The bHLH Gene<i>Hes1</i>as a Repressor of the Neuronal Commitment of CNS Stem Cells
Bibliographic record
Abstract
Hes1 is one of the basic helix-loop-helix transcription factors that regulate mammalian CNS development, and its loss- and gain-of-function phenotypes indicate that it negatively regulates neuronal differentiation. Here we report that Hes1(-/-) mice expressed both early (TuJ1 and Hu) and late (MAP2 and Neurofilament) neuronal markers prematurely, and that there were approximately twice the normal number of neurons in the Hes1(-/-) brain during early neural development. However, immunochemical analyses of sections and dissociated cells using neural progenitor markers, including nestin, failed to detect any changes in Hes1(-/-) progenitor population. Therefore, further characterization of neural progenitor cells that discriminated between multipotent and monopotent cells was performed using two culture methods, low-density culture, and a neurosphere assay. We demonstrate that the self-renewal activity of multipotent progenitor cells was reduced in the Hes1(-/-) brain, and that their subsequent commitment to the neuronal lineage was accelerated. The Hes1(-/-) neuronal progenitor cells were functionally abnormal, in that they divided, on average, only once, and then generated two neurons, (instead of one progenitor cell and one neuron), whereas wild-type progenitor cells divided more. In addition, some Hes1(-/-) progenitors followed an apoptotic fate. The overproduction of neurons in the early Hes1(-/-) brains may reflect this premature and immediate generation of neurons as well as a net increase in the number of neuronal progenitor cells. Taken together, we conclude that Hes1 is important for maintaining the self-renewing ability of progenitors and for repressing the commitment of multipotent progenitor cells to a neuronal fate, which is critical for the correct number of neurons to be produced and for the establishment of normal neuronal function.
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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".