A Comparative Transcriptomic Analysis Reveals Conserved Features of Stem Cell Pluripotency in Planarians and Mammals
Bibliographic record
Abstract
Many long-lived species of animals require the function of adult stem cells throughout their lives. However, the transcriptomes of stem cells in invertebrates and vertebrates have not been compared, and consequently, ancestral regulatory circuits that control stem cell populations remain poorly defined. In this study, we have used data from high-throughput RNA sequencing to compare the transcriptomes of pluripotent adult stem cells from planarians with the transcriptomes of human and mouse pluripotent embryonic stem cells. From a stringently defined set of 4,432 orthologs shared between planarians, mice and humans, we identified 123 conserved genes that are ≥5-fold differentially expressed in stem cells from all three species. Guided by this gene set, we used RNAi screening in adult planarians to discover novel stem cell regulators, which we found to affect the stem cell-associated functions of tissue homeostasis, regeneration, and stem cell maintenance. Examples of genes that disrupted these processes included the orthologs of TBL3, PSD12, TTC27, and RACK1. From these analyses, we concluded that by comparing stem cell transcriptomes from diverse species, it is possible to uncover conserved factors that function in stem cell biology. These results provide insights into which genes comprised the ancestral circuitry underlying the control of stem cell self-renewal and pluripotency.
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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 | 0.001 | 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) | 0.000 | 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".