A multiomic analysis of in situ coral–turf algal interactions
Bibliographic record
Abstract
Viruses, microbes, and host macroorganisms form ecological units called holobionts. Here, a combination of metagenomic sequencing, metabolomic profiling, and epifluorescence microscopy was used to investigate how the different components of the holobiont including bacteria, viruses, and their associated metabolites mediate ecological interactions between corals and turf algae. The data demonstrate that there was a microbial assemblage unique to the coral-turf algae interface displaying higher microbial abundances and larger microbial cells. This was consistent with previous studies showing that turf algae exudates feed interface and coral-associated microbial communities, often at the detriment of the coral. Further supporting this hypothesis, when the metabolites were assigned a nominal oxidation state of carbon (NOSC), we found that the turf algal metabolites were significantly more reduced (i.e., have higher potential energy) compared to the corals and interfaces. The algae feeding hypothesis was further supported when the ecological outcomes of interactions (e.g., whether coral was winning or losing) were considered. For example, coral holobionts losing the competition with turf algae had higher Bacteroidetes-to-Firmicutes ratios and an elevated abundance of genes involved in bacterial growth and division. These changes were similar to trends observed in the obese human gut microbiome, where overfeeding of the microbiome creates a dysbiosis detrimental to the long-term health of the metazoan host. Together these results show that there are specific biogeochemical changes at coral-turf algal interfaces that predict the competitive outcomes between holobionts and are consistent with algal exudates feeding coral-associated microbes.
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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 | 0.001 |
| 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) | 0.001 | 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".