The ABC transporter gene family of Caenorhabditis elegans has implications for the evolutionary dynamics of multidrug resistance in eukaryotes
Bibliographic record
Abstract
BACKGROUND: Many drugs of natural origin are hydrophobic and can pass through cell membranes. Hydrophobic molecules must be susceptible to active efflux systems if they are to be maintained at lower concentrations in cells than in their environment. Multi-drug resistance (MDR), often mediated by intrinsic membrane proteins that couple energy to drug efflux, provides this function. All eukaryotic genomes encode several gene families capable of encoding MDR functions, among which the ABC transporters are the largest. The number of candidate MDR genes means that study of the drug-resistance properties of an organism cannot be effectively carried out without taking a genomic perspective. RESULTS: We have annotated sequences for all 60 ABC transporters from the Caenorhabditis elegans genome, and performed a phylogenetic analysis of these along with the 49 human, 30 yeast, and 57 fly ABC transporters currently available in GenBank. Classification according to a unified nomenclature is presented. Comparison between genomes reveals much gene duplication and loss, and surprisingly little orthology among analogous genes. Proteins capable of conferring MDR are found in several distinct subfamilies and are likely to have arisen independently multiple times. CONCLUSIONS: ABC transporter evolution fits a pattern expected from a process termed 'dynamic-coherence'. This is an unusual result for such a highly conserved gene family as this one, present in all domains of cellular life. Mechanistically, this may result from the broad substrate specificity of some ABC proteins, which both reduces selection against gene loss, and leads to the facile sorting of functions among paralogs following gene duplication.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".