Inability To Catabolize Galactose Leads to Increased Ability To Compete for Nodule Occupancy in Sinorhizobium meliloti
Bibliographic record
Abstract
A mutant unable to utilize galactose was isolated in Sinorhizobium meliloti strain Rm1021. The mutation was found to be in a gene annotated dgoK1, a putative 2-keto-3-deoxygalactonokinase. The genetic region was isolated on a complementing cosmid and subsequently characterized. Based on genetic and bioinformatic evidence, the locus encodes all five enzymes (galD, dgoK, dgoA, SMc00883, and ilvD1) involved in the De Ley-Doudoroff pathway for galactose catabolism. Although all five genes are present, genetic analysis suggests that the galactonase (SMc00883) and the dehydratase (ilvD1) are dispensable with respect to the ability to catabolize galactose. In addition, we show that the transport of galactose is partially facilitated by the arabinose transporter (AraABC) and that both glucose and galactose compete with arabinose for transport. Quantitative reverse transcription-PCR (qRT-PCR) data show that in a dgoK background, the galactose locus is constitutively expressed, and the induction of the ara locus seems to be enhanced. Assays of competition for nodule occupancy show that the inability to catabolize galactose is correlated with an increased ability to compete for nodule occupancy.
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) | 0.001 | 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 | 0.000 |
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".