scholarly journals Gene expression profiles complement the analysis of genomic modifiers of the clinical onset of Huntington disease

2019 ◽  
Author(s):  
Galen E.B. Wright ◽  
Nicholas S. Caron ◽  
Bernard Ng ◽  
Lorenzo Casal ◽  
Xiaohong Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Huntington Disease ◽  
Neurodegenerative Disorder ◽  
Age Of Onset ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cag Repeat ◽  
Biologically Relevant ◽  
Clinical Onset ◽  
Genome Wide Data

ABSTRACTHuntington disease (HD) is a neurodegenerative disorder that is caused by a CAG repeat expansion in the HTT gene. In an attempt to identify genomic modifiers that contribute towards the age of onset of HD, we performed a transcriptome wide association study assessing heritable differences in genetically determined expression in diverse tissues, employing genome wide data from over 4,000 patients. This identified genes that showed evidence for colocalization and replication, with downstream functional validation being performed in isogenic HD stem cells and patient brains. Enrichment analyses detected associations with various biologically-relevant gene sets and striatal coexpression modules that are mediated by CAG length. Further, cortical coexpression modules that are relevant for HD onset were also associated with cognitive decline and HD-related traits in a longitudinal cohort. In summary, the combination of population-scale gene expression information with HD patient genomic data identified novel modifier genes for the disorder.

scholarly journals Gene expression profiles complement the analysis of genomic modifiers of the clinical onset of Huntington disease

2020 ◽  
Vol 29 (16) ◽  
pp. 2788-2802
Author(s):  
Galen E B Wright ◽  
Nicholas S Caron ◽  
Bernard Ng ◽  
Lorenzo Casal ◽  
William Casazza ◽  
...  
Keyword(s):  
Gene Expression ◽  
Huntington Disease ◽  
Neurodegenerative Disorder ◽  
Expression Profiles ◽  
Association Studies ◽  
Modifier Genes ◽  
Cag Repeat ◽  
Supporting Evidence ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Huntington disease (HD) is a neurodegenerative disorder that is caused by a CAG repeat expansion in HTT. The length of this repeat, however, only explains a proportion of the variability in age of onset in patients. Genome-wide association studies have identified modifiers that contribute toward a proportion of the observed variance. By incorporating tissue-specific transcriptomic information with these results, additional modifiers can be identified. We performed a transcriptome-wide association study assessing heritable differences in genetically determined expression in diverse tissues, with genome-wide data from over 4000 patients. Functional validation of prioritized genes was undertaken in isogenic HD stem cells and patient brains. Enrichment analyses were performed with biologically relevant gene sets to identify the core pathways. HD-associated gene coexpression modules were assessed for associations with neurological phenotypes in an independent cohort and to guide drug repurposing analyses. Transcriptomic analyses identified genes that were associated with age of HD onset and displayed colocalization with gene expression signals in brain tissue (FAN1, GPR161, PMS2, SUMF2), with supporting evidence from functional experiments. This included genes involved in DNA repair, as well as novel-candidate modifier genes that have been associated with other neurological conditions. Further, cortical coexpression modules were also associated with cognitive decline and HD-related traits in a longitudinal cohort. In summary, the combination of population-scale gene expression information with HD patient genomic data identified novel modifier genes for the disorder. Further, these analyses expanded the pathways potentially involved in modifying HD onset and prioritized candidate therapeutics for future study.

scholarly journals Identification of Monotonically Differentially Expressed Genes for IFN-β-Treated Multiple Sclerosis Patients

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Suyan Tian ◽  
Lei Zhang
Keyword(s):  
Gene Expression ◽  
Multiple Sclerosis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Biologically Relevant ◽  
Therapeutic Mechanism ◽  
The Central Nervous System ◽  
Multiple Sclerosis Patients ◽  
Term Response

Multiple sclerosis (MS) is a common neurological disability of the central nervous system. Immune-modulatory therapy with interferon-β (IFN-β) has been used as a first-line treatment to prevent relapses in MS patients. While the therapeutic mechanism of IFN-β has not been fully elucidated, the data of microarray experiments that collected longitudinal gene expression profiles to evaluate the long-term response of IFN-β treatment have been analyzed using statistical methods that were incapable of dealing with such data. In this study, the GeneRank method was applied to generate weighted gene expression values and the monotonically expressed genes (MEGs) for both IFN-β treatment responders and nonresponders were identified. The proposed procedure identified 13 MEGs for the responders and 2 MEGs for the nonresponders, most of which are biologically relevant to MS. Our work here provides some useful insight into the mechanism of IFN-β treatment for MS patients. A full understanding of the therapeutic mechanism will enable a more personalized treatment strategy possible.

scholarly journals Single-nucleus RNA-seq identifies Huntington disease astrocyte states

2019 ◽  
Author(s):  
Osama Al-Dalahmah ◽  
Alexander A Sosunov ◽  
A Shaik ◽  
Kenneth Ofori ◽  
Yang Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Huntington Disease ◽  
Expression Profiles ◽  
Lipid Synthesis ◽  
Gene Expression Profiles ◽  
Cag Repeats ◽  
Cell Gene Expression ◽  
Single Nucleus ◽  
Cell Gene

AbstractHuntington Disease (HD) is an inherited movement disorder caused by expanded CAG repeats in the Huntingtin gene. We have used single nucleus RNASeq (snRNASeq) to uncover cellular phenotypes that change in the disease, investigating single cell gene expression in cingulate cortex of patients with HD and comparing the gene expression to that of patients with no neurological disease. In this study, we focused on astrocytes, although we found significant gene expression differences in neurons, oligodendrocytes, and microglia as well. In particular, the gene expression profiles of astrocytes in HD showed multiple signatures, varying in phenotype from cells that had markedly upregulated metallothionein and heat shock genes, but had not completely lost the expression of genes associated with normal protoplasmic astrocytes, to astrocytes that had substantially upregulated GFAP and had lost expression of many normal protoplasmic astrocyte genes as well as metallothionein genes. When compared to astrocytes in control samples, astrocyte signatures in HD also showed downregulated expression of a number of genes, including several associated with protoplasmic astrocyte function and lipid synthesis. Thus, HD astrocytes appeared in variable transcriptional phenotypes, and could be divided into several different “states”, defined by patterns of gene expression. Ultimately, this study begins to fill the knowledge gap of single cell gene expression in HD and provide a more detailed understanding of the variation in changes in gene expression during astrocyte “reactions” to the disease.

scholarly journals ShapeCluster: Applying parametric regression to analyse time-series gene expression data

2016 ◽  
Author(s):  
Philip J Law ◽  
Vicky Buchanan-Wollaston ◽  
Andrew Mead
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Parametric Models ◽  
Linear Functions ◽  
Biologically Relevant ◽  
Research Areas ◽  
Biological Stress

High-throughput technologies have made it possible to perform genome-scale analyses to investigate a variety of research areas. From these analyses, vast amounts of data are generated. However, these data can be noisy, which could obscure the underlying signal. Here, a high-throughput regression analysis approach was developed, where a variety of linear and nonlinear parametric models were fitted to gene expression profiles from time course experiments. These models include the logistic, Gompertz, exponential, critical exponential, linear+exponential, Gaussian and linear functions. The fitted parameters from these models reflect aspects of the model shape, and thus allowed for the interpretation of gene expression profiles in terms of the underlying biology, such as the time of initial gene expression. This provides a potentially more mechanistic ap-proach to studying the genetic responses to stimuli. Together with a cluster analysis, termed ShapeCluster, it was possible to group genes based on these aspects of the expression profiles. By investigating different combinations of parameters, this added flexibility to the analysis and allowed for the investigation of the data in multiple ways, including the identification of groups of genes that may be co-regulated, or participate in response to the biological stress in question. Clusters from these methods were assessed for significance through the use of over-represented annotation terms and motifs, and found to pro-duce biologically relevant sets of genes. The ShapeCluster package is available from https://sourceforge.net/projects/shapecluster/.

scholarly journals Dysregulated gene-associated biomarkers for Alzheimer’s disease and aging

2021 ◽  
Vol 12 (1) ◽  
pp. 83-95
Author(s):  
Min Li ◽  
Rongxin Geng ◽  
Chen Li ◽  
Fantao Meng ◽  
Hongwei Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Neurodegenerative Disorder ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Potential Core ◽  
Regulatory Changes ◽  
Geo Database

Abstract Alzheimer’s disease (AD), the most common type of dementia, is a neurodegenerative disorder with a hidden onset, including difficult early detection and diagnosis. Nevertheless, the new crucial biomarkers for the diagnosis and pathogenesis of AD need to be explored further. Here, the common differentially expressed genes (DEGs) were identified through a comprehensive analysis of gene expression profiles from the Gene Expression Omnibus (GEO) database. Furthermore, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these DEGs were mainly associated with biological processes, cellular components, and molecular functions, which are involved in multiple cellular functions. Next, we found that 9 of the 24 genes showed the same regulatory changes in the blood of patients with AD compared to those in the GEO database, and 2 of the 24 genes showed a significant correlation with Montreal Cognitive Assessment scores. Finally, we determined that mice with AD and elderly mice had the same regulatory changes in the identified DEGs in both the blood and hippocampus. Our study identified several potential core biomarkers of AD and aging, which could contribute to the early detection, differential diagnosis, treatment, and pathological analysis of AD.

scholarly journals Clustering Alzheimer’s Disease Gene Expression Dataset Reveals Underlying Sexually Dimorphic and Disease Status Profiles

2021 ◽  
pp. 1-7
Author(s):  
Sigal Levy ◽  
Nili Guttmann-Beck ◽  
Dorit Shweiki
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sexual Dimorphism ◽  
Age Of Onset ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Disease Status ◽  
Microarray Dataset ◽  
Strategic Decisions

Background: The multiple appearance phenotypes in Alzheimer’s disease (AD) are manifested in epidemiologic sexual dimorphism, variation in age of onset, progress, and severity of the disease. Objective: In this study, we focused on sexual dimorphism, aiming to untie some of the complex interconnections in AD between sex, disease status, and gene expression profiles. Two strategic decisions guided our study: 1) to value transcriptomic multi-layered profiles over alterations in single genes expression; and 2) to embrace a sexual dimorphism centered approach, as we suspect that transcriptomic profiles may dramatically differ not only between healthy and sick individuals but between men and women as well. Methods: Microarray dataset GSE15222, fulfilling our strict criteria, was retrieved from the GEO repository. We performed cluster analysis for each sex separately, comparing the proportion of healthy and AD individuals in each cluster. Results: We were able to identify a biased, female, AD-typified cluster. Furthermore, we showed that this female AD-typified cluster is highly similar to one of the male clusters. While the female cluster constitutes mostly sick individuals, the male cluster constitutes healthy and sick individuals in almost identical proportion. Conclusion: Our results clearly indicate that similar transcriptomic profiles in the two sexes are “physiologically translated” in to a very different, dramatic outcome. Thus, our results suggest the need for a sex-based and transcriptomic profile-based study, for a better understanding of the onset and progression of AD.

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