Habitat generalists or specialists, insights from comparative genomic analyses of <i>Thermosipho</i> lineages
Bibliographic record
Abstract
Abstract Thermosipho species inhabit various extreme environments such as marine hydrothermal vents, petroleum reservoirs and terrestrial hot springs. A 16S rRNA phylogeny of available Thermosipho spp. sequences suggested habitat specialists adapted to living in hydrothermal vents only, and habitat generalists inhabiting oil reservoirs, hydrothermal vents and hotsprings. Comparative genomics and recombination analysis of the genomes of 15 Thermosipho isolates separated them into three species with different habitat distributions, the widely distributed T. africanus and the more specialized, T. melanesiensis and T. affectus . The three Thermosipho species can also be differentiated on the basis of genome content. For instance the T. africanus genomes had the largest repertoire of carbohydrate metabolism, which could explain why these isolates were obtained from ecologically more divergent habitats. The three species also show different capacities for defense against foreign DNA. T. melanesiensis and T. africanus both had a complete RM system, while this was missing in T. affectus . These observations also correlated with Pacbio sequencing, which revealed a methylated T. melanesiensis BI431 genome, while no methylation was detected among two T. affectus isolates. All the genomes carry CRISPR arrays accompanied by more or less complete CRISPR-cas systems. Interestingly, some isolates of both T. melanesiensis and T. africanus carry integrated prophage elements, with spacers matching these in their CRISPR arrays. Taken together, the comparative genomic analyses of Thermosipho spp. revealed genetic variation allowing habitat differentiation within the genus as well as differentiation with respect to invading mobile DNA that is present in subsurface ecosystems.
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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 | 0.001 |
| 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".