Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add more filters










Database
Language
Publication year range
1.
Cell ; 186(9): 1985-2001.e19, 2023 04 27.
Article in English | MEDLINE | ID: mdl-37075754

ABSTRACT

Aneuploidy, the presence of chromosome gains or losses, is a hallmark of cancer. Here, we describe KaryoCreate (karyotype CRISPR-engineered aneuploidy technology), a system that enables the generation of chromosome-specific aneuploidies by co-expression of an sgRNA targeting chromosome-specific CENPA-binding ɑ-satellite repeats together with dCas9 fused to mutant KNL1. We design unique and highly specific sgRNAs for 19 of the 24 chromosomes. Expression of these constructs leads to missegregation and induction of gains or losses of the targeted chromosome in cellular progeny, with an average efficiency of 8% for gains and 12% for losses (up to 20%) validated across 10 chromosomes. Using KaryoCreate in colon epithelial cells, we show that chromosome 18q loss, frequent in gastrointestinal cancers, promotes resistance to TGF-ß, likely due to synergistic hemizygous deletion of multiple genes. Altogether, we describe an innovative technology to create and study chromosome missegregation and aneuploidy in the context of cancer and beyond.


Subject(s)
Centromere , Genetic Techniques , Humans , Aneuploidy , Centromere/genetics , Chromosome Deletion , Neoplasms/genetics , Clustered Regularly Interspaced Short Palindromic Repeats
2.
Nat Biotechnol ; 41(8): 1117-1129, 2023 08.
Article in English | MEDLINE | ID: mdl-36702896

ABSTRACT

Cys2His2 zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate engagement of ZF domains with DNA has made their design challenging. Here we describe the screening of 49 billion protein-DNA interactions and the development of a deep-learning model, ZFDesign, that solves ZF design for any genomic target. ZFDesign is a modern machine learning method that models global and target-specific differences induced by a range of library environments and specifically takes into account compatibility of neighboring fingers using a novel hierarchical transformer architecture. We demonstrate the versatility of designed ZFs as nucleases as well as activators and repressors by seamless reprogramming of human transcription factors. These factors could be used to upregulate an allele of haploinsufficiency, downregulate a gain-of-function mutation or test the consequence of regulation of a single gene as opposed to the many genes that a transcription factor would normally influence.


Subject(s)
Deep Learning , Transcription Factors , Humans , Transcription Factors/genetics , Transcription Factors/metabolism , Zinc Fingers/genetics , Gene Expression Regulation , DNA/genetics
SELECTION OF CITATIONS
SEARCH DETAIL
...