CRISPR-Mediated Transcriptional Activation of Squalene Synthase to Increase the Biosynthesis of Anticancer Metabolite Inotodiol in Inonotus Obliquus: A Research Protocol
Bibliographic record
Abstract
Chaga (Inonotus obliquus) is a parasitic fungus of birch trees used in the traditional medicine of Russia, China, and other Eurasian countries. The secondary metabolites produced by I. obliquus are known to possess anticancer, anti-inflammatory, antiviral, antioxidant, and hypoglycemic activity, while posing no known adverse effects. The therapeutic properties of I. obliquus are thus of particular medical interest. Several studies have demonstrated that the triterpenoids produced by I. obliquus provide the anticancer effects. Specifically, the triterpenoid inotodiol has demonstrated promising antitumor effects in human cervical cancer HeLa cells. Unfortunately, the limited natural abundance of I. obliquus impedes use of this fungus as a source of inotodiol for clinical applications. Furthermore, attempts to culture I. obliquus in a laboratory setting are limited by the low expression of biosynthetic gene clusters. While chemical syntheses of inotodiol avoid these challenges, they are hindered by low yield, cost, and time. To address these challenges, we propose using CRISPR-mediated transcriptional activation (CRISPRa) to boost the expression of the enzyme (squalene synthase (SQS)) that mediates the biosynthesis of inotodiol in I. obliquus, thereby increasing production of the therapeutic metabolite. The level of inotodiol production achieved by endonuclease deficient Cas9s fused to transcriptional activator domains (dCas9-VPR) will be compared using a control group with no single guide RNA (sgRNA) as well as three test groups with sgRNA sequences, varying in their distance upstream from the SQS gene. To evaluate the proposed CRISPRa system, transcriptomic, proteomic, and metabolomic analyses will be implemented. To our knowledge, CRISPRa methodology has not yet been used to improve the yield of I. obliquus metabolites.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".