Abstract
Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental condition with no current treatment available. Although advances in genetics and genomics have identified hundreds of genes associated with ASD, very little is known about the pathophysiology of ASD and the functional contribution of specific genes to ASD phenotypes. Improved understanding of the biological function of ASD-associated genes and how this heterogeneous group of genetic variants leads to the disease is needed in order to develop therapeutic strategies. Here, we review the current state of ASD research related to gene discovery and examples of emerging molecular mechanisms (protein translation and alternative splicing). In addition, we discuss how patient-derived three-dimensional brain organoids might provide an opportunity to model specific genetic variants in order to define molecular and cellular defects that could be amenable for developing and screening personalized therapies related to ASD.
Keywords: autism, genomics, genetics, iPSCs, organoids, single-cell RNA-sequencing
Introduction
Autism spectrum disorder (ASD) is a phenotypically and genetically heterogeneous neurodevelopmental condition that manifests as deficits in reciprocal social interaction, repetitive behavior patterns, and restricted interests 1. The prevalence of ASD is as high as 1 in 68 children 2 in the US, and ASD has a profound impact at the individual, family, and societal levels. Although environmental factors likely play some role in the etiology of ASD 3, family and twin studies show that genetics contribute to the majority of the risk associated with ASD 4– 9. Genome-wide studies using genotyping microarrays, whole exome sequencing (WES), and whole genome sequencing have identified a rapidly growing number of genes linked to ASD 10– 21, providing a window into the molecular underpinnings of the disorder. However, our understanding of molecular mechanisms anchored to this heterogeneous group of genetic variants is not entirely clear. The paucity of disease-modifying therapies or molecular diagnostic tools for ASD makes identifying molecular disease mechanisms critical to assist developing rationally designed therapies. Additionally, details regarding the time course of molecular alterations in ASD can inform diagnostic biomarkers and quantitative measures to indicate disease severity and evaluate the efficacy of future therapeutic approaches.
Here, we review the recent progress in understanding the underlying genetics of ASD, including the identification of inherited, de novo, and somatic mutations linked to the disease. We then discuss how convergent disease mechanisms in ASD can potentially translate into the most appropriate biomarker development and treatment strategies for individuals or subtypes (or both) with ASD. Finally, we consider the unprecedented premise of patient-derived three-dimensional (3D) brain organoids as appropriate models to test and validate the functional impact of identified genetic variants as accessible and flexible platforms to screen and test for therapeutic agents.
The complex genetic makeup of autism spectrum disorder
The importance of heritable genetic variability in ASD pathogenesis has been highlighted in twin and family studies. The increased prevalence of the disease in siblings of ASD patients and greater ASD concordance rates in monozygotic twins compared with dizygotic twins has prompted significant efforts toward understanding the genetic architecture of ASD pathophysiology. Although the identification of mutations linked to monogenic syndromic forms of ASD, including Fragile X 22, Rett 23, MECP2 duplication 24, tuberous sclerosis complex 25, PTEN macrocephaly 26, and Timothy 27 syndromes, provided key insights into the genetic basis of ASD, these rare syndromes collectively account for only about 5% of ASD cases 28, leaving the etiology of non-syndromic ASD cases mostly unknown. The highly heterogeneous disease presentation of non-syndromic ASD initially posed serious impediments for identifying reproducible ASD-associated mutations. Despite these challenges, the assembly of large patient cohorts along with advances in genomic technologies within the last decade has facilitated the identification of ASD-associated variants in hundreds of genes, including single-nucleotide variant (SNVs) and copy number variants (CNVs) 29. The use of WES and whole genome sequencing in family cohorts with sporadic ASD (simplex) and with more than one affected individual (multiplex) led to the discovery of both rare inherited and de novo ASD risk variants. Rare recessive mutations have been reported in genes such as CNTNAP2 30, SLC9A9 31, AMT 20, PEX7 20, CC2D1A 32, and BCKDK 33 in consanguineous families with ASD and epilepsy, highlighting the role of recessive inheritance of deleterious mutations associated with ASD. The role of inherited variants in ASD was further supported through WES in larger cohorts of unrelated families 34, 35. WES in large cohorts of simplex families (one affected child sequenced together with unaffected parents) provided substantial insight into the role of de novo (or spontaneous) genetic variants in ASD. Numerous studies have reported increased rates of rare de novo CNVs and SNVs in individuals with ASD 10, 16– 18, 36 and have identified high-confidence ASD genes, including CHD8 16, 18, 37– 39, SYNGAP1 21, 40, 41, DYRK1A 42, and SCN2A 16. Moreover, targeted sequencing approaches confirmed the recurrence of some of these de novo mutations in independent cohorts, substantiating their role in ASD pathogenesis 10, 36, 39. Finally, one very interesting group of genetic variants that has recently been implicated in ASD is somatic mutations 43– 45. Somatic mutations can occur during development and yield mosaic individuals with distinct cellular genomes in subsets of their somatic cells 46. Whereas routine genetic sampling from blood misses the disease-associated somatic variants in the brain, targeted sequencing on ASD post-mortem tissue has detected increased rates of deleterious somatic mutations in cases compared with controls 45. Interestingly, there may be some overlap of genes at risk for both germline and de novo somatic mutations (for example, SCN2A) 43. Future single-cell sequencing approaches 47 will be informative to identify and characterize cells that carry disease-related somatic mutations 48.
Taken together, recent advances in ASD gene discovery highlight the complexity of the genetic landscape of the disease while beginning to shed light on some of the biological pathways at risk in ASD. This complexity is underscored by the potential for certain combinations of common genetic variants contributing to ASD by increasing an individual’s susceptibility to pathogenic effects of rare inherited, de novo, or somatic mutations. Given the progress in identifying high-confidence risk genes for ASD, investigators can now direct their attention to understanding the pathogenicity of this genetic variance and identifying potential common convergent disease mechanisms as molecular targets for future treatment strategies.
Convergent molecular mechanisms
One approach to understand pathogenesis and identify therapeutic targets amid a complex genetic architecture is to elucidate downstream pathways commonly affected across ASD cases with distinct genetic etiologies. One example of a convergent molecular mechanism includes defects in the regulation of protein synthesis and alternative splicing (AS) as potential unifying pathways for ASD 49.
Precise regulation of translation at synapses during the tight window of a learning experience has been shown to be extremely critical for the formation and maintenance of long-term memory 50. Several mutations in translation factors and regulators such as eIF4E 51, 52, TSC1/2 53, and PTEN 54 are associated with ASD, underscoring the involvement of translational defects in ASD pathogenesis. Furthermore, there is emerging evidence showing dysregulated translational activity in cells derived from non-syndromic ASD patients, including aberrant activity of mammalian target of rapamycin (mTOR), a key regulator of translation, suggesting translational dysregulation as a shared pathogenic mechanism in genetically distinct ASD cases 55, 56. The inhibition of aberrant translation directly via compounds targeting translation factors (for example, 4EGI-1 57) or by modulating the mTOR pathway 58 has been shown to prevent autism-relevant phenotypes in mice and has been proposed as a therapeutic strategy to correct dysregulated protein synthesis in ASD 58.
AS is co- or post-transcriptionally regulated by RNA-binding proteins (RBPs) and tightly controlled during developmental stages in a tissue-specific manner 59. Given the limited number of protein-coding genes in the human genome, AS is recognized as an essential source of transcriptomic and proteomic diversity driving the species-specific features of the human brain 60– 62. Dysregulation of AS in post-mortem brain tissue from ASD patients with distinct etiologies has been increasingly apparent as a convergent mechanism in ASD 63– 65. The transcripts that are misspliced in ASD are enriched for neuronal RBP targets, including those of RBFOX1 63, 65, SRRM4 65, and PTBP1 65, suggesting that defective RBP function is a common feature of ASD. Genetic evidence showing ASD-linked chromosomal translocations and copy number variations in RBFOX1 also supports a prominent role for loss or dysregulation (or both) of RBFOX1 activity in ASD pathogenesis 13, 66– 68. Loss of RBFOX1 in mice causes deficits in synaptic transmission 69 and corticogenesis 70. Neuronal-specific, activity-dependent, 3- to 27-nucleotide microexons are frequently misspliced in ASD 64, 71. This group of genes that are subject to microexon splicing is enriched for synaptic functions and ASD genes 64, 71. These microexons are regulated primarily by the neuronal RBP, SRRM4, which is downregulated in ASD brains 64. Haploinsufficiency of SRRM4 in mice resulted in microexon misregulation and ASD-like features, including altered social behaviors 71. These data highlight the function of RBPs, including RBFOX1 and SRRM4, as essential for cortical development and function and at risk in ASD. Taken together, global dysregulation of RNA processing and protein translation is likely to be a common feature of genetically diverse ASD cases, and the regulation of these processes might be a viable target for therapeutic approaches.
Patient-specific disease models
The high degree of genetic heterogeneity in ASD requires personalized approaches to understand the underlying individual pathogenic mechanisms and develop efficient treatments. In addition, there is a need for improved model systems with appropriate genetic backgrounds to test identified convergent biological mechanisms such as the ones discussed above. Advances in stem cell biology in the last decade have yielded protocols for the generation of human neurons from accessible somatic tissue (for example, skin), overcoming the unavailability of human neurons from specific developmental stages or disease states. Briefly, human induced pluripotent stem cells (hiPSCs) are generated by the ectopic expression of specific transcription factors in somatic cells that then can be differentiated into neurons or glia harboring the genetic features of the human individual from whom the cells are derived, either the patients or matched unaffected controls 72. In addition, isogenic neurons generated by introducing mutations in control iPSCs via gene editing technologies—that is, CRISPR-Cas9 73, 74 and TALENs 75—can be used to study the functional impact of disease-related mutations on a non-disease genetic background.
Research adopting iPSC-based models has begun to impact the understanding of the biological underpinnings of several ASD-related genetic variants 76, 77. In several instances, syndromic forms of ASD, including Fragile X 78, 79, Rett 80, Timothy 81, 82, and Phelan–McDermid 83 syndromes, have been modeled by using iPSCs. These studies have defined disease-related defects in patient-derived neurons, including reduced synaptic density, impaired excitatory transmission, and aberrant signaling. Additionally, a recent study of iPSCs from an ASD patient with a de novo mutation in TRPC6 confirmed the potential for patient-specific disease modeling of rare ASD variants 84.
Breakthroughs in iPSC culture systems have facilitated the generation of more complex differentiation programs that yield organ-like structures. These 3D brain organoids have been established with the goal of improved recapitulation of brain development and connectivity in vitro, providing an unprecedented opportunity to study human brain features in a dish 85– 87. A major goal of using patient-derived 3D organoids is to perform high-throughput drug screens to correct ASD-relevant cellular defects and reliably predict drug responses specific to each individual. In the future, standardization of 3D human brain organoid generation is needed for reliable and reproducible disease modeling. Defining the functional properties and molecular signatures of brain organoids derived from unaffected iPSCs at several time points will provide insights into how this model system follows in vivo human brain development and baseline information for disease modeling. It will be important to address how the differentiation process of 3D brain organoids corresponds to stages of human brain development. This will be essential to identify and translate the critical time window for successful therapeutic intervention. Recent advances in single-cell RNA sequencing facilitate the identification of cell types and differentiation states of diverse human neuronal populations in fetal brain in vivo 88 and have also proven to be very useful for characterizing brain organoids 89. Integration of cell-specific gene expression profiles with regional and developmental timing mechanisms has been elegantly carried out from human fetal tissue 88, and these data can be superimposed on the data derived from patient organoids to identify aberrant profiles. Inherent limitations of 3D brain organoids such as lack of behavioral output and circuit-based studies should be addressed with complementary studies using animal models 19.
In terms of cell-specific profiling, most research has gone into characterizing the neuronal defects in ASD; however, the involvement of glia has recently been implicated in many neuropsychiatric diseases 90. For example, a recent iPSC model provided evidence that defects in astrocytes can contribute to non-syndromic ASD 91 with unknown genetic cause. Therefore, future strategies to develop therapies for ASD should not only focus on neurons but also include all cell types in the brain. In addition, these data support the promise of using iPSC models from individuals with genetically complex etiologies to narrow the therapeutic search window to common pathogenic mechanisms. Improvements to brain organoid models that include many cell types such as glia and endothelial cells from non-syndromic ASD patients should further facilitate the identification of patient-specific cellular deficits.
Conclusions
Technological and conceptual advances in genomics, stem cell biology, and gene editing together with large cohorts of patients are providing opportunities to identify genetic causes of ASD and develop functionally relevant disease models. Integrative studies that include post-mortem tissue, genomics, and single-cell transcriptomics will continue to provide insights into human brain development and how this process is disrupted in ASD. By improved modeling of the disease using patient tissues and incorporating data from genomic and gene expression studies into these models, the field should move closer to developing personalized therapeutic approaches as well as identifying common druggable molecular pathways. Thus, persistent pursuit of all of the strategies discussed above will be needed to define optimal personalized treatments that potentially could involve several drugs in combination for additive or synergistic effects.
Abbreviations
3D, three-dimensional; AS, alternative splicing; ASD, autism spectrum disorder; CNV, copy number variant; hiPSC, human induced pluripotent stem cell; mTOR, mammalian target of rapamycin; RBP, RNA-binding protein; SNV, single-nucleotide variant; WES, whole exome sequencing.
Acknowledgments
The authors wish to thank Maria Chahrour for her helpful comments and suggestions. GK is a Jon Heighten Scholar in Autism Research at UT Southwestern.
Editorial Note on the Review Process
F1000 Faculty Reviews are commissioned from members of the prestigious F1000 Faculty and are edited as a service to readers. In order to make these reviews as comprehensive and accessible as possible, the referees provide input before publication and only the final, revised version is published. The referees who approved the final version are listed with their names and affiliations but without their reports on earlier versions (any comments will already have been addressed in the published version).
The referees who approved this article are:
Laia Rodriguez-Revenga, Biochemistry and Molecular Genetics Department, Hospital Clinic, Villarroel 170, Barcelona, Spain
M. Chiara Manzini, GW Institute for Neurosciences, Department of Pharmacology and Physiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
Alysson R. Muotri, Department of Pediatrics/Rady Children׳s Hospital San Diego, Department of Cellular & Molecular Medicine, Stem Cell Program, School of Medicine, University of California San Diego, La Jolla, CA, USA
Funding Statement
This work is supported by grants from the National Institutes of Health (R01DC014702 and R01MH102603 to GK and T32DA007290-24 to FA), the Simons Foundation Autism Research Initiative (project 401220 to GK), and a James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition – Scholar Award to GK.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
[version 1; referees: 3 approved]
References
- 1. American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders: DSM-5.5th edn. American Psychiatric Association, Arlington, Virginia, USA2013. 10.1176/appi.books.9780890425596 [DOI] [Google Scholar]
- 2. Christensen DL, Baio J, Van Naarden Braun K, et al. : Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years--Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. 2016;65(3):1–23. 10.15585/mmwr.ss6503a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Kim YS, Leventhal BL: Genetic epidemiology and insights into interactive genetic and environmental effects in autism spectrum disorders. 2015;77(1):66–74. 10.1016/j.biopsych.2014.11.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Rosenberg RE, Law JK, Yenokyan G, et al. : Characteristics and concordance of autism spectrum disorders among 277 twin pairs. 2009;163(10):907–14. 10.1001/archpediatrics.2009.98 [DOI] [PubMed] [Google Scholar]
- 5. Bailey A, Le Couteur A, Gottesman I, et al. : Autism as a strongly genetic disorder: evidence from a British twin study. 1995;25(1):63–77. 10.1017/S0033291700028099 [DOI] [PubMed] [Google Scholar]
- 6. Ozonoff S, Young GS, Carter A, et al. : Recurrence risk for autism spectrum disorders: a Baby Siblings Research Consortium study. 2011;128(3):e488–95. 10.1542/peds.2010-2825 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 7. Hallmayer J, Cleveland S, Torres A, et al. : Genetic heritability and shared environmental factors among twin pairs with autism. 2011;68(11):1095–102. 10.1001/archgenpsychiatry.2011.76 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 8. Sandin S, Lichtenstein P, Kuja-Halkola R, et al. : The familial risk of autism. 2014;311(17):1770–7. 10.1001/jama.2014.4144 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Colvert E, Tick B, McEwen F, et al. : Heritability of Autism Spectrum Disorder in a UK Population-Based Twin Sample. 2015;72(5):415–23. 10.1001/jamapsychiatry.2014.3028 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. De Rubeis S, He X, Goldberg AP, et al. : Synaptic, transcriptional and chromatin genes disrupted in autism. 2014;515(7526):209–15. 10.1038/nature13772 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Gaugler T, Klei L, Sanders SJ, et al. : Most genetic risk for autism resides with common variation. 2014;46(8):881–5. 10.1038/ng.3039 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. C Yuen RK, Merico D, Bookman M, et al. : Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. 2017;20(4):602–11. 10.1038/nn.4524 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Sebat J, Lakshmi B, Malhotra D, et al. : Strong association of de novo copy number mutations with autism. 2007;316(5823):445–9. 10.1126/science.1138659 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 14. Sanders SJ, Ercan-Sencicek AG, Hus V, et al. : Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. 2011;70(5):863–85. 10.1016/j.neuron.2011.05.002 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 15. Gilman SR, Iossifov I, Levy D, et al. : Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. 2011;70(5):898–907. 10.1016/j.neuron.2011.05.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Sanders SJ, Murtha MT, Gupta AR, et al. : De novo mutations revealed by whole-exome sequencing are strongly associated with autism. 2012;485(7397):237–41. 10.1038/nature10945 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 17. Iossifov I, Ronemus M, Levy D, et al. : De novo gene disruptions in children on the autistic spectrum. 2012;74(2):285–99. 10.1016/j.neuron.2012.04.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Neale BM, Kou Y, Liu L, et al. : Patterns and rates of exonic de novo mutations in autism spectrum disorders. 2012;485(7397):242–5. 10.1038/nature11011 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Chahrour M, O'Roak BJ, Santini E, et al. : Current Perspectives in Autism Spectrum Disorder: From Genes to Therapy. 2016;36(45):11402–10. 10.1523/JNEUROSCI.2335-16.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Yu TW, Chahrour MH, Coulter ME, et al. : Using whole-exome sequencing to identify inherited causes of autism. 2013;77(2):259–73. 10.1016/j.neuron.2012.11.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. O'Roak BJ, Stessman HA, Boyle EA, et al. : Recurrent de novo mutations implicate novel genes underlying simplex autism risk. 2014;5:5595. 10.1038/ncomms6595 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Verkerk AJ, Pieretti M, Sutcliffe JS, et al. : Identification of a gene ( FMR-1) containing a CGG repeat coincident with a breakpoint cluster region exhibiting length variation in fragile X syndrome. 1991;65(5):905–14. 10.1016/0092-8674(91)90397-H [DOI] [PubMed] [Google Scholar]
- 23. Amir RE, Van den Veyver IB, Wan M, et al. : Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2. 1999;23(2):185–8. 10.1038/13810 [DOI] [PubMed] [Google Scholar]
- 24. Van Esch H, Bauters M, Ignatius J, et al. : Duplication of the MECP2 region is a frequent cause of severe mental retardation and progressive neurological symptoms in males. 2005;77(3):442–53. 10.1086/444549 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 25. Fryer AE, Chalmers A, Connor JM, et al. : Evidence that the gene for tuberous sclerosis is on chromosome 9. 1987;1(8534):659–61. 10.1016/S0140-6736(87)90416-8 [DOI] [PubMed] [Google Scholar]
- 26. Butler MG, Dasouki MJ, Zhou XP, et al. : Subset of individuals with autism spectrum disorders and extreme macrocephaly associated with germline PTEN tumour suppressor gene mutations. 2005;42(4):318–21. 10.1136/jmg.2004.024646 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Splawski I, Timothy KW, Sharpe LM, et al. : Ca V1.2 calcium channel dysfunction causes a multisystem disorder including arrhythmia and autism. 2004;119(1):19–31. 10.1016/j.cell.2004.09.011 [DOI] [PubMed] [Google Scholar]; F1000 Recommendation
- 28. Sztainberg Y, Zoghbi HY: Lessons learned from studying syndromic autism spectrum disorders. 2016;19(11):1408–17. 10.1038/nn.4420 [DOI] [PubMed] [Google Scholar]; F1000 Recommendation
- 29. Geschwind DH, State MW: Gene hunting in autism spectrum disorder: On the path to precision medicine. 2015;14(11):1109–20. 10.1016/S1474-4422(15)00044-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Strauss KA, Puffenberger EG, Huentelman MJ, et al. : Recessive symptomatic focal epilepsy and mutant contactin-associated protein-like 2. 2006;354(13):1370–7. 10.1056/NEJMoa052773 [DOI] [PubMed] [Google Scholar]; F1000 Recommendation
- 31. Morrow EM, Yoo SY, Flavell SW, et al. : Identifying autism loci and genes by tracing recent shared ancestry. 2008;321(5886):218–23. 10.1126/science.1157657 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 32. Manzini MC, Xiong L, Shaheen R, et al. : CC2D1A regulates human intellectual and social function as well as NF-κB signaling homeostasis. 2014;8(3):647–55. 10.1016/j.celrep.2014.06.039 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Novarino G, El-Fishawy P, Kayserili H, et al. : Mutations in BCKD-kinase lead to a potentially treatable form of autism with epilepsy. 2012;338(6105):394–7. 10.1126/science.1224631 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 34. Chahrour MH, Yu TW, Lim ET, et al. : Whole-exome sequencing and homozygosity analysis implicate depolarization-regulated neuronal genes in autism. 2012;8(4):e1002635. 10.1371/journal.pgen.1002635 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Lim ET, Raychaudhuri S, Sanders SJ, et al. : Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. 2013;77(2):235–42. 10.1016/j.neuron.2012.12.029 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 36. Iossifov I, O'Roak BJ, Sanders SJ, et al. : The contribution of de novo coding mutations to autism spectrum disorder. 2014;515(7526):216–21. 10.1038/nature13908 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. O'Roak BJ, Vives L, Girirajan S, et al. : Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. 2012;485(7397):246–50. 10.1038/nature10989 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 38. Bernier R, Golzio C, Xiong B, et al. : Disruptive CHD8 mutations define a subtype of autism early in development. 2014;158(2):263–76. 10.1016/j.cell.2014.06.017 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 39. O'Roak BJ, Vives L, Fu W, et al. : Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. 2012;338(6114):1619–22. 10.1126/science.1227764 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 40. Hamdan FF, Daoud H, Piton A, et al. : De novo SYNGAP1 mutations in nonsyndromic intellectual disability and autism. 2011;69(9):898–901. 10.1016/j.biopsych.2010.11.015 [DOI] [PubMed] [Google Scholar]
- 41. Parker MJ, Fryer AE, Shears DJ, et al. : De novo, heterozygous, loss-of-function mutations in SYNGAP1 cause a syndromic form of intellectual disability. 2015;167A(10):2231–7. 10.1002/ajmg.a.37189 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. van Bon BW, Coe BP, Bernier R, et al. : Disruptive de novo mutations of DYRK1A lead to a syndromic form of autism and ID. 2016;21(1):126–32. 10.1038/mp.2015.5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Lim ET, Uddin M, De Rubeis S, et al. : Rates, distribution and implications of postzygotic mosaic mutations in autism spectrum disorder. 2017;20(9):1217–24. 10.1038/nn.4598 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 44. Krupp DR, Barnard RA, Duffourd Y, et al. : Exonic Mosaic Mutations Contribute Risk for Autism Spectrum Disorder. 2017;101(3):369–90. 10.1016/j.ajhg.2017.07.016 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 45. D'Gama AM, Pochareddy S, Li M, et al. : Targeted DNA Sequencing from Autism Spectrum Disorder Brains Implicates Multiple Genetic Mechanisms. 2015;88(5):910–7. 10.1016/j.neuron.2015.11.009 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 46. Poduri A, Evrony GD, Cai X, et al. : Somatic mutation, genomic variation, and neurological disease. 2013;341(6141):1237758. 10.1126/science.1237758 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Poulin JF, Tasic B, Hjerling-Leffler J, et al. : Disentangling neural cell diversity using single-cell transcriptomics. 2016;19(9):1131–41. 10.1038/nn.4366 [DOI] [PubMed] [Google Scholar]; F1000 Recommendation
- 48. McConnell MJ, Moran JV, Abyzov A, et al. : Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network. 2017;356(6336): pii: eaal1641. 10.1126/science.aal1641 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. de la Torre-Ubieta L, Won H, Stein JL, et al. : Advancing the understanding of autism disease mechanisms through genetics. 2016;22(4):345–61. 10.1038/nm.4071 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50. Buffington SA, Huang W, Costa-Mattioli M: Translational control in synaptic plasticity and cognitive dysfunction. 2014;37:17–38. 10.1146/annurev-neuro-071013-014100 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51. Autism Genome Project Consortium, Szatmari P, Paterson AD, et al. : Mapping autism risk loci using genetic linkage and chromosomal rearrangements. 2007;39(3):319–28. 10.1038/ng1985 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 52. Yonan AL, Alarcón M, Cheng R, et al. : A genomewide screen of 345 families for autism-susceptibility loci. 2003;73(4):886–97. 10.1086/378778 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53. Jeste SS, Sahin M, Bolton P, et al. : Characterization of autism in young children with tuberous sclerosis complex. 2008;23(5):520–5. 10.1177/0883073807309788 [DOI] [PubMed] [Google Scholar]
- 54. Krumm N, O'Roak BJ, Shendure J, et al. : A de novo convergence of autism genetics and molecular neuroscience. 2014;37(2):95–105. 10.1016/j.tins.2013.11.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55. Suzuki AM, Griesi-Oliveira K, de Oliveira Freitas Machado C, et al. : Altered mTORC1 signaling in multipotent stem cells from nearly 25% of patients with nonsyndromic autism spectrum disorders. 2015;20(5):551–2. 10.1038/mp.2014.175 [DOI] [PubMed] [Google Scholar]; F1000 Recommendation
- 56. Poopal AC, Schroeder LM, Horn PS, et al. : Increased expression of the PI3K catalytic subunit p110δ underlies elevated S6 phosphorylation and protein synthesis in an individual with autism from a multiplex family. 2016;7:3. 10.1186/s13229-015-0066-4 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 57. Santini E, Huynh TN, MacAskill AF, et al. : Exaggerated translation causes synaptic and behavioural aberrations associated with autism. 2013;493(7432):411–5. 10.1038/nature11782 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58. Tsai PT, Hull C, Chu Y, et al. : Autistic-like behaviour and cerebellar dysfunction in Purkinje cell Tsc1 mutant mice. 2012;488(7413):647–51. 10.1038/nature11310 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 59. Scotti MM, Swanson MS: RNA mis-splicing in disease. 2016;17(1):19–32. 10.1038/nrg.2015.3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60. Pan Q, Shai O, Lee LJ, et al. : Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. 2008;40(12):1413–5. 10.1038/ng.259 [DOI] [PubMed] [Google Scholar]
- 61. Lin L, Shen S, Jiang P, et al. : Evolution of alternative splicing in primate brain transcriptomes. 2010;19(15):2958–73. 10.1093/hmg/ddq201 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62. Wang ET, Sandberg R, Luo S, et al. : Alternative isoform regulation in human tissue transcriptomes. 2008;456(7221):470–6. 10.1038/nature07509 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 63. Voineagu I, Wang X, Johnston P, et al. : Transcriptomic analysis of autistic brain reveals convergent molecular pathology. 2011;474(7351):380–4. 10.1038/nature10110 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 64. Irimia M, Weatheritt RJ, Ellis JD, et al. : A highly conserved program of neuronal microexons is misregulated in autistic brains. 2014;159(7):1511–23. 10.1016/j.cell.2014.11.035 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 65. Parikshak NN, Swarup V, Belgard TG, et al. : Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. 2016;540(7633):423–7. 10.1038/nature20612 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66. Martin CL, Duvall JA, Ilkin Y, et al. : Cytogenetic and molecular characterization of A2BP1/FOX1 as a candidate gene for autism. 2007;144B(7):869–76. 10.1002/ajmg.b.30530 [DOI] [PubMed] [Google Scholar]
- 67. Mikhail FM, Lose EJ, Robin NH, et al. : Clinically relevant single gene or intragenic deletions encompassing critical neurodevelopmental genes in patients with developmental delay, mental retardation, and/or autism spectrum disorders. 2011;155A(10):2386–96. 10.1002/ajmg.a.34177 [DOI] [PubMed] [Google Scholar]
- 68. Davis LK, Maltman N, Mosconi MW, et al. : Rare inherited A2BP1 deletion in a proband with autism and developmental hemiparesis. 2012;158A(7):1654–61. 10.1002/ajmg.a.35396 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69. Gehman LT, Stoilov P, Maguire J, et al. : The splicing regulator Rbfox1 (A2BP1) controls neuronal excitation in the mammalian brain. 2011;43(7):706–11. 10.1038/ng.841 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70. Hamada N, Ito H, Iwamoto I, et al. : Role of the cytoplasmic isoform of RBFOX1/A2BP1 in establishing the architecture of the developing cerebral cortex. 2015;6:56. 10.1186/s13229-015-0049-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71. Quesnel-Vallières M, Dargaei Z, Irimia M, et al. : Misregulation of an Activity-Dependent Splicing Network as a Common Mechanism Underlying Autism Spectrum Disorders. 2016;64(6):1023–34. 10.1016/j.molcel.2016.11.033 [DOI] [PubMed] [Google Scholar]; F1000 Recommendation
- 72. Paşca SP, Panagiotakos G, Dolmetsch RE: Generating human neurons in vitro and using them to understand neuropsychiatric disease. 2014;37:479–501. 10.1146/annurev-neuro-062012-170328 [DOI] [PubMed] [Google Scholar]
- 73. Le Cong, Ran FA, Cox D, et al. : Multiplex genome engineering using CRISPR/Cas systems. 2013;339(6121):819–23. 10.1126/science.1231143 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 74. Mali P, Yang L, Esvelt KM, et al. : RNA-guided human genome engineering via Cas9. 2013;339(6121):823–6. 10.1126/science.1232033 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 75. Miller JC, Tan S, Qiao G, et al. : A TALE nuclease architecture for efficient genome editing. 2011;29(2):143–8. 10.1038/nbt.1755 [DOI] [PubMed] [Google Scholar]
- 76. Nestor MW, Phillips AW, Artimovich E, et al. : Human Inducible Pluripotent Stem Cells and Autism Spectrum Disorder: Emerging Technologies. 2016;9(5):513–35. 10.1002/aur.1570 [DOI] [PubMed] [Google Scholar]; F1000 Recommendation
- 77. Beltrão-Braga PC, Muotri AR: Modeling autism spectrum disorders with human neurons. 2017;1656:49–54. 10.1016/j.brainres.2016.01.057 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 78. Liu J, Koscielska KA, Cao Z, et al. : Signaling defects in iPSC-derived fragile X premutation neurons. 2012;21(17):3795–805. 10.1093/hmg/dds207 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79. Doers ME, Musser MT, Nichol R, et al. : iPSC-derived forebrain neurons from FXS individuals show defects in initial neurite outgrowth. 2014;23(15):1777–87. 10.1089/scd.2014.0030 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 80. Marchetto MC, Carromeu C, Acab A, et al. : A model for neural development and treatment of Rett syndrome using human induced pluripotent stem cells. 2010;143(4):527–39. 10.1016/j.cell.2010.10.016 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 81. Tian Y, Voineagu I, Paşca SP, et al. : Alteration in basal and depolarization induced transcriptional network in iPSC derived neurons from Timothy syndrome. 2014;6(10):75. 10.1186/s13073-014-0075-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 82. Krey JF, Paşca SP, Shcheglovitov A, et al. : Timothy syndrome is associated with activity-dependent dendritic retraction in rodent and human neurons. 2013;16(2):201–9. 10.1038/nn.3307 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83. Shcheglovitov A, Shcheglovitova O, Yazawa M, et al. : SHANK3 and IGF1 restore synaptic deficits in neurons from 22q13 deletion syndrome patients. 2013;503(7475):267–71. 10.1038/nature12618 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 84. Griesi-Oliveira K, Acab A, Gupta AR, et al. : Modeling non-syndromic autism and the impact of TRPC6 disruption in human neurons. 2015;20(11):1350–65. 10.1038/mp.2014.141 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 85. Lancaster MA, Knoblich JA: Generation of cerebral organoids from human pluripotent stem cells. 2014;9(10):2329–40. 10.1038/nprot.2014.158 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 86. Giandomenico SL, Lancaster MA: Probing human brain evolution and development in organoids. 2017;44:36–43. 10.1016/j.ceb.2017.01.001 [DOI] [PubMed] [Google Scholar]
- 87. Camp JG, Treutlein B: Human organomics: a fresh approach to understanding human development using single-cell transcriptomics. 2017;144(9):1584–7. 10.1242/dev.150458 [DOI] [PubMed] [Google Scholar]
- 88. Nowakowski TJ, Bhaduri A, Pollen AA, et al. : Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. 2017;358:1318–23. 10.1126/science.aap8809 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 89. Camp JG, Badsha F, Florio M, et al. : Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. 2015;112(51):15672–7. 10.1073/pnas.1520760112 [DOI] [PMC free article] [PubMed] [Google Scholar]; F1000 Recommendation
- 90. Salter MW, Stevens B: Microglia emerge as central players in brain disease. 2017;23(9):1018–27. 10.1038/nm.4397 [DOI] [PubMed] [Google Scholar]
- 91. Russo FB, Freitas BC, Pignatari GC, et al. : Modeling the Interplay Between Neurons and Astrocytes in Autism Using Human Induced Pluripotent Stem Cells. 2018;83(7):569–78. 10.1016/j.biopsych.2017.09.021 [DOI] [PubMed] [Google Scholar]; F1000 Recommendation