A saturating mutagenesis CRISPR-Cas9 mediated functional genomic screen identifies <i>cis-</i> and <i>trans-</i> regulatory elements of <i>Oct4</i> in murine ESCs
Bibliographic record
Abstract
Abstract Regulatory elements (REs) consist of enhancers and promoters that occupy a significant portion of the non-coding genome and control gene expression programs either in –cis or in – trans . Putative REs have been identified largely based on their regulatory features (co-occupancy of ESC-specific transcription factors, enhancer histone marks and DNase hypersensitivity) in mouse embryonic stem cells (mESCs). However, less has been established regarding their regulatory functions in their native context. We deployed cis- and trans- regulatory elements scanning through saturating mutagenesis and sequencing (ctSCAN-SMS) to target elements within the ∼12kb cis -region ( Cis- REs; CREs) of the Oct4 gene locus, as well as genome-wide 2,613 high-confidence trans- REs (TREs), in mESCs. ctSCAN-SMS identified 10 CREs and 12 TREs, as novel candidate REs of the Oct4 gene in mESCs. Furthermore, deletions of these candidate REs confirmed that the majority of the REs are functionally active, and CREs are more active than TREs in controlling Oct4 gene expression. A subset of active CREs and TREs physically interact with the Oct4 promoter to varying degrees; specifically, a greater number of active CREs compared to active TREs, physically interact with the Oct4 promoter. Moreover, comparative genomics analysis reveals that more number of active CREs than active TREs are evolutionary conserved between mouse and primates, including human. Taken together, our study demonstrates the reliability and robustness of ctSCAN-SMS screening to identify critical REs, and investigate their roles in the regulation of transcriptional output of a target gene (in this case Oct4) in their native context.
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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 | 0.001 |
| 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".