<i>Thermosipho</i> spp. immune system differences affect variation in genome size and geographical distributions
Bibliographic record
Abstract
Abstract Thermosipho species inhabit thermal 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 of 15 Thermosipho genomes separated them into three distinct species with different habitat distributions: the widely distributed T. africanus and the more specialized, T. melanesiensis and T. affectus . Moreover, the species can be differentiated on the basis of genome size, genome content and immune system composition. For instance, the T. africanus genomes are largest and contained the most carbohydrate metabolism genes, which could explain why these isolates were obtained from ecologically more divergent habitats. Nonetheless, all the Thermosipho genomes, like other Thermotogae genomes, show evidence of genome streamlining. Genome size differences between the species could further be correlated to differences in defense capacities against foreign DNA, which influence recombination via HGT. The smallest genomes are found in T. affectus that contain both CRISPR-cas Type I and III systems, but no RM system genes. We suggest that this has caused these genomes to be almost devoid of mobile elements, contrasting the two other species genomes that contain a higher abundance of mobile elements combined with different immune system configurations. 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.
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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) | 0.002 | 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".