Pyrosequencing of the bacteria associated with Platygyra carnosus corals with skeletal growth anomalies reveals differences in bacterial community composition in apparently healthy and diseased tissues
Bibliographic record
Abstract
Corals are rapidly declining globally due to coral diseases. Skeletal growth anomalies (SGA) or "coral tumors" are a group of coral diseases that affect coral reefs worldwide, including Hong Kong waters in the Indo-Pacific region. To better understand how bacterial communities may vary in corals with SGA, for the first time, we examined the bacterial composition associated with the apparently healthy and the diseased tissues of SGA-affected Platgyra carnosus using 16S ribosomal rRNA gene pyrosequencing. Taxonomic analysis revealed Proteobacteria, Bacteroidetes, Cyanobacteria, and Actinobacteria as the main phyla in both the apparently healthy and the diseased tissues. A significant difference in the bacterial community composition was observed between the two conditions at the OTU level. Diseased tissues were associated with higher abundances of Acidobacteria and Gemmatimonadetes, and a lower abundance of Spirochaetes. Several OTUs belonging to Rhodobacteraceae, Rhizobiales, Gammaproteobacteria, and Cytophaga-Flavobacterium-Bacteroidetes (CFB) were strongly associated with the diseased tissues. These groups of bacteria may contain potential pathogens involved with the development of SGA or opportunistic secondary or tertiary colonizers that proliferated upon the health-compromised coral host. We suggest that these bacterial groups to be further studied based on inoculation experiments and testing of Koch's postulates in efforts to understand the etiology and progression of SGA.
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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 | 0.000 | 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".