Genomic insights into cryptic cycles of microbial hydrocarbon production and degradation in contiguous freshwater and marine microbiomes
Bibliographic record
Abstract
BACKGROUND: Cyanobacteria and eukaryotic phytoplankton produce long-chain alkanes and generate around 100 times greater quantities of hydrocarbons in the ocean compared to natural seeps and anthropogenic sources. Yet, these compounds do not accumulate in the water column, suggesting rapid biodegradation by co-localized microbial populations. Despite their ecological importance, the identities of microbes involved in this cryptic hydrocarbon cycle are mostly unknown. Here, we identified genes encoding enzymes involved in the hydrocarbon cycle across the salinity gradient of a remote, vertically stratified, seawater-containing High Arctic lake that is isolated from anthropogenic petroleum sources and natural seeps. Metagenomic analysis revealed diverse hydrocarbon cycling genes and populations, with patterns of variation along gradients of light, salinity, oxygen, and sulfur that are relevant to freshwater, oceanic, hadal, and anoxic deep sea ecosystems. RESULTS: Analyzing genes and metagenome-assembled genomes down the water column of Lake A in the Canadian High Arctic, we detected microbial hydrocarbon production and degradation pathways at all depths, from surface freshwaters to dark, saline, anoxic waters. In addition to Cyanobacteria, members of the phyla Flavobacteria, Nitrospina, Deltaproteobacteria, Planctomycetes, and Verrucomicrobia had pathways for alkane and alkene production, providing additional sources of biogenic hydrocarbons. Known oil-degrading microorganisms were poorly represented in the system, while long-chain hydrocarbon degradation genes were identified in various freshwater and marine lineages such as Actinobacteria, Schleiferiaceae, and Marinimicrobia. Genes involved in sulfur and nitrogen compound transformations were abundant in hydrocarbon producing and degrading lineages, suggesting strong interconnections with nitrogen and sulfur cycles and a potential for widespread distribution in the ocean. CONCLUSIONS: Our detailed metagenomic analyses across water column gradients in a remote petroleum-free lake derived from the Arctic Ocean suggest that the current estimation of bacterial hydrocarbon production in the ocean could be substantially underestimated by neglecting non-phototrophic production and by not taking low oxygen zones into account. Our findings also suggest that biogenic hydrocarbons may sustain a large fraction of freshwater and oceanic microbiomes, with global biogeochemical implications for carbon, sulfur, and nitrogen cycles. Video Abstract.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".