System-wide analyses of the fission yeast poly(A)+ RNA interactome reveal insights into organisation and function of RNA-protein complexes
Bibliographic record
Abstract
Abstract Production, function, and turnover of mRNA are orchestrated by multi-subunit machineries that play a central role in gene expression. Within these molecular machines, interactions with the target mRNA are mediated by RNA-binding proteins (RBPs), and the accuracy and dynamics of these RNA-protein interactions are essential for their function. Here, we show that fission yeast whole cell poly(A)+ RNA-protein crosslinking data provides system-wide information on the organisation and function of the RNA-protein complexes. We evaluate relative enrichment of cellular RBPs on poly(A)+ RNA to identify interactors with high RNA-binding activity and provide key information about the RNA-binding properties of large multi-protein complexes, such as the mRNA 3’ end processing machinery (cleavage and polyadenylation factor, CPF) and the RNA exosome. We demonstrate that different functional modules within CPF differ in their ability to interact with RNA. Importantly, we reveal that CPF forms additional contacts with RNA via the Fip1 subunit of the polyadenylation module and two subunits of the nuclease module. In addition, our data highlights the central role of the RNA helicase Mtl1 in RNA degradation by the exosome as mutations in Mtl1 lead to disengagement of the exosome from RNA. We examine how routes of substrate access to the complex are affected upon mutation of exosome subunits. Our results provide important insights into how different components of the exosome contribute to engagement of the complex with substrate RNA. Overall, our data uncover how multi-subunit cellular machineries interact with RNA, on a proteome-wide scale.
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) | 0.001 | 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".