Genomic structure predicts metabolite dynamics in microbial communities.

The metabolic activities of microbial communities play a defining role in the evolution and persistence of life on Earth, driving redox reactions that give rise to global biogeochemical cycles. Community metabolism emerges from a hierarchy of processes, including gene expression, ecological interactions, and environmental factors. In wild communities, gene content is correlated with environmental context, but predicting metabolite dynamics from genomes remains elusive. Here, we show, for the process of denitrification, that metabolite dynamics of a community are predictable from the genes each member of the community possesses. A simple linear regression reveals a sparse and generalizable mapping from gene content to metabolite dynamics for genomically diverse bacteria. A consumer-resource model correctly predicts community metabolite dynamics from single-strain phenotypes. Our results demonstrate that the conserved impacts of metabolic genes can predict community metabolite dynamics, enabling the prediction of metabolite dynamics from metagenomes, designing denitrifying communities, and discovering how genome evolution impacts metabolism.

A Reverse Ecology Approach Based on a Biological Definition of Microbial Populations.

Delineating ecologically meaningful populations among microbes is important for identifying their roles in environmental and host-associated microbiomes. Here, we introduce a metric of recent gene flow, which when applied to co-existing microbes, identifies congruent genetic and ecological units separated by strong gene flow discontinuities from their next of kin. We then develop a pipeline to identify genome regions within these units that show differential adaptation and allow mapping of populations onto environmental variables or host associations. Using this reverse ecology approach, we show that the human commensal bacterium Ruminococcus gnavus breaks up into sharply delineated populations that show different associations with health and disease. Defining populations by recent gene flow in this way will facilitate the analysis of bacterial and archaeal genomes using ecological and evolutionary theory developed for plants and animals, thus allowing for testing unifying principles across all biology.

Charting the Complexity of the Marine Microbiome through Single-Cell Genomics.

Marine bacteria and archaea play key roles in global biogeochemistry. To improve our understanding of this complex microbiome, we employed single-cell genomics and a randomized, hypothesis-agnostic cell selection strategy to recover 12,715 partial genomes from the tropical and subtropical euphotic ocean. A substantial fraction of known prokaryoplankton coding potential was recovered from a single, 0.4 mL ocean sample, which indicates that genomic information disperses effectively across the globe. Yet, we found each genome to be unique, implying limited clonality within prokaryoplankton populations. Light harvesting and secondary metabolite biosynthetic pathways were numerous across lineages, highlighting the value of single-cell genomics to advance the identification of ecological roles and biotechnology potential of uncultured microbial groups. This genome collection enabled functional annotation and genus-level taxonomic assignments for >80% of individual metagenome reads from the tropical and subtropical surface ocean, thus offering a model to improve reference genome databases for complex microbiomes.

Gene Expression Changes and Community Turnover Differentially Shape the Global Ocean Metatranscriptome.

Ocean microbial communities strongly influence the biogeochemistry, food webs, and climate of our planet. Despite recent advances in understanding their taxonomic and genomic compositions, little is known about how their transcriptomes vary globally. Here, we present a dataset of 187 metatranscriptomes and 370 metagenomes from 126 globally distributed sampling stations and establish a resource of 47 million genes to study community-level transcriptomes across depth layers from pole-to-pole. We examine gene expression changes and community turnover as the underlying mechanisms shaping community transcriptomes along these axes of environmental variation and show how their individual contributions differ for multiple biogeochemically relevant processes. Furthermore, we find the relative contribution of gene expression changes to be significantly lower in polar than in non-polar waters and hypothesize that in polar regions, alterations in community activity in response to ocean warming will be driven more strongly by changes in organismal composition than by gene regulatory mechanisms. VIDEO ABSTRACT.

Are Bacteria Leaky? Mechanisms of Metabolite Externalization in Bacterial Cross-Feeding.

The metabolism of a bacterial cell stretches beyond its boundaries, often connecting with the metabolism of other cells to form extended metabolic networks that stretch across communities, and even the globe. Among the least intuitive metabolic connections are those involving cross-feeding of canonically intracellular metabolites. How and why are these intracellular metabolites externalized? Are bacteria simply leaky? Here I consider what it means for a bacterium to be leaky, and I review mechanisms of metabolite externalization from the context of cross-feeding. Despite common claims, diffusion of most intracellular metabolites across a membrane is unlikely. Instead, passive and active transporters are likely involved, possibly purging excess metabolites as part of homeostasis. Re-acquisition of metabolites by a producer limits the opportunities for cross-feeding. However, a competitive recipient can stimulate metabolite externalization and initiate a positive-feedback loop of reciprocal cross-feeding.

Microbial Interspecies Interactions and Their Impact on the Emergence and Spread of Antimicrobial Resistance.

Bacteria are social organisms that commonly live in dense communities surrounded by a multitude of other species. The competitive and cooperative interactions between these species not only shape the bacterial communities but also influence their susceptibility to antimicrobials. While several studies have shown that mixed-species communities are more tolerant toward antimicrobials than their monospecies counterparts, only limited empirical data are currently available on how interspecies interactions influence resistance development. We here propose a theoretic framework outlining the potential impact of interspecies social behavior on different aspects of resistance development. We identify factors by which interspecies interactions might influence resistance evolution and distinguish between their effect on (a) the emergence of a resistant mutant and (b) the spread of this resistance throughout the population. Our analysis indicates that considering the social life of bacteria is imperative to the rational design of more effective antibiotic treatment strategies with a minimal hazard for resistance development.

Endogenous virophages are active and mitigate giant virus infection in the marine protist Cafeteria burkhardae.

Endogenous viral elements (EVEs) are common genetic passengers in various protists. Some EVEs represent viral fossils, whereas others are still active. The marine heterotrophic flagellate Cafeteria burkhardae contains several EVE types related to the virophage mavirus, a small DNA virus that parasitizes the lytic giant virus CroV. We hypothesized that endogenous virophages may act as an antiviral defense system in protists, but no protective effect of virophages in wild host populations has been shown so far. Here, we tested the activity of virophage EVEs and studied their impact on giant virus replication. We found that endogenous mavirus-like elements (EMALEs) from globally distributed Cafeteria populations produced infectious virus particles specifically in response to CroV infection. However, reactivation was stochastic, often inefficient, and poorly reproducible. Interestingly, only one of eight EMALE types responded to CroV infection, implying that other EMALEs may be linked to different giant viruses. We isolated and cloned several reactivated virophages and characterized their particles, genomes, and infection dynamics. All tested virophages inhibited the production of CroV during coinfection, thereby preventing lysis of the host cultures in a dose-dependent manner. Comparative genomics of different C. burkhardae strains revealed that inducible EMALEs are common and are not linked to specific geographic locations. We demonstrate that naturally occurring virophage EVEs reactivate upon giant virus infection, thus providing a striking example that eukaryotic EVEs can become active under specific conditions. Moreover, our results support the hypothesis that virophages can act as an adaptive antiviral defense system in protists.

3D intrusions transport active surface microbial assemblages to the dark ocean.

Subtropical oceans contribute significantly to global primary production, but the fate of the picophytoplankton that dominate in these low-nutrient regions is poorly understood. Working in the subtropical Mediterranean, we demonstrate that subduction of water at ocean fronts generates 3D intrusions with uncharacteristically high carbon, chlorophyll, and oxygen that extend below the sunlit photic zone into the dark ocean. These contain fresh picophytoplankton assemblages that resemble the photic-zone regions where the water originated. Intrusions propagate depth-dependent seasonal variations in microbial assemblages into the ocean interior. Strikingly, the intrusions included dominant biomass contributions from nonphotosynthetic bacteria and enrichment of enigmatic heterotrophic bacterial lineages. Thus, the intrusions not only deliver material that differs in composition and nutritional character from sinking detrital particles, but also drive shifts in bacterial community composition, organic matter processing, and interactions between surface and deep communities. Modeling efforts paired with global observations demonstrate that subduction can flux similar magnitudes of particulate organic carbon as sinking export, but is not accounted for in current export estimates and carbon cycle models. Intrusions formed by subduction are a particularly important mechanism for enhancing connectivity between surface and upper mesopelagic ecosystems in stratified subtropical ocean environments that are expanding due to the warming climate.

Mechanics limits ecological diversity and promotes heterogeneity in confined bacterial communities.

Multispecies bacterial populations often inhabit confined and densely packed environments where spatial competition determines the ecological diversity of the community. However, the role of mechanical interactions in shaping the ecology is still poorly understood. Here, we study a model system consisting of two populations of nonmotile Escherichia coli bacteria competing within open, monolayer microchannels. The competitive dynamics is observed to be biphasic: After seeding, either one strain rapidly fixates or both strains orient into spatially stratified, stable communities. We find that mechanical interactions with other cells and local spatial constraints influence the resulting community ecology in unexpected ways, severely limiting the overall diversity of the communities while simultaneously allowing for the establishment of stable, heterogeneous populations of bacteria displaying disparate growth rates. Surprisingly, the populations have a high probability of coexisting even when one strain has a significant growth advantage. A more coccus morphology is shown to provide a selective advantage, but agent-based simulations indicate this is due to hydrodynamic and adhesion effects within the microchannel and not from breaking of the nematic ordering. Our observations are qualitatively reproduced by a simple Pólya urn model, which suggests the generality of our findings for confined population dynamics and highlights the importance of early colonization conditions on the resulting diversity and ecology of bacterial communities. These results provide fundamental insights into the determinants of community diversity in dense confined ecosystems where spatial exclusion is central to competition as in organized biofilms or intestinal crypts.

Targeted hypermutation of putative antigen sensors in multicellular bacteria.

Diversity-generating retroelements (DGRs) are used by bacteria, archaea, and viruses as a targeted mutagenesis tool. Through error-prone reverse transcription, DGRs introduce random mutations at specific genomic loci, enabling rapid evolution of these targeted genes. However, the function and benefits of DGR-diversified proteins in cellular hosts remain elusive. We find that 82% of DGRs from one of the major monophyletic lineages of DGR reverse transcriptases are encoded by multicellular bacteria, which often have two or more DGR loci in their genomes. Using the multicellular purple sulfur bacterium Thiohalocapsa sp. PB-PSB1 as an example, we characterized nine distinct DGR loci capable of generating 10282 different combinations of target proteins. With environmental metagenomes from individual Thiohalocapsa aggregates, we show that most of PB-PSB1's DGR target genes are diversified across its biogeographic range, with spatial heterogeneity in the diversity of each locus. In Thiohalocapsa PB-PSB1 and other bacteria hosting this lineage of cellular DGRs, the diversified target genes are associated with NACHT-domain anti-phage defenses and putative ternary conflict systems previously shown to be enriched in multicellular bacteria. We propose that these DGR-diversified targets act as antigen sensors that confer a form of adaptive immunity to their multicellular consortia, though this remains to be experimentally tested. These findings could have implications for understanding the evolution of multicellularity, as the NACHT-domain anti-phage systems and ternary systems share both domain homology and conceptual similarities with the innate immune and programmed cell death pathways of plants and metazoans.

Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes.

Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the identification of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just [Formula: see text]1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.

Lipid biomarkers for algal resistance to viral infection in the ocean.

Marine viruses play a key role in regulating phytoplankton populations, greatly affecting the biogeochemical cycling of major nutrients in the ocean. Resistance to viral infection has been reported for various phytoplankton species under laboratory conditions. Nevertheless, the occurrence of resistant cells in natural populations is underexplored due to the lack of sensitive tools to detect these rare phenotypes. Consequently, our current understanding of the ecological importance of resistance and its underlying mechanisms is limited. Here, we sought to identify lipid biomarkers for the resistance of the bloom-forming alga Emiliania huxleyi to its specific virus, E. huxleyi virus (EhV). By applying an untargeted lipidomics approach, we identified a group of glycosphingolipid (GSL) biomarkers that characterize resistant E. huxleyi strains and were thus termed resistance-specific GSLs (resGSLs). Further, we detected these lipid biomarkers in E. huxleyi isolates collected from induced E. huxleyi blooms and in samples collected during an open-ocean E. huxleyi bloom, indicating that resistant cells predominantly occur during the demise phase of the bloom. Last, we show that the GSL composition of E. huxleyi cultures that recover following infection and gain resistance to the virus resembles that of resistant strains. These findings highlight the metabolic plasticity and coevolution of the GSL biosynthetic pathway and underscore its central part in this host-virus arms race.

Species invasions shift microbial phenology in a two-decade freshwater time series.

Invasive species impart abrupt changes on ecosystems, but their impacts on microbial communities are often overlooked. We paired a 20 y freshwater microbial community time series with zooplankton and phytoplankton counts, rich environmental data, and a 6 y cyanotoxin time series. We observed strong microbial phenological patterns that were disrupted by the invasions of spiny water flea (Bythotrephes cederströmii) and zebra mussels (Dreissena polymorpha). First, we detected shifts in Cyanobacteria phenology. After the spiny water flea invasion, Cyanobacteria dominance crept earlier into clearwater; and after the zebra mussel invasion, Cyanobacteria abundance crept even earlier into the diatom-dominated spring. During summer, the spiny water flea invasion sparked a cascade of shifting diversity where zooplankton diversity decreased and Cyanobacteria diversity increased. Second, we detected shifts in cyanotoxin phenology. After the zebra mussel invasion, microcystin increased in early summer and the duration of toxin production increased by over a month. Third, we observed shifts in heterotrophic bacteria phenology. The Bacteroidota phylum and members of the acI Nanopelagicales lineage were differentially more abundant. The proportion of the bacterial community that changed differed by season; spring and clearwater communities changed most following the spiny water flea invasion that lessened clearwater intensity, while summer communities changed least following the zebra mussel invasion despite the shifts in Cyanobacteria diversity and toxicity. A modeling framework identified the invasions as primary drivers of the observed phenological changes. These long-term invasion-mediated shifts in microbial phenology demonstrate the interconnectedness of microbes with the broader food web and their susceptibility to long-term environmental change.

Universal abundance fluctuations across microbial communities, tropical forests, and urban populations.

The growth of complex populations, such as microbial communities, forests, and cities, occurs over vastly different spatial and temporal scales. Although research in different fields has developed detailed, system-specific models to understand each individual system, a unified analysis of different complex populations is lacking; such an analysis could deepen our understanding of each system and facilitate cross-pollination of tools and insights across fields. Here, we use a shared framework to analyze time-series data of the human gut microbiome, tropical forest, and urban employment. We demonstrate that a single, three-parameter model of stochastic population dynamics can reproduce the empirical distributions of population abundances and fluctuations in all three datasets. The three parameters characterizing a species measure its mean abundance, deterministic stability, and stochasticity. Our analysis reveals that, despite the vast differences in scale, all three systems occupy a similar region of parameter space when time is measured in generations. In other words, although the fluctuations observed in these systems may appear different, this difference is primarily due to the different physical timescales associated with each system. Further, we show that the distribution of temporal abundance fluctuations is described by just two parameters and derive a two-parameter functional form for abundance fluctuations to improve risk estimation and forecasting.

Resource competition can explain simplicity in microbial community assembly.

Predicting the composition and diversity of communities is a central goal in ecology. While community assembly is considered hard to predict, laboratory microcosms often follow a simple assembly rule based on the outcome of pairwise competitions. This assembly rule predicts that a species that is excluded by another species in pairwise competition cannot survive in a multispecies community with that species. Despite the empirical success of this bottom-up prediction, its mechanistic origin has remained elusive. In this study, we elucidate how this simple pattern in community assembly can emerge from resource competition. Our geometric analysis of a consumer-resource model shows that trio community assembly is always predictable from pairwise outcomes when one species grows faster than another species on every resource. We also identify all possible trio assembly outcomes under three resources and find that only two outcomes violate the assembly rule. Simulations demonstrate that pairwise competitions accurately predict trio assembly with up to 100 resources and the assembly of larger communities containing up to twelve species. We then further demonstrate accurate quantitative prediction of community composition using the harmonic mean of pairwise fractions. Finally, we show that cross-feeding between species does not decrease assembly rule prediction accuracy. Our findings highlight that simple community assembly can emerge even in ecosystems with complex underlying dynamics.

A case for the importance of following antibiotic resistant bacteria throughout the soil food web.

It is necessary to complement next-generation sequencing data on the soil resistome with theoretical knowledge provided by ecological studies regarding the spread of antibiotic resistant bacteria (ARB) in the abiotic and, especially, biotic fraction of the soil ecosystem. Particularly, when ARB enter agricultural soils as a consequence of the application of animal manure as fertilizer, from a microbial ecology perspective, it is important to know their fate along the soil food web, that is, throughout that complex network of feeding interactions among members of the soil biota that has crucial effects on species richness and ecosystem productivity and stability. It is critical to study how the ARB that enter the soil through the application of manure can reach other taxonomical groups (e.g., fungi, protists, nematodes, arthropods, earthworms), paying special attention to their presence in the gut microbiomes of mesofauna-macrofauna and to the possibilities for horizontal gene transfer of antibiotic resistant genes.

Boat encounter with the 2019 Java bioluminescent milky sea: Views from on-deck confirm satellite detection.

"Milky seas" are massive swaths of uniformly and steadily glowing ocean seen at night. The phenomenon is thought to be caused by luminous bacteria, but details of milky sea composition, structure, cause, and implications in nature remain largely uncertain. Between late July and early September 2019, specialized low-light satellite sensors detected a possible bioluminescent milky sea south of Java, Indonesia, spanning >100,000 km2. Upon learning of these findings, crew members of the yacht Ganesha reached out to confirm and share details of their personal encounter with this same event. Here, we document Ganesha's experience as recalled by the crew, compare their course to satellite data, and assess their photography of this milky sea.

The dynamic trophic architecture of open-ocean protist communities revealed through machine-guided metatranscriptomics.

Intricate networks of single-celled eukaryotes (protists) dominate carbon flow in the ocean. Their growth, demise, and interactions with other microorganisms drive the fluxes of biogeochemical elements through marine ecosystems. Mixotrophic protists are capable of both photosynthesis and ingestion of prey and are dominant components of open-ocean planktonic communities. Yet the role of mixotrophs in elemental cycling is obscured by their capacity to act as primary producers or heterotrophic consumers depending on factors that remain largely uncharacterized. Here, we develop and apply a machine learning model that predicts the in situ trophic mode of aquatic protists based on their patterns of gene expression. This approach leverages a public collection of protist transcriptomes as a training set to identify a subset of gene families whose transcriptional profiles predict trophic mode. We applied our model to nearly 100 metatranscriptomes obtained during two oceanographic cruises in the North Pacific and found community-level and population-specific evidence that abundant open-ocean mixotrophic populations shift their predominant mode of nutrient and carbon acquisition in response to natural gradients in nutrient supply and sea surface temperature. Metatranscriptomic data from ship-board incubation experiments revealed that abundant mixotrophic prymnesiophytes from the oligotrophic North Pacific subtropical gyre rapidly remodeled their transcriptome to enhance photosynthesis when supplied with limiting nutrients. Coupling this approach with experiments designed to reveal the mechanisms driving mixotroph physiology provides an avenue toward understanding the ecology of mixotrophy in the natural environment.

Microbial functional diversity across biogeochemical provinces in the central Pacific Ocean.

Enzymes catalyze key reactions within Earth's life-sustaining biogeochemical cycles. Here, we use metaproteomics to examine the enzymatic capabilities of the microbial community (0.2 to 3 µm) along a 5,000-km-long, 1-km-deep transect in the central Pacific Ocean. Eighty-five percent of total protein abundance was of bacterial origin, with Archaea contributing 1.6%. Over 2,000 functional KEGG Ontology (KO) groups were identified, yet only 25 KO groups contributed over half of the protein abundance, simultaneously indicating abundant key functions and a long tail of diverse functions. Vertical attenuation of individual proteins displayed stratification of nutrient transport, carbon utilization, and environmental stress. The microbial community also varied along horizontal scales, shaped by environmental features specific to the oligotrophic North Pacific Subtropical Gyre, the oxygen-depleted Eastern Tropical North Pacific, and nutrient-rich equatorial upwelling. Some of the most abundant proteins were associated with nitrification and C1 metabolisms, with observed interactions between these pathways. The oxidoreductases nitrite oxidoreductase (NxrAB), nitrite reductase (NirK), ammonia monooxygenase (AmoABC), manganese oxidase (MnxG), formate dehydrogenase (FdoGH and FDH), and carbon monoxide dehydrogenase (CoxLM) displayed distributions indicative of biogeochemical status such as oxidative or nutritional stress, with the potential to be more sensitive than chemical sensors. Enzymes that mediate transformations of atmospheric gases like CO, CO2, NO, methanethiol, and methylamines were most abundant in the upwelling region. We identified hot spots of biochemical transformation in the central Pacific Ocean, highlighted previously understudied metabolic pathways in the environment, and provided rich empirical data for biogeochemical models critical for forecasting ecosystem response to climate change.

Vertical stratification of the air microbiome in the lower troposphere.

The troposphere constitutes the final frontier of global ecosystem research due to technical challenges arising from its size, low biomass, and gaseous state. Using a vertical testing array comprising a meteorological tower and a research aircraft, we conducted synchronized measurements of meteorological parameters and airborne biomass (n = 480) in the vertical air column up to 3,500 m. The taxonomic analysis of metagenomic data revealed differing patterns of airborne microbial community composition with respect to time of day and height above ground. The temporal and spatial resolution of our study demonstrated that the diel cycle of airborne microorganisms is a ground-based phenomenon that is entirely absent at heights >1,000 m. In an integrated analysis combining meteorological and biological data, we demonstrate that atmospheric turbulence, identified by potential temperature and high-frequency three-component wind measurements, is the key driver of bioaerosol dynamics in the lower troposphere. Multivariate regression analysis shows that at least 50% of identified airborne microbial taxa (n = ∼10,000) are associated with either ground or height, allowing for an understanding of dispersal patterns of microbial taxa in the vertical air column. Due to the interconnectedness of atmospheric turbulence and temperature, the dynamics of microbial dispersal are likely to be impacted by rising global temperatures, thereby also affecting ecosystems on the planetary surface.