TEG-1 CD2BP2 controls miRNA levels by regulating miRISC stability in<i>C. elegans</i>and human cells
Bibliographic record
Abstract
MiRNAs post-transcriptionally regulate gene expression by recruiting the miRNA-induced silencing complex (miRISC) to target mRNAs. However, the mechanisms by which miRISC components are maintained at appropriate levels for proper function are largely unknown. Here, we demonstrate that Caenorhabditis elegans TEG-1 regulates the stability of two miRISC effectors, VIG-1 and ALG-1, which in turn affects the abundance of miRNAs in various families. We demonstrate that TEG-1 physically interacts with VIG-1, and complexes with mature let-7 miRNA. Also, loss of teg-1 in vivo phenocopies heterochronic defects observed in let-7 mutants, suggesting the association of TEG-1 with miRISC is necessary for let-7 to function properly during development. Loss of TEG-1 function also affects the abundance and function of other microRNAs, suggesting that TEG-1's role is not specific to let-7. We further demonstrate that the human orthologs of TEG-1, VIG-1 and ALG-1 (CD2BP2, SERBP1/PAI-RBP1 and AGO2) are found in a complex in HeLa cells, and knockdown of CD2BP2 results in reduced miRNA levels; therefore, TEG-1's role in affecting miRNA levels and function is likely conserved. Together, these data demonstrate that TEG-1 CD2BP2 stabilizes miRISC and mature miRNAs, maintaining them at levels necessary to properly regulate target gene expression.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".