Year-around survey and manipulation experiments reveal differential sensitivities of soil prokaryotic and fungal communities to saltwater intrusion in Florida Everglades wetlands.

Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased episodic or chronic saltwater exposure, leading to shifts in biogeochemistry, plant- and microbial communities, as well as ecological services. Yet, it is still difficult to predict how soil microbial communities respond to the saltwater exposure because of poorly understood microbial sensitivity within complex wetland soil microbial communities, as well as the high spatial and temporal heterogeneity of wetland soils and saltwater exposure. To address this, we first conducted a two-year survey of microbial community structure and bottom water chemistry in submerged surface soils from 14 wetland sites across the Florida Everglades. We identified ecosystem-specific microbial biomarker taxa primarily associated with variation in salinity. Bacterial, archaeal and fungal community composition differed between freshwater, mangrove, and marine seagrass meadow sites, irrespective of soil type or season. Especially, methanogens, putative denitrifying methanotrophs and sulfate reducers shifted in relative abundance and/or composition between wetland types. Methanogens and putative denitrifying methanotrophs declined in relative abundance from freshwater to marine wetlands, whereas sulfate reducers showed the opposite trend. A four-year experimental simulation of saltwater intrusion in a pristine freshwater site and a previously saltwater-impacted site corroborated the highest sensitivity and relative increase of sulfate reducers, as well as taxon-specific sensitivity of methanogens, in response to continuously pulsing of saltwater treatment. Collectively, these results suggest that besides increased salinity, saltwater-mediated increased sulfate availability leads to displacement of methanogens by sulfate reducers even at low or temporal salt exposure. These changes of microbial composition could affect organic matter degradation pathways in coastal freshwater wetlands exposed to sea-level rise, with potential consequences, such as loss of stored soil organic carbon.

Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats.

Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats-instructions, templates, and tools for consistently formatting data within a discipline-can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.

Competitive Exclusion and Metabolic Dependency among Microorganisms Structure the Cellulose Economy of an Agricultural Soil.

Microorganisms that degrade cellulose utilize extracellular reactions that yield free by-products which can promote interactions with noncellulolytic organisms. We hypothesized that these interactions determine the ecological and physiological traits governing the fate of cellulosic carbon (C) in soil. We performed comparative genomics with genome bins from a shotgun metagenomic-stable isotope probing experiment to characterize the attributes of cellulolytic and noncellulolytic taxa accessing 13C from cellulose. We hypothesized that cellulolytic taxa would exhibit competitive traits that limit access, while noncellulolytic taxa would display greater metabolic dependency, such as signatures of adaptive gene loss. We tested our hypotheses by evaluating genomic traits indicative of competitive exclusion or metabolic dependency, such as antibiotic production, growth rate, surface attachment, biomass degrading potential, and auxotrophy. The most 13C-enriched taxa were cellulolytic Cellvibrio (Gammaproteobacteria) and Chaetomium (Ascomycota), which exhibited a strategy of self-sufficiency (prototrophy), rapid growth, and competitive exclusion via antibiotic production. Auxotrophy was more prevalent in cellulolytic Actinobacteria than in cellulolytic Proteobacteria, demonstrating differences in dependency among cellulose degraders. Noncellulolytic taxa that accessed 13C from cellulose (Planctomycetales, Verrucomicrobia, and Vampirovibrionales) were also more dependent, as indicated by patterns of auxotrophy and 13C labeling (i.e., partial labeling or labeling at later stages). Major 13C-labeled cellulolytic microbes (e.g., Sorangium, Actinomycetales, Rhizobiales, and Caulobacteraceae) possessed adaptations for surface colonization (e.g., gliding motility, hyphae, attachment structures) signifying the importance of surface ecology in decomposing particulate organic matter. Our results demonstrated that access to cellulosic C was accompanied by ecological trade-offs characterized by differing degrees of metabolic dependency and competitive exclusion.IMPORTANCE Our study reveals the ecogenomic traits of microorganisms participating in the cellulose economy of soil. We identified three major categories of participants in this economy: (i) independent primary degraders, (ii) interdependent primary degraders, and (iii) secondary consumers (mutualists, opportunists, and parasites). Trade-offs between independent primary degraders, whose adaptations favor antagonism and competitive exclusion, and interdependent and secondary degraders, whose adaptations favor complex interspecies interactions, are expected to affect the fate of microbially processed carbon in soil. Our findings provide useful insights into the ecological relationships that govern one of the planet's most abundant resources of organic carbon. Furthermore, we demonstrate a novel gradient-resolved approach for stable isotope probing, which provides a cultivation-independent, genome-centric perspective into soil microbial processes.

Draft Genome Sequence of Pseudarthrobacter sp. Strain ATCC 49442 (Formerly Micrococcus luteus), a Pyridine-Degrading Bacterium.

We present here the draft genome sequence of a pyridine-degrading bacterium, Micrococcus luteus ATCC 49442, which was reclassified as Pseudarthrobacter sp. strain ATCC 49442 based on its draft genome sequence. Its genome length is 4.98 Mbp, with 64.81% GC content.

Representing the function and sensitivity of coastal interfaces in Earth system models.

Between the land and ocean, diverse coastal ecosystems transform, store, and transport material. Across these interfaces, the dynamic exchange of energy and matter is driven by hydrological and hydrodynamic processes such as river and groundwater discharge, tides, waves, and storms. These dynamics regulate ecosystem functions and Earth's climate, yet global models lack representation of coastal processes and related feedbacks, impeding their predictions of coastal and global responses to change. Here, we assess existing coastal monitoring networks and regional models, existing challenges in these efforts, and recommend a path towards development of global models that more robustly reflect the coastal interface.

Soil Bacterial Diversity Is Associated with Human Population Density in Urban Greenspaces.

Urban greenspaces provide extensive ecosystem services, including pollutant remediation, water management, carbon maintenance, and nutrient cycling. However, while the urban soil microbiota underpin these services, we still have limited understanding of the factors that influence their distribution. We characterized soil bacterial communities from turf-grasses associated with urban parks, streets, and residential sites across a major urban environment, including a gradient of human population density. Bacterial diversity was significantly positively correlated with the population density; and species diversity was greater in park and street soils, compared to residential soils. Population density and greenspace type also led to significant differences in the microbial community composition that was also significantly correlated with soil pH, moisture, and texture. Co-occurrence network analysis revealed that microbial guilds in urban soils were well correlated. Abundant soil microbes in high density population areas had fewer interactions, while abundant bacteria in high moisture soils had more interactions. These results indicate the significant influence of changes in urban demographics and land-use on soil microbial communities. As urbanization is rapidly growing across the planet, it is important to improve our understanding of the consequences of urban zoning on the soil microbiota.

Metabolic Reconstruction and Modeling Microbial Electrosynthesis.

Microbial electrosynthesis is a renewable energy and chemical production platform that relies on microbial cells to capture electrons from a cathode and fix carbon. Yet despite the promise of this technology, the metabolic capacity of the microbes that inhabit the electrode surface and catalyze electron transfer in these systems remains largely unknown. We assembled thirteen draft genomes from a microbial electrosynthesis system producing primarily acetate from carbon dioxide, and their transcriptional activity was mapped to genomes from cells on the electrode surface and in the supernatant. This allowed us to create a metabolic model of the predominant community members belonging to Acetobacterium, Sulfurospirillum, and Desulfovibrio. According to the model, the Acetobacterium was the primary carbon fixer, and a keystone member of the community. Transcripts of soluble hydrogenases and ferredoxins from Acetobacterium and hydrogenases, formate dehydrogenase, and cytochromes of Desulfovibrio were found in high abundance near the electrode surface. Cytochrome c oxidases of facultative members of the community were highly expressed in the supernatant despite completely sealed reactors and constant flushing with anaerobic gases. These molecular discoveries and metabolic modeling now serve as a foundation for future examination and development of electrosynthetic microbial communities.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation.

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.

Modeling central metabolism and energy biosynthesis across microbial life.

BACKGROUND: Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. RESULTS: To overcome this challenge, we developed methods and tools ( http://coremodels.mcs.anl.gov ) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. CONCLUSIONS: We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.

Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction.

Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339-2345, 2016. © 2016 Wiley Periodicals, Inc.

Network-based metabolic analysis and microbial community modeling.

Network inference is being applied to studies of microbial ecology to visualize and characterize microbial communities. Network representations can allow examination of the underlying organizational structure of a microbial community, and identification of key players or environmental conditions that influence community assembly and stability. Microbial co-association networks provide information on the dynamics of community structure as a function of time or other external variables. Community metabolic networks can provide a mechanistic link between species through identification of metabolite exchanges and species specific resource requirements. When used together, co-association networks and metabolic networks can provide a more in-depth view of the hidden rules that govern the stability and dynamics of microbial communities.

Carbon constrains fungal endophyte assemblages along the timberline.

The alpha diversity of foliar fungal endophytes (FEs) in leaves of Betula ermanii in a subalpine timberline ecotone on Changbai Mountain, China increased with elevation. There were also significant differences in beta diversity along the elevation gradient. Among the environmental variables analysed, leaf carbon significantly increased with elevation, and was the most significant environmental factor that constrained the alpha and beta diversity in the FE communities. Tree height and the cellulose, lignin, and carbon/nitrogen ratio of the leaves also affected the FE assemblages. When controlled for the effects of elevation, leaf carbon was still the main driver of changes in evenness, Shannon diversity and FE community composition. The results offered clues of the carbon acquisition strategy of the foliar FEs across this cold terrain. There was strong multicollinearity between both annual precipitation and temperature, with elevation (|Pearson r| > 0.986), so the effects of these climatic variables were impossible to separate; however, they may play key roles, and the direct effects of both warrant further investigation. As pioneer decomposers of leaf litter, variations in diversity and community composition of FE measured here may feedback and influence carbon cycling and dynamics in these forest ecosystems.

The soil microbiome influences grapevine-associated microbiota.

UNLABELLED: Grapevine is a well-studied, economically relevant crop, whose associated bacteria could influence its organoleptic properties. In this study, the spatial and temporal dynamics of the bacterial communities associated with grapevine organs (leaves, flowers, grapes, and roots) and soils were characterized over two growing seasons to determine the influence of vine cultivar, edaphic parameters, vine developmental stage (dormancy, flowering, preharvest), and vineyard. Belowground bacterial communities differed significantly from those aboveground, and yet the communities associated with leaves, flowers, and grapes shared a greater proportion of taxa with soil communities than with each other, suggesting that soil may serve as a bacterial reservoir. A subset of soil microorganisms, including root colonizers significantly enriched in plant growth-promoting bacteria and related functional genes, were selected by the grapevine. In addition to plant selective pressure, the structure of soil and root microbiota was significantly influenced by soil pH and C:N ratio, and changes in leaf- and grape-associated microbiota were correlated with soil carbon and showed interannual variation even at small spatial scales. Diazotrophic bacteria, e.g., Rhizobiaceae and Bradyrhizobium spp., were significantly more abundant in soil samples and root samples of specific vineyards. Vine-associated microbial assemblages were influenced by myriad factors that shape their composition and structure, but the majority of organ-associated taxa originated in the soil, and their distribution reflected the influence of highly localized biogeographic factors and vineyard management. IMPORTANCE: Vine-associated bacterial communities may play specific roles in the productivity and disease resistance of their host plant. Also, the bacterial communities on grapes have the potential to influence the organoleptic properties of the wine, contributing to a regional terroir. Understanding that factors that influence these bacteria may provide insights into management practices to shape and craft individual wine properties. We show that soil serves as a key source of vine-associated bacteria and that edaphic factors and vineyard-specific properties can influence the native grapevine microbiome preharvest.

Genome-wide metabolic network reconstruction of the picoalga Ostreococcus.

The green picoalga Ostreococcus is emerging as a simple plant model organism, and two species, O. lucimarinus and O. tauri, have now been sequenced and annotated manually. To evaluate the completeness of the metabolic annotation of both species, metabolic networks of O. lucimarinus and O. tauri were reconstructed from the KEGG database, thermodynamically constrained, elementally balanced, and functionally evaluated. The draft networks contained extensive gaps and, in the case of O. tauri, no biomass components could be produced due to an incomplete Calvin cycle. To find and remove gaps from the networks, an extensive reference biochemical reaction database was assembled using a stepwise approach that minimized the inclusion of microbial reactions. Gaps were then removed from both Ostreococcus networks using two existing gap-filling methodologies. In the first method, a bottom-up approach, a minimal list of reactions was added to each model to enable the production of all metabolites included in our biomass equation. In the second method, a top-down approach, all reactions in the reference database were added to the target networks and subsequently trimmed away based on the sequence alignment scores of identified orthologues. Because current gap-filling methods do not produce unique solutions, a quality metric that includes a weighting for phylogenetic distance and sequence similarity was developed to distinguish between gap-filling results automatically. The draft O. lucimarinus and O. tauri networks required the addition of 56 and 70 reactions, respectively, in order to produce the same biomass precursor metabolites that were produced by our plant reference database.