Fli-1 overexpression in erythroleukemic cells promotes erythroid de-differentiation while Spi-1/PU.1 exerts the opposite effect
Bibliographic record
Abstract
The ETS transcription factors play a critical role during hematopoiesis. In F-MuLV-induced erythroleukemia, Fli‑1 insertional activation producing high expression of this transcription factor required to promote proliferation. How deregulated Fli‑1 expression alters the balance between erythroid differentiation and proliferation is unknown. To address this issue, we exogenously overexpressed Fli‑1 in an erythroleukemic cell harboring activation of spi‑1/PU.1, another ETS gene involved in erythroleukemogenesis. While the proliferation in culture remains unaffected, Fli‑1 overexpression imparts morphological and immunohistochemical characteristics of immature erythroid progenitors. Fli‑1 overexpression in erythroleukemic cells increased the numbers of erythroid colonies on methylcellulose and reduced tumorigenicity as evidenced by increase latency of erythroleukemogenesis in mice inoculated with these cells. Although all transplanted mice developed enlargement of the spleen and liver due to leukemic infiltration, Fli‑1 overexpression altered the hematopoietic phenotype, significantly increasing the expression of regulatory hematopoietic genes cKIT, SCA-1, CD41 and CD71. In contrast, expression of Spi‑1/PU.1 in a Fli‑1 producing erythroleukemia cell line in which fli‑1 is activated, resulted in increased proliferation through activation of growth promoting proteins MAPK, AKT, cMYC and JAK2. Importantly, these progenitors express high levels of markers such as CD71 and TER119 associated with more mature erythroid cells. Thus, Fli‑1 overexpression induces a de-differentiation program by reverting CFU-E to BFU-E erythroid progenitor activity, while Spi‑1/PU.1 promoting maturation from BFU-E to CFU-E.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".