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Gumusgoz, E.

Publications and source records attributed to Gumusgoz, E..

2 recordsLinked to original sources

Targeting Gys1 with AAV-SaCas9 decreases pathogenic polyglucosan bodies and neuroinflammation in Adult Polyglucosan Body and Lafora disease mouse models

Many adult and most childhood neurological diseases have a genetic basis. CRISPR/Cas9 biotechnology holds great promise in neurological therapy, pending the clearance of major delivery, efficiency and specificity hurdles. We apply CRISPR/Cas9 genome editing in its simplest modality, namely inducing gene sequence disruption, to one adult and one pediatric disease. Adult polyglucosan body disease is a neurodegenerative disease resembling amyotrophic lateral sclerosis. Lafora disease is a severe late childhood onset progressive myoclonus epilepsy. The pathogenic insult in both is formation in the brain of glycogen with overlong branches, which precipitates and accumulates into polyglucosan bodies that drive neuroinflammation and neurodegeneration. We packaged Staphylococcus aureus Cas9 and a guide RNA targeting the glycogen synthase gene Gys1 responsible for brain glycogen branch elongation in AAV9 virus, which we delivered by neonatal intracerebroventricular injection to one mouse model of adult polyglucosan body disease and two mouse models of Lafora disease. This resulted, in all three models, in editing of approximately 17% of Gys1 alleles and a similar extent of reduction of Gys1 mRNA across the brain. The latter led to approximately 50% reductions of GYS1 protein, of abnormal glycogen accumulation and of polyglucosan bodies, as well as corrections of neuroinflammatory markers in all three models. Our work represents proof of principle for virally-delivered CRISPR/Cas9 neurotherapeutics in an adult-onset (adult polyglucosan body) and a childhood-onset (Lafora) neurological diseases.

neuroscience↗

A cross-organism framework for supervised enhancer prediction with epigenetic pattern recognition and targeted validation

Enhancers are important noncoding elements, but they have been traditionally hard to characterize experimentally. Only a few mammalian enhancers have been validated, making it difficult to train statistical models for their identification properly. Instead, postulated patterns of genomic features have been used heuristically for identification. The development of massively parallel assays allows for the characterization of large numbers of enhancers for the first time. Here, we developed a framework that uses Drosophila STARR-seq data to create shape-matching filters based on enhancer-associated meta-profiles of epigenetic features. We combined these features with supervised machine learning algorithms (e.g., support vector machines) to predict enhancers. We demonstrated that our model could be applied to predict enhancers in mammalian species (i.e., mouse and human). We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mouse and transduction-based reporter assays in human cell lines. Overall, the validations involved 153 enhancers in 6 mouse tissues and 4 human cell lines. The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription-factor binding patterns at predicted enhancers and promoters in human cell lines. We demonstrated that these patterns enable the construction of a secondary model effectively discriminating between enhancers and promoters.

bioinformatics↗