Phenotypic Subtyping and Re-analyses of Existing Transcriptomic Data from Autistic Probands in Simplex Families Reveal Differentially Expressed and ASD Trait-Associated Genes
Journal: 2020/December - Frontiers in Neurology
Abstract:
Autism spectrum disorder (ASD) describes a collection of neurodevelopmental disorders characterized by core symptoms that include social communication deficits and repetitive, stereotyped behaviors often coupled with restricted interests. Primary challenges to understanding and treating ASD are the genetic and phenotypic heterogeneity of cases that complicates all omics analyses as well as a lack of information on relationships among genes, pathways, and autistic traits. In this study, we re-analyze existing transcriptomic data from simplex families by subtyping individuals with ASD according to multivariate cluster analyses of clinical ADI-R scores that encompass a broad range of behavioral symptoms. We also correlate multiple ASD traits, such as deficits in verbal and non-verbal communication, play and social skills, ritualistic behaviors, and savant skills, with expression profiles using Weighted Gene Correlation Network Analyses (WGCNA). Our results show that subtyping greatly enhances the ability to identify differentially expressed genes involved in specific canonical pathways and biological functions associated with ASD within each phenotypic subgroup. Moreover, using WGCNA, we identify gene modules that correlate significantly with specific ASD traits. Network prediction analyses of the genes in these modules reveal canonical pathways as well as neurological functions and disorders relevant to the pathobiology of ASD. Finally, we compare the WGCNA-derived data on autistic traits in simplex families with analogous data from multiplex families using transcriptomic data from our previous studies. The comparison reveals overlapping trait-associated pathways as well as upstream regulators of the module-associated genes that may serve as useful targets for a precision medicine approach to ASD.
Keywords: ASD subgroups; ASD trait-associated genes; comparison with multiplex population; simplex families; transcriptomic analysis.
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Front Neurol 11: 578972

Phenotypic Subtyping and Re-analyses of Existing Transcriptomic Data from Autistic Probands in Simplex Families Reveal Differentially Expressed and ASD Trait-Associated Genes

Supplementary Table 1

Demographic information on the individuals from the SSC population included in this study.

Click here for additional data file.(13K, docx)

Supplementary Table 2

Clinical autistic traits classification and included trait-associated ADI-R items.

Click here for additional data file.(14K, docx)

Supplementary Table 3

Differentially expressed genes in the Language subtype of ASD.

Click here for additional data file.(38K, xlsx)

Supplementary Table 4

Differentially expressed genes in the Intermediate subtype of ASD.

Click here for additional data file.(27K, xlsx)

Supplementary Table 5

Differentially expressed genes in the Mild subtype of ASD.

Click here for additional data file.(24K, xlsx)

Supplementary Table 6

Differentially expressed genes in the combined case group.

Click here for additional data file.(26K, xlsx)

Supplementary Table 7

All significant over-represented canonical pathways associated with DEGs in the subtypes of ASD and the combined case group.

Click here for additional data file.(20K, xlsx)

Supplementary Table 8

All significant over-represented canonical pathways associated with module genes corresponding to verbal, non-verbal, social, and all (combined) traits in the simplex population.

Click here for additional data file.(59K, xlsx)

Supplementary Table 9

All significant over-represented canonical pathways associated with module genes corresponding to verbal, non-verbal, play, insistence on sameness, savant, and all (combined) traits in the multiplex population.

Click here for additional data file.(111K, xlsx)
Department of Biochemistry and Molecular Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, United States
Edited by: Lidia V. Gabis, Sheba Medical Center, Israel
Reviewed by: Mariangela Gulisano, University of Catania, Italy; Francesca Felicia Operto, University of Salerno, Italy
*Correspondence: Valerie W. Hu ude.uwg@uhlav
This article was submitted to Pediatric Neurology, a section of the journal Frontiers in Neurology
Department of Biochemistry and Molecular Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, United States
Edited by: Lidia V. Gabis, Sheba Medical Center, Israel
Reviewed by: Mariangela Gulisano, University of Catania, Italy; Francesca Felicia Operto, University of Salerno, Italy
Received 2020 Jul 1; Accepted 2020 Oct 21.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Abstract

Autism spectrum disorder (ASD) describes a collection of neurodevelopmental disorders characterized by core symptoms that include social communication deficits and repetitive, stereotyped behaviors often coupled with restricted interests. Primary challenges to understanding and treating ASD are the genetic and phenotypic heterogeneity of cases that complicates all omics analyses as well as a lack of information on relationships among genes, pathways, and autistic traits. In this study, we re-analyze existing transcriptomic data from simplex families by subtyping individuals with ASD according to multivariate cluster analyses of clinical ADI-R scores that encompass a broad range of behavioral symptoms. We also correlate multiple ASD traits, such as deficits in verbal and non-verbal communication, play and social skills, ritualistic behaviors, and savant skills, with expression profiles using Weighted Gene Correlation Network Analyses (WGCNA). Our results show that subtyping greatly enhances the ability to identify differentially expressed genes involved in specific canonical pathways and biological functions associated with ASD within each phenotypic subgroup. Moreover, using WGCNA, we identify gene modules that correlate significantly with specific ASD traits. Network prediction analyses of the genes in these modules reveal canonical pathways as well as neurological functions and disorders relevant to the pathobiology of ASD. Finally, we compare the WGCNA-derived data on autistic traits in simplex families with analogous data from multiplex families using transcriptomic data from our previous studies. The comparison reveals overlapping trait-associated pathways as well as upstream regulators of the module-associated genes that may serve as useful targets for a precision medicine approach to ASD.

Keywords: ASD subgroups, transcriptomic analysis, simplex families, ASD trait-associated genes, comparison with multiplex population
Abstract

Acknowledgments

We are grateful to both the SSC and AGRE for ADI-R scoresheets from individuals with ASD in both the simplex and multiplex families, respectively, as well as to the families who have generously allowed this information to be publicly available for research.

Acknowledgments
Click here for additional data file.(13K, docx)Click here for additional data file.(14K, docx)Click here for additional data file.(38K, xlsx)Click here for additional data file.(27K, xlsx)Click here for additional data file.(24K, xlsx)Click here for additional data file.(26K, xlsx)Click here for additional data file.(20K, xlsx)Click here for additional data file.(59K, xlsx)Click here for additional data file.(111K, xlsx)

References

  • 1. American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders: DSM-5. Arlington, VA: American Psychiatric Association; (2013). [PubMed]
  • 2. Bruining H, de Sonneville L, Swaab H, de Jonge M, Kas M, van Engeland H, et al. . Dissecting the clinical heterogeneity of autism spectrum disorders through defined genotypes. PLoS ONE. (2010) 5:e10887. 10.1371/journal.pone.0010887 ] [
  • 3. Cholemkery H, Medda J, Lempp T, Freitag CM. Classifying autism spectrum disorders by ADI-R: subtypes or severity gradient?J Autism Dev Disord. (2016) 46:2327–39. 10.1007/s10803-016-2760-2 [] [[PubMed]
  • 4. Hu VW, Steinberg ME. Novel clustering of items from the Autism Diagnostic Interview-Revised to define phenotypes within autism spectrum disorders. Autism Res. (2009) 2:67–77. 10.1002/aur.72 ] [
  • 5. Nurmi EL, Dowd M, Tadevosyan-Leyfer O, Haines JL, Folstein SE, Sutcliffe JS. Exploratory subsetting of autism families based on savant skills improves evidence of genetic linkage to 15q11-q13. J Am Acad Child Adolesc Psychiatry. (2003) 42:856–63. 10.1097/01.CHI.0000046868.56865.0F [] [[PubMed]
  • 6. Lord C, Rutter M, Couteur AL. Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Discord. (1994) 24:659–85. 10.1007/BF02172145 [] [[PubMed]
  • 7. Hu VW, Sarachana T, Kim KS, Nguyen A, Kulkarni S, Steinberg ME, et al. . Gene expression profiling differentiates autism case-controls and phenotypic variants of autism spectrum disorders: evidence for circadian rhythm dysfunction in severe autism. Autism Res. (2009) 2:78–97. 10.1002/aur.73 ] [
  • 8. Hu VW, Lai Y. Developing a predictive gene classifier for autism spectrum disorders based upon differential gene expression profiles of phenotypic subgroups. N Am J Med Sci. (2013) 6:107–16. 10.7156/najms.2013.0603107 ] [
  • 9. Hu VW, Addington A, Hyman A. Novel autism subtype-dependent genetic variants are revealed by quantitative trait and subphenotype association analyses of published GWAS Data. PLoS One. (2011) 6:e19067. 10.1371/journal.pone.0019067 ] [
  • 10. Hu VW, Devlin CA, Debski JJ. ASD phenotype-genotype associations in concordant and discordant monozygotic and dizygotic twins stratified by severity of autistic traits. Int J Mol Sci. (2019) 20:38804. 10.3390/ijms20153804 ] [
  • 11. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. (2008) 9:559. 10.1186/1471-2105-9-559 ] [
  • 12. Sun Y, Lin J, Zhang L. Application of weighted gene co-expression network analysis to explore the key genes in Alzheimer's disease. Ann Transl Med. (2019) 7:800. 10.21037/atm.2019.12.59 ] [
  • 13. Liu Y, Gu H-Y, Zhu J, Niu Y-M, Zhang C, Guo G-L. Identification of hub genes and key pathways associated with bipolar disorder based on weighted gene co-expression network analysis. Front Physiol. (2019) 10:1081. 10.3389/fphys.2019.01081 ] [
  • 14. Zhou Z, Cheng Y, Jiang Y, Liu S, Zhang M, Liu J, et al. . Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis. Int J Biol Sci. (2018) 14:124–36. 10.7150/ijbs.22619 ] [
  • 15. Xiao H, Chen P, Zeng G, Xu D, Wang X, Zhang X. Three novel hub genes and their clinical significance in clear cell renal cell carcinoma. J Cancer. (2019) 10:6779–91. 10.7150/jca.35223 ] [
  • 16. Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, et al. . Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. (2011) 474:380–4. 10.1038/nature10110 ] [
  • 17. Parikshak NN, Luo R, Zhang A, Won H, Lowe JK, Chandran V, et al. . Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell. (2013) 155:1008. 10.1016/j.cell.2013.10.031 ] [
  • 18. Parikshak NN, Swarup V, Belgard TG, Irimia M, Ramaswami G, Gandal MJ, et al. . Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature. (2016) 540:423–7. 10.1038/nature20612 ] [
  • 19. Konopka G, Wexler E, Rosen E, Mukamel Z, Osborn GE, Chen L, et al. . Modeling the functional genomics of autism using human neurons. Mol Psychiatry. (2012) 17:202–14. 10.1038/mp.2011.60 ] [
  • 20. Gudenas BL, Srivastava AK, Wang L. Integrative genomic analyses for identification and prioritization of long non-coding RNAs associated with autism. PLoS One. (2017) 12:e0178532. 10.1371/journal.pone.0178532 ] [
  • 21. Gupta S, Ellis SE, Ashar FN, Moes A, Bader JS, Zhan J, et al. . Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat Commun. (2014) 5:5748. 10.1038/ncomms6748 ] [
  • 22. Luo R, Sanders SJ, Tian Y, Voineagu I, Huang N, Chu SH, et al. . Genome-wide transcriptome profiling reveals the functional impact of rare de novo and recurrent CNVs in autism spectrum disorders. Am J Hum Genet. (2012) 91:38–55. 10.1016/j.ajhg.2012.05.011 ] [
  • 23. Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, et al. . TM4: a free, open-source system for microarray data management and analysis. Biotechniques. (2003) 34:374–8. 10.2144/03342mt01 [] [[PubMed]
  • 24. Basu SN, Kollu R, Banerjee-Basu S. AutDB: a gene reference resource for autism research. Nucleic Acids Res. (2009) 37:D832–D6. 10.1093/nar/gkn835 ] [
  • 25. Oliveros JC. Venny. An Interactive Tool for Comparing Lists With Venn's Diagrams. (2007). Available online at: [PubMed]
  • 26. Bauman ML. Medical comorbidities in autism: challenges to diagnosis and treatment. Neurotherapeutics. (2010) 7:320–7. 10.1016/j.nurt.2010.06.001 ] [
  • 27. Lai M-C, Lombardo MV, Baron-Cohen S. Autism. Lancet. (2014) 383:896–910. 10.1016/S0140-6736(13)61539-1 [] [[PubMed]
  • 28. Muhle R, Trentacoste SV, Rapin I. The genetics of autism. Pediatrics. (2004) 113:e472–e486. 10.1542/peds.113.5.e472 [] [[PubMed]
  • 29. Rose S, Niyazov DM, Rossignol DA, Goldenthal M, Kahler SG, Frye RE. Clinical and molecular characteristics of mitochondrial dysfunction in autism spectrum disorder. Mol Diagn Ther. (2018) 22:571–93. 10.1007/s40291-018-0352-x ] [
  • 30. Griffiths KK, Levy RJ. Evidence of mitochondrial dysfunction in autism: biochemical links, genetic-based associations, and non-energy-related mechanisms. Oxidative Med Cell Longevity. (2017) 2017:4314025. 10.1155/2017/4314025 ] [
  • 31. Weissman JR, Kelley RI, Bauman ML, Cohen BH, Murray KF, Mitchell RL, et al. . Mitochondrial disease in autism spectrum disorder patients: a cohort analysis. PLoS One. (2008) 3:e3815. 10.1371/journal.pone.0003815 ] [
  • 32. Frye RE, Rossignol DA. Mitochondrial dysfunction can connect the diverse medical symptoms associated with autism spectrum disorders. Pediatr Res. (2011) 69:41R−47R. 10.1203/PDR.0b013e318212f16b ] [
  • 33. Aspromonte MC, Bellini M, Gasparini A, Carraro M, Bettella E, Polli R, et al Characterization of intellectual disability and autism comorbidity through gene panel sequencing. Hum Mutat. (2019) 40:1346–63. 10.1002/humu.23822 ] [[Google Scholar]
  • 34. Lee EC, Hu VW. Phenotypic subtyping and re-analysis of existing methylation data from autistic probands in simplex families reveal ASD subtype-associated differentially methylated genes and biological functions. Int J Mol Sci. (2020) 21:e6877. 10.3390/ijms21186877 ] [
  • 35. Auyeung B, Baron-Cohen S, Ashwin E, Knickmeyer R, Taylor K, Hackett GF, et altestosterone and autistic traits Br J Psychol. (2009) 100:1–22. 10.1348/000712608X311731 [] [[PubMed][Google Scholar]
  • 36. Winden KD, Ebrahimi-Fakhari D, Sahin M. Abnormal mTOR activation in autism. Ann Rev Neurosci. (2018) 41:1–23. 10.1146/annurev-neuro-080317-061747 [] [[PubMed]
  • 37. Ganesan H, Balasubramanian V, Mahalaxmi I, Venugopal A, Subramaniam MD, Cho SG, et al. . mTOR signalling pathway - a root cause for idiopathic autism?BMB Rep. (2019) 52:424–33. 10.5483/BMBRep.2019.52.7.137 ] [
  • 38. McCarthy MM, Wright CL. Convergence of sex differences and the neuroimmune system in autism spectrum disorder. Biol Psychiatry. (2017) 81:402–10. 10.1016/j.biopsych.2016.10.004 ] [
  • 39. Quartier A, Chatrousse L, Redin C, Keime C, Haumesser N, Maglott-Roth A, et al. . Genes and pathways regulated by androgens in human neural cells, potential candidates for the male excess in autism spectrum disorder. Biol Psychiatry. (2018) 84:239–52. 10.1016/j.biopsych.2018.01.002 [] [[PubMed]
  • 40. Baron-Cohen S, Auyeung B, Nørgaard-Pedersen B, Hougaard DM, Abdallah MW, Melgaard L, et al. . Elevated fetal steroidogenic activity in autism. Mol Psychiatry. (2014) 20:369–76. 10.1038/mp.2014.48 ] [
  • 41. Marchetto MC, Belinson H, Tian Y, Freitas BC, Fu C, Vadodaria KC, et al. . Altered proliferation and networks in neural cells derived from idiopathic autistic individuals. Mol Psychiatry. (2017) 22:820–35. 10.1038/mp.2016.95 ] [
  • 42. McCarthy MM. Estradiol and the developing brain. Physiol Rev. (2008) 88:91–124. 10.1152/physrev.00010.2007 ] [
  • 43. Wright CL, Schwarz JS, Dean SL, McCarthy MM. Cellular mechanisms of estradiol-mediated sexual differentiation of the brain. Trends Endocrinol Metab. (2010) 21:553–61. 10.1016/j.tem.2010.05.004 ] [
  • 44. Baron-Cohen S, Tsompanidis A, Auyeung B, Nørgaard-Pedersen B, Hougaard DM, Abdallah M, et al. . Foetal oestrogens and autism. Mol. Psychiatry. (2019) 25:2970–78. 10.1038/s41380-019-0454-9 ] [
  • 45. Doi H, Fujisawa TX, Iwanaga R, Matsuzaki J, Kawasaki C, Tochigi M, et al. . Association between single nucleotide polymorphisms in estrogen receptor 1/2 genes and symptomatic severity of autism spectrum disorder. Res Dev Disabil. (2018) 82:20–6. 10.1016/j.ridd.2018.02.014 [] [[PubMed]
  • 46. Yeh MM, Bosch DE, Daoud SS. Role of hepatocyte nuclear factor 4-alpha in gastrointestinal and liver diseases. World J Gastroenterol. (2019) 25:4074–91. 10.3748/wjg.v25.i30.4074 ] [
  • 47. Santiago JA, Potashkin JA. Network-based metaanalysis identifies HNF4A and PTBP1 as longitudinally dynamic biomarkers for Parkinson's disease. Proc Natl Acad Sci U S A. (2015) 112:2257–62. 10.1073/pnas.1423573112 ] [
  • 48. Yamanishi K, Doe N, Sumida M, Watanabe Y, Yoshida M, Yamamoto H, et al. . Hepatocyte nuclear factor 4 alpha is a key factor related to depression and physiological homeostasis in the mouse brain. PLoS One. (2015) 10:e0119021. 10.1371/journal.pone.0119021 ] [
  • 49. Qu M, Duffy T, Hirota T, Kay SA. Nuclear receptor HNF4A transrepresses CLOCK: BMAL1 and modulates tissue-specific circadian networks. Proc Natl Acad Sci U S A. (2018) 115:E12305–E12. 10.1073/pnas.1816411115 ] [
  • 50. Melke J, Goubran Botros H, Chaste P, Betancur C, Nygren G, Anckarsäter H, et al. . Abnormal melatonin synthesis in autism spectrum disorders. Mol Psychiatry. (2008) 13:90–8. 10.1038/sj.mp.4002016 ] [
  • 51. Veatch OJ, Pendergast JS, Allen MJ, Leu RM, Johnson CH, Elsea SH, et al. . Genetic variation in melatonin pathway enzymes in children with autism spectrum disorder and comorbid sleep onset delay. J Autism Dev Disord. (2014) 45:100–10. 10.1007/s10803-014-2197-4 ] [
  • 52. Bourgeron T. The possible interplay of synaptic and clock genes in autism spectrum disorders. Cold Spring Harb Symp Quant Biol. (2007) 72:645–54. 10.1101/sqb.2007.72.020 [] [[PubMed]
  • 53. Buie T., Campbell D. B., Fuchs I. I. I., G. J., Furuta G. T., Levy J., et al. . Evaluation, diagnosis, and treatment of gastrointestinal disorders in individuals with ASDs: a consensus report. Pediatrics. (2010) 125:S1–S18. 10.1542/peds.2009-1878C [] [[PubMed]
  • 54. Campbell DB, Buie TM, Winter H, Bauman M, Sutcliffe JS, Perrin JM, et al. . Distinct genetic risk based on association of MET in families with co-occurring autism and gastrointestinal conditions. Pediatrics. (2009) 123:1018–24. 10.1542/peds.2008-0819 [] [[PubMed]
  • 55. Walker SJ, Langefeld CD, Zimmerman K, Schwartz MZ, Krigsman A. A molecular biomarker for prediction of clinical outcome in children with ASD, constipation, and intestinal inflammation. Sci Rep. (2019) 9:5987. 10.1038/s41598-019-42568-1 ] [
  • 56. Glatt SJ, Tsuang MT, Winn M, Chandler SD, Collins M, Lopez L, et al. . Blood-based gene expression signatures of infants and toddlers with autism. J Am Acad Child Adolesc Psychiatry. (2012) 51:934–44.e2. 10.1016/j.jaac.2012.07.007 ] [
  • 57. Hu VW, Frank BC, Heine S, Lee NH, Quackenbush J. Gene expression profiling of lymphoblastoid cell lines from monozygotic twins discordant in severity of autism reveals differential regulation of neurologically relevant genes. BMC Genomics. (2006) 7:118. 10.1186/1471-2164-7-118 ] [
  • 58. Enstrom AM, Lit L, Onore CE, Gregg JP, Hansen RL, Pessah IN, et al. . Altered gene expression and function of peripheral blood natural killer cells in children with autism. Brain Behav Immun. (2009) 23:124–33. 10.1016/j.bbi.2008.08.001 ] [
  • 59. Gregg JP, Lit L, Baron CA, Hertz-Picciotto I, Walker W, Davis RA, et al. . Gene expression changes in children with autism. Genomics. (2008) 91:22–9. 10.1016/j.ygeno.2007.09.003 [] [[PubMed]
  • 60. Baron CA, Liu SY, Hicks C, Gregg JP. Utilization of lymphoblastoid cell lines as a system for the molecular modeling of autism. J Autism Dev Discord. (2006) 36:973–82. 10.1007/s10803-006-0134-x [] [[PubMed]
  • 61. Nishimura Y, Martin CL, Vazquez-Lopez A, Spence SJ, Alvarez-Retuerto AI, Sigman M, et al. . Genome-wide expression profiling of lymphoblastoid cell lines distinguishes different forms of autism and reveals shared pathways. Hum Mol Genet. (2007) 16:1682–98. 10.1093/hmg/ddm116 [] [[PubMed]
  • 62. Kong SW, Shimizu-Motohashi Y, Campbell MG, Lee IH, Collins CD, Brewster SJ, et al. . Peripheral blood gene expression signature differentiates children with autism from unaffected siblings. Neurogenetics. (2013) 14:143–52. 10.1007/s10048-013-0363-z ] [
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