Evolutionary conservation of human ketodeoxynonulosonic acid production is independent of sialoglycan biosynthesis
Bibliographic record
Abstract
Human metabolic incorporation of nonhuman sialic acid (Sia) N-glycolylneuraminic acid into endogenous glycans generates inflammation via preexisting antibodies, which likely contributes to red meat-induced atherosclerosis acceleration. Exploring whether this mechanism affects atherosclerosis in end-stage renal disease (ESRD), we instead found serum accumulation of 2-keto-3-deoxy-d-glycero-d-galacto-2-nonulosonic acid (Kdn), a Sia prominently expressed in cold-blooded vertebrates. In patients with ESRD, levels of the Kdn precursor mannose also increased, but within a normal range. Mannose ingestion by healthy volunteers raised the levels of urinary mannose and Kdn. Kdn production pathways remained conserved in mammals but were diminished by an M42T substitution in a key biosynthetic enzyme, N-acetylneuraminate synthase. Remarkably, reversion to the ancestral methionine then occurred independently in 2 lineages, including humans. However, mammalian glycan databases contain no Kdn-glycans. We hypothesize that the potential toxicity of excess mannose in mammals is partly buffered by conversion to free Kdn. Thus, mammals probably conserve Kdn biosynthesis and modulate it in a lineage-specific manner, not for glycosylation, but to control physiological mannose intermediates and metabolites. However, human cells can be forced to express Kdn-glycans via genetic mutations enhancing Kdn utilization, or by transfection with fish enzymes producing cytidine monophosphate-Kdn (CMP-Kdn). Antibodies against Kdn-glycans occur in pooled human immunoglobulins. Pathological conditions that elevate Kdn levels could therefore result in antibody-mediated inflammatory pathologies.
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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".