Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases
TL;DR: Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non‐demented controls, this work identified a 242‐gene subnetwork enriched for independent AD/HD signatures, which revealed a surprising dichotomy of gained/lost correlations among two inter‐connected processes, chromatin organization and neural differentiation.
Abstract: Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer’s disease (AD) and Huntington’s disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10 � 12 ), while Dnmt3a KO signature does not (P = 0.017).
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TL;DR: A major rearrangement of transcriptional patterns in MDD is shown, with limited overlap between males and females, and key regulators of sex-specific gene networks underlying MDD are identified and confirmed.
Abstract: Major depressive disorder (MDD) is a leading cause of disease burden worldwide. While the incidence, symptoms and treatment of MDD all point toward major sex differences, the molecular mechanisms underlying this sexual dimorphism remain largely unknown. Here, combining differential expression and gene coexpression network analyses, we provide a comprehensive characterization of male and female transcriptional profiles associated with MDD across six brain regions. We overlap our human profiles with those from a mouse model, chronic variable stress, and capitalize on converging pathways to define molecular and physiological mechanisms underlying the expression of stress susceptibility in males and females. Our results show a major rearrangement of transcriptional patterns in MDD, with limited overlap between males and females, an effect seen in both depressed humans and stressed mice. We identify key regulators of sex-specific gene networks underlying MDD and confirm their sex-specific impact as mediators of stress susceptibility. For example, downregulation of the female-specific hub gene Dusp6 in mouse prefrontal cortex mimicked stress susceptibility in females, but not males, by increasing ERK signaling and pyramidal neuron excitability. Such Dusp6 downregulation also recapitulated the transcriptional remodeling that occurs in prefrontal cortex of depressed females. Together our findings reveal marked sexual dimorphism at the transcriptional level in MDD and highlight the importance of studying sex-specific treatments for this disorder.
480 citations
TL;DR: Proteomic analyses of 129 human cortical tissues reveal protein- and disease-specific pathways involved in the etiology, initiation, and progression of Alzheimer's disease.
Abstract: Here, we report proteomic analyses of 129 human cortical tissues to define changes associated with the asymptomatic and symptomatic stages of Alzheimer's disease (AD). Network analysis revealed 16 modules of co-expressed proteins, 10 of which correlated with AD phenotypes. A subset of modules overlapped with RNA co-expression networks, including those associated with neurons and astroglial cell types, showing altered expression in AD, even in the asymptomatic stages. Overlap of RNA and protein networks was otherwise modest, with many modules specific to the proteome, including those linked to microtubule function and inflammation. Proteomic modules were validated in an independent cohort, demonstrating some module expression changes unique to AD and several observed in other neurodegenerative diseases. AD genetic risk loci were concentrated in glial-related modules in the proteome and transcriptome, consistent with their causal role in AD. This multi-network analysis reveals protein- and disease-specific pathways involved in the etiology, initiation, and progression of AD.
331 citations
TL;DR: Recent advances in epigenetic regulation are presented, with a focus on histone modifications and the implications for several neurodegenerative diseases including Alzheimer's disease, Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS).
212 citations
TL;DR: This study profiled 169,496 nuclei from the prefrontal cortical samples of AD patients and healthy controls by single-nucleus RNA sequencing, revealing a role of antigen presentation by angiogenic endothelial cells in AD and offering important insights into the therapeutic potential of targeting glial- and endothelial-specific pathways to restore brain homeostasis in AD.
Abstract: Alzheimer’s disease (AD) is the most common form of dementia but has no effective treatment. A comprehensive investigation of cell type-specific responses and cellular heterogeneity in AD is required to provide precise molecular and cellular targets for therapeutic development. Accordingly, we perform single-nucleus transcriptome analysis of 169,496 nuclei from the prefrontal cortical samples of AD patients and normal control (NC) subjects. Differential analysis shows that the cell type-specific transcriptomic changes in AD are associated with the disruption of biological processes including angiogenesis, immune activation, synaptic signaling, and myelination. Subcluster analysis reveals that compared to NC brains, AD brains contain fewer neuroprotective astrocytes and oligodendrocytes. Importantly, our findings show that a subpopulation of angiogenic endothelial cells is induced in the brain in patients with AD. These angiogenic endothelial cells exhibit increased expression of angiogenic growth factors and their receptors (i.e., EGFL7, FLT1, and VWF) and antigen-presentation machinery (i.e., B2M and HLA-E). This suggests that these endothelial cells contribute to angiogenesis and immune response in AD pathogenesis. Thus, our comprehensive molecular profiling of brain samples from patients with AD reveals previously unknown molecular changes as well as cellular targets that potentially underlie the functional dysregulation of endothelial cells, astrocytes, and oligodendrocytes in AD, providing important insights for therapeutic development.
174 citations
Cites result from "Common dysregulation network in the..."
...GSE33000), who performed bulk transcriptome microarray analysis of prefrontal cortical tissues in a large cohort (AD: n = 310; NC: n = 157) (18)....
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...Among the DEGs identified in our snRNA-seq analysis, 1,113 and 764 genes were significantly differentially expressed in the microarray data from the prefrontal cortex and temporal cortex, respectively (adjusted P 0.05) (SI Appendix, Table S4)....
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...To validate the AD-associated transcriptomic changes detailed above, we compared our results with microarray data from large cohort studies that examined samples from the prefrontal cortex (AD: n = 310; NC: n = 157) or temporal cortex (AD: n = 106; NC: n = 135) (SI Appendix, Table S4) (18, 19)....
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TL;DR: DGCA is an R package for systematically assessing the difference in gene-gene regulatory relationships under different conditions that will greatly facilitate the application of differential correlation analysis in many biological studies and thus will help identification of novel signaling pathways, biomarkers, and targets in complex biological systems and diseases.
Abstract: Dissecting the regulatory relationships between genes is a critical step towards building accurate predictive models of biological systems. A powerful approach towards this end is to systematically study the differences in correlation between gene pairs in more than one distinct condition. In this study we develop an R package, DGCA (for Differential Gene Correlation Analysis), which offers a suite of tools for computing and analyzing differential correlations between gene pairs across multiple conditions. To minimize parametric assumptions, DGCA computes empirical p-values via permutation testing. To understand differential correlations at a systems level, DGCA performs higher-order analyses such as measuring the average difference in correlation and multiscale clustering analysis of differential correlation networks. Through a simulation study, we show that the straightforward z-score based method that DGCA employs significantly outperforms the existing alternative methods for calculating differential correlation. Application of DGCA to the TCGA RNA-seq data in breast cancer not only identifies key changes in the regulatory relationships between TP53 and PTEN and their target genes in the presence of inactivating mutations, but also reveals an immune-related differential correlation module that is specific to triple negative breast cancer (TNBC). DGCA is an R package for systematically assessing the difference in gene-gene regulatory relationships under different conditions. This user-friendly, effective, and comprehensive software tool will greatly facilitate the application of differential correlation analysis in many biological studies and thus will help identification of novel signaling pathways, biomarkers, and targets in complex biological systems and diseases.
174 citations
Cites background or methods from "Common dysregulation network in the..."
...Differential co-expression analysis can start with coexpressed gene modules or clusters based on the similarity of their gene expression in each condition using WGCNA [5] and MEGENA [6] and then computes module overlap statistics between conditions [7] or the average modular differential connectivity [8, 9]....
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...Perhaps as a result, some investigators have used only one permutation of the data in such analyses [9]....
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...correlation calculation that was previously described and used [8, 9]....
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References
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Book•
01 Jan 1985
TL;DR: In this article, the authors present a model for estimating the effect size from a series of experiments using a fixed effect model and a general linear model, and combine these two models to estimate the effect magnitude.
Abstract: Preface. Introduction. Data Sets. Tests of Statistical Significance of Combined Results. Vote-Counting Methods. Estimation of a Single Effect Size: Parametric and Nonparametric Methods. Parametric Estimation of Effect Size from a Series of Experiments. Fitting Parametric Fixed Effect Models to Effect Sizes: Categorical Methods. Fitting Parametric Fixed Effect Models to Effect Sizes: General Linear Models. Random Effects Models for Effect Sizes. Multivariate Models for Effect Sizes. Combining Estimates of Correlation Coefficients. Diagnostic Procedures for Research Synthesis Models. Clustering Estimates of Effect Magnitude. Estimation of Effect Size When Not All Study Outcomes Are Observed. Meta-Analysis in the Physical and Biological Sciences. Appendix. References. Index.
9,769 citations
TL;DR: This work proposes an approach to measuring statistical significance in genomewide studies based on the concept of the false discovery rate, which offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted.
Abstract: With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.
9,239 citations
"Common dysregulation network in the..." refers methods in this paper
...To make differential co-expression calls from the Q statistics of all gene pairs taken together, we used a global permutation-based approach that both accounts for multiple testing and is robust to any violations of parametric assumptions (Storey & Tibshirani, 2003)....
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Brigham and Women's Hospital1, University of California, San Diego2, University of California, Davis3, Rush University Medical Center4, University of Washington5, Washington University in St. Louis6, University of Tokyo7, Mayo Clinic8, Oregon Health & Science University9, University of Texas at Dallas10, University of Melbourne11, Eli Lilly and Company12, Columbia University13, University of California, San Francisco14, Alzheimer's Association15, National Institutes of Health16
TL;DR: A conceptual framework and operational research criteria are proposed, based on the prevailing scientific evidence to date, to test and refine these models with longitudinal clinical research studies and it is hoped that these recommendations will provide a common rubric to advance the study of preclinical AD.
Abstract: The pathophysiological process of Alzheimer's disease (AD) is thought to begin many years before the diagnosis of AD dementia. This long "preclinical" phase of AD would provide a critical opportunity for therapeutic intervention; however, we need to further elucidate the link between the pathological cascade of AD and the emergence of clinical symptoms. The National Institute on Aging and the Alzheimer's Association convened an international workgroup to review the biomarker, epidemiological, and neuropsychological evidence, and to develop recommendations to determine the factors which best predict the risk of progression from "normal" cognition to mild cognitive impairment and AD dementia. We propose a conceptual framework and operational research criteria, based on the prevailing scientific evidence to date, to test and refine these models with longitudinal clinical research studies. These recommendations are solely intended for research purposes and do not have any clinical implications at this time. It is hoped that these recommendations will provide a common rubric to advance the study of preclinical AD, and ultimately, aid the field in moving toward earlier intervention at a stage of AD when some disease-modifying therapies may be most efficacious.
5,671 citations
"Common dysregulation network in the..." refers background in this paper
...…could result from the normal aging process, accelerated or premature aging induced by AD, or age-independent pathological mechanisms, and disentangling the effect of these factors remains open (Sperling et al, 2011) despite some recent advances (Cao et al, 2010; Podtelezhnikov et al, 2011)....
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TL;DR: A general framework for `soft' thresholding that assigns a connection weight to each gene pair is described and several node connectivity measures are introduced and provided empirical evidence that they can be important for predicting the biological significance of a gene.
Abstract: Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.
4,448 citations
TL;DR: An online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs) is developed, well-suited to guide future investigations of the role of common variants in complex disease etiology.
Abstract: We have developed an online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs). Reported TASs were common [median risk allele frequency 36%, interquartile range (IQR) 21%−53%] and were associated with modest effect sizes [median odds ratio (OR) 1.33, IQR 1.20–1.61]. Among 20 genomic annotation sets, reported TASs were significantly overrepresented only in nonsynonymous sites [OR = 3.9 (2.2−7.0), p = 3.5 × 10−7] and 5kb-promoter regions [OR = 2.3 (1.5−3.6), p = 3 × 10−4] compared to SNPs randomly selected from genotyping arrays. Although 88% of TASs were intronic (45%) or intergenic (43%), TASs were not overrepresented in introns and were significantly depleted in intergenic regions [OR = 0.44 (0.34−0.58), p = 2.0 × 10−9]. Only slightly more TASs than expected by chance were predicted to be in regions under positive selection [OR = 1.3 (0.8−2.1), p = 0.2]. This new online resource, together with bioinformatic predictions of the underlying functionality at trait/disease-associated loci, is well-suited to guide future investigations of the role of common variants in complex disease etiology.
4,041 citations
"Common dysregulation network in the..." refers background in this paper
...Several common variants have been shown to associate with AD based on genome-wide association studies (GWAS) catalog (Hindorff et al, 2009), and rare variants in certain genes have also been identified through Mendelian inheritance based on OMIM database (Supplementary Table S2 and Supplementary…...
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