Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Genome-wide, integrative analysis implicates microRNA dysregulation in autism spectrum disorder

Nature Neuroscience volume 19, pages 1463–1476 (2016)Cite this article

Subjects

Abstract

Genetic variants conferring risk for autism spectrum disorder (ASD) have been identified, but the role of post-transcriptional mechanisms in ASD is not well understood. We performed genome-wide microRNA (miRNA) expression profiling in post-mortem brains from individuals with ASD and controls and identified miRNAs and co-regulated modules that were perturbed in ASD. Putative targets of these ASD-affected miRNAs were enriched for genes that have been implicated in ASD risk. We confirmed regulatory relationships between several miRNAs and their putative target mRNAs in primary human neural progenitors. These include hsa-miR-21-3p, a miRNA of unknown CNS function that is upregulated in ASD and that targets neuronal genes downregulated in ASD, and hsa_can_1002-m, a previously unknown, primate-specific miRNA that is downregulated in ASD and that regulates the epidermal growth factor receptor and fibroblast growth factor receptor signaling pathways involved in neural development and immune function. Our findings support a role for miRNA dysregulation in ASD pathophysiology and provide a rich data set and framework for future analyses of miRNAs in neuropsychiatric diseases.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: miRNA expression changes in post-mortem ASD cortex.
Figure 2: miRNA coexpression modules dysregulated in post-mortem ASD cortex.
Figure 3: Enrichment of ASD risk genes among the top targets of ASD-affected miRNAs and miRNA modules.
Figure 4: Relationship between miRNA and mRNA expression changes in post-mortem ASD cortex.
Figure 5: Enrichment for ASD-affected mRNAs and mRNA modules in the top targets of ASD-affected miRNAs.
Figure 6: hsa-miR-21-3p targets neuronal genes downregulated in ASD.
Figure 7: hsa_can_1002-m is primate specific and targets genes in the EGFR and the FGFR signaling pathways.
Figure 8: Experimental validation of targets of other candidate miRNAs.

References

  1. Bourgeron, T. From the genetic architecture to synaptic plasticity in autism spectrum disorder. Nat. Rev. Neurosci. 16, 551–563 (2015).

    Article  CAS  PubMed  Google Scholar 

  2. Geschwind, D.H. & State, M.W. Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurol. 14, 1109–1120 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Gaugler, T. et al. Most genetic risk for autism resides with common variation. Nat. Genet. 46, 881–885 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Geschwind, D.H. & Flint, J. Genetics and genomics of psychiatric disease. Science 349, 1489–1494 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ha, M. & Kim, V.N. Regulation of microRNA biogenesis. Nat. Rev. Mol. Cell Biol. 15, 509–524 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Friedman, R.C., Farh, K.K.H., Burge, C.B. & Bartel, D.P. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19, 92–105 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Im, H.-I. & Kenny, P.J. MicroRNAs in neuronal function and dysfunction. Trends Neurosci. 35, 325–334 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Amaral, D.G., Schumann, C.M. & Nordahl, C.W. Neuroanatomy of autism. Trends Neurosci. 31, 137–145 (2008).

    Article  CAS  PubMed  Google Scholar 

  9. Friedländer, M.R., Mackowiak, S.D., Li, N., Chen, W. & Rajewsky, N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 40, 37–52 (2012).

    Article  PubMed  CAS  Google Scholar 

  10. Friedländer, M.R. et al. Evidence for the biogenesis of more than 1,000 novel human microRNAs. Genome Biol. 15, R57 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Voineagu, I. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Oldham, M.C. et al. Functional organization of the transcriptome in human brain. Nat. Neurosci. 11, 1271–1282 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Parikshak, N.N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005).

    Article  PubMed  Google Scholar 

  15. Parikshak, N.N., Gandal, M.J. & Geschwind, D.H. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat. Rev. Genet. 16, 441–458 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Langfelder, P., Luo, R., Oldham, M.C. & Horvath, S. Is my network module preserved and reproducible? PLoS Comput. Biol. 7, e1001057 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Arbiza, L. et al. Genome-wide inference of natural selection on human transcription factor binding sites. Nat. Genet. 45, 723–729 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ronan, J.L., Wu, W. & Crabtree, G.R. From neural development to cognition: unexpected roles for chromatin. Nat. Rev. Genet. 14, 347–359 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Lewis, B.P., Burge, C.B. & Bartel, D.P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20 (2005).

    Article  CAS  PubMed  Google Scholar 

  20. Grimson, A. et al. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol. Cell 27, 91–105 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Garcia, D.M. et al. Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat. Struct. Mol. Biol. 18, 1139–1146 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hsu, S.-D. et al. miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 42, D78–D85 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Basu, S.N., Kollu, R. & Banerjee-Basu, S. AutDB: a gene reference resource for autism research. Nucleic Acids Res. 37, D832–D836 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Darnell, J.C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bayés, A. et al. Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nat. Neurosci. 14, 19–21 (2011).

    Article  CAS  PubMed  Google Scholar 

  27. Kang, H.J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sanders, S.J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Anney, R. et al. Individual common variants exert weak effects on the risk for autism spectrum disorders. Hum. Mol. Genet. 21, 4781–4792 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang, K. et al. Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature 459, 528–533 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

  32. Lee, P.H., O'Dushlaine, C., Thomas, B. & Purcell, S.M. INRICH: interval-based enrichment analysis for genome-wide association studies. Bioinformatics 28, 1797–1799 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Article  CAS  PubMed Central  Google Scholar 

  34. Lambert, J.-C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat. Genet. 45, 1452–1458 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hirokawa, N., Niwa, S. & Tanaka, Y. Molecular motors in neurons: transport mechanisms and roles in brain function, development, and disease. Neuron 68, 610–638 (2010).

    Article  CAS  PubMed  Google Scholar 

  36. Lasiecka, Z.M. & Winckler, B. Mechanisms of polarized membrane trafficking in neurons -- focusing in on endosomes. Mol. Cell. Neurosci. 48, 278–287 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wan, Q.-F. et al. SV2 acts via presynaptic calcium to regulate neurotransmitter release. Neuron 66, 884–895 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Redies, C., Hertel, N. & Hübner, C.A. Cadherins and neuropsychiatric disorders. Brain Res. 1470, 130–144 (2012).

    Article  CAS  PubMed  Google Scholar 

  39. Wong, R.W.C. & Guillaud, L. The role of epidermal growth factor and its receptors in mammalian CNS. Cytokine Growth Factor Rev. 15, 147–156 (2004).

    Article  CAS  PubMed  Google Scholar 

  40. Turner, C.A., Akil, H., Watson, S.J. & Evans, S.J. The fibroblast growth factor system and mood disorders. Biol. Psychiatry 59, 1128–1135 (2006).

    Article  CAS  PubMed  Google Scholar 

  41. Mellios, N. & Sur, M. The emerging role of microRNAs in schizophrenia and autism spectrum disorders. Front. Psychiatry 3, 39 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Rani, N. et al. A primate lncRNA mediates Notch signaling during neuronal development by sequestering miRNA. Neuron 90, 1174–1188 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Mundalil Vasu, M. et al. Serum microRNA profiles in children with autism. Mol. Autism 5, 40 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Mor, M., Nardone, S., Sams, D.S. & Elliott, E. Hypomethylation of miR-142 promoter and upregulation of microRNAs that target the oxytocin receptor gene in the autism prefrontal cortex. Mol. Autism 6, 46 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Geschwind, D.H. & Rakic, P. Cortical evolution: judge the brain by its cover. Neuron 80, 633–647 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Geschwind, D.H. Advances in autism. Annu. Rev. Med. 60, 367–380 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Mariani, J. et al. FOXG1-dependent dysregulation of GABA/glutamate neuron differentiation in autism spectrum disorders. Cell 162, 375–390 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Gawad, C., Koh, W. & Quake, S.R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).

    Article  CAS  PubMed  Google Scholar 

  49. Quinn, E.M. et al. Development of strategies for SNP detection in RNA-seq data: application to lymphoblastoid cell lines and evaluation using 1000 Genomes data. PLoS One 8, e58815 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Friedländer, M.R. et al. Discovering microRNAs from deep sequencing data using miRDeep. Nat. Biotechnol. 26, 407–415 (2008).

    Article  PubMed  CAS  Google Scholar 

  51. Hackenberg, M., Sturm, M., Langenberger, D., Falcón-Pérez, J.M. & Aransay, A.M. miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res. 37, W68–W76 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Hackenberg, M., Rodríguez-Ezpeleta, N. & Aransay, A.M. miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res. 39, W132–W138 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Hansen, K.D., Irizarry, R.A. & Wu, Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics 13, 204–216 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Leek, J.T., Johnson, W.E., Parker, H.S., Jaffe, A.E. & Storey, J.D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Miller, J.A., Horvath, S. & Geschwind, D.H. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc. Natl. Acad. Sci. USA 107, 12698–12703 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2008).

    Article  CAS  PubMed  Google Scholar 

  59. Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal Complex Systems 1695 (2006).

  60. Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Zambon, A.C., et al. GO-Elite: a flexible solution for pathway and ontology over-representation. Bioinformatics 28, 2209–2210 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Stein, J.L. et al. A quantitative framework to evaluate modeling of cortical development by neural stem cells. Neuron 83, 69–86 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Li, H. et al. The sequence alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Anders, S., Pyl, P.T. & Huber, W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  67. Rossin, E.J. et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 7, e1001273 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Lage, K. et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309–316 (2007).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to acknowledge the BrainSpan Consortium principal investigator N. Sestan (Yale University) for providing BrainSpan microRNA data and J. Ou for excellent technical assistance. This work was supported by US National Institutes of Health grant R01MH094714 to D.H.G. and is part of the PsychEncode Consortium.

Author information

Author notes

  1. T Grant Belgard

    Present address: Present address: Verge Genomics, San Francisco, California, USA.,

Authors and Affiliations

  1. Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.,

    Ye E Wu, Neelroop N Parikshak, T Grant Belgard & Daniel H Geschwind

  2. Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.,

    Ye E Wu, T Grant Belgard & Daniel H Geschwind

  3. Interdepartmental Program in Neuroscience, University of California, Los Angeles, Los Angeles, California, USA.,

    Neelroop N Parikshak

  4. Department of Neurology, Program in Neurogenetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.,

    Neelroop N Parikshak & Daniel H Geschwind

  5. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.,

    Daniel H Geschwind

Authors
  1. Ye E Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neelroop N Parikshak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T Grant Belgard
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel H Geschwind
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.N.P. and T.G.B. performed brain sample dissections. Y.E.W. performed the other experiments and data analyses. N.N.P. provided code for differential gene expression, coexpression and enrichment analyses. D.H.G. provided guidance and oversight on all experiments and analyses. Y.E.W. and D.H.G. wrote the manuscript.

Corresponding author

Correspondence to Daniel H Geschwind.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Brain sample characteristics.

(a-b) Number of reads mapped to the hg19 reference genome (a) and mean read quality scores (b) for 131 ASD and 111 CTL brain tissue samples sequenced in this study. (c-e) Numbers of mapped reads, mean read quality scores, distribution of age, RIN, PMI, numbers of male and female individuals, numbers of prefrontal (FC) and temporal cortex (TC) samples, and numbers of samples from different brain banks (H, Harvard-ATP; N, NICHD-BTB) are shown for the ASD and CTL groups in 95 cortex samples used for the main DGE analysis (c), 109 cortex samples used for the main WGCNA analysis (d), and 47 cerebellum samples used for DGE analysis (e). Boxplot whiskers encompass data points within 1.5-fold interquartile ranges of the lower and upper quartiles.

Supplementary Figure 2 Principal component analysis and hierarchical sample clustering.

(a) Pearson correlations between different covariates and principal components (PCs) 1-5 of miRNA expression data (normalized for library size) for all 216 samples that passed quality control. Pearson correlation coefficients (R) and P values are indicated. Diagnosis (ASD vs. CTL), sex (female vs. male), and library preparation method (RiboZero selection vs. total RNA) were treated as binary numeric variables. Region (frontal cortex, temporal cortex, or cerebellum) and brain bank were treated as multi-level factor variables and adjusted R2 was calculated using a linear model between these two variables and other variables. Between region and brain bank, a chi-square test was performed and the P value was indicated in the heatmap (R was non-applicable). (b) Scatter plot of 216 samples based on PCs 1 and 2 of the miRNA expression data (normalized for library size). Dots are colored according to brain region. (c) Hierarchical clustering of all 216 samples using expression data (normalized for library size) of all expressed miRNAs. Information on diagnosis, age, sex, brain region, co-morbidity of seizures, psychiatric medication history, RIN, PMI, brain bank, and library preparation method is indicated with color bars below the dendrogram according to the legend on the right. (d) Pearson correlations between different covariates and PCs 1-5 of miRNA expression data (normalized for library size) for 95 cortex samples used for the main DGE analysis. Pearson correlation coefficients (R) and P values are indicated. Diagnosis (ASD vs. CTL), sex (female vs. male), region (frontal vs. temporal), and brain bank (Harvard-ATP vs. NICHD-BTB) were treated as binary numeric variables. (e) Scatter plot of 95 cortex samples based on PCs 1 and 2 of the miRNA expression data (normalized for library size). Dots are colored according to brain region. (f) Hierarchical clustering of 95 cortex samples using expression data (normalized for library size and other technical covariates) of all expressed miRNAs. Information on diagnosis, age, sex, brain region, co-morbidity of seizures, psychiatric medication history, RIN, PMI, and brain bank is indicated with color bars below the dendrogram according to the legend on the right.

Supplementary Figure 3 Robustness of DGE results.

(a) Comparison of miRNA fold changes in ASD vs. CTL between 10 rounds of random sampling of 70% of the samples and the original 95 cortex samples. Pearson correlation coefficients (R) and P values are indicated. Red line, y=x. (b-d) Comparison of miRNA fold changes in ASD vs. CTL between all 95 cortex samples and subsets of samples with RIN ≥ 5 (b), PMI ≤ 30 hrs (c), or no 15q duplication (d). Pearson correlation coefficients (R) and P values are indicated. Red line, y=x. (e) Comparison of miRNA fold changes in ASD vs. CTL between 95 cortex and 47 cerebellum samples. Red line, y=x; black line, linear regression between fold changes in the cerebellum and the cortex for miRNAs differentially expressed in the cortex (FDR < 0.05). Pearson’s R and P values are indicated. (f-g) Normalized log2(expression level) of down-regulated (f) and up-regulated (g) miRNAs in ASD (red) and control (blue) samples (n = 5 - 8) detected by qRT-PCR. Statistical significance was assessed using two-sided t-tests assuming unequal variance.

Supplementary Figure 4 Robustness, age dependence, and preservation of miRNA co-expression modules.

(a) Construction of consensus modules by bootstrapping for 200 rounds. Module assignment based on the original 109 samples, consensus module assignment based on 200 rounds of bootstrapping, and module assignment in each of the 200 resampled networks are shown below the dendrogram. The three ASD-associated modules are largely stable with perturbations to the initial subject and regional identity. The fraction of times each gene was assigned to the same module as in the consensus module is reported in Supplementary Table 2. (b-c) Comparison of ASD vs. CTL fold changes between samples from younger (15 - 30 years) and older (> 30 years) individuals for miRNAs in the yellow (b) and magenta (c) modules. Red line, y=x; black line, regression line between fold changes in the younger individuals and the older individuals for miRNAs differentially expressed (P < 0.05) in the younger set (orange and magenta dots). (d-g) Preservation of modules defined in ASD (d), CTL (e), TC (f), or FC (g) samples only in CTL (d), ASD (e), FC (f), or TC samples (g). (h-i) Module preservation analysis in two independent datasets: 31 independent cortex samples sequenced in this study (h) (Methods) and 167 samples covering 16 brain regions (neocortex, subcortical regions, thalamus, and cerebellum) and different ages (4 months to 19 years old) from the BrainSpan project (i). A module is considered not preserved if preservation Zsummary < 2, moderately preserved if 2 ≤ Zsummary < 10, and highly preserved if Zsummary ≥ 10.

Supplementary Figure 5 Experimental validation of TargetScan predicted miRNA targets.

Distribution (left) and cumulative distribution (right) of mRNA log2(fold change) in response to over-expression of hsa-miR-21-5p in hNPCs. Orange line, the strongest mRNA targets predicted by TargetScan (summed context+ score ≤ -0.1); purple line, the most conserved mRNA targets predicted by TargetScan (branch length in the top 25%, context+ score ≤ -0.05); red line, all mRNAs predicted to be hsa-miR-21-5p targets by TargetScan (the above two categories combined); green line, hsa-miR-21-5p targets documented in miRTarBase (452 miRNAs); black line, mRNAs not predicted to be hsa-miR-21-5p targets by TargetScan. Statistical significance between target groups and non-targets was assessed using ones-sided t-tests assuming unequal variance.

Supplementary Figure 6 Enrichment of ASD risk genes within the top targets of ASD-affected miRNAs and miRNA modules while controlling for 3’ UTR length.

(a) Heatmap showing enrichment of ASD SFARI genes, ID genes, ASD rare variants, FMRP targets, PSD genes, embryonically expressed genes, and chromatin modifiers, assessed using a logistic model that incorporates gene 3’ UTR length. P values were FDR corrected across 10 target groups for each gene list. (b) Heatmap showing enrichment of genes affected by multiple categories of de novo variants (DNVs), assessed using a logistic model that incorporates gene coding region length and gene 3’ UTR length. P values were FDR corrected across 10 target groups for each DNV category. (c) Heatmap showing enrichment of ASD-associated developmental gene co-expression modules in human cortex, assessed using a logistic model that incorporates gene 3’ UTR length. P values were FDR corrected across 10 target groups for each developmental module. Enrichment odds ratios (OR) and FDR corrected P values are shown for enrichments with FDR < 0.05.

Supplementary Figure 7 GO analysis for the predicted targets of differentially expressed miRNAs and ASD-related miRNA modules.

(a-b) Top relevant gene ontology categories (GO-Elite software, uncorrected P < 0.01, number of enriched genes > 5) for the strongest (a) or the most conserved (b) targets of the down-regulated and up-regulated miRNAs and the ASD-related miRNA modules. Enrichment Z scores represent relative enrichment in the targets compared to the background (Methods), with the red line at Z = 2. Asterisks indicate GO terms with FDR-corrected P values < 0.10.

Supplementary Figure 8 Effect of miRNAs on mRNA expression changes.

(a-h) Comparison of log2(fold change) distribution of down-regulated (a,b,e,f) or up-regulated (c,d,g,h) mRNAs (FDR < 0.05) that are predicted to be the strongest (a-d) or the most conserved (e-h) targets of the down-regulated or up-regulated miRNAs (a,c,e,g) or miRNA modules (b,d,f,h) to log2(fold change) distribution of differentially expressed mRNAs that are not predicted targets. The number of mRNAs in each group is indicated in brackets. One-tailed Wilcoxon rank sum tests were performed to compare each target group to the non-targets. *P < 0.05, **P < 0.01, ***P < 0.001. mRNAs that are predicted targets of both down-regulated and up-regulated miRNAs and miRNA modules were not included in the analysis.

Supplementary Figure 9 Correlation between differentially expressed miRNAs and mRNAs.

(a-c) Correlations between the PC1s of differentially expressed miRNAs (FDR < 0.05, |log2(fold change)| ≥ 0.3) and differentially expressed mRNAs (FDR < 0.05) that are predicted targets. (a) All differentially expressed miRNAs vs. all differentially expressed mRNAs; (b) up-regulated miRNAs vs. down-regulated mRNAs; (c) down-regulated miRNAs vs. up-regulated mRNAs. Pearson correlation coefficients (R) and P values within 47 CTL or 54 ASD samples alone, or the combined samples together are shown below the plots.

Supplementary Figure 10 Further characterization of hsa-miR-21-3p and hsa_can_1002-m.

(a) miRNA expression fold changes in the cortex plotted against percentile rank of mean expression levels across 95 cortex samples, with hsa-miR-21-3p and hsa_can_1002-m highlighted. (b-c) Views from the UCSC Genome Browser (https://genome.ucsc.edu) showing the chromosome locations and pre-miRNA sequences of hsa-miR-21-3p (b) and hsa_can_1002-m (c). The mature sequences and seed regions are indicated with black lines and green rectangles, respectively. Within the seed region of hsa_can_1002-m, a single-nucleotide difference indicated with a red asterisk was observed between the human and all other primate sequences. Gene annotations, multiple alignments of corresponding regions in different vertebrates, and measurements of evolutionary conservation using phyloP are shown. (d-g) Boxplots showing expression patterns of hsa-miR-21-3p (d,e) and hsa_can_1002-m (f,g) in different human brain regions (d,f) and at different developmental stages (e,g). DFC, dorsolateral prefrontal cortex; VFC, ventrolateral prefrontal cortex; MFC, medial prefrontal cortex; OFC, orbital frontal cortex; M1C, primary motor cortex; S1C primary somatosensory cortex; IPC, posteroinferior parietal cortex; A1C, primary auditory cortex; STC, posterior superior temporal cortex; ITC, inferolateral temporal cortex; V1C, primary visual cortex; HIP, hippocampus; AMY, amygdaloid complex; STR, striatum; MD, mediodorsal nucleus of thalamus; CBC, cerebellar cortex. Infancy, 4 months - 1 year; early childhood, 2 - 4 years; late childhood, 8 - 13 years; adolescence, 15 - 19 years; adulthood, 21 - 40 years. Boxplot whiskers indicate data points within 1.5-fold interquartile ranges of the lower and upper quartiles.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10 (PDF 3967 kb)

Supplementary Methods Checklist (PDF 539 kb)

Supplementary Table 1

Brain sample metadata (XLSX 95 kb)

Supplementary Table 2

Information on predicted miRNAs, raw read counts, and DGE and WGCNA results (XLSX 1594 kb)

Supplementary Table 3

Enrichment of potential TF/CR targets in miRNA modules (XLSX 25 kb)

Supplementary Table 4

Numbers of miRNA targets and overlap between target groups (XLSX 41 kb)

Supplementary Table 5

Gene expression changes in hNPCs with miRNA over-expression (XLSX 21534 kb)

Supplementary Table 6

Enrichment analysis for miRNA targets (XLSX 107 kb)

Supplementary Code 1

Code for differential gene expression analysis using a linear mixed-effects model (TXT 3 kb)

Supplementary Code 2

Code for weighted gene co-expression network analysis using a bootstrapping-based method (TXT 7 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wu, Y., Parikshak, N., Belgard, T. et al. Genome-wide, integrative analysis implicates microRNA dysregulation in autism spectrum disorder. Nat Neurosci 19, 1463–1476 (2016). https://doi.org/10.1038/nn.4373

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.4373

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing