Microorganisms in sea ice melt pools as a source of ultra-violet radiation absorbing metabolites
Bibliographic record
Abstract
Natural products have many uses in today’s society, from disease therapeutics to\ncosmetic applications. One such application of natural products is use as an active\ningredient in commercial sunscreens. Ultra-violet (UV) radiation exposure can result in\na range of harmful side effects from a minor burn to the induction of melanoma. Due to\nthe hazards associated with UV exposure, there is a need for safe and effective natural\nsunscreens. Microbes are known to produce structurally diverse natural products with\ngreatly varied functions. One potential role of microbial natural products is to act as UV\nprotectants for the producing organism. For this thesis we wanted to describe the\ncultivable microbial community of sea ice melt pools to determine if microbes in this\nhabitat are resistant to UVB radiation, and if their mechanism of resistance was via the\nproduction of UV protectants. Thus, microbes living in high UV intense habitats are of\ninterest for this thesis. One such habitat is sea ice melt pools in Canada’s Arctic. During\nthe early summer months the sea ice begins to melt forming melt pools. Due to the\nconstant sun exposure, coupled with the reflective property of the ice, microbes present\nin these pools endure extreme levels of UV radiation. Microorganisms have three\nmechanisms in which they can survive exposure to UV radiation. They can produce\nspores, have DNA repair mechanisms, or they can produce UV-absorbing metabolites.\nWith this knowledge it was hypothesized that microbes living in these melt pools would\nbe resistant to UV radiation via the production of UV-absorbing metabolites. Water\nsamples were collected from sea ice melt pools in Nunavut and the cultivable microbial\ncommunity was identified via sequencing of the 16S rRNA gene (bacteria) and the\nITS/28S rRNA genes (fungi). Phylogenetic analysis revealed that the microbes belonged to 26 different species. Of these 26 species two of the organisms, Frigidiomyces\naurantiacum and Polaromyces triangulaformis, were discovered in this study and were\ndescribed during the course of the research. Each of the 26 organism’s temperature\ngrowth range, nutrient requirements, ability to survive a freeze thaw cycle, and\nresistance to UVB radiation were determined. Post exposure to UVB radiation,\ncompounds produced by each organism was extracted to determine if UV-absorbing\nmetabolites were being produced. The crude extracts were then analyzed using HPLCHRMS.\nOf the 26 organisms, seven were true psychrophiles, 17 could survive with\nminimal nutrients, all of the organisms tested remained viable after a single freeze-thaw\ncycle, and 20 were resistant to exposure to UVB radiation. HPLC-HRMS analysis of\nthe crude extracts revealed that four strains produced mycosporines or mycosporine-like\namino acids. One bacterium, Rhodococcus sp. RKAT245, produced a single\nmycosporine-like amino acid, shinorine. From the fungal library, Bulleromyces albus\nproduced mycosporine-glutaminol, Dioszegia sp. RKAT 238 produced mycosporineglutaminol,\nmycosporine-glutaminol-glucoside, and mycosporine-glutamicol-glucoside,\nwhile Frigidiomyces aurantiacum produced mycosporine-glutaminol-glucoside.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
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".