Dissecting the abilities of murine Siglecs to interact with gangliosides
Bibliographic record
Abstract
Siglecs are cell surface receptors whose functions are tied to the binding of their sialoglycan ligands. Recently, we developed an optimized liposome formulation and used it to investigate the binding of human Siglecs (hSiglec) against a panel of gangliosides. Animal models, more specifically murine models, are used to understand human biology; however, species-specific differences can complicate the interpretation of the results. Herein, we used our optimized liposome formulation to dissect the interactions between murine Siglecs (mSiglecs) and gangliosides to assess the appropriateness of mSiglecs as a proxy to better understand the biological roles of hSiglec-ganglioside interactions. Using our optimized liposome formulation, we found that ganglioside binding is generally conserved between mice and humans with mSiglec-1, -E, -F, and -15 binding multiple gangliosides like their human counterparts. However, in contrast to the hSiglecs, we observed little to no binding between the mSiglecs and ganglioside GM1a. Detailed analysis of mSiglec-1 interacting with GM1a and its structural isomer, GM1b, suggests that mSiglec-1 preferentially binds α2-3-linked sialic acids presented from the terminal galactose residue. The ability of mSiglecs to interact or not interact with gangliosides, particularly GM1a, has implications for using mice to study neurodegenerative diseases, infections, and cancer, where interactions between Siglecs and glycolipids have been proposed to modulate these human diseases.
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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) | 0.000 | 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".