Using regulatory variants to detect gene-gene interactions identifies networks of genes linked to cell immortalisation.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
17 01 2020
17 01 2020
Historique:
received:
28
11
2018
accepted:
19
11
2019
entrez:
19
1
2020
pubmed:
19
1
2020
medline:
10
4
2020
Statut:
epublish
Résumé
The extent to which the impact of regulatory genetic variants may depend on other factors, such as the expression levels of upstream transcription factors, remains poorly understood. Here we report a framework in which regulatory variants are first aggregated into sets, and using these as estimates of the total cis-genetic effects on a gene we model their non-additive interactions with the expression of other genes in the genome. Using 1220 lymphoblastoid cell lines across platforms and independent datasets we identify 74 genes where the impact of their regulatory variant-set is linked to the expression levels of networks of distal genes. We show that these networks are predominantly associated with tumourigenesis pathways, through which immortalised cells are able to rapidly proliferate. We consequently present an approach to define gene interaction networks underlying important cellular pathways such as cell immortalisation.
Identifiants
pubmed: 31953380
doi: 10.1038/s41467-019-13762-6
pii: 10.1038/s41467-019-13762-6
pmc: PMC6969137
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
343Subventions
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/F019394/1
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BBS/E/D/10002071
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/K026992/1
Pays : United Kingdom
Références
Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).
pubmed: 25439723
pmcid: 4225595
doi: 10.1016/j.ajhg.2014.10.004
Aschard, H. et al. Challenges and opportunities in genome-wide environmental interaction (GWEI) studies. Hum. Genet. 131, 1591–1613 (2012).
pubmed: 22760307
pmcid: 3677711
doi: 10.1007/s00439-012-1192-0
Montgomery, S. B. & Dermitzakis, E. T. From expression QTLs to personalized transcriptomics. Nat. Rev. Genet. 12, 277–282 (2011).
pubmed: 21386863
doi: 10.1038/nrg2969
GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).
pubmed: 24037378
pmcid: 3918453
doi: 10.1038/nature12531
Stranger, B. E. et al. Patterns of cis regulatory variation in diverse human populations. PLOS Genet. 8, e1002639 (2012).
pubmed: 22532805
pmcid: 3330104
doi: 10.1371/journal.pgen.1002639
Brown, A. A. et al. Genetic interactions affecting human gene expression identified by variance association mapping. eLife 3, e01381 (2014).
pubmed: 24771767
pmcid: 4017648
doi: 10.7554/eLife.01381
Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).
pubmed: 26258848
pmcid: 4552594
doi: 10.1038/ng.3367
Deplancke, B., Alpern, D. & Gardeux, V. The genetics of transcription factor DNA binding variation. Cell 166, 538–554 (2016).
pubmed: 27471964
doi: 10.1016/j.cell.2016.07.012
Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017).
pubmed: 27918533
doi: 10.1038/ng.3737
Castaldi, P. J. et al. Screening for interaction effects in gene expression data. PLoS ONE 12, e0173847 (2017).
pubmed: 28301596
pmcid: 5354413
doi: 10.1371/journal.pone.0173847
Weiser, M., Mukherjee, S. & Furey, T. S. Novel distal eQTL analysis demonstrates effect of population genetic architecture on detecting and interpreting associations. Genetics 198, 879–893 (2014).
pubmed: 25230953
pmcid: 4224177
doi: 10.1534/genetics.114.167791
Hemani, G. et al. Detection and replication of epistasis influencing transcription in humans. Nature 508, 249–253 (2014).
pubmed: 24572353
pmcid: 3984375
doi: 10.1038/nature13005
Luijk, R. et al. Genome-wide identification of directed gene networks using large-scale population genomics data. Nat. Commun. 9, 3097 (2018).
pubmed: 30082726
pmcid: 6079029
doi: 10.1038/s41467-018-05452-6
Cohen, J. I., Fauci, A. S., Varmus, H. & Nabel, G. J. Epstein-Barr virus: an important vaccine target for cancer prevention. Sci. Transl. Med. 3, 107fs7-107fs7 (2011).
doi: 10.1126/scitranslmed.3002878
Jiang, S. et al. The Epstein-Barr virus regulome in lymphoblastoid cells. Cell Host Microbe 22, 561–573.e4 (2017).
pubmed: 29024646
pmcid: 5662195
doi: 10.1016/j.chom.2017.09.001
Ersing, I., Bernhardt, K. & Gewurz, B. E. NF-κB and IRF7 pathway activation by Epstein-Barr virus latent membrane protein 1. Viruses 5, 1587–1606 (2013).
pubmed: 23793113
pmcid: 3717723
doi: 10.3390/v5061587
Deary, I. J., Gow, A. J., Pattie, A. & Starr, J. M. Cohort profile: the Lothian Birth Cohorts of 1921 and 1936. Int. J. Epidemiol. 41, 1576–1584 (2012).
pubmed: 22253310
doi: 10.1093/ije/dyr197
Corley, J., Cox, S. R. & Deary, I. J. Healthy cognitive ageing in the Lothian Birth Cohort studies: marginal gains not magic bullet. Psychol. Med. 48, 187–207 (2018).
pubmed: 28595670
doi: 10.1017/S0033291717001489
Paré, G., Cook, N. R., Ridker, P. M. & Chasman, D. I. On the use of variance per genotype as a tool to identify quantitative trait interaction effects: a report from the Women’s Genome Health Study. PLoS Genet. 6, e1000981–e1000981 (2010).
pubmed: 20585554
pmcid: 2887471
doi: 10.1371/journal.pgen.1000981
Lu, Y. et al. MYC targeted long noncoding RNA DANCR promotes cancer in part by reducing p21 levels. Cancer Res. 78, 64–74 (2018).
pubmed: 29180471
doi: 10.1158/0008-5472.CAN-17-0815
Wang, S. & Jiang, M. The long non-coding RNA-DANCR exerts oncogenic functions in non-small cell lung cancer via miR-758-3p. Biomed. Pharmacother. 103, 94–100 (2018).
pubmed: 29635134
doi: 10.1016/j.biopha.2018.03.053
Wan, G. et al. SLFN5 suppresses cancer cell migration and invasion by inhibiting MT1-MMP expression via AKT/GSK-3β/β-catenin pathway. Cell. Signal. 59, 1–12 (2019).
pubmed: 30844429
doi: 10.1016/j.cellsig.2019.03.004
Sassano, A. et al. Human Schlafen 5 (SLFN5) is a regulator of motility and invasiveness of renal cell carcinoma cells. Mol. Cell. Biol. 35, 2684–2698 (2015).
pubmed: 26012550
pmcid: 4524119
doi: 10.1128/MCB.00019-15
Liu, Y. et al. Long non-coding RNA FLVCR1-AS1 sponges miR-155 to promote the tumorigenesis of gastric cancer by targeting c-Myc. Am. J. Transl. Res. 11, 793–805 (2019).
pubmed: 30899380
pmcid: 6413279
Edwards, Y. H. et al. Locus determining the human sperm-specific lactate dehydrogenase, LDHC, is syntenic with LDHA. Dev. Genet. 8, 219–232 (1987).
pubmed: 2844458
doi: 10.1002/dvg.1020080406
Koslowski, M. et al. Multiple splice variants of lactate dehydrogenase C selectively expressed in human cancer. Cancer Res. 62, 6750–6755 (2002).
pubmed: 12438276
Tang, H. & Goldberg, E. Homo sapiens Lactate Dehydrogenase c (Ldhc) gene expression in cancer cells is regulated by transcription factor Sp1, CREB, and CpG island methylation. J. Androl. 30, 157–167 (2009).
pubmed: 18930904
doi: 10.2164/jandrol.108.005785
Arslan, A. D. et al. Human SLFN5 is a transcriptional co-repressor of STAT1-mediated interferon responses and promotes the malignant phenotype in glioblastoma. Oncogene 36, 6006–6019 (2017).
pubmed: 28671669
pmcid: 5821504
doi: 10.1038/onc.2017.205
Young, L. S. & Dawson, C. W. Epstein-Barr virus and nasopharyngeal carcinoma. Chin. J. Cancer 33, 581–590 (2014).
pubmed: 25418193
pmcid: 4308653
doi: 10.5732/cjc.014.10208
Ichikawa, T. et al. Regulation of Epstein-Barr virus life cycle and cell proliferation by histone H3K27 methyltransferase EZH2 in Akata Cells. mSphere 3, e00478–18 (2018).
pubmed: 30487153
pmcid: 6262262
doi: 10.1128/mSphere.00478-18
Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
pubmed: 25613900
doi: 10.1126/science.1260419
Markert, C. L. Lactate dehydrogenase. Biochemistry and function of lactate dehydrogenase. Cell Biochem. Funct. 2, 131–134 (1984).
pubmed: 6383647
doi: 10.1002/cbf.290020302
DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G. & Thompson, C. B. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 7, 11–20 (2008).
pubmed: 18177721
doi: 10.1016/j.cmet.2007.10.002
Darekar, S. et al. Epstein-Barr virus immortalization of human B-cells leads to stabilization of hypoxia-induced factor 1 alpha, congruent with the Warburg effect. PLoS ONE 7, e42072 (2012).
pubmed: 22848707
pmcid: 3407085
doi: 10.1371/journal.pone.0042072
Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).
doi: 10.1126/science.1160809
Chen, Z., Lu, W., Garcia-Prieto, C. & Huang, P. The Warburg effect and its cancer therapeutic implications. J. Bioenerg. Biomembr. 39, 267 (2007).
pubmed: 17551814
doi: 10.1007/s10863-007-9086-x
pmcid: 17551814
Liberti, M. V. & Locasale, J. W. The Warburg effect: how does it benefit cancer cells? Trends Biochem. Sci. 41, 211–218 (2016).
pubmed: 26778478
pmcid: 4783224
doi: 10.1016/j.tibs.2015.12.001
Hui, K. F., Yiu, S. P. T., Tam, K. P. & Chiang, A. K. S. Viral-targeted strategies against EBV-associated lymphoproliferative diseases. Front. Oncol. 9, 81 (2019).
pubmed: 30873380
pmcid: 6400835
doi: 10.3389/fonc.2019.00081
Morrison, T. E., Mauser, A., Wong, A., Ting, J. P. & Kenney, S. C. Inhibition of IFN-gamma signaling by an Epstein-Barr virus immediate-early protein. Immunity 15, 787–799 (2001).
pubmed: 11728340
doi: 10.1016/S1074-7613(01)00226-6
Crawford, L., Zeng, P., Mukherjee, S. & Zhou, X. Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits. PLoS Genet. 13, e1006869 (2017).
pubmed: 28746338
pmcid: 5550000
doi: 10.1371/journal.pgen.1006869
Wang, G., Yang, E., Brinkmeyer-Langford, C. L. & Cai, J. J. Additive, epistatic, and environmental effects through the lens of expression variability QTL in a twin cohort. Genetics 196, 413–425 (2014).
pubmed: 24298061
doi: 10.1534/genetics.113.157503
Grundberg, E. et al. Mapping cis and trans-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).
pubmed: 22941192
pmcid: 3784328
doi: 10.1038/ng.2394
McKenna, A. et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
pubmed: 20644199
pmcid: 2928508
doi: 10.1101/gr.107524.110
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
pubmed: 21653522
pmcid: 3137218
doi: 10.1093/bioinformatics/btr330
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
pubmed: 1950838
pmcid: 1950838
doi: 10.1086/519795
Zhao, H. et al. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics 30, 1006–1007 (2014).
pubmed: 24351709
doi: 10.1093/bioinformatics/btt730
Harris, S. E. et al. Age-related gene expression changes, and transcriptome wide association study of physical and cognitive aging traits, in the Lothian Birth Cohort 1936. Aging 9, 2489–2503 (2017).
pubmed: 29207374
pmcid: 5764388
doi: 10.18632/aging.101333
Du, P., Kibbe, W. A. & Lin, S. M. lumi: a pipeline for processing Illumina microarray. Bioinformatics 24, 1547–1548 (2008).
pubmed: 18467348
doi: 10.1093/bioinformatics/btn224
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
doi: 10.18637/jss.v067.i01
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. Nat. Protoc. 7, 500–507 (2012).
pubmed: 22343431
pmcid: 3398141
doi: 10.1038/nprot.2011.457
Delaneau, O. et al. A complete tool set for molecular QTL discovery and analysis. Nat. Commun. 8, 15452 (2017).
pubmed: 28516912
pmcid: 5454369
doi: 10.1038/ncomms15452
Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).
pubmed: 26708335
doi: 10.1093/bioinformatics/btv722
Watanabe, K., Taskesen, E., Van, A. B. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826–1826 (2017).
pubmed: 29184056
pmcid: 5705698
doi: 10.1038/s41467-017-01261-5
Zhou, X. & Wang, T. Using the Wash U Epigenome Browser to examine genome-wide sequencing data. Curr. Protoc. Bioinformatics. https://doi.org/10.1002/0471250953.bi1010s40 (2012).
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
doi: 10.18637/jss.v028.i05