Differential activity of transcribed enhancers in the prefrontal cortex of 537 cases with schizophrenia and controls


Transcription at enhancers is a widespread phenomenon which produces so-called enhancer RNA (eRNA) and occurs in an activity-dependent manner. However, the role of eRNA and its utility in exploring disease-associated changes in enhancer function, and the downstream coding transcripts that they regulate, is not well established. We used transcriptomic and epigenomic data to interrogate the relationship of eRNA transcription to disease status and how genetic variants alter enhancer transcriptional activity in the human brain. We combined RNA-seq data from 537 postmortem brain samples from the CommonMind Consortium with cap analysis of gene expression and enhancer identification, using the assay for transposase-accessible chromatin followed by sequencing (ATACseq). We find 118 differentially transcribed eRNAs in schizophrenia and identify schizophrenia-associated gene/eRNA co-expression modules. Perturbations of a key module are associated with the polygenic risk scores. Furthermore, we identify genetic variants affecting expression of 927 enhancers, which we refer to as enhancer expression quantitative loci or eeQTLs. Enhancer expression patterns are consistent across studies, including differentially expressed eRNAs and eeQTLs. Combining eeQTLs with a genome-wide association study of schizophrenia identifies a genetic variant that alters enhancer function and expression of its target gene, GOLPH3L. Our novel approach to analyzing enhancer transcription is adaptable to other large-scale, non-poly-A depleted, RNA-seq studies.

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  1. 1.

    Roussos P, Mitchell Amanda C, Voloudakis G, Fullard John F, Pothula Venu M, Tsang J, et al. A role for noncoding variation in schizophrenia. Cell Rep. 2014;9:1417–29.

  2. 2.

    Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci. 2016;19:1442–53.

  3. 3.

    Hauberg ME, Roussos P, Grove J, Børglum AD, Mattheisen M. Analyzing the role of MicroRNAs in schizophrenia in the context of common genetic risk variants. JAMA Psychiatry. 2016;73:369–77.

  4. 4.

    Hu J, Xu J, Pang L, Zhao H, Li F, Deng Y, et al. Systematically characterizing dysfunctional long intergenic non-coding RNAs in multiple brain regions of major psychosis. Oncotarget. 2016;7:71087–98.

  5. 5.

    Kim T-K, Hemberg M, Gray JM, Costa AM, Bear DM, Wu J, et al. Widespread transcription at neuronal activity-regulated enhancers. Nature. 2010;465:182–7.

  6. 6.

    Li W, Notani D, Rosenfeld MG. Enhancers as non-coding RNA transcription units: recent insights and future perspectives. Nat Rev Genet. 2016;17:207–23.

  7. 7.

    Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, Boyd M, et al. An atlas of active enhancers across human cell types and tissues. Nature. 2014;507:455–61.

  8. 8.

    Arner E, Daub CO, Vitting-Seerup K, Andersson R, Lilje B, Drabløs F, et al. Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells. Science. 2015;347:1010–4.

  9. 9.

    Roussos P, Katsel P, Davis KL, Siever LJ, Haroutunian V. A system-level transcriptomic analysis of schizophrenia using postmortem brain tissue samples. Arch Gen Psychiatry. 2012;69:1205–13.

  10. 10.

    Bora E, Fornito A, Radua J, Walterfang M, Seal M, Wood SJ, et al. Neuroanatomical abnormalities in schizophrenia: a multimodal voxelwise meta-analysis and meta-regression analysis. Schizophr Res. 2011;127:46–57.

  11. 11.

    Barch DM, Sheffield JM. Cognitive impairments in psychotic disorders: common mechanisms and measurement. World Psychiatry. 2014;13:224–32.

  12. 12.

    Liao Y, Smyth GK, Shi W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 2013;41:e108–e108.

  13. 13.

    Law CW, Chen Y, Shi W, Smyth GK. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15:R29.

  14. 14.

    Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11:R14.

  15. 15.

    Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinform. 2013;14:7.

  16. 16.

    Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:Article 17.

  17. 17.

    Zhu L, Lei J, Devlin B, Roeder K. Testing high-dimensional covariance matrices, with application to detecting schizophrenia risk genes. Ann Appl Stat. 2017;11:1810–31.

  18. 18.

    Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28:1353–8.

  19. 19.

    Millstein J, Zhang B, Zhu J, Schadt EE. Disentangling molecular relationships with a causal inference test. BMC Genet. 2009;10:23.

  20. 20.

    PGC-SCZ. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.

  21. 21.

    Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481–7.

  22. 22.

    Blockus H, Chédotal A. Slit-Robo signaling. Development. 2016;143:3037–44.

  23. 23.

    Jaffe AE, Gao Y, Deep-Soboslay A, Tao R, Hyde TM, Weinberger DR, et al. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat Neurosci. 2016;19:40–47.

  24. 24.

    Degner JF, Pai AA, Pique-Regi R, Veyrieras JB, Gaffney DJ, Pickrell JK, et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature. 2012;482:390–4.

  25. 25.

    Lam MTY, Li W, Rosenfeld MG, Glass CK. Enhancer RNAs and regulated transcriptional programs. Trends Biochem Sci. 2014;39:170–82.

  26. 26.

    Ng MM, Dippold HC, Buschman MD, Noakes CJ, Field SJ. GOLPH3L antagonizes GOLPH3 to determine Golgi morphology. Mol Biol Cell. 2013;24:796–808.

  27. 27.

    Landek-Salgado MA, Faust TE, Sawa A. Molecular substrates of schizophrenia: homeostatic signaling to connectivity. Mol Psychiatry. 2016;21:10–28.

  28. 28.

    Fullard JF, Halene TB, Giambartolomei C, Haroutunian V, Akbarian S, Roussos P. Understanding the genetic liability to schizophrenia through the neuroepigenome. Schizophr Res. 2016;177:115–24.

  29. 29.

    Brennand KJ, Simone A, Jou J, Gelboin-Burkhart C, Tran N, Sangar S, et al. Modelling schizophrenia using human induced pluripotent stem cells. Nature. 2011;473:221–5.

  30. 30.

    Brennand K, Savas JN, Kim Y, Tran N, Simone A, Hashimoto-Torii K, et al. Phenotypic differences in hiPSC NPCs derived from patients with schizophrenia. Mol Psychiatry. 2015;20:361–8.

  31. 31.

    Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, et al. Spatio-temporal transcriptome of the human brain. Nature. 2011;478:483–9.

  32. 32.

    Psych EC, Akbarian S, Liu C, Knowles JA, Vaccarino FM, Farnham PJ, et al. The PsychENCODE project. Nat Neurosci. 2015;18:1707–12.

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This work was supported by the National Institutes of Health—R01AG050986 (Roussos), R01MH109677 (Roussos), R37MH057881 (Devlin, Roeder) and R01MH109900 (Roeder)—Brain Behavior Research Foundation (20540 Roussos), Alzheimer’s Association (NIRG-340998 Roussos) and the Veterans Affairs (Merit grant BX002395 Roussos). This study was additionally funded by The Lundbeck Foundation, Denmark (Grant number R102-A9118). Further, this work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. This paper is dedicated to the memory of PS. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. RNA-seq and genotyping data were generated as part of the CommonMind Consortium supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffman-La Roche Ltd. and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, R01-MH-075916, P50M096891, P50MH084053S1, R37MH057881 and R37MH057881S1, HHSN271201300031C, AG02219, AG05138 and MH06692. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer’s Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories and the NIMH Human Brain Collection Core. CMC Leadership: PS, Joseph Buxbaum (Icahn School of Medicine at Mount Sinai), BD, David Lewis (University of Pittsburgh), Raquel Gur, Chang-Gyu Hahn (University of Pennsylvania), Keisuke Hirai, Hiroyoshi Toyoshiba (Takeda Pharmaceuticals Company Limited), ED, Laurent Essioux (F. Hoffman-La Roche Ltd.), Lara Mangravite, MP (Sage Bionetworks), Thomas Lehner, Barbara Lipska (NIMH).

The CommonMind Consortium

Menachem Fromer, Douglas M Ruderfer, Hardik R Shah, Lambertus L Klei, Kristen K Dang, Thanneer M Perumal, Benjamin A Logsdon, Milind C Mahajan, Lara M Mangravite, Laurent Essioux, Hiroyoshi Toyoshiba, Raquel E Gur, Chang-Gyu Hahn, David A Lewis, Vahram Haroutunian, Barbara K Lipska, Joseph D Buxbaum, Keisuke Hirai

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Correspondence to Panos Roussos.

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