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A Crowdsourced Analysis to Identify Ab Initio Molecular Signatures Predictive of Susceptibility to Viral Infection Publisher Pubmed



Fourati S1 ; Talla A1 ; Mahmoudian M2, 3 ; Burkhart JG4, 5 ; Klen R2 ; Henao R6, 7 ; Yu T8 ; Aydin Z9 ; Yeung KY10 ; Ahsen ME11 ; Almugbel R10 ; Jahandideh S12 ; Liang X10 ; Nordling TEM13 Show All Authors
Authors
  1. Fourati S1
  2. Talla A1
  3. Mahmoudian M2, 3
  4. Burkhart JG4, 5
  5. Klen R2
  6. Henao R6, 7
  7. Yu T8
  8. Aydin Z9
  9. Yeung KY10
  10. Ahsen ME11
  11. Almugbel R10
  12. Jahandideh S12
  13. Liang X10
  14. Nordling TEM13
  15. Shiga M14
  16. Stanescu A11, 15
  17. Vogel R11, 16
  18. Abdallah EB21, 22
  19. Aghababazadeh FA23
  20. Amadoz A24
  21. Bhalla S25
  22. Bleakley K26, 27
  23. Bongen E28
  24. Borzacchielo D22, 29
  25. Bucher P30, 31
  26. Carbonellcaballero J32
  27. Chaudhary K33
  28. Chinesta F34
  29. Chodavarapu P35
  30. Chow RD36
  31. Cokelaer T37
  32. Cubuk C38
  33. Dhanda SK39
  34. Dopazo J38
  35. Faux T2
  36. Feng Y40
  37. Flinta C41
  38. Guziolowski C21, 22
  39. He D42
  40. Hidalgo MR38
  41. Hou J43
  42. Inoue K44, 45
  43. Jaakkola MK2, 46
  44. Ji J47
  45. Kumar R48
  46. Kumar S30, 31
  47. Kursa MB49
  48. Li Q50, 51
  49. Lopuszynski M49
  50. Lu P51
  51. Magnin M21, 22, 44
  52. Mao W52, 53
  53. Miannay B21
  54. Nikolayeva I54, 55, 56
  55. Obradovic Z57
  56. Pak C58
  57. Rahman MM10
  58. Razzaq M21, 22
  59. Ribeiro T21, 22, 44
  60. Roux O21, 22
  61. Saghapour E59
  62. Saini H60
  63. Sarhadi S61
  64. Sato H62
  65. Schwikowski B54
  66. Sharma A63, 64, 65
  67. Sharma R65, 66
  68. Singla D67
  69. Stojkovic I57, 68
  70. Suomi T2
  71. Suprun M69
  72. Tian C70, 71
  73. Tomalin LE72
  74. Xie L73
  75. Yu X74
  76. Pandey G11
  77. Chiu C17
  78. Mcclain MT6, 18, 19
  79. Woods CW6, 18, 19
  80. Ginsburg GS6, 19
  81. Elo LL2
  82. Tsalik EL6, 19, 20
  83. Mangravite LM8
  84. Sieberts SK8

Source: Nature Communications Published:2018


Abstract

The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses. © 2018, The Author(s).
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