Regulation of progranulin expression in myeloid cells
Bibliographic record
Abstract
Progranulin (pgrn; granulin-epithelin precursor, PC-cell-derived growth factor, or acrogranin) is a multifunctional secreted glycoprotein implicated in tumorigenesis, development, inflammation, and repair. It is highly expressed in macrophage and monocyte-derived dendritic cells. Here we investigate its regulation in myeloid cells. All-trans retinoic acid (ATRA) increased pgrn mRNA levels in myelomonocytic cells (CD34(+) progenitors; monoblastic U-937; monocytic THP-1; progranulocytic HL-60; macrophage RAW 264.7) but not in nonmyeloid cells tested. Interleukin-4 impaired basal expression of pgrn in U-937. Differentiation agents DMSO, and, in U-937 only, phorbol ester [phorbol 12-myristate,13-acetate (PMA)] elevated pgrn mRNA expression late in differentiation, suggestive of roles for pgrn in more mature terminally differentiated granulocyte/monocytes rather than during growth or differentiation. The response of pgrn mRNA to ATRA differs in U-937 and HL-60 lineages. In U-937, ATRA and chemical differentiation agents greatly increased pgrn mRNA stability, whereas, in HL-60, ATRA accelerated pgrn mRNA turnover. The initial upregulation of pgrn mRNA after stimulation with ATRA was independent of de novo protein synthesis in U-937 but not HL-60. Chemical blockade of nuclear factor-kappaB (NF-kappaB) activation impaired ATRA-stimulated pgrn expression in HL-60 but not U-937, whereas in U-937 it blocked PMA-induced pgrn mRNA expression, suggestive of cell-specific roles for NF-kappaB in determining pgrn mRNA levels. We propose that: 1) ATRA regulates pgrn mRNA levels in myelomonocytic cells; 2) ATRA acts in a cell-specific manner involving the differential control of mRNA stability and differential requirement for NF-kappaB signaling; and 3) elevated pgrn mRNA expression is characteristic of more mature cells and does not stimulate differentiation.
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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".