Protist Predation Influences the Temperature Response of Bacterial Communities.

Temperature strongly influences microbial community structure and function, in turn contributing to global carbon cycling that can fuel further warming. Recent studies suggest that biotic interactions among microbes may play an important role in determining the temperature responses of these communities. However, how predation regulates these microbiomes under future climates is still poorly understood. Here, we assess whether predation by a key global bacterial consumer-protists-influences the temperature response of the community structure and function of a freshwater microbiome. To do so, we exposed microbial communities to two cosmopolitan protist species-Tetrahymena thermophila and Colpidium sp.-at two different temperatures, in a month-long microcosm experiment. While microbial biomass and respiration increased with temperature due to community shifts, these responses changed over time and in the presence of protists. Protists influenced microbial biomass and respiration rate through direct and indirect effects on bacterial community structure, and predator presence actually reduced microbial respiration at elevated temperature. Indicator species analyses showed that these predator effects were mostly determined by phylum-specific bacterial responses to protist density and cell size. Our study supports previous findings that temperature is an important driver of microbial communities but also demonstrates that the presence of a large predator can mediate these responses to warming.

Alkaline mine drainage drives stream sediment microbial community structure and function.

With advances in eDNA metabarcoding, environmental microbiomes are increasingly used as cost-effective tools for monitoring ecosystem health. Stream ecosystems in Central Appalachia, heavily impacted by alkaline drainage from mountaintop coal mining, present ideal opportunities for biomonitoring using stream microbiomes, but the structural and functional responses of microbial communities in different environmental compartments are not well understood. We investigated sediment microbiomes in mining impacted streams to determine how community composition and function respond to mining and to look for potential microbial bioindicators. Using 16s rRNA gene amplicon sequencing, we found that mining leads to shifts in microbial community structure, with the phylum Planctomycetes enriched by 1-6% at mined sites. We observed ~51% increase in species richness in bulk sediments. In contrast, of the 31 predicted metabolic pathways that changed significantly with mining, 23 responded negatively. Mining explained 15-18% of the variance in community structure and S, Se, %C and %N were the main drivers of community and functional pathway composition. We identified 12 microbial indicators prevalent in the ecosystem and sensitive to mining. Overall, alkaline mountaintop mining drainage causes a restructuration of the sediment microbiome, and our study identified promising microbial indicators for the long-term monitoring of these impacted streams.

Guided by Microbes: Applying Community Coalescence Principles for Predictive Microbiome Engineering.

Every seed germinating in soils, wastewater treatment, and stream confluence exemplify microbial community coalescence-the blending of previously isolated communities. Here, we present theoretical and experimental knowledge on how separated microbial communities mix, with particular focus on managed ecosystems. We adopt the community coalescence framework, which integrates metacommunity theory and meta-ecosystem dynamics, and highlight the prevalence of these coalescence events within microbial systems. Specifically, we (i) describe fundamental types of community coalescences using naturally occurring and managed examples, (ii) offer ways forward to leverage community coalescence in managed systems, and (iii) emphasize the importance of microbial ecological theory to achieving desired coalescence outcomes. Further, considering the massive dispersal events of microbiomes and their coalescences is pivotal to better predict microbial community dynamics and responses to disturbances. We conclude our piece by highlighting some challenges and unanswered question yet to be tackled.

Consistent declines in aquatic biodiversity across diverse domains of life in rivers impacted by surface coal mining.

The rivers of Appalachia (United States) are among the most biologically diverse freshwater ecosystems in the temperate zone and are home to numerous endemic aquatic organisms. Throughout the Central Appalachian ecoregion, extensive surface coal mines generate alkaline mine drainage that raises the pH, salinity, and trace element concentrations in downstream waters. Previous regional assessments have found significant declines in stream macroinvertebrate and fish communities after draining these mined areas. Here, we expand these assessments with a more comprehensive evaluation across a broad range of organisms (bacteria, algae, macroinvertebrates, all eukaryotes, and fish) using high-throughput amplicon sequencing of environmental DNA (eDNA). We collected water samples from 93 streams in Central Appalachia (West Virginia, United States) spanning a gradient of mountaintop coal mining intensity and legacy to assess how this land use alters downstream water chemistry and affects aquatic biodiversity. For each group of organisms, we identified the sensitive and tolerant taxa along the gradient and calculated stream specific conductivity thresholds in which large synchronous declines in diversity were observed. Streams below mining operations had steep declines in diversity (-18 to -41%) and substantial shifts in community composition that were consistent across multiple taxonomic groups. Overall, large synchronous declines in bacterial, algal, and macroinvertebrate communities occurred even at low levels of mining impact at stream specific conductivity thresholds of 150-200 µS/cm that are substantially below the current U.S. Environmental Protection Agency aquatic life benchmark of 300 µS/cm for Central Appalachian streams. We show that extensive coal surface mining activities led to the extirpation of 40% of biodiversity from impacted rivers throughout the region and that current water quality criteria are likely not protective for many groups of aquatic organisms.

Rare microbial taxa emerge when communities collide: freshwater and marine microbiome responses to experimental mixing.

Whole microbial communities regularly merge with one another, often in tandem with their environments, in a process called community coalescence. Such events impose substantial changes: abiotic perturbation from environmental blending and biotic perturbation of community merging. We used an aquatic mixing experiment to unravel the effects of these perturbations on the whole microbiome response and on the success of individual taxa when distinct freshwater and marine communities coalesce. We found that an equal mix of freshwater and marine habitats and blended microbiomes resulted in strong convergence of the community structure toward that of the marine microbiome. The enzymatic potential of these blended microbiomes in mixed media also converged toward that of the marine, with strong correlations between the multivariate response patterns of the enzymes and of community structure. Exposing each endmember inocula to an axenic equal mix of their freshwater and marine source waters led to a 96% loss of taxa from our freshwater microbiomes and a 66% loss from our marine microbiomes. When both inocula were added together to this mixed environment, interactions amongst the communities led to a further loss of 29% and 49% of freshwater and marine taxa, respectively. Under both the axenic and competitive scenarios, the diversity lost was somewhat counterbalanced by increased abundance of microbial taxa that were too rare to detect in the initial inocula. Our study emphasizes the importance of the rare biosphere as a critical component of microbial community responses to community coalescence.

In search of microbial indicator taxa: shifts in stream bacterial communities along an urbanization gradient.

A majority of environmental studies describe microbiomes at coarse scales of taxonomic resolution (bacterial community, phylum), ignoring key ecological knowledge gained from finer-scales and microbial indicator taxa. Here, we characterized the distribution of 940 bacterial taxa from 41 streams along an urbanization gradient (0%-83% developed watershed area) in the Raleigh-Durham area of North Carolina (USA). Using statistical approaches derived from macro-organismal ecology, we found that more bacterial taxa were classified as intolerant than as tolerant to increasing watershed urbanization (143 vs 48 OTUs), and we identified a threshold of 12.1% developed watershed area beyond which the majority of intolerant taxa were lost from streams. Two bacterial families strongly decreased with urbanization: Acidobacteriaceae (Acidobacteria) and Xanthobacteraceae (Alphaproteobacteria). Tolerant taxa were broadly distributed throughout the bacterial phylogeny, with members of the Comamonadaceae family (Betaproteobacteria) presenting the highest number of tolerant taxa. Shifts in microbial community structure were strongly correlated with a stream biotic index, based on macroinvertebrate composition, suggesting that microbial assemblages could be used to establish biotic criteria for monitoring aquatic ecosystems. In addition, our study shows that classic methods in community ecology can be applied to microbiome datasets to identify reliable microbial indicator taxa and determine the environmental constraints on individual taxa distributions along environmental gradients.

Plant and Microbial Responses to Repeated Cu(OH)2 Nanopesticide Exposures Under Different Fertilization Levels in an Agro-Ecosystem.

The environmental fate and potential impacts of nanopesticides on agroecosystems under realistic agricultural conditions are poorly understood. As a result, the benefits and risks of these novel formulations compared to the conventional products are currently unclear. Here, we examined the effects of repeated realistic exposures of the Cu(OH)2 nanopesticide, Kocide 3000, on simulated agricultural pastureland in an outdoor mesocosm experiment over 1 year. The Kocide applications were performed alongside three different mineral fertilization levels (Ambient, Low, and High) to assess the environmental impacts of this nanopesticide under low-input or conventional farming scenarios. The effects of Kocide over time were monitored on forage biomass, plant mineral nutrient content, plant-associated non-target microorganisms (i.e., N-fixing bacteria or mycorrhizal fungi) and six soil microbial enzyme activities. We observed that three sequential Kocide applications had no negative effects on forage biomass, root mycorrhizal colonization or soil nitrogen fixation rates. In the Low and High fertilization treatments, we observed a significant increase in aboveground plant biomass after the second Kocide exposure (+14% and +27%, respectively). Soil microbial enzyme activities were significantly reduced in the short-term after the first exposure (day 15) in the Ambient (-28% to -82%) and Low fertilization (-25% to -47%) but not in the High fertilization treatment. However, 2 months later, enzyme activities were similar across treatments and were either unresponsive or responded positively to subsequent Kocide additions. There appeared to be some long-term effects of Kocide exposure, as 6 months after the last Kocide exposure (day 365), both beta-glucosidase (-57% in Ambient and -40% in High fertilization) and phosphatase activities (-47% in Ambient fertilization) were significantly reduced in the mesocosms exposed to the nanopesticide. These results suggest that when used in conventional farming with high fertilization rates, Kocide applications did not lead to marked adverse effects on forage biomass production and key plant-microorganism interactions over a growing season. However, in the context of low-input organic farming for which this nanopesticide is approved, Kocide applications may have some unintended detrimental effects on microbially mediated soil processes involved in carbon and phosphorus cycling.

Gold nanoparticle biodissolution by a freshwater macrophyte and its associated microbiome.

Predicting nanoparticle fate in aquatic environments requires mimicking of ecosystem complexity to observe the geochemical processes affecting their behaviour. Here, 12 nm Au nanoparticles were added weekly to large-scale freshwater wetland mesocosms. After six months, ~70% of Au was associated with the macrophyte Egeria densa, where, despite the thermodynamic stability of Au0 in water, the pristine Au0 nanoparticles were fully oxidized and complexed to cyanide, hydroxyls or thiol ligands. Extracted biofilms growing on E. densa leaves were shown to dissolve Au nanoparticles within days. The Au biodissolution rate was highest for the biofilm with the lowest prevalence of metal-resistant taxa but the highest ability to release cyanide, known to promote Au0 oxidation and complexation. Macrophytes and the associated microbiome thus form a biologically active system that can be a major sink for nanoparticle accumulation and transformations. Nanoparticle biotransformation in these compartments should not be ignored, even for nanoparticles commonly considered to be stable in the environment.

Understanding how microbiomes influence the systems they inhabit.

Translating the ever-increasing wealth of information on microbiomes (environment, host or built environment) to advance our understanding of system-level processes is proving to be an exceptional research challenge. One reason for this challenge is that relationships between characteristics of microbiomes and the system-level processes that they influence are often evaluated in the absence of a robust conceptual framework and reported without elucidating the underlying causal mechanisms. The reliance on correlative approaches limits the potential to expand the inference of a single relationship to additional systems and advance the field. We propose that research focused on how microbiomes influence the systems they inhabit should work within a common framework and target known microbial processes that contribute to the system-level processes of interest. Here, we identify three distinct categories of microbiome characteristics (microbial processes, microbial community properties and microbial membership) and propose a framework to empirically link each of these categories to each other and the broader system-level processes that they affect. We posit that it is particularly important to distinguish microbial community properties that can be predicted using constituent taxa (community-aggregated traits) from those properties that cannot currently be predicted using constituent taxa (emergent properties). Existing methods in microbial ecology can be applied to more explicitly elucidate properties within each of these three categories of microbial characteristics and connect them with each other. We view this proposed framework, gleaned from a breadth of research on environmental microbiomes and ecosystem processes, as a promising pathway with the potential to advance discovery and understanding across a broad range of microbiome science.

Detecting macroecological patterns in bacterial communities across independent studies of global soils.

The emergence of high-throughput DNA sequencing methods provides unprecedented opportunities to further unravel bacterial biodiversity and its worldwide role from human health to ecosystem functioning. However, despite the abundance of sequencing studies, combining data from multiple individual studies to address macroecological questions of bacterial diversity remains methodically challenging and plagued with biases. Here, using a machine-learning approach that accounts for differences among studies and complex interactions among taxa, we merge 30 independent bacterial data sets comprising 1,998 soil samples from 21 countries. Whereas previous meta-analysis efforts have focused on bacterial diversity measures or abundances of major taxa, we show that disparate amplicon sequence data can be combined at the taxonomy-based level to assess bacterial community structure. We find that rarer taxa are more important for structuring soil communities than abundant taxa, and that these rarer taxa are better predictors of community structure than environmental factors, which are often confounded across studies. We conclude that combining data from independent studies can be used to explore bacterial community dynamics, identify potential 'indicator' taxa with an important role in structuring communities, and propose hypotheses on the factors that shape bacterial biogeography that have been overlooked in the past.

The Microbiome Stress Project: Toward a Global Meta-Analysis of Environmental Stressors and Their Effects on Microbial Communities.

Microbial community structure is highly sensitive to natural (e.g., drought, temperature, fire) and anthropogenic (e.g., heavy metal exposure, land-use change) stressors. However, despite an immense amount of data generated, systematic, cross-environment analyses of microbiome responses to multiple disturbances are lacking. Here, we present the Microbiome Stress Project, an open-access database of environmental and host-associated 16S rRNA amplicon sequencing studies collected to facilitate cross-study analyses of microbiome responses to stressors. This database will comprise published and unpublished datasets re-processed from the raw sequences into exact sequence variants using our standardized computational pipeline. Our database will provide insight into general response patterns of microbiome diversity, structure, and stability to environmental stressors. It will also enable the identification of cross-study associations between single or multiple stressors and specific microbial clades. Here, we present a proof-of-concept meta-analysis of 606 microbiomes (from nine studies) to assess microbial community responses to: (1) one stressor in one environment: soil warming across a variety of soil types, (2) a range of stressors in one environment: soil microbiome responses to a comprehensive set of stressors (incl. temperature, diesel, antibiotics, land use change, drought, and heavy metals), (3) one stressor across a range of environments: copper exposure effects on soil, sediment, activated-sludge reactors, and gut environments, and (4) the general trends of microbiome stressor responses. Overall, we found that stressor exposure significantly decreases microbiome alpha diversity and increases beta diversity (community dispersion) across a range of environments and stressor types. We observed a hump-shaped relationship between microbial community resistance to stressors (i.e., the average pairwise similarity score between the control and stressed communities) and alpha diversity. We used Phylofactor to identify microbial clades and individual taxa as potential bioindicators of copper contamination across different environments. Using standardized computational and statistical methods, the Microbiome Stress Project will leverage thousands of existing datasets to build a general framework for how microbial communities respond to environmental stress.

Historical climate controls soil respiration responses to current soil moisture.

Ecosystem carbon losses from soil microbial respiration are a key component of global carbon cycling, resulting in the transfer of 40-70 Pg carbon from soil to the atmosphere each year. Because these microbial processes can feed back to climate change, understanding respiration responses to environmental factors is necessary for improved projections. We focus on respiration responses to soil moisture, which remain unresolved in ecosystem models. A common assumption of large-scale models is that soil microorganisms respond to moisture in the same way, regardless of location or climate. Here, we show that soil respiration is constrained by historical climate. We find that historical rainfall controls both the moisture dependence and sensitivity of respiration. Moisture sensitivity, defined as the slope of respiration vs. moisture, increased fourfold across a 480-mm rainfall gradient, resulting in twofold greater carbon loss on average in historically wetter soils compared with historically drier soils. The respiration-moisture relationship was resistant to environmental change in field common gardens and field rainfall manipulations, supporting a persistent effect of historical climate on microbial respiration. Based on these results, predicting future carbon cycling with climate change will require an understanding of the spatial variation and temporal lags in microbial responses created by historical rainfall.

Relationships between protein-encoding gene abundance and corresponding process are commonly assumed yet rarely observed.

For any enzyme-catalyzed reaction to occur, the corresponding protein-encoding genes and transcripts are necessary prerequisites. Thus, a positive relationship between the abundance of gene or transcripts and corresponding process rates is often assumed. To test this assumption, we conducted a meta-analysis of the relationships between gene and/or transcript abundances and corresponding process rates. We identified 415 studies that quantified the abundance of genes or transcripts for enzymes involved in carbon or nitrogen cycling. However, in only 59 of these manuscripts did the authors report both gene or transcript abundance and rates of the appropriate process. We found that within studies there was a significant but weak positive relationship between gene abundance and the corresponding process. Correlations were not strengthened by accounting for habitat type, differences among genes or reaction products versus reactants, suggesting that other ecological and methodological factors may affect the strength of this relationship. Our findings highlight the need for fundamental research on the factors that control transcription, translation and enzyme function in natural systems to better link genomic and transcriptomic data to ecosystem processes.

Rhizosphere stoichiometry: are C : N : P ratios of plants, soils, and enzymes conserved at the plant species-level?

As a consequence of the tight linkages among soils, plants and microbes inhabiting the rhizosphere, we hypothesized that soil nutrient and microbial stoichiometry would differ among plant species and be correlated within plant rhizospheres. We assessed plant tissue carbon (C) : nitrogen (N) : phosphorus (P) ratios for eight species representing four different plant functional groups in a semiarid grassland during near-peak biomass. Using intact plant species-specific rhizospheres, we examined soil C : N : P, microbial biomass C : N, and soil enzyme C : N : P nutrient acquisition activities. We found that few of the plant species' rhizospheres demonstrated distinct stoichiometric properties from other plant species and unvegetated soil. Plant tissue nutrient ratios and components of below-ground rhizosphere stoichiometry predominantly differed between the C4 plant species Buchloe dactyloides and the legume Astragalus laxmannii. The rhizospheres under the C4 grass B. dactyloides exhibited relatively higher microbial C and lower soil N, indicative of distinct soil organic matter (SOM) decomposition and nutrient mineralization activities. Assessing the ecological stoichiometry among plant species' rhizospheres is a high-resolution tool useful for linking plant community composition to below-ground soil microbial and nutrient characteristics. By identifying how rhizospheres differ among plant species, we can better assess how plant-microbial interactions associated with ecosystem-level processes may be influenced by plant community shifts.

High-throughput fluorometric measurement of potential soil extracellular enzyme activities.

Microbes in soils and other environments produce extracellular enzymes to depolymerize and hydrolyze organic macromolecules so that they can be assimilated for energy and nutrients. Measuring soil microbial enzyme activity is crucial in understanding soil ecosystem functional dynamics. The general concept of the fluorescence enzyme assay is that synthetic C-, N-, or P-rich substrates bound with a fluorescent dye are added to soil samples. When intact, the labeled substrates do not fluoresce. Enzyme activity is measured as the increase in fluorescence as the fluorescent dyes are cleaved from their substrates, which allows them to fluoresce. Enzyme measurements can be expressed in units of molarity or activity. To perform this assay, soil slurries are prepared by combining soil with a pH buffer. The pH buffer (typically a 50 mM sodium acetate or 50 mM Tris buffer), is chosen for the buffer's particular acid dissociation constant (pKa) to best match the soil sample pH. The soil slurries are inoculated with a nonlimiting amount of fluorescently labeled (i.e. C-, N-, or P-rich) substrate. Using soil slurries in the assay serves to minimize limitations on enzyme and substrate diffusion. Therefore, this assay controls for differences in substrate limitation, diffusion rates, and soil pH conditions; thus detecting potential enzyme activity rates as a function of the difference in enzyme concentrations (per sample). Fluorescence enzyme assays are typically more sensitive than spectrophotometric (i.e. colorimetric) assays, but can suffer from interference caused by impurities and the instability of many fluorescent compounds when exposed to light; so caution is required when handling fluorescent substrates. Likewise, this method only assesses potential enzyme activities under laboratory conditions when substrates are not limiting. Caution should be used when interpreting the data representing cross-site comparisons with differing temperatures or soil types, as in situ soil type and temperature can influence enzyme kinetics.

Changes in microbial community structure in the wake of Hurricanes Katrina and Rita.

Hurricanes have the potential to alter the structures of coastal ecosystems and generate pathogen-laden floodwaters thatthreaten public health. To examine the impact of hurricanes on urban systems, we compared microbial community structures in samples collected after Hurricane Katrina and before and after Hurricane Rita. We extracted environmental DNA and sequenced small-subunit rRNA (SSU rRNA) gene clone libraries to survey microbial communities in floodwater, water, and sediment samples collected from Lake Charles, Lake Pontchartrain, the 17th Street and Industrial Canals in New Orleans, and raw sewage. Correspondence analysis showed that microbial communities associated with sediments formed one cluster while communities associated with lake and Industrial Canal water formed a second. Communities associated with water from the 17th Street Canal and floodwaters collected in New Orleans showed similarity to communities in raw sewage and contained a number of sequences associated with possible pathogenic microbes. This suggests that a distinct microbial community developed in floodwaters following Hurricane Katrina and that microbial community structures as a whole might be sensitive indicators of ecosystem health and serve as "sentinels" of water quality in the environment.