Proteomic profiling unveils compensatory physiological mechanisms of an annelid living across a natural persistent deoxygenation gradient
Bibliographic record
Abstract
Dissolved oxygen is a major environmental driver in aquatic environments, and its decline in the global ocean over recent decades threatens marine fauna, particularly benthic invertebrates. These organisms, often sessile or sedentary, cannot escape persistent environmental hypoxia and must rely on the adjustment of physiological mechanisms, such as energy metabolism and cell functioning pathways, underpinning their ability to cope with these challenging conditions. However, the molecular bases of such mechanisms, particularly under in situ conditions, are yet poorly understood. Here, we characterised the proteomic profile of the annelid Neoleanira tetragona, a species widespread in the North Atlantic Ocean, across the permanent deoxygenation gradient of the Estuary and Gulf of St. Lawrence (EGSL). Specifically, whole specimens were collected from four regions of the EGSL deoxygenation gradient and were analysed using high-resolution LC-MS/MS with a shotgun proteomics approach. Region pairwise comparisons through linear models (LIMMA) showed no differentially abundant proteins, but generalised linear latent variables models identified 59 proteins with differential abundance linked to environmental oxygen and/or food availability. An overrepresentation of tricarboxylic acid cycle via citrate synthase activity was supported in response to low oxygen and high food availability for the annelid. Our results suggest that N. tetragona possesses a compensatory mechanism to cope with the in situ persistent deoxygenation, which involves the accumulation of key proteins that are responsible for maintaining steady energy metabolism under in situ persistent deoxygenation. Our findings contribute to shed light on physiological strategies that benthic marine invertebrates can employ to cope with ongoing and future environmental challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".