Integrated systems analysis reveals a molecular network underlying autism spectrum disorders
Open accessAbstract
Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA‐sequencing of the corpus callosum from patients with autism exhibited extensive gene mis‐expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology.
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LPL is an autism candidate gene.
“LPL was identified as a gene affected in multiple hyperlipidemic conditions including familial combined hyperlipidemia [99] and is an autism candidate gene [100].”
From Sterol and lipid analyses identifies hypolipidemia and apolipoprotein disorders in autism associated with adaptive functioning deficits (2021) · cites this paper as [100]
The Results section on DNA sequencing identifies LRP2—not LPL—as an autism spectrum disorder candidate gene. Searches for “LPL” and “lipoprotein lipase” found no passage in our copy addressing whether LPL is an autism candidate gene. (LLM, conf=0.77)
Experimental — accuracy not yet measuredThis analysis found a PPI module enriched for synaptic function genes and weakly enriched for mutations from ASD individuals.
“This identified a PPI module that is enriched for genes related to synaptic function and weakly enriched for mutations from individuals with ASD.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [123]
Tissue-specific data are essential for neurobiological interpretation of PPI modules.
“demonstrating that tissue-specific data are essential for a neurobiological interpretation of PPI modules123.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [123]
Integration with transcriptomics annotated that module as highly expressed in oligodendrocytes and corpus callosum.
“Integration with transcriptomics annotated the module as highly expressed in oligodendrocytes and the corpus callosum”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [123]
A genome-wide network analysis using global literature-curated PPI interactions identified modules enriched for ASD-associated genes.
“Recently, one study used global literature-curated PPI interactions in a genome-wide network analysis to identify modules that are enriched for ASD-associated genes123.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [123]
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“Importantly, mutations in this gene were identified among ASD patients from our exome/genome sequencing and also in an earlier study from a different patient cohort (Sicca et al , 2011).”
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“Network and transcriptome analyses of this ASD module collectively revealed that the corpus callosum is likely a potential tissue of origin underlying ASD, in line with its morphological alterations that have been described in patients with ASD (Boger-Megiddo et al , 2006; Frazier et al , 2012).”
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“Module #13 was also more enriched for ASD genes (21% in the module) than genes involved in schizophrenia (Jia et al , 2010) (10% in the module) and intellectual disability (Parikshak et al , 2013) (9.2% in the module), whereas no enrichment was observed for Alzheimer’s disease (Bertram et al ,…”
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A Two-Year Longitudinal MRI Study of the Corpus Callosum in Autism
Frazier, T. W., Keshavan, M. S., Minshew, N. J., & Hardan, A. Y. (2012) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-012-1478-z · Cited by 81 · cited 1× · see citing works
“Network and transcriptome analyses of this ASD module collectively revealed that the corpus callosum is likely a potential tissue of origin underlying ASD, in line with its morphological alterations that have been described in patients with ASD (Boger-Megiddo et al , 2006; Frazier et al , 2012).”
An, J., Cristino, A. S., Zhao, Q., Edson, J., Williams, S. M., Ravine, D., Wray, J. A., Marshall, V. M., Hunt, A., Whitehouse, A., et al. (2014) · Translational Psychiatry · doi:10.1038/tp.2014.38 · Cited by 78 · cited 2× · see citing works
“Related studies were focused on a set of proteins potentially implicated in ASD and characterized their inter-acting partners to identify molecular pathways underlying ASD (Sakai et al , 2011; Corominas et al , 2014; Cristino et al , 2014).”
Ionita-Laza, I., Makarov, V., & Buxbaum, J. D. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.04.010 · Cited by 57 · cited 1× · see citing works
“In addition, another example of a gene that would be missed by differential expression studies is LRP2, whose implication in ASD was found by this study and also an earlier investigation (Ionita-Laza et al , 2012), but it did not exhibit altered expression in ASD patients.”
Noh, H. J., Ponting, C. P., Boulding, H. C., Meader, S., Betancur, C., Buxbaum, J. D., Pinto, D., Marshall, C. R., Lionel, A. C., Scherer, S. W., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003523 · Cited by 54 · see citing works
Genetic analysis of biological pathway data through genomic randomization
Yaspan, B. L., Bush, W. S., Torstenson, E. S., Ma, D., Pericak-Vance, M. A., Ritchie, M. D., Sutcliffe, J. S., & Haines, J. L. (2011) · Human Genetics · doi:10.1007/s00439-011-0956-2 · Cited by 54 · cited 1× · see citing works
“Common variants from genome-wide association studies (GWAS) were also tested against KEGG pathways, suggesting a possible association with a pathway for ketone body metabolism (Yaspan et al , 2011).”
Functional Genomics Assistant (FUGA): a toolbox for the analysis of complex biological networks
Drozdov, I. A., Ouzounis, C. A., Shah, A. M., & Tsoka, S. (2011) · BMC Research Notes · doi:10.1186/1756-0500-4-462 · Cited by 22 · see citing works
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Lord, C., Brugha, T. S., Charman, T., Cusack, J., Dumas, G., Frazier, T., Jones, E. J. H., Jones, R. M., Pickles, A., State, M. W., et al. (2020) · Nature Reviews Disease Primers · doi:10.1038/s41572-019-0138-4 · Cited by 1,859 · see citing works
Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci
Sanders, S. J., He, X., Willsey, A. J., Ercan-Sencicek, A. G., Samocha, K. E., Cicek, A. E., Murtha, M. T., Bal, V. H., Bishop, S. L., Dong, S., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.016 · Cited by 1,430 · see citing works
“These 65 ASD risk genes show enrichment for protein-protein interactions (PPIs) and coalesce into a PPI network with two distinct sub-networks corresponding to genes involved in chromatin regulation and the synapse (Figure 7), consistent with previous analyses (Chang et al., 2015; Li et al., 2014; Parikshak et al., 20…”
ResultIntegrative omics for health and disease
Karczewski, K. J., & Snyder, M. P. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2018.4 · Cited by 1,158 · see citing works
“A measure of openness of chromatin, as measured by its sensitivity to cleavage by DNase I. and characterized their function 31,32 .”
BackgroundMethodSelf-citationDefining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders
Sullivan, P. F., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.01.015 · Cited by 452 · see citing works
“These efforts point at similar pathways and/or convergence of risk loci on similar biological processes (Corominas et al., 2014; Gilman et al., 2012; Li et al., 2014).”
BackgroundSystems biology and gene networks in neurodevelopmental and neurodegenerative disorders
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430 · see citing works
“Integration with transcriptomics annotated the module as highly expressed in oligodendrocytes and the corpus callosum, demonstrating that tissue-specific data are essential for a neurobiological interpretation of PPI modules 123 .”
BackgroundMethodKrishnan, A., Zhang, R., Yao, V., Theesfeld, C. L., Wong, A. K., Tadych, A., Volfovsky, N., Packer, A. I., Lash, A., & Troyanskaya, O. G. (2016) · Nature Neuroscience · doi:10.1038/nn.4353 · Cited by 427 · see citing works
“This finding is largely consistent with previous studies 16,18,30,31 , as well as recent findings on the possible role of callosal projection neurons 17 and on the neuropathology of autism brain 32 .”
ResultBaron-Cohen, S., Tsompanidis, A., Auyeung, B., Nørgaard-Pedersen, B., Hougaard, D. M., Abdallah, M., Cohen, A., & Pohl, A. (2020) · Molecular Psychiatry · doi:10.1038/s41380-019-0454-9 · Cited by 200 · see citing works
“In autism, synapse formation [29], neuronal differentiation [30] as well as the GABAergic system [31] are all atypical.”
BackgroundBorovac, J., Bosch, M., & Okamoto, K. (2018) · Molecular and Cellular Neuroscience · doi:10.1016/j.mcn.2018.07.001 · Cited by 181 · see citing works
Behavioral phenotypes of genetic mouse models of autism
Kazdoba, T. M., Leach, P. T., & Crawley, J. N. (2015) · Genes Brain & Behavior · doi:10.1111/gbb.12256 · Cited by 179 · see citing works
“…genes and CNVs for ASD have been published, each one appearing in only a relatively small number of individuals (Butler et al. 2005; Coe et al. 2014; De Rubeis et al. 2014; Gaugler et al. 2014; Iossifov et al. 2014; Li et al. 2014; Parikshak et al. 2013; Pinto et al. 2014; Willsey & State 2015).”
BackgroundMav, D., Shah, R., Howard, B. E., Auerbach, S. S., Bushel, P. R., Collins, J. B., Gerhold, D. L., Judson, R., Karmaus, A. L., Maull, E. A., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191105 · Cited by 156 · see citing works
Neuroglia in the autistic brain: evidence from a preclinical model
Bronzuoli, M. R., Facchinetti, R., Ingrassia, D., Sarvadio, M., Schiavi, S., Steardo, L., Verkhratsky, A., Trezza, V., & Scuderi, C. (2018) · Molecular Autism · doi:10.1186/s13229-018-0254-0 · Cited by 107 · see citing works
Decoding the Genomics of Abdominal Aortic Aneurysm
Li, J., Pan, C., Zhang, S., Spin, J. M., Deng, A. C., Leung, L. L. K., Dalman, R. L., Tsao, P. S., & Snyder, M. P. (2018) · Cell · doi:10.1016/j.cell.2018.07.021 · Cited by 95 · see citing works
Self-citationA Subset of Autism-Associated Genes Regulate the Structural Stability of Neurons
Lin, Y., Frei, J. A., Kilander, M. B. C., Shen, W., & Blatt, G. J. (2016) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2016.00263 · Cited by 91 · see citing works
“…2011, 2012a; Tarabeux et al., 2011; de Ligt et al., 2012; Griswold et al., 2012; Prasad et al., 2012; Talkowski et al., 2012; Yoo et al., 2012; Dimassi et al., 2013; De Rubeis et al., 2014; Kenny et al., 2014; Li et al., 2014a; Namjou et al., 2014; Poot, 2014; Aller et al., 2015; Pan et al., 2015).”
Spence, E. F., Dube, S., Uezu, A., Locke, M., Soderblom, E. J., & Soderling, S. H. (2019) · Nature Communications · doi:10.1038/s41467-019-08288-w · Cited by 88 · see citing works
First glimpses of the neurobiology of autism spectrum disorder
Sanders, S. J. (2015) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2015.10.002 · Cited by 84 · see citing works
Luo, Y., Eran, A., Palmer, N., Avillach, P., Levy-Moonshine, A., Szolovits, P., & Kohane, I. S. (2020) · Nature Medicine · doi:10.1038/s41591-020-1007-0 · Cited by 78 · see citing works
Expression of lncRNAs in Low-Grade Gliomas and Glioblastoma Multiforme: An In Silico Analysis
Reon, B. J., Anaya, J., Zhang, Y., Mandell, J. W., Purow, B. W., Abounader, R., & Dutta, A. (2016) · PLoS Medicine · doi:10.1371/journal.pmed.1002192 · Cited by 80 · see citing works
Berto, S., Usui, N., Konopka, G., & Fogel, B. L. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw110 · Cited by 80 · see citing works
Williams, S. M., An, J., Edson, J., Watts, M. E., Murigneux, V., Whitehouse, A., Jackson, C. J., Bellgrove, M. A., Cristino, A. S., & Claudianos, C. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0049-x · Cited by 75 · see citing works
“2 Enrichment of the filtered regulatory variants and the proteincoding variants from ASD cases in different ASD-associated datasets AXAS-ASD (n= 2664) [30], SFARI (n= 397) [47], M13 - Li 2014 (n= 115) [48], and GWAS-ASD (ASD associated genes as described in the NHGRI-EBI GWAS Catalog).”
BackgroundMethodInfluentialTBR1 regulates autism risk genes in the developing neocortex
Notwell, J. H., Heavner, W. E., Darbandi, S. F., Katzman, S., McKenna, W. L., Ortiz-Londono, C. F., Tastad, D., Eckler, M. J., Rubenstein, J. L. R., McConnell, S. K., et al. (2016) · Genome Research · doi:10.1101/gr.203612.115 · Cited by 74 · see citing works
“Additional gene lists were obtained from references (Parikshak et al. 2013; Li et al. 2014; Hormozdiari et al. 2015) and mapped using the same procedures.”
BackgroundMethodResultInfluentialFrom UBE3A to Angelman syndrome: a substrate perspective
Sell, G. L., & Margolis, S. S. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00322 · Cited by 70 · see citing works
Large neutral amino acid levels tune perinatal neuronal excitability and survival
Knaus, L. S., Basilico, B., Malzl, D., Gerykova Bujalkova, M., Smogavec, M., Schwarz, L. A., Gorkiewicz, S., Amberg, N., Pauler, F. M., Knittl-Frank, C., et al. (2023) · Cell · doi:10.1016/j.cell.2023.02.037 · Cited by 70 · see citing works
Genetic heterogeneity in autism: From single gene to a pathway perspective
An, J., & Claudianos, C. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.06.013 · Cited by 64 · see citing works
Brain-specific functional relationship networks inform autism spectrum disorder gene prediction
Duda, M., Zhang, H., Li, H., Wall, D. P., Burmeister, M., & Guan, Y. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0098-6 · Cited by 63 · see citing works
Autism spectrum disorder: prospects for treatment using gene therapy
Benger, M., Kinali, M., & Mazarakis, N. D. (2018) · Molecular Autism · doi:10.1186/s13229-018-0222-8 · Cited by 67 · see citing works
Dendritic spine actin cytoskeleton in autism spectrum disorder
Joensuu, M., Lanoue, V., & Hotulainen, P. (2018) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2017.08.023 · Cited by 65 · see citing works
Lin, Y., Afshar, S., Rajadhyaksha, A. M., Potash, J. B., & Han, S. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.500064 · Cited by 59 · see citing works
“To leverage these functional relationships, several studies have explored integrating known risk genes using a protein-protein interaction (PPI) network to identify novel genes involved in ASD (Gilman et al., 2011; Li et al., 2014; Hormozdiari et al., 2015; Liu et al., 2015).”
BackgroundGupta, C., Chandrashekar, P. B., Jin, T., He, C., Khullar, S., Chang, Q., & Wang, D. (2022) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-022-09438-w · Cited by 64 · see citing works
Detecting local genetic correlations with scan statistics
Guo, H., Li, J. J., Lu, Q., & Hou, L. (2021) · Nature Communications · doi:10.1038/s41467-021-22334-6 · Cited by 52 · see citing works
Self-citationCoba, M. P., Ramaker, M. J., Ho, E. V., Thompson, S. L., Komiyama, N. H., Grant, S. G. N., Knowles, J. A., & Dulawa, S. C. (2018) · Scientific Reports · doi:10.1038/s41598-018-20610-y · Cited by 48 · see citing works
“We evaluated Dlgap1 wild type (WT), heterozygous (HT), and knockout (KO) mice for protein interactions within the PSD, and behavioral phenotypes relevant to OCD, ASD, and/or schizophrenia.”
BackgroundResultInfluentialLi, J., Ma, Z., Shi, M., Malty, R. H., Aoki, H., Minić, Z., Phanse, S., Jin, K., Wall, D. P., Zhang, Z., et al. (2015) · Cell Systems · doi:10.1016/j.cels.2015.11.002 · Cited by 48 · see citing works
“In a previous study, we leveraged a human PPI network to derive the high-level functional modules involving proteins associated with ASD (Li et al., 2014).”
BackgroundMethodInfluentialSelf-citationMerging Data from Genetic and Epigenetic Approaches to Better Understand Autistic Spectrum Disorder
Grayson, D. R., & Guidotti, A. (2015) · Epigenomics · doi:10.2217/epi.15.92 · Cited by 49 · see citing works
Jin, Z., Li, Z., Liu, Z., Jiang, Y. W., Cai, X., & Wu, J. (2017) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12383 · Cited by 48 · see citing works
“For example, two PPI modules were significantly enriched in 383 ASD-risk genes from the SFARI gene list (Li et al., 2014).”
BackgroundSubcellular organization of UBE3A in neurons
Burette, A. C., Judson, M. C., Burette, S., Phend, K. D., Philpot, B. D., & Weinberg, R. J. (2016) · The Journal of Comparative Neurology · doi:10.1002/cne.24063 · Cited by 46 · see citing works
“Despite a relatively low concentration in PSDs, UBE3A may nevertheless participate locally in postsynaptic regulation, perhaps by regulating the turnover of SAP97 and other synaptic proteins within the cytoplasm of dendritic spines (Matsumoto et al., 2006; Li et al., 2014).”
BackgroundVLDL-specific increases of fatty acids in autism spectrum disorder correlate with social interaction
Usui, N., Iwata, K., Miyachi, T., Takagai, S., Wakusawa, K., Nara, T., Tsuchiya, K. J., Matsumoto, K., Kurita, D., Kameno, Y., et al. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.102917 · Cited by 45 · see citing works
“VLDL is synthesised in the liver by microsomal triglyceride transfer protein (MTTP), and is degraded by LPL.”
BackgroundInfluentialBanerjee, A., Luong, J. A., Ho, A., Saib, A., & Ploski, J. E. (2016) · Molecular Autism · doi:10.1186/s13229-016-0077-9 · Cited by 43 · see citing works
“05) [64, 110–122].”
Sadeghi, I., Gispert, J. D., Palumbo, E., Muñoz-Aguirre, M., Wucher, V., D’Argenio, V., Santpere, G., Navarro, A., Guigó, R., & Vilor‐Tejedor, N. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.037 · Cited by 39 · see citing works
Rubinstein, M., Patowary, A., Stanaway, I. B., McCord, E., Nesbitt, R., Archer, M., Scheuer, T., Nickerson, D. A., Raskind, W. H., Wijsman, E. M., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2016.222 · Cited by 39 · see citing works
“However we cannot exclude the possibility of SCN1A involvement as mutations in this gene were associated with autism without seizures as well 47, 48 .”
Brown, E., Lautz, J. D., Davis, T. R., Gniffke, E. P., VanSchoiack, A., Neier, S. C., Tashbook, N., Nicolini, C., Fahnestock, M. L., Schrum, A. G., et al. (2018) · Molecular Autism · doi:10.1186/s13229-018-0229-1 · Cited by 40 · see citing works
Nassir, N., Bankapur, A., Samara, B., Ali, A. Y., Ahmed, A., Inuwa, I. M., Zarrei, M., Shabestari, S. A. S., Albanna, A., Howe, J., et al. (2021) · Human Genomics · doi:10.1186/s40246-021-00368-7 · Cited by 38 · see citing works
Squair, J. W., Tigchelaar, S., Moon, K., Liu, J., Tetzlaff, W., Kwon, B. K., Krassioukov, A. V., West, C. R., Foster, L. J., & Skinnider, M. A. (2018) · eLife · doi:10.7554/elife.39188 · Cited by 38 · see citing works
Haploinsufficiency in the ANKS1B gene encoding AIDA-1 leads to a neurodevelopmental syndrome
Carbonell, A. U., Cho, C. H., Tindi, J. O., Counts, P. A., Bates, J. C., Erdjument‐Bromage, H., Cvejic, S., Iaboni, A., Kvint, I., Rosensaft, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11437-w · Cited by 38 · see citing works
Howell, B. W., & Smith, K. M. (2019) · Pharmacological Research · doi:10.1016/j.phrs.2018.11.019 · Cited by 35 · see citing works
Novel Gene-Based Analysis of ASD GWAS: Insight Into the Biological Role of Associated Genes
Alonso-González, A., Calaza, M., Rodriguez‐Fontenla, C., & Carracedo, Á. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00733 · Cited by 32 · see citing works
A comparative study of the genetic components of three subcategories of autism spectrum disorder
Li, J., Hu, S., Zhang, K., Shi, L., Zhang, Y., Zhao, T., Wang, L., He, X., Xia, K., Liu, C., et al. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0081-x · Cited by 30 · see citing works
Self-citationIntegrated analysis of human transcriptome data for Rett syndrome finds a network of involved genes
Ehrhart, F., Coort, S. L., Eijssen, L., Cirillo, E., Smeets, E., Sangani, N. B., Evelo, C. T., & Curfs, L. (2019) · The World Journal of Biological Psychiatry · doi:10.1080/15622975.2019.1593501 · Cited by 31 · see citing works
Yousaf, A., Waltes, R., Haslinger, D., Klauck, S. M., Duketis, E., Sachse, M., Voran, A., Biscaldi, M., Schulte‐Rüther, M., Cichon, S., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00906-2 · Cited by 27 · see citing works
Fricano-Kugler, C. J., Gordon, A., Shin, G. S., Gao, K., Nguyen, J., Berg, J. M., Starks, M., & Geschwind, D. H. (2019) · Molecular Autism · doi:10.1186/s13229-019-0278-0 · Cited by 27 · see citing works
Tierney, E., Remaley, A. T., Thurm, A., Jager, L. R., Wassif, C. A., Kratz, L. E., Bailey-Wilson, J. E., Bukelis, I., Sarphare, G., Jung, E. S., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01580-8 · Cited by 26 · see citing works
Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis
Guan, J., Yang, E., Yang, J., Zeng, Y., Ji, G., & Cai, J. J. (2016) · Human Genetics · doi:10.1007/s00439-016-1673-7 · Cited by 27 · see citing works
“Disturbed ASD genetic networks have been noticed previously (Hormozdiari et al. 2015; Li et al. 2014; Parikshak et al. 2013; Pramparo et al. 2015; Willsey et al. 2013).”
BackgroundInfluentialQuinlan, M. A., Robson, M. J., Ye, R., Rose, K. L., Schey, K. L., & Blakely, R. (2020) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2020.00089 · Cited by 26 · see citing works
Niemsiri, V., Rosenthal, S. B., Nievergelt, C. M., Maihofer, A. X., Marchetto, M. C., Santos, R., Shekhtman, T., Alliey‐Rodriguez, N., Anand, A., Balaraman, Y., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-022-01909-9 · Cited by 26 · see citing works
“Network-based methods require comprehensive information but have been shown to produce promising insights in studies of various diseases, including psychiatric disorders [21, 22].”
BackgroundWu, S., Chen, D., & Snyder, M. P. (2022) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.102101 · Cited by 25 · see citing works
Self-citationSAPAP Scaffold Proteins: From Synaptic Function to Neuropsychiatric Disorders
Bai, Y., Wang, H., & Li, C. (2022) · Cells · doi:10.3390/cells11233815 · Cited by 24 · see citing works
“genome‐wide network analysis [133] Two SNPs located within an intron of”
BackgroundReview on Graph Clustering and Subgraph Similarity Based Analysis of Neurological Disorders
Thomas, J., Seo, D., & Sael, L. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17060862 · Cited by 24 · see citing works
“[56] proposed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes.”
BackgroundMosca, E., Bersanelli, M., Gnocchi, M., Moscatelli, M., Castellani, G., Milanesi, L., & Mezzelani, A. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00129 · Cited by 24 · see citing works
“For convenience we will refer to these network-based gene lists—which contains input and predicted genes—as SFARIh n ∗ , dnCNVn n ∗ , dnCNV3s n ∗ , rCNV n ∗ , dMUT n ∗ , and mMUT n ∗ .”
BackgroundMethodInfluentialThe ANKS1B Gene and its Associated Phenotypes: Focus on CNS Drug Response
Younis, R. M., Taylor, R. M., Beardsley, P. M., & McClay, J. L. (2019) · Pharmacogenomics · doi:10.2217/pgs-2019-0015 · Cited by 24 · see citing works
Kumari, E., Velloso, F. J., Nasuhidehnavi, A., Somasundaram, A., Savanur, V. H., Buono, K. D., & Levison, S. W. (2020) · Stem Cell Reports · doi:10.1016/j.stemcr.2020.03.019 · Cited by 22 · see citing works
“At this stage of development, the SVZ is expanding rapidly, giving rise to distinct sets of late-developing interneurons and glial progenitors that have been shown to be affected in ASD (Gogolla et al., 2009; Li et al., 2014; Marie et al., 2018).”
BackgroundVelloso, F. J., Wadhwa, A., Kumari, E., Carcea, I., Bozdagi-Gunal, O., & Levison, S. W. (2021) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2021.12.015 · Cited by 24 · see citing works
Autism Spectrum Disorder: A Neuro-Immunometabolic Hypothesis of the Developmental Origins
Frasch, M. G., Yoon, B.-J., Helbing, D. L., Snir, G., Antonelli, M. C., & Bauer, R. (2023) · Biology · doi:10.3390/biology12070914 · Cited by 21 · see citing works
“DLG4 [5] Disks large homolog 4; modulates stress reactivity and anxiety [35,36] Down Down”
BackgroundThe proteome and its dynamics: A missing piece for integrative multi-omics in schizophrenia
Borgmann-Winter, K., Wang, K., Bandyopadhyay, S., Torshizi, A. D., Blair, I., & Hahn, C. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2019.07.025 · Cited by 20 · see citing works
“For instance, in patients with autism spectrum disorder (ASD), genes harboring de novo missense or nonsense mutations are enriched for genes with high degrees of connectivity in PPI networks to all other genes and particularly previously ASD-implicated genes (Li et al., 2014; Neale et al., 2012).”
BackgroundLi, X., Zhang, Y., Wang, L., Lin, Y., Gao, Z., Zhan, X., Huang, Y., Sun, C., Wang, D., Liang, S., et al. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00706 · Cited by 21 · see citing works
“RNA-sequencing data of CC (GSE62098) (14) have provided DEGs between ASD children and typically developing controls and was used to confirm the involvement of a protein interaction module (with GO enrichment for synaptic transmission) in ASD.”
BackgroundMethodInfluentialHlushchenko, I., Khanal, P., Abouelezz, A., Paavilainen, V. O., & Hotulainen, P. (2018) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2018.00217 · Cited by 21 · see citing works
Carbonell, A. U., Freire‐Cobo, C., Deyneko, I. V., Dobariya, S., Erdjument‐Bromage, H., Clipperton‐Allen, A. E., Page, D. T., Neubert, T. A., & Jordan, B. A. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1152562 · Cited by 18 · see citing works
Genome-Wide Changes in Protein Translation Efficiency Are Associated with Autism
Rogozin, I. B., Gertz, E. M., Baranov, P. V., Poliakov, E., & Schäffer, A. A. (2018) · Genome Biology and Evolution · doi:10.1093/gbe/evy146 · Cited by 19 · see citing works
Homozygous deletions implicate non-coding epigenetic marks in Autism spectrum disorder
Schmitz‐Abe, K., Sánchez‐Schmitz, G., Doan, R. N., Hill, R. S., Chahrour, M. H., Mehta, B. K., Servattalab, S. E., Ataman, B., Lam, A.-T. N., Morrow, E. M., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-70656-0 · Cited by 18 · see citing works
Bristy, S. A., Islam, A., Andalib, K. M. S., Khan, U., Awal, M. A., & Rahman, M. H. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100871 · Cited by 18 · see citing works
Ji, C., Tang, Y., Zhang, Y., Huang, X., Li, C., Yang, Y., Wu, Q., Xia, X., Cai, Q., Qi, X.-R., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112712 · Cited by 16 · see citing works
Lin, C., Chang, K., Lin, C.-Y., Wu, J.-Y., Coon, H. H., Huang, P., Ho, H., Akbarian, S., Gau, S. S., & Huang, H. (2018) · Scientific Reports · doi:10.1038/s41598-018-22753-4 · Cited by 16 · see citing works
The dark side of synaptic proteins in tumours
Li, J., Xu, Y., Zhu, H., Wang, Y., Li, P., & Wang, D. (2022) · British Journal of Cancer · doi:10.1038/s41416-022-01863-x · Cited by 15 · see citing works
Self-citationTranslational Research in Pediatrics IV: Solid Tissue Collection and Processing
Gillio‐Meina, C., Zielke, H. R., & Fraser, D. D. (2015) · PEDIATRICS · doi:10.1542/peds.2015-0490 · Cited by 15 · see citing works
Roy, B., Amemasor, E., Hussain, S., & Castro, K. (2023) · Diseases · doi:10.3390/diseases12010007 · Cited by 14 · see citing works
Dysregulation of Alternative Poly-adenylation as a Potential Player in Autism Spectrum Disorder
Szkop, K. J., Cooke, P. I. C., Humphries, J. A., Kalna, V., Moss, D. S., Schuster, E. F., & Nobeli, I. (2017) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2017.00279 · Cited by 12 · see citing works
Three Brothers With Autism Carry a Stop‐Gain Mutation in the HPA‐Axis Gene NR3C2
Cukier, H. N., Griswold, A. J., Hofmann, N. K., Gomez, L., Whitehead, P. L., Abramson, R. K., Gilbert, J. R., Cuccaro, M. L., Dykxhoorn, D. M., & Pericak‐Vance, M. A. (2020) · Autism Research · doi:10.1002/aur.2269 · Cited by 13 · see citing works
“This is because the GRIK3 deletion was intronic and evidence for the role of this gene in autism is minimal, given that only a single individual with autism has been previously reported with a GRIK3 mutation [Li et al., 2014; Lim et al., 2017].”
BackgroundGao, H., Ni, Y., Mo, X., Li, D., Teng, S., Huang, Q., Huang, S., Liu, G., Zhang, S., Tang, Y., et al. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.046 · Cited by 13 · see citing works
“Raw data for the four datasets (GSE102741, GSE59288, GSE51264, GSE62098) from the GEO database were converted to ‘‘.fastq” format files using the fasterq-dump tool.”
MethodNanni, N. D., Bersanelli, M., Cupaioli, F. A., Milanesi, L., Mezzelani, A., & Mosca, E. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20133363 · Cited by 13 · see citing works
“Interestingly, it seems that SHANK3, strongly linked to ASDs, works in organization of HCN-channels [29] and that its expression negatively influences those of HCN2 [30], so variations in the SHANK3 gene are reflected in pacemaker current abnormalities.”
BackgroundInfluentialMakrodimitris, S., Pronk, B., Abdelaal, T., & Reinders, M. J. T. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad416 · Cited by 11 · see citing works
“This has enabled the discovery of complex biological mechanisms and lead to deeper understanding of biological processes (e.g. [2, 3]).”
BackgroundWang, Y. J., Zhang, X., Lam, C. K., Guo, H., Wang, C., Zhang, S., Wu, J. C., Snyder, M. P., & Li, J. (2022) · Cell Systems · doi:10.1016/j.cels.2022.09.001 · Cited by 11 · see citing works
Self-citationWu, X., Xiong, D., Liu, R., Lai, X., Tian, Y., Xie, Z., Chen, L., Hu, L., Duan, J., Gao, X., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58275-7 · Cited by 11 · see citing works
Network Structure Analysis Identifying Key Genes of Autism and Its Mechanism
Wang, Y., Kou, Y., & Meng, D. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/3753080 · Cited by 11 · see citing works
“[20] integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression, and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes.”
MethodAshitha, S. N. M., & Ramachandra, N. B. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.04.051 · Cited by 10 · see citing works
Lei, J., Deng, Y., & Ma, S. (2022) · Brain and Behavior · doi:10.1002/brb3.2610 · Cited by 10 · see citing works
“Integrated bioinformatics analyzing tools including the GEO database have been largely used for the identification of keymolecules implicated in the development of diseases, including autism (Li et al., 2014).”
MethodAssociation of oligodendrocytes differentiation regulator geneDUSP15with autism
Tian, Y., Wang, L., Jia, M., Lu, T., Ruan, Y., Wu, Z., Wang, L., Liu, J., & Zhang, D. (2016) · The World Journal of Biological Psychiatry · doi:10.1080/15622975.2016.1178395 · Cited by 10 · see citing works
“Oligodendrocytes are the predominant cell type in human corpus callosum (Li et al. 2014), which is likely a potential tissue of origin underlying autism.”
BackgroundKarunakaran, K. B., Jain, S., Brahmachari, S. K., Balakrishnan, N., & Ganapathiraju, M. K. (2024) · Schizophrenia · doi:10.1038/s41537-024-00439-3 · Cited by 9 · see citing works
“Following the methodology detailed by Li et al. 96 , we decomposed the human interactome into 241 topological modules using the Cytoscape plugin MCODE (see Supplementary Methods) 97 .”
MethodMolecular Architecture of Spinal Cord Injury Protein Interaction Network
Alawieh, A. M., Sabra, M., Sabra, Z., Tomlinson, S., & Zaraket, F. A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0135024 · Cited by 9 · see citing works
“Compared to previous literature-based studies that used data from microarray and proteomics databases (Examples [26,27]), a novel feature of our approach is to incorporate data originating from “non-omics” (genomics, transcriptomics or proteomics) approaches, including experiments on single or a limited number of prot…”
MethodZhang, J., Yang, J., Yang, N., Ma, J., Lu, D., Dong, Y., Liang, H., Liu, D., & Cang, M. (2020) · Lipids in Health and Disease · doi:10.1186/s12944-020-01230-w · Cited by 9 · see citing works
“Genetic Dlgap1 variants are associated with neuropsychiatric disorders, including schizophrenia [35], autism spectrum disorder [36], and obsessive-compulsive disorder [37].”
Cameli, C., Viggiano, M., Rochat, M. J., Maresca, A., Caporali, L., Fiorini, C., Palombo, F., Magini, P., Duardo, R. C., Ceroni, F., et al. (2021) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.16161 · Cited by 9 · see citing works
Systems genetics of nonsyndromic orofacial clefting provides insights into its complex aetiology
Razaghi‐Moghadam, Z., Namipashaki, A., Farahmand, S., & Ansari‐Pour, N. (2018) · European Journal of Human Genetics · doi:10.1038/s41431-018-0263-7 · Cited by 9 · see citing works
Prats, C., Fatjó‐Vilas, M., Penzol, M. J., Kébir, O., Pina‐Camacho, L., Demontis, D., Crespo‐Facorro, B., Peralta, V., González-Pinto, A. M., Pomarol‐Clotet, E., et al. (2021) · The World Journal of Biological Psychiatry · doi:10.1080/15622975.2021.1939155 · Cited by 8 · see citing works
“In detail, Li et al. (2014) identified a functional module linked to autism, revealing a subset of genes expressed in oligodendrocytes at the corpus callosum, a structure associated with ASD (Wolff et al. 2015).”
BackgroundIncreased plasma lipoprotein lipase activity in males with autism spectrum disorder
Hirai, T., Usui, N., Iwata, K., Miyachi, T., Tsuchiya, K. J., Xie, M.-J., Nakamura, K., Tsujii, M., Sugiyama, T., & Matsuzaki, H. (2020) · Research in Autism Spectrum Disorders · doi:10.1016/j.rasd.2020.101630 · Cited by 7 · see citing works
Integrated multifactor analysis explores core dysfunctional modules in autism spectrum disorder
Huang, Y., Chang, Z., Li, X., Liang, S., Yi, Y., & Wu, L. (2018) · International Journal of Biological Sciences · doi:10.7150/ijbs.24624 · Cited by 7 · see citing works
“The RNA-seq data (GSE62098 [42]) was collected from the NCBI Gene Expression Omnibus [43] (GEO) database, which contained 6 patients with autism spectrum disorders and 6 normal individuals (tissue: corpus callosum).”
Methodrecount-brain : a curated repository of human brain RNA-seq datasets metadataPreprint
Razmara, A., Ellis, S., Sokolowski, D., Davis, S. R., Wilson, M. D., Leek, J. T., Jaffe, A. E., & Collado‐Torres, L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/618025 · Cited by 6 · see citing works
“The two sets of studies are set 1: SRP017933, SRP032539, SRP032540, SRP048683, SRP056604 (Boudreau et al., 2014; Li et al., 2014; Magistri et al., 2015; Pardo et al., 2013) ; set 2: ERP001304, SRP051844, SRP007483, SRP058181, SRP019762 (Dumitriu et al., 2016; Khrameeva et al., 2014; Labadorf et al., 2015; Voineagu et…”
BackgroundMethodInfluentialScheller, U., Pfisterer, K., Uebe, S., Ekici, A. B., Reis, A., Jamra, R. A., & Ferrazzi, F. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0358-6 · Cited by 6 · see citing works
“For analyses on a protein level a human protein-protein interaction network (PPI) published by Li et al. [27] was employed.”
MethodLombardo, S. D., Battaglia, G., Petralia, M. C., Mangano, K., Basile, M. S., Bruno, V., Fagone, P., Bella, R., Nicoletti, F., & Cavalli, E. (2020) · Brain Sciences · doi:10.3390/brainsci10040200 · Cited by 5 · see citing works
“0 Expression BeadChip [15] [16] Cerebellum n = 8 ASD n = 8 HD * GSE62098 Corpus callosum n = 6 ASD n = 6 HD * Illumina HiSeq 2000 (Homo sapiens) [17]”
BackgroundVeneruso, I., Ranieri, A., Falcone, N., Tripodi, L., Scarano, C., Monica, I. L., Pastore, L., Lombardo, B., & D’Argenio, V. (2023) · Genes · doi:10.3390/genes14081651 · Cited by 4 · see citing works
“(Arg270His) in the LPL gene, associated to familial lipoprotein lipase deficiency [39].”
BackgroundSpatiotemporal expression patterns of anxiety disorder-associated genes
Karunakaran, K. B., & Amemori, K. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02693-y · Cited by 4 · see citing works
LaFlamme, B. A. (2015) · Nature Genetics · doi:10.1038/ng.3210 · Cited by 3 · see citing works
Synaptic protein mutations in autism
Sauer, A. K., Walsh, J. A., Curran, D., Zohourian, N., Stanton, J. E., Zabetakis, I., Brown, J. A. L., & Grabrucker, A. M. (2026) · Neurobiology of Disease · doi:10.1016/j.nbd.2026.107365 · Cited by 3 · see citing works
Stewart, A., Nguyen, M., Song, C., & Kalueff, A. V. (2015) · Journal of Psychopharmacology · doi:10.1177/0269881115593904 · Cited by 2 · see citing works
“For example, applied to complex neuropsychiatric disorders, recent sophisticated omics-based analyses of clinical samples (Chang et al., 2015; Li et al., 2014) and preclinical data (Heinzmann et al.”
BackgroundLiu, Y., Teng, X., Bai, D., Fu, X., Chang, Q., Li, J., Gao, S., Liu, W., & Guo, Y. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.142130 · Cited by 2 · see citing works
Self-citation
- Authors
- Li, J., Shi, M., Ma, Z., Zhao, S., Euskirchen, G., Ziskin, J., Urban, A., Hallmayer, J., & Snyder, M.
- Affiliations
- Stanford Medicine (US)
- Corresponding author
- Snyder, Michael
- Venue
- Molecular Systems Biology
- Published
- 2014-12-01
- Volume
- 10
- Issue
- 12
- Pages
- 774-774
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC4300495
- OpenAlex
- W2098734267
- Cited by
- 132
- References
- 61
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 2 · 2023: 12 · 2022: 10 · 2021: 11 · 2020: 16 · 2019: 14 · 2018: 19 · 2017: 6 · 2016: 17 · 2015: 11 · 2014: 1
- FWCI
- 6.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Systems Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- NIH
- Department of Genetics
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- autism spectrum disorder, protein-protein interaction network, autism candidate genes, corpus callosum, oligodendrocyte development, molecular pathology
- MeSH terms
- Autism Spectrum Disorder, Animals, Corpus Callosum, Humans, Male, Oligodendroglia, Reproducibility of Results, Cohort Studies, Gene Expression, Genome, Human, Case-Control Studies, Sequence Analysis, DNA, Sequence Analysis, RNA, Systems Biology, Mice, Gene Regulatory Networks, Protein Interaction Maps
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