Transcription Activator-Like Effector Proteins Induce the Expression of the Frataxin Gene
Bibliographic record
Abstract
Genes encoding transcription activator-like effector (TALE) proteins may be engineered to target specific DNA sequences. TALEs fused with a transcription activator can be used to specifically induce the expression of a gene. This could lead to completely new therapies for several diseases. We have applied this potential therapeutic approach to Friedreich ataxia (FRDA), as an example. FRDA is due to reduced expression of frataxin because of elongation of a trinucleotide (GAA) repeat in intron 1. Our aim was to develop a potential treatment for FRDA by increasing the expression of the frataxin gene. We engineered 12 TALE genes (TALE(Frat)) encoding TALE(Frat) proteins, each specifically targeting different 14-bp DNA sequences within the proximal region of the human frataxin promoter. When the genes encoding these TALE(Frat) proteins were fused with a transcription activator, that is, four VP16 peptides (i.e., VP64), the resulting TALE(Frat)-VP64 proteins induced the expression of an mCherry reporter gene fused to a mini-cytomegalovirus promoter able to be activated by the insertion of the frataxin proximal promoter upstream to the minipromoter. These TALE(Frat)-VP64 proteins also increased, by 2- to 3-fold, frataxin gene expression (detected by qRT-PCR) in the cells. We conclude that TALE(Frat) proteins targeting the frataxin promoter may be used to increase the expression of frataxin mRNA and potentially could alleviate the symptoms of Friedreich ataxia. TALE methodology opens a new field of research, which could be used to develop TALE proteins to treat other diseases by inducing the expression of specific genes.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".