Abstract:
BACKGROUND:Single nucleotide variants account for approximately 90% of all known pathogenic variants responsible for human diseases. Recently discovered CRISPR/Cas9 base editors can correct individual nucleotides without cutting DNA and inducing double-stranded breaks. We aimed to find all possible pathogenic variants which can be efficiently targeted by any of the currently described base editors and to present them for further selection and development of targeted therapies. METHODS:ClinVar database (GRCh37_clinvar_20171203) was used to search and select mutations available for current single-base editing systems. We included only pathogenic and likely pathogenic variants for further analysis. For every potentially editable mutation we checked the presence of PAM. If a PAM was found, we analyzed the sequence to find possibility to edit only one nucleotide without changing neighboring nucleotides. The code of the script to search Clinvar database and to analyze the sequences was written in R and is available in the appendix. RESULTS:We analyzed 21 editing system currently reported in 9 publications. Every system has different working characteristics such as the editing window and PAM sequence. C > T base editors can precisely target 3196 mutations (46% of all pathogenic T > C variants), and A > G editors - 6900 mutations (34% of all pathogenic G > A variants). CONCLUSIONS:Protein engineering helps to develop new enzymes with a narrower window of base editors as well as using new Cas9 enzymes with different PAM sequences. But, even now the list of mutations which can be targeted with currently available systems is huge enough to choose and develop new targeted therapies.
journal_name
BMC Med Genomicsjournal_title
BMC medical genomicsauthors
Lavrov AV,Varenikov GG,Skoblov MYdoi
10.1186/s12920-020-00735-8subject
Has Abstractpub_date
2020-09-18 00:00:00pages
80issue
Suppl 8issn
1755-8794pii
10.1186/s12920-020-00735-8journal_volume
13pub_type
杂志文章abstract:BACKGROUND:One of the tasks in the 2017 iDASH secure genome analysis competition was to enable training of logistic regression models over encrypted genomic data. More precisely, given a list of approximately 1500 patient records, each with 18 binary features containing information on specific mutations, the idea was f...
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