A genome-wide approach identifies distinct but overlapping subsets of cellular mRNAs associated with Staufen1- and Staufen2-containing ribonucleoprotein complexes
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Bibliographic record
Abstract
Messenger RNAs are associated with multiple RNA-binding proteins to form ribonucleoprotein (mRNP) complexes. These proteins are important regulators of the fate of their target mRNAs. In human cells, Staufen1 and Staufen2 proteins, coded by two different genes, are double-stranded RNA-binding proteins involved in several cellular functions including mRNA localization, translation, and decay. Although 51% identical, these proteins are nevertheless found in different RNA particles. In addition, differential splicing events generate Staufen2 isoforms that only differ at their N-terminal extremities. In this paper, we used a genome-wide approach to identify and compare the mRNA targets of mammalian Staufen proteins. The mRNA content of Staufen mRNPs was identified by probing DNA microarrays with probes derived from mRNAs isolated from immunopurified Staufen-containing complexes following transfection of HEK293T cells with Stau1(55)-HA, Stau2(59)-HA, or Stau2(62)-HA expressors. Our results indicate that 7% and 11% of the cellular RNAs expressed in HEK293T cells are found in Stau1- and in Stau2-containing mRNPs, respectively. A comparison of Stau1- and Stau2-containing mRNAs identifies a relatively low percentage of common mRNAs; the percentage of common mRNAs highly increases when mRNAs in Stau2(59)-HA- and Stau2(62)-containing mRNPs are compared. There is a predominance of mRNAs involved in cell metabolism, transport, transcription, regulation of cell processes, and catalytic activity. All these subsets of mRNAs are mostly distinct from those associated with FMRP or IMP, although some mRNAs overlap. Consistent with a model of post-transcriptional gene regulation, our results show that Stau1- and Stau2-mRNPs associate with distinct but overlapping sets of cellular mRNAs.
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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 | 0.000 | 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 it