Impact of Plant Growth-Promoting Rhizobacteria Inoculation and Grafting on Tolerance of Tomato to Combined Water and Nutrient Stress Assessed via Metabolomics Analysis
Bibliographic record
Abstract
In the current study, inoculation with plant growth-promoting rhizobacteria (PGPR) and grafting were tested as possible cultural practices that may enhance resilience of tomato to stress induced by combined water and nutrient shortage. The roots of tomato grown on perlite were either inoculated or not with PGPR, applying four different treatments. These were PGPR-T1, a mix of two Enterobacter sp. strains (C1.2 and C1.5); PGPR-T2, Paenibacillus sp. strain DN1.2; PGPR-T3, Enterobacter mori strain C3.1; and PGPR-T4, Lelliottia sp. strain D2.4. PGPR-treated plants were either self-grafted or grafted onto Solanum lycopersicum cv. M82 and received either full or 50% of their standard water, nitrogen, and phosphorus needs. The vegetative biomass of plants subjected to PGPR-T1 was not reduced when plants were cultivated under combined stress, while it was reduced by stress to the rest of the PGPR treatments. However, PGPR-T3 increased considerably plant biomass of non-stressed tomato plants than did all other treatments. PGPR application had no impact on fruit biomass, while grafting onto ’M82’ increased fruit production than did self-grafting. Metabolomics analysis in tomato leaves revealed that combined stress affects several metabolites, most of them already described as stress-related, including trehalose, myo-inositol, and monopalmitin. PGPR inoculati o n with E. mori strain C3.1 affected metabolites, which are important for plant/microbe symbiosis (myo-inositol and monopalmitin). The rootstock M82 did not affect many metabolites in plant leaves, but it clearly decreased the levels of malate and D -fructose and imposed an accumulation of oleic acid. In conclusion, PGPR are capable of increasing tomato tolerance to combined stress. However, further research is required to evaluate more strains and refine protocols for their application. Metabolites that were discovered as biomarkers could be used to accelerate the screening process for traits such as stress tolerance to abiotic and/or abiotic stresses. Finally, ‘M82’ is a suitable rootstock for tomato, as it is capable of increasing fruit biomass production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".