Metagenomics Evidence of a Novel Thermoplasmata-like Archaeon Decomposing Heterodisulfide in Nonacidic Estuary-Ocean Sediment
Bibliographic record
Abstract
Thermoplasmata archaea, renowned for their acidophilic and thermophilic traits, have been implicated in methanogenic metabolism across diverse environments. However, their metabolic capabilities in nonacidic settings remain understudied. This metagenomic investigation unveils the microbiome dynamics and metabolic processes within nonacidic estuary-ocean sediments across 19 sites (seven groups) in the Pearl River Estuary and South China Sea regions. Remarkably, the microbiomes exhibited rapid adaptation and compositional consistency despite spatial heterogeneity, suggesting the overriding influences of salinity, pH, and nutrient availability. Metabolic pathway reconstruction revealed prevalent modules for amino acid, nucleotide, lipid, vitamin, and carbohydrate metabolism, indicating community adaptation to this environment. Additionally, the high occurrence of heterodisulfide reductase ( hdrA ) genes implicated potential roles in CoM-S-S-CoB degradation. Notably, we discovered a novel Thermoplasmata-like genome (MAG3 in g3) that, despite sharing core genomic traits with known Thermoplasmata, harbored distinct genetic variabilities. Phylogenetic evidence robustly affiliated the Thermoplasmata-like archaeon with methanogenesis pathways, suggesting its potential involvement in CoM-S-S-CoB degradation under nonacidic conditions. This study enhances the understanding of microbiome composition and metabolic processes and highlights the pivotal contribution of Thermoplasmata archaea to sulfur cycling within nonacidic estuarine-ocean sediments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".