Biology subjects

Bouchonville, K. J.

Publications and source records attributed to Bouchonville, K. J..

3 recordsLinked to original sources

bioRxiv2023

Germline cis variant determines epigenetic regulation of the anti-cancer drug metabolism gene dihydropyrimidine dehydrogenase (DPYD)

Enhancers are critical for regulating tissue-specific gene expression, and genetic variants within enhancer regions have been suggested to contribute to various cancer-related processes, including therapeutic resistance. However, the precise mechanisms remain elusive. Using a well-defined drug-gene pair, we identified an enhancer region for dihydropyrimidine dehydrogenase (DPD, DPYD gene) expression that is relevant to the metabolism of the anti-cancer drug 5-fluorouracil (5-FU). Using reporter systems, CRISPR genome edited cell models, and human liver specimens, we demonstrated in vitro and vivo that genotype status for the common germline variant (rs4294451; 27% global minor allele frequency) located within this novel enhancer controls DPYD transcription and alters resistance to 5-FU. The variant genotype increases recruitment of the transcription factor CEBPB to the enhancer and alters the level of direct interactions between the enhancer and DPYD promoter. Our data provide insight into the regulatory mechanisms controlling sensitivity and resistance to 5-FU.

pharmacology and toxicology↗

bioRxiv2022

A Robust Bayesian Approach to Bulk Gene Expression Deconvolution withNoisy Reference Signatures

BackgroundDifferential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportion of cell types within the sample. Expression deconvolution methods can help differentiate these scenarios and enable more accurate inference of gene regulation by estimating the contributions of individual cell types to bulk transcriptomic profiles. However, the accuracy of these methods is sensitive to technical and biological differences between bulk profiles and the cell type-signatures required by them as references. ResultsWe present BEDwARS, a Bayesian deconvolution method specifically designed to address differences between reference signatures and the unknown true signatures underlying bulk transcriptomic profiles. Through extensive benchmarking utilizing eight different datasets derived from pancreas and brain, we demonstrate that BEDwARS outperforms leading in-class methods for estimating cell type proportions and signatures. Furthermore, we systematically show that BEDwARS is more robust to noisy reference signatures than all compared methods. Finally, we apply BEDwARS to newly generated RNA-seq and scRNA-seq data on over 100 induced pluripotent stem cell-derived neural organoids to study mechanisms underlying a rare pediatric condition (Dihydropyridine Dehydrogenase deficiency), identifying the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder. ConclusionWe propose a new approach to bulk gene expression deconvolution which estimates the cell type proportions and cell type signatures simultaneously and is robust to commonly seen mismatches between reference and true cell type signatures. Application of our method lead to novel findings about mechanisms of a rare pediatric condition.

bioinformatics↗

bioRxiv2021

Defining proximity proteomics of post-translationally modified proteins by antibody-mediated protein A-APEX2 labeling

Proximity labeling catalyzed by promiscuous enzymes, such as APEX2, has emerged as a powerful approach to characterize multiprotein complexes and protein-protein interactions. However, current methods depend on the expression of exogenous fusion proteins and cannot be applied to post-translational modifications. To address this limitation, we developed a new method to label proximal proteins of interest by antibody-mediated protein A-APEX2 labeling (AMAPEX). In this method, a modified protein is bound in situ by a specific antibody, which then tethers a protein A-APEX2 (pA-APEX2) fusion protein. Activation of APEX2 labels the nearby proteins with biotin; these proteins are then purified using streptavidin beads and are identified by mass spectrometry. We demonstrate the utility of this approach by profiling the binding proteins of histone modifications including H3K27me3, H3K9me3, H3K4me3, H4K5ac and H4K12ac, and we verified the genome-wide colocalization of these identified proteins with bait proteins by published ChIP-seq analysis. Overall, AMAPEX is an efficient tool to identify proteins that interact with modified proteins.

molecular biology↗