A multi-omics approach to microbial nitrogen and sulfur cycling in the oxygen starved ocean
Bibliographic record
Abstract
Microbial communities mediate biogeochemical processes of Carbon (C), Nitrogen (N) and Sulfur (S) cycling in the ocean on global scales. Oxygen (O₂) availability is a key driver in these processes and shapes microbial community structure and metabolisms. As O₂ decreases, microbes utilize alternative terminal electron acceptors, nitrate (NO₃–), nitrite, sulfate and carbon dioxide, depleting biologically available nitrogen and producing greenhouse gases nitrous oxide (N₂O) and methane (CH₄). Marine oxygen minimum zones (OMZs) are areas of O₂-depletion (O₂ < 20µM) in sub-surface waters due to the respiration of organic matter from the surface. In areas of acute O₂-depletion or where OMZs contact underlying sediments, hydrogen sulfide (H₂S) and CH₄ accumulate within OMZ waters, drastically altering microbial community structure and metabolism. In this thesis, I explore microbial cycles along defined gradients of O₂, NO₃- and H₂S in Saanich Inlet, a seasonally anoxic fjord on the coast of British Columbia Canada. I develop a time-resolved multi-omic dataset consisting of small subunit ribosomal RNA amplicon sequences, single cell amplified genomes (SAGs), metagenomes, -transcriptomes and -proteomes, coupled with geochemical measurements, enabling robust microbial metabolic reconstruction at the individual, population and community levels of organization. Using metaproteomics, I construct a conceptual model of metabolic interactions involving N and S cycling, and carbon fixation, forming the basis for a collaborative effort to build a gene-centric numerical model, identifying an unrecognized niche for N₂O reduction. Using single cell amplified genomes (SAGs) from Saanich Inlet, I identify genes for N₂O reduction, nosZ, within the dark matter phylum Marinimicrobia clade SHBH1141, filling the proposed niche of non-denitrifying N₂O-reducers. Using globally sourced Marinimicrobia SAGs, I further analyze energy metabolism and biogeography of several Marinimicrobia clades, revealing roles in C, N and S cycling along eco-thermodynamic gradients throughout the ocean. Finally, I chart the global abundance and distribution of nosZ genes and transcripts within the ocean, identifying previously unappreciated potential sinks for N₂O. As OMZs continue to expand and intensify due to climate change, defining metabolic processes and interactions along gradients of O₂-depletion becomes increasingly important. This thesis provides foundational knowledge related to the microbial communities driving coupled biogeochemical cycling in OMZs. [This dissertation was updated to include a missing chapter on 2018-10-05.]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".