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Biology subjects

Andrew, A. A.

Publications and source records attributed to Andrew, A. A..

2 recordsLinked to original sources

CRISPR-Cas9 cytidine and adenosine base editing of splice-sites mediates highly-efficient disruption of proteins in primary cells

Base editors allow for precise nucleotide editing without the need for genotoxic double-stranded breaks. Prior work has used base editors to knockout genes by introducing premature stop codons or by disrupting conserved splice-sites, but no direct comparison exists between these methods. Additionally, while base editor mediated disruption of splice sites has been used to shift the functional isoform pool, its utility for gene knockout requires further validation. To address these needs, we developed the program SpliceR (z.umn.edu/spliceR) to design cytidine-deaminase base editor (CBE) and adenosine-deaminase base editor (ABE) splice-site targeting guides. We compared the splice-site targeting and premature stop codon introduction in a knockout screen against the TCR-CD3 immune synapse in primary human T-cells. Our data suggests that 1) the CBE, BE4 is more reliable than the ABE, ABE7.10 for splice-site targeting knockout and 2) for both CBEs and ABEs, splice-donor targeting is the most reliable approach for base editing induced knockout.

bioengineering↗

Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis

SummaryWhile many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear.\n\nTo identify these effects, we performed cis- and trans-expression quantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium.\n\nWe observed that cis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to use cis-eQTLs to pinpoint causal genes within susceptibility loci.\n\nIn contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology.\n\nWe observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.

genomics↗