TEMPERATURE REGULATION OF PLANT- RHIZOBACTERIA INTERACTIONS WITHIN THE SOIL-RHIZOSPHERE- RHIZOPLANE INTERFACE
Bibliographic record
Abstract
An optimal microbiome is important for plant health and can prime subsequent plant immune responses to enhance stress resilience. However, the molecular mechanisms that dynamically drive host-microbiome interactions and subsequent induced systemic resistance (ISR) responses in plants under elevated environmental temperatures remain underexplored. To address this major knowledge gap, this thesis further investigated Canadian soil-inhabiting rhizobacteria that had been previously shown to activate ISR in tomato (Solanum lycopersicum) plants. Specifically, this study characterized the in vitro physiology of these rhizobacterial strains under different temperatures, including growth, phosphate solubilization abilities and direct anti- pathogenic capabilities. Temperature-sensitivity of these parameters were largely species- dependent, with an observed distinction between Bacillus and Pseudomonas proliferation in various rhizosphere fractions. In situ, bacterial proliferation of a Gram-positive Bacillus velezensis strain and Gram-negative Pseudomonas defensor WCS374 was monitored in the rhizosphere and on the rhizoplane of tomato plants at two temperatures. It was determined that the rhizobacterial competency and the host plant’s epiphytic recruitment of microbes were species-dependent, and not significantly influenced by the temperature changes within the experimental parameters. Finally, gene expression analyses by RT-qPCR revealed that Bacillus and Pseudomonas did not significantly influence the expression of defence hormone-related gene expression in the tomato phyllosphere (i.e., aboveground parts of the plant). Overall, this thesis demonstrates the effect of temperature on plant interactions with, and responses to, rhizosphere microbiota. This research lays the foundation for future investigations of these experimental ISR- inducing rhizobacterial strains and is vital to potentially advance the development of microbiome-based technologies facing global warming.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".