Reference nodule transcriptomes for <scp> <i>Melilotus officinalis</i> </scp> and <scp> <i>Medicago sativa</i> </scp> cv. Algonquin
Bibliographic record
Abstract
Abstract Host/symbiont compatibility is a hallmark of the symbiotic nitrogen‐fixing interaction between rhizobia and legumes, mediated in part by plant‐produced nodule‐specific cysteine‐rich (NCR) peptides and the bacterial BacA membrane protein that can act as a NCR peptide transporter. In addition, the genetic and metabolic properties supporting symbiotic nitrogen fixation often differ between compatible partners, including those sharing a common partner, highlighting the need for multiple study systems. Here, we report high‐quality nodule transcriptome assemblies for Medicago sativa cv. Algonquin and Melilotus officinalis , two legumes able to form compatible symbioses with Sinorhizobium meliloti . The compressed M. sativa and M. officinalis assemblies consisted of 79,978 and 64,593 contigs, respectively, of which 33,341 and 28,278 were assigned putative annotations, respectively. As expected, the two transcriptomes showed broad similarity at a global level. We were particularly interested in the NCR peptide profiles of these plants, as these peptides drive bacterial differentiation during the symbiosis. A total of 412 and 308 NCR peptides were predicted from the M. sativa and M. officinalis transcriptomes, respectively, with approximately 9% of the transcriptome of both species consisting of NCR transcripts. Notably, transcripts encoding highly cationic NCR peptides (isoelectric point > 9.5), which are known to have antimicrobial properties, were ∼2‐fold more abundant in M. sativa than in M. officinalis , and ∼27‐fold more abundant when considering only NCR peptides in the six‐cysteine class. We hypothesize that the difference in abundance of highly cationic NCR peptides explains our previous observation that some rhizobial bacA alleles which can support symbiosis with M. officinalis are unable to support symbiosis with M. sativa .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".