Specialization in a Nitrogen-Fixing Symbiosis: Proteome Differences Between <i>Sinorhizobium medicae</i> Bacteria and Bacteroids
Bibliographic record
Abstract
Using tandem mass spectrometry (MS/MS), we analyzed the proteome of Sinorhizobium medicae WSM419 growing as free-living cells and in symbiosis with Medicago truncatula. In all, 3,215 proteins were identified, over half of the open reading frames predicted from the genomic sequence. The abundance of 1,361 proteins displayed strong lifestyle bias. In total, 1,131 proteins had similar levels in bacteroids and free-living cells, and the low levels of 723 proteins prevented statistically significant assignments. Nitrogenase subunits comprised approximately 12% of quantified bacteroid proteins. Other major bacteroid proteins included symbiosis-specific cytochromes and FixABCX, which transfer electrons to nitrogenase. Bacteroids had normal levels of proteins involved in amino acid biosynthesis, glycolysis or gluconeogenesis, and the pentose phosphate pathway; however, several amino acid degradation pathways were repressed. This suggests that bacteroids maintain a relatively independent anabolic metabolism. Tricarboxylic acid cycle proteins were highly expressed in bacteroids and no other catabolic pathway emerged as an obvious candidate to supply energy and reductant to nitrogen fixation. Bacterial stress response proteins were induced in bacteroids. Many WSM419 proteins that are not encoded in S. meliloti Rm1021 were detected, and understanding the functions of these proteins might clarify why S. medicae WSM419 forms a more effective symbiosis with M. truncatula than S. meliloti Rm1021. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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 | 0.000 |
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".