Decoding the Heterogeneity and Specialized Function of Translation Machinery Through Ribosome Profiling in Yeast Mutants of Initiation Factors
Bibliographic record
Abstract
The nuanced heterogeneity and specialized functions of translation machinery are increasingly recognized as crucial for precise translational regulation. Here, high-throughput ribosomal profiling (ribo-seq) is used to analyze the specialized roles of eukaryotic initiation factors (eIFs) in the budding yeast. By examining changes in ribosomal distribution across the genome resulting from knockouts of eIF4A, eIF4B, eIF4G1, CAF20, or EAP1, or knockdowns of eIF1, eIF1A, eIF4E, or PAB1, two distinct initiation-factor groups, the "looping" and "scanning" groups are discerned, based on similarities in the ribosomal landscapes their perturbation induced. The study delves into the cis-regulatory sequence features of genes influenced predominantly by each group, revealing that genes more dependent on the looping-group factors generally have shorter transcripts and poly(A) tails. In contrast, genes more dependent on the scanning-group factors often possess upstream open reading frames and exhibit a higher GC content in their 5' untranslated regions. From the ribosomal RNA fragments identified in the ribo-seq data, ribosomal heterogeneity associated with perturbation of specific initiation factors is further identified, suggesting their potential roles in regulating ribosomal components. Collectively, the study illuminates the complexity of translational regulation driven by heterogeneity and specialized functions of translation machinery, presenting potential approaches for targeted gene translation manipulation.
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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) | 0.000 | 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 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".