Hostname: page-component-8448b6f56d-xtgtn Total loading time: 0 Render date: 2024-04-20T14:35:25.403Z Has data issue: false hasContentIssue false

Integrative omics analysis identifies differential biological pathways that are associated with regional grey matter volume changes in major depressive disorder

Published online by Cambridge University Press:  29 July 2020

Zhiqiang Sha*
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
Layla Banihashemi
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
*
Author for correspondence: Zhiqiang Sha, E-mail: sha.zhiqiang@163.com

Abstract

Background

Major depressive disorder (MDD) is accompanied by alterations in grey matter volume. However, the biological processes associated with regional structural perturbations remain elusive.

Methods

We applied integrative omics analysis to investigate specialized transcriptome signatures and translational determinants associated with regional grey matter variations in 2737 MDD patients relative to 3098 controls by summarizing the results from gene co-expression network analysis of Allen human brain transcriptome profiles in six donors, enrichment analysis of gene-sets and cellular structure from rodents and mediation analysis of BrainSpan proteome profile in six donors.

Results

We found convergent alterations of grey matter volume in MDD were associated with transcriptome profiles enriched for synaptic transmission, metabolism, immune processes and transmembrane transport. Genes with abnormal expression in post-mortem tissue in MDD were also associated with transcriptome signatures. Further gene co-expression network and enrichment analysis of MDD-related genes in these signatures revealed the modules with higher neuronal expression were enriched in the medial temporal cortex and temporo-parietal junction with genes differentially associated with neuronal development and metabolism. Also, the modules with higher non-neuronal (e.g. astrocyte and oligodendrocyte) expression were concentrated in the rostral and dorsal anterior cingulate cortex and were separately associated with immune response and transmembrane transport. Moreover, proteins as the gene expression products mediated the association between transcriptome signatures and brain volume changes in the visual and dorsolateral prefrontal cortex.

Conclusions

Our multidimensional analyses offer a novel approach to detect specific biological pathways that capture regional structural variations in MDD, which suggests structural endophenotypes associated with MDD.

Type
Original Article
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdi, H., & Williams, L. J. (2013). Partial least squares methods: partial least squares correlation and partial least square regression. Methods in Molecular Biology, 930, 549579. doi:10.1007/978-1-62703-059-5_23.CrossRefGoogle ScholarPubMed
Anderson, B. J. (2011). Plasticity of gray matter volume: the cellular and synaptic plasticity that underlies volumetric change. Developmental Psychobiology, 53(5), 456465. doi:10.1002/dev.20563.CrossRefGoogle ScholarPubMed
Biver, F., Goldman, S., Delvenne, V., Luxen, A., De Maertelaer, V., Hubain, P., … Lotstra, F. (1994). Frontal and parietal metabolic disturbances in unipolar depression. Biological Psychiatry, 36(6), 381388.CrossRefGoogle ScholarPubMed
Bora, E., Fornito, A., Pantelis, C., & Yucel, M. (2012). Gray matter abnormalities in major depressive disorder: a meta-analysis of voxel based morphometry studies. Journal of Affective Disorders, 138(1–2), 918. doi:10.1016/j.jad.2011.03.049.CrossRefGoogle ScholarPubMed
Carlyle, B. C., Kitchen, R. R., Kanyo, J. E., Voss, E. Z., Pletikos, M., Sousa, A. M. M., … Nairn, A. C. (2017). A multiregional proteomic survey of the postnatal human brain. Nature Neuroscience, 20(12), 17871795. doi:10.1038/s41593-017-0011-2.CrossRefGoogle ScholarPubMed
Chen, J., Bardes, E. E., Aronow, B. J., & Jegga, A. G. (2009). Toppgene suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Research, 37(Web Server issue), W305W311. doi:10.1093/nar/gkp427.CrossRefGoogle ScholarPubMed
Choudary, P. V., Molnar, M., Evans, S. J., Tomita, H., Li, J. Z., Vawter, M. P., … Jones, E. G. (2005). Altered cortical glutamatergic and GABAergic signal transmission with glial involvement in depression. Proceedings of the National Academy of Sciences of the United States of America, 102(43), 1565315658. doi:10.1073/pnas.0507901102.CrossRefGoogle ScholarPubMed
Congdon, E., Poldrack, R. A., & Freimer, N. B. (2010). Neurocognitive phenotypes and genetic dissection of disorders of brain and behavior. Neuron, 68(2), 218230. doi:10.1016/j.neuron.2010.10.007.CrossRefGoogle ScholarPubMed
Cotter, D., Mackay, D., Landau, S., Kerwin, R., & Everall, I. (2001). Reduced glial cell density and neuronal size in the anterior cingulate cortex in major depressive disorder. Archives of General Psychiatry, 58(6), 545553.CrossRefGoogle ScholarPubMed
Dantzer, R., O'Connor, J. C., Freund, G. G., Johnson, R. W., & Kelley, K. W. (2008). From inflammation to sickness and depression: when the immune system subjugates the brain. Nature Reviews Neuroscience, 9(1), 4656. doi:10.1038/nrn2297.CrossRefGoogle ScholarPubMed
Duman, R. S., & Aghajanian, G. K. (2012). Synaptic dysfunction in depression: potential therapeutic targets. Science (New York, N.Y.), 338(6103), 6872. doi:10.1126/science.1222939.CrossRefGoogle ScholarPubMed
Duric, V., Banasr, M., Stockmeier, C. A., Simen, A. A., Newton, S. S., Overholser, J. C., … Duman, R. S. (2013). Altered expression of synapse and glutamate related genes in post-mortem hippocampus of depressed subjects. International Journal of Neuropsychopharmacology, 16(1), 6982. doi:10.1017/S1461145712000016.CrossRefGoogle ScholarPubMed
Eden, E., Lipson, D., Yogev, S., & Yakhini, Z. (2007). Discovering motifs in ranked lists of DNA sequences. PLoS Computational Biology, 3(3), e39. doi:10.1371/journal.pcbi.0030039.CrossRefGoogle ScholarPubMed
Eden, E., Navon, R., Steinfeld, I., Lipson, D., & Yakhini, Z. (2009). GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics, 10, 48. doi:10.1186/1471-2105-10-48.CrossRefGoogle ScholarPubMed
Eyre, T. A., Wright, M. W., Lush, M. J., & Bruford, E. A. (2007). HCOP: a searchable database of human orthology predictions. Briefings in Bioinformatics, 8(1), 25. doi:10.1093/bib/bbl030.CrossRefGoogle ScholarPubMed
Frodl, T., Koutsouleris, N., Bottlender, R., Born, C., Jager, M., Morgenthaler, M., … Meisenzahl, E. M. (2008). Reduced gray matter brain volumes are associated with variants of the serotonin transporter gene in major depression. Molecular Psychiatry, 13(12), 10931101. doi:10.1038/mp.2008.62.CrossRefGoogle ScholarPubMed
Frodl, T., Schule, C., Schmitt, G., Born, C., Baghai, T., Zill, P., … Meisenzahl, E. M. (2007). Association of the brain-derived neurotrophic factor Val66Met polymorphism with reduced hippocampal volumes in major depression. Archives of General Psychiatry, 64(4), 410416. doi:10.1001/archpsyc.64.4.410.CrossRefGoogle ScholarPubMed
Gavard, J. A., Lustman, P. J., & Clouse, R. E. (1993). Prevalence of depression in adults with diabetes. An epidemiological evaluation. Diabetes Care, 16(8), 11671178.CrossRefGoogle ScholarPubMed
Geschwind, D. H., Miller, B. L., DeCarli, C., & Carmelli, D. (2002). Heritability of lobar brain volumes in twins supports genetic models of cerebral laterality and handedness. Proceedings of the National Academy of Sciences of the United States of America, 99(5), 31763181. doi:10.1073/pnas.052494999.CrossRefGoogle ScholarPubMed
Gittins, R. A., & Harrison, P. J. (2011). A morphometric study of glia and neurons in the anterior cingulate cortex in mood disorder. Journal of Affective Disorders, 133(1–2), 328332. doi:10.1016/j.jad.2011.03.042.CrossRefGoogle ScholarPubMed
Hawrylycz, M., Miller, J. A., Menon, V., Feng, D., Dolbeare, T., Guillozet-Bongaarts, A. L., … Lein, E. (2015). Canonical genetic signatures of the adult human brain. Nature Neuroscience, 18(12), 18321844. doi:10.1038/nn.4171.CrossRefGoogle ScholarPubMed
Hieronymus, F., Emilsson, J. F., Nilsson, S., & Eriksson, E. (2016). Consistent superiority of selective serotonin reuptake inhibitors over placebo in reducing depressed mood in patients with major depression. Molecular Psychiatry, 21(4), 523530. doi:10.1038/mp.2015.53.CrossRefGoogle ScholarPubMed
Jans, L. A., Riedel, W. J., Markus, C. R., & Blokland, A. (2007). Serotonergic vulnerability and depression: assumptions, experimental evidence and implications. Molecular Psychiatry, 12(6), 522543. doi:10.1038/sj.mp.4001920.CrossRefGoogle ScholarPubMed
Jansen, R., Penninx, B. W., Madar, V., Xia, K., Milaneschi, Y., Hottenga, J. J., … Sullivan, P. F. (2016). Gene expression in major depressive disorder. Molecular Psychiatry, 21(3), 444. doi:10.1038/mp.2015.94.CrossRefGoogle ScholarPubMed
Johnson, W. E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics (Oxford, England), 8(1), 118127. doi:10.1093/biostatistics/kxj037.CrossRefGoogle ScholarPubMed
Johnston-Wilson, N. L., Sims, C. D., Hofmann, J. P., Anderson, L., Shore, A. D., Torrey, E. F., & Yolken, R. H. (2000). Disease-specific alterations in frontal cortex brain proteins in schizophrenia, bipolar disorder, and major depressive disorder. The Stanley Neuropathology Consortium. Molecular Psychiatry, 5(2), 142149.CrossRefGoogle ScholarPubMed
Kang, H. J., Voleti, B., Hajszan, T., Rajkowska, G., Stockmeier, C. A., Licznerski, P., … Duman, R. S. (2012). Decreased expression of synapse-related genes and loss of synapses in major depressive disorder. Nature Medicine, 18(9), 14131417. doi:10.1038/nm.2886.CrossRefGoogle ScholarPubMed
Kennedy, S. H., Evans, K. R., Kruger, S., Mayberg, H. S., Meyer, J. H., McCann, S., … Vaccarino, F. J. (2001). Changes in regional brain glucose metabolism measured with positron emission tomography after paroxetine treatment of major depression. American Journal of Psychiatry, 158(6), 899905. doi:10.1176/appi.ajp.158.6.899.CrossRefGoogle ScholarPubMed
Klempan, T. A., Sequeira, A., Canetti, L., Lalovic, A., Ernst, C., Ffrench-Mullen, J., & Turecki, G. (2009). Altered expression of genes involved in ATP biosynthesis and GABAergic neurotransmission in the ventral prefrontal cortex of suicides with and without major depression. Molecular Psychiatry, 14(2), 175189. doi:10.1038/sj.mp.4002110.CrossRefGoogle ScholarPubMed
Lai, C. H. (2013). Gray matter volume in major depressive disorder: a meta-analysis of voxel-based morphometry studies. Psychiatry Research, 211(1), 3746. doi:10.1016/j.pscychresns.2012.06.006.CrossRefGoogle ScholarPubMed
Lancaster, J. L., Tordesillas-Gutierrez, D., Martinez, M., Salinas, F., Evans, A., Zilles, K., … Fox, P. T. (2007). Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Human Brain Mapping, 28(11), 11941205. doi:10.1002/hbm.20345.CrossRefGoogle ScholarPubMed
Langfelder, P., & Horvath, S. (2007). Eigengene networks for studying the relationships between co-expression modules. BMC Systems Biology, 1, 54. doi:10.1186/1752-0509-1-54.CrossRefGoogle ScholarPubMed
Langfelder, P., & Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9, 559. doi:10.1186/1471-2105-9-559.CrossRefGoogle Scholar
Langfelder, P., Zhang, B., & Horvath, S. (2008). Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics (Oxford, England), 24(5), 719720. doi:10.1093/bioinformatics/btm563.CrossRefGoogle ScholarPubMed
Leday, G. G. R., Vertes, P. E., Richardson, S., Greene, J. R., Regan, T., Khan, S., … Bullmore, E. T. (2018). Replicable and coupled changes in innate and adaptive immune gene expression in two case-control studies of blood microarrays in major depressive disorder. Biological Psychiatry, 83(1), 7080. doi:10.1016/j.biopsych.2017.01.021.CrossRefGoogle ScholarPubMed
Maes, M. (1995). Evidence for an immune response in major depression: a review and hypothesis. Progress in Neuro-psychopharmacology & Biological Psychiatry, 19(1), 1138.CrossRefGoogle ScholarPubMed
Martins-de-Souza, D., Guest, P. C., Harris, L. W., Vanattou-Saifoudine, N., Webster, M. J., Rahmoune, H., & Bahn, S. (2012). Identification of proteomic signatures associated with depression and psychotic depression in post-mortem brains from major depression patients. Translational Psychiatry, 2, e87. doi:10.1038/tp.2012.13.CrossRefGoogle ScholarPubMed
McColgan, P., Gregory, S., Seunarine, K. K., Razi, A., Papoutsi, M., Johnson, E., … Track-On, H. D. I. (2017). Brain regions showing white matter loss in huntington's disease are enriched for synaptic and metabolic genes. Biological Psychiatry, 83(5), 456465. doi:10.1016/j.biopsych.2017.10.019.CrossRefGoogle ScholarPubMed
Milaneschi, Y., Lamers, F., Peyrot, W. J., Baune, B. T., Breen, G., & Dehghan, A., … the Major Depressive Disorder Working Group of the Psychiatric Genomics, C. (2017). Genetic association of major depression with atypical features and obesity-related immunometabolic dysregulations. JAMA Psychiatry, 74(12), 12141225. doi:10.1001/jamapsychiatry.2017.3016.CrossRefGoogle ScholarPubMed
Milaneschi, Y., Simmons, W. K., van Rossum, E. F. C., & Penninx, B. W. (2019). Depression and obesity: evidence of shared biological mechanisms. Molecular Psychiatry, 24(1), 1833. doi:10.1038/s41380-018-0017-5.CrossRefGoogle ScholarPubMed
Miller, A. H., Maletic, V., & Raison, C. L. (2009). Inflammation and its discontents: the role of cytokines in the pathophysiology of major depression. Biological Psychiatry, 65(9), 732741. doi:10.1016/j.biopsych.2008.11.029.CrossRefGoogle ScholarPubMed
Miller, A. H., & Raison, C. L. (2016). The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nature Reviews Immunology, 16(1), 2234. doi:10.1038/nri.2015.5.CrossRefGoogle Scholar
Oudega, M. L., van Exel, E., Stek, M. L., Wattjes, M. P., van der Flier, W. M., Comijs, H. C., … van den Heuvel, O. A. (2014). The structure of the geriatric depressed brain and response to electroconvulsive therapy. Psychiatry Research, 222(1–2), 19. doi:10.1016/j.pscychresns.2014.03.002.CrossRefGoogle ScholarPubMed
Peper, J. S., Brouwer, R. M., Boomsma, D. I., Kahn, R. S., & Hulshoff Pol, H. E. (2007). Genetic influences on human brain structure: a review of brain imaging studies in twins. Human Brain Mapping, 28(6), 464473. doi:10.1002/hbm.20398.CrossRefGoogle ScholarPubMed
Preacher, K. J., & Hayes, A. F. (2004). SPSS And SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717731.CrossRefGoogle ScholarPubMed
Radua, J., Mataix-Cols, D., Phillips, M. L., El-Hage, W., Kronhaus, D. M., Cardoner, N., & Surguladze, S. (2012). A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. European Psychiatry, 27(8), 605611. doi:10.1016/j.eurpsy.2011.04.001.CrossRefGoogle ScholarPubMed
Rajkowska, G., Miguel-Hidalgo, J. J., Wei, J., Dilley, G., Pittman, S. D., Meltzer, H. Y., … Stockmeier, C. A. (1999). Morphometric evidence for neuronal and glial prefrontal cell pathology in major depression. Biological Psychiatry, 45(9), 10851098.CrossRefGoogle ScholarPubMed
Ramasamy, A., Trabzuni, D., Guelfi, S., Varghese, V., Smith, C., Walker, R., … Weale, M. E. (2014). Genetic variability in the regulation of gene expression in ten regions of the human brain. Nature Neuroscience, 17(10), 14181428. doi:10.1038/nn.3801.CrossRefGoogle ScholarPubMed
Major Depressive Disorder Working Group of the Psychiatric, G. C., Ripke, S., Wray, N. R., Lewis, C. M., Hamilton, S. P., Weissman, M. M., … Sullivan, P. F. (2013). A mega-analysis of genome-wide association studies for major depressive disorder. Molecular Psychiatry, 18(4), 497511. doi:10.1038/mp.2012.21.Google ScholarPubMed
Romero-Garcia, R., Warrier, V., Bullmore, E. T., Baron-Cohen, S., & Bethlehem, R. A. I. (2018). Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism. Molecular Psychiatry, 24(7), 10531064. doi:10.1038/s41380-018-0023-7.CrossRefGoogle ScholarPubMed
Romme, I. A., de Reus, M. A., Ophoff, R. A., Kahn, R. S., & van den Heuvel, M. P. (2017). Connectome disconnectivity and cortical gene expression in patients with schizophrenia. Biological Psychiatry, 81(6), 495502. doi:10.1016/j.biopsych.2016.07.012.CrossRefGoogle ScholarPubMed
Rothman, J. S., Cathala, L., Steuber, V., & Silver, R. A. (2009). Synaptic depression enables neuronal gain control. Nature, 457(7232), 10151018. doi:10.1038/nature07604.CrossRefGoogle ScholarPubMed
Schmaal, L., Veltman, D. J., van Erp, T. G., Samann, P. G., Frodl, T., Jahanshad, N., … Hibar, D. P. (2016). Subcortical brain alterations in major depressive disorder: findings from the ENIGMA major depressive disorder working group. Molecular Psychiatry, 21(6), 806812. doi:10.1038/mp.2015.69.CrossRefGoogle ScholarPubMed
Shelton, R. C., Claiborne, J., Sidoryk-Wegrzynowicz, M., Reddy, R., Aschner, M., Lewis, D. A., & Mirnics, K. (2011). Altered expression of genes involved in inflammation and apoptosis in frontal cortex in major depression. Molecular Psychiatry, 16(7), 751762. doi:10.1038/mp.2010.52.CrossRefGoogle ScholarPubMed
Spijker, S., Van Zanten, J. S., De Jong, S., Penninx, B. W., van Dyck, R., Zitman, F. G., … Hoogendijk, W. J. (2010). Stimulated gene expression profiles as a blood marker of major depressive disorder. Biological Psychiatry, 68(2), 179186. doi:10.1016/j.biopsych.2010.03.017.CrossRefGoogle ScholarPubMed
Stockmeier, C. A., Mahajan, G. J., Konick, L. C., Overholser, J. C., Jurjus, G. J., Meltzer, H. Y., … Rajkowska, G. (2004). Cellular changes in the postmortem hippocampus in major depression. Biological Psychiatry, 56(9), 640650. doi:10.1016/j.biopsych.2004.08.022.CrossRefGoogle ScholarPubMed
Svenningsson, P., Kim, Y., Warner-Schmidt, J., Oh, Y. S., & Greengard, P. (2013). P11 and its role in depression and therapeutic responses to antidepressants. Nature Reviews. Neuroscience, 14(10), 673680. doi:10.1038/nrn3564.CrossRefGoogle Scholar
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., … von Mering, C. (2015). STRING V10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Research, 43(Database issue), D447D452. doi:10.1093/nar/gku1003.CrossRefGoogle ScholarPubMed
van Tol, M. J., van der Wee, N. J., van den Heuvel, O. A., Nielen, M. M., Demenescu, L. R., Aleman, A., … Veltman, D. J. (2010). Regional brain volume in depression and anxiety disorders. Archives of General Psychiatry, 67(10), 10021011. doi:10.1001/archgenpsychiatry.2010.121.CrossRefGoogle ScholarPubMed
Wehry, A. M., McNamara, R. K., Adler, C. M., Eliassen, J. C., Croarkin, P., Cerullo, M. A., … Strawn, J. R. (2015). Neurostructural impact of co-occurring anxiety in pediatric patients with major depressive disorder: a voxel-based morphometry study. Journal of Affective Disorders, 171, 5459. doi:10.1016/j.jad.2014.09.004.CrossRefGoogle ScholarPubMed
Whitaker, K. J., Vertes, P. E., Romero-Garcia, R., Vasa, F., Moutoussis, M., Prabhu, G., … Bullmore, E. T. (2016). Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome. Proceedings of the National Academy of Sciences of the United States of America, 113(32), 91059110. doi:10.1073/pnas.1601745113.CrossRefGoogle ScholarPubMed
Wise, T., Radua, J., Via, E., Cardoner, N., Abe, O., Adams, T. M., … Arnone, D. (2017). Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis. Molecular Psychiatry, 22(10), 14551463. doi:10.1038/mp.2016.72.CrossRefGoogle ScholarPubMed
Wray, N. R., Pergadia, M. L., Blackwood, D. H., Penninx, B. W., Gordon, S. D., Nyholt, D. R., … Sullivan, P. F. (2012). Genome-wide association study of major depressive disorder: new results, meta-analysis, and lessons learned. Molecular Psychiatry, 17(1), 3648. doi:10.1038/mp.2010.109.CrossRefGoogle ScholarPubMed
Zhang, Y., Chen, K., Sloan, S. A., Bennett, M. L., Scholze, A. R., O'Keeffe, S., … Wu, J. Q. (2014). An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. Journal of Neuroscience, 34(36), 1192911947. doi:10.1523/JNEUROSCI.1860-14.2014.CrossRefGoogle ScholarPubMed
Zhao, B., Luo, T., Li, T., Li, Y., Zhang, J., Shan, Y., … Zhu, H. (2019). Genome-wide association analysis of 19629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nature Genetics, 51(11), 16371644. doi:10.1038/s41588-019-0516-6.CrossRefGoogle ScholarPubMed
Zou, K., Deng, W., Li, T., Zhang, B., Jiang, L., Huang, C., … Sun, X. (2010). Changes of brain morphometry in first-episode, drug-naive, non-late-life adult patients with major depression: an optimized voxel-based morphometry study. Biological Psychiatry, 67(2), 186188. doi:10.1016/j.biopsych.2009.09.014.CrossRefGoogle Scholar
Supplementary material: File

Sha and Banihashemi Supplementary Materials

Sha and Banihashemi Supplementary Materials

Download Sha and Banihashemi Supplementary Materials(File)
File 437.2 KB