Bibliographic record
Abstract
Somatic cells can be reprogrammed to induced pluripotent stem (iPS) cells by expression of defined embryonic factors. My thesis is focused on exploring the mechanisms underlying reprogramming using a secondary mouse embryonic fibroblast model that forms iPS cells with high efficiency upon inducible expression of Oct4, Klf4, c-Myc and Sox2. My analyses of the temporal changes in gene expression reveal that reprogramming is a multi-step process characterized by initiation, maturation and stabilization phases. Using functional RNAi screening, I discovered a key role for BMP signaling and the induction of mesenchymal-to-epithelial transition (MET) during the initiation phase. I showed that MET induction was linked to BMP-dependent induction of miR-205 and the miR-200 family of microRNAs. These studies thus defined a multi-step mechanism that incorporates a BMP-miRNA-MET axis during somatic cell reprogramming.\nNext I focused on the two later phases of reprogramming, maturation and stabilization. I showed the stabilization phase and acquisition of pluripotency is dependent on removal of transgene expression late in the maturation phase. Clonal analysis of reprogramming cells revealed subsets of stabilization competent (SC) versus stabilization incompetent (SI) cells. SC clones robustly entered the pluripotent state upon transgene withdrawal in the late, but not early maturation phase, whereas SI clones failed to reprogram at either stage. Transcriptome profiling by RNA-Seq revealed that SC clones acquire a competency gene expression signature late in the maturation phase. Functional RNAi screening of SC signature genes further identified regulators of transition to the stabilization phase, while screening of the same signature in iPS cells revealed a distinct subset of genes required for maintenance of pluripotency. These studies reveal that the acquisition and subsequent maintenance of pluripotency are controlled by distinct molecular networks and uncover a novel regulatory program that is required for transition to transgene independence.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".