Genomic resources for identification of the minimal <scp> <scp> N <sub>2</sub> </scp> </scp> ‐fixing symbiotic genome
Bibliographic record
Abstract
The lack of an appropriate genomic platform has precluded the use of gain-of-function approaches to study the rhizobium-legume symbiosis, preventing the establishment of the genes necessary and sufficient for symbiotic nitrogen fixation (SNF) and potentially hindering synthetic biology approaches aimed at engineering this process. Here, we describe the development of an appropriate system by reverse engineering Sinorhizobium meliloti. Using a novel in vivo cloning procedure, the engA-tRNA-rmlC (ETR) region, essential for cell viability and symbiosis, was transferred from Sinorhizobium fredii to the ancestral location on the S. meliloti chromosome, rendering the ETR region on pSymB redundant. A derivative of this strain lacking both the large symbiotic replicons (pSymA and pSymB) was constructed. Transfer of pSymA and pSymB back into this strain restored symbiotic capabilities with alfalfa. To delineate the location of the single-copy genes essential for SNF on these replicons, we screened a S. meliloti deletion library, representing > 95% of the 2900 genes of the symbiotic replicons, for their phenotypes with alfalfa. Only four loci, accounting for < 12% of pSymA and pSymB, were essential for SNF. These regions will serve as our preliminary target of the minimal set of horizontally acquired genes necessary and sufficient for SNF.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".