Genetic characterization of a complex locus necessary for the transport and catabolism of erythritol, adonitol and l-arabitol in Sinorhizobium meliloti
Bibliographic record
Abstract
The Sinorhizobium meliloti locus necessary for the utilization of erythritol as a sole carbon source, contains 17 genes, including genes that encode an ABC transporter necessary for the transport of erythritol, as well as the genes encoding EryA, EryB, EryC, TpiB and the regulators EryD and EryR (SMc01615). Construction of defined deletions and complementation experiments show that the other genes at this locus encode products that are necessary for the catabolism of adonitol (ribitol) and l-arabitol, but not d-arabitol. These analyses show that aside from one gene that is specific for the catabolism of l-arabitol (SMc01619, lalA), the rest of the catabolic genes are necessary for both polyols (SMc01617, rbtC; SMc01618, rbtB; SMc01622, rbtA). Genetic and biochemical data show that in addition to utilizing erythritol as a substrate, EryA is also capable of utilizing adonitol and l-arabitol. Similarly, transport experiments using labelled erythritol show that adonitol, l-arabitol and erythritol share a common transporter (MptABCDE). Quantitative RT-PCR experiments show that transcripts containing genes necessary for adonitol and l-arabitol utilization are induced by these sugars in an eryA-dependent manner.
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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) | 0.001 | 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 | 0.001 |
| 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".