Gary D. Bader | Search bioRxiv

bioRxiv2016

ARGLU1 is a Glucocorticoid Receptor Coactivator and Splicing Modulator Important in Stress Hormone Signaling and Brain Development

Prolonged exposure to glucocorticoid stress hormones precipitates mood and cognitive disorders. We identified arginine and glutamate rich 1 (ARGLU1) in a screen for new modulators of glucocorticoid signaling in the CNS. Biochemical studies found that the glutamate rich C-terminus coactivates the glucocorticoid receptor (GR) and the arginine rich N-terminus interacts with splicing factors and RNA. RNA-seq of neuronal cells {+/-}siARGLU1found significant changes in the expression and alternative splicing of distinct genes involved in neurogenesis. Loss of ARGLU1 was embryonic lethal in mice, and knockdown in zebrafish caused neurodevelopmental and heart defects. Treatment with dexamethasone, a GR activator, also induced changes in the pattern of alternatively spliced genes, highlighting an underappreciated global mechanism of glucocorticoid action in neuronal cells. Thus, in addition to its basal role, ARGLU1 links glucocorticoid-mediated transcription and alternative splicing in neural cells, providing new avenues from which to investigate the molecular underpinnings of cognitive stress disorders.

Molecular Biology↗

bioRxiv2016

Integrative pharmacogenomics to infer large-scale drug taxonomy

Identification of drug targets and mechanism of action (MoA) for new and uncharacterlzed drugs is important for optimization of drug efficacy. Current MoA prediction approaches largely rely on prior information including side effects, therapeutic indication and/or chemo-informatics. Such information is not transferable or applicable for newly identified, previously uncharacterlzed small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary towards development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose a new integrative computational pharmacogenomlc approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that relies only on basic drug characteristics towards elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. We demonstrate that the DNF taxonomy succeeds in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. We highlight how the scalability of DNF facilitates identification of key drug relationships across different drug categories, and poses as a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on the compound MoA and potential insights for drug repurposlng.

Bioinformatics↗