Differences in codon bias and GC content contribute to the balanced expression of TLR7 and TLR9
Bibliographic record
Abstract
The innate immune system detects diverse microbial species with a limited repertoire of immune receptors that recognize nucleic acids. The cost of this immune surveillance strategy is the potential for inappropriate recognition of self-derived nucleic acids and subsequent autoimmune disease. The relative expression of two closely related receptors, Toll-like receptor (TLR) 7 and TLR9, is balanced to allow recognition of microbial nucleic acids while limiting recognition of self-derived nucleic acids. Situations that tilt this balance toward TLR7 promote inappropriate responses, including autoimmunity; therefore, tight control of expression is critical for proper homeostasis. Here we report that differences in codon bias limit TLR7 expression relative to TLR9. Codon optimization of Tlr7 increases protein levels as well as responses to ligands, but, unexpectedly, these changes only modestly affect translation. Instead, we find that much of the benefit attributed to codon optimization is actually the result of enhanced transcription. Our findings, together with other recent examples, challenge the dogma that codon optimization primarily increases translation. We propose that suboptimal codon bias, which correlates with low guanine-cytosine (GC) content, limits transcription of certain genes. This mechanism may establish low levels of proteins whose overexpression leads to particularly deleterious effects, such as TLR7.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".