Computational resources to define alleles and altered regulatory motifs at genomically edited candidate response elements

• Published in: Nucleic acids research Vol. 49; no. 16; pp. 9117 - 9131
• Main Authors: Ehmsen, Kirk T, Knuesel, Matthew T, Martinez, Delsy, Asahina, Masako, Aridomi, Haruna, Yamamoto, Keith R
• Format: Journal Article
• Language: English
• Published: Oxford University Press 20.09.2021
• Subjects: Data Resources and Analyses
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkab700

Abstract Unequivocal functional assessment of candidate genomic regulatory regions, such as transcriptional response elements, requires genetic alteration at their native chromosomal loci. Targeted DNA cleavage by Cas9 or other programmable nucleases enables analysis at virtually any genomic region, and diverse alleles generated by editing can be defined by deep sequencing for functional analysis. Interpretation of disrupted response elements, however, presents a special challenge, as these regions typically comprise clustered DNA binding motifs for multiple transcriptional regulatory factors (TFs); DNA sequence differences, natural or engineered, that affect binding by one TF can confer loss or gain of binding sites for other TFs. To address these and other analytical complexities, we created three computational tools that together integrate, in a single experiment, allele definition and TF binding motif evaluation for up to 9216 clones isolated, sequenced and propagated from Cas9-treated cell populations. We demonstrate 1) the capacity to functionally assess edited TF binding sites to query response element function, and 2) the efficacy and utility of these tools, by analyzing cell populations targeted by Cas9 for disruption of example glucocorticoid receptor (GR) binding motifs near FKBP5, a GR-regulated gene in the human adenocarcinoma cell line A549. Graphical Abstract Graphical Abstract Workflow to evaluate (A) a genetically heterogeneous sample population includes (B) clonal isolation and PCR-barcoding of one or more target loci from diverse samples, followed by (C) computational resources to define alleles and altered transcription factor binding sites (TFBSs) that inform clone selection and regulatory analysis.