A meta-analysis of changes in bacterial and archaeal communities with time
TL;DR: A meta-analysis of temporal dynamics in microbial communities, including 76 sites representing air, aquatic, soil, brewery wastewater treatment, human- and plant-associated microbial biomes, found that temporal variability in both within- and between-community diversity was consistent among microbial communities from similar environments.
Abstract: Ecologists have long studied the temporal dynamics of plant and animal communities with much less attention paid to the temporal dynamics exhibited by microbial communities. As a result, we do not know if overarching temporal trends exist for microbial communities or if changes in microbial communities are generally predictable with time. Using microbial time series assessed via high-throughput sequencing, we conducted a meta-analysis of temporal dynamics in microbial communities, including 76 sites representing air, aquatic, soil, brewery wastewater treatment, human- and plant-associated microbial biomes. We found that temporal variability in both within- and between-community diversity was consistent among microbial communities from similar environments. Community structure changed systematically with time in less than half of the cases, and the highest rates of change were observed within ranges of 1 day to 1 month for all communities examined. Microbial communities exhibited species-time relationships (STRs), which describe the accumulation of new taxa to a community, similar to those observed previously for plant and animal communities, suggesting that STRs are remarkably consistent across a broad range of taxa. These results highlight that a continued integration of microbial ecology into the broader field of ecology will provide new insight into the temporal patterns of microbial and 'macro'-bial communities alike.
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University of California, San Diego1, University of Southern Mississippi2, Atlantic Oceanographic and Meteorological Laboratory3, University of California, San Francisco4, Indiana University5, IBM6, Broad Institute7, Massachusetts Institute of Technology8, Harvard University9, Northern Arizona University10, Pacific Northwest National Laboratory11, Argonne National Laboratory12, University of Illinois at Chicago13, University of Colorado Boulder14, Cooperative Institute for Research in Environmental Sciences15, University of Southern California16, University of Chicago17, Michigan State University18
TL;DR: A meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project is presented, creating 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.
Abstract: 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.
1,676 citations
TL;DR: It is surmised that plants secrete blends of compounds and specific phytochemicals in the root exudates that are differentially produced at distinct stages of development to help orchestrate rhizosphere microbiome assemblage.
Abstract: There is a concerted understanding of the ability of root exudates to influence the structure of rhizosphere microbial communities. However, our knowledge of the connection between plant development, root exudation and microbiome assemblage is limited. Here, we analyzed the structure of the rhizospheric bacterial community associated with Arabidopsis at four time points corresponding to distinct stages of plant development: seedling, vegetative, bolting and flowering. Overall, there were no significant differences in bacterial community structure, but we observed that the microbial community at the seedling stage was distinct from the other developmental time points. At a closer level, phylum such as Acidobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria and specific genera within those phyla followed distinct patterns associated with plant development and root exudation. These results suggested that the plant can select a subset of microbes at different stages of development, presumably for specific functions. Accordingly, metatranscriptomics analysis of the rhizosphere microbiome revealed that 81 unique transcripts were significantly (P<0.05) expressed at different stages of plant development. For instance, genes involved in streptomycin synthesis were significantly induced at bolting and flowering stages, presumably for disease suppression. We surmise that plants secrete blends of compounds and specific phytochemicals in the root exudates that are differentially produced at distinct stages of development to help orchestrate rhizosphere microbiome assemblage.
987 citations
Cites background from "A meta-analysis of changes in bacte..."
...Recently, it was shown that microbial succession is similar to that of previously described plant and animal succession with respect to species-time relationships (Shade et al., 2013) but what this could mean in the rhizosphere is unclear....
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University of Colorado Boulder1, University of Colorado Denver2, Vrije Universiteit Brussel3, Katholieke Universiteit Leuven4, Massachusetts Institute of Technology5, Chinese Academy of Sciences6, University of Oklahoma7, Stanford University8, University of Pennsylvania9, University of California, San Diego10, University of Southern California11, Tsinghua University12, Lawrence Berkeley National Laboratory13
TL;DR: This work benchmarks the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts.
Abstract: Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.
522 citations
Cites background from "A meta-analysis of changes in bacte..."
...…vary based on signal, sampling frequency and time shift Correlations in time-series data are well studied in other fields, but microbiological studies are just beginning to show predictable shifts in microbial communities over time (Caporaso et al., 2011; Gonzalez et al., 2012; Shade et al., 2013)....
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...Correlations in time-series data are well studied in other fields, but microbiological studies are just beginning to show predictable shifts in microbial communities over time (Caporaso et al., 2011; Gonzalez et al., 2012; Shade et al., 2013)....
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TL;DR: This work introduces a new method to detect typically rare microbial taxa that occasionally become very abundant (conditionally rare taxa [CRT]) and quantifies their contributions to temporal shifts in community structure and reveals that many rareTaxa contribute a greater amount to microbial community dynamics than is apparent from their low proportional abundances.
Abstract: Microbial communities typically contain many rare taxa that make up the majority of the observed membership, yet the contribution of this microbial "rare biosphere" to community dynamics is unclear. Using 16S rRNA amplicon sequencing of 3,237 samples from 42 time series of microbial communities from nine different ecosystems (air; marine; lake; stream; adult hu- man skin, tongue, and gut; infant gut; and brewery wastewater treatment), we introduce a new method to detect typically rare microbial taxa that occasionally become very abundant (conditionally rare taxa (CRT)) and then quantify their contributions to temporal shifts in community structure. We discovered that CRT made up 1.5 to 28% of the community membership, repre- sented a broad diversity of bacterial and archaeal lineages, and explained large amounts of temporal community dissimilarity (i.e., up to 97% of Bray-Curtis dissimilarity). Most of the CRT were detected at multiple time points, though we also identified "one-hit wonder" CRT that were observed at only one time point. Using a case study from a temperate lake, we gained additional insights into the ecology of CRT by comparing routine community time series to large disturbance events. Our results reveal that many rare taxa contribute a greater amount to microbial community dynamics than is apparent from their low proportional abundances. This observation was true across a wide range of ecosystems, indicating that these rare taxa are essential for under- standing community changes over time. IMPORTANCE Microbial communities and their processes are the foundations of ecosystems. The ecological roles of rare microor- ganisms are largely unknown, but it is thought that they contribute to community stability by acting as a reservoir that can rap- idly respond to environmental changes. We investigated the occurrence of typically rare taxa that very occasionally become more prominent in their communities ("conditionally rare"). We quantified conditionally rare taxa in time series from a wide variety of ecosystems and discovered that not only were conditionally rare taxa present in all of the examples, but they also contributed disproportionately to temporal changes in diversity when they were most abundant. This result indicates an important and gen- eral role for rare microbial taxa within their communities.
512 citations
Cites background or methods from "A meta-analysis of changes in bacte..."
...Again, because of the differences in sampling and sequencing strategies across data sets (28), we encourage readers to consider the general trends in CRT rather than absolute values....
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...Our previous analysis suggested that the longer a community is observed, the more the perceived magnitude of the changes in community structure is reduced, suggesting very low rates of community change over long-term observations (28)....
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...The descriptions, quality control, and normalization of these data sets also are detailed elsewhere (28)....
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...These data sets were previously analyzed by using a closed-reference operational taxonomic unit (OTU)-picking protocol (27) for direct comparison of their temporal patterns (see Table S1 in the supplemental material) (28)....
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TL;DR: It is argued that it is urgently necessary to incorporate microbial traits into biogeochemical ecosystem modeling in order to increase the estimation reliability of N2O emissions and proposed a molecular methodology oriented framework from gene to ecosystem scales for more robust prediction and mitigation of future N1O emissions.
Abstract: The continuous increase of the greenhouse gas nitrous oxide (N2O) in the atmosphere due to increasing anthropogenic nitrogen input in agriculture has become a global concern. In recent years, identification of the microbial assemblages responsible for soil N2O production has substantially advanced with the development of molecular technologies and the discoveries of novel functional guilds and new types of metabolism. However, few practical tools are available to effectively reduce in situ soil N2O flux. Combating the negative impacts of increasing N2O fluxes poses considerable challenges and will be ineffective without successfully incorporating microbially regulated N2O processes into ecosystem modeling and mitigation strategies. Here, we synthesize the latest knowledge of (i) the key microbial pathways regulating N2O production and consumption processes in terrestrial ecosystems and the critical environmental factors influencing their occurrence, and (ii) the relative contributions of major biological pathways to soil N2O emissions by analyzing available natural isotopic signatures of N2O and by using stable isotope enrichment and inhibition techniques. We argue that it is urgently necessary to incorporate microbial traits into biogeochemical ecosystem modeling in order to increase the estimation reliability of N2O emissions. We further propose a molecular methodology oriented framework from gene to ecosystem scales for more robust prediction and mitigation of future N2O emissions.
499 citations
Cites background from "A meta-analysis of changes in bacte..."
...quencing data facilitates our ability to measure the enormous microbial diversity and the highly spatiotemporal dynamics of soil microbes (Lauber et al. 2009; Shade et al. 2013), and to char-...
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...…sequencing data facilitates our ability to measure the enormous microbial diversity and the highly spatiotemporal dynamics of soil microbes (Lauber et al. 2009; Shade et al. 2013), and to characterize and predict the response of microbes to environmental parameters (Fierer et al. 2011)....
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References
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Journal Article•
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.
272,030 citations
"A meta-analysis of changes in bacte..." refers methods in this paper
...Bray–Curtis was used as a taxon-based metric of differences in community composition (beta diversity), and the dissimilarities were calculated from the rarefied OTU tables in R using the vegan package (Oksanen et al., 2011; R Development Core Team, 2011)....
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...All analyses were performed using the R environment for statistical computing (R Development Core Team, 2011), with the aid of the vegan and ggplot2 packages (Wickham, 2009; Oksanen et al., 2011)....
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TL;DR: An overview of the analysis pipeline and links to raw data and processed output from the runs with and without denoising are provided.
Abstract: Supplementary Figure 1 Overview of the analysis pipeline. Supplementary Table 1 Details of conventionally raised and conventionalized mouse samples. Supplementary Discussion Expanded discussion of QIIME analyses presented in the main text; Sequencing of 16S rRNA gene amplicons; QIIME analysis notes; Expanded Figure 1 legend; Links to raw data and processed output from the runs with and without denoising.
28,911 citations
"A meta-analysis of changes in bacte..." refers methods in this paper
...1, (Caporaso et al., 2010) was used for constructing weighted and unweighted UniFrac distances....
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...QIIME (version 1.2.1, (Caporaso et al., 2010) was used for constructing weighted and unweighted UniFrac distances....
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TL;DR: The traditional view of natural systems, therefore, might well be less a meaningful reality than a perceptual convenience.
Abstract: Individuals die, populations disappear, and species become extinct. That is one view of the world. But another view of the world concentrates not so much on presence or absence as upon the numbers of organisms and the degree of constancy of their numbers. These are two very different ways of viewing the behavior of systems and the usefulness of the view depends very much on the properties of the system concerned. If we are examining a particular device designed by the engineer to perform specific tasks under a rather narrow range of predictable external conditions, we are likely to be more concerned with consistent nonvariable performance in which slight departures from the performance goal are immediately counteracted. A quantitative view of the behavior of the system is, therefore, essential. With attention focused upon achieving constancy, the critical events seem to be the amplitude and frequency of oscillations. But if we are dealing with a system profoundly affected by changes external to it, and continually confronted by the unexpected, the constancy of its behavior becomes less important than the persistence of the relationships. Attention shifts, therefore, to the qualitative and to questions of existence or not. Our traditions of analysis in theoretical and empirical ecology have been largely inherited from developments in classical physics and its applied variants. Inevitably, there has been a tendency to emphasize the quantitative rather than the qualitative, for it is important in this tradition to know not just that a quantity is larger than another quantity, but precisely how much larger. It is similarly important, if a quantity fluctuates, to know its amplitude and period of fluctuation. But this orientation may simply reflect an analytic approach developed in one area because it was useful and then transferred to another where it may not be. Our traditional view of natural systems, therefore, might well be less a meaningful reality than a perceptual convenience. There can in some years be more owls and fewer mice and in others, the reverse. Fish populations wax and wane as a natural condition, and insect populations can range over extremes that only logarithmic
13,447 citations
TL;DR: The results illustrate that UniFrac provides a new way of characterizing microbial communities, using the wealth of environmental rRNA sequences, and allows quantitative insight into the factors that underlie the distribution of lineages among environments.
Abstract: We introduce here a new method for computing differences between microbial communities based on phylogenetic information. This method, UniFrac, measures the phylogenetic distance between sets of taxa in a phylogenetic tree as the fraction of the branch length of the tree that leads to descendants from either one environment or the other, but not both. UniFrac can be used to determine whether communities are significantly different, to compare many communities simultaneously using clustering and ordination techniques, and to measure the relative contributions of different factors, such as chemistry and geography, to similarities between samples. We demonstrate the utility of UniFrac by applying it to published 16S rRNA gene libraries from cultured isolates and environmental clones of bacteria in marine sediment, water, and ice. Our results reveal that (i) cultured isolates from ice, water, and sediment resemble each other and environmental clone sequences from sea ice, but not environmental clone sequences from sediment and water; (ii) the geographical location does not correlate strongly with bacterial community differences in ice and sediment from the Arctic and Antarctic; and (iii) bacterial communities differ between terrestrially impacted seawater (whether polar or temperate) and warm oligotrophic seawater, whereas those in individual seawater samples are not more similar to each other than to those in sediment or ice samples. These results illustrate that UniFrac provides a new way of characterizing microbial communities, using the wealth of environmental rRNA sequences, and allows quantitative insight into the factors that underlie the distribution of lineages among environments.
6,679 citations
"A meta-analysis of changes in bacte..." refers background or methods in this paper
...…pairwise dissimilarity in community composition and incorporates information about differences in phylogenetic composition of community members (Lozupone and Knight, 2005; Lozupone et al., 2011), with weighted UniFrac accounting for differences in the relative abundances of community members....
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...UniFrac is a commonly used phylogenetic distance metric to assess pairwise dissimilarity in community composition and incorporates information about differences in phylogenetic composition of community members (Lozupone and Knight, 2005; Lozupone et al., 2011), with weighted UniFrac accounting for differences in the relative abundances of community members....
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