CD33 forms functional dimers on cell surface to modulate Alzheimer risk
Bibliographic record
Abstract
Abstract Background The sialic‐acid binding immunoglobulin‐like lectin 3 receptor (Siglec‐3 / CD33) is one of the highly associated AD risk genes. Previous studies revealed that the non‐coding AD‐risk alleles (rs3865444 and rs12459419) are associated with increased total levels of CD33 expression and a higher relative expression of the long CD33 M splice form. However, the molecular basis of the immuno‐inhibitory function of CD33 remains unclear. Method To confirm the presence of CD33 dimers, we conducted multiple experiments. Blue Native Gel electrophoresis and co‐immunoprecipitation assays were used to detect CD33 bands corresponding to the expected molecular weights. Flow cytometry with specific antibodies was performed to quantify cell‐surface CD33. Additionally, single‐molecule fluorescence resonance energy transfer (smFRET) combined with TIRF imaging was employed to visualize CD33 dimers on the cell surface. Furthermore, Western Blotting of phosphorylation of CD33 and its downstream molecule were performed to verify if the dimers were functional. Result Biochemical analyses demonstrated that CD33 M and CD33 m can form homodimers or heterodimers. Flow cytometry confirmed that CD33 M isoforms are selectively trafficked to the cell surface, while smFRET imaging verified the presence of dimers on the cell surface. The elevation of the CD33 pathway following stimulation with CD33‐specific ligands provided evidence that CD33 M homodimers are functional. Conclusion This study reveals the critical role of CD33 M in AD pathology by elucidating its molecular mechanisms. We provide direct evidence that CD33 M and CD33 m isoforms can form both homodimers and heterodimers. However, only CD33 M isoforms are preferentially trafficked to the cell surface and form functional dimers. These findings advance our understanding of the molecular basis of CD33's immuno‐inhibitory function and offer new insights into its involvement in AD risk, potentially paving the way for the development of targeted therapeutic strategies.
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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) | 0.002 | 0.001 |
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".