Genetic Modification of the HSP90 Gene Using CRISPR-Cas9 to Enhance Thermotolerance in T. Suecica
Bibliographic record
Abstract
Phytoplankton are marine microorganisms that play a key role in the production of oxygen and serve as the foundation of the marine food chain. Over the past century, the population of phytoplankton has declined significantly with the onset of climate change. Although phytoplankton have the capacity to adapt to rising ocean temperatures, rapid environmental changes, including increased top-down control and thermal stratification, reduce populations before adaptations are incorporated into the genome. To enhance survival rates, thermotolerance in common algal strains can be enhanced through increased expression of the conserved Heat Shock Protein 90 (HSP90). Trials will be conducted on the common algal species, Tetraselmis suecica (T. suecica), for its considerable size, photosynthetic rate, and nutrient-rich properties. Thermotolerance will be augmented by splicing the HSP90 gene into the T. suecica metallothionein (Mt) promoter using CRISPR-Cas9. A period of incubation in a copper sulphate solution ensures Mt promoter stimulation, thereby increasing HSP90 expression. The efficacy of the proposed methods will be measured by comparing HSP90 protein production between transgenic and wild-type T. suecica cultures. The genomic incorporation of the modified HSP90 gene enables future populations to exhibit thermotolerance in the presence of heavy metals in the ocean beyond its basal level of expression. By accelerating the adaptation of thermotolerance, the overall fitness of T. suecica can be increased to re-establish its population under warmer oceanic conditions. By applying similar methods to other phytoplankton, the repopulation of various species can increase biodiversity and global net primary productivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".