Drought-Driven Rhizosphere Microbiome and Metabolome Remodeling in Wild vs. Cultivated Saccharum arundinaceum
Bibliographic record
Abstract
Sugarcane is highly sensitive to the variations in soil moisture content capacity, and upregulated water stress efficiency restricts its development and crop output. Rhizospheric microbes and metabolites play key roles to mitigate the adverse effects of abiotic stresses, i.e., drought stress. The drought-tolerant wild sugarcane relative, Saccharum arundinaceum Retz., remains poorly characterized with respect to its rhizosphere microbial community dynamics under water limitation. To address this, we analyzed drought-associated shifts in the rhizosphere microbiome and metabolome by comparing native plants from a long-term arid habitat in Guangxi, China, with plants from an irrigated cultivation environment. We analyzed the effects of agronomic traits, soil properties, enzyme activities, and 16S rRNA sequencing and untargeted metabolomics to characterize microbial communities and metabolites, with correlation analyses. Results demonstrated that wild plants possessed thicker stems, higher proline levels, and increased antioxidant enzyme activity. Their rhizospheres were enriched with Actinobacteria, Proteobacteria, and Chloroflexi, which exhibited upregulated urease and acid phosphatase activities. Metabolites linked to phosphotransferase systems and sugar metabolisms were also more abundant. Positive correlations between these microbes, metabolites, and drought traits reveal site-specific microbial–metabolic modules that confer drought resilience, providing valuable insights for sugarcane breeding programs.
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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 | 0.001 |
| 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) | 0.001 | 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".