Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors

Deciphering microbial interactions and detecting keystone species with co-occurrence networks

Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. We then construct co-occurrence networks and evaluate how well networks reveal the underlying interactions and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.

Specificity and temporal dynamics of complex bacteria--sponge symbiotic interactions

Microbes are known to form intricate and intimate relationships with most animal and plant taxa. Microbe--host symbiotic associations are poorly explored in comparison with other species interaction networks. The current paradigm on symbiosis research stems from species-poor systems where pairwise and reciprocally specialized interactions between a single microbe and a single host that coevolve are the norm. These symbioses involving just a few species are fascinating in their own right, but more diverse and complex host-associated microbial communities are increasingly found, with new emerging questions that require new paradigms and approaches. Here we adopt an intermediate complexity approach to study the specificity, phylogenetic community structure, and temporal variability of the subset of the most abundant bacteria associated with different sponge host species with diverse eco-evolutionary characteristics. We do so by using a monthly resolved annual temporal series of host-associated and free-living bacteria. Bacteria are very abundant and diverse within marine sponges, and these symbiotic interactions are hypothesized to have a very ancient origin. We show that host-bacteria reciprocal specialization depends on the temporal scale and level of taxonomic aggregation considered. Sponge hosts with similar eco-evolutionary characteristics (e.g., volume of tissue corresponding to microbes, water filtering rates, and microbial transmission type) have similar bacterial phylogenetic community structure when looking at interactions aggregated over time. In general, sponge hosts hypothesized to form more intricate relationships with bacteria show a remarkably persistent bacterial community over time. Other hosts, however, show a large turnover similar to that observed for free-living bacterioplankton. Our study highlights the importance of exploring temporal variability in host--microbe interaction networks if we aim to determine how specific and persistent these poorly explored but extremely common interactions are.

Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota

The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka–Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.

Recent advances in DNA sequencing and metagenomics are opening a window into the human microbiome revealing novel associations between certain microbial consortia and disease. However, most of these studies are cross-sectional and lack a mechanistic understanding of this ecosystem's structure and its response to external perturbations, therefore not allowing accurate temporal predictions. In this article, we develop a method to analyze temporal community data accounting also for time-dependent external perturbations. In particular, this method combines the classical Lotka–Volterra model of population dynamics with regression techniques to obtain mechanistically descriptive coefficients which can be further used to construct predictive models of ecosystem dynamics. Using then data from a mouse experiment under antibiotic perturbations, we are able to predict and recover the microbiota temporal dynamics and study the concept of alternative stable states and antibiotic-induced transitions. As a result, our method reveals a group of commensal microbes that potentially protect against infection by the pathogen Clostridium difficile and proposes a possible mechanism how the antibiotic makes the host more susceptible to infection.

miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions

MicroRNAs (miRNAs) are small non-coding RNA molecules capable of negatively regulating gene expression to control many cellular mechanisms. The miRTarBase database (http://mirtarbase.mbc.nctu.edu.tw/) provides the most current and comprehensive information of experimentally validated miRNA-target interactions. The database was launched in 2010 with data sources for >100 published studies in the identification of miRNA targets, molecular networks of miRNA targets and systems biology, and the current release (2013, version 4) includes significant expansions and enhancements over the initial release (2010, version 1). This article reports the current status of and recent improvements to the database, including (i) a 14-fold increase to miRNA-target interaction entries, (ii) a miRNA-target network, (iii) expression profile of miRNA and its target gene, (iv) miRNA target-associated diseases and (v) additional utilities including an upgrade reminder and an error reporting/user feedback system.

Top-down controls on bacterial community structure: microbial network analysis of bacteria, T4-like viruses and protists

Characterizing ecological relationships between viruses, bacteria and protists in the ocean are critical to understanding ecosystem function, yet these relationships are infrequently investigated together. We evaluated these relationships through microbial association network analysis of samples collected approximately monthly from March 2008 to January 2011 in the surface ocean (0-5 m) at the San Pedro Ocean Time series station. Bacterial, T4-like myoviral and protistan communities were described by Automated Ribosomal Intergenic Spacer Analysis and terminal restriction fragment length polymorphism of the gene encoding the major capsid protein (g23) and 18S ribosomal DNA, respectively. Concurrent shifts in community structure suggested similar timing of responses to environmental and biological parameters. We linked T4-like myoviral, bacterial and protistan operational taxonomic units by local similarity correlations, which were then visualized as association networks. Network links (correlations) potentially represent synergistic and antagonistic relationships such as viral lysis, grazing, competition or other interactions. We found that virus-bacteria relationships were more cross-linked than protist-bacteria relationships, suggestive of increased taxonomic specificity in virus-bacteria relationships. We also found that 80% of bacterial-protist and 74% of bacterial-viral correlations were positive, with the latter suggesting that at monthly and seasonal timescales, viruses may be following their hosts more often than controlling host abundance.

Campylobacter jejuni colonization is associated with a dysbiosis in the cecal microbiota of mice in the absence of prominent inflammation

Background

Campylobacter jejuni causes enterocolitis in humans, but does not incite disease in asymptomatic carrier animals. To survive in the intestine, C. jejuni must successfully compete with the microbiota and overcome the host immune defense. Campylobacter jejuni colonization success varies considerably amongst individual mice, and we examined the degree to which the intestinal microbiota was affected in mice (i.e. a model carrier animal) colonized by C. jejuni at high relative to low densities.

Methods

Mice were inoculated with C. jejuni or buffer, and pathogen shedding and intestinal colonization were measured. Histopathologic scoring and quantification of mRNA expression for α-defensins, toll-like receptors, and cytokine genes were conducted. Mucosa-associated bacterial communities were characterized by two approaches: multiplexed barcoded pyrosequencing and terminal restriction fragment length polymorphism analysis.

Results

Two C. jejuni treatments were established based on the degree of cecal and colonic colonization; C. jejuni Group A animals were colonized at high cell densities, and C. jejuni Group B animals were colonized at lower cell densities. Histological examination of cecal and colonic tissues indicated that C. jejuni did not incite visible pathologic changes. Although there was no significant difference among treatments in expression of mRNA for α-defensins, toll-like receptors, or cytokine genes, a trend for increased expression of toll-like receptors and cytokine genes was observed for C. jejuni Group A. The results of the two methods to characterize bacterial communities indicated that the composition of the cecal microbiota of C. jejuni Group A mice differed significantly from C. jejuni Group B and Control mice. This difference was due to a reduction in load, diversity and richness of bacteria associated with the cecal mucosa of C. jejuni Group A mice.

Conclusions

High density colonization by C. jejuni is associated with a dysbiosis in the cecal microbiota independent of prominent inflammation.

Temporal variability and coherence of euphotic zone bacterial communities over a decade in the Southern California Bight

Time-series are critical to understanding long-term natural variability in the oceans. Bacterial communities in the euphotic zone were investigated for over a decade at the San Pedro Ocean Time-series station (SPOT) off southern California. Community composition was assessed by Automated Ribosomal Intergenic Spacer Analysis (ARISA) and coupled with measurements of oceanographic parameters for the surface ocean (0-5 m) and deep chlorophyll maximum (DCM, average depth ≈ 30 m). SAR11 and cyanobacterial ecotypes comprised typically more than one-third of the measured community; diversity within both was temporally variable, although a few operational taxonomic units (OTUs) were consistently more abundant. Persistent OTUs, mostly Alphaproteobacteria (SAR11 clade), Actinobacteria and Flavobacteria, tended to be abundant, in contrast to many rarer yet intermittent and ephemeral OTUs. Association networks revealed potential niches for key OTUs from SAR11, cyanobacteria, SAR86 and other common clades on the basis of robust correlations. Resilience was evident by the average communities drifting only slightly as years passed. Average Bray-Curtis similarity between any pair of dates was ≈ 40%, with a slight decrease over the decade and obvious near-surface seasonality; communities 8-10 years apart were slightly more different than those 1-4 years apart with the highest rate of change at 0-5 m between communities <4 years apart. The surface exhibited more pronounced seasonality than the DCM. Inter-depth Bray-Curtis similarities repeatedly decreased as the water column stratified each summer. Environmental factors were better predictors of shifts in community composition than months or elapsed time alone; yet, the best predictor was community composition at the other depth (that is, 0-5 m versus DCM).

Life in the "plastisphere": microbial communities on plastic marine debris

Plastics are the most abundant form of marine debris, with global production rising and documented impacts in some marine environments, but the influence of plastic on open ocean ecosystems is poorly understood, particularly for microbial communities. Plastic marine debris (PMD) collected at multiple locations in the North Atlantic was analyzed with scanning electron microscopy (SEM) and next-generation sequencing to characterize the attached microbial communities. We unveiled a diverse microbial community of heterotrophs, autotrophs, predators, and symbionts, a community we refer to as the "Plastisphere". Pits visualized in the PMD surface conformed to bacterial shapes suggesting active hydrolysis of the hydrocarbon polymer. Small-subunit rRNA gene surveys identified several hydrocarbon-degrading bacteria, supporting the possibility that microbes play a role in degrading PMD. Some Plastisphere members may be opportunistic pathogens (the authors, unpublished data) such as specific members of the genus Vibrio that dominated one of our plastic samples. Plastisphere communities are distinct from surrounding surface water, implying that plastic serves as a novel ecological habitat in the open ocean. Plastic has a longer half-life than most natural floating marine substrates, and a hydrophobic surface that promotes microbial colonization and biofilm formation, differing from autochthonous substrates in the upper layers of the ocean.

Evidence for a persistent microbial seed bank throughout the global ocean

Do bacterial taxa demonstrate clear endemism, like macroorganisms, or can one site's bacterial community recapture the total phylogenetic diversity of the world's oceans? Here we compare a deep bacterial community characterization from one site in the English Channel (L4-DeepSeq) with 356 datasets from the International Census of Marine Microbes (ICoMM) taken from around the globe (ranging from marine pelagic and sediment samples to sponge-associated environments). At the L4-DeepSeq site, increasing sequencing depth uncovers greater phylogenetic overlap with the global ICoMM data. This site contained 31.7-66.2% of operational taxonomic units identified in a given ICoMM biome. Extrapolation of this overlap suggests that 1.93 × 10(11) sequences from the L4 site would capture all ICoMM bacterial phylogenetic diversity. Current technology trends suggest this limit may be attainable within 3 y. These results strongly suggest the marine biosphere maintains a previously undetected, persistent microbial seed bank.

Beyond the genome: community-level analysis of the microbial world

The development of culture-independent strategies to study microbial diversity and function has led to a revolution in microbial ecology, enabling us to address fundamental questions about the distribution of microbes and their influence on Earth's biogeochemical cycles. This article discusses some of the progress that scientists have made with the use of so-called "omic" techniques (metagenomics, metatranscriptomics, and metaproteomics) and the limitations and major challenges these approaches are currently facing. These 'omic methods have been used to describe the taxonomic structure of microbial communities in different environments and to discover new genes and enzymes of industrial and medical interest. However, microbial community structure varies in different spatial and temporal scales and none of the 'omic techniques are individually able to elucidate the complex aspects of microbial communities and ecosystems. In this article we highlight the importance of a spatiotemporal sampling design, together with a multilevel 'omic approach and a community analysis strategy (association networks and modeling) to examine and predict interacting microbial communities and their impact on the environment.

Short-term observations of marine bacterial and viral communities: patterns, connections and resilience

Observation of short-term temporal variation in bacterial and viral communities is important for understanding patterns of aquatic microbial diversity. We collected surface seawater once daily for 38 consecutive days with seven more samples interspersed over 40 more days at one location ∼2 km from Santa Catalina Island, California. Bacterial communities were analyzed by automated ribosomal intergenic spacer analysis (ARISA) and viral communities were analyzed by terminal restriction fragment length polymorphism (TRFLP) of the conserved T4-like myoviral gene encoding the major capsid protein (g23). Common bacterial and viral taxa were consistently dominant, and relatively few displayed dramatic increases/decreases or 'boom/bust' patterns that might be expected from dynamic predator-prey interactions. Association network analysis showed most significant covariations (associations) occurred among bacterial taxa or among viral taxa and there were several modular (highly-interconnected) associations (P≤0.005). Associations observed between bacteria and viruses (P≤0.005) occurred with a median time lag of 2 days. Regression of all pairwise Bray-Curtis similarities between samples indicated a rate of bacterial community change that slows from 2.1%-0.18% per day over a week to 2 months; the rate stays around 0.4% per day for viruses. Our interpretation is that, over the scale of days, individual bacterial and viral OTUs can be dynamic and patterned; resulting in statistical associations regarded as potential ecological interactions. However, over the scale of weeks, average bacterial community variation is slower, suggesting that there is strong community-level ecological resilience, that is, a tendency to converge towards a 'mean' microbial community set by longer-term controlling factors.

Unexpected diversity during community succession in the apple flower microbiome

Despite its importance to the host, the flower microbiome is poorly understood. We report a culture-independent, community-level assessment of apple flower microbial diversity and dynamics. We collected flowers from six apple trees at five time points, starting before flowers opened and ending at petal fall. We applied streptomycin to half of the trees when flowers opened. Assessment of microbial diversity using tag pyrosequencing of 16S rRNA genes revealed that the apple flower communities were rich and diverse and dominated by members of TM7 and Deinococcus-Thermus, phyla about which relatively little is known. From thousands of taxa, we identified six successional groups with coherent dynamics whose abundances peaked at different times before and after bud opening. We designated the groups Pioneer, Early, Mid, Late, Climax, and Generalist communities. The successional pattern was attributed to a set of prevalent taxa that were persistent and gradually changing in abundance. These taxa had significant associations with other community members, as demonstrated with a cooccurrence network based on local similarity analysis. We also detected a set of less-abundant, transient taxa that contributed to general tree-to-tree variability but not to the successional pattern. Communities on trees sprayed with streptomycin had slightly lower phylogenetic diversity than those on unsprayed trees but did not differ in structure or succession. Our results suggest that changes in apple flower microbial community structure are predictable over the life of the flower, providing a basis for ecological understanding and disease management.

IMPORTANCE

Flowering plants (angiosperms) represent a diverse group of an estimated 400,000 species, and their successful cultivation is essential to agriculture. Yet fundamental knowledge of flower-associated microbiotas remains largely unknown. Even less well understood are the changes that flower microbial communities experience through time. Flowers are particularly conducive to comprehensive temporal studies because they are, by nature, ephemeral organs. Here, we present the first culture-independent time series of bacterial and archaeal communities associated with the flowers of apple, an economically important crop. We found unexpected diversity on apple flowers, including a preponderance of taxa affiliated with Deinococcus-Thermus and TM7, phyla that are understudied but thought to be tolerant to an array of environmental stresses. Our results also suggest that changes in microbial community structure on the apple flower may be predictable over the life of the flower, providing the basis for ecological understanding and disease management.

Expanding the boundaries of local similarity analysis

Background

Pairwise comparison of time series data for both local and time-lagged relationships is a computationally challenging problem relevant to many fields of inquiry. The Local Similarity Analysis (LSA) statistic identifies the existence of local and lagged relationships, but determining significance through a p-value has been algorithmically cumbersome due to an intensive permutation test, shuffling rows and columns and repeatedly calculating the statistic. Furthermore, this p-value is calculated with the assumption of normality -- a statistical luxury dissociated from most real world datasets.

Results

To improve the performance of LSA on big datasets, an asymptotic upper bound on the p-value calculation was derived without the assumption of normality. This change in the bound calculation markedly improved computational speed from O(pm²n) to O(m²n), where p is the number of permutations in a permutation test, m is the number of time series, and n is the length of each time series. The bounding process is implemented as a computationally efficient software package, FASTLSA, written in C and optimized for threading on multi-core computers, improving its practical computation time. We computationally compare our approach to previous implementations of LSA, demonstrate broad applicability by analyzing time series data from public health, microbial ecology, and social media, and visualize resulting networks using the Cytoscape software.

Conclusions

The FASTLSA software package expands the boundaries of LSA allowing analysis on datasets with millions of co-varying time series. Mapping metadata onto force-directed graphs derived from FASTLSA allows investigators to view correlated cliques and explore previously unrecognized network relationships. The software is freely available for download at: http://www.cmde.science.ubc.ca/hallam/fastLSA/.

Fundamentals of microbial community resistance and resilience

Microbial communities are at the heart of all ecosystems, and yet microbial community behavior in disturbed environments remains difficult to measure and predict. Understanding the drivers of microbial community stability, including resistance (insensitivity to disturbance) and resilience (the rate of recovery after disturbance) is important for predicting community response to disturbance. Here, we provide an overview of the concepts of stability that are relevant for microbial communities. First, we highlight insights from ecology that are useful for defining and measuring stability. To determine whether general disturbance responses exist for microbial communities, we next examine representative studies from the literature that investigated community responses to press (long-term) and pulse (short-term) disturbances in a variety of habitats. Then we discuss the biological features of individual microorganisms, of microbial populations, and of microbial communities that may govern overall community stability. We conclude with thoughts about the unique insights that systems perspectives – informed by meta-omics data – may provide about microbial community stability.

Efficient statistical significance approximation for local similarity analysis of high-throughput time series data

MOTIVATION

Local similarity analysis of biological time series data helps elucidate the varying dynamics of biological systems. However, its applications to large scale high-throughput data are limited by slow permutation procedures for statistical significance evaluation.

RESULTS

We developed a theoretical approach to approximate the statistical significance of local similarity analysis based on the approximate tail distribution of the maximum partial sum of independent identically distributed (i.i.d.) random variables. Simulations show that the derived formula approximates the tail distribution reasonably well (starting at time points > 10 with no delay and > 20 with delay) and provides P-values comparable with those from permutations. The new approach enables efficient calculation of statistical significance for pairwise local similarity analysis, making possible all-to-all local association studies otherwise prohibitive. As a demonstration, local similarity analysis of human microbiome time series shows that core operational taxonomic units (OTUs) are highly synergetic and some of the associations are body-site specific across samples.

AVAILABILITY

The new approach is implemented in our eLSA package, which now provides pipelines for faster local similarity analysis of time series data. The tool is freely available from eLSA's website: http://meta.usc.edu/softs/lsa.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CONTACT

fsun@usc.edu.

Co-occurrence patterns of plants and soil bacteria in the high-alpine subnival zone track environmental harshness

Plants and soil microorganisms interact to play a central role in ecosystem functioning. To determine the potential importance of biotic interactions in shaping the distributions of these organisms in a high-alpine subnival landscape, we examine co-occurrence patterns between plant species and bulk soil bacteria abundances. In this context, a co-occurrence relationship reflects a combination of several assembly processes: that both parties can disperse to the site, that they can survive the abiotic environmental conditions, and that interactions between the biota either facilitate survival or allow for coexistence. Across the entire landscape, 31% of the bacterial sequences in this dataset were significantly correlated to the abundance distribution of one or more plant species. These sequences fell into 14 clades, 6 of which are related to bacteria that are known to form symbioses with plants in other systems. Abundant plant species were more likely to have significant as well as stronger correlations with bacteria and these patterns were more prevalent in lower altitude sites. Conversely, correlations between plant species abundances and bacterial relative abundances were less frequent in sites near the snowline. Thus, plant-bacteria associations became more common as environmental conditions became less harsh and plants became more abundant. This pattern in co-occurrence strength and frequency across the subnival landscape suggests that plant-bacteria interactions are important for the success of life, both below- and above-ground, in an extreme environment.

Stochastic and deterministic assembly processes in subsurface microbial communities

A major goal of microbial community ecology is to understand the forces that structure community composition. Deterministic selection by specific environmental factors is sometimes important, but in other cases stochastic or ecologically neutral processes dominate. Lacking is a unified conceptual framework aiming to understand why deterministic processes dominate in some contexts but not others. Here we work toward such a framework. By testing predictions derived from general ecological theory we aim to uncover factors that govern the relative influences of deterministic and stochastic processes. We couple spatiotemporal data on subsurface microbial communities and environmental parameters with metrics and null models of within and between community phylogenetic composition. Testing for phylogenetic signal in organismal niches showed that more closely related taxa have more similar habitat associations. Community phylogenetic analyses further showed that ecologically similar taxa coexist to a greater degree than expected by chance. Environmental filtering thus deterministically governs subsurface microbial community composition. More importantly, the influence of deterministic environmental filtering relative to stochastic factors was maximized at both ends of an environmental variation gradient. A stronger role of stochastic factors was, however, supported through analyses of phylogenetic temporal turnover. Although phylogenetic turnover was on average faster than expected, most pairwise comparisons were not themselves significantly non-random. The relative influence of deterministic environmental filtering over community dynamics was elevated, however, in the most temporally and spatially variable environments. Our results point to general rules governing the relative influences of stochastic and deterministic processes across micro- and macro-organisms.

Microbial interactions: from networks to models

Metagenomics and 16S pyrosequencing have enabled the study of ecosystem structure and dynamics to great depth and accuracy. Co-occurrence and correlation patterns found in these data sets are increasingly used for the prediction of species interactions in environments ranging from the oceans to the human microbiome. In addition, parallelized co-culture assays and combinatorial labelling experiments allow high-throughput discovery of cooperative and competitive relationships between species. In this Review, we describe how these techniques are opening the way towards global ecosystem network prediction and the development of ecosystem-wide dynamic models.

Microbial ecology of expanding oxygen minimum zones

Dissolved oxygen concentration is a crucial organizing principle in marine ecosystems. As oxygen levels decline, energy is increasingly diverted away from higher trophic levels into microbial metabolism, leading to loss of fixed nitrogen and to production of greenhouse gases, including nitrous oxide and methane. In this Review, we describe current efforts to explore the fundamental factors that control the ecological and microbial biodiversity in oxygen-starved regions of the ocean, termed oxygen minimum zones. We also discuss how recent advances in microbial ecology have provided information about the potential interactions in distributed co-occurrence and metabolic networks in oxygen minimum zones, and we provide new insights into coupled biogeochemical processes in the ocean.

Validating T-RFLP as a sensitive and high-throughput approach to assess bacterial diversity patterns in human anterior nares

While recent works aimed to thoroughly characterize the bacterial community of the human anterior nares of a few candidates, this work sought to analyse a greater cross-section by sampling 100 volunteers. After optimizing and validating the method of terminal restriction fragment length polymorphism against six previously pyrosequenced samples, abundant species could be discriminated and their relative abundances measured in a high-throughput manner. The 100 volunteers could be statistically clustered into 12 groups, where two-thirds of volunteers shared more than 40% similarity in respect to their bacterial community structure, while the remaining third clustered into smaller groups being dominated by Dolosigranulum pigrum, Moraxella spp. or Staphylococcus aureus. Moraxella spp. was present predominantly in women rather than in men. The use of network analysis charting bacterial ecological co-occurrences revealed new evidence of likely positive associations between some core human nasal species. So, in the age of post 'omics' and 'deep sequencing', there is still a place for these well-tried and well-tested methods that can offer a rapid, reproducible and economical alternative, whereby also yielding valuable new information.

Distinct and diverse anaerobic bacterial communities in boreal lakes dominated by candidate division OD1

Lakes have a central role in the carbon cycle of the boreal landscape. These systems typically stratify in summer and their hypolimnetic microbial communities influence burial of biogenic organic matter in sediments. The composition of bacterial communities in these suboxic habitats was studied by pyrosequencing of 16S rRNA amplicons from five lakes with variable dissolved organic carbon (DOC) concentrations. Bacterioplankton communities in the hypolimnetic waters were clearly different from the surface layer with candidate division OD1, Chlorobi and Bacteroidetes as dominant community members. Several operational taxonomic units (OTUs) affiliated with candidate division OD1 were abundant and consistently present in the suboxic hypolimnion in these boreal lakes. The overall representation of this group was positively correlated with DOC and methane concentrations. Network analysis of time-series data revealed contrasting temporal patterns but suggested similar ecological roles among the abundant OTUs affiliated with candidate division OD1. Together, stable isotope data and taxonomic classification point to methane oxidation and autotrophic denitrification as important processes in the suboxic zone of boreal lakes. Our data revealed that while hypolimnetic bacterial communities are less dynamic, they appear to be more diverse than communities from the oxic surface layer. An appreciable proportion of the hypolimnetic bacteria belong to poorly described phyla.

Distribution, abundance, and diversity patterns of the thermoacidophilic "deep-sea hydrothermal vent euryarchaeota 2"

Cultivation-independent studies have shown that taxa belonging to the “deep-sea hydrothermal vent euryarchaeota 2” (DHVE2) lineage are widespread at deep-sea hydrothermal vents. While this lineage appears to be a common and important member of the microbial community at vent environments, relatively little is known about their overall distribution and phylogenetic diversity. In this study, we examined the distribution, relative abundance, co-occurrence patterns, and phylogenetic diversity of cultivable thermoacidophilic DHVE2 in deposits from globally distributed vent fields. Results of quantitative polymerase chain reaction assays with primers specific for the DHVE2 and Archaea demonstrate the ubiquity of the DHVE2 at deep-sea vents and suggest that they are significant members of the archaeal communities of established vent deposit communities. Local similarity analysis of pyrosequencing data revealed that the distribution of the DHVE2 was positively correlated with 10 other Euryarchaeota phylotypes and negatively correlated with mostly Crenarchaeota phylotypes. Targeted cultivation efforts resulted in the isolation of 12 axenic strains from six different vent fields, expanding the cultivable diversity of this lineage to vents along the East Pacific Rise and Mid-Atlantic Ridge. Eleven of these isolates shared greater than 97% 16S rRNA gene sequence similarity with one another and the only described isolate of the DHVE2, Aciduliprofundum boonei T469^T. Sequencing and phylogenetic analysis of five protein-coding loci, atpA, EF-2, radA, rpoB, and secY, revealed clustering of isolates according to geographic region of isolation. Overall, this study increases our understanding of the distribution, abundance, and phylogenetic diversity of the DHVE2.

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

Background

The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval.

Results

We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified.

Conclusions

The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa.

Characterizing microbial communities through space and time

Until recently, the study of microbial diversity has mainly been limited to descriptive approaches, rather than predictive model-based analyses. The development of advanced analytical tools and decreasing cost of high-throughput multi-omics technologies has made the later approach more feasible. However, consensus is lacking as to which spatial and temporal scales best facilitate understanding of the role of microbial diversity in determining both public and environmental health. Here, we review the potential for combining these new technologies with both traditional and nascent spatio-temporal analysis methods. The fusion of proper spatio-temporal sampling, combined with modern multi-omics and computational tools, will provide insight into the tracking, development and manipulation of microbial communities.

Beyond the Venn diagram: the hunt for a core microbiome

Discovering a core microbiome is important for understanding the stable, consistent components across complex microbial assemblages. A core is typically defined as the suite of members shared among microbial consortia from similar habitats, and is represented by the overlapping areas of circles in Venn diagrams, in which each circle contains the membership of the sample or habitats being compared. Ecological insight into core microbiomes can be enriched by 'omics approaches that assess gene expression, thereby extending the concept of the core beyond taxonomically defined membership to community function and behaviour. Parameters defined by traditional ecology theory, such as composition, phylogeny, persistence and connectivity, will also create a more complex portrait of the core microbiome and advance understanding of the role of key microorganisms and functions within and across ecosystems.

Using network analysis to explore co-occurrence patterns in soil microbial communities

Exploring large environmental datasets generated by high-throughput DNA sequencing technologies requires new analytical approaches to move beyond the basic inventory descriptions of the composition and diversity of natural microbial communities. In order to investigate potential interactions between microbial taxa, network analysis of significant taxon co-occurrence patterns may help to decipher the structure of complex microbial communities across spatial or temporal gradients. Here, we calculated associations between microbial taxa and applied network analysis approaches to a 16S rRNA gene barcoded pyrosequencing dataset containing >160 000 bacterial and archaeal sequences from 151 soil samples from a broad range of ecosystem types. We described the topology of the resulting network and defined operational taxonomic unit categories based on abundance and occupancy (that is, habitat generalists and habitat specialists). Co-occurrence patterns were readily revealed, including general non-random association, common life history strategies at broad taxonomic levels and unexpected relationships between community members. Overall, we demonstrated the potential of exploring inter-taxa correlations to gain a more integrated understanding of microbial community structure and the ecological rules guiding community assembly.

Coherent dynamics and association networks among lake bacterioplankton taxa

Bacteria have important roles in freshwater food webs and in the cycling of elements in the ecosystem. Yet specific ecological features of individual phylogenetic groups and interactions among these are largely unknown. We used 454 pyrosequencing of 16S rRNA genes to study associations of different bacterioplankton groups to environmental characteristics and their co-occurrence patterns over an annual cycle in a dimictic lake. Clear seasonal succession of the bacterioplankton community was observed. After binning of sequences into previously described and highly resolved phylogenetic groups (tribes), their temporal dynamics revealed extensive synchrony and associations with seasonal events such as ice coverage, ice-off, mixing and phytoplankton blooms. Coupling between closely and distantly related tribes was resolved by time-dependent rank correlations, suggesting ecological coherence that was often dependent on taxonomic relatedness. Association networks with the abundant freshwater Actinobacteria and Proteobacteria in focus revealed complex interdependencies within bacterioplankton communities and contrasting linkages to environmental conditions. Accordingly, unique ecological features can be inferred for each tribe and reveal the natural history of abundant cultured and uncultured freshwater bacteria.

Marine bacterial, archaeal and protistan association networks reveal ecological linkages

Microbes have central roles in ocean food webs and global biogeochemical processes, yet specific ecological relationships among these taxa are largely unknown. This is in part due to the dilute, microscopic nature of the planktonic microbial community, which prevents direct observation of their interactions. Here, we use a holistic (that is, microbial system-wide) approach to investigate time-dependent variations among taxa from all three domains of life in a marine microbial community. We investigated the community composition of bacteria, archaea and protists through cultivation-independent methods, along with total bacterial and viral abundance, and physico-chemical observations. Samples and observations were collected monthly over 3 years at a well-described ocean time-series site of southern California. To find associations among these organisms, we calculated time-dependent rank correlations (that is, local similarity correlations) among relative abundances of bacteria, archaea, protists, total abundance of bacteria and viruses and physico-chemical parameters. We used a network generated from these statistical correlations to visualize and identify time-dependent associations among ecologically important taxa, for example, the SAR11 cluster, stramenopiles, alveolates, cyanobacteria and ammonia-oxidizing archaea. Negative correlations, perhaps suggesting competition or predation, were also common. The analysis revealed a progression of microbial communities through time, and also a group of unknown eukaryotes that were highly correlated with dinoflagellates, indicating possible symbioses or parasitism. Possible 'keystone' species were evident. The network has statistical features similar to previously described ecological networks, and in network parlance has non-random, small world properties (that is, highly interconnected nodes). This approach provides new insights into the natural history of microbes.

Co-occurrence patterns for abundant marine archaeal and bacterial lineages in the deep chlorophyll maximum of coastal California

Microorganisms remineralize and respire half of marine primary production, yet the niches occupied by specific microbial groups, and how these different groups may interact, are poorly understood. In this study, we identify co-occurrence patterns for marine Archaea and specific bacterial groups in the chlorophyll maximum of the Southern California Bight. Quantitative PCR time series of marine group 1 (MG1) Crenarchaeota 16S rRNA genes varied substantially over time but were well-correlated (r(2)=0.94, P<0.001) with ammonia monooxygenase subunit A (amoA) genes, and were more weakly related to 16S rRNA genes for all Archaea (r(2)=0.39), indicating that other archaeal groups (for example, Euryarchaeota) were numerically important. These data sets were compared with variability in bacterial community composition based on automated ribosomal intergenic spacer analysis (ARISA). We found that archaeal amoA gene copies and a SAR11 (or Pelagibacter) group Ib operational taxonomic unit (OTU) displayed strong co-variation through time (r(2)=0.55, P<0.05), and archaeal amoA and MG1 16S rRNA genes also co-occurred with two SAR86 and two Bacteroidetes OTUs. The relative abundance of these groups increased and decreased in synchrony over the course of the time series, and peaked during periods of seasonal transition. By using a combination of quantitative and relative abundance estimates, our findings show that abundant microbial OTUs-including the marine Crenarchaeota, SAR11, SAR86 and the Bacteroidetes-co-occur non-randomly; they consequently have important implications for our understanding of microbial community ecology in the sea.

Functional molecular ecological networks

Biodiversity and its responses to environmental changes are central issues in ecology and for society. Almost all microbial biodiversity research focuses on "species" richness and abundance but not on their interactions. Although a network approach is powerful in describing ecological interactions among species, defining the network structure in a microbial community is a great challenge. Also, although the stimulating effects of elevated CO(2) (eCO(2)) on plant growth and primary productivity are well established, its influences on belowground microbial communities, especially microbial interactions, are poorly understood. Here, a random matrix theory (RMT)-based conceptual framework for identifying functional molecular ecological networks was developed with the high-throughput functional gene array hybridization data of soil microbial communities in a long-term grassland FACE (free air, CO(2) enrichment) experiment. Our results indicate that RMT is powerful in identifying functional molecular ecological networks in microbial communities. Both functional molecular ecological networks under eCO(2) and ambient CO(2) (aCO(2)) possessed the general characteristics of complex systems such as scale free, small world, modular, and hierarchical. However, the topological structures of the functional molecular ecological networks are distinctly different between eCO(2) and aCO(2), at the levels of the entire communities, individual functional gene categories/groups, and functional genes/sequences, suggesting that eCO(2) dramatically altered the network interactions among different microbial functional genes/populations. Such a shift in network structure is also significantly correlated with soil geochemical variables. In short, elucidating network interactions in microbial communities and their responses to environmental changes is fundamentally important for research in microbial ecology, systems microbiology, and global change.

Temporal patterns in glycolate-utilizing bacterial community composition correlate with phytoplankton population dynamics in humic lakes

Previous observations of correlated community dynamics between phytoplankton and bacteria in lakes indicate that phytoplankton populations may influence bacterial community structure. To investigate the possibility that bacterial use of phytoplankton exudates contributes to observed patterns of community change, we characterized the diversity and dynamics of heterotrophic bacterioplankton with genetic potential to use glycolate, a photorespiration-specific exudate, in five lakes over a 15-week period. Culture-independent approaches were used to track different bacterial phylotypes represented by DNA sequence variation in the functional gene glycolate oxidase subunit D (glcD). glcD gene sequences from freshwater bacteria exhibited broad phylogenetic diversity, including sequences representing the Alpha-, Beta-, and Gammaproteobacteria, Actinobacteria, Bacteroidetes, Firmicutes, and Verrucomicrobia. The majority of glcD gene sequences were betaproteobacterial, with 48% of the sequences clustering with the glcD gene from the cosmopolitan freshwater species Polynucleobacter necessarius. Terminal restriction fragment length polymorphism fingerprinting of the glcD gene revealed changes in glycolate-utilizing assemblages over time. An average of 39% of within-lake temporal variation in glycolate-utilizing assemblages across five lakes was explained by phytoplankton community composition and dynamics. The interaction between phytoplankton populations and the environment explained an additional 17% of variation on average. These observations offer new insight into the diversity and temporal dynamics of freshwater bacteria with genetic potential to use glycolate and support the hypothesis that algal exudates influence the structure of bacterial communities.

A global network of coexisting microbes from environmental and whole-genome sequence data

Microbes are the most abundant and diverse organisms on Earth. In contrast to macroscopic organisms, their environmental preferences and ecological interdependencies remain difficult to assess, requiring laborious molecular surveys at diverse sampling sites. Here, we present a global meta-analysis of previously sampled microbial lineages in the environment. We grouped publicly available 16S ribosomal RNA sequences into operational taxonomic units at various levels of resolution and systematically searched these for co-occurrence across environments. Naturally occurring microbes, indeed, exhibited numerous, significant interlineage associations. These ranged from relatively specific groupings encompassing only a few lineages, to larger assemblages of microbes with shared habitat preferences. Many of the coexisting lineages were phylogenetically closely related, but a significant number of distant associations were observed as well. The increased availability of completely sequenced genomes allowed us, for the first time, to search for genomic correlates of such ecological associations. Genomes from coexisting microbes tended to be more similar than expected by chance, both with respect to pathway content and genome size, and outliers from these trends are discussed. We hypothesize that groupings of lineages are often ancient, and that they may have significantly impacted on genome evolution.

Differential bacterial dynamics promote emergent community robustness to lake mixing: an epilimnion to hypolimnion transplant experiment

Lake mixing disrupts chemical and physical gradients that structure bacterial communities. A transplant experiment was designed to investigate the influence of post-mixing environmental conditions and biotic interactions on bacterial community composition. The experimental design was 3x2 factorial, where water was incubated from three different sources (epilimnion, hypolimnion, and mixed epilimnion and hypolimnion) at two different locations in the water column (epilimnion or hypolimnion). Three replicate mesocosms of each treatment were removed every day for 5 days for bacterial community profiling, assessed by automated ribosomal intergenic spacer analysis. There were significant treatment effects observed, and temperature was the strongest measured driver of community change (r=-0.66). Epilimnion-incubated communities changed more than hypolimnion-incubated. Across all treatments, we classified generalist, layer-preferential and layer-specialist populations based on occurrence patterns. Most classified populations were generalists that occurred in both strata, suggesting that communities were robust to mixing. In a network analysis of the mixed-inocula treatments, there was correlative evidence of inter-population biotic interactions, where many of these interactions involved generalists. These results reveal differential responses of bacterial populations to lake mixing and highlight the role of generalist taxa in structuring an emergent community-level response.