Diane Y. Kim

Marine bacterial, archaeal and protistan association networks reveal ecological linkages

Microbes have central roles in ocean food webs and global biogeochemical processes, yet specific ecological relationships among these taxa are largely unknown. This is in part due to the dilute, microscopic nature of the planktonic microbial community, which prevents direct observation of their interactions. Here, we use a holistic (that is, microbial system-wide) approach to investigate time-dependent variations among taxa from all three domains of life in a marine microbial community. We investigated the community composition of bacteria, archaea and protists through cultivation-independent methods, along with total bacterial and viral abundance, and physico-chemical observations. Samples and observations were collected monthly over 3 years at a well-described ocean time-series site of southern California. To find associations among these organisms, we calculated time-dependent rank correlations (that is, local similarity correlations) among relative abundances of bacteria, archaea, protists, total abundance of bacteria and viruses and physico-chemical parameters. We used a network generated from these statistical correlations to visualize and identify time-dependent associations among ecologically important taxa, for example, the SAR11 cluster, stramenopiles, alveolates, cyanobacteria and ammonia-oxidizing archaea. Negative correlations, perhaps suggesting competition or predation, were also common. The analysis revealed a progression of microbial communities through time, and also a group of unknown eukaryotes that were highly correlated with dinoflagellates, indicating possible symbioses or parasitism. Possible ‘keystone’ species were evident. The network has statistical features similar to previously described ecological networks, and in network parlance has non-random, small world properties (that is, highly interconnected nodes). This approach provides new insights into the natural history of microbes.

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.

Investigating Microbial Eukaryotic Diversity from a Global Census: Insights from a Comparison of Pyrotag and Full-Length Sequences of 18S rRNA Genes

ABSTRACT Next-generation DNA sequencing (NGS) approaches are rapidly surpassing Sanger sequencing for characterizing the diversity of natural microbial communities. Despite this rapid transition, few comparisons exist between Sanger sequences and the generally much shorter reads of NGS. Operational taxonomic units (OTUs) derived from full-length (Sanger sequencing) and pyrotag (454 sequencing of the V9 hypervariable region) sequences of 18S rRNA genes from 10 global samples were analyzed in order to compare the resulting protistan community structures and species richness. Pyrotag OTUs called at 98% sequence similarity yielded numbers of OTUs that were similar overall to those for full-length sequences when the latter were called at 97% similarity. Singleton OTUs strongly influenced estimates of species richness but not the higher-level taxonomic composition of the community. The pyrotag and full-length sequence data sets had slightly different taxonomic compositions of rhizarians, stramenopiles, cryptophytes, and haptophytes, but the two data sets had similarly high compositions of alveolates. Pyrotag-based OTUs were often derived from sequences that mapped to multiple full-length OTUs at 100% similarity. Thus, pyrotags sequenced from a single hypervariable region might not be appropriate for establishing protistan species-level OTUs. However, nonmetric multidimensional scaling plots constructed with the two data sets yielded similar clusters, indicating that beta diversity analysis results were similar for the Sanger and NGS sequences. Short pyrotag sequences can provide holistic assessments of protistan communities, although care must be taken in interpreting the results. The longer reads (>500 bp) that are now becoming available through NGS should provide powerful tools for assessing the diversity of microbial eukaryotic assemblages.

Monthly to interannual variability of microbial eukaryote assemblages at four depths in the eastern North Pacific

The monthly, seasonal and interannual variability of microbial eukaryote assemblages were examined at 5 m, the deep chlorophyll maximum, 150 m and 500 m at the San Pedro Ocean Time-series station (eastern North Pacific). The depths spanned transitions in temperature, light, nutrients and oxygen, and included a persistently hypoxic environment at 500 m. Terminal restriction fragment length polymorphism was used for the analysis of 237 samples that were collected between September 2000 and December 2010. Spatiotemporal variability patterns of microeukaryote assemblages indicated the presence of distinct shallow and deep communities at the SPOT station, presumably reflecting taxa that were specifically adapted for the conditions in those environments. Community similarity values between assemblages collected 1 month apart at each depth ranged between ∼20% and ∼84% (averages were ∼50–59%). The assemblage at 5 m was temporally more dynamic than deeper assemblages and also displayed substantial interannual variability during the first ∼3 years of the study. Evidence of seasonality was detected for the microbial eukaryote assemblage at 5 m between January 2008 and December 2010 and at 150 m between September 2000 and December 2003. Seasonality was not detected for assemblages at the deep chlorophyll a maximum, which varied in depth seasonally, or at 500 m. Microbial eukaryote assemblages exhibited cyclical patterns in at least 1 year at each depth, implying an annual resetting of communities. Substantial interannual variability was detected for assemblages at all depths and represented the largest source of temporal variability in this temperate coastal ecosystem.

Estimating Protistan Diversity Using High‐Throughput Sequencing

Sequencing hypervariable regions from the 18S rRNA gene is commonly employed to characterize protistan biodiversity, yet there are concerns that short reads do not provide the same taxonomic resolution as full‐length sequences. A total of 7,432 full‐length sequences were used to perform an in silico analysis of how sequences of various lengths and target regions impact downstream ecological interpretations. Sequences that were longer than 400 nucleotides and included the V4 hypervariable region generated results similar to those derived from full‐length 18S rRNA gene sequences. Present high‐throughput sequencing capabilities are approaching protistan diversity estimation comparable to whole gene sequences.

A combined sequence-based and fragment-based characterization of microbial eukaryote assemblages provides taxonomic context for the Terminal Restriction Fragment Length Polymorphism (T-RFLP) method

Microbial eukaryotes in seawater samples collected from two depths (5 m and 500 m) at the USC Microbial Observatory off the coast of Southern California, USA, were characterized by cloning and sequencing of 18S rRNA genes, as well as DNA fragment analysis of these genes. The sequenced genes were assigned to operational taxonomic units (OTUs), and taxonomic information for the sequence-based OTUs was obtained by comparison to public sequence databases. The sequences were then subjected to in silico digestion to predict fragment sizes, and that information was compared to the results of the T-RFLP method applied to the same samples in order to provide taxonomic context for the environmental T-RFLP fragments. A total of 663 and 678 sequences were analyzed for the 5m and 500 m samples, respectively, which clustered into 157 OTUs and 183 OTUs. The sequences yielded substantially fewer taxonomic units as in silico fragment lengths (i.e., following in silico digestion), and the environmental T-RFLP resulted in the fewest unique OTUs (unique fragments). Bray-Curtis similarity analysis of protistan assemblages was greater using the T-RFLP dataset compared to the sequence-based OTU dataset, presumably due to the inability of the fragment method to differentiate some taxa and an inability to detect many rare taxa relative to the sequence-based approach. Nonetheless, fragments in our analysis generally represented the dominant sequence-based OTUs and putative identifications could be assigned to a majority of the fragments in the environmental T-RFLP results. Our empirical examination of the T-RFLP method identified limitations relative to sequence-based community analysis, but the relative ease and low cost of fragment analysis make this method a useful approach for characterizing the dominant taxa within complex assemblages of microbial eukaryotes in large datasets.