Splicing buffers suboptimal codon usage in human cells
Bibliographic record
Abstract
Although multiple studies have addressed the effects of codon usage on gene expression, such studies were typically performed in unspliced model genes. In the human genome, most genes undergo splicing and patterns of codon usage are splicing-dependent: guanine and cytosine (GC) content is highest within single-exon genes and within first exons of multi-exon genes. Intrigued by this observation, we measured the effects of splicing on expression in a panel of synonymous variants of GFP and mKate2 reporter genes that varied in nucleotide composition. We found that splicing promotes the expression of adenine and thymine (AT)-rich variants by increasing their steady-state protein and mRNA levels, in part through promoting cytoplasmic localization of mRNA. Splicing had little or no effect on the expression of GC-rich variants. In the absence of splicing, high GC content at the 5' end, but not at the 3' end of the coding sequence positively correlated with expression. Among endogenous human protein-coding transcripts, GC content has a more positive effect on various expression measures of unspliced, relative to spliced mRNAs. We propose that splicing promotes the expression of AT-rich genes, leading to selective pressure for the retention of introns in the human genome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".