Evolutionary analysis reveals the origin of sodium coupling in glutamate transporters
Bibliographic record
Abstract
Secondary active membrane transporters harness the energy of ion gradients to concentrate their substrates. Homologous transporters evolved to couple transport to different ions in response to changing environments and needs. The bases of such diversification and, thus, principles of ion coupling are unexplored. Here, using phylogenetics and ancestral protein reconstruction, we investigated sodium-coupled transport in prokaryotic glutamate transporters, a mechanism ubiquitous across life domains and critical to neurotransmitter recycling in humans by excitatory amino acid transporters from the solute carrier 1 family. By inferring ancestral prokaryotic transporter sequences during a change in the ion-coupling mechanism, we found an evolutionary transition from sodium-dependent to independent substrate binding and transport. Structural and functional experiments on ancestral transporters suggest that the transition involved allosteric mutations, rendering sodium binding dispensable without affecting the ion-binding sites. Allosteric tuning of transporters’ energy landscapes might be a widespread route of their functional diversification. Reddy et al. used ancestral protein reconstruction, cryo-electron microscopy and functional assays to elucidate how a secondary active transporter evolved to harness the energy of sodium gradients to power the concentrative uptake of its substrate.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".