Evolution of Transcription Factors in<i>Caenorhabditis</i>
Bibliographic record
Abstract
Abstract Transcription factors (TFs) are regulatory proteins controlling gene expression by binding specific motifs associated with their target genes and playing essential roles during development and physiological response to stimuli. The evolution of transcriptional regulation is an important source of phenotypic diversity. The genome of the nematode Caenorhabditis elegans encodes a similar proportion of TFs as the human genome, although TF families show different patterns of expansion in human and in worm. The TF repertoire of C. elegans is well diversified with over 50 TF families. Alternative splicing and gene duplication contribute to the functional diversity of TFs and the rapid evolution of TF protein sequence suggests that TF evolution plays important roles in developmental evolution. Many of the principles that guided the diversification of the worm TFs are likely to be applicable to the evolution of TFs in other organisms. Key Concepts: The genome of C. elegans encodes 988 TFs belonging to 50 distinct families based on the type of DNA‐binding domain. The worm and human genomes encode a similar proportion of TFs, and in both species a third of the TFs show tissue‐specific gene expression. Alternative splicing increases the number of TF transcripts by 22%. Gene duplication contributes significantly to the functional diversification of TFs, and several examples illustrate the different evolutionary trajectories taken by TF duplicates. Tissue‐specific expression, alternative splicing, gene duplication and the combinatorial regulatory activity of TFs reduce the pleiotropic constraints operating on TF sequence evolution. TF factor sequence evolution plays an unappreciated role in developmental evolution. TFs can harbour high level of amino acid variation both between species and within populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".