Molecular signatures from multi-omics of autism spectrum disorders and schizophrenia.
autism spectrum disorder
enrichment analysis
gene ontology
neurodevelopmental disorder
protein-protein interaction network
schizophrenia
Journal
Journal of neurochemistry
ISSN: 1471-4159
Titre abrégé: J Neurochem
Pays: England
ID NLM: 2985190R
Informations de publication
Date de publication:
11 2021
11 2021
Historique:
revised:
11
08
2021
received:
01
05
2021
accepted:
07
09
2021
pubmed:
20
9
2021
medline:
7
1
2022
entrez:
19
9
2021
Statut:
ppublish
Résumé
The genetic and phenotypic heterogeneity of autism spectrum disorder (ASD) impedes the unification of multiple biological hypotheses in an attempt to explain the complex features of ASD, such as impaired social communication, social interaction deficits, and restricted and repetitive patterns of behavior. However, recent psychiatric genetic studies have identified numerous risk genes and chromosome loci (copy number variation: CNV) which enable us to analyze at the single gene level and utilize system-level approaches. In this review, we focus on ASD as a major neurodevelopmental disorder and review recent findings mainly from the bioinformatics of omics studies. Additionally, by comparing these data with other major psychiatric disorders, including schizophrenia (SCZ), we identify unique characteristics of both diseases from multiple enrichment, pathway, and protein-protein interaction networks (PPIs) analyses using susceptible genes found in recent large-scale genetic studies. These unified, systematic approaches highlight unique characteristics of both disorders from multiple aspects and demonstrate how convergent pathways can contribute to an understanding of the complex etiology of such neurodevelopmental and neuropsychiatric disorders.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
647-659Informations de copyright
© 2021 International Society for Neurochemistry.
Références
Abrahams, B. S., Arking, D. E., Campbell, D. B., Mefford, H. C., Morrow, E. M., Weiss, L. A., Menashe, I., Wadkins, T., Banerjee-basu, S., & Packer, A. (2013). SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Molecular Autism, 4, 36. https://doi.org/10.1186/2040-2392-4-36
Bader, G. D., Hogue, C. W., & (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4(2), https://doi.org/10.1186/1471-2105-4-2
Bjørklund, G., Pivina, L., Dadar, M., Meguid, N. A., Semenova, Y., Anwar, M., & Chirumbolo, S. (2020). Gastrointestinal alterations in autism spectrum disorder: What do we know? Neuroscience and Biobehavioral Reviews, 118, 111-120. https://doi.org/10.1016/j.neubiorev.2020.06.033
Briscoe, J., & Marín, O. (2020). Looking at neurodevelopment through a big data lens. Science, 369(6510), eaaz8627. https://doi.org/10.1126/science.aaz8627
Buniello, A., MacArthur, J. A. L., Cerezo, M., Harris, L. W., Hayhurst, J., Malangone, C., McMahon, A., Morales, J., Mountjoy, E., Sollis, E., Suveges, D., Vrousgou, O., Whetzel, P. L., Amode, R., Guillen, J. A., Riat, H. S., Trevanion, S. J., Hall, P., Junkins, H., … Parkinson, H. (2019). The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Research, 47(D1), D1005-D1012. https://doi.org/10.1093/nar/gky1120
Burré, J., & Volknandt, W. (2007). The synaptic vesicle proteome. Journal of Neurochemistry, 101, 1448-1462. https://doi.org/10.1111/j.1471-4159.2007.04453.x
Cantor, R. M., Lange, K., & Sinsheimer, J. S. (2010). Prioritizing GWAS results: A review of statistical methods and recommendations for their application. American Journal of Human Genetics, 86(1), 6-22. https://doi.org/10.1016/j.ajhg.2009.11.017
Cichon, S., Craddock, N., Daly, M., Faraone, S. V., Gejman, P. V., Kelsoe, J., Lehner, T., Levinson, D. F., Moran, A., & Sklar, P. (2009). Genomewide association studies: History, rationale, and prospects for psychiatric disorders. American Journal of Psychiatry, 166, 540-556. https://doi.org/10.1176/appi.ajp.2008.08091354
Cizeron, M., Qiu, Z., Koniaris, B., Gokhale, R., Komiyama, N. H., Fransén, E., & Grant, S. G. N. (2020). A brainwide atlas of synapses across the mouse life span. Science, 369, 270-275. https://doi.org/10.1126/science.aba3163
De Rubeis, S., He, X., Goldberg, A. P., Poultney, C. S., Samocha, K., Ercument Cicek, A., Kou, Y., Liu, L. I., Fromer, M., Walker, S., Singh, T., Klei, L., Kosmicki, J., Fu, S.-C., Aleksic, B., Biscaldi, M., Bolton, P. F., Brownfeld, J. M., Cai, J., … Buxbaum, J. D. (2014). Synaptic, transcriptional and chromatin genes disrupted in autism. Nature, 515(7526), 209-215. https://doi.org/10.1038/nature13772
Dong, X., Liu, C., & Dozmorov, M. (2021). Review of multi-omics data resources and integrative analysis for human brain disorders. Briefings in Functional Genomics, 20(4), 1-12. https://doi.org/10.1093/bfgp/elab024
Doyle, G. A., Crist, R. C., Karatas, E. T., Hammond, M. J., Ewing, A. D., Ferraro, T. N., Hahn, C. G., & Berrettini, W. H. (2017). Analysis of LINE-1 elements in DNA from postmortem brains of individuals with schizophrenia. Neuropsychopharmacology, 42, 2602-2611. https://doi.org/10.1038/npp.2017.115
Duan, W., Wang, K., Duan, Y., Chu, X., Ma, R., Hu, P., & Xiong, B. (2020). Integrated transcriptome analyses revealed key target genes in mouse models of autism. Autism Research, 13, 352-368. https://doi.org/10.1002/aur.2240
Escott-Price, V., Kirov, G., Rees, E., Isles, A. R., Owen, M. J., & O’Donovan, M. C. (2015). No evidence for enrichment in schizophrenia for common allelic associations at imprinted loci. PLoS One, 10, 1-8. https://doi.org/10.1371/journal.pone.0144172
Fernandes, H. J. R., Patikas, N., Foskolou, S., Field, S. F., Park, J. E., Byrne, M. L., Bassett, A. R., & Metzakopian, E. (2020). Single-cell transcriptomics of parkinson’s disease human in vitro models reveals dopamine neuron-specific stress responses. Cell Reports, 33, 108263. https://doi.org/10.1016/j.celrep.2020.108263
Gandal, M. J., Zhang, P., Hadjimichael, E., Walker, R. L., Chen, C., Liu, S., Won, H., van Bakel, H., Varghese, M., Wang, Y., Shieh, A. W., Haney, J., Parhami, S., Belmont, J., Kim, M., Moran Losada, P., Khan, Z., Mleczko, J., Xia, Y., … Geschwind, D. H. (2018). Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science, 362(6420), eaat8127. https://doi.org/10.1126/science.aat8127
Gokool, A., Anwar, F., & Voineagu, I. (2020). The Landscape of Circular RNA Expression in the Human Brain. Biological Psychiatry, 87, 294-304. https://doi.org/10.1016/j.biopsych.2019.07.029
Grant, S. G. N. (2019). Synapse diversity and synaptome architecture in human genetic disorders. Human Molecular Genetics, 28, R219-R225. https://doi.org/10.1093/hmg/ddz178
Han, H., Cho, J.-W., Lee, S., Yun, A., Kim, H., Bae, D., Yang, S., Kim, C. Y., Lee, M., Kim, E., Lee, S., Kang, B., Jeong, D., Kim, Y., Jeon, H.-N., Jung, H., Nam, S., Chung, M., Kim, J.-H., & Lee, I. (2018). TRRUST v2: An expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Research, 46(D1), D380-D386. https://doi.org/10.1093/nar/gkx1013
Hossain, M. M., Khan, N., Sultana, A., Ma, P., McKyer, E. L. J., Ahmed, H. U., & Purohit, N. (2020). Prevalence of comorbid psychiatric disorders among people with autism spectrum disorder: An umbrella review of systematic reviews and meta-analyses. Psychiatry Research, 287, 112922. https://doi.org/10.1016/j.psychres.2020.112922
Jacob, S., Wolff, J. J., Steinbach, M. S., Doyle, C. B., Kumar, V., & Elison, J. T. (2019). Neurodevelopmental heterogeneity and computational approaches for understanding autism. Translational Psychiatry, 9, 1-12. https://doi.org/10.1038/s41398-019-0390-0
Jaffe, A. E., Hoeppner, D. J., Saito, T., Blanpain, L., Ukaigwe, J., Burke, E. E., Collado-Torres, L., Tao, R., Tajinda, K., Maynard, K. R., Tran, M. N., Martinowich, K., Deep-Soboslay, A., Shin, J. H., Kleinman, J. E., Weinberger, D. R., Matsumoto, M., & Hyde, T. M. (2020). Profiling gene expression in the human dentate gyrus granule cell layer reveals insights into schizophrenia and its genetic risk. Nature Neuroscience, 23(4), 510-519. https://doi.org/10.5281/zenodo.3605718
Jin, X., Simmons, S. K., Guo, A., Shetty, A. S., Ko, M., Nguyen, L., Jokhi, V., Robinson, E., Oyler, P., Curry, N., Deangeli, G., Lodato, S., Levin, J.Z., Regev, A., Zhang, F., & Arlotta, P. (2020). In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes. Science, 370(6520), eaaz6063. https://doi.org/10.1126/science.aaz6063
Kaizuka, T., & Takumi, T. (2018). Postsynaptic density proteins and their involvement in neurodevelopmental disorders. Journal of Biochemistry, 163, 447-455. https://doi.org/10.1093/jb/mvy022
Karczewski, K. J., & Snyder, M. P. (2018). Integrative omics for health and disease. Nature Reviews Genetics, 19, 299-310. https://doi.org/10.1038/nrg.2018.4
Katayama, Y., Nishiyama, M., Shoji, H., Ohkawa, Y., Kawamura, A., Sato, T., Suyama, M., Takumi, T., Miyakawa, T., & Nakayama, K. I. (2016). CHD8 haploinsufficiency results in autistic-like phenotypes in mice. Nature, 537, 675-679. https://doi.org/10.1038/nature19357
Klindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast, C., Horn, M., & Glöckner, F. O. (2013). Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Research, 41(1), e1. https://doi.org/10.1093/nar/gks808
Lalli, M. A., Avey, D., Dougherty, J. D., Milbrandt, J., & Mitra, R. D. (2020). High-throughput single-cell functional elucidation of neurodevelopmental disease-associated genes reveals convergent mechanisms altering neuronal differentiation. Genome Research, 30, 1317-1331. https://doi.org/10.1101/gr.262295.120
Leng, K., Li, E., Eser, R., Piergies, A., Sit, R., Tan, M., Neff, N., Li, S. H., Rodriguez, R. D., Suemoto, C. K., & Leite, R. E. (2021). Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease. Nature Neuroscience, 24, 276-287. https://doi.org/10.1038/s41593-020-00764-7
Li, T., Wernersson, R., Hansen, R. B., Horn, H., Mercer, J., Slodkowicz, G., Workman, C. T., Rigina, O., Rapacki, K., Staerfeldt, H. H., & Brunak, S. (2016). A scored human protein-protein interaction network to catalyze genomic interpretation. Nature Methods, 14, 61-64. https://doi.org/10.1038/nmeth.4083
Liu, D., Zhuang, Y., Zhang, L., Gao, H., Neavin, D., Carrillo-Roa, T., Wang, Y., Yu, J., Qin, S., Kim, D. C., Liu, E., Nguyen, T. T. L., Biernacka, J. M., Kaddurah-Daouk, R., Dunlop, B. W., Craighead, W. E., Mayberg, H. S., Binder, E. B., Frye, M. A., … Weinshilboum, R. M. (2020). ERICH3: vesicular association and antidepressant treatment response. Molecular Psychiatry, 26(6), 2415-2428. https://doi.org/10.1038/s41380-020-00940-y
Malhotra, D., & Sebat, J. (2012). CNVs: Harbingers of a rare variant revolution in psychiatric genetics. Cell, 148, 1223-1241. https://doi.org/10.1016/j.cell.2012.02.039.
Maniatis, S., Äijö, T., Vickovic, S., Braine, C., Kang, K., Mollbrink, A., Fagegaltier, D., Andrusivová, Ž., Saarenpää, S., Saiz-Castro, G., & Cuevas, M. (2019). Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science, 364, 89-93. https://doi.org/10.1126/science.aav9776
Mannion, A., & Leader, G. (2013). Comorbidity in autism spectrum disorder: A literature review. Research in Autism Spectrum Disorders, 7(12), 1595-1616. https://doi.org/10.1016/j.rasd.2013.09.006
Maynard, K. R., Collado-Torres, L., Weber, L. M., Uytingco, C., Barry, B. K., Williams, S. R., Catallini, J. L., Tran, M. N., Besich, Z., Tippani, M., & Chew, J. (2021). Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nature Neuroscience, 24, 425-436. https://doi.org/10.1038/s41593-021-00817-5
Mejias, R., Adamczyk, A., Anggono, V., Niranjan, T., Thomas, G. M., & Sharma, K. (2011). Gain-of-function glutamate receptor interacting protein 1 variants alter GluA2 recycling and surface distribution in patients with autism. Proceedings of the National Academy of Sciences, 108(12), 4920-4925. https://doi.org/10.1073/pnas.1102233108
Ming, X., Stein, T. P., Barnes, V., Rhodes, N., & Guo, L. (2012). Metabolic perturbance in autism spectrum disorders: A metabolomics study. Journal of Proteome Research, 11, 5856-5862. https://doi.org/10.1021/pr300910n
Muhle, R. A., Reed, H. E., Stratigos, K. A., & Veenstra-VanderWeele, J. (2018). The emerging clinical neuroscience of autism spectrum disorder a review. JAMA Psychiatry, 75, 514-523. https://doi.org/10.1001/jamapsychiatry.2017.4685
Murtaza, N., Uy, J., & Singh, K. K. (2020). Emerging proteomic approaches to identify the underlying pathophysiology of neurodevelopmental and neurodegenerative disorders. Molecular Autism, 11, 1-15. https://doi.org/10.1186/s13229-020-00334-5
Nagy, C., Maitra, M., Tanti, A., Suderman, M., Théroux, J. F., Davoli, M. A., Perlman, K., Yerko, V., Wang, Y. C., Tripathy, S. J., & Pavlidis, P. (2020). Single-nucleus transcriptomics of the prefrontal cortex in major depressive disorder implicates oligodendrocyte precursor cells and excitatory neurons. Nature Neuroscience, 23, 771-781. https://doi.org/10.1038/s41593-020-0621-y
Nelson, S. B., & Valakh, V. (2015). Review excitatory/inhibitory balance and circuit homeostasis in autism spectrum disorders. Neuron, 87, 684-698. https://doi.org/10.1016/j.neuron.2015.07.033
Nomura, J., Kannan, G., & Takumi, T. (2017). Rodent models of genetic and chromosomal variations in psychiatric disorders. Psychiatry and Clinical Neurosciences, 71, 508-517. https://doi.org/10.1111/pcn.12524
Nomura, Y., Nomura, J., Kamiguchi, H., Nishikawa, T., & Takumi, T. (2021). Transcriptome analysis of human neural cells derived from isogenic embryonic stem cells with 16p11.2 deletion. Neuroscience Research, 171, 114-123. https://doi.org/10.1016/j.neures.2021.03.005
Oron, O., Getselter, D., Shohat, S., Reuveni, E., Lukic, I., Shifman, S., & Elliott, E. (2019). Gene network analysis reveals a role for striatal glutamatergic receptors in dysregulated risk-assessment behavior of autism mouse models. Translational Psychiatry, 9, 1-15. https://doi.org/10.1038/s41398-019-0584-5
Oughtred, R., Stark, C., Breitkreutz, B.-J., Rust, J., Boucher, L., Chang, C., Kolas, N., O’Donnell, L., Leung, G., McAdam, R., Zhang, F., Dolma, S., Willems, A., Coulombe-Huntington, J., Chatr-aryamontri, A., Dolinski, K., & Tyers, M. (2019). The BioGRID interaction database: 2019 update. Nucleic Acids Research, 47, D529-D541. https://doi.org/10.1093/nar/gky1079.
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015). Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nature Reviews Genetics, 16, 441-458. https://doi.org/10.1038/nrg3934
Parikshak, N. N., Swarup, V., Belgard, T. G., Irimia, M., Ramaswami, G., Gandal, M. J., Hartl, C., Leppa, V., Ubieta, L. D. L. T., Huang, J., Lowe, J. K., Blencowe, B. J., Horvath, S., & Geschwind, D. H. (2016). Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature, 540, 423-427. https://doi.org/10.1038/nature20612
Pérez, M. J., Ivanyuk, D., Panagiotakopoulou, V., Di Napoli, G., Kalb, S., Brunetti, D., Al-Shaana, R., Kaeser, S. A., Fraschka, S.-K., Jucker, M., Zeviani, M., Viscomi, C., & Deleidi, M. (2020). Loss of function of the mitochondrial peptidase PITRM1 induces proteotoxic stress and Alzheimer’s disease-like pathology in human cerebral organoids. Molecular Psychiatry, online ahead of print. https://doi.org/10.1038/s41380-020-0807-4
Quesnel-Vallières, M., Weatheritt, R. J., Cordes, S. P., & Blencowe, B. J. (2019). Autism spectrum disorder: insights into convergent mechanisms from transcriptomics. Nature Reviews Genetics, 20, 51-63. https://doi.org/10.1038/s41576-018-0066-2
Ramaswami, G., Won, H., Gandal, M. J., Haney, J., Wang, J. C., Wong, C. C. Y., Sun, W., Prabhakar, S., Mill, J., & Geschwind, D. H. (2020). Integrative genomics identifies a convergent molecular subtype that links epigenomic with transcriptomic differences in autism. Nature Communications, 11, 1-14. https://doi.org/10.1038/s41467-020-18526-1
Rees, E., & Kirov, G. (2021). Copy number variation and neuropsychiatric illness. Current Opinion in Genetics and Development, 68, 57-63. https://doi.org/10.1016/j.gde.2021.02.014
Ripke, S., Neale, B. M., Corvin, A., Walters, J. T. R., Farh, K. H., Holmans, P. A., Lee, P., Bulik-Sullivan, B., Collier, D. A., Huang, H., & Pers, T. H. (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511, 421-427. https://doi.org/10.1038/nature13595
Satterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J.-Y., Peng, M., Collins, R., Grove, J., Klei, L., Stevens, C., Reichert, J., Mulhern, M. S., Artomov, M., Gerges, S., Sheppard, B., Xu, X., Bhaduri, A., Norman, U., … Walters, R. K. (2020). Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell, 180, 568-584. https://doi.org/10.1016/j.cell.2019.12.036
Sawada, T., Chater, T. E., Sasagawa, Y., Yoshimura, M., Fujimori-Tonou, N., Tanaka, K., Benjamin, K. J. M., Paquola, A. C., Erwin, J. A., Goda, Y., Nikaido, I., & Kato, T. (2020). Developmental excitation-inhibition imbalance underlying psychoses revealed by single-cell analyses of discordant twins-derived cerebral organoids. Molecular Psychiatry, 25, 2695-2711. https://doi.org/10.1038/s41380-020-0844-z
Smail, M. A., Wu, X., Henkel, N. D., Eby, H. M., Herman, J. P., McCullumsmith, R. E., & Shukla, R. (2021). Similarities and dissimilarities between psychiatric cluster disorders. Molecular Psychiatry. https://doi.org/10.1038/s41380-021-01030-3
Statello, L., Guo, C. J., Chen, L. L., & Huarte, M. (2021). Gene regulation by long non-coding RNAs and its biological functions. Nature Reviews Molecular Cell Biology, 22, 96-118. https://doi.org/110.1038/s41580-021-00330-4
Sun, W., Poschmann, J., Cruz-Herrera del Rosario, R., Parikshak, N. N., Hajan, H. S., Kumar, V., Ramasamy, R., Belgard, T. G., Elanggovan, B., Wong, C. C. Y., Mill, J., Geschwind, D. H., & Prabhakar, S. (2016). Histone acetylome-wide association study of autism spectrum disorder. Cell, 167, 1385-1397.e11. https://doi.org/10.1016/j.cell.2016.10.031
Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., & Mering, C. (2019). STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47, D607-D613. https://doi.org/10.1093/nar/gky1131
Takumi, T., & Tamada, K. (2018). CNV biology in neurodevelopmental disorders. Current Opinion in Neurobiology, 48, 183-192. https://doi.org/10.1016/j.conb.2017.12.004
Takumi, T., Tamada, K., Hatanaka, F., Nakai, N., & Bolton, P. F. (2020). Behavioral neuroscience of autism. Neuroscience and Biobehavioral Reviews, 110, 60-76. https://doi.org/10.1016/j.neubiorev.2019.04.012
Tandon, N., & Tandon, R. (2019). Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype. Schizophrenia Research, 214, 70-75. https://doi.org/10.1016/j.schres.2019.08.032
Türei, D., Korcsmáros, T., & Saez-Rodriguez, J. (2016). OmniPath: Guidelines and gateway for literature-curated signaling pathway resources. Nature Methods, 13, 966-967. https://doi.org/10.1038/nmeth.4077
Van Daalen, E., Kemner, C., Verbeek, N. E., Van Der Zwaag, B., Burbach, J. P. H., Kristian, H., Van Amstel, P. Hochstenbach, R., Brilstra, E. H., & Poot, M. (2011) Social responsiveness scale-aided analysis of the clinical impact of copy number variations in autism. Neurogenetics, 12, 315-323. https://doi.org/10.1007/s10048-011-0297-2
Velmeshev, D., Magistri, M., Mazza, E. M. C., Lally, P., Khoury, N., D’Elia, E. R., Bicciato, S., & Faghihi, M. A. (2020). Cell-type-specific analysis of molecular pathology in autism identifies common genes and pathways affected across neocortical regions. Molecular Neurobiology, 57, 2279-2289. https://doi.org/10.1007/s12035-020-01879-5
Velmeshev, D., Schirmer, L., Jung, D., Haeussler, M., Perez, Y., Mayer, S., Bhaduri, A., Goyal, N., Rowitch, D. H., & Kriegstein, A. R. (2019). Single-cell genomics identifies cell type-specific molecular changes in autism. Science, 364, 685-689. https://doi.org/10.1126/science.aav8130
Watson, C. N., Belli, A., & Di Pietro, V. (2019). Small non-coding RNAs: New class of biomarkers and potential therapeutic targets in neurodegenerative disease. Frontiers in Genetics, 10, 1-14. https://doi.org/10.3389/fgene.2019.00364
Whitton, L., Cosgrove, D., Clarkson, C., Harold, D., Kendall, K., Richards, A., Mantripragada, K., Owen, M. J., O'Donovan, M. C., Walters, J., & Hartmann, A. (2016). Cognitive analysis of schizophrenia risk genes that function as epigenetic regulators of gene expression. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 171, 1170-1179. https://doi.org/10.1002/ajmg.b.32503
Willsey, A. J., Morris, M. T., Wang, S., Willsey, H. R., Sun, N., Teerikorpi, N., Baum, T. B., Cagney, G., Bender, K. J., Desai, T. A., Srivastava, D., Davis, G. W., Doudna, J., Chang, E., Sohal, V., Lowenstein, D. H., Li, H., Agard, D., Keiser, M. J., … Krogan, N. J. (2018). The psychiatric cell map initiative: A convergent systems biological approach to illuminating key molecular pathways in neuropsychiatric disorders. Cell, 174, 505-520. https://doi.org/10.1016/j.cell.2018.06.016
Wong, A. C. N., Holmes, A., Ponton, F., Lihoreau, M., Wilson, K., Raubenheimer, D., & Simpson, S. J. (2015). Behavioral microbiomics: A multi-dimensional approach to microbial influence on behavior. Frontiers in Microbiology, 6, 1-8. https://doi.org/10.3389/fmicb.2015.01359
Yang, S., Lim, K. H., Kim, S. H., & Joo, J. Y. (2020). Molecular landscape of long noncoding RNAs in brain disorders. Molecular Psychiatry, 26(4), 1060-1074. https://doi.org/10.1038/s41380-020-00947-5
Yang, Y. J., Baltus, A. E., Mathew, R. S., Murphy, E. A., Evrony, G. D., Gonzalez, D. M., Wang, E. P., Marshall-Walker, C. A., Barry, B. J., Murn, J., Tatarakis, A., Mahajan, M. A., Samuels, H. H., Shi, Y., Golden, J. A., Mahajnah, M., Shenhav, R., & Walsh, C. A. (2012). Microcephaly gene links trithorax and REST/NRSF to control neural stem cell proliferation and differentiation. Cell, 151, 1097-1112. https://doi.org/10.1016/j.cell.2012.10.043
Yoshizaki, K., Kimura, R., Kobayashi, H., Oki, S., Kikkawa, T., Mai, L., Koike, K., Mochizuki, K., Inada, H., Matsui, Y., Kono, T., & Osumi, N. (2021). Paternal age affects offspring via an epigenetic mechanism involving REST/NRSF. EMBO Reports, 22, e51524. https://doi.org/10.15252/embr.202051524
Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., Benner, C., & Chanda, S. K. (2019). Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications, 10(1), 1-10. https://doi.org/10.1038/s41467-019-09234-6