Tensor decomposition of stimulated monocyte and macrophage gene expression profiles identifies neurodegenerative disease-specific trans-eQTLs
Autoři:
Satesh Ramdhani aff001; Elisa Navarro aff001; Evan Udine aff001; Anastasia G. Efthymiou aff001; Brian M. Schilder aff001; Madison Parks aff001; Alison Goate aff001; Towfique Raj aff001
Působiště autorů:
Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
aff001; Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
aff002; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
aff003
Vyšlo v časopise:
Tensor decomposition of stimulated monocyte and macrophage gene expression profiles identifies neurodegenerative disease-specific trans-eQTLs. PLoS Genet 16(2): e1008549. doi:10.1371/journal.pgen.1008549
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008549
Souhrn
Recent human genetic studies suggest that cells of the innate immune system have a primary role in the pathogenesis of neurodegenerative diseases. However, the results from these studies often do not elucidate how the genetic variants affect the biology of these cells to modulate disease risk. Here, we applied a tensor decomposition method to uncover disease associated gene networks linked to distal genetic variation in stimulated human monocyte and macrophage gene expression profiles. We report robust evidence that some disease associated genetic variants affect the expression of multiple genes in trans. These include a Parkinson’s disease locus influencing the expression of genes mediated by a protease that controls lysosomal function, and Alzheimer’s disease loci influencing the expression of genes involved in type 1 interferon signaling, myeloid phagocytosis, and complement cascade pathways. Overall, we uncover gene networks in induced innate immune cells linked to disease associated genetic variants, which may help elucidate the underlying biology of disease.
Klíčová slova:
Alzheimer's disease – Gene expression – Genetic loci – Genome-wide association studies – Interferons – Macrophages – Monocytes – Parkinson disease
Zdroje
1. Stranger BE, Stahl EA, Raj T. Progress and promise of genome-wide association studies for human complex trait genetics. Genetics. 2011;187(2):367–83. doi: 10.1534/genetics.110.120907 21115973.
2. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. American journal of human genetics. 2017;101(1):5–22. doi: 10.1016/j.ajhg.2017.06.005 28686856.
3. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337(6099):1190–5. doi: 10.1126/science.1222794 22955828.
4. Consortium GT. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348(6235):648–60. Epub 2015/05/09. doi: 10.1126/science.1262110 25954001.
5. Franzen O, Ermel R, Cohain A, Akers NK, Di Narzo A, Talukdar HA, et al. Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science. 2016;353(6301):827–30. doi: 10.1126/science.aad6970 27540175.
6. Ramasamy A, Trabzuni D, Guelfi S, Varghese V, Smith C, Walker R, et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nature neuroscience. 2014;17(10):1418–28. doi: 10.1038/nn.3801 25174004.
7. Raj T, Rothamel K, Mostafavi S, Ye C, Lee MN, Replogle JM, et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science. 2014;344(6183):519–23. doi: 10.1126/science.1249547 24786080.
8. Stranger BE, Montgomery SB, Dimas AS, Parts L, Stegle O, Ingle CE, et al. Patterns of cis regulatory variation in diverse human populations. PLoS genetics. 2012;8(4):e1002639. doi: 10.1371/journal.pgen.1002639 22532805.
9. Lappalainen T, Sammeth M, Friedlander MR, t Hoen PA, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501(7468):506–11. doi: 10.1038/nature12531 24037378.
10. Ye CJ, Feng T, Kwon HK, Raj T, Wilson MT, Asinovski N, et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science. 2014;345(6202):1254665. doi: 10.1126/science.1254665 25214635.
11. Lee MN, Ye C, Villani AC, Raj T, Li W, Eisenhaure TM, et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science. 2014;343(6175):1246980. doi: 10.1126/science.1246980 24604203.
12. Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D, Lau E, et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science. 2014;343(6175):1246949. doi: 10.1126/science.1246949 24604202.
13. Westra HJ, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013;45(10):1238–43. Epub 2013/09/10. doi: 10.1038/ng.2756 24013639.
14. Hore V, Vinuela A, Buil A, Knight J, McCarthy MI, Small K, et al. Tensor decomposition for multiple-tissue gene expression experiments. Nature genetics. 2016;48(9):1094–100. doi: 10.1038/ng.3624 27479908.
15. Fagny M, Paulson JN, Kuijjer ML, Sonawane AR, Chen CY, Lopes-Ramos CM, et al. Exploring regulation in tissues with eQTL networks. Proceedings of the National Academy of Sciences of the United States of America. 2017;114(37):E7841–E50. doi: 10.1073/pnas.1707375114 28851834.
16. Rotival M, Zeller T, Wild PS, Maouche S, Szymczak S, Schillert A, et al. Integrating genome-wide genetic variations and monocyte expression data reveals trans-regulated gene modules in humans. PLoS genetics. 2011;7(12):e1002367. doi: 10.1371/journal.pgen.1002367 22144904.
17. Rakitsch B, Stegle O. Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression. Genome Biol. 2016;17:33. Epub 2016/02/26. doi: 10.1186/s13059-016-0895-2 26911988.
18. Braak H, de Vos RA, Jansen EN, Bratzke H, Braak E. Neuropathological hallmarks of Alzheimer’s and Parkinson’s diseases. Progress in brain research. 1998;117:267–85. doi: 10.1016/s0079-6123(08)64021-2 9932414.
19. Ramanan VK, Saykin AJ. Pathways to neurodegeneration: mechanistic insights from GWAS in Alzheimer’s disease, Parkinson’s disease, and related disorders. American journal of neurodegenerative disease. 2013;2(3):145–75. 24093081.
20. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45(12):1452–8. 24162737.
21. Sims R, van der Lee SJ, Naj AC, Bellenguez C, Badarinarayan N, Jakobsdottir J, et al. Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nature genetics. 2017;49(9):1373–84. doi: 10.1038/ng.3916 28714976.
22. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing. Nat Genet. 2019;51(3):414–30. doi: 10.1038/s41588-019-0358-2 30820047.
23. Chang D, Nalls MA, Hallgrimsdottir IB, Hunkapiller J, van der Brug M, Cai F, et al. A meta-analysis of genome-wide association studies identifies 17 new Parkinson’s disease risk loci. Nat Genet. 2017;49(10):1511–6. doi: 10.1038/ng.3955 28892059.
24. Mike A Nalls CB, Costanza L Vallerga, Karl Heilbron, Sara Bandres-Ciga, Diana Chang, Manuela Tan, Demis A Kia, Alastair J Noyce, Angli Xue, Jose Bras, Emily Young, Ranier von Coelln, Javier Simon-Sanchez, Claudia Schulte, Manu Sharma, Lynne Krohn, Lasse Pihlstrom, Ari Siitonen, Hirotaka Iwaki, Hampton Leonard, Faraz Faghri, J Raphael Gibbs, Dena G Hernandez, Sonja W Scholz, Juan A Botia, Maria Martinez, Jean-Chrstophe Corvol, Suzanne Lesage, Joseph Jankovic, Lisa M Shulman, The 23andMe Research Team, System Genomics of Parkinson’s Disease (SGPD) Consortium, Margaret Sutherland, Pentti Tienari, Kari Majamaa, Mathias Toft, Alexis Brice, Jian Yang, Ziv Gan-Orr, Thomas M Gasser, Peter M Heutink, Joshua M Shulman, Nicolas A Wood, David A Hinds, John R Hardy, Huw R Morris, Jacob M Gratten, Peter M Visscher, Robert R Graham, Andrew B Singleton, International Parkinson’s Disease Genomics Consortium. Expanding Parkinson’s disease genetics: novel risk loci, genomic context, causal insights and heritable risk. bioRxiv 388165. 2019. https://doi.org/10.1101/388165.
25. Gagliano SA, Pouget JG, Hardy J, Knight J, Barnes MR, Ryten M, et al. Genomics implicates adaptive and innate immunity in Alzheimer’s and Parkinson’s diseases. Annals of clinical and translational neurology. 2016;3(12):924–33. doi: 10.1002/acn3.369 28097204.
26. Keren-Shaul H, Spinrad A, Weiner A, Matcovitch-Natan O, Dvir-Szternfeld R, Ulland TK, et al. A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease. Cell. 2017;169(7):1276–90 e17. doi: 10.1016/j.cell.2017.05.018 28602351.
27. Amor S, Puentes F, Baker D, van der Valk P. Inflammation in neurodegenerative diseases. Immunology. 2010;129(2):154–69. doi: 10.1111/j.1365-2567.2009.03225.x 20561356.
28. Krasemann S, Madore C, Cialic R, Baufeld C, Calcagno N, El Fatimy R, et al. The TREM2-APOE Pathway Drives the Transcriptional Phenotype of Dysfunctional Microglia in Neurodegenerative Diseases. Immunity. 2017;47(3):566–81 e9. doi: 10.1016/j.immuni.2017.08.008 28930663.
29. Middeldorp J, Lehallier B, Villeda SA, Miedema SS, Evans E, Czirr E, et al. Preclinical Assessment of Young Blood Plasma for Alzheimer Disease. JAMA neurology. 2016;73(11):1325–33. doi: 10.1001/jamaneurol.2016.3185 27598869.
30. Garnier S, Truong V, Brocheton J, Zeller T, Rovital M, Wild PS, et al. Genome-wide haplotype analysis of cis expression quantitative trait loci in monocytes. PLoS genetics. 2013;9(1):e1003240. doi: 10.1371/journal.pgen.1003240 23382694.
31. de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11(4):e1004219. Epub 2015/04/18. doi: 10.1371/journal.pcbi.1004219 25885710.
32. Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet. 2015;47(11):1228–35. Epub 2015/09/29. doi: 10.1038/ng.3404 26414678.
33. Salih Dervis A B S, Guelfi Sebastian, Reynolds Regina H, Shoai Maryam, Ryten Mina, Brenton Jonathan, Zhang David, Matarin Mar, Botia Juan A, Shah Runil, Brookes Keeley J, Guetta-Baranes Tamar, Morgan Kevin, Bellou Eftychia, Cummings Damian M, Escott-Price Valentina, Hardy John. Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk. Brain Communications. 2019. https://doi.org/10.1093/braincomms/fcz022.
34. Mathys H, Adaikkan C, Gao F, Young JZ, Manet E, Hemberg M, et al. Temporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution. Cell Rep. 2017;21(2):366–80. doi: 10.1016/j.celrep.2017.09.039 29020624.
35. Sala Frigerio C, Wolfs L, Fattorelli N, Thrupp N, Voytyuk I, Schmidt I, et al. The Major Risk Factors for Alzheimer’s Disease: Age, Sex, and Genes Modulate the Microglia Response to Abeta Plaques. Cell Rep. 2019;27(4):1293–306 e6. doi: 10.1016/j.celrep.2019.03.099 31018141.
36. Filiano AJ, Xu Y, Tustison NJ, Marsh RL, Baker W, Smirnov I, et al. Unexpected role of interferon-gamma in regulating neuronal connectivity and social behaviour. Nature. 2016;535(7612):425–9. Epub 2016/07/15. doi: 10.1038/nature18626 27409813.
37. Gandal MJ, Zhang P, Hadjimichael E, Walker RL, Chen C, Liu S, et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science. 2018;362(6420). Epub 2018/12/14. doi: 10.1126/science.aat8127 30545856.
38. McDowell I, Pai A, Guo C, Vockley CM, Brown CD, Reddy TE, et al. Many long intergenic non-coding RNAs distally regulate mRNA gene expression levels. bioRxiv. 2016:044719.
39. Li YI, Wong G, Humphrey J, Raj T. Prioritizing Parkinson’s disease genes using population-scale transcriptomic data. Nat Commun. 2019;10(1):994. doi: 10.1038/s41467-019-08912-9 30824768.
40. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS genetics. 2014;10(5):e1004383. doi: 10.1371/journal.pgen.1004383 24830394.
41. Raj T, Li YI, Wong G, Humphrey J, Wang M, Ramdhani S, et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nature genetics. 2018. doi: 10.1038/s41588-018-0238-1 30297968.
42. Snoek BL, Sterken MG, Bevers RPJ, Volkers RJM, Van’t Hof A, Brenchley R, et al. Contribution of trans regulatory eQTL to cryptic genetic variation in C. elegans. BMC genomics. 2017;18(1):500. doi: 10.1186/s12864-017-3899-8 28662696.
43. Kita R, Venkataram S, Zhou Y, Fraser HB. High-resolution mapping of cis-regulatory variation in budding yeast. Proceedings of the National Academy of Sciences of the United States of America. 2017;114(50):E10736–E44. doi: 10.1073/pnas.1717421114 29183975.
44. Ha SD, Ham B, Mogridge J, Saftig P, Lin S, Kim SO. Cathepsin B-mediated autophagy flux facilitates the anthrax toxin receptor 2-mediated delivery of anthrax lethal factor into the cytoplasm. The Journal of biological chemistry. 2010;285(3):2120–9. doi: 10.1074/jbc.M109.065813 19858192.
45. Plotegher N, Duchen MR. Crosstalk between Lysosomes and Mitochondria in Parkinson’s Disease. Frontiers in cell and developmental biology. 2017;5:110. doi: 10.3389/fcell.2017.00110 29312935.
46. Raj T, Ryan KJ, Replogle JM, Chibnik LB, Rosenkrantz L, Tang A, et al. CD33: increased inclusion of exon 2 implicates the Ig V-set domain in Alzheimer’s disease susceptibility. Human molecular genetics. 2014;23(10):2729–36. doi: 10.1093/hmg/ddt666 24381305.
47. Bradshaw EM, Chibnik LB, Keenan BT, Ottoboni L, Raj T, Tang A, et al. CD33 Alzheimer’s disease locus: altered monocyte function and amyloid biology. Nature neuroscience. 2013;16(7):848–50. doi: 10.1038/nn.3435 23708142.
48. Huang KL, Marcora E, Pimenova AA, Di Narzo AF, Kapoor M, Jin SC, et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nature neuroscience. 2017;20(8):1052–61. doi: 10.1038/nn.4587 28628103.
49. Hong S, Beja-Glasser VF, Nfonoyim BM, Frouin A, Li S, Ramakrishnan S, et al. Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science. 2016;352(6286):712–6. doi: 10.1126/science.aad8373 27033548.
50. Veerhuis R, Janssen I, Hack CE, Eikelenboom P. Early complement components in Alzheimer’s disease brains. Acta neuropathologica. 1996;91(1):53–60. doi: 10.1007/s004019570001 8773146.
51. Zhang B, Gaiteri C, Bodea LG, Wang Z, McElwee J, Podtelezhnikov AA, et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell. 2013;153(3):707–20. doi: 10.1016/j.cell.2013.03.030 23622250.
52. Ye CJ, Chen J, Villani AC, Gate RE, Subramaniam M, Bhangale T, et al. Genetic analysis of isoform usage in the human anti-viral response reveals influenza-specific regulation of ERAP2 transcripts under balancing selection. Genome Res. 2018;28(12):1812–25. Epub 2018/11/18. doi: 10.1101/gr.240390.118 30446528.
53. Stegle O, Parts L, Piipari M, Winn J, Durbin R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nature protocols. 2012;7(3):500–7. doi: 10.1038/nprot.2011.457 22343431.
54. Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28(10):1353–8. doi: 10.1093/bioinformatics/bts163 22492648.
55. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B. 57 (1): 289–300.
56. Kwon AT, Arenillas DJ, Worsley Hunt R, Wasserman WW. oPOSSUM-3: advanced analysis of regulatory motif over-representation across genes or ChIP-Seq datasets. G3. 2012;2(9):987–1002. doi: 10.1534/g3.112.003202 22973536.
57. Alexa A, Rahnenfuhrer J, Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006;22(13):1600–7. doi: 10.1093/bioinformatics/btl140 16606683.
58. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics C, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291–5. Epub 2015/02/03. doi: 10.1038/ng.3211 25642630.
Článek vyšel v časopise
PLOS Genetics
2020 Číslo 2
- FDA varuje před selfmonitoringem cukru pomocí chytrých hodinek. Jak je to v Česku?
- Proč se v Česku šíří několik „vymýcených“ infekcí najednou? Ptáme se odborníků
- Prof. Jan Škrha: Metformin je bezpečný, ale je třeba jej bezpečně užívat a léčbu kontrolovat
- Jak souvisí postcovidový syndrom s poškozením mozku?
- Umělá inteligence pomůže s výběrem chřipkových kmenů pro výrobu vakcín
Nejčtenější v tomto čísle
- Planarian EGF repeat-containing genes megf6 and hemicentin are required to restrict the stem cell compartment
- Evolutionary dynamics of microRNA target sites across vertebrate evolution
- Rab11 activation by Ik2 kinase is required for dendrite pruning in Drosophila sensory neurons
- Tensor decomposition of stimulated monocyte and macrophage gene expression profiles identifies neurodegenerative disease-specific trans-eQTLs