Inter-comparison of marine microbiome sampling protocols

Research on marine microbial communities is growing, but studies are hard to compare because of variation in seawater sampling protocols. To help researchers in the inter-comparison of studies that use different seawater sampling methodologies, as well as to help them design future sampling campaigns, we developed the EuroMarine Open Science Exploration initiative (EMOSE). Within the EMOSE framework, we sampled thousands of liters of seawater from a single station in the NW Mediterranean Sea (Service d'Observation du Laboratoire Arago [SOLA], Banyuls-sur-Mer), during one single day. The resulting dataset includes multiple seawater processing approaches, encompassing different material-type kinds of filters (cartridge membrane and flat membrane), three different size fractionations (>0.22 µm, 0.22-3 µm, 3-20 µm and >20 µm), and a number of different seawater volumes ranging from 1 L up to 1000 L. We show that the volume of seawater that is filtered does not have a significant effect on prokaryotic and protist diversity, independently of the sequencing strategy. However, there was a clear difference in alpha and beta diversity between size fractions and between these and "whole water" (with no pre-fractionation). Overall, we recommend care when merging data from datasets that use filters of different pore size, but we consider that the type of filter and volume should not act as confounding variables for the tested sequencing strategies. To the best of our knowledge, this is the first time a publicly available dataset effectively allows for the clarification of the impact of marine microbiome methodological options across a wide range of protocols, including large-scale variations in sampled volume.

Symbiotic UCYN-A strains co-occurred with El Niño, relaxed upwelling, and varied eukaryotes over 10 years off Southern California

Biological nitrogen fixation, the conversion of N2 gas into a bioavailable form, is vital to sustaining marine primary production. Studies have shifted beyond traditionally studied tropical diazotrophs. Candidatus Atelocyanobacterium thalassa (or UCYN-A) has emerged as a focal point due to its streamlined metabolism, intimate partnership with a haptophyte host, and broad distribution. Here, we explore the environmental parameters that govern UCYN-A's presence at the San Pedro Ocean Time-series (SPOT), its host specificity, and statistically significant interactions with non-host eukaryotes from 2008-2018. 16S and 18S rRNA gene sequences were amplified by "universal primers" from monthly samples and resolved into Amplicon Sequence Variants, allowing us to observe multiple UCYN-A symbioses. UCYN-A1 relative abundances increased following the 2015-2016 El Niño event. This "open ocean ecotype" was present when coastal upwelling declined, and Ekman transport brought tropical waters into the region. Network analyses reveal all strains of UCYN-A co-occur with dinoflagellates including Lepidodinium, a potential predator, and parasitic Syndiniales. UCYN-A2 appeared to pair with multiple hosts and was not tightly coupled to its predominant host, while UCYN-A1 maintained a strong host-symbiont relationship. These biological relationships are particularly important to study in the context of climate change, which will alter UCYN-A distribution at regional and global scales.

Constraining the composition and quantity of organic matter used by abundant marine Thaumarchaeota

Marine Group I (MGI) Thaumarchaeota were originally described as chemoautotrophic nitrifiers, but molecular and isotopic evidence suggests heterotrophic and/or mixotrophic capabilities. Here, we investigated the quantity and composition of organic matter assimilated by individual, uncultured MGI cells from the Pacific Ocean to constrain their potential for mixotrophy and heterotrophy. We observed that most MGI cells did not assimilate carbon from any organic substrate provided (glucose, pyruvate, oxaloacetate, protein, urea, and amino acids). The minority of MGI cells that did assimilate it did so exclusively from nitrogenous substrates (urea, 15% of MGI and amino acids, 36% of MGI), and only as an auxiliary carbon source (<20% of that subset's total cellular carbon was derived from those substrates). At the population level, MGI assimilation of organic carbon comprised just 0.5%-11% of total biomass carbon. We observed extensive assimilation of inorganic carbon and urea- and amino acid-derived nitrogen (equal to that from ammonium), consistent with metagenomic and metatranscriptomic analyses performed here and previously showing a widespread potential for MGI to perform autotrophy and transport and degrade organic nitrogen. Our results constrain the quantity and composition of organic matter used by MGI and suggest they use it primarily to meet nitrogen demands for anabolism and nitrification.

Diverse Marine T4-like Cyanophage Communities Are Primarily Comprised of Low-Abundance Species Including Species with Distinct Seasonal, Persistent, Occasional, or Sporadic Dynamics

Cyanophages exert important top-down controls on their cyanobacteria hosts; however, concurrent analysis of both phage and host populations is needed to better assess phage-host interaction models. We analyzed picocyanobacteria Prochlorococcus and Synechococcus and T4-like cyanophage communities in Pacific Ocean surface waters using five years of monthly viral and cellular fraction metagenomes. Cyanophage communities contained thousands of mostly low-abundance (<2% relative abundance) species with varying temporal dynamics, categorized as seasonally recurring or non-seasonal and occurring persistently, occasionally, or sporadically (detected in ≥85%, 15-85%, or <15% of samples, respectively). Viromes contained mostly seasonal and persistent phages (~40% each), while cellular fraction metagenomes had mostly sporadic species (~50%), reflecting that these sample sets capture different steps of the infection cycle-virions from prior infections or within currently infected cells, respectively. Two groups of seasonal phages correlated to Synechococcus or Prochlorococcus were abundant in spring/summer or fall/winter, respectively. Cyanophages likely have a strong influence on the host community structure, as their communities explained up to 32% of host community variation. These results support how both seasonally recurrent and apparent stochastic processes, likely determined by host availability and different host-range strategies among phages, are critical to phage-host interactions and dynamics, consistent with both the Kill-the-Winner and the Bank models.

ViralCC retrieves complete viral genomes and virus-host pairs from metagenomic Hi-C data

The introduction of high-throughput chromosome conformation capture (Hi-C) into metagenomics enables reconstructing high-quality metagenome-assembled genomes (MAGs) from microbial communities. Despite recent advances in recovering eukaryotic, bacterial, and archaeal genomes using Hi-C contact maps, few of Hi-C-based methods are designed to retrieve viral genomes. Here we introduce ViralCC, a publicly available tool to recover complete viral genomes and detect virus-host pairs using Hi-C data. Compared to other Hi-C-based methods, ViralCC leverages the virus-host proximity structure as a complementary information source for the Hi-C interactions. Using mock and real metagenomic Hi-C datasets from several different microbial ecosystems, including the human gut, cow fecal, and wastewater, we demonstrate that ViralCC outperforms existing Hi-C-based binning methods as well as state-of-the-art tools specifically dedicated to metagenomic viral binning. ViralCC can also reveal the taxonomic structure of viruses and virus-host pairs in microbial communities. When applied to a real wastewater metagenomic Hi-C dataset, ViralCC constructs a phage-host network, which is further validated using CRISPR spacer analyses. ViralCC is an open-source pipeline available at https://github.com/dyxstat/ViralCC .

Contrasting diversity patterns of prokaryotes and protists over time and depth at the San-Pedro Ocean Time series

Community dynamics are central in microbial ecology, yet we lack studies comparing diversity patterns among marine protists and prokaryotes over depth and multiple years. Here, we characterized microbes at the San-Pedro Ocean Time series (2005-2018), using SSU rRNA gene sequencing from two size fractions (0.2-1 and 1-80 μm), with a universal primer set that amplifies from both prokaryotes and eukaryotes, allowing direct comparisons of diversity patterns in a single set of analyses. The 16S + 18S rRNA gene composition in the small size fraction was mostly prokaryotic (>92%) as expected, but the large size fraction unexpectedly contained 46-93% prokaryotic 16S rRNA genes. Prokaryotes and protists showed opposite vertical diversity patterns; prokaryotic diversity peaked at mid-depth, protistan diversity at the surface. Temporal beta-diversity patterns indicated prokaryote communities were much more stable than protists. Although the prokaryotic communities changed monthly, the average community stayed remarkably steady over 14 years, showing high resilience. Additionally, particle-associated prokaryotes were more diverse than smaller free-living ones, especially at deeper depths, contributed unexpectedly by abundant and diverse SAR11 clade II. Eukaryotic diversity was strongly correlated with the diversity of particle-associated prokaryotes but not free-living ones, reflecting that physical associations result in the strongest interactions, including symbioses, parasitism, and decomposer relationships.

OUP accepted manuscript

Motivation

Phage–host associations play important roles in microbial communities. But in natural communities, as opposed to culture-based lab studies where phages are discovered and characterized metagenomically, their hosts are generally not known. Several programs have been developed for predicting which phage infects which host based on various sequence similarity measures or machine learning approaches. These are often based on whole viral and host genomes, but in metagenomics-based studies, we rarely have whole genomes but rather must rely on contigs that are sometimes as short as hundreds of bp long. Therefore, we need programs that predict hosts of phage contigs on the basis of these short contigs. Although most existing programs can be applied to metagenomic datasets for these predictions, their accuracies are generally low. Here, we develop ContigNet, a convolutional neural network-based model capable of predicting phage–host matches based on relatively short contigs, and compare it to previously published VirHostMatcher (VHM) and WIsH.

Results

On the validation set, ContigNet achieves 72–85% area under the receiver operating characteristic curve (AUROC) scores, compared to the maximum of 68% by VHM or WIsH for contigs of lengths between 200 bps to 50 kbps. We also apply the model to the Metagenomic Gut Virus (MGV) catalogue, a dataset containing a wide range of draft genomes from metagenomic samples and achieve 60–70% AUROC scores compared to that of VHM and WIsH of 52%. Surprisingly, ContigNet can also be used to predict plasmid-host contig associations with high accuracy, indicating a similar genetic exchange between mobile genetic elements and their hosts.

Availability and implementation

The source code of ContigNet and related datasets can be downloaded from https://github.com/tianqitang1/ContigNet.

Immune lag is a major cost of prokaryotic adaptive immunity during viral outbreaks

Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas adaptive immune systems enable bacteria and archaea to efficiently respond to viral pathogens by creating a genomic record of previous encounters. These systems are broadly distributed across prokaryotic taxa, yet are surprisingly absent in a majority of organisms, suggesting that the benefits of adaptive immunity frequently do not outweigh the costs. Here, combining experiments and models, we show that a delayed immune response which allows viruses to transiently redirect cellular resources to reproduction, which we call ‘immune lag’, is extremely costly during viral outbreaks, even to completely immune hosts. Critically, the costs of lag are only revealed by examining the early, transient dynamics of a host–virus system occurring immediately after viral challenge. Lag is a basic parameter of microbial defence, relevant to all intracellular, post-infection antiviral defence systems, that has to-date been largely ignored by theoretical and experimental treatments of host-phage systems.

Comprehensive single-PCR 16S and 18S rRNA community analysis validated with mock communities, and estimation of sequencing bias against 18S

Universal primers for SSU rRNA genes allow profiling of natural communities by simultaneously amplifying templates from Bacteria, Archaea, and Eukaryota in a single PCR reaction. Despite the potential to show relative abundance for all rRNA genes, universal primers are rarely used, due to various concerns including amplicon length variation and its effect on bioinformatic pipelines. We thus developed 16S and 18S rRNA mock communities and a bioinformatic pipeline to validate this approach. Using these mocks, we show that universal primers (515Y/926R) outperformed eukaryote-specific V4 primers in observed versus expected abundance correlations (slope = 0.88 vs. 0.67-0.79), and mock community members with single mismatches to the primer were strongly underestimated (threefold to eightfold). Using field samples, both primers yielded similar 18S beta-diversity patterns (Mantel test, p < 0.001) but differences in relative proportions of many rarer taxa. To test for length biases, we mixed mock communities (16S + 18S) before PCR and found a twofold underestimation of 18S sequences due to sequencing bias. Correcting for the twofold underestimation, we estimate that, in Southern California field samples (1.2-80 μm), there were averages of 35% 18S, 28% chloroplast 16S, and 37% prokaryote 16S rRNA genes. These data demonstrate the potential for universal primers to generate comprehensive microbiome profiles.

Metagenomics and Quantitative Stable Isotope Probing Offer Insights into Metabolism of Polycyclic Aromatic Hydrocarbon Degraders in Chronically Polluted Seawater

Bacterial biodegradation is a significant contributor to remineralization of polycyclic aromatic hydrocarbons (PAHs)-toxic and recalcitrant components of crude oil as well as by-products of partial combustion chronically introduced into seawater via atmospheric deposition. The Deepwater Horizon oil spill demonstrated the speed at which a seed PAH-degrading community maintained by chronic inputs responds to acute pollution. We investigated the diversity and functional potential of a similar seed community in the chronically polluted Port of Los Angeles (POLA), using stable isotope probing with naphthalene, deep-sequenced metagenomes, and carbon incorporation rate measurements at the port and in two sites in the San Pedro Channel. We demonstrate the ability of the community of degraders at the POLA to incorporate carbon from naphthalene, leading to a quick shift in microbial community composition to be dominated by the normally rare Colwellia and Cycloclasticus We show that metagenome-assembled genomes (MAGs) belonged to these naphthalene degraders by matching their 16S-rRNA gene with experimental stable isotope probing data. Surprisingly, we did not find a full PAH degradation pathway in those genomes, even when combining genes from the entire microbial community, leading us to hypothesize that promiscuous dehydrogenases replace canonical naphthalene degradation enzymes in this site. We compared metabolic pathways identified in 29 genomes whose abundance increased in the presence of naphthalene to generate genomic-based recommendations for future optimization of PAH bioremediation at the POLA, e.g., ammonium as opposed to urea, heme or hemoproteins as an iron source, and polar amino acids.IMPORTANCE Oil spills in the marine environment have a devastating effect on marine life and biogeochemical cycles through bioaccumulation of toxic hydrocarbons and oxygen depletion by hydrocarbon-degrading bacteria. Oil-degrading bacteria occur naturally in the ocean, especially where they are supported by chronic inputs of oil or other organic carbon sources, and have a significant role in degradation of oil spills. Polycyclic aromatic hydrocarbons are the most persistent and toxic component of crude oil. Therefore, the bacteria that can break those molecules down are of particular importance. We identified such bacteria at the Port of Los Angeles (POLA), one of the busiest ports worldwide, and characterized their metabolic capabilities. We propose chemical targets based on those analyses to stimulate the activity of these bacteria in case of an oil spill in the Port POLA.

Microbial tropicalization driven by a strengthening western ocean boundary current

Western boundary currents (WBCs) redistribute heat and oligotrophic seawater from the tropics to temperate latitudes, with several displaying substantial climate change-driven intensification over the last century. Strengthening WBCs have been implicated in the poleward range expansion of marine macroflora and fauna, however, the impacts on the structure and function of temperate microbial communities are largely unknown. Here we show that the major subtropical WBC of the South Pacific Ocean, the East Australian Current (EAC), transports microbial assemblages that maintain tropical and oligotrophic (k-strategist) signatures, to seasonally displace more copiotrophic (r-strategist) temperate microbial populations within temperate latitudes of the Tasman Sea. We identified specific characteristics of EAC microbial assemblages compared with non-EAC assemblages, including strain transitions within the SAR11 clade, enrichment of Prochlorococcus, predicted smaller genome sizes and shifts in the importance of several functional genes, including those associated with cyanobacterial photosynthesis, secondary metabolism and fatty acid and lipid transport. At a temperate time-series site in the Tasman Sea, we observed significant reductions in standing stocks of total carbon and chlorophyll a, and a shift towards smaller phytoplankton and carnivorous copepods, associated with the seasonal impact of the EAC microbial assemblage. In light of the substantial shifts in microbial assemblage structure and function associated with the EAC, we conclude that climate-driven expansions of WBCs will expand the range of tropical oligotrophic microbes, and potentially profoundly impact the trophic status of temperate waters.

A network-based integrated framework for predicting virus-prokaryote interactions

Metagenomic sequencing has greatly enhanced the discovery of viral genomic sequences; however, it remains challenging to identify the host(s) of these new viruses. We developed VirHostMatcher-Net, a flexible, network-based, Markov random field framework for predicting virus–prokaryote interactions using multiple, integrated features: CRISPR sequences and alignment-free similarity measures (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$s_2^*$\end{document} and WIsH). Evaluation of this method on a benchmark set of 1462 known virus–prokaryote pairs yielded host prediction accuracy of 59% and 86% at the genus and phylum levels, representing 16–27% and 6–10% improvement, respectively, over previous single-feature prediction approaches. We applied our host prediction tool to crAssphage, a human gut phage, and two metagenomic virus datasets: marine viruses and viral contigs recovered from globally distributed, diverse habitats. Host predictions were frequently consistent with those of previous studies, but more importantly, this new tool made many more confident predictions than previous tools, up to nearly 3-fold more (n > 27 000), greatly expanding the diversity of known virus–host interactions.

Assessment of metagenomic assemblers based on hybrid reads of real and simulated metagenomic sequences

In metagenomic studies of microbial communities, the short reads come from mixtures of genomes. Read assembly is usually an essential first step for the follow-up studies in metagenomic research. Understanding the power and limitations of various read assembly programs in practice is important for researchers to choose which programs to use in their investigations. Many studies evaluating different assembly programs used either simulated metagenomes or real metagenomes with unknown genome compositions. However, the simulated datasets may not reflect the real complexities of metagenomic samples and the estimated assembly accuracy could be misleading due to the unknown genomes in real metagenomes. Therefore, hybrid strategies are required to evaluate the various read assemblers for metagenomic studies. In this paper, we benchmark the metagenomic read assemblers by mixing reads from real metagenomic datasets with reads from known genomes and evaluating the integrity, contiguity and accuracy of the assembly using the reads from the known genomes. We selected four advanced metagenome assemblers, MEGAHIT, MetaSPAdes, IDBA-UD and Faucet, for evaluation. We showed the strengths and weaknesses of these assemblers in terms of integrity, contiguity and accuracy for different variables, including the genetic difference of the real genomes with the genome sequences in the real metagenomic datasets and the sequencing depth of the simulated datasets. Overall, MetaSPAdes performs best in terms of integrity and continuity at the species-level, followed by MEGAHIT. Faucet performs best in terms of accuracy at the cost of worst integrity and continuity, especially at low sequencing depth. MEGAHIT has the highest genome fractions at the strain-level and MetaSPAdes has the overall best performance at the strain-level. MEGAHIT is the most efficient in our experiments. Availability: The source code is available at https://github.com/ziyewang/MetaAssemblyEval.

Identifying viruses from metagenomic data using deep learning

BACKGROUND

The recent development of metagenomic sequencing makes it possible to massively sequence microbial genomes including viral genomes without the need for laboratory culture. Existing reference-based and gene homology-based methods are not efficient in identifying unknown viruses or short viral sequences from metagenomic data.

METHODS

Here we developed a reference-free and alignment-free machine learning method, DeepVirFinder, for identifying viral sequences in metagenomic data using deep learning.

RESULTS

Trained based on sequences from viral RefSeq discovered before May 2015, and evaluated on those discovered after that date, DeepVirFinder outperformed the state-of-the-art method VirFinder at all contig lengths, achieving AUROC 0.93, 0.95, 0.97, and 0.98 for 300, 500, 1000, and 3000 bp sequences respectively. Enlarging the training data with additional millions of purified viral sequences from metavirome samples further improved the accuracy for identifying virus groups that are under-represented. Applying DeepVirFinder to real human gut metagenomic samples, we identified 51,138 viral sequences belonging to 175 bins in patients with colorectal carcinoma (CRC). Ten bins were found associated with the cancer status, suggesting viruses may play important roles in CRC.

CONCLUSIONS

Powered by deep learning and high throughput sequencing metagenomic data, DeepVirFinder significantly improved the accuracy of viral identification and will assist the study of viruses in the era of metagenomics.

Characterizing Chemoautotrophy and Heterotrophy in Marine Archaea and Bacteria With Single-Cell Multi-isotope NanoSIP

Characterizing and quantifying in situ metabolisms remains both a central goal and challenge for environmental microbiology. Here, we used a single-cell, multi-isotope approach to investigate the anabolic activity of marine microorganisms, with an emphasis on natural populations of Thaumarchaeota. After incubating coastal Pacific Ocean water with ¹³C-bicarbonate and ¹⁵N-amino acids, we used nanoscale secondary ion mass spectrometry (nanoSIMS) to isotopically screen 1,501 individual cells, and 16S rRNA amplicon sequencing to assess community composition. We established isotopic enrichment thresholds for activity and metabolic classification, and with these determined the percentage of anabolically active cells, the distribution of activity across the whole community, and the metabolic lifestyle—chemoautotrophic or heterotrophic—of each cell. Most cells (>90%) were anabolically active during the incubation, and 4–17% were chemoautotrophic. When we inhibited bacteria with antibiotics, the fraction of chemoautotrophic cells detected via nanoSIMS increased, suggesting archaea dominated chemoautotrophy. With fluorescence in situ hybridization coupled to nanoSIMS (FISH-nanoSIMS), we confirmed that most Thaumarchaeota were living chemoautotrophically, while bacteria were not. FISH-nanoSIMS analysis of cells incubated with dual-labeled (¹³C,¹⁵N-) amino acids revealed that most Thaumarchaeota cells assimilated amino-acid-derived nitrogen but not carbon, while bacteria assimilated both. This indicates that some Thaumarchaeota do not assimilate intact amino acids, suggesting intra-phylum heterogeneity in organic carbon utilization, and potentially their use of amino acids for nitrification. Together, our results demonstrate the utility of multi-isotope nanoSIMS analysis for high-throughput metabolic screening, and shed light on the activity and metabolism of uncultured marine archaea and bacteria.

Long-term stability and Red Queen-like strain dynamics in marine viruses

Viruses that infect microorganisms dominate marine microbial communities numerically, with impacts ranging from host evolution to global biogeochemical cycles^1,2. However, virus community dynamics, necessary for conceptual and mechanistic model development, remains difficult to assess. Here, we describe the long-term stability of a viral community by analysing the metagenomes of near-surface 0.02-0.2 μm samples from the San Pedro Ocean Time-series³ that were sampled monthly over 5 years. Of 19,907 assembled viral contigs (>5 kb, mean 15 kb), 97% were found in each sample (by >98% ID metagenomic read recruitment) to have relative abundances that ranged over seven orders of magnitude, with limited temporal reordering of rank abundances along with little change in richness. Seasonal variations in viral community composition were superimposed on the overall stability; maximum community similarity occurred at 12-month intervals. Despite the stability of viral genotypic clusters that had 98% sequence identity, viral sequences showed transient variations in single-nucleotide polymorphisms (SNPs) and constant turnover of minor population variants, each rising and falling over a few months, reminiscent of Red Queen dynamics⁴. The rise and fall of variants within populations, interpreted through the perspective of known virus-host interactions⁵, is consistent with the hypothesis that fluctuating selection acts on a microdiverse cloud of strains, and this succession is associated with ever-shifting virus-host defences and counterdefences. This results in long-term virus-host coexistence that is facilitated by perpetually changing minor variants.

Microbial rhodopsins are major contributors to the solar energy captured in the sea

All known phototrophic metabolisms on Earth rely on one of three categories of energy-converting pigments: chlorophyll-a (rarely -d), bacteriochlorophyll-a (rarely -b), and retinal, which is the chromophore in rhodopsins. While the significance of chlorophylls in solar energy capture has been studied for decades, the contribution of retinal-based phototrophy to this process remains largely unexplored. We report the first vertical distributions of the three energy-converting pigments measured along a contrasting nutrient gradient through the Mediterranean Sea and the Atlantic Ocean. The highest rhodopsin concentrations were observed above the deep chlorophyll-a maxima, and their geographical distribution tended to be inversely related to that of chlorophyll-a. We further show that proton-pumping proteorhodopsins potentially absorb as much light energy as chlorophyll-a-based phototrophy and that this energy is sufficient to sustain bacterial basal metabolism. This suggests that proteorhodopsins are a major energy-transducing mechanism to harvest solar energy in the surface ocean.

A hydrocarbon-contaminated aquifer reveals a Piggyback-the-Persistent viral strategy

Subsurface environments hold the largest reservoir of microbes in the biosphere. They play essential roles in transforming nutrients, degrading contaminants and recycling organic matter. Here, we propose a previously unrecognised fundamental microbial process that influences aquifer bioremediation dynamics and that applies to all microbial communities. In contrast to previous models, our proposed Piggyback-the-Persistent (PtP) mechanism occurs when viruses become more dominated by those exhibiting temperate rather than lytic lifestyles driven by persistent chemicals (in our case chlorinated-hydrocarbon pollutants) that provide long-term carbon sources and that refocus the aquifer carbon cycle, thus altering the microbial community. In this ultra-oligotrophic system, the virus:microbial ratio (VMR) ranges from below the detection limit of 0.0001 to 0.6, well below the common aquatic range of 3-10. Shortest-average-path network analysis revealed VMR and trichlorethene (TCE) as nodes through which ecosystem information and biomass most efficiently pass. Novel network rearrangement revealed a hierarchy of Kill-the-Winner (KtW), Piggyback-the-Winner (PtW) and PtP nodes. We propose that KtW, PtW and PtP occur simultaneously as competing strategies, with their relative importance depending on conditions at a particular time and location with unusual nutrient sources, such as TCE, appearing to contribute to a shift in this balance between viral mechanisms.

CAFE: aCcelerated Alignment-FrEe sequence analysis

Alignment-free genome and metagenome comparisons are increasingly important with the development of next generation sequencing (NGS) technologies. Recently developed state-of-the-art k-mer based alignment-free dissimilarity measures including CVTree, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^S$\end{document} are more computationally expensive than measures based solely on the k-mer frequencies. Here, we report a standalone software, aCcelerated Alignment-FrEe sequence analysis (CAFE), for efficient calculation of 28 alignment-free dissimilarity measures. CAFE allows for both assembled genome sequences and unassembled NGS shotgun reads as input, and wraps the output in a standard PHYLIP format. In downstream analyses, CAFE can also be used to visualize the pairwise dissimilarity measures, including dendrograms, heatmap, principal coordinate analysis and network display. CAFE serves as a general k-mer based alignment-free analysis platform for studying the relationships among genomes and metagenomes, and is freely available at https://github.com/younglululu/CAFE.

Multi-year dynamics of fine-scale marine cyanobacterial populations are more strongly explained by phage interactions than abiotic, bottom-up factors

Currently defined ecotypes in marine cyanobacteria Prochlorococcus and Synechococcus likely contain subpopulations that themselves are ecologically distinct. We developed and applied high-throughput sequencing for the 16S-23S rRNA internally transcribed spacer (ITS) to examine ecotype and fine-scale genotypic community dynamics for monthly surface water samples spanning 5 years at the San Pedro Ocean Time-series site. Ecotype-level structure displayed regular seasonal patterns including succession, consistent with strong forcing by seasonally varying abiotic parameters (e.g. temperature, nutrients, light). We identified tens to thousands of amplicon sequence variants (ASVs) within ecotypes, many of which exhibited distinct patterns over time, suggesting ecologically distinct populations within ecotypes. Community structure within some ecotypes exhibited regular, seasonal patterns, but not for others, indicating other more irregular processes such as phage interactions are important. Network analysis including T4-like phage genotypic data revealed distinct viral variants correlated with different groups of cyanobacterial ASVs including time-lagged predator-prey relationships. Variation partitioning analysis indicated that phage community structure more strongly explains cyanobacterial community structure at the ASV level than the abiotic environmental factors. These results support a hierarchical model whereby abiotic environmental factors more strongly shape niche partitioning at the broader ecotype level while phage interactions are more important in shaping community structure of fine-scale variants within ecotypes.

Influence of Light on Particulate Organic Matter Utilization by Attached and Free-Living Marine Bacteria

Light plays a central role on primary productivity of aquatic systems. Yet, its potential impact on the degradation of photosynthetically produced biomass is not well understood. We investigated the patterns of light-induced particle breakdown and bacterial assimilation of detrital C and N using ¹³C and ¹⁵N labeled freeze-thawed diatom cells incubated in laboratory microcosms with a marine microbial community freshly collected from the Pacific Ocean. Particles incubated in the dark resulted in increased bacterial counts and dissolved organic carbon concentrations compared to those incubated in the light. Light also influenced the attached and free-living microbial community structure as detected by 16S rRNA gene amplicon sequencing. For example, Sphingobacteriia were enriched on dark-incubated particles and taxa from the family Flavobacteriaceae and the genus Pseudoalteromonas were numerically enriched on particles in the light. Isotope incorporation analysis by phylogenetic microarray and NanoSIMS (a method called Chip-SIP) identified free-living and attached microbial taxa able to incorporate N and C from the particles. Some taxa, including members of the Flavobacteriaceae and Cryomorphaceae, exhibited increased isotope incorporation in the light, suggesting the use of photoheterotrophic metabolisms. In contrast, some members of Oceanospirillales and Rhodospirillales showed decreased isotope incorporation in the light, suggesting that their heterotrophic metabolism, particularly when occurring on particles, might increase at night or may be inhibited by sunlight. These results show that light influences particle degradation and C and N incorporation by attached bacteria, suggesting that the transfer between particulate and free-living phases are likely affected by external factors that change with the light regime, such as time of day, water column depth and season.

Optimizing de novo genome assembly from PCR-amplified metagenomes

BACKGROUND

Metagenomics has transformed our understanding of microbial diversity across ecosystems, with recent advances enabling de novo assembly of genomes from metagenomes. These metagenome-assembled genomes are critical to provide ecological, evolutionary, and metabolic context for all the microbes and viruses yet to be cultivated. Metagenomes can now be generated from nanogram to subnanogram amounts of DNA. However, these libraries require several rounds of PCR amplification before sequencing, and recent data suggest these typically yield smaller and more fragmented assemblies than regular metagenomes.

METHODS

Here we evaluate de novo assembly methods of 169 PCR-amplified metagenomes, including 25 for which an unamplified counterpart is available, to optimize specific assembly approaches for PCR-amplified libraries. We first evaluated coverage bias by mapping reads from PCR-amplified metagenomes onto reference contigs obtained from unamplified metagenomes of the same samples. Then, we compared different assembly pipelines in terms of assembly size (number of bp in contigs ≥ 10 kb) and error rates to evaluate which are the best suited for PCR-amplified metagenomes.

RESULTS

Read mapping analyses revealed that the depth of coverage within individual genomes is significantly more uneven in PCR-amplified datasets versus unamplified metagenomes, with regions of high depth of coverage enriched in short inserts. This enrichment scales with the number of PCR cycles performed, and is presumably due to preferential amplification of short inserts. Standard assembly pipelines are confounded by this type of coverage unevenness, so we evaluated other assembly options to mitigate these issues. We found that a pipeline combining read deduplication and an assembly algorithm originally designed to recover genomes from libraries generated after whole genome amplification (single-cell SPAdes) frequently improved assembly of contigs ≥10 kb by 10 to 100-fold for low input metagenomes.

CONCLUSIONS

PCR-amplified metagenomes have enabled scientists to explore communities traditionally challenging to describe, including some with extremely low biomass or from which DNA is particularly difficult to extract. Here we show that a modified assembly pipeline can lead to an improved de novo genome assembly from PCR-amplified datasets, and enables a better genome recovery from low input metagenomes.

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.

Proteorhodopsins dominate the expression of phototrophic mechanisms in seasonal and dynamic marine picoplankton communities

The most abundant and ubiquitous microbes in the surface ocean use light as an energy source, capturing it via complex chlorophyll-based photosystems or simple retinal-based rhodopsins. Studies in various ocean regimes compared the abundance of these mechanisms, but few investigated their expression. Here we present the first full seasonal study of abundance and expression of light-harvesting mechanisms (proteorhodopsin, PR; aerobic anoxygenic photosynthesis, AAnP; and oxygenic photosynthesis, PSI) from deep-sequenced metagenomes and metatranscriptomes of marine picoplankton (<1 µm) at three coastal stations of the San Pedro Channel in the Pacific Ocean. We show that, regardless of season or sampling location, the most common phototrophic mechanism in metagenomes of this dynamic region was PR (present in 65–104% of the genomes as estimated by single-copy recA), followed by PSI (5–104%) and AAnP (5–32%). Furthermore, the normalized expression (RNA to DNA ratio) of PR genes was higher than that of oxygenic photosynthesis (average ± standard deviation 26.2 ± 8.4 vs. 11 ± 9.7), and the expression of the AAnP marker gene was significantly lower than both mechanisms (0.013 ± 0.02). We demonstrate that PR expression was dominated by the SAR11-cluster year-round, followed by other Alphaproteobacteria, unknown-environmental clusters and Gammaproteobacteria. This highly dynamic system further allowed us to identify a trend for PR spectral tuning, in which blue-absorbing PR genes dominate in areas with low chlorophyll-a concentrations (<0.25 µgL⁻¹). This suggests that PR phototrophy is not an accessory function but instead a central mechanism that can regulate photoheterotrophic population dynamics.

Discovery of several novel, widespread, and ecologically distinct marine Thaumarchaeota viruses that encode amoC nitrification genes

Much of the diversity of prokaryotic viruses has yet to be described. In particular, there are no viral isolates that infect abundant, globally significant marine archaea including the phylum Thaumarchaeota. This phylum oxidizes ammonia, fixes inorganic carbon, and thus contributes to globally significant nitrogen and carbon cycles in the oceans. Metagenomics provides an alternative to culture-dependent means for identifying and characterizing viral diversity. Some viruses carry auxiliary metabolic genes (AMGs) that are acquired via horizontal gene transfer from their host(s), allowing inference of what host a virus infects. Here we present the discovery of 15 new genomically and ecologically distinct Thaumarchaeota virus populations, identified as contigs that encode viral capsid and thaumarchaeal ammonia monooxygenase genes (amoC). These viruses exhibit depth and latitude partitioning and are distributed globally in various marine habitats including pelagic waters, estuarine habitats, and hydrothermal plume water and sediments. We found evidence of viral amoC expression and that viral amoC AMGs sometimes comprise up to half of total amoC DNA copies in cellular fraction metagenomes, highlighting the potential impact of these viruses on N cycling in the oceans. Phylogenetics suggest they are potentially tailed viruses and share a common ancestor with related marine Euryarchaeota viruses. This work significantly expands our view of viruses of globally important marine Thaumarchaeota.

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.

Mosaic patterns of B-vitamin synthesis and utilization in a natural marine microbial community

Aquatic environments contain large communities of microorganisms whose synergistic interactions mediate the cycling of major and trace nutrients, including vitamins. B-vitamins are essential coenzymes that many organisms cannot synthesize. Thus, their exchange among de novo synthesizers and auxotrophs is expected to play an important role in the microbial consortia and explain some of the temporal and spatial changes observed in diversity. In this study, we analyzed metatranscriptomes of a natural marine microbial community, diel sampled quarterly over one year to try to identify the potential major B-vitamin synthesizers and consumers. Transcriptomic data showed that the best-represented taxa dominated the expression of synthesis genes for some B-vitamins but lacked transcripts for others. For instance, Rhodobacterales dominated the expression of vitamin-B₁₂ synthesis, but not of vitamin-B₇ , whose synthesis transcripts were mainly represented by Flavobacteria. In contrast, bacterial groups that constituted less than 4% of the community (e.g., Verrucomicrobia) accounted for most of the vitamin-B₁ synthesis transcripts. Furthermore, ambient vitamin-B₁ concentrations were higher in samples collected during the day, and were positively correlated with chlorophyll-a concentrations. Our analysis supports the hypothesis that the mosaic of metabolic interdependencies through B-vitamin synthesis and exchange are key processes that contribute to shaping microbial communities in nature.

Short-term dynamics and interactions of marine protist communities during the spring-summer transition

We examined the short-term variability, by daily to weekly sampling, of protist assemblages from March to July in surface water of the San Pedro Ocean Time-series station (eastern North Pacific), by V4 Illumina sequencing of the 18S rRNA gene. The sampling period encompassed a spring bloom followed by progression to summer conditions. Several protistan taxa displayed sharp increases and declines, with whole community Bray-Curtis dissimilarities of adjacent days being 66% in March and 40% in May. High initial abundance of parasitic Cercozoa Cryothecomonas longipes and Protaspis grandis coincided with a precipitous decline of blooming Pseudo-nitzschia diatoms, possibly suggesting their massive infection by these parasites; these cercozoans were hardly detectable afterwards. Canonical correspondence analysis indicated a limited predictability of community variability from environmental factors. This indicates that other factors are relevant in explaining changes in protist community composition at short temporal scales, such as interspecific relationships, stochastic processes, mixing with adjacent water, or advection of patches with different protist communities. Association network analysis revealed that interactions between the many parasitic OTUs and other taxa were overwhelmingly positive and suggest that although sometimes parasites may cause a crash of host populations, they may often follow their hosts and do not regularly cause enough mortality to potentially create negative correlations at the daily to weekly time scales we studied.

Towards enhanced and interpretable clustering/classification in integrative genomics

High-throughput technologies have led to large collections of different types of biological data that provide unprecedented opportunities to unravel molecular heterogeneity of biological processes. Nevertheless, how to jointly explore data from multiple sources into a holistic, biologically meaningful interpretation remains challenging. In this work, we propose a scalable and tuning-free preprocessing framework, Heterogeneity Rescaling Pursuit (Hetero-RP), which weighs important features more highly than less important ones in accord with implicitly existing auxiliary knowledge. Finally, we demonstrate effectiveness of Hetero-RP in diverse clustering and classification applications. More importantly, Hetero-RP offers an interpretation of feature importance, shedding light on the driving forces of the underlying biology. In metagenomic contig binning, Hetero-RP automatically weighs abundance and composition profiles according to the varying number of samples, resulting in markedly improved performance of contig binning. In RNA-binding protein (RBP) binding site prediction, Hetero-RP not only improves the prediction performance measured by the area under the receiver operating characteristic curves (AUC), but also uncovers the evidence supported by independent studies, including the distribution of the binding sites of IGF2BP and PUM2, the binding competition between hnRNPC and U2AF2, and the intron–exon boundary of U2AF2 [availability: https://github.com/younglululu/Hetero-RP].

COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment and paired-end read LinkAge

Motivation

The advent of next-generation sequencing technologies enables researchers to sequence complex microbial communities directly from the environment. Because assembly typically produces only genome fragments, also known as contigs, instead of an entire genome, it is crucial to group them into operational taxonomic units (OTUs) for further taxonomic profiling and down-streaming functional analysis. OTU clustering is also referred to as binning. We present COCACOLA, a general framework automatically bin contigs into OTUs based on sequence composition and coverage across multiple samples.

Results

The effectiveness of COCACOLA is demonstrated in both simulated and real datasets in comparison with state-of-art binning approaches such as CONCOCT, GroopM, MaxBin and MetaBAT. The superior performance of COCACOLA relies on two aspects. One is using L 1 distance instead of Euclidean distance for better taxonomic identification during initialization. More importantly, COCACOLA takes advantage of both hard clustering and soft clustering by sparsity regularization. In addition, the COCACOLA framework seamlessly embraces customized knowledge to facilitate binning accuracy. In our study, we have investigated two types of additional knowledge, the co-alignment to reference genomes and linkage of contigs provided by paired-end reads, as well as the ensemble of both. We find that both co-alignment and linkage information further improve binning in the majority of cases. COCACOLA is scalable and faster than CONCOCT, GroopM, MaxBin and MetaBAT.

Availability and implementation

The software is available at https://github.com/younglululu/COCACOLA .

Contact

fsun@usc.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

A communal catalogue reveals Earth's multiscale microbial diversity

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

Author Correction: Re-examination of the relationship between marine virus and microbial cell abundances

The original publication of this Article included analysis of virus and microbial cell abundances and virus-to-microbial cell ratios. Data in the Article came from 25 studies intended to be exclusively from marine sites. However, 3 of the studies included in the original unified dataset were erroneously classified as marine sites during compilation. The records with mis-recorded longitude and latitude values were, in fact, taken from inland, freshwater sources. The three inland, freshwater datasets are ELA, TROUT and SWAT. The data from these three studies represent 163 of the 5,671 records in the original publication. In the updated version of the Article, all analyses have been recalculated using the same statistical analysis pipeline released via GitHub as part of the original publication. Removal of the three studies reduces the unified dataset to 5,508 records. Analyses involving all grouped datasets have been updated with changes noted in each figure. All key results remain qualitatively unchanged. All data and scripts used in this correction have been made available as a new, updated GitHub release to reflect the updated dataset and figures.

Marine archaeal dynamics and interactions with the microbial community over 5 years from surface to seafloor

Marine archaea are critical contributors to global carbon and nitrogen redox cycles, but their temporal variability and microbial associations across the water column are poorly known. We evaluated seasonal variability of free living (0.2-1 μm size fraction) Thaumarchaea Marine Group I (MGI) and Euryarchaea Marine Group II (MGII) communities and their associations with the microbial community from surface to seafloor (890 m) over 5 years by 16S rRNA V4-V5 gene sequencing. MGI and MGII communities demonstrated distinct compositions at different depths, and seasonality at all depths. Microbial association networks at 150 m, 500 m and 890 m, revealed diverse assemblages of MGI (presumed ammonia oxidizers) and Nitrospina taxa (presumed dominant nitrite oxidizers, completing the nitrification process), suggesting distinct MGI-Nitrospina OTUs are responsible for nitrification at different depths and seasons, and depth- related and seasonal variability in nitrification could be affected by alternating MGI-Nitrospina assemblages. MGII taxa also showed distinct correlations to possibly heterotrophic bacteria, most commonly to members of Marine Group A, Chloroflexi, Marine Group B, and SAR86. Thus, both MGI and MGII likely have dynamic associations with bacteria based on similarities in activity or other interactions that select for distinct microbial assemblages over time. The importance of MGII taxa as members of the heterotrophic community previously reported for photic zone appears to apply throughout the water column.

VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data

BACKGROUND

Identifying viral sequences in mixed metagenomes containing both viral and host contigs is a critical first step in analyzing the viral component of samples. Current tools for distinguishing prokaryotic virus and host contigs primarily use gene-based similarity approaches. Such approaches can significantly limit results especially for short contigs that have few predicted proteins or lack proteins with similarity to previously known viruses.

METHODS

We have developed VirFinder, the first k-mer frequency based, machine learning method for virus contig identification that entirely avoids gene-based similarity searches. VirFinder instead identifies viral sequences based on our empirical observation that viruses and hosts have discernibly different k-mer signatures. VirFinder's performance in correctly identifying viral sequences was tested by training its machine learning model on sequences from host and viral genomes sequenced before 1 January 2014 and evaluating on sequences obtained after 1 January 2014.

RESULTS

VirFinder had significantly better rates of identifying true viral contigs (true positive rates (TPRs)) than VirSorter, the current state-of-the-art gene-based virus classification tool, when evaluated with either contigs subsampled from complete genomes or assembled from a simulated human gut metagenome. For example, for contigs subsampled from complete genomes, VirFinder had 78-, 2.4-, and 1.8-fold higher TPRs than VirSorter for 1, 3, and 5 kb contigs, respectively, at the same false positive rates as VirSorter (0, 0.003, and 0.006, respectively), thus VirFinder works considerably better for small contigs than VirSorter. VirFinder furthermore identified several recently sequenced virus genomes (after 1 January 2014) that VirSorter did not and that have no nucleotide similarity to previously sequenced viruses, demonstrating VirFinder's potential advantage in identifying novel viral sequences. Application of VirFinder to a set of human gut metagenomes from healthy and liver cirrhosis patients reveals higher viral diversity in healthy individuals than cirrhosis patients. We also identified contig bins containing crAssphage-like contigs with higher abundance in healthy patients and a putative Veillonella genus prophage associated with cirrhosis patients.

CONCLUSIONS

This innovative k-mer based tool complements gene-based approaches and will significantly improve prokaryotic viral sequence identification, especially for metagenomic-based studies of viral ecology.

VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data

BACKGROUND

Identifying viral sequences in mixed metagenomes containing both viral and host contigs is a critical first step in analyzing the viral component of samples. Current tools for distinguishing prokaryotic virus and host contigs primarily use gene-based similarity approaches. Such approaches can significantly limit results especially for short contigs that have few predicted proteins or lack proteins with similarity to previously known viruses.

METHODS

We have developed VirFinder, the first k-mer frequency based, machine learning method for virus contig identification that entirely avoids gene-based similarity searches. VirFinder instead identifies viral sequences based on our empirical observation that viruses and hosts have discernibly different k-mer signatures. VirFinder's performance in correctly identifying viral sequences was tested by training its machine learning model on sequences from host and viral genomes sequenced before 1 January 2014 and evaluating on sequences obtained after 1 January 2014.

RESULTS

VirFinder had significantly better rates of identifying true viral contigs (true positive rates (TPRs)) than VirSorter, the current state-of-the-art gene-based virus classification tool, when evaluated with either contigs subsampled from complete genomes or assembled from a simulated human gut metagenome. For example, for contigs subsampled from complete genomes, VirFinder had 78-, 2.4-, and 1.8-fold higher TPRs than VirSorter for 1, 3, and 5 kb contigs, respectively, at the same false positive rates as VirSorter (0, 0.003, and 0.006, respectively), thus VirFinder works considerably better for small contigs than VirSorter. VirFinder furthermore identified several recently sequenced virus genomes (after 1 January 2014) that VirSorter did not and that have no nucleotide similarity to previously sequenced viruses, demonstrating VirFinder's potential advantage in identifying novel viral sequences. Application of VirFinder to a set of human gut metagenomes from healthy and liver cirrhosis patients reveals higher viral diversity in healthy individuals than cirrhosis patients. We also identified contig bins containing crAssphage-like contigs with higher abundance in healthy patients and a putative Veillonella genus prophage associated with cirrhosis patients.

CONCLUSIONS

This innovative k-mer based tool complements gene-based approaches and will significantly improve prokaryotic viral sequence identification, especially for metagenomic-based studies of viral ecology.

Genome and epigenome of a novel marine Thaumarchaeota strain suggest viral infection, phosphorothioation DNA modification and multiple restriction systems

Marine Thaumarchaeota are abundant ammonia-oxidizers but have few representative laboratory-cultured strains. We report the cultivation of Candidatus Nitrosomarinus catalina SPOT01, a novel strain that is less warm-temperature tolerant than other cultivated Thaumarchaeota. Using metagenomic recruitment, strain SPOT01 comprises a major portion of Thaumarchaeota (4-54%) in temperate Pacific waters. Its complete 1.36 Mbp genome possesses several distinguishing features: putative phosphorothioation (PT) DNA modification genes; a region containing probable viral genes; and putative urea utilization genes. The PT modification genes and an adjacent putative restriction enzyme (RE) operon likely form a restriction modification (RM) system for defence from foreign DNA. PacBio sequencing showed >98% methylation at two motifs, and inferred PT guanine modification of 19% of possible TGCA sites. Metagenomic recruitment also reveals the putative virus region and PT modification and RE genes are present in 18-26%, 9-14% and <1.5% of natural populations at 150 m with ≥85% identity to strain SPOT01. The presence of multiple probable RM systems in a highly streamlined genome suggests a surprising importance for defence from foreign DNA for dilute populations that infrequently encounter viruses or other cells. This new strain provides new insights into the ecology, including viral interactions, of this important group of marine microbes.

Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters

Numerous ecological processes, such as bacteriophage infection and phytoplankton-bacterial interactions, often occur via strain-specific mechanisms. Therefore, studying the causes of microbial dynamics should benefit from highly resolving taxonomic characterizations. We sampled daily to weekly over 5 months following a phytoplankton bloom off Southern California and examined the extent of microdiversity, that is, significant variation within 99% sequence similarity clusters, operational taxonomic units (OTUs), of bacteria, archaea, phytoplankton chloroplasts (all via 16S or intergenic spacer (ITS) sequences) and T4-like-myoviruses (via g23 major capsid protein gene sequence). The extent of microdiversity varied between genes (ITS most, g23 least) and only temporally common taxa were highly microdiverse. Overall, 60% of taxa exhibited microdiversity; 59% of these had subtypes that changed significantly as a proportion of the parent taxon, indicating ecologically distinct taxa. Pairwise correlations between prokaryotes and myoviruses or phytoplankton (for example, highly microdiverse Chrysochromulina sp.) improved when using single-base variants. Correlations between myoviruses and SAR11 increased in number (172 vs 9, Spearman>0.65) and became stronger (0.61 vs 0.58, t-test: P<0.001) when using SAR11 ITS single-base variants vs OTUs. Whole-community correlation between SAR11 and myoviruses was much improved when using ITS single-base variants vs OTUs, with Mantel rho=0.49 vs 0.27; these results are consistent with strain-specific interactions. Mantel correlations suggested >1 μm (attached/large) prokaryotes are a major myovirus source. Consideration of microdiversity improved observation of apparent host and virus networks, and provided insights into the ecological and evolutionary factors influencing the success of lineages, with important implications to ecosystem resilience and microbial function.

Prediction of virus-host infectious association by supervised learning methods

Background

The study of virus-host infectious association is important for understanding the functions and dynamics of microbial communities. Both cellular and fractionated viral metagenomic data generate a large number of viral contigs with missing host information. Although relative simple methods based on the similarity between the word frequency vectors of viruses and bacterial hosts have been developed to study virus-host associations, the problem is significantly understudied. We hypothesize that machine learning methods based on word frequencies can be efficiently used to study virus-host infectious associations.

Methods

We investigate four different representations of word frequencies of viral sequences including the relative word frequency and three normalized word frequencies by subtracting the number of expected from the observed word counts. We also study five machine learning methods including logistic regression, support vector machine, random forest, Gaussian naive Bayes and Bernoulli naive Bayes for separating infectious from non-infectious viruses for nine bacterial host genera with at least 45 infecting viruses. Area under the receiver operating characteristic curve (AUC) is used to compare the performance of different machine learning method and feature combinations. We then evaluate the performance of the best method for the identification of the hosts of contigs in metagenomic studies. We also develop a maximum likelihood method to estimate the fraction of true infectious viruses for a given host in viral tagging experiments.

Results

Based on nine bacterial host genera with at least 45 infectious viruses, we show that random forest together with the relative word frequency vector performs the best in identifying viruses infecting particular hosts. For all the nine host genera, the AUC is over 0.85 and for five of them, the AUC is higher than 0.98 when the word size is 6 indicating the high accuracy of using machine learning approaches for identifying viruses infecting particular hosts. We also show that our method can predict the hosts of viral contigs of length at least 1kbps in metagenomic studies with high accuracy. The random forest together with word frequency vector outperforms current available methods based on Manhattan and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$d_{2}^{*}$\end{document}d2∗ dissimilarity measures. Based on word frequencies, we estimate that about 95% of the identified T4-like viruses in viral tagging experiment infect Synechococcus, while only about 29% of the identified non-T4-like viruses and 30% of the contigs in the study potentially infect Synechococcus.

Conclusions

The random forest machine learning method together with the relative word frequencies as features of viruses can be used to predict viruses and viral contigs for specific bacterial hosts. The maximum likelihood approach can be used to estimate the fraction of true infectious associated viruses in viral tagging experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1473-7) contains supplementary material, which is available to authorized users.

Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences

Viruses and their host genomes often share similar oligonucleotide frequency (ONF) patterns, which can be used to predict the host of a given virus by finding the host with the greatest ONF similarity. We comprehensively compared 11 ONF metrics using several k-mer lengths for predicting host taxonomy from among ∼32 000 prokaryotic genomes for 1427 virus isolate genomes whose true hosts are known. The background-subtracting measure [Formula: see text] at k = 6 gave the highest host prediction accuracy (33%, genus level) with reasonable computational times. Requiring a maximum dissimilarity score for making predictions (thresholding) and taking the consensus of the 30 most similar hosts further improved accuracy. Using a previous dataset of 820 bacteriophage and 2699 bacterial genomes, [Formula: see text] host prediction accuracies with thresholding and consensus methods (genus-level: 64%) exceeded previous Euclidian distance ONF (32%) or homology-based (22-62%) methods. When applied to metagenomically-assembled marine SUP05 viruses and the human gut virus crAssphage, [Formula: see text]-based predictions overlapped (i.e. some same, some different) with the previously inferred hosts of these viruses. The extent of overlap improved when only using host genomes or metagenomic contigs from the same habitat or samples as the query viruses. The [Formula: see text] ONF method will greatly improve the characterization of novel, metagenomic viruses.

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.

Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples

Microbial community analysis via high-throughput sequencing of amplified 16S rRNA genes is an essential microbiology tool. We found the popular primer pair 515F (515F-C) and 806R greatly underestimated (e.g. SAR11) or overestimated (e.g. Gammaproteobacteria) common marine taxa. We evaluated marine samples and mock communities (containing 11 or 27 marine 16S clones), showing alternative primers 515F-Y (5'-GTGYCAGCMGCCGCGGTAA) and 926R (5'-CCGYCAATTYMTTTRAGTTT) yield more accurate estimates of mock community abundances, produce longer amplicons that can differentiate taxa unresolvable with 515F-C/806R, and amplify eukaryotic 18S rRNA. Mock communities amplified with 515F-Y/926R yielded closer observed community composition versus expected (r(2)  = 0.95) compared with 515F-Y/806R (r(2)  ∼ 0.5). Unexpectedly, biases with 515F-Y/806R against SAR11 in field samples (∼4-10-fold) were stronger than in mock communities (∼2-fold). Correcting a mismatch to Thaumarchaea in the 515F-C increased their apparent abundance in field samples, but not as much as using 926R rather than 806R. With plankton samples rich in eukaryotic DNA (> 1 μm size fraction), 18S sequences averaged ∼17% of all sequences. A single mismatch can strongly bias amplification, but even perfectly matched primers can exhibit preferential amplification. We show that beyond in silico predictions, testing with mock communities and field samples is important in primer selection.

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.

The ocean sampling day consortium

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits.

A multitrophic model to quantify the effects of marine viruses on microbial food webs and ecosystem processes

Viral lysis of microbial hosts releases organic matter that can then be assimilated by nontargeted microorganisms. Quantitative estimates of virus-mediated recycling of carbon in marine waters, first established in the late 1990s, were originally extrapolated from marine host and virus densities, host carbon content and inferred viral lysis rates. Yet, these estimates did not explicitly incorporate the cascade of complex feedbacks associated with virus-mediated lysis. To evaluate the role of viruses in shaping community structure and ecosystem functioning, we extend dynamic multitrophic ecosystem models to include a virus component, specifically parameterized for processes taking place in the ocean euphotic zone. Crucially, we are able to solve this model analytically, facilitating evaluation of model behavior under many alternative parameterizations. Analyses reveal that the addition of a virus component promotes the emergence of complex communities. In addition, biomass partitioning of the emergent multitrophic community is consistent with well-established empirical norms in the surface oceans. At steady state, ecosystem fluxes can be probed to characterize the effects that viruses have when compared with putative marine surface ecosystems without viruses. The model suggests that ecosystems with viruses will have (1) increased organic matter recycling, (2) reduced transfer to higher trophic levels and (3) increased net primary productivity. These model findings support hypotheses that viruses can have significant stimulatory effects across whole-ecosystem scales. We suggest that existing efforts to predict carbon and nutrient cycling without considering virus effects are likely to miss essential features of marine food webs that regulate global biogeochemical cycles.

Beta diversity of marine bacteria depends on temporal scale

Factors controlling the spatial distribution of bacterial diversity have been intensely studied, whereas less is known about temporal changes. To address this, we tested whether the mechanisms that underlie bacterial temporal beta-diversity vary across different scales in three marine microbial communities. While seasonal turnover was detected, at least 73% of the community variation occurred at intra-seasonal temporal scales, suggesting that episodic events are important in structuring marine microbial communities. In addition, turnover at different temporal scales appeared to be driven by different factors. Intra-seasonal turnover was significantly correlated to environmental variables such as phosphate and silicate concentrations, while seasonal and interannual turnover were related to nitrate concentration and temporal distance. We observed a strong link between the magnitude of environmental variation and bacterial beta-diversity in different communities. Analogous to spatial biogeography, we found different rates of community changes across temporal scales.

Performance of viruses and bacteriophages for fecal source determination in a multi-laboratory, comparative study

An inter-laboratory study of the accuracy of microbial source tracking (MST) methods was conducted using challenge fecal and sewage samples that were spiked into artificial freshwater and provided as unknowns (blind test samples) to the laboratories. The results of the Source Identification Protocol Project (SIPP) are presented in a series of papers that cover 41 MST methods. This contribution details the results of the virus and bacteriophage methods targeting human fecal or sewage contamination. Human viruses used as source identifiers included adenoviruses (HAdV), enteroviruses (EV), norovirus Groups I and II (NoVI and NoVII), and polyomaviruses (HPyVs). Bacteriophages were also employed, including somatic coliphages and F-specific RNA bacteriophages (FRNAPH) as general indicators of fecal contamination. Bacteriophage methods targeting human fecal sources included genotyping of FRNAPH isolates and plaque formation on bacterial hosts Enterococcus faecium MB-55, Bacteroides HB-73 and Bacteroides GB-124. The use of small sample volumes (≤50 ml) resulted in relatively insensitive theoretical limits of detection (10-50 gene copies or plaques × 50 ml(-1)) which, coupled with low virus concentrations in samples, resulted in high false-negative rates, low sensitivity, and low negative predictive values. On the other hand, the specificity of the human virus methods was generally close to 100% and positive predictive values were ∼40-70% with the exception of NoVs, which were not detected. The bacteriophage methods were generally much less specific toward human sewage than virus methods, although FRNAPH II genotyping was relatively successful, with 18% sensitivity and 85% specificity. While the specificity of the human virus methods engenders great confidence in a positive result, better concentration methods and larger sample volumes must be utilized for greater accuracy of negative results, i.e. the prediction that a human contamination source is absent.

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.