The use of high throughput DNA sequence analysis to assess the endophytic microbiome of date palm roots grown under different levels of salt stress.
Bibliographic record
Abstract
Date palms are able to grow under diverse abiotic stress conditions including in saline soils, where microbial communities may be help in the plant's salinity tolerance. These communities able to produce specific growth promoting substances can enhance date palm growth in a saline environment. However, these communities are poorly defined. In the work reported here, the date palm endophytic bacterial and fungal communities were identified using the pyrosequencing method, and the microbial differential abundance in the root upon exposure to salinity stress was estimated. Approximately 150,061 reads were produced from the analysis of six ribosomal DNA libraries, which were prepared from endophytic microorganisms colonizing date palm root tissues. DNA sequence analysis of these libraries predicted the presence of a variety of bacterial and fungal endophytic species, some known and others unknown. The microbial community compositions of 30% and 8% of the bacterial and fungal species, respectively, were significantly (p ≤ 0.05) altered in response to salinity stress. Differential enrichment analysis showed that microbe diversity indicated by the Chao, Shannon and Simpson indices were slightly reduced, however, the overall microbial community structures were not significantly affected as a consequence of salinity. This may reflect a buffering effect by the host plant on the internal environments that these communities are colonizing. Some of the endophytes identified in this study were strains that were previously isolated from saline and marine environments. This suggests possible interactions with the plant that are favorable to salinity tolerance in date palm. [Int Microbiol 19(3):143-155 (2016)].
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".