Inorganic salts and compatible solutes help mesophilic bacteria inhabit the high temperature waters of a Trans-Himalayan sulfur-borax spring
Bibliographic record
Abstract
Abstract While geographically-/geologically-distinct hot springs harbor different levels of microbial diversity, some of them encompass several such taxa which have no strain reported for laboratory growth at >45°C. We, therefore, hypothesized that native geomicrobial factors could be potent determinants of the microbial habitability of hot spring environments. To test this hypothesis, aquatic microbial communities were revealed metataxonomically, and considered in the context of spring-water chemistry, along the 85-14°C hydrothermal gradient of a sulfur-boron spring named Lotus Pond located at 4,436 m, within the Puga geothermal area of the Indian Trans-Himalayan region of Ladakh. Water samples were studied from four distinct sites along Lotus Pond ’s spring-water transit from the vent to an adjacent river called Rulang . Insinuations obtained from geomicrobiological data were tested via pure-culture growth experiments in habitat-inspired media. Microbial diversities were found to be high at all the sample-sites; majority of the genera identified at the 70-85°C sites were found to have no report of laboratory growth at >45°C; concurrently, these sample-sites had high concentrations of the kosmotropic solutes boron, lithium, sodium, sulfide, thiosulfate and sulfate, which are known to biophysically stabilize macromolecules. Based on the universal thermodynamic status of these solutes, we conjectured that they may be instrumental in helping mesophiles withstand high in situ temperatures. Corroboratively, growth experiments with a mesophilic, 80°C-isolate, Paracoccus SMMA_5 showed that at 50°C and 70°C, depending on the incubation-time, lithium/boron/sulfate/sodium/glycine-betaine either increases the number of colony-forming units present in the culture or arrests decline of the same. Incubations at 70°C, followed by fluorescein diacetate staining and flow cytometry, showed that these solutes keep more cells under viable condition than in ready-to-divide state. We concluded that kosmotropes and compatible solutes help mesophiles overcome the chaotropic effects of heat by augmenting such indigenous, entropy-minimizing biophysical mechanisms that apparently trade-off cell division for cell viability.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".