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Inferring linkages between microbial metabolism and dissolved organic matter (DOM) across environmental gradients is a promising avenue to improve biogeochemical predictions at large spatial scales. Despite decades of metagenomic studies identifying microbial functional trait-environment patterns at small spatial scales, general patterns at continental or global scales that may improve large-scale models remain unresolved. Recent influx of multi-omics datasets that represent diverse environmental conditions has enabled scalable analyses linking microbial metabolic niche breadths with key environmental processes, such as carbon and nutrient transformations.Here, we leveraged publicly available microbial metagenome assembled genomes (MAGs) derived from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) data paired with metabolomic (FTICR-MS) and sediment chemistry data to link microbial metabolic potential with organic chemistry. We annotated 1,384 MAGs representing 65 sites using the R tool microTrait, which categorizes functional traits under the YAS (growth yield-resource acquisition-stress tolerance) framework. Following Hutchinsonian niche theory, we modeled microbial trait combinations as n-dimensional hypervolumes and observed trait-DOM patterns at the continental scale, showing microbial functional tradeoffs along gradients of organic carbon. We expect that at the continental scale, microbial trait profiles will be distinct across climatic regions, and that niche breadth (i.e. the size of individual hypervolumes in trait space) will correlate with DOM/metabolite diversity. The results of this work will distill generalizable patterns of microbe-DOM availability and diversity at large spatial scales, thus identifying information to improve current biogeochemical models. 

Although river ecosystems constitute a small fraction of Earth’s total area, they are critical modulators of microbially and virally orchestrated global biogeochemical cycles. However, most studies either use data that is not spatially resolved or is collected at timepoints that do not reflect the short life cycles of microorganisms. To address this gap, we assessed how viral and microbial communities change over a 48-hour period by sampling surface water and pore water compartments of the wastewater-impacted River Erpe in Germany. We sampled every 3 hours resulting in 32 samples for which we obtained metagenomes along with geochemical and metabolite measurements. From our metagenomes, we identified 6,500 viral and 1,033 microbial metagenome assembled genomes (MAGs) and found distinct community membership and abundance associated with each river compartment (e.g.,Competibacteraceaein surfacewater andSulfurimonadaceaein pore water). We show that 17% of our viral MAGs clustered to viruses from other ecosystems like wastewater treatment plants and rivers. Our results also indicated that 70% of the viral community was persistent in surface waters, whereas only 13% were persistent in the pore waters taken from the hyporheic zone. Finally, we predicted linkages between 73 viral genomes and 38 microbial genomes. These putatively linked hosts included members of theCompetibacteraceae, which we suggest are potential contributors to river carbon and nitrogen cycling via denitrification and nitrogen fixation. Together, these findings demonstrate that members of the surface water microbiome from this urban river are stable over multiple diurnal cycles. These temporal insights raise important considerations for ecosystem models attempting to constrain dynamics of river biogeochemical cycles. 

Abstract Although time series in ecosystem metabolism are well characterized in small and medium rivers, patterns in the world's largest rivers are almost unknown. Large rivers present technical difficulties, including depth measurements, gas exchange (, ) estimates, and the presence of large dams, which can supersaturate gases. We estimated reach‐scale metabolism for the Hanford Reach of the Columbia River (Washington state, USA), a free‐flowing stretch with an average discharge of 3173 . We calculated from semi‐empirical models and directly estimated it from tracer measurements. We fixed at the median value from these calculations (0.5 ), and used maximum likelihood to estimate reach‐scale, open‐channel metabolism. Both gross primary production (GPP) and ecosystem respiration (ER) were high (GPP range: 0.3–30.8 g , ER range: 0.8–30.6 g ), with peak GPP and ER occurring in the late summer or early fall. GPP increased exponentially with temperature, consistent with metabolic theory, while light was seasonally saturating. Annual average GPP, estimated at 1500 g carbon , was in the top 2% of estimates for other rivers. GPP and ER were tightly coupled and 90% of GPP was immediately respired, resulting in net ecosystem production near 0. Patterns in the Hanford Reach contrast with those in small‐medium rivers, suggesting that metabolism magnitudes and patterns in large rivers may not be simply scaled from knowledge of smaller rivers. 

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth’s environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment. 

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyze 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical, and gene neighborhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter. 

Abstract. A comprehensive set of measurements and calculated metricsdescribing physical, chemical, and biological conditions in the rivercorridor is presented. These data were collected in a catchment-wide,synoptic campaign in the H. J. Andrews ExperimentalForest (Cascade Mountains, Oregon, USA) in summer 2016 during low-dischargeconditions. Extensive characterization of 62 sites including surface water,hyporheic water, and streambed sediment was conducted spanning 1st- through5th-order reaches in the river network. The objective of the sample designand data acquisition was to generate a novel data set to support scaling ofriver corridor processes across varying flows and morphologic forms presentin a river network. The data are available at https://doi.org/10.4211/hs.f4484e0703f743c696c2e1f209abb842 (Ward, 2019).