Bridging the Gap: Type III Secretion Systems in Plant-Beneficial Bacteria
Bibliographic record
Abstract
Type III secretion systems (T3SSs) are bacterial membrane-embedded nanomachines translocating effector proteins into the cytoplasm of eukaryotic cells. They have been intensively studied for their important roles in animal and plant bacterial diseases. Over the past two decades, genome sequencing has unveiled their ubiquitous distribution in many taxa of Gram-negative bacteria, including plant-beneficial ones. Here, we discuss the distribution and functions of the T3SS in two agronomically important bacterial groups: the symbiotic nodule-forming nitrogen-fixing rhizobia and the free-living plant-beneficial Pseudomonas spp. In legume-rhizobia symbiosis, T3SSs and their cognate effectors play important roles, including the modulation of the plant immune response and the initiation of the nodulation process in some cases. In plant-beneficial Pseudomonas spp., the roles of T3SSs are not fully understood, but pertain to plant immunity suppression, biocontrol against eukaryotic plant pathogens, mycorrhization facilitation, and possibly resistance against protist predation. The diversity of T3SSs in plant-beneficial bacteria points to their important roles in multifarious interkingdom interactions in the rhizosphere. We argue that the gap in research on T3SSs in plant-beneficial bacteria must be bridged to better understand bacteria/eukaryotes rhizosphere interactions and to support the development of efficient plant-growth promoting microbial inoculants.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".