A communal catalogue reveals Earth's multiscale microbial diversity

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.Peer reviewe

A communal catalogue reveals Earth’s multiscale microbial diversity

Our growing awareness of the microbial world’s importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity

Coping with Polychlorinated Biphenyl (PCB) Toxicity: Physiological and Genome-Wide Responses of Burkholderia xenovorans LB400 to PCB-Mediated Stress

The biodegradation of polychlorinated biphenyls (PCBs) relies on the ability of aerobic microorganisms such as Burkholderia xenovorans sp. LB400 to tolerate two potential modes of toxicity presented by PCB degradation: passive toxicity, as hydrophobic PCBs potentially disrupt membrane and protein function, and degradation-dependent toxicity from intermediates of incomplete degradation. We monitored the physiological characteristics and genome-wide expression patterns of LB400 in response to the presence of Aroclor 1242 (500 ppm) under low expression of the structural biphenyl pathway (succinate and benzoate growth) and under induction by biphenyl. We found no inhibition of growth or change in fatty acid profile due to PCBs under nondegrading conditions. Moreover, we observed no differential gene expression due to PCBs themselves. However, PCBs did have a slight effect on the biosurface area of LB400 cells and caused slight membrane separation. Upon activation of the biphenyl pathway, we found growth inhibition from PCBs beginning after exponential-phase growth suggestive of the accumulation of toxic compounds. Genome-wide expression profiling revealed 47 differentially expressed genes (0.56% of all genes) under these conditions. The biphenyl and catechol pathways were induced as expected, but the quinoprotein methanol metabolic pathway and a putative chloroacetaldehyde dehydrogenase were also highly expressed. As the latter protein is essential to conversion of toxic metabolites in dichloroethane degradation, it may play a similar role in the degradation of chlorinated aliphatic compounds resulting from PCB degradation

Key Edaphic Properties Largely Explain Temporal and Geographic Variation in Soil Microbial Communities across Four Biomes

<div><p>Soil microbial communities play a critical role in nutrient transformation and storage in all ecosystems. Quantifying the seasonal and long-term temporal extent of genetic and functional variation of soil microorganisms in response to biotic and abiotic changes within and across ecosystems will inform our understanding of the effect of climate change on these processes. We examined spatial and seasonal variation in microbial communities based on 16S rRNA gene sequencing and phospholipid fatty acid (PLFA) composition across four biomes: a tropical broadleaf forest (Hawaii), taiga (Alaska), semiarid grassland-shrubland (Utah), and a subtropical coniferous forest (Florida). In this study, we used a team-based instructional approach leveraging the iPlant Collaborative to examine publicly available National Ecological Observatory Network (NEON) 16S gene and PLFA measurements that quantify microbial diversity, composition, and growth. Both profiling techniques revealed that microbial communities grouped strongly by ecosystem and were predominately influenced by three edaphic factors: pH, soil water content, and cation exchange capacity. Temporal variability of microbial communities differed by profiling technique; 16S-based community measurements showed significant temporal variability only in the subtropical coniferous forest communities, specifically through changes within subgroups of <i>Acidobacteria</i>. Conversely, PLFA-based community measurements showed seasonal shifts in taiga and tropical broadleaf forest systems. These differences may be due to the premise that 16S-based measurements are predominantly influenced by large shifts in the abiotic soil environment, while PLFA-based analyses reflect the metabolically active fraction of the microbial community, which is more sensitive to local disturbances and biotic interactions. To address the technical issue of the response of soil microbial communities to sample storage temperature, we compared 16S-based community structure in soils stored at -80°C and -20°C and found no significant differences in community composition based on storage temperature. Free, open access datasets and data sharing platforms are powerful tools for integrating research and teaching in undergraduate and graduate student classrooms. They are a valuable resource for fostering interdisciplinary collaborations, testing ecological theory, model development and validation, and generating novel hypotheses. Training in data analysis and interpretation of large datasets in university classrooms through project-based learning improves the learning experience for students and enables their use of these significant resources throughout their careers.</p></div

Average values for all measured (A) soil environmental variables, (B) dominant 16S rRNA-determined bacterial phyla, and (C) grouped lipids ± 95% confidence intervals for all soil samples at all time points.

<p>Columns in gray indicate time points closest to peak greenness at each site, which are used for cross-site comparisons. Significant differences over time, within sites, are indicated with * (p < 0.05), ** (p < 0.01), *** (p < 0.001), as compared to the time point at peak greenness using repeated measures ANOVA. Differences between sites are not indicated here, but are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135352#pone.0135352.g002" target="_blank">Fig 2A</a>.</p

ANOVA and pairwise Tukey’s analysis to determine differences between sites at peak greenness, based on PLFA and 16S analysis (corresponds to Fig 2).

<p>Superscript letter values indicate statistical differences between clusters of communities associated with MDS1 (x-axis) and MDS2 (y-axis). SD indicates standard deviation.</p><p>ANOVA and pairwise Tukey’s analysis to determine differences between sites at peak greenness, based on PLFA and 16S analysis (corresponds to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135352#pone.0135352.g002" target="_blank">Fig 2</a>).</p

Factors explaining the variation in 16S-based and lipid-based communities across seasons at four ecosystem sites, as described by PERMANOVA analysis.

<p>NS indicates not significant.</p

ANOVA and pairwise Tukey analysis to determine differences between sites and sample dates, based on PLFA and 16S analysis all dates and all sites (corresponds to Fig 3).

<p>Superscript letter values indicate statistical differences between clusters of communities associated with MDS1 (x-axis) and MDS2 (y-axis). SD indicates standard deviation.</p><p>ANOVA and pairwise Tukey analysis to determine differences between sites and sample dates, based on PLFA and 16S analysis all dates and all sites (corresponds to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135352#pone.0135352.g003" target="_blank">Fig 3</a>).</p

Changes in 16S rRNA-based community composition in Florida soils over time.

<p>(A) Relative abundances of all taxa classified at the phylum level and unclassified taxa over time. (B) NMDS ordination of all order-level taxa classified within the phylum <i>Proteobacteria</i>, which did not vary over time (ANOSIM R = 0.03968, p = 0.148). (C) NMDS ordination of all order-level taxa classified within the phylum <i>Actinobacteria</i>, which did not vary over time (ANOSIM R = 0.04861, p = 0.171). (D) NMDS ordination of all order level taxa classified within the phylum <i>Acidobacteria</i>, which varied significantly over time (ANOSIM R = 0.2057, p = 0.003).</p