Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis

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Citações na Scopus
14
Tipo de produção
article
Data de publicação
2023
Título da Revista
ISSN da Revista
Título do Volume
Editora
NATURE PORTFOLIO
Autores
WEINSTOCK, Joshua S.
GOPAKUMAR, Jayakrishnan
BURUGULA, Bala Bharathi
UDDIN, Md Mesbah
JAHN, Nikolaus
BELK, Julia A.
BOUZID, Hind
DANIEL, Bence
MIAO, Zhuang
LY, Nghi
Citação
NATURE, v.616, n.7958, p.755-+, 2023
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Mutations in a diverse set of driver genes increase the fitness of haematopoietic stem cells (HSCs), leading to clonal haematopoiesis(1). These lesions are precursors for blood cancers(2-6), but the basis of their fitness advantage remains largely unknown, partly owing to a paucity of large cohorts in which the clonal expansion rate has been assessed by longitudinal sampling. Here, to circumvent this limitation, we developed a method to infer the expansion rate from data from a single time point. We applied this method to 5,071 people with clonal haematopoiesis. A genome-wide association study revealed that a common inherited polymorphism in the TCL1A promoter was associated with a slower expansion rate in clonal haematopoiesis overall, but the effect varied by driver gene. Those carrying this protective allele exhibited markedly reduced growth rates or prevalence of clones with driver mutations in TET2, ASXL1, SF3B1 and SRSF2, but this effect was not seen in clones with driver mutations in DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 led to the expression of TCL1A protein and the expansion of HSCs in vitro. The protective allele restricted TCL1A expression and expansion of mutant HSCs, as did experimental knockdown of TCL1A expression. Forced expression of TCL1A promoted the expansion of human HSCs in vitro and mouse HSCs in vivo. Our results indicate that the fitness advantage of several commonly mutated driver genes in clonal haematopoiesis may be mediated by TCL1A activation.
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Referências
  1. Abelson S, 2018, NATURE, V559, P400, DOI 10.1038/s41586-018-0317-6
  2. Aguet F, 2020, SCIENCE, V369, P1318, DOI 10.1126/science.aaz1776
  3. Alexandrov LB, 2015, NAT GENET, V47, P1402, DOI 10.1038/ng.3441
  4. [Anonymous], 2020, STAN MOD LANG US GUI
  5. Asuni N, 2018, bioRxiv, DOI [10.1101/473744, 10.1101/473744v3, DOI 10.1101/473744V3]
  6. Bates D., 2019, Matrix: Sparse and Dense Matrix Classes and Methods, V1, P4
  7. Beauchamp EM, 2021, BLOOD CANCER DISCOV, V2, P500, DOI 10.1158/2643-3230.BCD-20-0224
  8. Bezanson J, 2017, SIAM REV, V59, P65, DOI 10.1137/141000671
  9. Bick AG, 2020, NATURE, V586, P763, DOI 10.1038/s41586-020-2819-2
  10. Bick AG, 2020, CIRCULATION, V141, P124, DOI 10.1161/CIRCULATIONAHA.119.044362
  11. Brunet A, 1999, CELL, V96, P857, DOI 10.1016/S0092-8674(00)80595-4
  12. Butler A, 2018, NAT BIOTECHNOL, V36, P411, DOI 10.1038/nbt.4096
  13. Carvalho-Silva D, 2019, NUCLEIC ACIDS RES, V47, pD1056, DOI 10.1093/nar/gky1133
  14. Cibulskis K, 2013, NAT BIOTECHNOL, V31, P213, DOI 10.1038/nbt.2514
  15. Cingolani P, 2012, FLY, V6, P80, DOI 10.4161/fly.19695
  16. Corces M. R., 2017, Protoc Exch, DOI [10.1038/protex.2017.096, DOI 10.1038/PROTEX.2017.096]
  17. Corces MR, 2016, NAT GENET, V48, P1193, DOI 10.1038/ng.3646
  18. Desai P, 2018, NAT MED, V24, P1015, DOI 10.1038/s41591-018-0081-z
  19. Dobin A, 2013, BIOINFORMATICS, V29, P15, DOI 10.1093/bioinformatics/bts635
  20. Eijkelenboom A, 2013, NAT REV MOL CELL BIO, V14, P83, DOI 10.1038/nrm3507
  21. Fabre MA, 2022, NATURE, V606, P335, DOI 10.1038/s41586-022-04785-z
  22. Fishilevich S, 2017, DATABASE-OXFORD, DOI 10.1093/database/bax028
  23. Genovese G, 2014, NEW ENGL J MED, V371, P2477, DOI 10.1056/NEJMoa1409405
  24. Giambartolomei C, 2014, PLOS GENET, V10, DOI 10.1371/journal.pgen.1004383
  25. Gogarten SM, 2019, BIOINFORMATICS, V35, P5346, DOI 10.1093/bioinformatics/btz567
  26. Hiatt JB, 2013, GENOME RES, V23, P843, DOI 10.1101/gr.147686.112
  27. Jaiswal S, 2017, NEW ENGL J MED, V377, P111, DOI 10.1056/NEJMoa1701719
  28. Jaiswal S, 2014, NEW ENGL J MED, V371, P2488, DOI 10.1056/NEJMoa1408617
  29. Jun G, 2015, GENOME RES, V25, P918, DOI 10.1101/gr.176552.114
  30. Kakiuchi N, 2021, NAT REV CANCER, V21, P239, DOI 10.1038/s41568-021-00335-3
  31. Kim D, 2019, NAT BIOTECHNOL, V37, P907, DOI 10.1038/s41587-019-0201-4
  32. Koboldt DC, 2012, GENOME RES, V22, P568, DOI 10.1101/gr.129684.111
  33. Laine J, 2000, MOL CELL, V6, P395, DOI 10.1016/S1097-2765(00)00039-3
  34. Lee-Six H, 2018, NATURE, V561, P473, DOI 10.1038/s41586-018-0497-0
  35. Li ZL, 2019, AM J HUM GENET, V104, P802, DOI 10.1016/j.ajhg.2019.03.002
  36. Love MI, 2014, GENOME BIOL, V15, DOI 10.1186/s13059-014-0550-8
  37. Ma C, 2013, GENET EPIDEMIOL, V37, P539, DOI 10.1002/gepi.21742
  38. Malcovati L, 2017, BLOOD, V129, P3371, DOI 10.1182/blood-2017-01-763425
  39. Martincorena I, 2018, SCIENCE, V362, P911, DOI 10.1126/science.aau3879
  40. Martincorena I, 2015, SCIENCE, V348, P880, DOI 10.1126/science.aaa6806
  41. Miller CA, 2022, J MOL DIAGN, V24, P219, DOI 10.1016/j.jmoldx.2021.10.013
  42. mimips, 2020, About us
  43. Mitchell E, 2022, NATURE, V606, P343, DOI 10.1038/s41586-022-04786-y
  44. Narducci MG, 1997, CANCER RES, V57, P5452
  45. Osorio FG, 2018, CELL REP, V25, P2308, DOI 10.1016/j.celrep.2018.11.014
  46. Pedersen BS, 2017, BIOINFORMATICS, V33, P1867, DOI 10.1093/bioinformatics/btx057
  47. Pietras EM, 2015, CELL STEM CELL, V17, P35, DOI 10.1016/j.stem.2015.05.003
  48. Psaila B, 2020, MOL CELL, V78, P477, DOI 10.1016/j.molcel.2020.04.008
  49. Regev Aviv, 2017, Elife, V6, DOI 10.7554/eLife.27041
  50. Regier AA, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-018-06159-4
  51. Robertson NA, 2022, NAT MED, V28, P1439, DOI 10.1038/s41591-022-01883-3
  52. Robinson JT, 2011, NAT BIOTECHNOL, V29, P24, DOI 10.1038/nbt.1754
  53. Stan Development Team, 2020, RSTAN R INT STAN V 2
  54. Steensma DP, 2015, BLOOD, V126, P9, DOI 10.1182/blood-2015-03-631747
  55. Steffan-Dewenter I., 2002, Ecology, V83, P1421, DOI 10.1890/0012-9658(2002)083[1421:SDEOLC]2.0.CO;2
  56. Taliun D, 2021, NATURE, V590, DOI 10.1038/s41586-021-03205-y
  57. Thompson DJ, 2019, NATURE, V575, P652, DOI 10.1038/s41586-019-1765-3
  58. Trapnell C, 2014, NAT BIOTECHNOL, V32, P381, DOI 10.1038/nbt.2859
  59. Uddin MM, 2022, IMMUN AGEING, V19, DOI 10.1186/s12979-022-00278-9
  60. van Deuren RC, 2021, bioRxiv, DOI [10.1101/2021.05.12.443095, 10.1101/2021.05.12.443095, DOI 10.1101/2021.05.12.443095]
  61. van Zeventer IA, 2020, BLOOD, V135, P1161, DOI 10.1182/blood.2019004362
  62. Velten L, 2021, NAT COMMUN, V12, DOI 10.1038/s41467-021-21650-1
  63. Venables WN., 2002, Modern Applied Statistics with S, DOI 10.1007/978-0-387-21706-2
  64. Voss K., 2017, F1000RESEARCH, DOI 10.7490/F1000RESEARCH.1114631.1
  65. Wang G, 2020, J R STAT SOC B, V82, P1273, DOI 10.1111/rssb.12388
  66. Watson CJ, 2020, SCIENCE, V367, P1449, DOI 10.1126/science.aay9333
  67. Williams N, 2022, NATURE, V602, P162, DOI 10.1038/s41586-021-04312-6
  68. Xie MC, 2014, NAT MED, V20, P1472, DOI 10.1038/nm.3733
  69. Young AL, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms12484
  70. Zerbino DR, 2015, GENOME BIOL, V16, DOI 10.1186/s13059-015-0621-5
  71. Zhou W, 2018, NAT GENET, V50, P1335, DOI 10.1038/s41588-018-0184-y
  72. Zink F, 2017, BLOOD, V130, P742, DOI 10.1182/blood-2017-02-769869