Paralog buffering contributes to the variable essentiality of genes in cancer cell lines


Autoři: Barbara De Kegel aff001;  Colm J. Ryan aff001
Působiště autorů: School of Computer Science and Systems Biology Ireland, University College Dublin, Belfield, Ireland aff001
Vyšlo v časopise: Paralog buffering contributes to the variable essentiality of genes in cancer cell lines. PLoS Genet 15(10): e32767. doi:10.1371/journal.pgen.1008466
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008466

Souhrn

What makes a gene essential for cellular survival? In model organisms, such as budding yeast, systematic gene deletion studies have revealed that paralog genes are less likely to be essential than singleton genes and that this can partially be attributed to the ability of paralogs to buffer each other's loss. However, the essentiality of a gene is not a fixed property and can vary significantly across different genetic backgrounds. It is unclear to what extent paralogs contribute to this variation, as most studies have analyzed genes identified as essential in a single genetic background. Here, using gene essentiality profiles of 558 genetically heterogeneous tumor cell lines, we analyze the contribution of paralogy to variable essentiality. We find that, compared to singleton genes, paralogs are less frequently essential and that this is more evident when considering genes with multiple paralogs or with highly sequence-similar paralogs. In addition, we find that paralogs derived from whole genome duplication exhibit more variable essentiality than those derived from small-scale duplications. We provide evidence that in 13–17% of cases the variable essentiality of paralogs can be attributed to buffering relationships between paralog pairs, as evidenced by synthetic lethality. Paralog pairs derived from whole genome duplication and pairs that function in protein complexes are significantly more likely to display such synthetic lethal relationships. Overall we find that many of the observations made using a single strain of budding yeast can be extended to understand patterns of essentiality in genetically heterogeneous cancer cell lines.

Klíčová slova:

Cancer screening – CRISPR – Genetic polymorphism – Genetic screens – Nonsense mutation – Protein complexes – Saccharomyces cerevisiae – Sequence alignment


Zdroje

1. Giaever G, Nislow C. The yeast deletion collection: a decade of functional genomics. Genetics. 2014;197: 451–465. doi: 10.1534/genetics.114.161620 24939991

2. Giaever G, Chu AM, Ni L, Connelly C, Riles L, Véronneau S, et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature. 2002;418: 387–391. doi: 10.1038/nature00935 12140549

3. Hart GT, Lee I, Marcotte ER. A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality. BMC Bioinformatics. 2007;8: 236. doi: 10.1186/1471-2105-8-236 17605818

4. Ryan CJ, Krogan NJ, Cunningham P, Cagney G. All or nothing: protein complexes flip essentiality between distantly related eukaryotes. Genome Biol Evol. 2013;5: 1049–1059. doi: 10.1093/gbe/evt074 23661563

5. Gu Z, Steinmetz LM, Gu X, Scharfe C, Davis RW, Li W-H. Role of duplicate genes in genetic robustness against null mutations. Nature. 2003;421: 63–66. doi: 10.1038/nature01198 12511954

6. Kamath RS, Fraser AG, Dong Y, Poulin G, Durbin R, Gotta M, et al. Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Nature. 2003;421: 231–237. doi: 10.1038/nature01278 12529635

7. Rancati G, Moffat J, Typas A, Pavelka N. Emerging and evolving concepts in gene essentiality. Nat Rev Genet. 2017;19: 34. doi: 10.1038/nrg.2017.74 29033457

8. Liu G, Yong MYJ, Yurieva M, Srinivasan KG, Liu J, Lim JSY, et al. Gene Essentiality Is a Quantitative Property Linked to Cellular Evolvability. Cell. 2015;163: 1388–1399. doi: 10.1016/j.cell.2015.10.069 26627736

9. Li J, Wang H-T, Wang W-T, Zhang X-R, Suo F, Ren J-Y, et al. Systematic analysis reveals the prevalence and principles of bypassable gene essentiality. Nat Commun. 2019;10: 1002. doi: 10.1038/s41467-019-08928-1 30824696

10. Vu V, Verster AJ, Schertzberg M, Chuluunbaatar T, Spensley M, Pajkic D, et al. Natural Variation in Gene Expression Modulates the Severity of Mutant Phenotypes. Cell. 2015;162: 391–402. doi: 10.1016/j.cell.2015.06.037 26186192

11. Dowell RD, Ryan O, Jansen A, Cheung D, Agarwala S, Danford T, et al. Genotype to phenotype: a complex problem. Science. 2010;328: 469. doi: 10.1126/science.1189015 20413493

12. Meyers RM, Bryan JG, McFarland JM, Weir BA, Sizemore AE, Xu H, et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat Genet. 2017;49: 1779. doi: 10.1038/ng.3984 29083409

13. Wang T, Yu H, Hughes NW, Liu B, Kendirli A, Klein K, et al. Gene Essentiality Profiling Reveals Gene Networks and Synthetic Lethal Interactions with Oncogenic Ras. Cell. 2017;168: 890–903.e15. doi: 10.1016/j.cell.2017.01.013 28162770

14. Behan FM, Iorio F, Picco G, Gonçalves E, Beaver CM, Migliardi G, et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature. 2019;568: 511–516. doi: 10.1038/s41586-019-1103-9 30971826

15. Conant GC, Wolfe KH. Turning a hobby into a job: how duplicated genes find new functions. Nat Rev Genet. 2008;9: 938–950. doi: 10.1038/nrg2482 19015656

16. White JK, Gerdin A-K, Karp NA, Ryder E, Buljan M, Bussell JN, et al. Genome-wide generation and systematic phenotyping of knockout mice reveals new roles for many genes. Cell. 2013;154: 452–464. doi: 10.1016/j.cell.2013.06.022 23870131

17. Blomen VA, Májek P, Jae LT, Bigenzahn JW, Nieuwenhuis J, Staring J, et al. Gene essentiality and synthetic lethality in haploid human cells. Science. 2015;350: 1092–1096. doi: 10.1126/science.aac7557 26472760

18. DeLuna A, Vetsigian K, Shoresh N, Hegreness M, Colón-González M, Chao S, et al. Exposing the fitness contribution of duplicated genes. Nat Genet. 2008;40: 676–681. doi: 10.1038/ng.123 18408719

19. Dean EJ, Davis JC, Davis RW, Petrov DA. Pervasive and persistent redundancy among duplicated genes in yeast. PLoS Genet. 2008;4: e1000113. doi: 10.1371/journal.pgen.1000113 18604285

20. VanderSluis B, Bellay J, Musso G, Costanzo M, Papp B, Vizeacoumar FJ, et al. Genetic interactions reveal the evolutionary trajectories of duplicate genes. Mol Syst Biol. 2010;6: 429. doi: 10.1038/msb.2010.82 21081923

21. Hakes L, Pinney JW, Lovell SC, Oliver SG, Robertson DL. All duplicates are not equal: the difference between small-scale and genome duplication. Genome Biol. 2007;8: R209. doi: 10.1186/gb-2007-8-10-r209 17916239

22. Guan Y, Dunham MJ, Troyanskaya OG. Functional analysis of gene duplications in Saccharomyces cerevisiae. Genetics. 2007;175: 933–943. doi: 10.1534/genetics.106.064329 17151249

23. Fares MA, Keane OM, Toft C, Carretero-Paulet L, Jones GW. The roles of whole-genome and small-scale duplications in the functional specialization of Saccharomyces cerevisiae genes. PLoS Genet. 2013;9: e1003176. doi: 10.1371/journal.pgen.1003176 23300483

24. Fortin J-P, Tan J, Gascoigne KE, Haverty PM, Forrest WF, Costa MR, et al. Multiple-gene targeting and mismatch tolerance can confound analysis of genome-wide pooled CRISPR screens. Genome Biol. 2019;20: 21. doi: 10.1186/s13059-019-1621-7 30683138

25. Morgens DW, Wainberg M, Boyle EA, Ursu O, Araya CL, Tsui CK, et al. Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens. Nat Commun. 2017;8: 15178. doi: 10.1038/ncomms15178 28474669

26. Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res. 2018;46: D754–D761. doi: 10.1093/nar/gkx1098 29155950

27. Aguirre AJ, Meyers RM, Weir BA, Vazquez F, Zhang C-Z, Ben-David U, et al. Genomic Copy Number Dictates a Gene-Independent Cell Response to CRISPR/Cas9 Targeting. Cancer Discov. 2016;6: 914–929.

28. Hart T, Brown KR, Sircoulomb F, Rottapel R, Moffat J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Mol Syst Biol. 2014;10: 733. doi: 10.15252/msb.20145216 24987113

29. Yates B, Braschi B, Gray KA, Seal RL, Tweedie S, Bruford EA. Genenames.org: the HGNC and VGNC resources in 2017. Nucleic Acids Res. 2017;45: D619–D625. doi: 10.1093/nar/gkw1033 27799471

30. Wang T, Birsoy K, Hughes NW, Krupczak KM, Post Y, Wei JJ, et al. Identification and characterization of essential genes in the human genome. Science. 2015;350: 1096–1101. doi: 10.1126/science.aac7041 26472758

31. Makino T, McLysaght A. Ohnologs in the human genome are dosage balanced and frequently associated with disease. Proc Natl Acad Sci U S A. 2010;107: 9270–9274. doi: 10.1073/pnas.0914697107 20439718

32. Singh PP, Arora J, Isambert H. Identification of Ohnolog Genes Originating from Whole Genome Duplication in Early Vertebrates, Based on Synteny Comparison across Multiple Genomes. PLoS Comput Biol. 2015;11: e1004394. doi: 10.1371/journal.pcbi.1004394 26181593

33. Helming KC, Wang X, Wilson BG, Vazquez F, Haswell JR, Manchester HE, et al. ARID1B is a specific vulnerability in ARID1A-mutant cancers. Nat Med. 2014;20: 251–254. doi: 10.1038/nm.3480 24562383

34. Hoffman GR, Rahal R, Buxton F, Xiang K, McAllister G, Frias E, et al. Functional epigenetics approach identifies BRM/SMARCA2 as a critical synthetic lethal target in BRG1-deficient cancers. Proc Natl Acad Sci U S A. 2014;111: 3128–3133. doi: 10.1073/pnas.1316793111 24520176

35. Oike T, Ogiwara H, Tominaga Y, Ito K, Ando O, Tsuta K. A synthetic lethality–based strategy to treat cancers harboring a genetic deficiency in the chromatin remodeling factor BRG1. Cancer Res. 2013; Available: http://cancerres.aacrjournals.org/content/73/17/5508.short

36. van der Lelij P, Lieb S, Jude J, Wutz G, Santos CP, Falkenberg K, et al. Synthetic lethality between the cohesin subunits STAG1 and STAG2 in diverse cancer contexts. Elife. 2017;6. doi: 10.7554/eLife.26980 28691904

37. Benedetti L, Cereda M, Monteverde L, Desai N, Ciccarelli FD. Synthetic lethal interaction between the tumour suppressor STAG2 and its paralog STAG1. Oncotarget. 2017;8: 37619–37632. doi: 10.18632/oncotarget.16838 28430577

38. Muller FL, Colla S, Aquilanti E, Manzo VE, Genovese G, Lee J, et al. Passenger deletions generate therapeutic vulnerabilities in cancer. Nature. 2012;488: 337–342. doi: 10.1038/nature11331 22895339

39. O’Leary MN, Schreiber KH, Zhang Y, Duc A-CE, Rao S, Hale JS, et al. The ribosomal protein Rpl22 controls ribosome composition by directly repressing expression of its own paralog, Rpl22l1. PLoS Genet. 2013;9: e1003708. doi: 10.1371/journal.pgen.1003708 23990801

40. Wilson BG, Roberts CWM. SWI/SNF nucleosome remodellers and cancer. Nat Rev Cancer. 2011;11: 481–492. doi: 10.1038/nrc3068 21654818

41. Giurgiu M, Reinhard J, Brauner B, Dunger-Kaltenbach I, Fobo G, Frishman G, et al. CORUM: the comprehensive resource of mammalian protein complexes—2019. Nucleic Acids Res. 2018;47: D559–D563.

42. Papp B, Pál C, Hurst LD. Dosage sensitivity and the evolution of gene families in yeast. Nature. 2003;424: 194–197. doi: 10.1038/nature01771 12853957

43. Musso G, Costanzo M, Huangfu M, Smith AM, Paw J, San Luis B-J, et al. The extensive and condition-dependent nature of epistasis among whole-genome duplicates in yeast. Genome Res. 2008;18: 1092–1099. doi: 10.1101/gr.076174.108 18463300

44. Hillenmeyer ME, Fung E, Wildenhain J, Pierce SE, Hoon S, Lee W, et al. The chemical genomic portrait of yeast: uncovering a phenotype for all genes. Science. 2008;320: 362–365. doi: 10.1126/science.1150021 18420932

45. Kuzmin E, VanderSluis B, Wang W, Tan G, Deshpande R, Chen Y, et al. Systematic analysis of complex genetic interactions. Science. 2018;360. doi: 10.1126/science.aao1729 29674565

46. Ihmels J, Collins SR, Schuldiner M, Krogan NJ, Weissman JS. Backup without redundancy: genetic interactions reveal the cost of duplicate gene loss. Mol Syst Biol. 2007;3: 86. doi: 10.1038/msb4100127 17389874

47. Nijhawan D, Zack TI, Ren Y, Strickland MR, Lamothe R, Schumacher SE, et al. Cancer vulnerabilities unveiled by genomic loss. Cell. 2012;150: 842–854. doi: 10.1016/j.cell.2012.07.023 22901813

48. Brough R, Frankum JR, Costa-Cabral S, Lord CJ, Ashworth A. Searching for synthetic lethality in cancer. Curr Opin Genet Dev. 2011;21: 34–41. doi: 10.1016/j.gde.2010.10.009 21255997

49. O’Neil NJ, Bailey ML, Hieter P. Synthetic lethality and cancer. Nat Rev Genet. 2017;18: 613–623. doi: 10.1038/nrg.2017.47 28649135

50. Viswanathan SR, Nogueira MF, Buss CG, Krill-Burger JM, Wawer MJ, Malolepsza E, et al. Genome-scale analysis identifies paralog lethality as a vulnerability of chromosome 1p loss in cancer. Nat Genet. 2018;50: 937–943. doi: 10.1038/s41588-018-0155-3 29955178

51. D’Antonio M, Guerra RF, Cereda M, Marchesi S, Montani F, Nicassio F, et al. Recessive cancer genes engage in negative genetic interactions with their functional paralogs. Cell Rep. 2013;5: 1519–1526. doi: 10.1016/j.celrep.2013.11.033 24360954

52. Dey P, Baddour J, Muller F, Wu CC, Wang H, Liao W-T, et al. Genomic deletion of malic enzyme 2 confers collateral lethality in pancreatic cancer. Nature. 2017;542: 119–123. doi: 10.1038/nature21052 28099419

53. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr, Kinzler KW. Cancer genome landscapes. Science. 2013;339: 1546–1558. doi: 10.1126/science.1235122 23539594

54. Zack TI, Schumacher SE, Carter SL, Cherniack AD, Saksena G, Tabak B, et al. Pan-cancer patterns of somatic copy number alteration. Nat Genet. 2013;45: 1134–1140. doi: 10.1038/ng.2760 24071852

55. Fares MA. The origins of mutational robustness. Trends Genet. 2015;31: 373–381. doi: 10.1016/j.tig.2015.04.008 26013677

56. Plata G, Vitkup D. Genetic robustness and functional evolution of gene duplicates. Nucleic Acids Res. 2014;42: 2405–2414. doi: 10.1093/nar/gkt1200 24288370

57. Zapata L, Pich O, Serrano L, Kondrashov FA, Ossowski S, Schaefer MH. Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome. Genome Biol. 2018;19: 67. doi: 10.1186/s13059-018-1434-0 29855388

58. McKinney W. pandas: a foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing. 2011;14. Available: https://www.dlr.de/sc/portaldata/15/resources/dokumente/pyhpc2011/submissions/pyhpc2011_submission_9.pdf

59. Jones E, Oliphant T, Peterson P, Others. SciPy: Open source scientific tools for Python. 2001;

60. Seabold S, Perktold J. Statsmodels: Econometric and statistical modeling with python. Proceedings of the 9th Python in Science Conference. Scipy; 2010. p. 61.

61. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011;12: 2825–2830.

62. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10: R25. doi: 10.1186/gb-2009-10-3-r25 19261174

63. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25: 2078–2079. doi: 10.1093/bioinformatics/btp352 19505943

64. Morgan M, Pages H, Obenchain V, Hayden N. Rsamtools: Binary alignment (BAM), FASTA, variant call (BCF), and tabix file import. R package version. 2016;1: 677–689.

65. Pagès H. BSgenome: Software infrastructure for efficient representation of full genomes and their SNPs. R package version. 2017;1: 10–18129.

66. Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, et al. Software for computing and annotating genomic ranges. PLoS Comput Biol. 2013;9: e1003118. doi: 10.1371/journal.pcbi.1003118 23950696

67. Cancer Cell Line Encyclopedia Consortium, Genomics of Drug Sensitivity in Cancer Consortium. Pharmacogenomic agreement between two cancer cell line data sets. Nature. 2015;528: 84–87. doi: 10.1038/nature15736 26570998

68. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483: 603–607. doi: 10.1038/nature11003 22460905

69. Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H, et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019; doi: 10.1093/nar/gkz369 31066453

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