Three-Dimensional Molecular Cartography of the Caribbean Reef-Building Coral Orbicella faveolata
Bibliographic record
Abstract
All organisms host a diversity of associated viruses, bacteria, and protists, collectively defined as the holobiont. While scientific advancements have enhanced the understanding of the functional roles played by various components of the holobiont, there is a growing need to integrate multiple types of molecular data into spatially and temporally resolved frameworks. To that end, we mapped 16S and 18S rDNA metabarcoding, metatranscriptomics, and metabolomic data onto three-dimensional reconstructions of coral colonies to examine microbial diversity, microbial gene expression, and biochemistry on two colonies of the ecologically important, reef-building coral,Orbicella faveolataand their competitors (i.e., adjacent organisms interacting with the corals: fleshy algae, turf algae, hydrozoans, and other corals). Overall, no statistically significant spatial patterns were observed among the samples for any of the data types; instead, strong signatures of the macroorganismal hosts (e.g., coral, algae, hydrozoa) were detected, in the microbiome, the transcriptome, and the metabolome. The 16S rDNA analysis demonstrated higher abundance of Firmicutes in the coral microbiome than in its competitors. A single bacterial amplicon sequence variant from the genusClostridiumwas found exclusively in allO. faveolatasamples. In contrast to microbial taxa, a portion of the functionally annotated bacterial RNA transcripts (6.86%) and metabolites (1.95%) were ubiquitous in all coral and competitor samples. Machine learning analysis of microbial transcripts revealed elevated T7-like cyanophage-encoded photosystem II transcripts inO. faveolatasamples, while sequences involved in bacterial cell division were elevated in turf algal and interface samples. Similar analysis of metabolites revealed that bacterial-produced antimicrobial and antifungal compounds were highly enriched in coral samples. This study provides insight into the spatial and biological patterning of the coral microbiome, transcriptome, and metabolome.
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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 | 0.002 | 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".