Polyglutamine Expanded Huntingtin Dramatically Alters the Genome-Wide Binding of HSF1

In Huntington's disease (HD), polyglutamine expansions in the huntingtin (Htt) protein cause subtle changes in cellular functions that, over-time, lead to neurodegeneration and death. Studies have indicated that activation of the heat shock response can reduce many of the effects of mutant Htt in disease models, suggesting that the heat shock response is impaired in the disease. To understand the basis for this impairment, we have used genome-wide chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) to examine the effects of mutant Htt on the master regulator of the heat shock response, HSF1. We find that, under normal conditions, HSF1 function is highly similar in cells carrying either wild-type or mutant Htt. However, polyQ-expanded Htt severely blunts the HSF1-mediated stress response. Surprisingly, we find that the HSF1 targets most affected upon stress are not directly associated with proteostasis, but with cytoskeletal binding, focal adhesion and GTPase activity. Our data raise the intriguing hypothesis that the accumulated damage from life-long impairment in these stress responses may contribute significantly to the etiology of Huntington's disease.National Institutes of Health (U.S.) (Grant R24 DK-090963)National Institutes of Health (U.S.) (Grant R01-GM089903)National Institutes of Health (U.S.) (Grant P30-ES002109)National Science Foundation (U.S.) (Award DB1-0821391

Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements

The immense and growing repositories of transcriptional data may contain critical insights for developing new therapies. Current approaches to mining these data largely rely on binary classifications of disease vs. control, and are not able to incorporate measures of disease severity. We report an analytical approach to integrate ordinal clinical information with transcriptomics. We apply this method to public data for a large cohort of Huntington's disease patients and controls, identifying and prioritizing phenotype-associated genes. We verify the role of a high-ranked gene in dysregulation of sphingolipid metabolism in the disease and demonstrate that inhibiting the enzyme, sphingosine-1-phosphate lyase 1 (SPL), has neuroprotective effects in Huntington's disease models. Finally, we show that one consequence of inhibiting SPL is intracellular inhibition of histone deacetylases, thus linking our observations in sphingolipid metabolism to a well-characterized Huntington's disease pathway. Our approach is easily applied to any data with ordinal clinical measurements, and may deepen our understanding of disease processes

Revealing disease-associated pathways and components by systematic integration of large-scale biological data

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2016.Cataloged from PDF version of thesis.Includes bibliographical references (pages 129-141).While technological advances have enabled measurements of thousands of molecules simultaneously, the data from each technology can only show a single-view of biological processes. Capturing a complete picture of these processes requires integrating data of different types, including clinical data, genomics, transcriptomics, proteomics and metabolomics. Here, we have demonstrated novel computational approaches for integrating a variety of biological data and used these methods to study Huntington's disease (HD). First, we established a computational approach for combining transcriptomics with qualitative, ordinal clinical information. Such data are available for a variety of diseases, but are rarely used in conjunction with molecular data. We adapted an ordinal regression model to analyze gene expression data from HD brains in conjunction with their grade of neuronal loss. This approach identified the SGPLl-encoded enzyme (SPL) as a potential therapeutic target for HD. Continuing our data-driven approach, we discovered the dysregulation of pathways associated with SPL and inferred molecular mechanisms by which SPL inhibition exerts protective effects. Then, we demonstrated a novel network-based, machine-learning algorithm for integrative analysis of untargeted metabolomic data. Metabolites are small molecules whose levels directly show cellular phenotypes. Despite their potential, the integrative analysis of metabolomic data has been limited because of challenges in metabolite identification. To address these challenges, we have developed a pioneering method for interpreting the large-scale metabolomic data in the context of other molecules such as proteins. We used our method to infer novel dysregulated pathways in a model of HD and experimentally verified our predictions. These two methods are extremely general and can be applied to a variety of diseases. As the costs of generating high-throughput data decrease, we anticipate that our approaches will have growing relevance to the discovery of therapeutic strategies for precision medicine.by Leila Pirhaji.Ph. D

Inferring interaction type in gene regulatory networks using co-expression data

Abstract Background Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction. Results This paper describes a novel algorithm, “Signing of Regulatory Networks” (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN. Conclusions SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It can serve as a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type

Revealing disease-associated pathways by network integration of untargeted metabolomics

Uncovering the molecular context of dysregulated metabolites is crucial to understand pathogenic pathways. However, their system-level analysis has been limited owing to challenges in global metabolite identification. Most metabolite features detected by untargeted metabolomics carried out by liquid-chromatography-mass spectrometry cannot be uniquely identified without additional, time-consuming experiments. We report a network-based approach, prize-collecting Steiner forest algorithm for integrative analysis of untargeted metabolomics (PIUMet), that infers molecular pathways and components via integrative analysis of metabolite features, without requiring their identification. We demonstrated PIUMet by analyzing changes in metabolism of sphingolipids, fatty acids and steroids in a Huntington's disease model. Additionally, PIUMet enabled us to elucidate putative identities of altered metabolite features in diseased cells, and infer experimentally undetected, disease-associated metabolites and dysregulated proteins. Finally, we established PIUMet's ability for integrative analysis of untargeted metabolomics data with proteomics data, demonstrating that this approach elicits disease-associated metabolites and proteins that cannot be inferred by individual analysis of these data.National Institutes of Health (U.S.) (grant R01-GM089903)National Institutes of Health (U.S.) (grant U54-NS091046)National Institutes of Health (U.S.) (grant U01-CA184898)National Cancer Institute (U.S.) (grant U54 CA112967)National Cancer Institute (U.S.) (grant P30 CA014051)Searle Scholars Progra

APOE4 is Associated with Differential Regional Vulnerability to Bioenergetic Deficits in Aged APOE Mice

© 2020, The Author(s). The ε4 allele of apolipoprotein E (APOE) is the dominant genetic risk factor for late-onset Alzheimer’s disease (AD). However, the reason for the association between APOE4 and AD remains unclear. While much of the research has focused on the ability of the apoE4 protein to increase the aggregation and decrease the clearance of Aβ, there is also an abundance of data showing that APOE4 negatively impacts many additional processes in the brain, including bioenergetics. In order to gain a more comprehensive understanding of APOE4′s role in AD pathogenesis, we performed a transcriptomics analysis of APOE4 vs. APOE3 expression in the entorhinal cortex (EC) and primary visual cortex (PVC) of aged APOE mice. This study revealed EC-specific upregulation of genes related to oxidative phosphorylation (OxPhos). Follow-up analysis utilizing the Seahorse platform showed decreased mitochondrial respiration with age in the hippocampus and cortex of APOE4 vs. APOE3 mice, but not in the EC of these mice. Additional studies, as well as the original transcriptomics data, suggest that multiple bioenergetic pathways are differentially regulated by APOE4 expression in the EC of aged APOE mice in order to increase the mitochondrial coupling efficiency in this region. Given the importance of the EC as one of the first regions to be affected by AD pathology in humans, the observation that the EC is susceptible to differential bioenergetic regulation in response to a metabolic stressor such as APOE4 may point to a causative factor in the pathogenesis of AD