Tolerance, Adaptation, and Cell Response Elicited by Micromonospora sp. Facing Tellurite Toxicity: A Biological and Physical-Chemical Characterization
Bibliographic record
Abstract
The intense use of tellurium (Te) in industrial applications, along with the improper disposal of Te-derivatives, is causing their accumulation in the environment, where oxyanion tellurite (TeO32−) is the most soluble, bioavailable, and toxic Te-species. On the other hand, tellurium is a rare metalloid element whose natural supply will end shortly with possible economic and technological effects. Thus, Te-containing waste represents the source from which Te should be recycled and recovered. Among the explored strategies, the microbial TeO32− biotransformation into less toxic Te-species is the most appropriate concerning the circular economy. Actinomycetes are ideal candidates in environmental biotechnology. However, their exploration in TeO32− biotransformation is scarce due to limited knowledge regarding oxyanion microbial processing. Here, this gap was filled by investigating the cell tolerance, adaptation, and response to TeO32− of a Micromonospora strain isolated from a metal(loid)-rich environment. To this aim, an integrated biological, physical-chemical, and statistical approach combining physiological and biochemical assays with confocal or scanning electron (SEM) microscopy and Fourier-transform infrared spectroscopy in attenuated total reflectance mode (ATR-FTIR) was designed. Micromonospora cells exposed to TeO32− under different physiological states revealed a series of striking cell responses, such as cell morphology changes, extracellular polymeric substance production, cell membrane damages and modifications, oxidative stress burst, protein aggregation and phosphorylation, and superoxide dismutase induction. These results highlight this Micromonospora strain as an asset for biotechnological purposes.
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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 | 0.000 | 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) | 0.000 | 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".