Succinoglycan Production Contributes to Acidic pH Tolerance in<i>Sinorhizobium meliloti</i>Rm1021
Bibliographic record
Abstract
In this work, the hypothesis that exopolysaccharide plays a role in the survival of Sinorhizobium meliloti at low pH levels is addressed. When S. meliloti was grown at pH 5.75, synthesis of succinoglycan increased, whereas synthesis of galactoglucan decreased. Succinoglycan that was isolated from cultures grown at low pH had a lower degree of polymerization relative to that which was isolated from cultures grown at neutral pH, suggesting that low-molecular weight (LMW) succinoglycan might play a role in adaptation to low pH. Mutants unable to produce succinoglycan or only able to produce high-molecular weight polysaccharide were found to be sensitive to low pH. However, strains unable to produce LMW polysaccharide were 10-fold more sensitive. In response to low pH, transcription of genes encoding proteins for succinoglycan, glycogen, and cyclic β(1-2) glucans biosynthesis increased, while those encoding proteins necessary for the biosynthesis of galactoglucan decreased. While changes in pH did not affect the production of glycogen or cyclic β(1-2) glucan, it was found that the inability to produce cyclic β(1-2) glucan did contribute to pH tolerance in the absence of succinoglycan. Finally, in addition to being sensitive to low pH, a strain carrying mutations in exoK and exsH, which encode the glycanases responsible for the cleavage of succinoglycan to LMW succinoglycan, exhibited a delay in nodulation and was uncompetitive for nodule occupancy. Taken together, the data suggest that the role for LMW succinoglycan in nodule development may be to enhance survival in the colonized curled root hair.
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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 | 0.000 |
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".