Soil bacterial communities associated with stony soils influence the tuber size of Tetrastigma hemsleyanum
Bibliographic record
Abstract
Abstract Background Plants grown in stony soils have better-developed root systems and higher crop yields than those grown in non-stony soils. The roles of various physical and chemical effects of stony soils on plant growth have been published, but the roles of soil microbiota and rhizosphere microbiota have not been investigated. Methods Tetrastigma hemsleyanum plants were cultivated for two years in stony soils and in the same soil from which rock-fragments had been removed. The microbiome and the tuber transcriptome were analyzed, using multiple bioinformatics methods. Results The soil microbiota of these two soils were markedly different, and the stony soils contained high abundances of bacterial taxa belonging to the Actinobacteria, Rokubacteria, Rhizobiales, Desulfarculaceae, and Chthoniobacteraceae. These discriminatory taxa in soils may promote the tuber growth of T. hemsleyanum, through releasing nutrients from rocks and colonizing the rhizosphere and tuber surface of T. hemsleyanum. In addition, stony soils induced a dramatic change in the tuber’s transcriptome, particularly with respect to the pathways of phytohormone biosynthesis, photosynthesis, and biotic stress resistance, expression levels of which showed strong correlations with the aforementioned bacterial taxa. Conclusions These results indicated that beneficial effects of stony soils on plant growth may be closely correlated with their specific microbiota, which can, in turn, influence multiple biological processes of host. This is the first study to reveal the role of stony soils-driven microbiota in tuber growth, and stony soils can represent a microbial repository for the screening of microbial isolates to increase plant yield.
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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) | 0.001 | 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".