#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Multiplexed assays reveal effects of missense variants in MSH2 and cancer predisposition


Autoři: Sofie V. Nielsen aff001;  Rasmus Hartmann-Petersen aff001;  Amelie Stein aff001;  Kresten Lindorff-Larsen aff001
Působiště autorů: Department of Biology, The Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, Copenhagen, Denmark aff001
Vyšlo v časopise: Multiplexed assays reveal effects of missense variants in MSH2 and cancer predisposition. PLoS Genet 17(4): e1009496. doi:10.1371/journal.pgen.1009496
Kategorie: Viewpoints
doi: https://doi.org/10.1371/journal.pgen.1009496


Zdroje

1. Dominguez-Valentin M, Sampson JR, Seppälä TT, Ten Broeke SW, Plazzer J-P, Nakken S, et al. Cancer risks by gene, age, and gender in 6350 carriers of pathogenic mismatch repair variants: findings from the Prospective Lynch Syndrome Database. Genet Med. 2020;22:15–25. doi: 10.1038/s41436-019-0596-9 31337882

2. Lynch HT, Snyder CL, Shaw TG, Heinen CD, Hitchins MP. Milestones of Lynch syndrome: 1895–2015. Nat Rev Cancer. 2015;15:181–94. doi: 10.1038/nrc3878 25673086

3. Ali H, Olatubosun A, Vihinen M. Classification of mismatch repair gene missense variants with PON-MMR. Hum Mutat. 2012;33:642–50. doi: 10.1002/humu.22038 22290698

4. Brnich SE, Abou Tayoun AN, Couch FJ, Cutting GR, Greenblatt MS, Heinen CD, et al. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med. 2019;12:3. doi: 10.1186/s13073-019-0690-2 31892348

5. Houlleberghs H, Dekker M, Lantermans H, Kleinendorst R, Dubbink HJ, Hofstra RMW, et al. Oligonucleotide-directed mutagenesis screen to identify pathogenic Lynch syndrome-associated MSH2 DNA mismatch repair gene variants. Proc Natl Acad Sci U S A. 2016;113:4128–33. doi: 10.1073/pnas.1520813113 26951660

6. Tricarico R, Kasela M, Mareni C, Thompson BA, Drouet A, Staderini L, et al. Assessment of the InSiGHT Interpretation Criteria for the Clinical Classification of 24 MLH1 and MSH2 Gene Variants. Hum Mutat. 2017;38:64–77. doi: 10.1002/humu.23117 27629256

7. Rasmussen LJ, Heinen CD, Royer-Pokora B, Drost M, Tavtigian S, Hofstra RMW, et al. Pathological assessment of mismatch repair gene variants in Lynch syndrome: past, present, and future. Hum Mutat. 2012;33:1617–25. doi: 10.1002/humu.22168 22833534

8. Fowler DM, Fields S. Deep mutational scanning: a new style of protein science. Nat Methods. 2014;11:801–7. doi: 10.1038/nmeth.3027 25075907

9. Starita LM, Ahituv N, Dunham MJ, Kitzman JO, Roth FP, Seelig G, et al. Variant Interpretation: Functional Assays to the Rescue. Am J Hum Genet. 2017;101:315–25. doi: 10.1016/j.ajhg.2017.07.014 28886340

10. Ollodart AR, Yeh C-LC, Miller AW, Shirts BH, Gordon AS, Dunham MJ. Multiplexing Mutation Rate Assessment: Determining Pathogenicity of Msh2 Variants in S. cerevisiae. Genetics (In press)

11. Jia X, Burugula BB, Chen V, Lemons RM, Jayakody S, Maksutova M, et al. Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk. Am J Hum Genet. 2020. doi: 10.1016/j.ajhg.2020.12.003 33357406

12. Brown KD, Rathi A, Kamath R, Beardsley DI, Zhan Q, Mannino JL, et al. The mismatch repair system is required for S-phase checkpoint activation. Nat Genet. 2003;33:80–4. doi: 10.1038/ng1052 12447371

13. Paquin CE, Adams J. Relative fitness can decrease in evolving asexual populations of S. cerevisiae. Nature. 1983;306:368–70. doi: 10.1038/306368a0 16752492

14. Lang GI, Murray AW. Estimating the per-base-pair mutation rate in the yeast Saccharomyces cerevisiae. Genetics. 2008;178:67–82. doi: 10.1534/genetics.107.071506 18202359

15. Gammie AE, Erdeniz N, Beaver J, Devlin B, Nanji A, Rose MD. Functional characterization of pathogenic human MSH2 missense mutations in Saccharomyces cerevisiae. Genetics. 2007;177:707–21. doi: 10.1534/genetics.107.071084 17720936

16. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434–43. doi: 10.1038/s41586-020-2308-7 32461654

17. Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019;176:535–548.e24. doi: 10.1016/j.cell.2018.12.015 30661751

18. Nielsen SV, Stein A, Dinitzen AB, Papaleo E, Tatham MH, Poulsen EG, et al. Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations. PLoS Genet. 2017;13:e1006739. doi: 10.1371/journal.pgen.1006739 28422960

19. Warren JJ, Pohlhaus TJ, Changela A, Iyer RR, Modrich PL, Beese LS. Structure of the human MutSalpha DNA lesion recognition complex. Mol Cell. 2007;26:579–92. doi: 10.1016/j.molcel.2007.04.018 17531815

20. Guerois R, Nielsen JE, Serrano L. Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol. 2002;320:369–87. doi: 10.1016/S0022-2836(02)00442-4 12079393

21. Balakrishnan S, Kamisetty H, Carbonell JG, Lee S-I, Langmead CJ. Learning generative models for protein fold families. Proteins. 2011;79:1061–78. doi: 10.1002/prot.22934 21268112

22. Abildgaard AB, Stein A, Nielsen SV, Schultz-Knudsen K, Papaleo E, Shrikhande A, et al. Computational and cellular studies reveal structural destabilization and degradation of MLH1 variants in Lynch syndrome. elife. 2019;8. doi: 10.7554/eLife.49138 31697235

23. Martinez SL, Kolodner RD. Functional analysis of human mismatch repair gene mutations identifies weak alleles and polymorphisms capable of polygenic interactions. Proc Natl Acad Sci U S A. 2010;107:5070–5. doi: 10.1073/pnas.1000798107 20176959

24. Bouvet D, Bodo S, Munier A, Guillerm E, Bertrand R, Colas C, et al. Methylation Tolerance-Based Functional Assay to Assess Variants of Unknown Significance in the MLH1 and MSH2 Genes and Identify Patients With Lynch Syndrome. Gastroenterology. 2019;157:421–31. doi: 10.1053/j.gastro.2019.03.071 30998989

25. Drost M, Lützen A, van Hees S, Ferreira D, Calléja F, Zonneveld JBM, et al. Genetic screens to identify pathogenic gene variants in the common cancer predisposition Lynch syndrome. Proc Natl Acad Sci U S A. 2013;110:9403–8. doi: 10.1073/pnas.1220537110 23690608

26. Bapat BV, Madlensky L, Temple LK, Hiruki T, Redston M, Baron DL, et al. Family history characteristics, tumor microsatellite instability and germline MSH2 and MLH1 mutations in hereditary colorectal cancer. Hum Genet. 1999;104:167–76. doi: 10.1007/s004390050931 10190329

27. Stein A, Fowler DM, Hartmann-Petersen R, Biophysical L-LK. Mechanistic Models for Disease-Causing Protein Variants. Trends Biochem Sci. 2019;44:575–88. doi: 10.1016/j.tibs.2019.01.003 30712981

28. Feinauer C, Weigt M. Context-Aware Prediction of Pathogenicity of Missense Mutations Involved in Human Disease. 2017. doi: 10.1101/103051

29. Hopf TA, Ingraham JB, Poelwijk FJ, Schärfe CPI, Springer M, Sander C, et al. Mutation effects predicted from sequence co-variation. Nat Biotechnol. 2017;35:128–35. doi: 10.1038/nbt.3769 28092658

30. Jepsen MM, Fowler DM, Hartmann-Petersen R, Stein A, Lindorff-Larsen K. Chapter 5—Classifying disease-associated variants using measures of protein activity and stability. In: Pey AL, editor. Protein Homeostasis Diseases. Academic Press; 2020. pp. 91–107.

31. Cagiada M, Johansson KE, Valančiūtė A, Nielsen SV, Hartmann-Petersen R, Yang JJ, et al. Understanding the origins of loss of protein function by analyzing the effects of thousands of variants on activity and abundance. bioRxiv. 2020. p. 2020.09.28.317040. doi: 10.1101/2020.09.28.317040

32. Chiasson MA, Rollins NJ, Stephany JJ, Sitko KA, Matreyek KA, Verby M, et al. Multiplexed measurement of variant abundance and activity reveals VKOR topology, active site and human variant impact. elife. 2020;9. doi: 10.7554/eLife.58026 32870157

33. Frazer J, Notin P, Dias M, Gomez A, Brock K, Gal Y, et al. Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning. bioRxiv. 2020. p. 2020.12.21.423785. doi: 10.1101/2020.12.21.423785

34. Livesey BJ, Marsh JA. Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations. Mol Syst Biol. 2020;16:e9380. doi: 10.15252/msb.20199380 32627955

35. Arlow T, Scott K, Wagenseller A, Gammie A. Proteasome inhibition rescues clinically significant unstable variants of the mismatch repair protein Msh2. Proc Natl Acad Sci U S A. 2013:246–51. doi: 10.1073/pnas.1215510110 23248292


Článek vyšel v časopise

PLOS Genetics


2021 Číslo 4
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Hypertenze a hypercholesterolémie – synergický efekt léčby
nový kurz
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Multidisciplinární zkušenosti u pacientů s diabetem
Autoři: Prof. MUDr. Martin Haluzík, DrSc., prof. MUDr. Vojtěch Melenovský, CSc., prof. MUDr. Vladimír Tesař, DrSc.

Úloha kombinovaných preparátů v léčbě arteriální hypertenze
Autoři: prof. MUDr. Martin Haluzík, DrSc.

Halitóza
Autoři: MUDr. Ladislav Korábek, CSc., MBA

Terapie roztroušené sklerózy v kostce
Autoři: MUDr. Dominika Šťastná, Ph.D.

Všechny kurzy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#