Additional file 2 of Disease-induced changes in plant microbiome assembly and functional adaptation
Bibliographic record
Abstract
Additional file 1. Fig. S1 Pathogen isolation, identification, and pathogenicity test. Fig. S2 Samples were divided into different compartments for preparing of DNA extraction. Fig. S3 NMDS of bacterial communities in soil, root, stem (3 sections), and fruit. Fig. S4 NMDS of fungal communities in soil, root, stem (3 sections), and fruit. Fig. S5 Changes of alpha diversity indices and taxonomic composition of bacterial and fungal communities. Fig. S6 Relative abundance of differentially abundant taxa between healthy and diseased plant. Fig. S7 The volcano plots illustrating the enrichment and depletion patterns of the bacterial and fungal microbiomes in FWD plant compartments compared with the healthy. Fig. S8 The volcano plots illustrating the enrichment and depletion patterns of the bacterial class in FWD plants compartments in Guiyang (top) and Huishui (bottom), when the healthy plants were used as a baseline. Fig. S9 The volcano plots illustrating the enrichment and depletion patterns of the fungal phylum in FWD plants compartments in Guiyang (top) and Huishui (bottom), when the healthy plants were used as a baseline. Fig. S10 The volcano plots illustrating the enrichment and depletion patterns of microbiome in FWD plants all compartments in Guiyang (left) and Huishui (right), when the healthy plants were used as a baseline. Fig. S11 Intra- and interkingdom co-occurrence networks at Guiyang and Huishui. Fig. S12 Interkingdom co-occurrence networks in soil, root, stem (3 sections), and fruit. Fig. S13 Taxonomic composition and differentially abundant taxa of bacterial and fungal communities between healthy and diseased root endosphere and upper stem epidermis from metagenomic sequencing data. Fig. S14 Changes of microbiome functional profiles between healthy and diseased root endosphere and upper stem epidermis. Table S1. Primers information used in this study. Table S2. PERMANOVA by adonis of all bacterial 16S and fungal ITS samples. Table S3. PERMANOVA by adonis of bacterial 16S conducted separately for each compartment. Table S4. PERMANOVA by adonis of fungal ITS conducted separately for each compartment. Table S5. Distance to centroid was calculated by analysis of beta-dispersion using Bray–Curtis dissimilarity. Table S6. Linear-mixed model (LMM) for alpha diversity indices. Table S7. Linear-mixed model for bacterial phylum and fungal class composition. Table S8. Differentially abundant analysis showing the enrichment and depletion patterns of bacterial microbiomes in diseased organs compared with healthy organs. Table S9. Differentially abundant analysis showing the enrichment and depletion patterns of fungal microbiomes in diseased organs compared with healthy organs. Table S10. Topology properties of the intra- and interkingdom networks. Table S11. The taxonomic composition of bacterial phylum and fungal class between healthy and diseased intra- and interkingdom networks. Table S12. The taxonomic position of top 10 hubs in intra- and interkingdom networks. Table S13. Numbers of enriched and depleted functions in diseased plant compared with the healthy plant. Table S14. Functional annotation of differentially abundant genes (top 20) between healthy and diseased plant calculated by LEfSe difference analysis.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.813 | 0.144 |
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".