Article series: Disease mechanisms

Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

Journal name:
Nature Reviews Genetics
Volume:
16,
Pages:
441–458
Year published:
DOI:
doi:10.1038/nrg3934
Published online

Abstract

Genetic and genomic approaches have implicated hundreds of genetic loci in neurodevelopmental disorders and neurodegeneration, but mechanistic understanding continues to lag behind the pace of gene discovery. Understanding the role of specific genetic variants in the brain involves dissecting a functional hierarchy that encompasses molecular pathways, diverse cell types, neural circuits and, ultimately, cognition and behaviour. With a focus on transcriptomics, this Review discusses how high-throughput molecular, integrative and network approaches inform disease biology by placing human genetics in a molecular systems and neurobiological context. We provide a framework for interpreting network biology studies and leveraging big genomics data sets in neurobiology.

At a glance

Figures

  1. Molecular systems and the neurobiological hierarchy.
    Figure 1: Molecular systems and the neurobiological hierarchy.

    a | Genetic variants exert their effects on cognitive and behavioural phenotypes associated with neurodevelopmental or neurodegenerative disease through a neurobiological hierarchy that includes multiple molecular levels (transcriptomic, proteomic and epigenomic) that can be modelled as networks on the basis of physical interactions and correlations within and across multiple molecular levels (Box 2). These molecular levels of organization can vary at multiple neurobiological phenotypic levels (cells, circuits, and cognition and behaviour) across the lifespan. b | Gene expression levels vary dramatically across development and ageing, brain regions and cell types, as illustrated by three genes: SMARCC2, which is a pan-regional neurodevelopmental gene; MET, a regionally patterned adult neuronal gene; and OLIG1, a gene most highly expressed in white matter and oligodendrocytes. Development and ageing data are from BrainCloud17, regional data are from Braineac16 and cell type expression data are from fluorescent-activated cell sorted transcriptomes from mouse cortex162 (http://web.stanford.edu/group/barres_lab/brain_rnaseq.html). c | Both the molecular and phenotypic levels exhibit a typical trajectory with normal variation during development and ageing that can be altered in disease, resulting in abnormal temporal trajectories. The x axis on this plot reflects the progression of time, and the y axis reflects theoretical deviation from the normal trajectory for any molecular or phenotypic measurement. CPi, inner cortical plate; CPo, outer cortical plate; CRBL, cerebellum; FCTX, frontal cortex; HIPP, hippocampus; ISVZ, inner subventricular zone; IZ, intermediate zone; lncRNA, long noncoding RNA; MEDU, brainstem medulla; miRNA, microRNA; OCTX, occipital cortex; OSVZ, outer subventricular zone; PUTM, putamen; SNIG, substantia nigra; SP, subplate; TCTX, temporal cortex; THAL, thalamus; VZ, ventricular zone; WHMT, subcortical white matter.

  2. Flowchart of transcriptomic analysis and illustration of seeded and genome-wide approaches to network analysis.
    Figure 2: Flowchart of transcriptomic analysis and illustration of seeded and genome-wide approaches to network analysis.

    A flowchart demonstrating the general approach to a transcriptomic study that uses differential gene expression (DGE) and network analysis (part a). Network-level features, such as connectivity ranking and module-level enrichment, allow the integration of many external data sources and experiments. Network analysis involves first (part b) connecting genetic or molecular nodes with information about pairwise relationships, which may be one or more of the following: statistical associations relating molecular patterns measured across experiments, such as variation in gene expression levels across brain regions; physical interaction data from experiments or curated from the literature such as transcription factor (TF) or RNA-binding protein (RNABP) binding or protein–protein interactions (PPIs); or computational predictions about TF or RNABP binding using motif enrichment analysis (here, U on the RNA motif is depicted as T). Next, the structure of the network is used to (part c) define modules using a seed-based or genome-wide approach, which groups together the genes that share similar edge-level properties. The seeded (prior-based) approach is shown on the left-hand side, and the unseeded (genome-wide) approach on the right-hand side. The seeded approach involves starting with genes of interest, expanding edges to bring in additional (unannotated) genes and identifying highly connected components as modules. The unseeded approach (right-hand side) involves starting with unannotated genes, using edges to identify interconnected components as modules and then evaluating where genes of interest fall in the resultant network structure. Modules from either approach can be further annotated with external information such as genetic associations and known pathways, integrated with additional data or used to prioritize targets for experimental validation (see Box 2 and Table 1 for more details). Alternative depictions of the network analysis process are also available elsewhere28, 41, 169. GO, Gene Ontology; KEGG, Kyoto Encyclopaedia of Genes and Genome Elements.

  3. Transcriptomic convergence and divergence across central nervous system disorders.
    Figure 3: Transcriptomic convergence and divergence across central nervous system disorders.

    Transcriptomics can systematically compare genes and pathways across neurobiological disorders. To provide a simple example, we compare genome-wide expression patterns in the cerebral cortex across published microarray studies of autism spectrum disorder (ASD)30, schizophrenia (SCZ)225 and Alzheimer disease (AD)226 (part a). We applied differential gene expression (DGE) analysis across these disorders in a pairwise manner and performed a meta-analysis with weighted gene co-expression network analysis (WGCNA). Please see Supplementary information S1 (box) for details. The bottom-left half of the comparisons shows pairwise comparison of DGE across conditions. ASD–SCZ and ASD–AD are significantly correlated in DGE changes, as demonstrated by Spearman correlations (ρ values) between genome-wide DGE effect sizes in each disorder. On the upper-right half of the comparisons, Gene Ontology (GO) term enrichment of pairwise shared upregulated and downregulated changes demonstrates that upregulated inflammation and downregulated synaptic function and oxidative phosphorylation are common to all three disorders. Results are shown as enrichment Z scores for pathway enrichment, Z > 2 suggests enrichment227. WGCNA across these three conditions identified five modules (labelled with different colours) that are perturbed across at least one condition, as demonstrated by differences in eigengene expression (*p < 0.05, **p < 0.01, ***p < 0.001, two-tailed t-test) (part b). The top ten interconnected (hub) genes in each module with edges reflecting the strength of correlation between genes reveals (part c) and GO term enrichment for each module (part d). MHC, major histocompatibility complex.

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Affiliations

  1. Program in Neurobehavioral Genetics, Semel Institute, and Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA.

    • Neelroop N. Parikshak,
    • Michael J. Gandal &
    • Daniel H. Geschwind
  2. Interdepartmental Program in Neuroscience, University of California, Los Angeles, California 90095, USA.

    • Neelroop N. Parikshak &
    • Daniel H. Geschwind
  3. Center for Autism Treatment and Research, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA.

    • Michael J. Gandal &
    • Daniel H. Geschwind
  4. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA.

    • Daniel H. Geschwind

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The authors declare no competing interests.

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  • Neelroop N. Parikshak

    Neelroop N. Parikshak is a trainee in the UCLA-Caltech Medical Scientist Training Program who did his dissertation research work in the Geschwind laboratory. He has a B.A. in biochemistry and mathematics from Rice University, Houston, Texas, USA, and a Ph.D. in neuroscience from University of California, Los Angeles (UCLA), USA. He has experience in computational neuroscience, neuroimaging and neurogenetics. He aims to apply quantitative methods to genomic, molecular and phenotypic data to identify therapeutically actionable interventions in disease.

  • Michael J. Gandal

    Michael J. Gandal is a research-track resident in psychiatry at University of California, Los Angeles (UCLA), USA, and the Semel Institute for Neuroscience and Human Behavior, and is a postdoctoral fellow in the Geschwind laboratory. He received a B.S. in engineering from Stanford University, California, USA, and an M.D. and Ph.D. in bioengineering from the University of Pennsylvania, Philadelphia, USA. He is applying systems-level and translational approaches to understand the neurobiology of psychiatric disease.

  • Daniel H. Geschwind

    Daniel H. Geschwind is the Gordon and Virginia MacDonald Distinguished Professor of Neurology, Psychiatry, and Human Genetics at University of California, Los Angeles (UCLA), USA. His laboratory integrates genetics and genomic methods with basic neurobiology to develop mechanistically based treatments for neurological and psychiatric disorders, focusing on autism and neurodegenerative disorders. He has also put considerable effort into fostering large-scale collaborative patient resources for genetic research and data sharing. He has published more than 300 papers and is a member of the American Association of Physicians and the Institute of Medicine of the National Academies. Geschwind laboratory homepage.

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    Methodology and analysis for cross-disorder transcriptomic analysis.

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