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      Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases

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          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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          Most cited references55

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          Genetics of gene expression and its effect on disease.

          Common human diseases result from the interplay of many genes and environmental factors. Therefore, a more integrative biology approach is needed to unravel the complexity and causes of such diseases. To elucidate the complexity of common human diseases such as obesity, we have analysed the expression of 23,720 transcripts in large population-based blood and adipose tissue cohorts comprehensively assessed for various phenotypes, including traits related to clinical obesity. In contrast to the blood expression profiles, we observed a marked correlation between gene expression in adipose tissue and obesity-related traits. Genome-wide linkage and association mapping revealed a highly significant genetic component to gene expression traits, including a strong genetic effect of proximal (cis) signals, with 50% of the cis signals overlapping between the two tissues profiled. Here we demonstrate an extensive transcriptional network constructed from the human adipose data that exhibits significant overlap with similar network modules constructed from mouse adipose data. A core network module in humans and mice was identified that is enriched for genes involved in the inflammatory and immune response and has been found to be causally associated to obesity-related traits.
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            Gene regulation and DNA damage in the ageing human brain.

            The ageing of the human brain is a cause of cognitive decline in the elderly and the major risk factor for Alzheimer's disease. The time in life when brain ageing begins is undefined. Here we show that transcriptional profiling of the human frontal cortex from individuals ranging from 26 to 106 years of age defines a set of genes with reduced expression after age 40. These genes play central roles in synaptic plasticity, vesicular transport and mitochondrial function. This is followed by induction of stress response, antioxidant and DNA repair genes. DNA damage is markedly increased in the promoters of genes with reduced expression in the aged cortex. Moreover, these gene promoters are selectively damaged by oxidative stress in cultured human neurons, and show reduced base-excision DNA repair. Thus, DNA damage may reduce the expression of selectively vulnerable genes involved in learning, memory and neuronal survival, initiating a programme of brain ageing that starts early in adult life.
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              Variations in DNA elucidate molecular networks that cause disease.

              Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.
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                Author and article information

                Journal
                Mol Syst Biol
                Mol. Syst. Biol
                msb
                Molecular Systems Biology
                Blackwell Publishing Ltd (Oxford, UK )
                1744-4292
                1744-4292
                July 2014
                30 July 2014
                : 10
                : 7
                : 743
                Affiliations
                [1 ]National Institute of Allergy and Infectious Diseases Bethesda, MD, USA
                [2 ]Department of Neuroscience, Icahn School of Medicine at Mount Sinai New York, NY, USA
                [3 ]Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai New York, NY, USA
                [4 ]Merck Research Laboratories, Merck & Co., Inc. Whitehouse Station, NJ, USA
                [5 ]Department of Integrative Biology and Physiology, University of California Los Angeles, CA, USA
                [6 ]Sage Bionetworks Seattle, WA, USA
                [7 ]National Heart, Lung and Blood Institute Bethesda, MD, USA
                [8 ]Department of Human Genetics, University of California Los Angeles, CA, USA
                [9 ]Icelandic Heart Association Kopavogur, Iceland
                [10 ]Faculty of Pharmaceutical Sciences, University of Iceland Reykjavik, Iceland
                Author notes
                *Corresponding author. Tel: +1 301 443 6005; Fax: +1 301 480 1660; E-mail: manikandan.narayanan@ 123456nih.gov
                **Corresponding author. Tel: +1 212 659 8942; Fax: +1 646 537 8660; E-mail: jun.zhu@ 123456mssm.edu

                Subject Categories Genome-Scale & Integrative Biology; Network Biology; Neuroscience

                Article
                10.15252/msb.20145304
                4299500
                25080494
                c57ed45b-5aa9-4d40-8c02-f5d6cd8970d9
                Published 2014. This article is a U.S. Government work and is in the public domain in the USA

                This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 March 2014
                : 11 June 2014
                : 20 June 2014
                Categories
                Articles

                Quantitative & Systems biology
                differential co-expression,dysregulatory gene networks,epigenetic regulation of neural differentiation,network alignment,neurodegenerative diseases

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