Back

Low RT-based Genome Editing Fidelity in Mouse Hepatocytes: Challenges and Solutions

Dunyak, M. T.; Hanna, P.; Nan, A. X.; von Stetina, J.; Pokharel, R.; O'Hara, M.; Estes, B. J.; Zheng, K.; Svenson, S.; Andresen, J.; Li, W.; Agrawal, M.; Hughes, C.; Spicer, M.; Choudhary, V.; Kelley, L.; Wang, J.; Meagher, M.; Xie, J.; Mukherjee, S.; Finn, J. D.

2024-11-03 bioengineering
10.1101/2024.10.31.621282 bioRxiv
Show abstract

Abstract/SummaryIntegrase-mediated Programmable Genomic Integration (I-PGI) uses a Cas9 nickase (nCas9) with a reverse transcriptase (RT), to write a large serine integrase (LSI) target site (attB/P, here called "beacon") in a programmed location. Co-delivery of the LSI and a DNA template containing the cognate recognition site results in precise integration of the template in a specific genomic location. While we were able to achieve high-fidelity beacon placement in a range of primate cycling and non-dividing cells, when translating our technology into an in vivo rodent model (liver) we surprisingly observed very low beacon fidelity, with the vast majority of beacons being unsuitable for integration. This phenomenon was independent of mouse strain, but was specific to non-dividing cells, as a cycling mouse hepatocyte cell line (Hepa1-6) demonstrated very high levels of fidelity. To address this issue we utilized neonatal mice, which have a much higher proportion of proliferating hepatocytes than adult mice. This resulted in a significant increase in the placement of high-fidelity beacons, and achieved functional gene expression after I-PGI in a therapeutically relevant target site. In an alternate approach, we engineered transgenic mice with intact beacons placed in specific genomic locations, allowing us to optimize integrase and DNA template dosing and kinetics. In summary, we have identified a previously undescribed challenge when using RT-based editing to write long sequences (~40 bp) in non-dividing rodent hepatocytes. This phenomenon was specific to rodents and was not observed in primate dividing or non-dividing cells. This previously unidentified challenge using RTs will limit the use of I-PGI in mouse models, however here we describe two methods that address this issue.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

1
PLOS ONE
4510 papers in training set
Top 16%
12.3%
2
Scientific Reports
3102 papers in training set
Top 10%
8.4%
3
The CRISPR Journal
33 papers in training set
Top 0.1%
3.6%
4
BMC Genomics
328 papers in training set
Top 0.9%
3.6%
5
BMC Biotechnology
10 papers in training set
Top 0.1%
3.6%
6
Cell Systems
167 papers in training set
Top 4%
3.6%
7
Cell Reports Methods
141 papers in training set
Top 0.9%
3.6%
8
Frontiers in Bioengineering and Biotechnology
88 papers in training set
Top 0.6%
3.6%
9
iScience
1063 papers in training set
Top 6%
3.1%
10
F1000Research
79 papers in training set
Top 0.7%
2.9%
11
Mobile DNA
27 papers in training set
Top 0.1%
2.9%
50% of probability mass above
12
Nucleic Acids Research
1128 papers in training set
Top 7%
2.7%
13
Philosophical Transactions of the Royal Society B
51 papers in training set
Top 2%
2.6%
14
Computational and Structural Biotechnology Journal
216 papers in training set
Top 4%
1.8%
15
Nature Communications
4913 papers in training set
Top 50%
1.8%
16
Journal of The Royal Society Interface
189 papers in training set
Top 3%
1.2%
17
eneuro
389 papers in training set
Top 8%
0.9%
18
PLOS Computational Biology
1633 papers in training set
Top 22%
0.9%
19
Frontiers in Bioinformatics
45 papers in training set
Top 0.7%
0.9%
20
Molecular Therapy
71 papers in training set
Top 2%
0.9%
21
PeerJ
261 papers in training set
Top 13%
0.9%
22
Cellular and Molecular Bioengineering
21 papers in training set
Top 0.3%
0.8%
23
Stem Cell Reports
118 papers in training set
Top 0.9%
0.8%
24
ACS Chemical Biology
150 papers in training set
Top 2%
0.8%
25
Biotechnology and Bioengineering
49 papers in training set
Top 0.9%
0.7%
26
PLOS Neglected Tropical Diseases
378 papers in training set
Top 5%
0.7%
27
BMC Bioinformatics
383 papers in training set
Top 7%
0.7%
28
Communications Biology
886 papers in training set
Top 24%
0.7%
29
International Journal of Molecular Sciences
453 papers in training set
Top 16%
0.7%
30
Frontiers in Cellular and Infection Microbiology
98 papers in training set
Top 6%
0.7%