The Use of Transposon Insertion Sequencing to Interrogate the Core Functional Genome of the Legume Symbiont Rhizobium leguminosarum
Bibliographic record
Abstract
The free-living legume symbiont Rhizobium leguminosarum is of significant economic value because of its ability to provide fixed nitrogen to globally important leguminous food crops, such as peas and lentils. Discovery based research into the genetics and physiology of R. leguminosarum provides the foundational knowledge necessary for understanding the bacterium’s complex lifestyle, and to subsequently improve its application as a crop biological. Transposon insertion sequencing (INSeq) facilitates high-throughput forward genetic screening at a genomic scale to identify individual genes required for growth in a specific environment. In this study we applied INSeq to screen the genome of R. leguminosarum bv. viciae strain 3841 (RLV3841) for genes required for growth on minimal mannitol containing medium. Results from this study were contrasted with a prior INSeq experiment screened on peptide rich media to identify a common set of functional genes necessary for basic physiology. Contrasting the two growth conditions indicated that approximately 10% of the chromosome was required for growth, under both growth conditions. Specific genes that were essential to singular growth conditions were also identified. Data from INSeq screening on mannitol as a sole carbon source were used to reconstruct a metabolic map summarizing growth impaired phenotypes observed in the Embden-Meyerhof-Parnas pathway, Entner-Doudoroff pathway, pentose phosphate pathway, and tricarboxylic acid cycle. This revealed the presence of mannitol dependent and independent metabolic pathways required for growth, along with identifying metabolic steps with isozymes or possible carbon flux by-passes. Additionally, genes were identified on plasmids pRL11 and pRL12 that are likely to encode for functional activities important to the central physiology of RLV3841.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".