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Shared mechanisms across the major psychiatric and neurodegenerative diseases

Wingo, Thomas S., Liu, Yue, Gerasimov, Ekaterina S., Vattathil, Selina M., Wynne, Meghan E., Liu, Jiaqi, Lori, Adriana, Faundez, Victor +4 more·2022·Nature Communications

Abstract

Several common psychiatric and neurodegenerative diseases share epidemiologic risk; however, whether they share pathophysiology is unclear and is the focus of our investigation. Using 25 GWAS results and LD score regression, we find eight significant genetic correlations between psychiatric and neurodegenerative diseases. We integrate the GWAS results with human brain transcriptomes ( n = 888) and proteomes ( n = 722) to identify cis - and trans - transcripts and proteins that are consistent with a pleiotropic or causal role in each disease, referred to as causal proteins for brevity. Within each disease group, we find many distinct and shared causal proteins. Remarkably, 30% (13 of 42) of the neurodegenerative disease causal proteins are shared with psychiatric disorders. Furthermore, we find 2.6-fold more protein-protein interactions among the psychiatric and neurodegenerative causal proteins than expected by chance. Together, our findings suggest these psychiatric and neurodegenerative diseases have shared genetic and molecular pathophysiology, which has important ramifications for early treatment and therapeutic development.

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  • A recently published work identified a set of causal transcripts and proteins for a range of psychiatric and neurodegenerative diseases.

    “We identified a set of causal transcripts and proteins for a range of psychiatric and neurodegenerative diseases in a recently published work26.”
  • Three of the four causal miRNAs we identified are located in proximity (< 500Kb) to one or more of the causal transcripts or proteins identified in the prior work.

    “Inspecting the four causal miRNAs we identified here, we found that three causal miRNAs are located in proximity (< 500Kb) to one or more of the causal transcripts or proteins identified in the prior work.”
  • miR-1908-5p is located within FADS1 and is close to TMEM258, which are causal mRNAs in both bipolar disorder and major depression.

    “Specifically, miR-1908–5p, causal in both bipolar disorder and major depression, is located within FADS1 and is close to TMEM258, which are causal mRNAs in both bipolar disorder and major depression26 (Figure 5; Supplementary Table 17).”
  • Among the 4 identified causal miRNAs in brain illnesses, two were regulated by miR-QTLs in proximity (±500Kb window) with eQTLs of causal mRNAs for the same brain illness.

    “Among the 4 identified causal miRNAs in brain illnesses, two were regulated by miR-QTLs in proximity (±500Kb window) with eQTLs of causal mRNAs for the same brain illness, and one was regulated by miR-QTLs in proximity with pQTLs for a causal protein for the same brain trait (Supplementary Table 17).”
  • Among the 4 identified causal miRNAs in brain illnesses, one was regulated by miR-QTLs in proximity with pQTLs for a causal protein for the same brain trait.

    “Among the 4 identified causal miRNAs in brain illnesses, two were regulated by miR-QTLs in proximity (±500Kb window) with eQTLs of causal mRNAs for the same brain illness, and one was regulated by miR-QTLs in proximity with pQTLs for a causal protein for the same brain trait (Supplementary Table 17).”

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  1. [1]Bellou, V. et al. Systematic evaluation of the associations between environmental risk factors and dementia: An umbrella review of systematic reviews and meta-analyses. Alzheimer’s Dement.13, 406–418 (2017). 27599208
  2. [2]Almeida OP Risk of dementia and death in community-dwelling older men with bipolar disorder Br. J. Psychiatry.: J. Ment. Sci. 2016 209 121 126 10.1192/bjp.bp.115.180059 27482038 · DOI
  3. [3]Cai L Huang J Schizophrenia and risk of dementia: a meta-analysis study Neuropsychiatr. Dis. Treat. 2018 14 2047 2055 10.2147/NDT.S172933 30147318 PMC6095111 · DOI
  4. [4]Gimson A Schlosser M Huntley JD Marchant NL Support for midlife anxiety diagnosis as an independent risk factor for dementia: a systematic review BMJ Open 2018 8 e019399 10.1136/bmjopen-2017-019399 29712690 PMC5969723 · DOI
  5. [5]Desmarais P The interplay between post-traumatic stress disorder and dementia: a systematic review Am. J. Geriatr. Psychiatry.: Off. J. Am. Assoc. Geriatr. Psychiatry. 2020 28 48 60 10.1016/j.jagp.2019.08.006 31488352 · DOI
  6. [6]Richmond-Rakerd, L.S., D’Souza, S., Milne, B.J., Caspi, A. & Moffitt, T.E. Longitudinal associations of mental disorders with dementia: 30-year analysis of 1.7 million New Zealand citizens. JAMA Psychiatry79, 333–340 (2022). PMC8851362 35171209
  7. [7]van der Linde RM Longitudinal course of behavioural and psychological symptoms of dementia: systematic review Br. J. Psychiatry 2016 209 366 377 10.1192/bjp.bp.114.148403 27491532 PMC5100633 · DOI
  8. [8]Smeland OB Genome-wide association analysis of Parkinson’s disease and Schizophrenia reveals shared genetic architecture and identifies novel risk loci Biol. Psychiatry 2021 89 227 235 10.1016/j.biopsych.2020.01.026 32201043 PMC7416467 · DOI
  9. [9]Mok KY Deletions at 22q11.2 in idiopathic Parkinson’s disease: a combined analysis of genome-wide association data Lancet Neurol. 2016 15 585 596 10.1016/S1474-4422(16)00071-5 27017469 PMC4828586 · DOI
  10. [10]Sperling RA Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease Alzheimer’s Dement. 2011 7 280 292 10.1016/j.jalz.2011.03.003 21514248 PMC3220946 · DOI · Paper page
  11. [12]Friedrich MJ Depression is the leading cause of disability around the world JAMA 2017 317 1517 28418490
  12. [13]Mullins, N. et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat. Genet.53, 817–829 (2021). PMC8192451 34002096
  13. [15]Akil H Treatment resistant depression: A multi-scale, systems biology approach Neurosci. Biobehav. Rev. 2018 84 272 288 10.1016/j.neubiorev.2017.08.019 28859997 PMC5729118 · DOI
  14. [18]La Cognata, V., Morello, G. & Cavallaro, S. Omics data and their integrative analysis to support stratified medicine in neurodegenerative diseases. Int. J. Mol. Sci.22, 4820 (2021). PMC8125201 34062930
  15. [20]Vogel C Marcotte EM Insights into the regulation of protein abundance from proteomic and transcriptomic analyses Nat. Rev. Genet. 2012 13 227 232 10.1038/nrg3185 22411467 PMC3654667 · DOI · Paper page
  16. [21]Seyfried NT A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer’s disease Cell Syst. 2017 4 60 72.e64 10.1016/j.cels.2016.11.006 27989508 PMC5269514 · DOI · Paper page
  17. [22]Wingo, T.S., et al. Brain proteome-wide association study implicates novel proteins in depression pathogenesis. Nat. Neurosci.24, 810–817 (2021). PMC8530461 33846625
  18. [23]Wingo AP Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer’s disease pathogenesis Nat. Genet. 2021 53 143 146 10.1038/s41588-020-00773-z 33510477 PMC8130821 · DOI · Paper page
  19. [24]Johnson ECB Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level Nat. Neurosci. 2022 25 213 225 10.1038/s41593-021-00999-y 35115731 PMC8825285 · DOI · Paper page
  20. [26]Gusev A Integrative approaches for large-scale transcriptome-wide association studies Nat. Genet. 2016 48 245 252 10.1038/ng.3506 26854917 PMC4767558 · DOI · Paper page
  21. [27]Zhu Z Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets Nat. Genet. 2016 48 481 487 10.1038/ng.3538 27019110 · DOI · Paper page
  22. [30]Stahl EA Genome-wide association study identifies 30 loci associated with bipolar disorder Nat. Genet. 2019 51 793 803 10.1038/s41588-019-0397-8 31043756 PMC6956732 · DOI · Paper page
  23. [32]Stein MB Genome-wide association analyses of post-traumatic stress disorder and its symptom subdomains in the Million Veteran Program Nat. Genet. 2021 53 174 184 10.1038/s41588-020-00767-x 33510476 PMC7972521 · DOI
  24. [33]Zhou H Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits Nat. Neurosci. 2020 23 809 818 10.1038/s41593-020-0643-5 32451486 PMC7485556 · DOI
  25. [34]Nagel M Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways Nat. Genet. 2018 50 920 927 10.1038/s41588-018-0151-7 29942085 · DOI
  26. [35]Jansen PR Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways Nat. Genet. 2019 51 394 403 10.1038/s41588-018-0333-3 30804565 · DOI
  27. [36]Jansen IE Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk Nat. Genet. 2019 51 404 413 10.1038/s41588-018-0311-9 30617256 PMC6836675 · DOI · Paper page
  28. [37]Kunkle BW Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing Nat. Genet. 2019 51 414 430 10.1038/s41588-019-0358-2 30820047 PMC6463297 · DOI · Paper page
  29. [38]Chia R Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture Nat. Genet. 2021 53 294 303 10.1038/s41588-021-00785-3 33589841 PMC7946812 · DOI · Paper page
  30. [40]Nalls MA Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies Lancet Neurol. 2019 18 1091 1102 10.1016/S1474-4422(19)30320-5 31701892 PMC8422160 · DOI · Paper page
  31. [41]Sargurupremraj M Cerebral small vessel disease genomics and its implications across the lifespan Nat. Commun. 2020 11 6285 10.1038/s41467-020-19111-2 33293549 PMC7722866 · DOI
  32. [42]Grasby, K.L., et al. The genetic architecture of the human cerebral cortex. Science 367 (2020). PMC7295264 32193296
  33. [44]Satizabal CL Genetic architecture of subcortical brain structures in 38,851 individuals Nat. Genet. 2019 51 1624 1636 10.1038/s41588-019-0511-y 31636452 PMC7055269 · DOI
  34. [45]Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Science360, eaap8757 (2018). PMC6097237 29930110
  35. [48]Hawrylycz MJ An anatomically comprehensive atlas of the adult human brain transcriptome Nature 2012 489 391 399 10.1038/nature11405 22996553 PMC4243026 · DOI · Paper page
  36. [50]Mathys H Single-cell transcriptomic analysis of Alzheimer’s disease Nature 2019 570 332 337 10.1038/s41586-019-1195-2 31042697 PMC6865822 · DOI · Paper page
  37. [51]Oughtred R The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions Protein Sci.: a Publ. Protein Soc. 2021 30 187 200 10.1002/pro.3978 PMC7737760 33070389 · DOI · Paper page
  38. [52]Sorokina O A unified resource and configurable model of the synapse proteome and its role in disease Sci. Rep. 2021 11 9967 10.1038/s41598-021-88945-7 33976238 PMC8113277 · DOI
  39. [53]Rath S MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations Nucleic Acids Res. 2021 49 D1541 D1547 10.1093/nar/gkaa1011 33174596 PMC7778944 · DOI · Paper page
  40. [54]Antonicka H A high-density human mitochondrial proximity interaction network Cell Metab. 2020 32 479 497.e479 10.1016/j.cmet.2020.07.017 32877691 · DOI
  41. [55]Kessler RC Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States. Results from the National Comorbidity Survey Arch. Gen. psychiatry 1994 51 8 19 10.1001/archpsyc.1994.03950010008002 8279933 · DOI · Paper page
  42. [56]Kessler RC Prevalence and treatment of mental disorders, 1990 to 2003 N. Engl. J. Med. 2005 352 2515 2523 10.1056/NEJMsa043266 15958807 PMC2847367 · DOI · Paper page
  43. [57]Wilson RS Gilley DW Bennett DA Beckett LA Evans DA Hallucinations, delusions, and cognitive decline in Alzheimer’s disease J. Neurol., Neurosurg., psychiatry 2000 69 172 177 10.1136/jnnp.69.2.172 10896689 PMC1737043 · DOI
  44. [58]Murray PS Kumar S Demichele-Sweet MA Sweet RA Psychosis in Alzheimer’s disease Biol. psychiatry 2014 75 542 552 10.1016/j.biopsych.2013.08.020 24103379 PMC4036443 · DOI
  45. [59]Shah C DeMichele-Sweet MA Sweet RA Genetics of psychosis of Alzheimer disease Am. J. Med. Genet. Part B, Neuropsychiatr. Genet.: Off. Publ. Int. Soc. Psychiatr. Genet. 2017 174 27 35 10.1002/ajmg.b.32413 PMC5154859 26756273 · DOI
  46. [60]Clement A Wiborg O Asuni AA Steps towards developing effective treatments for neuropsychiatric disturbances in alzheimer’s disease: insights from preclinical models, clinical data, and future directions Front. Aging Neurosci. 2020 12 56 10.3389/fnagi.2020.00056 32210790 PMC7068814 · DOI
  47. [61]Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science362, eaat7615 (2018). PMC6413317 30545854
  48. [63]Schwartzentruber J Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes Nat. Genet. 2021 53 392 402 10.1038/s41588-020-00776-w 33589840 PMC7610386 · DOI · Paper page
  49. [64]Huckins LM Gene expression imputation across multiple brain regions provides insights into schizophrenia risk Nat. Genet. 2019 51 659 674 10.1038/s41588-019-0364-4 30911161 PMC7034316 · DOI
  50. [65]Gulsuner S Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network Cell 2013 154 518 529 10.1016/j.cell.2013.06.049 23911319 PMC3894107 · DOI · Paper page
  51. [66]Das JSNARE Complex-Associated Proteins and Alcohol Alcohol., Clin. Exp. Res. 2020 44 7 18 10.1111/acer.14238 31724225 PMC6980881 · DOI
  52. [67]Chen F Dysfunction of the SNARE complex in neurological and psychiatric disorders Pharmacol. Res. 2021 165 105469 10.1016/j.phrs.2021.105469 33524541 · DOI
  53. [68]Margiotta, A. Role of SNAREs in neurodegenerative diseases. Cells10, 991 (2021). PMC8146804 33922505
  54. [69]Devine MJ Kittler JT Mitochondria at the neuronal presynapse in health and disease Nat. Rev. Neurosci. 2018 19 63 80 10.1038/nrn.2017.170 29348666 · DOI
  55. [70]Du H Early deficits in synaptic mitochondria in an Alzheimer’s disease mouse model Proc. Natl. Acad. Sci. USA 2010 107 18670 18675 10.1073/pnas.1006586107 20937894 PMC2972922 · DOI · Paper page
  56. [71]Reddy PH Amyloid-beta and mitochondria in aging and Alzheimer’s disease: implications for synaptic damage and cognitive decline J. Alzheimer’s Dis. 2010 20 S499 S512 10.3233/JAD-2010-100504 20413847 PMC3059092 · DOI
  57. [73]Liang WS Alzheimer’s disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons Proc. Natl Acad. Sci. USA 2008 105 4441 4446 10.1073/pnas.0709259105 18332434 PMC2393743 · DOI · Paper page
  58. [74]Mastroeni D Nuclear but not mitochondrial-encoded oxidative phosphorylation genes are altered in aging, mild cognitive impairment, and Alzheimer’s disease Alzheimer’s Dement. 2017 13 510 519 10.1016/j.jalz.2016.09.003 27793643 PMC5967608 · DOI
  59. [77]Pantazopoulos H Molecular signature of extracellular matrix pathology in schizophrenia Eur. J. Neurosci. 2021 53 3960 3987 10.1111/ejn.15009 33070392 PMC8359380 · DOI
  60. [78]Hu J Systematically characterizing dysfunctional long intergenic non-coding RNAs in multiple brain regions of major psychosis Oncotarget 2016 7 71087 71098 10.18632/oncotarget.12122 27661005 PMC5342065 · DOI
  61. [79]Bronner IF Comprehensive mRNA expression profiling distinguishes tauopathies and identifies shared molecular pathways PloS one 2009 4 e6826 10.1371/journal.pone.0006826 19714246 PMC2729393 · DOI
  62. [80]King EA Davis JW Degner JF Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval PLoS Genet. 2019 15 e1008489 10.1371/journal.pgen.1008489 31830040 PMC6907751 · DOI · Paper page
  63. [81]Levey DF Reproducible genetic risk loci for anxiety: results from ~200,000 participants in the million veteran program Am. J. psychiatry 2020 177 223 232 10.1176/appi.ajp.2019.19030256 31906708 PMC7869502 · DOI
  64. [82]Wingo AP Shared proteomic effects of cerebral atherosclerosis and Alzheimer’s disease on the human brain Nat. Neurosci. 2020 23 696 700 10.1038/s41593-020-0635-5 32424284 PMC7269838 · DOI · Paper page
  65. [83]Bennett DA Religious orders study and rush memory and aging project J. Alzheimer’s Dis. 2018 64 S161 S189 10.3233/JAD-179939 29865057 PMC6380522 · DOI · Paper page
  66. [84]Beach T Arizona study of aging and neurodegenerative disorders and brain and body donation program Neuropathology 2015 35 354 389 10.1111/neup.12189 25619230 PMC4593391 · DOI · Paper page
  67. [85]Wang M The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease Sci. data 2018 5 180185 10.1038/sdata.2018.185 30204156 PMC6132187 · DOI · Paper page
  68. [88]Ping L Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease Sci. data 2018 5 180036 10.1038/sdata.2018.36 29533394 PMC5848788 · DOI · Paper page
  69. [89]Mertins P Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry Nat. Protoc. 2018 13 1632 1661 10.1038/s41596-018-0006-9 29988108 PMC6211289 · DOI
  70. [90]Johnson, E.C.B. et al. Large-Scale Deep Multi-Layer Analysis of Alzheimer’s Disease Brain Reveals Strong Proteomic Disease-Related Changes Not Observed at the RNA Level. 2021.2004.2005.438450 (2021). PMC8825285 35115731
  71. [91]Leek JT Johnson WE Parker HS Jaffe AE Storey JD The sva package for removing batch effects and other unwanted variation in high-throughput experiments Bioinforma. 2012 28 882 883 10.1093/bioinformatics/bts034 PMC3307112 22257669 · DOI · Paper page
  72. [93]De Jager PL A genome-wide scan for common variants affecting the rate of age-related cognitive decline Neurobiol. Aging 2012 33 1017.e1011 1017.e1015 10.1016/j.neurobiolaging.2011.09.033 PMC3307898 22054870 · DOI
  73. [94]De Jager PL A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research Sci. Data 2018 5 1 13 10.1038/s41597-018-0002-5 30084846 PMC6080491 · DOI
  74. [95]Manichaikul A Robust relationship inference in genome-wide association studies Bioinformatics 2010 26 2867 2873 10.1093/bioinformatics/btq559 20926424 PMC3025716 · DOI · Paper page
  75. [96]Price AL Principal components analysis corrects for stratification in genome-wide association studies Nat. Genet. 2006 38 904 909 10.1038/ng1847 16862161 · DOI · Paper page
  76. [98]Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell10.1016/j.cell.2021.04.021 (2020). PMC8195859 34004146
  77. [99]Wynne, M. E. et al. Heterogeneous expression of nuclear encoded mitochondrial genes distinguishes inhibitory and excitatory neurons. eNeuro8, ENEURO.0232-21.2021 (2021). PMC8387155 34312306
  78. [100]Shannon P Cytoscape: a software environment for integrated models of biomolecular interaction networks Genome Res. 2003 13 2498 2504 10.1101/gr.1239303 14597658 PMC403769 · DOI · Paper page
  79. [101]Gockley J Multi-tissue neocortical transcriptome-wide association study implicates 8 genes across 6 genomic loci in Alzheimer’s disease Genome Med. 2021 13 76 10.1186/s13073-021-00890-2 33947463 PMC8094491 · DOI
  80. [102]Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Annals Stat.29, 1165–1188 (2001).
  81. [103]Purcell S PLINK: a toolset for whole-genome association and population-based linkage analysis Am. J. Hum. Genet. 2007 81 559 575 10.1086/519795 17701901 PMC1950838 · DOI · Paper page
  82. [104]Zambon AC GO-Elite: a flexible solution for pathway and ontology over-representation Bioinforma 2012 28 2209 2210 10.1093/bioinformatics/bts366 PMC3413395 22743224 · DOI
  83. [106]Martens M WikiPathways: connecting communities Nucleic Acids Res. 2021 49 D613 d621 10.1093/nar/gkaa1024 33211851 PMC7779061 · DOI
  84. [107]Giurgiu M CORUM: the comprehensive resource of mammalian protein complexes-2019 Nucleic Acids Res. 2019 47 D559 D563 10.1093/nar/gky973 30357367 PMC6323970 · DOI · Paper page
  85. [108]Jassal B The reactome pathway knowledgebase Nucleic Acids Res. 2020 48 D498 D503 31691815 PMC7145712

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Sorted by citations

Authors
Wingo, T. S., Liu, Y., Gerasimov, E. S., Vattathil, S. M., Wynne, M. E., Liu, J., Lori, A., Faundez, V., Bennett, D. A., Seyfried, N. T., Levey, A. I., & Wingo, A. P.
Venue
Nature Communications
Published
2022-07-26
Volume
13
Issue
1
ISSN
2041-1723
Cited by
199
References
22
Language
en
Funders
  • Center for Integrated Healthcare, U.S. Department of Veterans Affairs (I01 BX003853)
  • U.S. Department of Veterans Affairs (IK4 BX005219)

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