METAGENOMIC INSIGHTS INTO MICROBIAL COMMUNITY RESPONSES TO LONG-TERM ELEVATED CO2

Understanding how belowground microbial communities respond to increasing atmospheric CO2 is of crucial importance for global change biology, microbial ecology and predictive biology. However, our understanding of CO2 effects on microbial communities is still limited, especially due to the immense diversity and as yet-uncultivable nature of most soil microorganisms. By implementing next generation sequencing (NGS) technologies, we comprehensively surveyed the responses of microbial communities to elevated CO2 (eCO2) in the BioCON experimental site, a grassland ecosystem, which had been exposed to eCO2 for 12 years. In the beginning of this study, it was noticed that computational approaches to identify microbial strains/species from shotgum metagenomes are very limited, thus we have developed a computational algorithm, termed GSMer that identifies genome-specific markers (GSMs) from currently sequenced microbial genomes for strain/species identification in metagenomes. Although GSMer was not very successfully applied in our soil metagenomes due to the extremely low coverage and high diversity of soil microbial communities as well as short sequencing reads from early Illumina GAII performs, it was successfully used to analyze microbial communities with a good coverage of reference genomes, such as human microbiomes. Sensitivity evaluation against synthetic metagenomes with different levels of coverage suggested that 50 GSMs per strain were sufficient to identify most microbial strains with ≥ 0.25x coverage, and 10% of selected GSMs in a database should be detected for confident positive callings. Application of GSMs respectively identified 45 and 74 microbial strains/species significantly associated with type-2-diabetes (T2D) patients and obese/lean individuals from corresponding gastrointestinal tract metagenomes. Our results agreed well with previous studies, but provided strain-level information. In the following, we analyzed the biodiversity, composition, structure and functional potential of soil microbial communities in response to eCO2 at multiple (e.g., phylogenetic, taxonomic, genetic, functional) dimensions using next generation sequencing approaches. For each dimension of microbial biodiversity, all components of diversity, including alpha-, beta- and gamma-diversity were analyzed. Our results suggested that long-term eCO2 decreased the overall microbial biodiversity. Beta-diversity analysis suggested eCO2 decreased functional beta-diversity, but increased taxonomic and phylogenetic diversity, suggesting long-term eCO2 selected for microbial function rather than taxonomy. Further meta-analysis suggested that such decreased biodiversity was significantly negatively correlated with increased soil ammonification rate. Moreover, the abundance of gene families involved in ammonium producing pathways increased significantly as well, indicating a functional convergence process as a result of higher demand for biologically available nitrogen (N) by stimulated plant growth as a result of eCO2. Our findings present evidence that plant-microbe interactions for NH4+ as result of progressive nitrogen limitation were an important driving factor, responsible for decreased microbial biodiversity under eCO2. We also analyzed the response of fungal communities to long-term eCO2 by sequencing of 28S rRNA gene amplicons. Long-term eCO2 did not significantly alter the overall fungal community structure and species richness, but significantly increased community evenness and diversity. Relative abundances of 119 OTUs (~ 27% of the total captured sequences) were changed significantly. More interestingly, significantly changed OTUs under eCO2 were associated with increased overall relative abundance of Ascomycota, but decreased relative abundance of Basidiomycota. Co-occurrence ecological network analysis indicated that eCO2 increased fungal community interactions, as evidenced by higher intermodular and intramodular connectivity and shorter geodesic distance. In contrast, decreased connections for dominant fungal species were observed in the eCO2 network. Community reassembly of unrelated fungal species into highly connected dense modules was observed. Such changes in the network structure were significantly associated with altered soil and plant properties under eCO2, especially with increased plant biomass and NH4+ availability. This study provides novel insights into our understanding of how eCO2 shapes soil fungal communities in grassland ecosystems. Since it was noticed that changes of both belowground microbial biodiversity and fungal communities were significantly correlated with soil ammonification rate, and our previous studies showed that the abundance of nifH significantly increased at eCO2, we hypothesized that N2-fixing microorganisms would play important roles in response to eCO2. Therefore, we analyzed N2-fixing communities by sequencing of nifH gene amplicons as well as extraction of nifH fragments from shotgun metagenomes. Surprisingly, long-term eCO2 significantly increased the abundance of nifH genes, but did not change the overall nifH diversity and diazotrophic community structure. Taxonomic and phylogenetic analysis of amplified nifH sequences suggested a high diversity of nifH genes in the soil ecosystem, with the majority belonging to cluster I and II nifH genes. We then constructed a microbial ecological network using 16S rRNA gene and nifH gene profiles. Co-occurrence ecological network analysis suggested a clear preference of co-occurrence patterns between diazotrophs and other microbial species with different patterns observed for different subgroups of diazotrophs, such as Azospirillum/Actinobacteria, Mesorhizobium/Conexibacter, and Bradyrhizobium/Acidobacteria. This indicated a potential attraction of these non-N2-fixers by diazotrophs in the soil ecosystem. Interestingly, more complex co-occurrence patterns were found for free-living diazotrophs than commonly known symbiotic diazotrophs, consistent with the physical isolation nature of symbiotic diazotrophs from the environment by root nodules. The study provides novel insights of our understanding microbial ecology of soil diazotrophs in natural ecosystems. All studies included in this work provided novel insights into the long-term eCO2 effects on belowground microbial communities, which are of merit for next generation sequencing analysis for microbial ecologists, global change biologists and bioinformaticians

Oral microbiota of periodontal health and disease and their changes after nonsurgical periodontal therapy

This study examined the microbial diversity and community assembly of oral microbiota in periodontal health and disease and after nonsurgical periodontal treatment. The V4 region of 16S rRNA gene from DNA of 238 saliva and subgingival samples of 21 healthy and 48 diseased subjects was amplified and sequenced. Among 1979 OTUs identified, 28 were overabundant in diseased plaque. Six of these taxa were also overabundant in diseased saliva. Twelve OTUs were overabundant in healthy plaque. There was a trend for disease-associated taxa to decrease and health-associated taxa to increase after treatment with notable variations among individual sites. Network analysis revealed modularity of the microbial communities and identified several health- and disease-specific modules. Ecological drift was a major factor that governed community turnovers in both plaque and saliva. Dispersal limitation and homogeneous selection affected the community assembly in plaque, with the additional contribution of homogenizing dispersal for plaque within individuals. Homogeneous selection and dispersal limitation played important roles, respectively, in healthy saliva and diseased pre-treatment saliva between individuals. Our results revealed distinctions in both taxa and assembly processes of oral microbiota between periodontal health and disease. Furthermore, the community assembly analysis has identified potentially effective approaches for managing periodontitis

Functional Gene Array-Based Ultrasensitive and Quantitative Detection of Microbial Populations in Complex Communities.

While functional gene arrays (FGAs) have greatly expanded our understanding of complex microbial systems, specificity, sensitivity, and quantitation challenges remain. We developed a new generation of FGA, GeoChip 5.0, using the Agilent platform. Two formats were created, a smaller format (GeoChip 5.0S), primarily covering carbon-, nitrogen-, sulfur-, and phosphorus-cycling genes and others providing ecological services, and a larger format (GeoChip 5.0M) containing the functional categories involved in biogeochemical cycling of C, N, S, and P and various metals, stress response, microbial defense, electron transport, plant growth promotion, virulence, gyrB, and fungus-, protozoan-, and virus-specific genes. GeoChip 5.0M contains 161,961 oligonucleotide probes covering >365,000 genes of 1,447 gene families from broad, functionally divergent taxonomic groups, including bacteria (2,721 genera), archaea (101 genera), fungi (297 genera), protists (219 genera), and viruses (167 genera), mainly phages. Computational and experimental evaluation indicated that designed probes were highly specific and could detect as little as 0.05 ng of pure culture DNAs within a background of 1 μg community DNA (equivalent to 0.005% of the population). Additionally, strong quantitative linear relationships were observed between signal intensity and amount of pure genomic (∼99% of probes detected; r > 0.9) or soil (∼97%; r > 0.9) DNAs. Application of the GeoChip to a contaminated groundwater microbial community indicated that environmental contaminants (primarily heavy metals) had significant impacts on the biodiversity of the communities. This is the most comprehensive FGA to date, capable of directly linking microbial genes/populations to ecosystem functions.IMPORTANCE The rapid development of metagenomic technologies, including microarrays, over the past decade has greatly expanded our understanding of complex microbial systems. However, because of the ever-expanding number of novel microbial sequences discovered each year, developing a microarray that is representative of real microbial communities, is specific and sensitive, and provides quantitative information remains a challenge. The newly developed GeoChip 5.0 is the most comprehensive microarray available to date for examining the functional capabilities of microbial communities important to biogeochemistry, ecology, environmental sciences, and human health. The GeoChip 5 is highly specific, sensitive, and quantitative based on both computational and experimental assays. Use of the array on a contaminated groundwater sample provided novel insights on the impacts of environmental contaminants on groundwater microbial communities

Continental scale structuring of forest and soil diversity via functional traits.

Trait-based ecology claims to offer a mechanistic approach for explaining the drivers that structure biological diversity and predicting the responses of species, trophic interactions and ecosystems to environmental change. However, support for this claim is lacking across broad taxonomic groups. A framework for defining ecosystem processes in terms of the functional traits of their constituent taxa across large spatial scales is needed. Here, we provide a comprehensive assessment of the linkages between climate, plant traits and soil microbial traits at many sites spanning a broad latitudinal temperature gradient from tropical to subalpine forests. Our results show that temperature drives coordinated shifts in most plant and soil bacterial traits but these relationships are not observed for most fungal traits. Shifts in plant traits are mechanistically associated with soil bacterial functional traits related to carbon (C), nitrogen (N) and phosphorus (P) cycling, indicating that microbial processes are tightly linked to variation in plant traits that influence rates of ecosystem decomposition and nutrient cycling. Our results are consistent with hypotheses that diversity gradients reflect shifts in phenotypic optima signifying local temperature adaptation mediated by soil nutrient availability and metabolism. They underscore the importance of temperature in structuring the functional diversity of plants and soil microbes in forest ecosystems and how this is coupled to biogeochemical processes via functional traits

Biogeographic patterns of soil diazotrophic communities across six forests in North America.

Soil diazotrophs play important roles in ecosystem functioning by converting atmospheric N2 into biologically available ammonium. However, the diversity and distribution of soil diazotrophic communities in different forests and whether they follow biogeographic patterns similar to macroorganisms still remain unclear. By sequencing nifH gene amplicons, we surveyed the diversity, structure and biogeographic patterns of soil diazotrophic communities across six North American forests (126 nested samples). Our results showed that each forest harboured markedly different soil diazotrophic communities and that these communities followed traditional biogeographic patterns similar to plant and animal communities, including the taxa-area relationship (TAR) and latitudinal diversity gradient. Significantly higher community diversity and lower microbial spatial turnover rates (i.e. z-values) were found for rainforests (~0.06) than temperate forests (~0.1). The gradient pattern of TARs and community diversity was strongly correlated (r(2)  > 0.5) with latitude, annual mean temperature, plant species richness and precipitation, and weakly correlated (r(2)  < 0.25) with pH and soil moisture. This study suggests that even microbial subcommunities (e.g. soil diazotrophs) follow general biogeographic patterns (e.g. TAR, latitudinal diversity gradient), and indicates that the metabolic theory of ecology and habitat heterogeneity may be the major underlying ecological mechanisms shaping the biogeographic patterns of soil diazotrophic communities

Global diversity and biogeography of bacterial communities in wastewater treatment plants

Microorganisms in wastewater treatment plants (WWTPs) are essential for water purification to protect public and environmental health. However, the diversity of microorganisms and the factors that control it are poorly understood. Using a systematic global-sampling effort, we analysed the 16S ribosomal RNA gene sequences from ~1,200 activated sludge samples taken from 269 WWTPs in 23 countries on 6 continents. Our analyses revealed that the global activated sludge bacterial communities contain ~1 billion bacterial phylotypes with a Poisson lognormal diversity distribution. Despite this high diversity, activated sludge has a small, global core bacterial community (n = 28 operational taxonomic units) that is strongly linked to activated sludge performance. Meta-analyses with global datasets associate the activated sludge microbiomes most closely to freshwater populations. In contrast to macroorganism diversity, activated sludge bacterial communities show no latitudinal gradient. Furthermore, their spatial turnover is scale-dependent and appears to be largely driven by stochastic processes (dispersal and drift), although deterministic factors (temperature and organic input) are also important. Our findings enhance our mechanistic understanding of the global diversity and biogeography of activated sludge bacterial communities within a theoretical ecology framework and have important implications for microbial ecology and wastewater treatment processes

The Thermoanaerobacter Glycobiome Reveals Mechanisms of Pentose and Hexose Co-Utilization in Bacteria

Author Summary Renewable liquid fuels derived from lignocellulosic biomass could alleviate global energy shortage and climate change. Cellulose and hemicellulose are the main components of lignocellulosic biomass. Therefore, the ability to simultaneously utilize pentose and hexose (i.e., co-utilization) has been a crucial challenge for industrial microbes producing lignocellulosic biofuels. Certain thermoanaerobic bacteria demonstrate this unusual talent, but the genetic foundation and molecular mechanism of this process remain unknown. In this study, we reconstructed the structure and dynamics of the first genome-wide carbon utilization network of thermoanaerobes. This transcriptome-based co-expression network reveals that glucose, xylose, fructose, and cellobiose catabolism are each featured on distinct functional modules. Furthermore, the dynamics of the network suggests a distinct yet collaborative nature between glucose and xylose catabolism. In addition, we experimentally demonstrated that these novel network-derived features can be rationally exploited for product-yield enhancement via optimized timing and balanced loading of the carbon supply in a substrate-specific manner. Thus, the newly discovered modular and precisely regulated network elucidates unique features of thermoanaerobic glycobiomes and reveals novel perturbation strategies and targets for the enhanced thermophilic production of lignocellulosic biofuels.Yeshttp://www.plosgenetics.org/static/editorial#pee

Data_Sheet_1_Random sampling associated with microbial profiling leads to overestimated stochasticity inference in community assembly.docx

Revealing the mechanisms governing the complex community assembly over space and time is a central issue in ecology. Null models have been developed to quantitatively disentangle the relative importance of deterministic vs. stochastic processes in structuring the compositional variations of biological communities. Similar approaches have been recently extended to the field of microbial ecology. However, the profiling of highly diverse biological communities (e.g., microbial communities) is severely influenced by random sampling issues, leading to undersampled community profiles and overestimated β-diversity, which may further affect stochasticity inference in community assembly. By implementing simulated datasets, this study demonstrate that microbial stochasticity inference is also affected due to random sampling issues associated with microbial profiling. The effects on microbial stochasticity inference for the whole community and the abundant subcommunities were different using different randomization methods in generating null communities. The stochasticity of rare subcommunities, however, was persistently overestimated irrespective of which randomization method was used. Comparatively, the stochastic ratio approach was more sensitive to random sampling issues, whereas the Raup–Crick metric was more affected by randomization methods. As more studies begin to focus on the mechanisms governing abundant and rare subcommunities, we urge cautions be taken for microbial stochasticity inference based on β-diversity, especially for rare subcommunities. Randomization methods to generate null communities shall also be carefully selected. When necessary, the cutoff used for judging the relative importance of deterministic vs. stochastic processes shall be redefined.</p