Biology subjects

Cotter, K. A.

Publications and source records attributed to Cotter, K. A..

3 recordsLinked to original sources

bioRxiv2022

Capped nascent RNA sequencing reveals novel therapy-responsive enhancers in prostate cancer

Mounting evidence suggests that enhancer RNA (eRNA) transcription start sites (TSSs) provide higher sensitivity and specificity for enhancer identification than histone modifications and chromatin accessibility. The extent to which changes in eRNA transcription correspond to changes in enhancer activity, however, remains unclear. Here, we used precision run-on and capped RNA sequencing (PRO-cap) to assess changes in enhancer activity in response to treatment with the androgen receptor signaling inhibitor, enzalutamide (ENZ). We identified 6,189 high-confidence candidate enhancers in the human prostate cancer cell line, LNCaP; 853 of which demonstrated significant changes in activity in response to drug treatment. Notably, we found that 67% and 54% of drug-responsive enhancers did not show similar changes in activity in previous studies that utilized ChIP-seq and ATAC-seq, respectively. Strikingly, 79% of regions with increased eRNA transcription showed no other biochemical alterations, implying that PRO-cap can capture a set of precise changes in enhancer activity that classical approaches lack the sensitivity to detect. We performed in vivo functional validations of candidate enhancers and found that CRISPRi targeting of PRO-cap-specific drug-responsive enhancers impaired ENZ regulation of downstream target genes, suggesting that changes in eRNA TSSs mark true biological changes in enhancer activity with high sensitivity. Our study highlights the utility of using PRO-cap as a complementary approach to canonical biochemical methods for detecting precise changes in enhancer activity and, in particular, for better understanding disease progression and responses to treatment.

genomics↗

bioRxiv2021

Mapping of m6A and Its Regulatory Targets in Prostate Cancer Reveals a METTL3-low Induction of Therapy Resistance

Recent evidence has highlighted the role of N6-methyladenosine (m6A) in the regulation of mRNA expression, stability and translation, supporting a potential role for post-transcriptional regulation mediated by m6A in cancer. Here we explore prostate cancer as an exemplar and demonstrate that low levels of N6-adenosine-methyltransferase (METTL3) is associated with advanced metastatic disease. To explore this relationship, we generated the first prostate m6A maps, and further examined how METTL3 regulates expression at the level of transcription, translation, and protein. Significantly, transcripts encoding extracellular matrix proteins are consistently upregulated with METTL3 knockdown. We also examined the relationship between METTL3 and androgen signaling and discovered the upregulation of a hepatocyte nuclear factor-driven gene signature that is associated with therapy resistance in prostate cancer. Significantly, METTL3 knockdown rendered the cells resistant to androgen receptor antagonists, implicating changes in m6A as a mechanism for therapy resistance in metastatic prostate cancer.

cancer biology↗

bioRxiv2018

GRAM: A generalized model to predict the molecular effect of a non-coding variant in a cell-type specific manner

There has been much effort to prioritize genomic variants with respect to their impact on \"function\". However, function is often not precisely defined: Sometimes, it is the disease association of a variant; other times, it reflects a molecular effect on transcription or epigenetics. Here we coupled multiple genomic predictors to build GRAM, a generalized model, to predict a well-defined experimental target: the expression-modulating effect of a non-coding variant in a cell-specific manner. As a first step, we performed feature engineering: using a LASSO regularized linear model, we found transcription factor (TF) binding most predictive, especially for TFs that are hubs in the regulatory network; in contrast, evolutionary conservation, a popular feature in many other functional-impact predictors, has almost no contribution. Moreover, TF binding inferred from in vitro SELEX is as effective as that from in vivo ChIP-Seq. Second, we implemented GRAM integrating SELEX features and expression profiles. The program combines a universal regulatory score for a variant in a non-coding element with a modifier score reflecting the particular cell type. We benchmarked GRAM on a large-scale MPRA dataset in the GM12878 cell line, achieving a ROC score of [~]0.73; performance on the K562 cell line was similar. Finally, we evaluated the performance of GRAM on targeted regions using luciferase assays in MCF7 and K562 cell lines. We noted that changing the insertion position of the construct relative to the reporter gene gives very different results, highlighting the importance of carefully defining the functional target the model is predicting.\n\nAuthor SummaryNoncoding variants lie outside of protein-coding regions, and are found to have disease associations. However, knowledge on the molecular effect of these non-coding variants in a cell-specific context is very limited. Also, different output between multiple experiment platforms may introduce extra complexity in analyzing the molecular function of these variants. We developed GRAM, a generalized model to predict molecular effect of non-coding variants in multiple cell types for different experimental platforms. We first selected the most informative cell-independent SELEX transcription factor binding score on the variant locus as features and then combine cell-specific gene expression profile to build a multi-step prediction model. GRAM has been successfully tested on both MPRA and Luciferase assay, and on three different cell lines: GM12878, K562 and MCF7, shows high performance.

bioinformatics↗