Phyllospheric Microbial Composition and Diversity of the Tobacco Leaves Infected by Didymella segeticola
Bibliographic record
Abstract
A Myriad of biotic and abiotic factors inevitably affects the growth and production of tobacco (Nicotiana tabacum L.), which is a model crop and sought-after worldwide for its foliage. Among the various impacts the level of disease severity poses on plants, the influence on the dynamics of phyllospheric microbial diversity is of utmost importance. In China, recurring reports of a phyto-pathogen, Didymella segeticola, a causal agent of tobacco leaf spot, accentuate the need for its in-depth investigation. Here, a high-throughput sequencing technique, IonS5TMXL was employed to analyze tobacco leaves infected by D. segeticola at different disease severity levels, ranging from T1G (least disease index) to T4G (highest disease index), in an attempt to explore the composition and diversity of phyllospheric microbiota. In all healthy and diseased tobacco leaves, the most dominant fungal phylum was Ascomycota with a high prevalence of genus Didymella, followed by Boeremia, Meyerozyma and Alternaria, whereas in the case of bacterial phyla, Proteobacteria was prominent with Pseudomonas being a predominant genus, followed by Pantoea. The relative abundance of fungi, i.e., Didymella and Boeremia (Ascomycota) and bacteria, i.e., Pseudomonas and Pantoea (Proteobacteria) were higher in diseased groups compared to healthy groups. Healthy tissues exhibited relatively rich and diverse fungal communities in contrast with diseased groups. The infection of D. segeticola had a complex and significant effect on fungal as well as bacterial alpha diversity. FUNGuild analysis indicated that the relative abundance of pathotrophs and saprotrophs in diseased tissues proportionally increased with disease severity. PICRUSt analysis of diseased tissues indicated that the relative abundance of bacterial cell motility and membrane transport-related gene sequences elevated with an increase in disease severity from T1G to T3G and then tended to decrease at T4G. Conclusively, the current study shows the typical characteristics of the tobacco leaf microbiome and provides insights into the distinct microbiome shifts on tobacco leaves infected by D. segeticola.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".