The endoplasmic reticulum protein SEC22B interacts with NBEAL2 and is required for megakaryocyte α-granule biogenesis
Bibliographic record
Abstract
Studies of inherited platelet disorders have provided many insights into platelet development and function. Loss of function of neurobeachin-like 2 (NBEAL2) causes gray platelet syndrome (GPS), where the absence of platelet α-granules indicates NBEAL2 is required for their production by precursor megakaryocytes. The endoplasmic reticulum is a dynamic network that interacts with numerous intracellular vesicles and organelles and plays key roles in their development. The megakaryocyte endoplasmic reticulum is extensive, and in this study we investigated its role in the biogenesis of α-granules by focusing on the membrane-resident trafficking protein SEC22B. Coimmunoprecipitation (co-IP) experiments using tagged proteins expressed in human HEK293 and megakaryocytic immortalized megakaryocyte progenitor (imMKCL) cells established binding of NBEAL2 with SEC22B, and demonstrated that NBEAL2 can simultaneously bind SEC22B and P-selectin. NBEAL2-SEC22B binding was also observed for endogenous proteins in human megakaryocytes using co-IP, and immunofluorescence microscopy detected substantial overlap. SEC22B binding was localized to a region of NBEAL2 spanning amino acids 1798 to 1903, where 2 GPS-associated missense variants have been reported: E1833K and R1839C. NBEAL2 containing either variant did not bind SEC22B coexpressed in HEK293 cells. CRISPR/Cas9-mediated knockout of SEC22B in imMKCL cells resulted in decreased NBEAL2, but not vice versa. Loss of either SEC22B or NBEAL2 expression resulted in failure of α-granule production and reduced granule proteins in imMKCL cells. We conclude that SEC22B is required for α-granule biogenesis in megakaryocytes, and that interactions with SEC22B and P-selectin facilitate the essential role of NBEAL2 in granule development and cargo stability.
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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 | 0.001 |
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".