Stilianos Louca

Decoupling function and taxonomy in the global ocean microbiome

Microbial metabolism powers biogeochemical cycling in Earth’s ecosystems. The taxonomic composition of microbial communities varies substantially between environments, but the ecological causes of this variation remain largely unknown. We analyzed taxonomic and functional community profiles to determine the factors that shape marine bacterial and archaeal communities across the global ocean. By classifying >30,000 marine microorganisms into metabolic functional groups, we were able to disentangle functional from taxonomic community variation. We find that environmental conditions strongly influence the distribution of functional groups in marine microbial communities by shaping metabolic niches, but only weakly influence taxonomic composition within individual functional groups. Hence, functional structure and composition within functional groups constitute complementary and roughly independent “axes of variation” shaped by markedly different processes.

High taxonomic variability despite stable functional structure across microbial communities

Understanding the processes that are driving variation of natural microbial communities across space or time is a major challenge for ecologists. Environmental conditions strongly shape the metabolic function of microbial communities; however, other processes such as biotic interactions, random demographic drift or dispersal limitation may also influence community dynamics. The relative importance of these processes and their effects on community function remain largely unknown. To address this uncertainty, here we examined bacterial and archaeal communities in replicate ‘miniature’ aquatic ecosystems contained within the foliage of wild bromeliads. We used marker gene sequencing to infer the taxonomic composition within nine metabolic functional groups, and shotgun environmental DNA sequencing to estimate the relative abundances of these groups. We found that all of the bromeliads exhibited remarkably similar functional community structures, but that the taxonomic composition within individual functional groups was highly variable. Furthermore, using statistical analyses, we found that non-neutral processes, including environmental filtering and potentially biotic interactions, at least partly shaped the composition within functional groups and were more important than spatial dispersal limitation and demographic drift. Hence both the functional structure and taxonomic composition within functional groups of natural microbial communities may be shaped by non-neutral and roughly separate processes.

Calibration and analysis of genome-based models for microbial ecology

Microbial ecosystem modeling is complicated by the large number of unknown parameters and the lack of appropriate calibration tools. Here we present a novel computational framework for modeling microbial ecosystems, which combines genome-based model construction with statistical analysis and calibration to experimental data. Using this framework, we examined the dynamics of a community of Escherichia coli strains that emerged in laboratory evolution experiments, during which an ancestral strain diversified into two coexisting ecotypes. We constructed a microbial community model comprising the ancestral and the evolved strains, which we calibrated using separate monoculture experiments. Simulations reproduced the successional dynamics in the evolution experiments, and pathway activation patterns observed in microarray transcript profiles. Our approach yielded detailed insights into the metabolic processes that drove bacterial diversification, involving acetate cross-feeding and competition for organic carbon and oxygen. Our framework provides a missing link towards a data-driven mechanistic microbial ecology. DOI: http://dx.doi.org/10.7554/eLife.08208.001

Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone

Significance Modern molecular sequencing is beginning to provide great insight into microbial community structure and function at ecosystem scales. However, the quantitative integration of multiomic sequence information (DNA, mRNA, and protein) and geochemical processes has so far been elusive. Here, we develop a biogeochemical model that integrates geochemistry and multiomic sequence information to explain key metabolic processes in the oxygen-starved waters of Saanich Inlet, a model ecosystem for studying microbial community responses to oxygen minimum zone expansion. Our model largely explains DNA, mRNA, and protein distributions and sheds light on the metabolic networks coupling carbon, sulfur, and nitrogen transformations across a redox gradient. Our approach is extensible to other biogeochemical models incorporating feedbacks of global change on ecosystem functions. Microorganisms are the most abundant lifeform on Earth, mediating global fluxes of matter and energy. Over the past decade, high-throughput molecular techniques generating multiomic sequence information (DNA, mRNA, and protein) have transformed our perception of this microcosmos, conceptually linking microorganisms at the individual, population, and community levels to a wide range of ecosystem functions and services. Here, we develop a biogeochemical model that describes metabolic coupling along the redox gradient in Saanich Inlet—a seasonally anoxic fjord with biogeochemistry analogous to oxygen minimum zones (OMZs). The model reproduces measured biogeochemical process rates as well as DNA, mRNA, and protein concentration profiles across the redox gradient. Simulations make predictions about the role of ubiquitous OMZ microorganisms in mediating carbon, nitrogen, and sulfur cycling. For example, nitrite “leakage” during incomplete sulfide-driven denitrification by SUP05 Gammaproteobacteria is predicted to support inorganic carbon fixation and intense nitrogen loss via anaerobic ammonium oxidation. This coupling creates a metabolic niche for nitrous oxide reduction that completes denitrification by currently unidentified community members. These results quantitatively improve previous conceptual models describing microbial metabolic networks in OMZs. Beyond OMZ-specific predictions, model results indicate that geochemical fluxes are robust indicators of microbial community structure and reciprocally, that gene abundances and geochemical conditions largely determine gene expression patterns. The integration of real observational data, including geochemical profiles and process rate measurements as well as metagenomic, metatranscriptomic and metaproteomic sequence data, into a biogeochemical model, as shown here, enables holistic insight into the microbial metabolic network driving nutrient and energy flow at ecosystem scales.

Transient dynamics of competitive exclusion in microbial communities

Microbial metabolism drives our planets biogeochemistry and plays a central role in industrial processes. Molecular profiling in bioreactors has revealed that microbial community composition can be highly variable while maintaining constant functional performance. Furthermore, following perturbation bioreactor performance typically recovers rapidly, while community composition slowly returns to its original state. Despite its practical relevance, we still lack an understanding of the mechanisms causing the discrepancy between functional and compositional stability of microbial communities. Using a mathematical model for microbial competition, as well as simulations of a model for a nitrifying bioreactor, we explain these observations on grounds of slow non-equilibrium dynamics eventually leading to competitive exclusion. In the presence of several competing strains, metabolic niches are rapidly occupied by opportunistic populations, while subsequent species turnover and the eventual dominance of top competitors proceeds at a much slower rate. Hence, functional redundancy causes a separation of the time scales characterizing the functional and compositional stabilization of microbial communities. This effect becomes stronger with increasing richness because greater similarities between top competitors lead to longer transient population dynamics.

Functional structure of the bromeliad tank microbiome is strongly shaped by local geochemical conditions

Phytotelmata in tank-forming Bromeliaceae plants are regarded as potential miniature models for aquatic ecology, but detailed investigations of their microbial communities are rare. Hence, the biogeochemistry in bromeliad tanks remains poorly understood. Here we investigate the structure of bacterial and archaeal communities inhabiting the detritus within the tanks of two bromeliad species, Aechmea nudicaulis and Neoregelia cruenta, from a Brazilian sand dune forest. We used metagenomic sequencing for functional community profiling and 16S sequencing for taxonomic profiling. We estimated the correlation between functional groups and various environmental variables, and compared communities between bromeliad species. In all bromeliads, microbial communities spanned a metabolic network adapted to oxygen-limited conditions, including all denitrification steps, ammonification, sulfate respiration, methanogenesis, reductive acetogenesis and anoxygenic phototrophy. Overall, CO2 reducers dominated in abundance over sulfate reducers, and anoxygenic phototrophs largely outnumbered oxygenic photoautotrophs. Functional community structure correlated strongly with environmental variables, between and within a single bromeliad species. Methanogens and reductive acetogens correlated with detrital volume and canopy coverage, and exhibited higher relative abundances in N. cruenta. A comparison of bromeliads to freshwater lake sediments and soil from around the world, revealed stark differences in terms of taxonomic as well as functional microbial community structure.

Function and functional redundancy in microbial systems

Microbial communities often exhibit incredible taxonomic diversity, raising questions regarding the mechanisms enabling species coexistence and the role of this diversity in community functioning. On the one hand, many coexisting but taxonomically distinct microorganisms can encode the same energy-yielding metabolic functions, and this functional redundancy contrasts with the expectation that species should occupy distinct metabolic niches. On the other hand, the identity of taxa encoding each function can vary substantially across space or time with little effect on the function, and this taxonomic variability is frequently thought to result from ecological drift between equivalent organisms. Here, we synthesize the powerful paradigm emerging from these two patterns, connecting the roles of function, functional redundancy and taxonomy in microbial systems. We conclude that both patterns are unlikely to be the result of ecological drift, but are inevitable emergent properties of open microbial systems resulting mainly from biotic interactions and environmental and spatial processes.Microbial communities may often be composed of a wide diversity of taxa that perform similar functions. Here, the authors discuss the roles of function, functional redundancy and taxonomy in microbial community assembly and coexistence.

Moving forward in circles: challenges and opportunities in modelling population cycles

Population cycling is a widespread phenomenon, observed across a multitude of taxa in both laboratory and natural conditions. Historically, the theory associated with population cycles was tightly linked to pairwise consumer-resource interactions and studied via deterministic models, but current empirical and theoretical research reveals a much richer basis for ecological cycles. Stochasticity and seasonality can modulate or create cyclic behaviour in non-intuitive ways, the high-dimensionality in ecological systems can profoundly influence cycling, and so can demographic structure and eco-evolutionary dynamics. An inclusive theory for population cycles, ranging from ecosystem-level to demographic modelling, grounded in observational or experimental data, is therefore necessary to better understand observed cyclical patterns. In turn, by gaining better insight into the drivers of population cycles, we can begin to understand the causes of cycle gain and loss, how biodiversity interacts with population cycling, and how to effectively manage wildly fluctuating populations, all of which are growing domains of ecological research.

Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem

The 16S ribosomal RNA gene is the most widely used marker gene in microbial ecology. Counts of 16S sequence variants, often in PCR amplicons, are used to estimate proportions of bacterial and archaeal taxa in microbial communities. Because different organisms contain different 16S gene copy numbers (GCNs), sequence variant counts are biased towards clades with greater GCNs. Several tools have recently been developed for predicting GCNs using phylogenetic methods and based on sequenced genomes, in order to correct for these biases. However, the accuracy of those predictions has not been independently assessed. Here, we systematically evaluate the predictability of 16S GCNs across bacterial and archaeal clades, based on ∼ 6,800 public sequenced genomes and using several phylogenetic methods. Further, we assess the accuracy of GCNs predicted by three recently published tools (PICRUSt, CopyRighter, and PAPRICA) over a wide range of taxa and for 635 microbial communities from varied environments. We find that regardless of the phylogenetic method tested, 16S GCNs could only be accurately predicted for a limited fraction of taxa, namely taxa with closely to moderately related representatives (≲15% divergence in the 16S rRNA gene). Consistent with this observation, we find that all considered tools exhibit low predictive accuracy when evaluated against completely sequenced genomes, in some cases explaining less than 10% of the variance. Substantial disagreement was also observed between tools (R2<0.5) for the majority of tested microbial communities. The nearest sequenced taxon index (NSTI) of microbial communities, i.e., the average distance to a sequenced genome, was a strong predictor for the agreement between GCN prediction tools on non-animal-associated samples, but only a moderate predictor for animal-associated samples. We recommend against correcting for 16S GCNs in microbiome surveys by default, unless OTUs are sufficiently closely related to sequenced genomes or unless a need for true OTU proportions warrants the additional noise introduced, so that community profiles remain interpretable and comparable between studies.

Taxonomic variability and functional stability in microbial communities infected by phages

Microbial communities can display large variation in taxonomic composition, yet this variation can coincide with stable metabolic functional structure and performance. The mechanisms driving the taxonomic variation within functional groups remain largely unknown. Biotic interactions, such as predation by phages, have been suggested as potential cause of taxonomic turnover, but the conditions for this scenario have not been rigorously examined. Further, it is unknown how predation by phages affects community function, and how these effects are modulated by functional redundancy in the communities. Here, we address these questions using a model for a methanogenic microbial community that includes several interacting metabolic functional groups. Each functional group comprises multiple competing clades, and each clade is attacked by a specialist lytic phage. Our model predicts that phages induce intense taxonomic turnover, resembling the variability observed in previous experiments. The functional structure and performance of the community are also disturbed by phage predation, but they become more stable as the functional redundancy in the community increases. The extent of this stabilization depends on the particular functions considered. Our model suggests mechanisms by which functional redundancy stabilizes community function and supports the interpretation that biotic interactions promote taxonomic turnover within microbial functional groups.