Abstract
Neurodegenerative diseases share common pathologic features including neuroinflammation, mitochondrial dysfunction and protein aggregation, suggesting common underlying mechanisms of neurodegeneration. We undertook a meta-analysis of public gene expression data for neurodegenerative diseases to identify a common transcriptional signature of neurodegeneration. Using 1,270 post-mortem central nervous system tissue samples from 13 patient cohorts covering four neurodegenerative diseases, we identified 243 differentially expressed genes, which were similarly dysregulated in 15 additional patient cohorts of 205 samples including seven neurodegenerative diseases. This gene signature correlated with histologic disease severity. Metallothioneins featured prominently among differentially expressed genes, and functional pathway analysis identified specific convergent themes of dysregulation. MetaCore network analyses revealed various novel candidate hub genes (e.g. STAU2). Genes associated with M1-polarized macrophages and reactive astrocytes were strongly enriched in the meta-analysis data. Evaluation of genes enriched in neurons revealed 70 down-regulated genes, over half not previously associated with neurodegeneration. Comparison with aging brain data (3 patient cohorts, 221 samples) revealed 53 of these to be unique to neurodegenerative disease, many of which are strong candidates to be important in neuropathogenesis (e.g. NDN, NAP1L2). ENCODE ChIP-seq analysis predicted common upstream transcriptional regulators not associated with normal aging (REST, RBBP5, SIN3A, SP2, YY1, ZNF143, IKZF1). Finally, we removed genes common to neurodegeneration from disease-specific gene signatures, revealing uniquely robust immune response and JAK-STAT signaling in amyotrophic lateral sclerosis. Our results implicate pervasive bioenergetic deficits, M1-type microglial activation and gliosis as unifying themes of neurodegeneration, and identify numerous novel genes associated with neurodegenerative processes.
Highlights
Neurodegenerative diseases share common pathologic features including neuroinflammation, mitochondrial dysfunction and protein aggregation, suggesting common underlying mechanisms of neurodegeneration
Meta-analysis identifies a common gene signature of neurodegeneration For our discovery meta-analysis of neurodegenerative diseases, we collected microarray data sets containing 10 independent patient cohorts that profiled human post-mortem central nervous system (CNS) tissues in 285 samples (150 cases, 135 controls) (Table 1, Additional file 1: Table S1) [22,23,24,25,26,27,28,29]. These samples were obtained from various cortical regions, hippocampus, basal ganglia, and spinal cord in four neurodegenerative diseases (AD, Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS))
As some data sets do not provide raw data and optimal microarray pre-processing techniques differ across platforms, we downloaded processed signal intensities, and checked that all data were log2 transformed and quantile normalized across all samples in the specific experiment
Summary
Neurodegenerative diseases share common pathologic features including neuroinflammation, mitochondrial dysfunction and protein aggregation, suggesting common underlying mechanisms of neurodegeneration. Increasing numbers of transcriptome studies have addressed individual neurodegenerative diseases, including those focused on understanding regional susceptibility [13], disease progression [14], cell type-specific signals [15], and disease-specific meta-analysis [16] These studies are usually limited by relatively small sample sizes and significant heterogeneity between experiments, in the tissue sampled, expression analysis platform, sample procurement method, and background of the investigated patient populations. There are approximately 40 publicly available gene expression microarray studies that profiled brain tissue in neurodegenerative diseases These studies better represent the heterogeneity of neurodegeneration observed in the real world as different research groups carried out these experiments independently using different tissue samples and microarray technologies. We have successfully used this meta-analysis approach to reveal novel insights into lung cancer [19] and to predict FDA-approved drugs that can be repurposed to treat organ transplant patients [20]
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