Inoculation With the Plant-Growth-Promoting Rhizobacterium Pseudomonas fluorescens LBUM677 Impacts the Rhizosphere Microbiome of Three Oilseed Crops
Bibliographic record
Abstract
The bacterial communities inhabiting the rhizosphere play an important role in plant development and health. Here we studied the effect of inoculation with Pseudomonas fluorescens LBUM677, a plant growth promoting rhizobacterium that promotes seed oil accumulation, on the rhizosphere microbiome of three oilseed crops (Brassica napus, Buglossoides arvensis and Glycine max) over time. Next-Generation high-throughput sequencing targeting the V4 region of 16S rDNA was used to characterize the microbial communities associated with the three different crops, inoculated or not with LBUM677, over a time period of up to 90 days post-inoculation. A total of 1,627,231 amplicon sequence variants were obtained and were taxonomically grouped into 39 different phyla. LBUM677 inoculation and sampling date were found to significantly influence the rhizosphere microbiome of the three oil-producing crops under study. Specifically, inoculation with LBUM677 and sampling date, but not the plant species, were found to significantly alter the alpha- and the beta-diversity of the rhizosphere microbial communities. Differential abundance analyses found that 29 taxonomical bacterial groups were significantly more abundant in the LBUM677 treatments while 30 were significantly more abundant in the control treatments. Predicted functions of the microorganisms were also enriched, including 47 enzymatic pathways in LBUM677 treatments. These non-targeted effects on rhizosphere bacterial communities are discussed in the context of oilseed crops.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".