Marine microbial communities are engines of globally important processes, such as the marine carbon, nitrogen and sulphur cycles. Recent data on the structures of these communities show that they ...adhere to universal biological rules. Co-occurrence patterns can help define species identities, and systems-biology tools are revealing networks of interacting microorganisms. Some microbial systems are found to change predictably, helping us to anticipate how microbial communities and their activities will shift in a changing world.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are ...typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator and predicted maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity; extensions allow estimates from 16S rRNA sequences alone as well as weighted community estimates from metagenomes. We compared the growth rates of cultivated and uncultivated organisms to illustrate how culture collections are strongly biased toward organisms capable of rapid growth. Finally, we found that organisms naturally group into two growth classes and observed a bias in growth predictions for extremely slow-growing organisms. These observations ultimately led us to suggest evolutionary definitions of oligotrophy and copiotrophy based on the selective regime an organism occupies. We found that these growth classes are associated with distinct selective regimes and genomic functional potentials.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Summary
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 (r2 = 0.95) compared with 515F‐Y/806R (r2 ∼ 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.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Free-living and particle-associated marine prokaryotes have physiological, genomic, and phylogenetic differences, yet factors influencing their temporal dynamics remain poorly constrained. In this ...study, we quantify the entire microbial community composition monthly over several years, including viruses, prokaryotes, phytoplankton, and total protists, from the San-Pedro Ocean Time-series using ribosomal RNA sequencing and viral metagenomics. Canonical analyses show that in addition to physicochemical factors, the double-stranded DNA viral community is the strongest factor predicting free-living prokaryotes, explaining 28% of variability, whereas the phytoplankton (via chloroplast 16S rRNA) community is strongest with particle-associated prokaryotes, explaining 31% of variability. Unexpectedly, protist community explains little variability. Our findings suggest that biotic interactions are significant determinants of the temporal dynamics of prokaryotes, and the relative importance of specific interactions varies depending on lifestyles. Also, warming influenced the prokaryotic community, which largely remained oligotrophic summer-like throughout 2014-15, with cyanobacterial populations shifting from cold-water ecotypes to warm-water ecotypes.
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.
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NUK, SBMB, SBNM, UL, UM, UPUK
Marine microbial communities have been essential contributors to global biomass, nutrient cycling, and biodiversity since the early history of Earth, but so far their community distribution patterns ...remain unknown in most marine ecosystems.
The synthesis of 9.6 million bacterial V6-rRNA amplicons for 509 samples that span the global ocean's surface to the deep-sea floor shows that pelagic and benthic communities greatly differ, at all taxonomic levels, and share <10% bacterial types defined at 3% sequence similarity level. Surface and deep water, coastal and open ocean, and anoxic and oxic ecosystems host distinct communities that reflect productivity, land influences and other environmental constraints such as oxygen availability. The high variability of bacterial community composition specific to vent and coastal ecosystems reflects the heterogeneity and dynamic nature of these habitats. Both pelagic and benthic bacterial community distributions correlate with surface water productivity, reflecting the coupling between both realms by particle export. Also, differences in physical mixing may play a fundamental role in the distribution patterns of marine bacteria, as benthic communities showed a higher dissimilarity with increasing distance than pelagic communities.
This first synthesis of global bacterial distribution across different ecosystems of the World's oceans shows remarkable horizontal and vertical large-scale patterns in bacterial communities. This opens interesting perspectives for the definition of biogeographical biomes for bacteria of ocean waters and the seabed.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Recent advances in studying the dynamics of marine microbial communities have shown that the composition of these communities follows predictable patterns and involves complex network interactions, ...which shed light on the underlying processes regulating these globally important organisms. Such 'holistic' (or organism- and system-based) studies of these communities complement popular reductionist, often culture-based, approaches for understanding organism function one gene or protein at a time. In this Review, we summarize our current understanding of marine microbial community dynamics at various scales, from hours to decades. We also explain how the data illustrate community resilience and seasonality, and reveal interactions among microorganisms.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
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.
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.
The software is available at https://github.com/younglululu/COCACOLA .
fsun@usc.edu.
Supplementary data are available at Bioinformatics online.
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.
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.
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.
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.
Marine phytoplankton perform approximately half of global carbon fixation, with their blooms contributing disproportionately to carbon sequestration(1), and most phytoplankton production is ...ultimately consumed by heterotrophic prokaryotes(2). Therefore, phytoplankton and heterotrophic community dynamics are important in modelling carbon cycling and the impacts of global change(3). In a typical bloom, diatoms dominate initially, transitioning over several weeks to smaller and motile phytoplankton(4). Here, we show unexpected, rapid community variation from daily rRNA analysis of phytoplankton and prokaryotic community members following a bloom off southern California. Analysis of phytoplankton chloroplast 16S rRNA demonstrated ten different dominant phytoplankton over 18 days alone, including four taxa with animal toxin-producing strains. The dominant diatoms, flagellates and picophytoplankton varied dramatically in carbon export potential. Dominant prokaryotes also varied rapidly. Euryarchaea briefly became the most abundant organism, peaking over a few days to account for about 40% of prokaryotes. Phytoplankton and prokaryotic communities correlated better with each other than with environmental parameters. Extending beyond the traditional view of blooms being controlled primarily by physics and inorganic nutrients, these dynamics imply highly heterogeneous, continually changing conditions over time and/or space and suggest that interactions among microorganisms are critical in controlling plankton diversity, dynamics and fates.
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