Substrate promiscuity of Dicer toward precursors of the let-7 family and their 3′-end modifications
Bibliographic record
Abstract
The human let-7 miRNA family consists of thirteen members that play critical roles in many biological processes, including development timing and tumor suppression, and their levels are disrupted in several diseases. Dicer is the endoribonuclease responsible for processing the precursor miRNA (pre-miRNA) to yield the mature miRNA, and thereby plays a crucial role in controlling the cellular levels of let-7 miRNAs. It is well established that the sequence and structural features of pre-miRNA hairpins such as the 5'-phosphate, the apical loop, and the 2-nt 3'-overhang are important for the processing activity of Dicer. Exceptionally, nine precursors of the let-7 family (pre-let-7) contain a 1-nt 3'-overhang and get mono-uridylated in vivo, presumably to allow efficient processing by Dicer. Pre-let-7 are also oligo-uridylated in vivo to promote their degradation and likely prevent their efficient processing by Dicer. In this study, we systematically investigated the impact of sequence and structural features of all human let-7 pre-miRNAs, including their 3'-end modifications, on Dicer binding and processing. Through the combination of SHAPE structural probing, in vitro binding and kinetic studies using purified human Dicer, we show that despite structural discrepancies among pre-let-7 RNAs, Dicer exhibits remarkable promiscuity in binding and cleaving these substrates. Moreover, the 1- or 2-nt 3'-overhang, 3'-mono-uridylation, and 3'-oligo-uridylation of pre-let-7 substrates appear to have little effect on Dicer binding and cleavage rates. Thus, this study extends current knowledge regarding the broad substrate specificity of Dicer and provides novel insight regarding the effect of 3'-modifications on binding and cleavage by Dicer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
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".