Dual Organellar Targeting of Aminoacyl-tRNA Synthetases in Diatoms and Cryptophytes
Bibliographic record
Abstract
The internal compartmentation of eukaryotic cells not only allows separation of biochemical processes but it also creates the requirement for systems that can selectively transport proteins across the membrane boundaries. Although most proteins function in a single subcellular compartment, many are able to enter two or more compartments, a phenomenon known as dual or multiple targeting. The aminoacyl-tRNA synthetases (aaRSs), which catalyze the ligation of tRNAs to their cognate amino acids, are particularly prone to functioning in multiple subcellular compartments. They are essential for translation, so they are required in every compartment where translation takes place. In diatoms, there are three such compartments, the plastid, the mitochondrion, and the cytosol. In cryptophytes, translation also takes place in the periplastid compartment (PPC), which is the reduced cytoplasm of the plastid's red algal ancestor and which retains a reduced red algal nucleus. We searched the organelle and nuclear genomes of the cryptophyte Guillardia theta and the diatoms Phaeodactylum tricornutum and Thalassiosira pseudonana for aaRS genes and found an insufficient number of genes to provide each compartment with a complete set of aaRSs. We therefore inferred, with support from localization predictions, that many aaRSs are dual targeted. We tested four of the predicted dual targeted aaRSs with green fluorescent protein fusion localizations in P. tricornutum and found evidence for dual targeting to the mitochondrion and plastid in P. tricornutum and G. theta, and indications for dual targeting to the PPC and cytosol in G. theta. This is the first report of dual targeting in diatoms or cryptophytes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".