Genomic capacities for Reactive Oxygen Species metabolism across marine phytoplankton
Bibliographic record
Abstract
Marine phytoplankton produce and scavenge Reactive Oxygen Species, to support cellular processes, while limiting damaging reactions. Some prokaryotic picophytoplankton have, however, lost all genes encoding scavenging of hydrogen peroxide. Such losses of metabolic function can only apply to Reactive Oxygen Species which potentially traverse the cell membrane outwards, before provoking damaging intracellular reactions. We hypothesized that cell radius influences which elements of Reactive Oxygen Species metabolism are partially or fully dispensable from a cell. We therefore investigated genomes and transcriptomes from diverse marine eukaryotic phytoplankton, ranging from 0.4 to 44 μm radius, to analyze the genomic allocations encoding enzymes metabolizing Reactive Oxygen Species. Superoxide has high reactivity, short lifetimes and limited membrane permeability. Genes encoding superoxide scavenging are ubiquitous across phytoplankton, but the fractional gene allocation decreased with increasing cell radius, consistent with a nearly fixed set of core genes for scavenging superoxide pools. Hydrogen peroxide has lower reactivity, longer intracellular and extracellular lifetimes and readily crosses cell membranes. Genomic allocations to both hydrogen peroxide production and scavenging decrease with increasing cell radius. Nitric Oxide has low reactivity, long intracellular and extracellular lifetimes and readily crosses cell membranes. Neither Nitric Oxide production nor scavenging genomic allocations changed with increasing cell radius. Many taxa, however, lack the genomic capacity for nitric oxide production or scavenging. The probability of presence of capacity to produce nitric oxide decreases with increasing cell size, and is influenced by flagella and colony formation. In contrast, the probability of presence of capacity to scavenge nitric oxide increases with increasing cell size, and is again influenced by flagella and colony formation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".