Bacterial neuraminidase‐mediated erythrocyte desialylation provokes cell surface aminophospholipid exposure
Bibliographic record
Abstract
BACKGROUND: Surface desialylation is associated with erythrocyte aging and mediates phagocytic recognition and clearance of senescent erythrocytes. Neuraminidases, a family of glycohydrolytic enzymes, cleave the glycosidic linkages between sialic acid and mucopolysaccharides and have previously been implicated in erythrocyte dysfunction associated with sepsis. Erythrocytes in septic patients further display a phenotype of accelerated eryptosis characterized by membrane phospholipid scrambling resulting in phosphatidylserine (PS) externalization. Herein, we examined the impact of artificial erythrocyte desialylation on eryptosis. METHODS: Using flow cytometry and/or fluorescence microscopy, we analyzed desialylation patterns and eryptotic alterations in erythrocytes exposed to Clostridium perfringens-derived neuraminidase. RESULTS: levels in a dose- and time-dependent manner. Neuraminidase treatment significantly reduced fluorescence-tagged agglutinin binding, an effect temporally preceding the increase in PS externalization. Neuraminidase-induced PS exposure was significantly curtailed by pretreatment with the pan-sialidase inhibitor N-acetyl-2,3-dehydro-2-deoxyneuraminic acid. Neuraminidase treatment further induced hemolysis but did not significantly impact erythrocyte volume, ceramide abundance, or the generation of reactive oxygen species. CONCLUSION: Collectively, our data reveal that alteration of erythrocyte sialylation status by bacterial neuraminidase favors eryptotic cell death, an effect potentially contributing to reduced erythrocyte lifespan and anemia in sepsis.
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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 | 0.000 | 0.000 |
| 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".