Dataset Information

ChIP-seq dataset for H3K9ac in Identifying Novel Therapeutic Targets by Combining Transcriptional Data with Ordinal Clinical Measurements

ABSTRACT: The widespread adoption of transcriptional profiling has led to immense repositories of data that may contain critical insights for developing new therapies. Current approaches to mining these data largely rely on binary classifications of disease versus control. These methods are not able to incorporate measures of disease severity, which are often available. We report an analytical approach to integrate ordinal clinical information with transcriptomics. We have applied this method to public data for a large cohort of Huntington’s disease patients and controls, identifying and prioritizing phenotype-associated genes. To validate the approach, we have performed viability, siRNA knockdown, mRNA-seq, and ChIP-seq on striatal precursor cells expressing either full-length wild type (STHdh Q7) or mutant huntingtin (STHdh Q111). We have verified the role of a high-ranked gene in dysregulation of sphingolipid metabolism in the disease and demonstrated that inhibiting the enzyme, SPL, has neuroprotective effects in HD models. We have shown that one consequence of inhibiting SPL is the intracellular inhibition of HDACs, thus linking our observations in sphingolipid metabolism to a well-characterized HD pathway. Our approach is easily applied to any data with ordinal clinical measurements, and may deepen our understanding of disease processes.

ORGANISM(S): Mus musculus

PROVIDER: GSE98738 | GEO | 2017/05/11

SECONDARY ACCESSION(S): PRJNA386217

REPOSITORIES: GEO

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Project description:Huntington Disease (HD) is a dominantly inherited, relentlessly progressive neurodegenerative disease. Caused by a polyglutamine expansion in the mutant huntingtin protein (mhtt), HD pathogenesis impairs function in the cerebral cortex and in medium spiny neurons of the striatum and involves transcriptional dysregulation of a number of genes. Of these genes, silencing of genes related to mitochondrial function is believed to explain metabolic dysfunction in rodent models of HD. Here we show that transglutaminase 2 (TG2), which is upregulated in HD, exacerbates transcriptional dysregulation by acting as a selective corepressor of nuclear genes. TG2 inhibition by RNA knockdown, genetic deletion, or administration of a novel irreversible, peptide-based TG2 inhibitor (ZDON) de-repressed two established regulators of mitochondrial function, PGC-1? and cytochrome c in a cell model of HD. TG2 must localize to non-coding or coding regions of these mitochondrial metabolic genes to silence their transcription. As expected, TG2 inhibition reversed the increased susceptibility of HD mouse cells and human HD myoblasts to the mitochondrial toxin, 3-nitroproprionic acid (3-NP); however, protection mediated by TG2 inhibition was not associated with improved mitochondrial bioenergetics. Indeed, an unbiased array analysis indicated that TG2 inhibition leads to normalization of not only mitochondrial genes but of nearly 40% of genes that are dysregulated in HD mouse striatal neurons, including chaperone and histone genes. Indeed, TG2 interacts directly with Histone 3 in the nucleus. Moreover, TG2 inhibition significantly attenuated photoreceptor degeneration in a Drosophila model of HD and protected mouse HD striatal neurons (YAC128) from NMDA- induced toxicity. Altogether these findings demonstrate that TG2 mediates its deleterious effects in HD by contributing to broad transcriptional dysregulation of genes representing many cellular functions. These studies define a novel HDAC-independent epigenetic strategy for treating neurodegeneration. To evaluate the effect of TG2 inhibition (treatment with ZDON, Boc-DON, CystamineA, and Control) in striatal cell lines from a transgenic model of Huntington disease (Q111) vs. control (Q7). One sample (WT.boc.3, 4068264015_G) failed the QC test and was excluded from further analyses.

Dataset Information

ChIP-seq dataset for H3K9ac in Identifying Novel Therapeutic Targets by Combining Transcriptional Data with Ordinal Clinical Measurements

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