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Multiomics Approaches in Psychiatric Disorders

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Tasman’s Psychiatry

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Abstract

The past decade has seen the advent of high-throughput technologies to investigate many different levels of biology ranging from the genome and the epigenome via the transcriptome and the proteome all the way to the metabolome or the microbiome. The comprehensive, high-throughput, and often unbiased assessment of such a level of biology is referred to as “-omics.” Consequently, the study of multiple such levels of biology all at once is termed “multiomics.” This chapter addresses the application of multiomics to mental health disorders. In the first part, it introduces potential workflows for studying -omics of a single modality as well as in a multiomics format. It familiarizes the reader with both the great potential and the challenges inherent in working with -omics datasets. In the second part, proteomics and metabolomics and lipidomics serve as exemplary “-omics” dimensions and are used to highlight findings from studies that performed “-omics” analyses in blood and in central nervous system tissue as well as in animal and induced pluripotent stem cell models of mental disorders. As for most “-omics” studies, the aims of the depicted studies are both to provide a better understanding of the physiological and pathophysiological role of, in this case, proteins and metabolites in bringing about mental disorders and to establish potential biomarkers. The last section of the chapter poses the question of a potential clinical use of “-omics” technologies and the hurdles that need to be overcome on a path toward clinical translation.

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References

  • Aebersold, R., & Mann, M. (2016). Mass-spectrometric exploration of proteome structure and function. Nature, 537(7620), 347–355.

    Article  CAS  PubMed  Google Scholar 

  • Ahmed, A. T., MahmoudianDehkordi, S., Bhattacharyya, S., Arnold, M., Liu, D., Neavin, D., et al. (2020). Acylcarnitine metabolomic profiles inform clinically-defined major depressive phenotypes. Journal of Affective Disorders, 264, 90–97.

    Article  CAS  PubMed  Google Scholar 

  • Beauchemin, M., Geguchadze, R., Guntur, A. R., Nevola, K., Le, P. T., Barlow, D., et al. (2020). Exploring mechanisms of increased cardiovascular disease risk with antipsychotic medications: Risperidone alters the cardiac proteomic signature in mice. Pharmacological Research, 152, 104589.

    Article  CAS  PubMed  Google Scholar 

  • Brennand, K., Savas, J. N., Kim, Y., Tran, N., Simone, A., Hashimoto-Torii, K., et al. (2015). Phenotypic differences in hiPSC NPCs derived from patients with schizophrenia. Molecular Psychiatry, 20(3), 361–368.

    Article  CAS  PubMed  Google Scholar 

  • Cao, B., Wang, D., Pan, Z., Brietzke, E., McIntyre, R. S., Musial, N., et al. (2019a). Characterizing acyl-carnitine biosignatures for schizophrenia: A longitudinal pre- and post-treatment study. Translational Psychiatry, 9(1), 19.

    Article  PubMed  PubMed Central  Google Scholar 

  • Cao, B., Jin, M., Brietzke, E., McIntyre, R. S., Wang, D., Rosenblat, J. D., et al. (2019b). Serum metabolic profiling using small molecular water-soluble metabolites in individuals with schizophrenia: A longitudinal study using a pre-post-treatment design. Psychiatry and Clinical Neurosciences, 73(3), 100–108.

    Article  CAS  PubMed  Google Scholar 

  • Cassoli, J. S., Guest, P. C., Santana, A. G., & Martins-de-Souza, D. (2016). Employing proteomics to unravel the molecular effects of antipsychotics and their role in schizophrenia. Proteomics. Clinical Applications, 10(4), 442–455.

    Article  CAS  PubMed  Google Scholar 

  • Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y., Chen, R., et al. (2012). Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell, 148(6), 1293–1307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Comes, A. L., Papiol, S., Mueller, T., Geyer, P. E., Mann, M., & Schulze, T. G. (2018). Proteomics for blood biomarker exploration of severe mental illness: Pitfalls of the past and potential for the future. Translational Psychiatry, 8(1), 160.

    Article  PubMed  PubMed Central  Google Scholar 

  • Consortium, E. P. (2012). An integrated encyclopedia of DNA elements in the human genome. Nature, 489(7414), 57–74.

    Article  Google Scholar 

  • Cross-Disorder Group of the Psychiatric Genomics Consortium. (2019). Electronic address pmhe, Cross-Disorder Group of the Psychiatric Genomics C. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell, 179(7), 1469–1482. e11.

    Article  PubMed Central  Google Scholar 

  • Cuturic, M., Abramson, R. K., Breen, R. J., Edwards, A. C., & Levy, E. E. (2016). Comparison of serum carnitine levels and clinical correlates between outpatients and acutely hospitalised individuals with bipolar disorder and schizophrenia: A cross-sectional study. The World Journal of Biological Psychiatry, 17(6), 475–479.

    Article  PubMed  Google Scholar 

  • de Almeida, V., Alexandrino, G. L., Aquino, A., Gomes, A. F., Murgu, M., Dobrowolny, H., et al. (2020). Changes in the blood plasma lipidome associated with effective or poor response to atypical antipsychotic treatments in schizophrenia patients. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 101, 109945.

    Article  Google Scholar 

  • Fornito, A., & Harrison, B. J. (2012). Brain connectivity and mental illness. Frontiers in Psychiatry, 3, 72.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gadad, B. S., Jha, M. K., Czysz, A., Furman, J. L., Mayes, T. L., Emslie, M. P., et al. (2018). Peripheral biomarkers of major depression and antidepressant treatment response: Current knowledge and future outlooks. Journal of Affective Disorders, 233, 3–14.

    Article  CAS  PubMed  Google Scholar 

  • Garcia, S., Baldasso, P. A., Guest, P. C., & Martins-de-Souza, D. (2017). Depletion of highly abundant proteins of the human blood plasma: Applications in proteomics studies of psychiatric disorders. Methods in Molecular Biology, 1546, 195–204.

    Article  CAS  PubMed  Google Scholar 

  • Garcia-Rosa, S., Carvalho, B. S., Guest, P. C., Steiner, J., & Martins-de-Souza, D. (2020). Blood plasma proteomic modulation induced by olanzapine and risperidone in schizophrenia patients. Journal of Proteomics, 224, 103813.

    Article  CAS  PubMed  Google Scholar 

  • Ghosh, S., Dyer, R. A., & Beasley, C. L. (2017). Evidence for altered cell membrane lipid composition in postmortem prefrontal white matter in bipolar disorder and schizophrenia. Journal of Psychiatric Research, 95, 135–142.

    Article  PubMed  Google Scholar 

  • Gieger, C., Geistlinger, L., Altmaier, E., Hrabe de Angelis, M., Kronenberg, F., Meitinger, T., et al. (2008). Genetics meets metabolomics: A genome-wide association study of metabolite profiles in human serum. PLoS Genet, 4(11), e1000282.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gjoneska, E., Pfenning, A. R., Mathys, H., Quon, G., Kundaje, A., Tsai, L. H., et al. (2015). Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature, 518(7539), 365–369.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Glombik, K., Stachowicz, A., Trojan, E., Slusarczyk, J., Suski, M., Chamera, K., et al. (2018). Mitochondrial proteomics investigation of frontal cortex in an animal model of depression: Focus on chronic antidepressant drugs treatment. Pharmacological Reports, 70(2), 322–330.

    Article  CAS  PubMed  Google Scholar 

  • Graves, P. R., & Haystead, T. A. (2002). Molecular biologist’s guide to proteomics. Microbiology and Molecular Biology Reviews, 66(1), 39–63; table of contents.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Haggarty, S. J., Silva, M. C., Cross, A., Brandon, N. J., & Perlis, R. H. (2016). Advancing drug discovery for neuropsychiatric disorders using patient-specific stem cell models. Molecular and Cellular Neurosciences, 73, 104–115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hamazaki, K., Choi, K. H., & Kim, H. Y. (2010). Phospholipid profile in the postmortem hippocampus of patients with schizophrenia and bipolar disorder: No changes in docosahexaenoic acid species. Journal of Psychiatric Research, 44(11), 688–693.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hamazaki, K., Hamazaki, T., & Inadera, H. (2013). Abnormalities in the fatty acid composition of the postmortem entorhinal cortex of patients with schizophrenia, bipolar disorder, and major depressive disorder. Psychiatry Research, 210(1), 346–350.

    Article  CAS  PubMed  Google Scholar 

  • Hasin, Y., Seldin, M., & Lusis, A. (2017). Multi-omics approaches to disease. Genome Biology, 18(1), 83.

    Article  PubMed  PubMed Central  Google Scholar 

  • Heiles, S. (2021). Advanced tandem mass spectrometry in metabolomics and lipidomics-methods and applications. Analytical and Bioanalytical Chemistry, 413(24), 5927–5948.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Howard, D. M., Adams, M. J., Clarke, T. K., Hafferty, J. D., Gibson, J., Shirali, M., et al. (2019). Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature Neuroscience, 22(3), 343–352.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • John, A., Qin, B., Kalari, K. R., Wang, L., & Yu, J. (2020). Patient-specific multi-omics models and the application in personalized combination therapy. Future Oncology, 16(23), 1737–1750.

    CAS  PubMed  Google Scholar 

  • Koelmel, J. P. U., Ulmer, C. Z., Jones, C. M., Yost, R. A., & Bowden, J. A. (1862). Common cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation. Biochimica et Biophysica Acta, 2017, 766–770.

    Google Scholar 

  • Kaddurah-Daouk, R., McEvoy, J., Baillie, R., Zhu, H., Yao, J. K., Nimgaonkar, V. L., et al. (2012). Impaired plasmalogens in patients with schizophrenia. Psychiatry Research, 198(3), 347–352.

    Article  CAS  PubMed  Google Scholar 

  • Kettunen, J., Tukiainen, T., Sarin, A. P., Ortega-Alonso, A., Tikkanen, E., Lyytikainen, L. P., et al. (2012). Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nature Genetics, 44(3), 269–276.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021). Deep learning for small and big data in psychiatry. Neuropsychopharmacology, 46(1), 176–190.

    Article  PubMed  Google Scholar 

  • Kriisa, K., Leppik, L., Balotsev, R., Ottas, A., Soomets, U., Koido, K., et al. (2017). Profiling of acylcarnitines in first episode psychosis before and after antipsychotic treatment. Journal of Proteome Research, 16(10), 3558–3566.

    Article  CAS  PubMed  Google Scholar 

  • Lauwers, E., Goodchild, R., & Verstreken, P. (2016). Membrane lipids in presynaptic function and disease. Neuron, 90(1), 11–25.

    Article  CAS  PubMed  Google Scholar 

  • Lee, K. M., Hawi, Z. H., Parkington, H. C., Parish, C. L., Kumar, P. V., Polo, J. M., et al. (2020). The application of human pluripotent stem cells to model the neuronal and glial components of neurodevelopmental disorders. Molecular Psychiatry, 25(2), 368–378.

    Article  CAS  PubMed  Google Scholar 

  • Leppik, L., Parksepp, M., Janno, S., Koido, K., Haring, L., Vasar, E., et al. (2020). Profiling of lipidomics before and after antipsychotic treatment in first-episode psychosis. European Archives of Psychiatry and Clinical Neuroscience, 270(1), 59–70.

    Article  PubMed  Google Scholar 

  • Liu, X., Zheng, P., Zhao, X., Zhang, Y., Hu, C., Li, J., et al. (2015). Discovery and validation of plasma biomarkers for major depressive disorder classification based on liquid chromatography-mass spectrometry. Journal of Proteome Research, 14(5), 2322–2330.

    Article  CAS  PubMed  Google Scholar 

  • Liu, X., Li, J., Zheng, P., Zhao, X., Zhou, C., Hu, C., et al. (2016). Plasma lipidomics reveals potential lipid markers of major depressive disorder. Analytical and Bioanalytical Chemistry, 408(23), 6497–6507.

    Article  CAS  PubMed  Google Scholar 

  • McNamara, R. K., Hahn, C. G., Jandacek, R., Rider, T., Tso, P., Stanford, K. E., et al. (2007a). Selective deficits in the omega-3 fatty acid docosahexaenoic acid in the postmortem orbitofrontal cortex of patients with major depressive disorder. Biological Psychiatry, 62(1), 17–24.

    Article  CAS  PubMed  Google Scholar 

  • McNamara, R. K., Jandacek, R., Rider, T., Tso, P., Hahn, C. G., Richtand, N. M., et al. (2007b). Abnormalities in the fatty acid composition of the postmortem orbitofrontal cortex of schizophrenic patients: Gender differences and partial normalization with antipsychotic medications. Schizophrenia Research, 91(1–3), 37–50.

    Article  PubMed  Google Scholar 

  • McNamara, R. K., Jandacek, R., Rider, T., Tso, P., Stanford, K. E., Hahn, C. G., et al. (2008). Deficits in docosahexaenoic acid and associated elevations in the metabolism of arachidonic acid and saturated fatty acids in the postmortem orbitofrontal cortex of patients with bipolar disorder. Psychiatry Research, 160(3), 285–299.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019). Machine learning and integrative analysis of biomedical big data. Genes (Basel), 10(2).

    Google Scholar 

  • Misiak, B., Stanczykiewicz, B., Laczmanski, L., & Frydecka, D. (2018). Lipid profile disturbances in antipsychotic-naive patients with first-episode non-affective psychosis: Authors’ reply. Schizophrenia Research, 192, 471–472.

    Article  PubMed  Google Scholar 

  • Misiewicz, Z., Iurato, S., Kulesskaya, N., Salminen, L., Rodrigues, L., Maccarrone, G., et al. (2019). Multi-omics analysis identifies mitochondrial pathways associated with anxiety-related behavior. PLoS Genetics, 15(9), e1008358.

    Article  PubMed  PubMed Central  Google Scholar 

  • Nugent, A. A., Lin, K., van Lengerich, B., Lianoglou, S., Przybyla, L., Davis, S. S., et al. (2020). TREM2 regulates microglial cholesterol metabolism upon chronic phagocytic challenge. Neuron, 105(5), 837–854. e9.

    Article  CAS  PubMed  Google Scholar 

  • Oliveira, T. G., Chan, R. B., Bravo, F. V., Miranda, A., Silva, R. R., Zhou, B., et al. (2016). The impact of chronic stress on the rat brain lipidome. Molecular Psychiatry, 21(1), 80–88.

    Article  CAS  PubMed  Google Scholar 

  • Patti, G. J., Yanes, O., & Siuzdak, G. (2012). Innovation: Metabolomics: The apogee of the omics trilogy. Nature Reviews. Molecular Cell Biology, 13(4), 263–269.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pillinger, T., Beck, K., Stubbs, B., & Howes, O. D. (2017). Cholesterol and triglyceride levels in first-episode psychosis: Systematic review and meta-analysis. The British Journal of Psychiatry, 211(6), 339–349.

    Article  PubMed  PubMed Central  Google Scholar 

  • Piomelli, D., Astarita, G., & Rapaka, R. (2007). A neuroscientist’s guide to lipidomics. Nature Reviews. Neuroscience, 8(10), 743–754.

    Article  CAS  PubMed  Google Scholar 

  • Rahmoune, H., Martins-de-Souza, D., & Guest, P. C. (2017). Application of proteomic approaches to accelerate drug development for psychiatric disorders. Advances in Experimental Medicine and Biology, 974, 69–84.

    Article  CAS  PubMed  Google Scholar 

  • Rinschen, M. M., Ivanisevic, J., Giera, M., & Siuzdak, G. (2019). Identification of bioactive metabolites using activity metabolomics. Nature Reviews. Molecular Cell Biology, 20(6), 353–367.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A., & Kim, D. (2015). Methods of integrating data to uncover genotype-phenotype interactions. Nature Reviews. Genetics, 16(2), 85–97.

    Article  CAS  PubMed  Google Scholar 

  • Saia-Cereda, V. M., Cassoli, J. S., Martins-de-Souza, D., & Nascimento, J. M. (2017). Psychiatric disorders biochemical pathways unraveled by human brain proteomics. European Archives of Psychiatry and Clinical Neuroscience, 267(1), 3–17.

    Article  PubMed  Google Scholar 

  • Sarnyai, Z., Alsaif, M., Bahn, S., Ernst, A., Guest, P. C., Hradetzky, E., et al. (2011). Behavioral and molecular biomarkers in translational animal models for neuropsychiatric disorders. International Review of Neurobiology, 101, 203–238.

    Article  CAS  PubMed  Google Scholar 

  • Schizophrenia Working Group of the Psychiatric Genomics C. (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511(7510), 421–427.

    Article  Google Scholar 

  • Schwarz, E., Prabakaran, S., Whitfield, P., Major, H., Leweke, F. M., Koethe, D., et al. (2008). High throughput lipidomic profiling of schizophrenia and bipolar disorder brain tissue reveals alterations of free fatty acids, phosphatidylcholines, and ceramides. Journal of Proteome Research, 7(10), 4266–4277.

    Article  CAS  PubMed  Google Scholar 

  • Sethi, S., Hayashi, M. A., Sussulini, A., Tasic, L., & Brietzke, E. (2017). Analytical approaches for lipidomics and its potential applications in neuropsychiatric disorders. The World Journal of Biological Psychiatry, 18(7), 506–520.

    Article  PubMed  Google Scholar 

  • Shin, S. Y., Fauman, E. B., Petersen, A. K., Krumsiek, J., Santos, R., Huang, J., et al. (2014). An atlas of genetic influences on human blood metabolites. Nature Genetics, 46(6), 543–550.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shoemaker, L. D., & Kornblum, H. I. (2016). Neural Stem Cells (NSCs) and proteomics. Molecular & Cellular Proteomics, 15(2), 344–354.

    Article  CAS  Google Scholar 

  • Shukla, R., Henkel, N. D., Alganem, K., Hamoud, A. R., Reigle, J., Alnafisah, R. S., et al. (2021). Signature-based approaches for informed drug repurposing: Targeting CNS disorders. Neuropsychopharmacology, 46(1), 116–130.

    Article  PubMed  Google Scholar 

  • Sienski, G., Narayan, P., Bonner, J. M., Kory, N., Boland, S., Arczewska, A. A., et al. (2021). APOE4 disrupts intracellular lipid homeostasis in human iPSC-derived glia. Sci Transl Med, 13(583).

    Google Scholar 

  • Sliz, E., Shin, J., Syme, C., Black, S., Seshadri, S., Paus, T., et al. (2020). Thickness of the cerebral cortex shows positive association with blood levels of triacylglycerols carrying 18-carbon fatty acids. Communications Biology, 3(1), 456.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Stahl, E. A., Breen, G., Forstner, A. J., McQuillin, A., Ripke, S., Trubetskoy, V., et al. (2019). Genome-wide association study identifies 30 loci associated with bipolar disorder. Nature Genetics, 51(5), 793–803.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, 14, 1177932219899051.

    Article  PubMed  PubMed Central  Google Scholar 

  • Suhre, K., Shin, S. Y., Petersen, A. K., Mohney, R. P., Meredith, D., Wagele, B., et al. (2011). Human metabolic individuality in biomedical and pharmaceutical research. Nature, 477(7362), 54–60.

    Article  CAS  PubMed  Google Scholar 

  • Sullivan, C. R., Mielnik, C. A., O’Donovan, S. M., Funk, A. J., Bentea, E., DePasquale, E. A., et al. (2019). Connectivity analyses of bioenergetic changes in schizophrenia: Identification of novel treatments. Molecular Neurobiology, 56(6), 4492–4517.

    Article  CAS  PubMed  Google Scholar 

  • Symons, J. L., Cho, K. J., Chang, J. T., Du, G., Waxham, M. N., Hancock, J. F., et al. (2021). Lipidomic atlas of mammalian cell membranes reveals hierarchical variation induced by culture conditions, subcellular membranes, and cell lineages. Soft Matter, 17(2), 288–297.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Taha, A. Y., Cheon, Y., Ma, K., Rapoport, S. I., & Rao, J. S. (2013). Altered fatty acid concentrations in prefrontal cortex of schizophrenic patients. Journal of Psychiatric Research, 47(5), 636–643.

    Article  PubMed  PubMed Central  Google Scholar 

  • Taurines, R., Dudley, E., Grassl, J., Warnke, A., Gerlach, M., Coogan, A. N., et al. (2011). Proteomic research in psychiatry. Journal of Psychopharmacology, 25(2), 151–196.

    Article  CAS  PubMed  Google Scholar 

  • Tiihonen, J., Koskuvi, M., Storvik, M., Hyotylainen, I., Gao, Y., Puttonen, K. A., et al. (2019). Sex-specific transcriptional and proteomic signatures in schizophrenia. Nature Communications, 10(1), 3933.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tkachev, A., Stekolshchikova, E., Bobrovskiy, D. M., Anikanov, N., Ogurtsova, P., Park, D. I., et al. (2021). Long-term fluoxetine administration causes substantial lipidome alteration of the juvenile macaque brain. International Journal of Molecular Sciences, 22(15).

    Google Scholar 

  • Tobe, B. T. D., Crain, A. M., Winquist, A. M., Calabrese, B., Makihara, H., Zhao, W. N., et al. (2017). Probing the lithium-response pathway in hiPSCs implicates the phosphoregulatory set-point for a cytoskeletal modulator in bipolar pathogenesis. Proceedings of the National Academy of Sciences of the United States of America, 114(22), E4462–E4E71.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Tomasello, D. L., Kim, J. L., Khodour, Y., McCammon, J. M., Mitalipova, M., Jaenisch, R., et al. (2022). 16pdel lipid changes in iPSC-derived neurons and function of FAM57B in lipid metabolism and synaptogenesis. iScience, 25(1), 103551.

    Article  CAS  PubMed  Google Scholar 

  • Vadodaria, K. C., Jones, J. R., Linker, S., & Gage, F. H. (2020). Modeling brain disorders using induced pluripotent stem cells. Cold Spring Harbor Perspectives in Biology, 12(6).

    Google Scholar 

  • van Meer, G. (2005). Cellular lipidomics. The EMBO Journal, 24(18), 3159–3165.

    Article  PubMed  PubMed Central  Google Scholar 

  • Vancampfort, D., Wampers, M., Mitchell, A. J., Correll, C. U., De Herdt, A., Probst, M., et al. (2013). A meta-analysis of cardio-metabolic abnormalities in drug naive, first-episode and multi-episode patients with schizophrenia versus general population controls. World Psychiatry, 12(3), 240–250.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang, Q., Chen, R., Cheng, F., Wei, Q., Ji, Y., Yang, H., et al. (2019a). A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data. Nature Neuroscience, 22(5), 691–699.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang, D., Cheng, S. L., Fei, Q., Gu, H., Raftery, D., Cao, B., et al. (2019b). Metabolic profiling identifies phospholipids as potential serum biomarkers for schizophrenia. Psychiatry Research, 272, 18–29.

    Article  CAS  PubMed  Google Scholar 

  • Wang, M., Zhang, L., & Gage, F. H. (2020). Modeling neuropsychiatric disorders using human induced pluripotent stem cells. Protein & Cell, 11(1), 45–59.

    Article  CAS  Google Scholar 

  • Wang, D., Sun, X., Maziade, M., Mao, W., Zhang, C., Wang, J., et al. (2021a). Characterising phospholipids and free fatty acids in patients with schizophrenia: A case-control study. The World Journal of Biological Psychiatry, 22(3), 161–174.

    Article  CAS  PubMed  Google Scholar 

  • Wang, M., Li, A., Sekiya, M., Beckmann, N. D., Quan, X., Schrode, N., et al. (2021b). Transformative network modeling of multi-omics data reveals detailed circuits, key regulators, and potential therapeutics for Alzheimer’s disease. Neuron, 109(2), 257–272. e14.

    Article  CAS  PubMed  Google Scholar 

  • Wei, Y. G., Cai, D. B., Liu, J., Liu, R. X., Wang, S. B., Tang, Y. Q., et al. (2020). Cholesterol and triglyceride levels in first-episode patients with major depressive disorder: A meta-analysis of case-control studies. Journal of Affective Disorders, 266, 465–472.

    Article  CAS  PubMed  Google Scholar 

  • Wesseling, H., Elgersma, Y., & Bahn, S. (2017). A brain proteomic investigation of rapamycin effects in the Tsc1(+/−) mouse model. Molecular Autism, 8, 41.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wood, P. L., & Holderman, N. R. (2015). Dysfunctional glycosynapses in schizophrenia: Disease and regional specificity. Schizophrenia Research, 166(1–3), 235–237.

    Article  PubMed  Google Scholar 

  • Worheide, M. A., Krumsiek, J., Kastenmuller, G., & Arnold, M. (2021). Multi-omics integration in biomedical research – A metabolomics-centric review. Analytica Chimica Acta, 1141, 144–162.

    Article  PubMed  Google Scholar 

  • Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., et al. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics, 50(5), 668–681.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wu, J., Chai, T., Zhang, H., Huang, Y., Perry, S. W., Li, Y., et al. (2022). Changes in gut viral and bacterial species correlate with altered 1,2-diacylglyceride levels and structure in the prefrontal cortex in a depression-like non-human primate model. Translational Psychiatry, 12(1), 74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yan, L., Zhou, J., Wang, D., Si, D., Liu, Y., Zhong, L., et al. (2018). Unbiased lipidomic profiling reveals metabolomic changes during the onset and antipsychotics treatment of schizophrenia disease. Metabolomics, 14(6), 80.

    Article  PubMed  Google Scholar 

  • Yazd, H. S., Rubio, V. Y., Chamberlain, C. A., Yost, R. A., & Garrett, T. J. (2021). Metabolomic and lipidomic characterization of an X-chromosome deletion disorder in neural progenitor cells by UHPLC-HRMS. Journal of Mass Spectrometry and Advances in the Clinical Lab, 20, 11–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yu, Q., He, Z., Zubkov, D., Huang, S., Kurochkin, I., Yang, X., et al. (2020). Lipidome alterations in human prefrontal cortex during development, aging, and cognitive disorders. Molecular Psychiatry, 25(11), 2952–2969.

    Article  CAS  PubMed  Google Scholar 

  • Zhang, B., & Kuster, B. (2019). Proteomics is not an Island: Multi-omics integration is the key to understanding biological systems. Molecular & Cellular Proteomics, 18(8 suppl 1), S1–S4.

    Article  CAS  Google Scholar 

  • Zhang, Y., Fonslow, B. R., Shan, B., Baek, M. C., & Yates, J. R., 3rd. (2013). Protein analysis by shotgun/bottom-up proteomics. Chemical Reviews, 113(4), 2343–2394.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang, H., Liu, T., Zhang, Z., Payne, S. H., Zhang, B., McDermott, J. E., et al. (2016). Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell, 166(3), 755–765.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhou, C. H., Xue, S. S., Xue, F., Liu, L., Liu, J. C., Ma, Q. R., et al. (2020). The impact of quetiapine on the brain lipidome in a cuprizone-induced mouse model of schizophrenia. Biomedicine & Pharmacotherapy, 131, 110707.

    Article  CAS  Google Scholar 

  • Zuccoli, G. S., Martins-de-Souza, D., Guest, P. C., Rehen, S. K., & Nascimento, J. M. (2017). Combining patient-reprogrammed neural cells and proteomics as a model to study psychiatric disorders. Advances in Experimental Medicine and Biology, 974, 279–287.

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Psychiatry, University Medical Center, University of Munich, Munich, Germany

    Eva C. Schulte

  2. Institute of Psychiatric Phenomics and Genomics (IPPG), University Medical Center, University of Munich, Munich, Germany

    Eva C. Schulte, Mojtaba Oraki Kohshour & Thomas G. Schulze

  3. Institute of Human Genetics, University Hospital, Faculty of Medicine, University of Bonn, Bonn, Germany

    Eva C. Schulte

  4. Department of Psychiatry, University Hospital, Faculty of Medicine, University of Bonn, Bonn, Germany

    Eva C. Schulte

  5. Department of Immunology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

    Mojtaba Oraki Kohshour

  6. V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, Moscow, Russia

    Anna Tkachev & Philipp Khaitovich

  7. Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA

    Thomas G. Schulze

  8. Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Thomas G. Schulze

Authors
  1. Eva C. Schulte
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  2. Mojtaba Oraki Kohshour
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  3. Anna Tkachev
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  4. Philipp Khaitovich
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  5. Thomas G. Schulze
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Corresponding author

Correspondence to Eva C. Schulte .

Editor information

Editors and Affiliations

  1. Department of Psychiatry, University of Louisville, Louisville, KY, USA

    Allan Tasman

  2. Department of Psychiatry, University of Michigan Rogel Cancer Cent, Ann Arbor, MI, USA

    Michelle B. Riba

  3. Mayo Clinic College of Medicine, Oakland, CA, USA

    Renato D. Alarcón

  4. Columbia University Medical Center, New York, USA

    César A. Alfonso

  5. Kyushu University, Fukuoka, Japan

    Shigenobu Kanba

  6. Department of Psychiatry, University of Nairobi, Nairobi, Kenya

    David M. Ndetei

  7. Malaysian Research Inst on Ageing, Universiti Putra Malaysia, Selangor, Malaysia

    Chee H. Ng

  8. Department of Psychiatry, University of Munich, München, Bayern, Germany

    Thomas G. Schulze

  9. Serbian Academy of Sciences and Arts, Belgrade, Serbia

    Dusica Lecic-Tosevski

Section Editor information

  1. Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany

    Thomas G. Schulze

  2. Washington Behavioral Medicine Associates, LLC, Chevy Chase, MD, USA

    Gonzalo Laje

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Schulte, E.C., Kohshour, M.O., Tkachev, A., Khaitovich, P., Schulze, T.G. (2023). Multiomics Approaches in Psychiatric Disorders. In: Tasman, A., et al. Tasman’s Psychiatry. Springer, Cham. https://doi.org/10.1007/978-3-030-42825-9_30-1

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  • DOI: https://doi.org/10.1007/978-3-030-42825-9_30-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-42825-9

  • Online ISBN: 978-3-030-42825-9

  • eBook Packages: Springer Reference MedicineReference Module Medicine

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