Back

Anticodon Engineered Transfer RNAs (ACE-tRNAs) are a Platform Technology for Suppressing Nonsense Mutations

Ko, W.; Porter, J. J.; Spelier, S.; Couch, T.; van der Windt, I.; Bhatt, P.; Coote, K.; Mense, M.; Beekman, J.; Lueck, J. D.

2024-06-06 molecular biology
10.1101/2024.06.06.597760 bioRxiv
Show abstract

Nonsense mutations arise from single nucleotide substitutions that result in premature termination codons (PTCs). PTCs result in little to no full-length protein production and loss of mRNA expression through the nonsense-mediated mRNA decay (NMD) pathway. We demonstrate that anticodon engineered (ACE-) tRNAs efficiently suppress the most prevalent cystic fibrosis (CF) causing PTCs, promoting significant rescue of endogenous cystic fibrosis transmembrane conductance regulator (CFTR) transcript abundance and channel function in different model systems. We demonstrate that our best-performing ACE-tRNA, that decodes all UGA PTCs to a leucine amino acid, markedly rescues CFTR channel function from the most prevalent CF causing PTCs that arise from non-leucine encoding codons. Using this single ACE-tRNA variant, we demonstrate significant rescue of CFTR channel function in an immortalized airway cell line and two different primary CF patient-derived intestinal cell models with CFTR nonsense mutations. Thus, ACE-tRNAs have promise as a platform therapeutic for CF and other nonsense-associated diseases.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 2%
34.7%
Similar papers in this journal
2
eLife
● 5828 papers in training set
Top 13%
8.0%
Similar papers in this journal
3
Science Advances
● 1243 papers in training set
Top 6%
4.9%
Similar papers in this journal
4
Molecular Therapy
○ 81 papers in training set
Top 0.4%
4.4%
Similar papers in this journal
50% of probability mass above
5
Cell Reports
○ 1498 papers in training set
Top 10%
4.1%
Similar papers in this journal
6
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 14%
3.5%
7
Nucleic Acids Research
◐ 1281 papers in training set
Top 7%
2.4%
8
Communications Biology
○ 993 papers in training set
Top 12%
1.9%
9
Molecular Therapy - Nucleic Acids
○ 25 papers in training set
Top 0.3%
1.9%
10
iScience
○ 1154 papers in training set
Top 22%
1.5%
11
Cell Discovery
○ 57 papers in training set
Top 0.6%
1.5%
12
Circulation
○ 74 papers in training set
Top 2%
1.1%
13
Scientific Reports
○ 3612 papers in training set
Top 62%
1.1%
14
PLOS Genetics
● 862 papers in training set
Top 9%
1.1%
15
Nature Genetics
○ 286 papers in training set
Top 4%
1.1%
16
Cell
○ 431 papers in training set
Top 8%
1.1%
17
Molecular Cell
○ 350 papers in training set
Top 5%
0.9%
18
ACS Chemical Biology
● 167 papers in training set
Top 2%
0.9%
19
Cell Reports Medicine
○ 153 papers in training set
Top 5%
0.9%
20
eBioMedicine
○ 183 papers in training set
Top 6%
0.9%
21
Cell Reports Methods
○ 165 papers in training set
Top 3%
0.9%
22
JCI Insight
● 277 papers in training set
Top 7%
0.9%
23
Disease Models & Mechanisms
139 papers in training set
Top 3%
0.6%
24
Nature Cell Biology
○ 118 papers in training set
Top 3%
0.6%
25
Journal of Molecular Biology
○ 232 papers in training set
Top 4%
0.6%
26
Nature Biotechnology
○ 172 papers in training set
Top 5%
0.6%
27
PNAS Nexus
◐ 159 papers in training set
Top 4%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.