Bacterial ecology and evolution converge on seasonal and decadal scales.

Ecology and evolution are distinct theories, but the short lifespans and large population sizes of microbes allow evolution to unfold along contemporary ecological time scales. To document this in a natural system, we collected a two-decade, 471-metagenome time series from a single site in a freshwater lake, which we refer to as the TYMEFLIES dataset. This massive sampling and sequencing effort resulted in the reconstruction of 30,389 metagenomic-assembled genomes (MAGs) over 50% complete, which dereplicated into 2,855 distinct genomes (>96% nucleotide sequence identity). We found both ecological and evolutionary processes occurred at seasonal time scales. There were recurring annual patterns at the species level in abundances, nucleotide diversities (π), and single nucleotide variant (SNV) profiles for the majority of all taxa. During annual blooms, we observed both higher and lower nucleotide diversity, indicating that both ecological differentiation and competition drove evolutionary dynamics. Overlayed upon seasonal patterns, we observed long-term change in 20% of the species' SNV profiles including gradual changes, step changes, and disturbances followed by resilience. Most abrupt changes occurred in a single species, suggesting evolutionary drivers are highly specific. Nevertheless, seven members of the abundant Nanopelagicaceae family experienced abrupt change in 2012, an unusually hot and dry year. This shift coincided with increased numbers of genes under selection involved in amino acid and nucleic acid metabolism, suggesting fundamental organic nitrogen compounds drive strain differentiation in the most globally abundant freshwater family. Overall, we observed seasonal and decadal trends in both interspecific ecological and intraspecific evolutionary processes. The convergence of microbial ecology and evolution on the same time scales demonstrates that understanding microbiomes requires a new unified approach that views ecology and evolution as a single continuum.

Exploring novel alkane-degradation pathways in uncultured bacteria from the North Atlantic Ocean.

IMPORTANCE: Petroleum pollution in the ocean has increased because of rapid population growth and modernization, requiring urgent remediation. Our understanding of the metabolic response of native microbial communities to oil spills is not well understood. Here, we explored the baseline hydrocarbon-degrading communities of a subarctic Atlantic region to uncover the metabolic potential of the bacteria that inhabit the surface and subsurface water. We conducted enrichments with a 13C-labeled hydrocarbon to capture the fraction of the community actively using the hydrocarbon. We then combined this approach with metagenomics to identify the metabolic potential of this hydrocarbon-degrading community. This revealed previously undescribed uncultured bacteria with unique metabolic mechanisms involved in aerobic hydrocarbon degradation, indicating that temperature may be pivotal in structuring hydrocarbon-degrading baseline communities. Our findings highlight gaps in our understanding of the metabolic complexity of hydrocarbon degradation by native marine microbial communities.

Species invasions shift microbial phenology in a two-decade freshwater time series.

Invasive species impart abrupt changes on ecosystems, but their impacts on microbial communities are often overlooked. We paired a 20 y freshwater microbial community time series with zooplankton and phytoplankton counts, rich environmental data, and a 6 y cyanotoxin time series. We observed strong microbial phenological patterns that were disrupted by the invasions of spiny water flea (Bythotrephes cederströmii) and zebra mussels (Dreissena polymorpha). First, we detected shifts in Cyanobacteria phenology. After the spiny water flea invasion, Cyanobacteria dominance crept earlier into clearwater; and after the zebra mussel invasion, Cyanobacteria abundance crept even earlier into the diatom-dominated spring. During summer, the spiny water flea invasion sparked a cascade of shifting diversity where zooplankton diversity decreased and Cyanobacteria diversity increased. Second, we detected shifts in cyanotoxin phenology. After the zebra mussel invasion, microcystin increased in early summer and the duration of toxin production increased by over a month. Third, we observed shifts in heterotrophic bacteria phenology. The Bacteroidota phylum and members of the acI Nanopelagicales lineage were differentially more abundant. The proportion of the bacterial community that changed differed by season; spring and clearwater communities changed most following the spiny water flea invasion that lessened clearwater intensity, while summer communities changed least following the zebra mussel invasion despite the shifts in Cyanobacteria diversity and toxicity. A modeling framework identified the invasions as primary drivers of the observed phenological changes. These long-term invasion-mediated shifts in microbial phenology demonstrate the interconnectedness of microbes with the broader food web and their susceptibility to long-term environmental change.

Freshwater carbon and nutrient cycles revealed through reconstructed population genomes.

Although microbes mediate much of the biogeochemical cycling in freshwater, the categories of carbon and nutrients currently used in models of freshwater biogeochemical cycling are too broad to be relevant on a microbial scale. One way to improve these models is to incorporate microbial data. Here, we analyze both genes and genomes from three metagenomic time series and propose specific roles for microbial taxa in freshwater biogeochemical cycles. Our metagenomic time series span multiple years and originate from a eutrophic lake (Lake Mendota) and a humic lake (Trout Bog Lake) with contrasting water chemistry. Our analysis highlights the role of polyamines in the nitrogen cycle, the diversity of diazotrophs between lake types, the balance of assimilatory vs. dissimilatory sulfate reduction in freshwater, the various associations between types of phototrophy and carbon fixation, and the density and diversity of glycoside hydrolases in freshwater microbes. We also investigated aspects of central metabolism such as hydrogen metabolism, oxidative phosphorylation, methylotrophy, and sugar degradation. Finally, by analyzing the dynamics over time in nitrogen fixation genes and Cyanobacteria genomes, we show that the potential for nitrogen fixation is linked to specific populations in Lake Mendota. This work represents an important step towards incorporating microbial data into ecosystem models and provides a better understanding of how microbes may participate in freshwater biogeochemical cycling.

TaxAss: Leveraging a Custom Freshwater Database 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 data sets using the comprehensive SILVA database and the freshwater-specific FreshTrain database. TaxAss increased the percentage of the data set classified compared to using only SILVA, especially at fine-resolution family to species taxon levels, while across the freshwater test data sets classifications increased by as much as 11 to 40% of total reads. A similar increase in classifications was not observed in a control mouse gut data set, which was not expected to contain freshwater bacteria. TaxAss also maintained taxonomic richness compared to using only the FreshTrain across all taxon levels from phylum to species. Without TaxAss, most organisms not represented in the FreshTrain were unclassified, but at fine taxon levels, incorrect classifications became significant. We validated TaxAss using simulated amplicon data derived from full-length clone libraries and found that 96 to 99% of test sequences were correctly classified at fine resolution. TaxAss splits a data set's 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 is available at https://www.github.com/McMahonLab/TaxAssIMPORTANCE Microbial communities drive ecosystem processes, but microbial community composition analyses using 16S rRNA gene amplicon data sets are limited by the lack of fine-resolution taxonomy classifications. Coarse taxonomic groupings at the phylum, class, and order levels 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 data set dependent and cannot be compared between data sets. 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 data sets.

Ananke: temporal clustering reveals ecological dynamics of microbial communities.

Taxonomic markers such as the 16S ribosomal RNA gene are widely used in microbial community analysis. A common first step in marker-gene analysis is grouping genes into clusters to reduce data sets to a more manageable size and potentially mitigate the effects of sequencing error. Instead of clustering based on sequence identity, marker-gene data sets collected over time can be clustered based on temporal correlation to reveal ecologically meaningful associations. We present Ananke, a free and open-source algorithm and software package that complements existing sequence-identity-based clustering approaches by clustering marker-gene data based on time-series profiles and provides interactive visualization of clusters, including highlighting of internal OTU inconsistencies. Ananke is able to cluster distinct temporal patterns from simulations of multiple ecological patterns, such as periodic seasonal dynamics and organism appearances/disappearances. We apply our algorithm to two longitudinal marker gene data sets: faecal communities from the human gut of an individual sampled over one year, and communities from a freshwater lake sampled over eleven years. Within the gut, the segregation of the bacterial community around a food-poisoning event was immediately clear. In the freshwater lake, we found that high sequence identity between marker genes does not guarantee similar temporal dynamics, and Ananke time-series clusters revealed patterns obscured by clustering based on sequence identity or taxonomy. Ananke is free and open-source software available at https://github.com/beiko-lab/ananke.