Glycomic analysis reveals a conserved response to bacterial sepsis induced by different bacterial pathogens
Bibliographic record
Abstract
Abstract Sepsis is an extreme inflammatory response to infection (bacterial, viral, fungal) that occurs in the bloodstream and causes damage throughout the body. Currently, there are few diagnostic biomarkers of sepsis and new effective treatments have not been developed. There is a clear need to study the molecular underpinnings of this disease. Glycosylation is known to play a role in immunity and inflammation, but the role of glycans in sepsis is not well defined. Herein, we profiled the serum glycomes of experimental mouse sepsis models to identify changes induced by 4 different clinical bacterial pathogens (Gram-positive: Streptococcus pneumoniae, Staphylococcus aureus , Gram-negative: Escherichia coli and Salmonella Typhimurium) using our lectin microarray technology. We observed global shifts in the blood sera glycome that were conserved across all four species, regardless of whether they were Gram positive or negative. Bisecting GlcNAc was decreased upon sepsis and a strong increase in core 1/3 O -glycans was observed. Lectin blot analysis revealed a high molecular weight protein induced in sepsis by all four bacteria as the major cause of the core 1/3 O -glycan shift. While the identity of this protein remains to be elucidated, its presence indicates a common feature of bacterial sepsis associated with this glycomic signature.
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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".