Distinct Oceanic Microbiomes From Viruses to Protists Located Near the Antarctic Circumpolar Current.

Microbes occupy diverse ecological niches and only through recent advances in next generation sequencing technologies have the true microbial diversity been revealed. Furthermore, lack of perceivable marine barriers to genetic dispersal (i.e., mountains or islands) has allowed the speculation that organisms that can be easily transported by currents and therefore proliferate everywhere. That said, ocean currents are now commonly being recognized as barriers for microbial dispersal. Here we analyzed samples collected from a total of six stations, four located in the Indian Ocean, and two in the Southern Ocean. Amplicon sequencing was used to characterize both prokaryotic and eukaryotic plankton communities, while shotgun sequencing was used for the combined environmental DNA (eDNA), microbial eDNA (meDNA), and viral fractions. We found that Cyanobacteria dominated the prokaryotic component in the South-West Indian Ocean, while γ-Proteobacteria dominated the South-East Indian Ocean. A combination of γ- and α-Proteobacteria dominated the Southern Ocean. Alveolates dominated almost exclusively the eukaryotic component, with variation in the ratio of Protoalveolata and Dinoflagellata depending on station. However, an increase in haptophyte relative abundance was observed in the Southern Ocean. Similarly, the viral fraction was dominated by members of the order Caudovirales across all stations; however, a higher presence of nucleocytoplasmic large DNA viruses (mainly chloroviruses and mimiviruses) was observed in the Southern Ocean. To our knowledge, this is the first that a statistical difference in the microbiome (from viruses to protists) between the subtropical Indian and Southern Oceans. We also show that not all phylotypes can be found everywhere, and that meDNA is not a suitable resource for monitoring aquatic microbial diversity.

Phaeoviral Infections Are Present in Macrocystis, Ecklonia and Undaria (Laminariales) and Are Influenced by Wave Exposure in Ectocarpales.

Two sister orders of the brown macroalgae (class Phaeophyceae), the morphologically complex Laminariales (commonly referred to as kelp) and the morphologically simple Ectocarpales are natural hosts for the dsDNA phaeoviruses (family Phycodnaviridae) that persist as proviruses in the genomes of their hosts. We have previously shown that the major capsid protein (MCP) and DNA polymerase concatenated gene phylogeny splits phaeoviruses into two subgroups, A and B (both infecting Ectocarpales), while MCP-based phylogeny suggests that the kelp phaeoviruses form a distinct third subgroup C. Here we used MCP to better understand the host range of phaeoviruses by screening a further 96 and 909 samples representing 11 and 3 species of kelp and Ectocarpales, respectively. Sporophyte kelp samples were collected from their various natural coastal habitats spanning five continents: Africa, Asia, Australia, Europe, and South America. Our phylogenetic analyses showed that while most of the kelp phaeoviruses, including one from Macrocystispyrifera, belonged to the previously designated subgroup C, new lineages of Phaeovirus in 3 kelp species, Ecklonia maxima, Ecklonia radiata, Undaria pinnatifida, grouped instead with subgroup A. In addition, we observed a prevalence of 26% and 63% in kelp and Ectocarpales, respectively. Although not common, multiple phaeoviral infections per individual were observed, with the Ectocarpales having both intra- and inter-subgroup phaeoviral infections. Only intra-subgroup phaeoviral infections were observed in kelp. Furthermore, prevalence of phaeoviral infections within the Ectocarpales is also linked to their exposure to waves. We conclude that phaeoviral infection is a widely occurring phenomenon in both lineages, and that phaeoviruses have diversified with their hosts at least since the divergence of the Laminariales and Ectocarpales.

A Pelagic Microbiome (Viruses to Protists) from a Small Cup of Seawater.

The aquatic microbiome is composed of a multi-phylotype community of microbes, ranging from the numerically dominant viruses to the phylogenetically diverse unicellular phytoplankton. They influence key biogeochemical processes and form the base of marine food webs, becoming food for secondary consumers. Due to recent advances in next-generation sequencing, this previously overlooked component of our hydrosphere is starting to reveal its true diversity and biological complexity. We report here that 250 mL of seawater is sufficient to provide a comprehensive description of the microbial diversity in an oceanic environment. We found that there was a dominance of the order Caudovirales (59%), with the family Myoviridae being the most prevalent. The families Phycodnaviridae and Mimiviridae made up the remainder of pelagic double-stranded DNA (dsDNA) virome. Consistent with this analysis, the Cyanobacteria dominate (52%) the prokaryotic diversity. While the dinoflagellates and their endosymbionts, the superphylum Alveolata dominates (92%) the microbial eukaryotic diversity. A total of 834 prokaryotic, 346 eukaryotic and 254 unique virus phylotypes were recorded in this relatively small sample of water. We also provide evidence, through a metagenomic-barcoding comparative analysis, that viruses are the likely source of microbial environmental DNA (meDNA). This study opens the door to a more integrated approach to oceanographic sampling and data analysis.

Choice of molecular barcode will affect species prevalence but not bacterial community composition.

The rapid advancement of next generation sequencing protocols in recent years has led to the diversification in the methods used to study microbial communities; however, how comparable the data generated from these different methods are, remains unclear. In this study we compared the taxonomic composition and seasonal dynamics of the bacterial community determined by two distinct 16s amplicon sequencing protocols: sequencing of the V6 region of the 16s rRNA gene using 454 pyrosequencing vs the V4 region of the 16s rRNA gene using the Illumina Hiseq 2500 platform. Significant differences between relative abundances at all taxonomic levels were observed; however, their seasonal dynamics between phyla were largely consistent between methods. This study highlights that care must be taken when comparing datasets generated from different methods.

The impact of sampling, PCR, and sequencing replication on discerning changes in drinking water bacterial community over diurnal time-scales.

High-throughput and deep DNA sequencing, particularly amplicon sequencing, is being increasingly utilized to reveal spatial and temporal dynamics of bacterial communities in drinking water systems. Whilst the sampling and methodological biases associated with PCR and sequencing have been studied in other environments, they have not been quantified for drinking water. These biases are likely to have the greatest effect on the ability to characterize subtle spatio-temporal patterns influenced by process/environmental conditions. In such cases, intra-sample variability may swamp any underlying small, systematic variation. To evaluate this, we undertook a study with replication at multiple levels including sampling sites, sample collection, PCR amplification, and high throughput sequencing of 16S rRNA amplicons. The variability inherent to the PCR amplification and sequencing steps is significant enough to mask differences between bacterial communities from replicate samples. This was largely driven by greater variability in detection of rare bacteria (relative abundance <0.01%) across PCR/sequencing replicates as compared to replicate samples. Despite this, we captured significant changes in bacterial community over diurnal time-scales and find that the extent and pattern of diurnal changes is specific to each sampling location. Further, we find diurnal changes in bacterial community arise due to differences in the presence/absence of the low abundance bacteria and changes in the relative abundance of dominant bacteria. Finally, we show that bacterial community composition is significantly different across sampling sites for time-periods during which there are typically rapid changes in water use. This suggests hydraulic changes (driven by changes in water demand) contribute to shaping the bacterial community in bulk drinking water over diurnal time-scales.

Probabilistic models to describe the dynamics of migrating microbial communities.

In all but the most sterile environments bacteria will reside in fluid being transported through conduits and some of these will attach and grow as biofilms on the conduit walls. The concentration and diversity of bacteria in the fluid at the point of delivery will be a mix of those when it entered the conduit and those that have become entrained into the flow due to seeding from biofilms. Examples include fluids through conduits such as drinking water pipe networks, endotracheal tubes, catheters and ventilation systems. Here we present two probabilistic models to describe changes in the composition of bulk fluid microbial communities as they are transported through a conduit whilst exposed to biofilm communities. The first (discrete) model simulates absolute numbers of individual cells, whereas the other (continuous) model simulates the relative abundance of taxa in the bulk fluid. The discrete model is founded on a birth-death process whereby the community changes one individual at a time and the numbers of cells in the system can vary. The continuous model is a stochastic differential equation derived from the discrete model and can also accommodate changes in the carrying capacity of the bulk fluid. These models provide a novel Lagrangian framework to investigate and predict the dynamics of migrating microbial communities. In this paper we compare the two models, discuss their merits, possible applications and present simulation results in the context of drinking water distribution systems. Our results provide novel insight into the effects of stochastic dynamics on the composition of non-stationary microbial communities that are exposed to biofilms and provides a new avenue for modelling microbial dynamics in systems where fluids are being transported.

Spatial-temporal survey and occupancy-abundance modeling to predict bacterial community dynamics in the drinking water microbiome.

UNLABELLED: Bacterial communities migrate continuously from the drinking water treatment plant through the drinking water distribution system and into our built environment. Understanding bacterial dynamics in the distribution system is critical to ensuring that safe drinking water is being supplied to customers. We present a 15-month survey of bacterial community dynamics in the drinking water system of Ann Arbor, MI. By sampling the water leaving the treatment plant and at nine points in the distribution system, we show that the bacterial community spatial dynamics of distance decay and dispersivity conform to the layout of the drinking water distribution system. However, the patterns in spatial dynamics were weaker than those for the temporal trends, which exhibited seasonal cycling correlating with temperature and source water use patterns and also demonstrated reproducibility on an annual time scale. The temporal trends were driven by two seasonal bacterial clusters consisting of multiple taxa with different networks of association within the larger drinking water bacterial community. Finally, we show that the Ann Arbor data set robustly conforms to previously described interspecific occupancy abundance models that link the relative abundance of a taxon to the frequency of its detection. Relying on these insights, we propose a predictive framework for microbial management in drinking water systems. Further, we recommend that long-term microbial observatories that collect high-resolution, spatially distributed, multiyear time series of community composition and environmental variables be established to enable the development and testing of the predictive framework. IMPORTANCE: Safe and regulation-compliant drinking water may contain up to millions of microorganisms per liter, representing phylogenetically diverse groups of bacteria, archaea, and eukarya that affect public health, water infrastructure, and the aesthetic quality of water. The ability to predict the dynamics of the drinking water microbiome will ensure that microbial contamination risks can be better managed. Through a spatial-temporal survey of drinking water bacterial communities, we present novel insights into their spatial and temporal community dynamics and recommend steps to link these insights in a predictive framework for microbial management of drinking water systems. Such a predictive framework will not only help to eliminate microbial risks but also help to modify existing water quality monitoring efforts and make them more resource efficient. Further, a predictive framework for microbial management will be critical if we are to fully anticipate the risks and benefits of the beneficial manipulation of the drinking water microbiome.