities and make the case that community-level properties at least sometimes evolve repeatably. We discuss challenges faced in investigations of evolutionary repeatability. In particular, only a handful of studies enable us to quantify repeatability. We argue that quantifying repeatability at the community level is critical for approaching what we see as three major open questions in the field: (i) Is the observed degree of repeatability surprising? (ii) How is evolutionary repeatability at the community level related to repeatability at the level of traits of member species? (iii) What factors affect repeatability? We outline some theoretical and empirical approaches to addressing these questions. Advances in these directions will not only enrich our basic understanding of evolution and ecology but will also help us predict eco-evolutionary dynamics. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Formation and emergent dynamics of spatially organized microbial systems

Spatial organization is the norm rather than the exception in the microbial world. While the study of microbial physiology has been dominated by studies in well-mixed cultures, there is now increasing interest in understanding the role of spatial organization in microbial physiology, coexistence and evolution. Where studied, spatial organization has been shown to influence all three of these aspects. In this mini review and perspective article, we emphasize that the dynamics within spatially organized microbial systems (SOMS) are governed by feedbacks between local physico-chemical conditions, cell physiology and movement, and evolution. These feedbacks can give rise to emergent dynamics, which need to be studied through a combination of spatio-temporal measurements and mathematical models. We highlight the initial formation of SOMS and their emergent dynamics as two open areas of investigation for future studies. These studies will benefit from the development of model systems that can mimic natural ones in terms of species composition and spatial structure.

Chlamydomonas reinhardtii, a Reference Organism to Study Algal-Microbial Interactions: Why Can't They Be Friends?

The stability and harmony of ecological niches rely on intricate interactions between their members. During evolution, organisms have developed the ability to thrive in different environments, taking advantage of each other. Among these organisms, microalgae are a highly diverse and widely distributed group of major primary producers whose interactions with other organisms play essential roles in their habitats. Understanding the basis of these interactions is crucial to control and exploit these communities for ecological and biotechnological applications. The green microalga Chlamydomonas reinhardtii, a well-established model, is emerging as a model organism for studying a wide variety of microbial interactions with ecological and economic significance. In this review, we unite and discuss current knowledge that points to C. reinhardtii as a model organism for studying microbial interactions.

The Diversity and Functional Capacity of Microbes Associated with Coastal Macrophytes

Coastal marine macrophytes exhibit some of the highest rates of primary productivity in the world. They have been found to host a diverse set of microbes, many of which may impact the biology of their hosts through metabolisms that are unique to microbial taxa. Here, we characterized the metabolic functions of macrophyte-associated microbial communities using metagenomes collected from 2 species of kelp (Laminaria setchellii and Nereocystis luetkeana) and 3 marine angiosperms (Phyllospadix scouleri, P. serrulatus, and Zostera marina), including the rhizomes of two surfgrass species (Phyllospadix spp.), the seagrass Zostera marina, and the sediments surrounding P. scouleri and Z. marina. Using metagenomic sequencing, we describe 63 metagenome-assembled genomes (MAGs) that potentially benefit from being associated with macrophytes and may contribute to macrophyte fitness through their metabolic activity. Host-associated metagenomes contained genes for the use of dissolved organic matter from hosts and vitamin (B₁, B₂, B₇, B₁₂) biosynthesis in addition to a range of nitrogen and sulfur metabolisms that recycle dissolved inorganic nutrients into forms more available to the host. The rhizosphere of surfgrass and seagrass contained genes for anaerobic microbial metabolisms, including nifH genes associated with nitrogen fixation, despite residing in a well-mixed and oxygenated environment. The range of oxygen environments engineered by macrophytes likely explains the diversity of both oxidizing and reducing microbial metabolisms and contributes to the functional capabilities of microbes and their influences on carbon and nitrogen cycling in nearshore ecosystems. IMPORTANCE Kelps, seagrasses, and surfgrasses are ecosystem engineers on rocky shorelines, where they show remarkably high levels of primary production. Through analysis of their associated microbial communities, we found a variety of microbial metabolisms that may benefit the host, including nitrogen metabolisms, sulfur oxidation, and the production of B vitamins. In turn, these microbes have the genetic capabilities to assimilate the dissolved organic compounds released by their macrophyte hosts. We describe a range of oxygen environments associated with surfgrass, including low-oxygen microhabitats in their rhizomes that host genes for nitrogen fixation. The tremendous productivity of coastal seaweeds and seagrasses is likely due in part to the activities of associated microbes, and an increased understanding of these associations is needed.

Long-Term Stability of Bacterial Associations in a Microcosm of Ostreococcus tauri (Chlorophyta, Mamiellophyceae)

Phytoplankton-bacteria interactions rule over carbon fixation in the sunlit ocean, yet only a handful of phytoplanktonic-bacteria interactions have been experimentally characterized. In this study, we investigated the effect of three bacterial strains isolated from a long-term microcosm experiment with one Ostreococcus strain (Chlorophyta, Mamiellophyceae). We provided evidence that two Roseovarius strains (Alphaproteobacteria) had a beneficial effect on the long-term survival of the microalgae whereas one Winogradskyella strain (Flavobacteriia) led to the collapse of the microalga culture. Co-cultivation of the beneficial and the antagonistic strains also led to the loss of the microalga cells. Metagenomic analysis of the microcosm is consistent with vitamin B12 synthesis by the Roseovarius strains and unveiled two additional species affiliated to Balneola (Balneolia) and Muricauda (Flavobacteriia), which represent less than 4% of the reads, whereas Roseovarius and Winogradskyella recruit 57 and 39% of the reads, respectively. These results suggest that the low-frequency bacterial species may antagonize the algicidal effect of Winogradskyella in the microbiome of Ostreococcus tauri and thus stabilize the microalga persistence in the microcosm. Altogether, these results open novel perspectives into long-term stability of phytoplankton cultures.

An ensemble approach to the structure-function problem in microbial communities

The metabolic activity of microbial communities plays a primary role in the flow of essential nutrients throughout the biosphere. Molecular genetics has revealed the metabolic pathways that model organisms utilize to generate energy and biomass, but we understand little about how the metabolism of diverse, natural communities emerges from the collective action of its constituents. We propose that quantifying and mapping metabolic fluxes to sequencing measurements of genomic, taxonomic, or transcriptional variation across an ensemble of diverse communities, either in the laboratory or in the wild, can reveal low-dimensional descriptions of community structure that can explain or predict their emergent metabolic activity. We survey the types of communities for which this approach might be best suited, review the analytical techniques available for quantifying metabolite fluxes in communities, and discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data.