FRA ABSTRACT: Analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, plus a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects. Many of the genes implicated encode proteins for synaptic formation, transcriptional regulation and chromatin-remodelling pathways. These include voltage-gated ion channels regulating the propagation of action potentials, pacemaking and excitability–transcription coupling, as well as histone-modifying enzymes and chromatin remodellers—most prominently those that mediate post-translational lysine methylation/demethylation modifications of histones.
FRA ABSTRACT: Genes targeted by CHD8, a chromodomain helicase strongly associated with ASD, in human midfetal brain, human neural stem cells (hNSCs) and embryonic mouse cortex. CHD8 targets are strongly enriched for other ASD risk genes in both human and mouse neurodevelopment, and converge in ASD-associated co-expression networks in human midfetal cortex. CHD8 knockdown in hNSCs results in dysregulation of ASD risk genes directly targeted by CHD8. Integration of CHD8-binding data into ASD risk models improves detection of risk genes. These results suggest loss of CHD8 contributes to ASD by perturbing an ancient gene regulatory network during human brain development. –
two recent developments have sparked rapid progress in ASD gene discovery. First, it is now appreciated that de novo mutations contribute to ASD and often carry large effects5,6,7,8. Second, the advent of next-generation sequencing technologies has enabled hypothesis-naïve whole-exome surveys of large ASD cohorts to identify genes with de novo, ASD-associated damaging mutations9,10,11,12. This approach allows the level of ASD risk to be assessed for all genes using uniform statistical and genetic criteria, providing a quantitative definition of an ASD risk gene independent of prior hypotheses regarding gene functions or disease processes.
nine such high-confidence13 ASD risk genes have been identified: ANK2, CHD8, CUL3, DYRK1A, GRIN2B, KATNAL2, POGZ, SCN2A and TBR1. These genes encode proteins with a variety of functions, including chromatin modification and transcriptional regulation14, suggesting molecular mechanisms perturbed in ASD. Of these genes, CHD8 has the largest number of loss of function mutations in individuals with ASD, and therefore the strongest association with ASD risk.
Other studies suggest CHD8 may repress Wnt/β-catenin target genes and p53-dependent apoptosis17,21. These findings, coupled with the strong genetic evidence described above, suggest that loss of CHD8 function contributes to ASD pathology by disrupting the expression of genes regulated by CHD8.
networks of genes that were co-expressed with the nine known high-confidence ASD risk genes at specific brain regions and points in time. To define a larger set of potential ASD risk genes, Willsey et al. identified 122 genes that had a de novo loss of function in a single individual with ASD, but not in matched controls. These potential ASD risk genes show the most significant co-expression with high-confidence ASD risk genes in midfetal prefrontal and primary motor-somatosensory cortex (PFC-MSC). A parallel study also supported the convergence of ASD risk genes in co-expression networks at this developmental time point and location22. These findings suggest ASD risk genes are co-regulated, and may thus converge in regulatory networks associated with ASD. Owing to its chromatin remodelling activity, its association with other transcriptional regulators, and its increased expression during human midfetal development15, CHD8 is a prime candidate for contributing to the organization of such networks by regulating other ASD risk genes.
are ASD risk genes overrepresented among genes targeted by CHD8 in the developing brain? Second, are CHD8 targets overrepresented in ASD-associated co-expression networks in midfetal human brain? Third, does loss of CHD8 result in dysregulation of ASD risk genes that are targeted by CHD8?
CHD8 gene targets are overrepresented in the ASD-associated co-expression network identified in human midfetal brain13, supporting the hypothesis that CHD8 is a key regulator of genes in this network. After downregulation of CHD8 expression in hNSCs, ASD risk genes bound by CHD8 in multiple neurodevelopmental contexts are significantly dysregulated by CHD8 loss.
CHD8 seems to be a direct regulator of other ASD risk genes during human brain development.
[https://www.ncbi.nlm.nih.gov/pubmed/28255957 af Mastrototaro G, Zaghi M, Sessa A: link: https://link.springer.com/article/10.1007%2Fs12031-017-0900-6]
- Ronald, A. & Hoekstra, R. A. Autism spectrum disorders and autistic traits: a decade of new twin studies. Am. J. Med. Genet. B Neuropsychiatr. Genet. 156, 255–274 (2011)
- Sebat, J. et al. Strong association of de novo copy number mutations with autism. Science316, 445–449 (2007)
- Pinto, D. et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368–372 (2010)
- Klei, L. et al. Common genetic variants, acting additively, are a major source of risk for autism. Mol. Autism 3, 9 (2012)
- Gaugler, T. et al. Most inherited risk for autism resides with common variation. Nature Genet. 46, 881–885 (2014)
- Yu, T. W. et al. Using whole-exome sequencing to identify inherited causes of autism. Neuron 77, 259–273 (2013)
- Lim, E. T. et al. Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron 77, 235–242 (2013)
- Poultney, C. S. et al. Identification of small exonic CNV from whole-exome sequence data and application to autism spectrum disorder. Am. J. Hum. Genet. 93, 607–619 (2013)
- Betancur, C. Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res. 1380, 42–77 (2011)
- Glessner, J. T. et al. Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 459, 569–573 (2009)
- Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012)
- O’Roak, B. J. et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 338, 1619–1622 (2012)
- O’Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012)
- Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242–245 (2012)
- Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012)
- Willsey, A. J. et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155, 997–1007 (2013)
- DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genet. 43, 491–498 (2011)
- Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nature Methods 7, 248–249 (2010)
- Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nature Genet. 46, 944–950 (2014)
- He, X. et al. Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes. PLoS Genet. 9, e1003671 (2013)
- Girirajan, S. et al. Refinement and discovery of new hotspots of copy-number variation associated with autism spectrum disorder. Am. J. Hum. Genet. 92, 221–237 (2013)
- Pinto, D. et al. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am. J. Hum. Genet. 94, 677–694 (2014)
- Helsmoortel, C. et al. A SWI/SNF-related autism syndrome caused by de novo mutations in ADNP. Nature Genet. 46, 380–384 (2014)
- Long, H. et al. Myo9b and RICS modulate dendritic morphology of cortical neurons. Cereb. Cortex 23, 71–79 (2013)
- Yoon, K. J. et al. Mind bomb 1-expressing intermediate progenitors generate Notch signaling to maintain radial glial cells. Neuron 58, 519–531 (2008)
- Smrt, R. D. et al. MicroRNA miR-137 regulates neuronal maturation by targeting ubiquitin ligase Mind bomb-1. Stem Cells 28, 1060–1070 (2010)
- Ripke, S. et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nature Genet. 45, 1150–1159 (2013)
- Robinson, E. B., Lichtenstein, P., Anckarsater, H., Happe, F. & Ronald, A. Examining and interpreting the female protective effect against autistic behavior. Proc. Natl Acad. Sci. USA110, 5258–5262 (2013)
- Jacquemont, S. et al. A higher mutational burden in females supports a “female protective model” in neurodevelopmental disorders. Am. J. Hum. Genet. 94, 415–425 (2014)
- Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature506, 179–184 (2014)
- Darnell, J. C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011)
- Ascano, M., Jr. et al. FMRP targets distinct mRNA sequence elements to regulate protein expression. Nature 492, 382–386 (2012)
- Weyn-Vanhentenryck, S. M. et al. HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism. Cell Rep. 6, 1139–1152 (2014)
- Voineagu, I. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384 (2011)
- Collins, M. O. et al. Molecular characterization and comparison of the components and multiprotein complexes in the postsynaptic proteome. J. Neurochem. 97 (suppl. 1). 16–23 (2006)
- Liu, L. et al. DAWN: a framework to identify autism genes and subnetworks using gene expression and genetics. Mol. Autism 5, 22 (2014)
- Tan, C. M., Chen, E. Y., Dannenfelser, R., Clark, N. R. & Ma’ayan, A. Network2Canvas: network visualization on a canvas with enrichment analysis. Bioinformatics 29, 1872–1878 (2013)
- Vatta, M. et al. Genetic and biophysical basis of sudden unexplained nocturnal death syndrome (SUNDS), a disease allelic to Brugada syndrome. Hum. Mol. Genet. 11, 337–345 (2002)
- Volkers, L. et al. Nav 1.1 dysfunction in genetic epilepsy with febrile seizures-plus or Dravet syndrome. Eur. J. Neurosci. 34, 1268–1275 (2011)
- Scholl, U. I. et al. Somatic and germline CACNA1D calcium channel mutations in aldosterone-producing adenomas and primary aldosteronism. Nature Genet. 45, 1050–1054 (2013)
- Khare, S. P. et al. HIstome–a relational knowledgebase of human histone proteins and histone modifying enzymes. Nucleic Acids Res. 40, D337–D342 (2012)
- Feng, J. et al. Chronic cocaine-regulated epigenomic changes in mouse nucleus accumbens. Genome Biol. 15, R65 (2014)
- Lachmann, A. et al. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26, 2438–2444 (2010)
- Ronan, J. L., Wu, W. & Crabtree, G. R. From neural development to cognition: unexpected roles for chromatin. Nature Rev. Genet. 14, 347–359 (2013)
- Rauch, A. et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380, 1674–1682 (2012)
- Penzes, P., Cahill, M. E., Jones, K. A., VanLeeuwen, J. E. & Woolfrey, K. M. Dendritic spine pathology in neuropsychiatric disorders. Nature Neurosci. 14, 285–293 (2011)
- Zoghbi, H. Y. Postnatal neurodevelopmental disorders: meeting at the synapse? Science302, 826–830 (2003)
- Devlin, B. & Scherer, S. W. Genetic architecture in autism spectrum disorder. Curr. Opin. Genet. Dev. 22, 229–237 (2012) .
- 2.Jeste, S. S. & Geschwind, D. H. Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nat. Rev. Neurol. 10, 74–81 (2014) .
- + Show context
- 3.Krystal, J. H. & State, M. W. Psychiatric disorders: diagnosis to therapy. Cell 157, 201–214 (2014) .
- 4.State, M. W. & Levitt, P. The conundrums of understanding genetic risks for autism spectrum disorders. Nat. Neurosci. 14, 1499–1506 (2011) .
- 5.Sebat, J. et al. Strong association of de novo copy number mutations with autism. Science 316, 445–449 (2007) .
- 6.Gilman, S. R. et al. Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70, 898–907 (2011) .
- 7.Levy, D. et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70, 886–897 (2011) .
- 8.Sanders, S. J. et al. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70, 863–885 (2011) .
- 9.Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242–245 (2012) .
- 10.Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012) .
- 11.Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012) .
- 12.O’Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature485, 246–250 (2012) .
- 13.Willsey, A. J. et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155, 997–1007 (2013) .
- 14.Krumm, N., O'Roak, B. J., Shendure, J. & Eichler, E. E. A de novo convergence of autism genetics and molecular neuroscience. Trends Neurosci. 37, 95–105 (2014) .
- 15.Bernier, R. et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell 158, 263–276 (2014) .
- 16.O’Roak, B. J. et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science338, 1619–1622 (2012) .
- 17.Thompson, B. A., Tremblay, V., Lin, G. & Bochar, D. A. CHD8 is an ATP-dependent chromatin remodeling factor that regulates beta-catenin target genes. Mol. Cell. Biol. 28, 3894–3904 (2008) .
- 18.Yuan, C.-C. et al. CHD8 associates with human Staf and contributes to efficient U6 RNA polymerase III transcription. Mol. Cell. Biol. 27, 8729–8738 (2007) .
- 19.Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007) .
- 20.Subtil-Rodríguez, A. et al. The chromatin remodeller CHD8 is required for E2F-dependent transcription activation of S-phase genes. Nucleic Acids Res. 42, 2185–2196 (2013) .
- 21.Nishiyama, M. et al. CHD8 suppresses p53-mediated apoptosis through histone H1 recruitment during early embryogenesis. Nature 11, 172–182 (2009) .
- + Show context
- 22.Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell155, 1008–1021 (2013) .
- 23.Sugathan, A. et al. CHD8 regulates neurodevelopmental pathways associated with autism spectrum disorder in neural progenitors. Proc. Natl Acad Sci USA 111, E4468–E4468 (2014) .
- 24.Liu, L. et al. DAWN: a framework to identify autism genes and subnetworks using gene expression and genetics. Mol. Autism 5, 22 (2014) .
- 25.Chadwick, L. H. The NIH Roadmap Epigenomics Program data resource. Epigenomics 4, 317–324 (2012) .
- 26.Heintzman, N. D. et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459, 108–112 (2009) .
- 27.Beisel, C. & Paro, R. Silencing chromatin: comparing modes and mechanisms. Nat. Rev. Genet. 12, 123–135 (2011) .
- 28.Ishihara, K., Oshimura, M. & Nakao, M. CTCF-dependent chromatin insulator is linked to epigenetic remodeling. Mol. Cell23, 733–742 (2006) .
- 29.Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005) .
- 30.Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014) .
- 31.Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014) .
- 32.Cotney, J. et al. The evolution of lineage-specific regulatory activities in the human embryonic limb. Cell 154, 185–196 (2013) .
- 33.Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009) .
- 34.Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002) .
- 35.Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics (Oxford, Engl) 26, 841–842 (2010) .
- + Show context
- 36.Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009) .
- 37.Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010) .
- 38.Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009) .
- 39.Lynch, M. et al. Rate, molecular spectrum, and consequences of human mutation. PNAS 107, 961–968 (2010) .
- 40.Garber, M., Grabherr, M. G., Guttman, M. & Trapnell, C.Computational methods for transcriptome annotation and quantification using RNA-seq. Nat. Methods 8, 469–477 (2011) .
- 41.Anders, S., Pyl, P. T. & Huber, W. HTSeq A Python Framework to Work with High-Throughput Sequencing Data Cold Spring Harbor Labs Journals (2014) .
- + Show context
- 42.Nikolayeva, O. & Robinson, M. D. edgeR for differential RNA-seq and ChIP-seq analysis: an application to stem cell biology. Methods Mol. Biol. (Clifton, NJ) 1150, 45–79 (2014) .
- + Show context
- 43.He, X. et al. Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes. PLoS Genet. 9, e1003671 (2013) .
- 44.Meinshausen, N. & Bühlmann, P. High-dimensional graphs and variable selection with the lasso. Ann. Statist. (2006) .
- + Show context
- 45.Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005) .
- + Show context
- 46.Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011) .
Tegn abonnement på
BioNyt Videnskabens Verden (www.bionyt.dk) er Danmarks ældste populærvidenskabelige tidsskrift for naturvidenskab. Det er det eneste blad af sin art i Danmark, som er helliget international forskning inden for livsvidenskaberne.
Bladet bringer aktuelle, spændende forskningsnyheder inden for biologi, medicin og andre naturvidenskabelige områder som f.eks. klimaændringer, nanoteknologi, partikelfysik, astronomi, seksualitet, biologiske våben, ecstasy, evolutionsbiologi, kloning, fedme, søvnforskning, muligheden for liv på mars, influenzaepidemier, livets opståen osv.
Artiklerne roses for at gøre vanskeligt stof forståeligt, uden at den videnskabelige holdbarhed tabes.