Microbial diversity and abundance vary along salinity, oxygen, and particle size gradients in the Chesapeake Bay.

Marine snow and other particles are abundant in estuaries, where they drive biogeochemical transformations and elemental transport. Particles range in size, thereby providing a corresponding gradient of habitats for marine microorganisms. We used standard normalized amplicon sequencing, verified with microscopy, to characterize taxon-specific microbial abundances, (cells per litre of water and per milligrams of particles), across six particle size classes, ranging from 0.2 to 500 µm, along the main stem of the Chesapeake Bay estuary. Microbial communities varied in salinity, oxygen concentrations, and particle size. Many taxonomic groups were most densely packed on large particles (in cells/mg particles), yet were primarily associated with the smallest particle size class, because small particles made up a substantially larger portion of total particle mass. However, organisms potentially involved in methanotrophy, nitrite oxidation, and sulphate reduction were found primarily on intermediately sized (5-180 µm) particles, where species richness was also highest. All abundant ostensibly free-living organisms, including SAR11 and Synecococcus, appeared on particles, albeit at lower abundance than in the free-living fraction, suggesting that aggregation processes may incorporate them into particles. Our approach opens the door to a more quantitative understanding of the microscale and macroscale biogeography of marine microorganisms.

Explore mediated co-varying dynamics in microbial community using integrated local similarity and liquid association analysis.

BACKGROUND: Discovering the key microbial species and environmental factors of microbial community and characterizing their relationships with other members are critical to ecosystem studies. The microbial co-occurrence patterns across a variety of environmental settings have been extensively characterized. However, previous studies were limited by their restriction toward pairwise relationships, while there was ample evidence of third-party mediated co-occurrence in microbial communities. METHODS: We implemented and applied the triplet-based liquid association analysis in combination with the local similarity analysis procedure to microbial ecology data. We developed an intuitive scheme to visualize those complex triplet associations along with pairwise correlations. Using a time series from the marine microbial ecosystem as example, we identified pairs of operational taxonomic units (OTUs) where the strength of their associations appeared to relate to the values of a third "mediator" variable. These "mediator" variables appear to modulate the associations between pairs of bacteria. RESULTS: Using this analysis, we were able to assess the OTUs' ability to regulate its functional partners in the community, typically not manifested in the pairwise correlation patterns. For example, we identified Flavobacteria as a multifaceted player in the marine microbial ecosystem, and its clades were involved in mediating other OTU pairs. By contrast, SAR11 clades were not active mediators of the community, despite being abundant and highly correlated with other OTUs. Our results suggested that Flavobacteria are more likely to respond to situations where particles and unusual sources of dissolved organic material are prevalent, such as after a plankton bloom. On the other hand, SAR11s are oligotrophic chemoheterotrophs with inflexible metabolisms, and their relationships with other organisms may be less governed by environmental or biological factors. CONCLUSIONS: By integrating liquid association with local similarity analysis to explore the mediated co-varying dynamics, we presented a novel perspective and a useful toolkit to analyze and interpret time series data from microbial community. Our augmented association network analysis is thus more representative of the true underlying dynamic structure of the microbial community. The analytic software in this study was implemented as new functionalities of the ELSA (Extended local similarity analysis) tool, which is available for free download ( http://bitbucket.org/charade/elsa ).

Deep ocean nutrients imply large latitudinal variation in particle transfer efficiency.

The "transfer efficiency" of sinking organic particles through the mesopelagic zone and into the deep ocean is a critical determinant of the atmosphere-ocean partition of carbon dioxide (CO2). Our ability to detect large-scale spatial variations in transfer efficiency is limited by the scarcity and uncertainties of particle flux data. Here we reconstruct deep ocean particle fluxes by diagnosing the rate of nutrient accumulation along transport pathways in a data-constrained ocean circulation model. Combined with estimates of organic matter export from the surface, these diagnosed fluxes reveal a global pattern of transfer efficiency to 1,000 m that is high (∼25%) at high latitudes and low (∼5%) in subtropical gyres, with intermediate values in the tropics. This pattern is well correlated with spatial variations in phytoplankton community structure and the export of ballast minerals, which control the size and density of sinking particles. These findings accentuate the importance of high-latitude oceans in sequestering carbon over long timescales, and highlight potential impacts on remineralization depth as phytoplankton communities respond to a warming climate.

Vertical and Seasonal Patterns Control Bacterioplankton Communities at Two Horizontally Coherent Coastal Upwelling Sites off Galicia (NW Spain).

Analysis of seasonal patterns of marine bacterial community structure along horizontal and vertical spatial scales can help to predict long-term responses to climate change. Several recent studies have shown predictable seasonal reoccurrence of bacterial assemblages. However, only a few have assessed temporal variability over both horizontal and vertical spatial scales. Here, we simultaneously studied the bacterial community structure at two different locations and depths in shelf waters of a coastal upwelling system during an annual cycle. The most noticeable biogeographic patterns observed were seasonality, horizontal homogeneity, and spatial synchrony in bacterial diversity and community structure related with regional upwelling-downwelling dynamics. Water column mixing eventually disrupted bacterial community structure vertical heterogeneity. Our results are consistent with previous temporal studies of marine bacterioplankton in other temperate regions and also suggest a marked influence of regional factors on the bacterial communities inhabiting this coastal upwelling system. Bacterial-mediated carbon fluxes in this productive region appear to be mainly controlled by community structure dynamics in surface waters, and local environmental factors at the base of the euphotic zone.

Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains.

BACKGROUND: Local trend (i.e. shape) analysis of time series data reveals co-changing patterns in dynamics of biological systems. However, slow permutation procedures to evaluate the statistical significance of local trend scores have limited its applications to high-throughput time series data analysis, e.g., data from the next generation sequencing technology based studies. RESULTS: By extending the theories for the tail probability of the range of sum of Markovian random variables, we propose formulae for approximating the statistical significance of local trend scores. Using simulations and real data, we show that the approximate p-value is close to that obtained using a large number of permutations (starting at time points >20 with no delay and >30 with delay of at most three time steps) in that the non-zero decimals of the p-values obtained by the approximation and the permutations are mostly the same when the approximate p-value is less than 0.05. In addition, the approximate p-value is slightly larger than that based on permutations making hypothesis testing based on the approximate p-value conservative. The approximation enables efficient calculation of p-values for pairwise local trend analysis, making large scale all-versus-all comparisons possible. We also propose a hybrid approach by integrating the approximation and permutations to obtain accurate p-values for significantly associated pairs. We further demonstrate its use with the analysis of the Polymouth Marine Laboratory (PML) microbial community time series from high-throughput sequencing data and found interesting organism co-occurrence dynamic patterns. AVAILABILITY: The software tool is integrated into the eLSA software package that now provides accelerated local trend and similarity analysis pipelines for time series data. The package is freely available from the eLSA website: http://bitbucket.org/charade/elsa.

New insights into relationships between active and dormant organisms, phylogenetic diversity and ecosystem productivity.

Marine microbes make up a key part of ocean food webs and drive ocean chemistry through a range of metabolic processes. A fundamental question in ecology is whether the diversity of organisms in a community shapes the ecological functions of that community. While there is substantial evidence to support a positive link between diversity and ecological productivity for macro-organisms in terrestrial environments, this relationship has not previously been verified for marine microbial communities. One factor complicating the understanding of this relationship is that many marine microbes are dormant and are easily dispersed by ocean currents, making it difficult to ensure that the organisms found in a given environmental sample accurately reflect processes occurring in that environment. Another complication is that, due to microbes great range of genotypic and phenotypic variability, communities with distantly related species may have greater range of metabolic functions than communities have the same richness and evenness, but in which the species present are more closely related to each other. In this issue of Molecular Ecology, Galand et al. (2015) provide compelling evidence that the most metabolically active communities are those in which the nondormant portion of the microbial community has the highest phylogenetic diversity. They also illustrate that focusing on the active portion of the community allows for detection of temporal patterns in community structure that would not be otherwise evident. The authors' point out that the presence of many dormant organisms that do not contribute to ecosystem functioning is a feature that makes microbial ecosystems fundamentally different from macro-ecosystems and that this difference needs to be accounted for in microbial ecology theory.

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.

Associations between picocyanobacterial ecotypes and cyanophage host genes across ocean basins and depth.

Background: Cyanophages, viruses that infect cyanobacteria, are globally abundant in the ocean's euphotic zone and are a potentially important cause of mortality for marine picocyanobacteria. Viral host genes are thought to increase viral fitness by either increasing numbers of genes for synthesizing nucleotides for virus replication, or by mitigating direct stresses imposed by the environment. The encoding of host genes in viral genomes through horizontal gene transfer is a form of evolution that links viruses, hosts, and the environment. We previously examined depth profiles of the proportion of cyanophage containing various host genes in the Eastern Tropical North Pacific Oxygen Deficient Zone (ODZ) and at the subtropical North Atlantic (BATS). However, cyanophage host genes have not been previously examined in environmental depth profiles across the oceans. Methodology: We examined geographical and depth distributions of picocyanobacterial ecotypes, cyanophage, and their viral-host genes across ocean basins including the North Atlantic, Mediterranean Sea, North Pacific, South Pacific, and Eastern Tropical North and South Pacific ODZs using phylogenetic metagenomic read placement. We determined the proportion of myo and podo-cyanophage containing a range of host genes by comparing to cyanophage single copy core gene terminase (terL). With this large dataset (22 stations), network analysis identified statistical links between 12 of the 14 cyanophage host genes examined here with their picocyanobacteria host ecotypes. Results: Picyanobacterial ecotypes, and the composition and proportion of cyanophage host genes, shifted dramatically and predictably with depth. For most of the cyanophage host genes examined here, we found that the composition of host ecotypes predicted the proportion of viral host genes harbored by the cyanophage community. Terminase is too conserved to illuminate the myo-cyanophage community structure. Cyanophage cobS was present in almost all myo-cyanophage and did not vary in proportion with depth. We used the composition of cobS phylotypes to track changes in myo-cyanophage composition. Conclusions: Picocyanobacteria ecotypes shift with changes in light, temperature, and oxygen and many common cyanophage host genes shift concomitantly. However, cyanophage phosphate transporter gene pstS appeared to instead vary with ocean basin and was most abundant in low phosphate regions. Abundances of cyanophage host genes related to nutrient acquisition may diverge from host ecotype constraints as the same host can live in varying nutrient concentrations. Myo-cyanophage community in the anoxic ODZ had reduced diversity. By comparison to the oxic ocean, we can see which cyanophage host genes are especially abundant (nirA, nirC, and purS) or not abundant (myo psbA) in ODZs, highlighting both the stability of conditions in the ODZ and the importance of nitrite as an N source to ODZ endemic LLV Prochlorococcus.

Microbiota of Crassostrea virginica larvae during a hatchery crash and under normal production: Amplicon sequence data.

We present oyster larval microbiota from two feeding studies, in which wild type and low-salinity tolerant lines were either fed or starved. In one study, all larvae unexpectedly died, which was concurrent with an event in which all larvae in an adjoining oyster hatchery also died. In the other study, no crash occurred in either the study or hatchery. In both cases, larvae were collected and stored frozen, and microbial and host DNA was isolated by phenol-chloroform extraction. Both host 18 s rRNA genes and microbial 16 and 18 s rRNA genes were sequenced using universal primers. We present raw sequences, the pipeline that was used to quantify amplicon sequence variants, and our analysis pipeline that we used to describe how the overall microbial community varied between projects (crashed and non-crashed), feeding status (fed vs not), and strain (wild vs not). These data will be valuable to anyone interested in the microbiota of larval oysters, especially anyone interested in exploring hatchery crashes, effects of starvation, or strain level differences. They also contain a reproducible pipeline of amplicon analysis of host associated microbiota which may serve as a template for other studies. These data are a co-submission to a manuscript submitted to Aquaculture by Matthew grey et al. (2021) entitled Hatchery crashes among shellfish research hatcheries along the Atlantic coast of the United States: a case study at Horn Point Laboratory oyster hatchery. (Manuscript#: AQUACULTURE-D-21-01351R1).

NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data.

Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. In addition, the GAM transformation increased the predictive power (F1 score) of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.

Human gut microbiota is associated with HIV-reactive immunoglobulin at baseline and following HIV vaccination.

Antibodies that recognize commensal microbial antigens may be cross reactive with a part of the human immunodeficiency virus (HIV) envelope glycoprotein gp41. To improve understanding of the role of the microbiota in modulating the immune response to HIV vaccines, we studied the associations of the gut microbiota composition of participants in the HIV Vaccine Trials Network 096 clinical trial with their HIV-specific immune responses in response to vaccination with a DNA-prime, pox virus boost strategy designed to recapitulate the only efficacious HIV-vaccine trial (RV144). We observed that both levels of IgG antibodies to gp41 at baseline and post-vaccination levels of IgG antibodies to the Con.6.gp120.B, ZM96.gp140 and gp70 B.CaseA V1-V2 antigens were associated with three co-occurring clusters of family level microbial taxa. One cluster contained several families positively associated with gp41-specific IgG and negatively associated with vaccine-matched gp120, gp140 and V1-V2-specific IgG responses. A second cluster contained families that negatively associated with gp41 and positively associated with gp120, gp140 and V1-V2-specific IgG responses. A third cluster contained microbial groups that did not correlate with any immune responses. Baseline and post-vaccination levels of gp41 IgG were not significantly correlated, suggesting that factors beyond the microbiome that contribute to immune response heterogeneity. Sequence variant richness was positively associated with gp41, p24, pg140 and V1-V2 specific IgG responses, gp41 and p24 IgA responses, and CD4+ T cell responses to HIV-1 proteins. Our findings provide preliminary evidence that the gut microbiota may be an important predictor of vaccine response.

Utilizing gnotobiotic models to inform the role of the microbiome in vaccine response heterogeneity.

PURPOSE OF REVIEW: Gnotobiotic models have the potential to provide substantial insight into how the microbiome shapes its host's response to vaccines. This review aims to summarize literature about the role of the microbiome in shaping the immune system and vaccine response heterogeneity, summarize gnotobiotic and other murine models that help us understand the immune system and vaccine response, and suggest novel ways that these models could be used to further understand vaccine response heterogeneity. RECENT FINDINGS: Clinical data have suggested that numerous vaccines' effectiveness are regulated by the microbiome and often correlate with the abundance of specific taxa. Gnotobiotic and other animal models are beginning to illuminate the complex effects induced by the presence of particular microbial groups and communities. Such models have identified microbial groups that improve vaccine response to rotavirus vaccine and identified pathways by which the microbiome influences response to influenza and other vaccines. SUMMARY: By applying a range of vaccines across gnotobiotic mouse models, researchers may be able to identify the effects of single microorganisms as well as interacting communities of microorganisms on the immune response.

Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling.

Short timescale observations are valuable for understanding microbial ecological processes. We assessed dynamics in relative abundance and potential activities by sequencing the small sub-unit ribosomal RNA gene (rRNA gene) and rRNA molecules (rRNA) of Bacteria, Archaea, and Eukaryota once to twice daily between March 2014 and May 2014 from the surface ocean off Catalina Island, California. Typically Ostreococcus, Braarudosphaera, Teleaulax, and Synechococcus dominated phytoplankton sequences (including chloroplasts) while SAR11, Sulfitobacter, and Fluviicola dominated non-phytoplankton Bacteria and Archaea. We observed short-lived increases of diatoms, mostly Pseudo-nitzschia and Chaetoceros, with quickly responding Bacteria and Archaea including Flavobacteriaceae (Polaribacter & Formosa), Roseovarius, and Euryarchaeota (MGII), notably the exact amplicon sequence variants we observed responding similarly to another diatom bloom nearby, 3 years prior. We observed correlations representing known interactions among abundant phytoplankton rRNA sequences, demonstrating the biogeochemical and ecological relevance of such interactions: (1) The kleptochloroplastidic ciliate Mesodinium 18S rRNA gene sequences and a single Teleaulax taxon (via 16S rRNA gene sequences) were correlated (Spearman r = 0.83) yet uncorrelated to a Teleaulax 18S rRNA gene OTU, or any other taxon (consistent with a kleptochloroplastidic or karyokleptic relationship) and (2) the photosynthetic prymnesiophyte Braarudosphaera bigelowii and two strains of diazotrophic cyanobacterium UCYN-A were correlated and each taxon was also correlated to other taxa, including B. bigelowii to a verrucomicrobium and a dictyochophyte phytoplankter (all r > 0.8). We also report strong correlations (r > 0.7) between various ciliates, bacteria, and phytoplankton, suggesting interactions via currently unknown mechanisms. These data reiterate the utility of high-frequency time series to show rapid microbial reactions to stimuli, and provide new information about in situ dynamics of previously recognized and hypothesized interactions.

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision.

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.

Marine microbial community dynamics and their ecological interpretation.

Recent advances in studying the dynamics of marine microbial communities have shown that the composition of these communities follows predictable patterns and involves complex network interactions, which shed light on the underlying processes regulating these globally important organisms. Such 'holistic' (or organism- and system-based) studies of these communities complement popular reductionist, often culture-based, approaches for understanding organism function one gene or protein at a time. In this Review, we summarize our current understanding of marine microbial community dynamics at various scales, from hours to decades. We also explain how the data illustrate community resilience and seasonality, and reveal interactions among microorganisms.

Cross-depth analysis of marine bacterial networks suggests downward propagation of temporal changes.

Interactions among microbes and stratification across depths are both believed to be important drivers of microbial communities, though little is known about how microbial associations differ between and across depths. We have monitored the free-living microbial community at the San Pedro Ocean Time-series station, monthly, for a decade, at five different depths: 5 m, the deep chlorophyll maximum layer, 150 m, 500 m and 890 m (just above the sea floor). Here, we introduce microbial association networks that combine data from multiple ocean depths to investigate both within- and between-depth relationships, sometimes time-lagged, among microbes and environmental parameters. The euphotic zone, deep chlorophyll maximum and 890 m depth each contain two negatively correlated 'modules' (groups of many inter-correlated bacteria and environmental conditions) suggesting regular transitions between two contrasting environmental states. Two-thirds of pairwise correlations of bacterial taxa between depths lagged such that changes in the abundance of deeper organisms followed changes in shallower organisms. Taken in conjunction with previous observations of seasonality at 890 m, these trends suggest that planktonic microbial communities throughout the water column are linked to environmental conditions and/or microbial communities in overlying waters. Poorly understood groups including Marine Group A, Nitrospina and AEGEAN-169 clades contained taxa that showed diverse association patterns, suggesting these groups contain multiple ecological species, each shaped by different factors, which we have started to delineate. These observations build upon previous work at this location, lending further credence to the hypothesis that sinking particles and vertically migrating animals transport materials that significantly shape the time-varying patterns of microbial community composition.

Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years.

Microbial activities that affect global oceanographic and atmospheric processes happen throughout the water column, yet the long-term ecological dynamics of microbes have been studied largely in the euphotic zone and adjacent seasonally mixed depths. We investigated temporal patterns in the community structure of free-living bacteria, by sampling approximately monthly from 5 m, the deep chlorophyll maximum (∼15-40 m), 150, 500 and 890 m, in San Pedro Channel (maximum depth 900 m, hypoxic below ∼500 m), off the coast of Southern California. Community structure and biodiversity (inverse Simpson index) showed seasonal patterns near the surface and bottom of the water column, but not at intermediate depths. Inverse Simpson's index was highest in the winter in surface waters and in the spring at 890 m, and varied interannually at all depths. Biodiversity appeared to be driven partially by exchange of microbes between depths and was highest when communities were changing slowly over time. Meanwhile, communities from the surface through 500 m varied interannually. After accounting for seasonality, several environmental parameters co-varied with community structure at the surface and 890 m, but not at the intermediate depths. Abundant and seasonally variable groups included, at 890 m, Nitrospina, Flavobacteria and Marine Group A. Seasonality at 890 m is likely driven by variability in sinking particles, which originate in surface waters, pass transiently through the middle water column and accumulate on the seafloor where they alter the chemical environment. Seasonal subeuphotic groups are likely those whose ecology is strongly influenced by these particles. This surface-to-bottom, decade-long, study identifies seasonality and interannual variability not only of overall community structure, but also of numerous taxonomic groups and near-species level operational taxonomic units.

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).

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.

Accurate genome relative abundance estimation based on shotgun metagenomic reads.

Accurate estimation of microbial community composition based on metagenomic sequencing data is fundamental for subsequent metagenomics analysis. Prevalent estimation methods are mainly based on directly summarizing alignment results or its variants; often result in biased and/or unstable estimates. We have developed a unified probabilistic framework (named GRAMMy) by explicitly modeling read assignment ambiguities, genome size biases and read distributions along the genomes. Maximum likelihood method is employed to compute Genome Relative Abundance of microbial communities using the Mixture Model theory (GRAMMy). GRAMMy has been demonstrated to give estimates that are accurate and robust across both simulated and real read benchmark datasets. We applied GRAMMy to a collection of 34 metagenomic read sets from four metagenomics projects and identified 99 frequent species (minimally 0.5% abundant in at least 50% of the data-sets) in the human gut samples. Our results show substantial improvements over previous studies, such as adjusting the over-estimated abundance for Bacteroides species for human gut samples, by providing a new reference-based strategy for metagenomic sample comparisons. GRAMMy can be used flexibly with many read assignment tools (mapping, alignment or composition-based) even with low-sensitivity mapping results from huge short-read datasets. It will be increasingly useful as an accurate and robust tool for abundance estimation with the growing size of read sets and the expanding database of reference genomes.