Identification and cloning of the bacterial nodulation specificity gene in the <i>Sinorhizobium meliloti</i> <i>Medicago laciniata</i> symbiosis
Bibliographic record
Abstract
Medicago laciniata (cut-leaf medic) is an annual medic that is highly nodulation specific, nodulating only with a restricted range of Sinorhizobium meliloti. e.g., strain 102L4, but not with most strains that nodulate Medicago sativa (alfalfa), e.g., strains RCR2011 and Rm41. Our aim was to identify and clone the S. meliloti 102L4 gene implicated in the specific nodulation of M. laciniata and to characterize the adjacent nodulation (nod) region. An 11-kb EcoRI DNA fragment from S. meliloti 102L4 was shown to complement strain RCR2011 for nodulation of M. laciniata. Nucleotide sequencing revealed that this fragment contained nodABCIJ genes whose overall arrangement was similar to those found in strains RCR2011 and Rm41, which do not nodulate M. laciniata. Data for Tn5 mutagenesis of the nodABCIJ region of strain 102L4 suggested that the nodC gene was involved in the specific nodulation of M. laciniata. Tn5 insertions in the nodIJ genes gave mutants with nodulation delay phenotypes on both M. laciniata and M. sativa. Only subclones of the 11-kb DNA fragment containing a functional nodC gene from strain 102L4 were able to complement strain RCR2011 for nodulation of M. laciniata. The practical implications of these findings are discussed in the context of the development of a specific M. sativa - S. meliloti combination that excludes competition for nodulation by bacterial competitors resident in soil.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".