The logic of transcriptional regulator recruitment architecture at <i>cis</i>-regulatory modules controlling liver functions
Bibliographic record
Abstract
Control of gene transcription relies on concomitant regulation by multiple transcriptional regulators (TRs). However, how recruitment of a myriad of TRs is orchestrated at cis -regulatory modules (CRMs) to account for coregulation of specific biological pathways is only partially understood. Here, we have used mouse liver CRMs involved in regulatory activities of the hepatic TR, NR1H4 (FXR; farnesoid X receptor), as our model system to tackle this question. Using integrative cistromic, epigenomic, transcriptomic, and interactomic analyses, we reveal a logical organization where trans -regulatory modules (TRMs), which consist of subsets of preferentially and coordinately corecruited TRs, assemble into hierarchical combinations at hepatic CRMs. Different combinations of TRMs add to a core TRM, broadly found across the whole landscape of CRMs, to discriminate promoters from enhancers. These combinations also specify distinct sets of CRM differentially organized along the genome and involved in regulation of either housekeeping/cellular maintenance genes or liver-specific functions. In addition to these TRMs which we define as obligatory, we show that facultative TRMs, such as one comprising core circadian TRs, are further recruited to selective subsets of CRMs to modulate their activities. TRMs transcend TR classification into ubiquitous versus liver-identity factors, as well as TR grouping into functional families. Hence, hierarchical superimpositions of obligatory and facultative TRMs bring about independent transcriptional regulatory inputs defining different sets of CRMs with logical connection to regulation of specific gene sets and biological pathways. Altogether, our study reveals novel principles of concerted transcriptional regulation by multiple TRs at CRMs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".