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.