Symbiotic and non-symbiotic members of the genus <i>Ensifer</i> (syn. <i>Sinorhizobium</i> ) are separated into two clades based on comparative genomics and high-throughput phenotyping
Bibliographic record
Abstract
ABSTRACT Rhizobium – legume symbioses serve as a paradigmatic example for the study of mutualism evolution. The genus Ensifer (syn. Sinorhizobium ) contains diverse plant-associated bacteria, a subset of which can fix nitrogen in symbiosis with legumes. To gain insights into the evolution of symbiotic nitrogen fixation (SNF), and inter-kingdom mutualisms more generally, we performed extensive phenotypic, genomic, and phylogenetic analyses of the genus Ensifer . The data suggest that SNF emerged several times within the genus Ensifer , likely through independent horizontal gene transfer events. Yet, the majority (105 of 106) of the Ensifer strains with the nodABC and nifHDK nodulation and nitrogen fixation genes were found within a single, monophyletic clade. Comparative genomics highlighted several differences between the “symbiotic” and “non-symbiotic” clades, including divergences in their pangenome content. Additionally, strains of the symbiotic clade carried 325 fewer genes, on average, and appeared to have fewer rRNA operons than strains of the non-symbiotic clade. Characterizing a subset of ten Ensifer strains identified several phenotypic differences between the clades. Strains of the non-symbiotic clade could catabolize 25% more carbon sources, on average, than strains of the symbiotic clade, and they were better able to grow in LB medium and tolerate alkaline conditions. On the other hand, strains of the symbiotic clade were better able to tolerate heat stress and acidic conditions. We suggest that these data support the division of the genus Ensifer into two main subgroups, as well as the hypothesis that pre-existing genetic features are required to facilitate the evolution of SNF in bacteria.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".