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Newton, R. J.

Publications and source records attributed to Newton, R. J..

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Re-evaluating the salty divide: phylogenetic specificity of transitions between marine and freshwater systems

Marine and freshwater microbial communities are phylogenetically distinct and transitions between habitat types are thought to be infrequent. We compared the phylogenetic diversity of marine and freshwater microorganisms and identified specific lineages exhibiting notably high or low similarity between marine and freshwater ecosystems using a meta-analysis of 16S rRNA gene tag-sequencing datasets. As expected, marine and freshwater microbial communities differed in the relative abundance of major phyla and contained habitat-specific lineages; at the same time, however, many shared taxa were observed in both environments. Betaproteobacteria and Alphaproteobacteria sequences had the highest similarity between marine and freshwater sample pairs. Gammaproteobacteria and Alphaproteobacteria contained the highest number of Minimum Entropy Decomposition nodes shared by marine and freshwater samples. Shared nodes included lineages of the abundant alphaproteobacterial group SAR11 that have not previously been reported in 16S rRNA gene surveys of freshwater lakes. Our results suggest that shared taxa are numerous, but tend to occur sporadically and at low relative abundance in one habitat type, leading to an underestimation of transition frequency between marine and freshwater habitats. Lineages with a high degree of shared taxa or habitat-specific diversification represent targets for genome-scale investigations into microbial adaptations and evolutionary innovations. Rare taxa with abundances near or below detection, including lineages that appear to have crossed the salty divide relatively recently, may have novel adaptations enabling them to exploit opportunities for niche expansion when environments are disturbed or conditions change.\n\nImportanceThe distribution of microbial diversity across environments yields insight into processes that create and maintain this diversity as well as potential to infer how communities will respond to future environmental changes. We integrated datasets from dozens of freshwater lake and marine samples to compare diversity across open water habitats differing in salinity. Our novel combination of sequence-based approaches revealed phyla and proteobacterial classes inferred to include more or less recent transitions across habitat types as well as specific lineages that are shared by marine and freshwater environments at the level of 16S rRNA sequence types. Our findings contribute to understanding the ecological and evolutionary controls on microbial distributions, and open up new questions regarding the plasticity and adaptability of particular lineages.

microbiology

TaxAss: Leveraging Custom Databases Achieves Fine-Scale Taxonomic Resolution

Taxonomy assignment of freshwater microbial communities is limited by the minimally curated phylogenies used for large taxonomy databases. Here we introduce TaxAss, a taxonomy assignment workflow that classifies 16S rRNA gene amplicon data using two taxonomy reference databases: a large comprehensive database and a small ecosystem-specific database rigorously curated by scientists within a field. We applied TaxAss to five different freshwater datasets using the comprehensive Silva database and the freshwater-specific FreshTrain database. TaxAss increased the percent of the dataset classified compared to using only Silva, especially at fine-resolution family-species taxa levels, while across the freshwater test-datasets classifications increased by as much as 11-40 percent of total reads. A similar increase in classifications was not observed in a control mouse gut dataset, which was not expected to contain freshwater bacteria. TaxAss also maintained taxonomic richness compared to using only the FreshTrain across all taxa-levels from phylum to species. Without TaxAss, most organisms not represented in the FreshTrain were unclassified, but at fine taxa levels incorrect classifications became significant. We validated TaxAss using simulated amplicon data with known taxonomy and found that 96-99% of test sequences were correctly classified at fine resolution. TaxAss splits a datasets sequences into two groups based on their percent identity to reference sequences in the ecosystem-specific database. Sequences with high similarity to sequences in the ecosystem-specific database are classified using that database, and the others are classified using the comprehensive database. TaxAss is free and open source, and available at www.github.com/McMahonLab/TaxAss.\n\nIMPORTANCEMicrobial communities drive ecosystem processes, but microbial community composition analyses using 16S rRNA gene amplicon datasets are limited by the lack of fine-resolution taxonomy classifications. Coarse taxonomic groupings at phylum, class, and order level lump ecologically distinct organisms together. To avoid this, many researchers define operational taxonomic units (OTUs) based on clustered sequences, sequence variants, or unique sequences. These fine-resolution groupings are more ecologically relevant, but OTU definitions are dataset-dependent and cannot be compared between datasets. Microbial ecologists studying freshwater have curated a small, ecosystem-specific taxonomy database to provide consistent and up-to-date terminology. We created TaxAss, a workflow that leverages this database to assign taxonomy. We found that TaxAss improves fine-resolution taxonomic classifications (family, genus and species). Fine taxonomic groupings are more ecologically relevant, so they provide an alternative to OTU-based analyses that is consistent and comparable between datasets.

bioinformatics