Biology subjects

Brown, A. S.

Publications and source records attributed to Brown, A. S..

3 recordsLinked to original sources

bioRxiv2018

MTSS1/Src family kinase Dysregulation Underlies Multiple Inherited Ataxias

The genetically heterogeneous Spinocerebellar ataxias (SCAs) are caused by Purkinje neuron dysfunction and degeneration, but their underlying pathological mechanisms remain elusive. The Src family of non-receptor tyrosine kinases (SFK) are essential for nervous system homeostasis and are increasingly implicated in degenerative disease. Here we reveal that the SFK suppressor Missing-in-Metastasis (MTSS1) is a novel ataxia locus that links multiple SCAs. MTSS1 loss results in increased SFK activity, reduced Purkinje neuron arborization, and low basal firing rates, followed by cell death. Surprisingly, mouse models for SCA1, SCA2, and SCA5 show elevated SFK activity, with SCA1 and SCA2 displaying dramatically reduced MTSS1 protein levels through reduced gene expression and protein translation, respectively. Treatment of each SCA model with a clinically-approved Src inhibitor corrects Purkinje basal firing, and delays ataxia progression in MTSS1 mutants. Our results identify a common SCA therapeutic target and demonstrate a key role for MTSS1/SFK in Purkinje neuron survival and ataxia progression.\n\nSignificance StatementThe Src family of non-receptor tyrosine kinases (SFK) are essential for nervous system function, and may contribute to neurodegeneration. Spinocerebellar ataxias (SCAs) are neurodegenerative diseases where Purkinje neurons fire irregularly and degenerate leading to motor problems. We show that the SFK suppressor Missing-in-Metastasis (MTSS1) is a novel ataxia gene that links multiple SCAs. MTSS1 loss results in increased SFK activity, degenerating Purkinje neurons with low basal firing rates, and cell death. Surprisingly, mouse models for three different SCAs show elevated SFK activity, with SCA1 and SCA2 models displaying dramatically reduced MTSS1 protein levels. Treatment of each SCA model with SFK inhibitor corrects Purkinje basal firing, and delays ataxia progression in MTSS1 mutants. Our results identify a common link among disparate neurodegenerative diseases.

neuroscience↗

bioRxiv2017

Epigenome-Based Drug Repositioning in Acute Myeloid Leukemia

BackgroundRepositioning approved drugs for the treatment of new indications is a promising avenue to reduce the burden of drug development. Most currently available computational methods based on molecular evidence can only utilize gene expression for repositioning despite a growing interest in the epigenome in human disease. We recently described a novel repositioning method, ksRepo, that enables investigators to move beyond microarray-based gene expression and utilize a variety of other sources of molecular evidence, such as DNA methylation differences.\n\nMethodsWe downloaded differential DNA methylation data from two publicly available acute myeloid leukemia (AML) datasets, a cancer with known, extensive epigenomic perturbations. We consolidated CpGs-level to non-directional gene-level differential methylation using Browns correction to Fishers method. We then used ksRepo, which ignores directionality in disease- and gene-drug associations, to mine the resulting prioritized gene lists and and the Comparative Toxicogenomics Database (CTD) for predicted repositioning candidates.\n\nResultsWe successfully recovered four compounds that were significant (FDR < 0.05) in two AML datasets: cytarabine, alitretinoin, panobinostat, and progesterone. Cytarabine is the most commonly used frontline therapy for AML and alitretinoin, panobinostat, and progesterone have all been investigated for the treatment of AML.\n\nConclusions. Combining a method for consolidating CpG methylation to the gene level with ksRepo provides a pipeline for deriving drug repositioning hypotheses from differential DNA methylation. We claim that our platform can be extended to other diseases with epigenetic perturbations and to other epigenomic modalities, such as ChIP-seq.

bioinformatics↗

bioRxiv2017

Leveraging population-based clinical quantitative phenotyping for drug repositioning

Computational drug repositioning methods can scalably nominate approved drugs for new diseases, with reduced risk of unforeseen side effects. The majority of methods eschew individual-level phenotypes despite the promise of biomarker-driven repositioning. In this study, we propose a framework for discovering serendipitous interactions between drugs and routine clinical phenotypes in cross-sectional observational studies. Key to our strategy is the use of a healthy and non-diabetic population derived from the National Health and Nutrition Examination Survey, mitigating risk for confounding by indication. We combine complementary diagnostic phenotypes (fasting glucose and glucose response) and associate them with prescription drug usage. We then sought confirmation of phenotype-drug associations in un-identifiable member claims data from Aetna using a retrospective self-controlled case analysis approach. We identify bupropion hydrochloride as a plausible antidiabetic agent, suggesting that surveying otherwise healthy individuals cross-sectional studies can discover new drug repositioning hypotheses that have applicability to longitudinal clinical practice.

bioinformatics↗