A multi-tissue study of immune gene expression profiling highlights the key role of the nasal epithelium in COVID-19 severity

  1. Alberto Gómez-Carballa
  2. Irene Rivero-Calle
  3. Jacobo Pardo-Seco
  4. José Gómez-Rial
  5. Carmen Rivero-Velasco
  6. Nuria Rodríguez-Núñez
  7. Gema Barbeito-Castiñeiras
  8. Hugo Pérez-Freixo
  9. Miriam Cebey-López
  10. Ruth Barral-Arca
  11. Carmen Rodriguez-Tenreiro
  12. Ana Dacosta-Urbieta
  13. Xabier Bello
  14. Sara Pischedda
  15. María José Currás-Tuala
  16. Sandra Viz-Lasheras
  17. Federico Martinón-Torres
  18. Antonio Salas
  19. Aguilera Guirao Antonio
  20. Álvarez Escudero Julián
  21. Antela López Antonio
  22. Barbeito Castiñeiras Gema
  23. Bello Paderne Xabier
  24. Ben García Miriam
  25. Carral García María Victoria
  26. Cebey López Miriam
  27. Coira Nieto Amparo
  28. Conde Pájaro Mónica
  29. Costa Alcalde José Javier
  30. Currás Tuala María José
  31. Dacosta Urbieta Ana Isabel
  32. Díaz Esteban Blanca
  33. Domínguez Santalla María Jesús
  34. Fernández Pérez Cristina
  35. Fernández Villaverde Juan
  36. Galbán Rodríguez Cristóbal
  37. García Allut José Luis
  38. García Vicente Luisa
  39. Giráldez Vázquez Elena
  40. Gómez Carballa Alberto
  41. Gómez Rial José
  42. González Barcala Francisco Javier
  43. Guerra Liñares Beatriz
  44. Leboráns Iglesias Pilar
  45. Lence Massa Beatriz
  46. López Franco Montserrat
  47. López Lago Ana
  48. Martinón-Torres Federico
  49. Antonio Salas
  50. Navarro De la Cruz Daniel
  51. Núñez Masid Eloína
  52. Ortolá Devesa Juan Bautista
  53. Pardo Seco Jacobo
  54. Pazo Núñez María
  55. Pérez del Molino Bernal Marisa
  56. Pérez Freixo Hugo
  57. Piñeiro Rodríguez Lidia
  58. Pischedda Sara
  59. Portela Romero Manuel
  60. Pose Reino Antonio
  61. Prada Hervella Gloria María
  62. Queiro Verdes Teresa
  63. Redondo Collazo Lorenzo
  64. Regueiro Casuso Patricia
  65. Rey García Susana
  66. Rey Vázquez Sara
  67. Riveiro Blanco Vanessa
  68. Rivero Calle Irene
  69. Rivero Velasco Carmen
  70. Rodríguez Núñez Nuria
  71. Rodríguez-Tenreiro Sánchez Carmen
  72. Saborido Paz Eva
  73. Sadiki Orayyou José Miguel
  74. Saito Villanueva Carla
  75. Serén Fernández Sonia
  76. Souto Sanmartín Pablo
  77. Taboada Muñiz Manuel
  78. Trastoy Pena Rocío
  79. Treviño Castellano Mercedes
  80. Valdés Cuadrado Luis
  81. Varela García Pablo
  82. Vilas Iglesias María Soledad
  83. Viz Lasheras Sandra
  84. Ferreiro-Iglesias Rocio
  85. Bastón-Rey iria
  86. Calviño-Suárez Cristina

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Abstract

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  1. Version published to 10.1016/j.envres.2022.112890
  2. ScreenIT

    SciScore for 10.1101/2021.10.27.466206: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIRB: The study was approved by the Ethical Committee of Clinical Investigation of Galicia (CEIC ref.
    Consent: Written informed consent was obtained for subjects before study inclusion.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    After isolation, RNA amount and integrity was checked using TapeStation 4200 (Agilent), and DV200 values were calculated to both ensure that >50% of the of the RNA fragments were above 200nt and estimate the optimal sample input for the nCounter analysis.
    Agilent
    suggested: (Agilent Bravo NGS, RRID:SCR_019473)
    The GeNorm algorithm (38) implemented in the R package CrtlGene (39) was used to test for the most stable and optimal number of genes for normalization.
    GeNorm
    suggested: (geNORM, RRID:SCR_006763)
    Data normalization was performed through a combined approach using both DESeq2 (40) and RUVSeq (41) as described in (42).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Volcano plots and heatmaps were built with EnhancedVolcano (43) and ComplexHeatmap (44) R packages, respectively.
    ComplexHeatmap
    suggested: (ComplexHeatmap, RRID:SCR_017270)
    All graphics were created using R (www.r-project.org) software and the ggplot2 package (45).
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    For GSEA we used all available molecular measurements (log2FC) from the genes included in the DE to detect coordinated changes in the expression of genes from same pathway.
    GSEA
    suggested: (SeqGSEA, RRID:SCR_005724)
    Analyses were carried out using the Clusterprofiler (47) R package.
    Clusterprofiler
    suggested: (clusterProfiler, RRID:SCR_016884)
    We tested both the GO (Gene Ontology) and Reactome databases.
    Reactome
    suggested: (Reactome, RRID:SCR_003485)
    Microarray and RNA-seq data were downloaded from the public gene expression microarray repository Gene Expression Omnibus (GEO) under accession numbers: GSE64456 (49), GSE40012 (50), GSE42026 (51), GSE60244 (52), those also included in Thair et al. (23) as viral cohorts; and GSE72829 (53), GSE69529 (54).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Next, we used Limma package (59) to detect DEG and calculate log2FC values between groups.
    Limma
    suggested: (LIMMA, RRID:SCR_010943)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.10.27.466206v1 on bioRxiv