Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

  1. German COVID-19 Omics Initiative (DeCOI)
  2. Anna C. Aschenbrenner
  3. Maria Mouktaroudi
  4. Benjamin Krämer
  5. Marie Oestreich
  6. Nikolaos Antonakos
  7. Melanie Nuesch-Germano
  8. Konstantina Gkizeli
  9. Lorenzo Bonaguro
  10. Nico Reusch
  11. Kevin Baßler
  12. Maria Saridaki
  13. Rainer Knoll
  14. Tal Pecht
  15. Theodore S. Kapellos
  16. Sarandia Doulou
  17. Charlotte Kröger
  18. Miriam Herbert
  19. Lisa Holsten
  20. Arik Horne
  21. Ioanna D. Gemünd
  22. Nikoletta Rovina
  23. Shobhit Agrawal
  24. Kilian Dahm
  25. Martina van Uelft
  26. Anna Drews
  27. Lena Lenkeit
  28. Niklas Bruse
  29. Jelle Gerretsen
  30. Jannik Gierlich
  31. Matthias Becker
  32. Kristian Händler
  33. Michael Kraut
  34. Heidi Theis
  35. Simachew Mengiste
  36. Elena De Domenico
  37. Jonas Schulte-Schrepping
  38. Lea Seep
  39. Jan Raabe
  40. Christoph Hoffmeister
  41. Michael ToVinh
  42. Verena Keitel
  43. Gereon Rieke
  44. Valentina Talevi
  45. Dirk Skowasch
  46. N. Ahmad Aziz
  47. Peter Pickkers
  48. Frank L. van de Veerdonk
  49. Mihai G. Netea
  50. Joachim L. Schultze
  51. Matthijs Kox
  52. Monique M. B. Breteler
  53. Jacob Nattermann
  54. Antonia Koutsoukou
  55. Evangelos J. Giamarellos-Bourboulis
  56. Thomas Ulas

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system.

Methods

In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings.

Results

Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.

Conclusions

Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.

Article activity feed

  1. Version published to 10.1186/s13073-020-00823-5
  2. Version published to 10.1101/2020.07.07.20148395v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.07.07.20148395: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    SUBJECT DETAILS: Flow cytometry techniques: Whole blood cells were incubated for 15 minutes in the dark with the monoclonal antibodies anti-CD14 FITC, anti-CD3 FITC, anti-CD4 FITC and anti-CD19 FITC (fluorescein isothiocyanate, emission 525nm,Beckman Coulter); with anti-CD4 PE, anti-CD8 PE, and anti-CD(16+56) PE (phycoerythrin, emission 575nm, Beckman Coulter); and with anti-CD45 PC5 (emission 667nm, Beckman Coulter).
    anti-CD14
    suggested: None
    anti-CD3 FITC
    suggested: (Beckman Coulter Cat# 6607073, RRID:AB_1575973)
    anti-CD4 FITC
    suggested: None
    anti-CD19 FITC
    suggested: None
    anti-CD4 PE
    suggested: (Cell Sciences Cat# CMC145PE, RRID:AB_2291371)
    anti-CD8 PE
    suggested: None
    anti-CD(16+56
    suggested: None
    anti-CD45
    suggested: None
    Software and Algorithms
    SentencesResources
    DESeq2 was used for the calculation of normalized counts for each transcript using default parameters.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Gene set “c5.bp.v7.0.symbols.gmt” was obtained from the Molecular Signatures Database (MSigDB) (89). compareCluster and enrichGo functions from the R package ClusterProfiler (v3.12.0) (90) were used to determine significant enrichment (q-value<0.05) of biological processes.
    ClusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)
    Agglomerative hierarchical clustering was performed using the hclust function, defining the method with a setting for ward.D2 method linkage.
    hclust
    suggested: (HCLUST, RRID:SCR_009154)
    The algorithm was subsequently run with 1,000 permutations and the proportions of cell types were visualized with ggplot2 (v3.2.1) (93).
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Data Integration for Disease Comparison: To describe the differences and similarities between COVID-19 and other diseases, we searched in databases for genomics data such as Gene Expression Omnibus (GEO) (101) and ArrayExpress (102) [for studies that fulfill certain criteria: I) having at least 20 samples, II) the disease of study was of relevance (other infections, such as bacterial and viral, plus diseases that mainly involve immune dysregulation, such as autoimmune disease) and III) library preparation and sequencing technology differ as little as possible from our COVID-19 protocol.
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    ArrayExpress
    suggested: (ArrayExpress, RRID:SCR_002964)
    The fastq files of 18 additional studies (588242, GSE101705, GSE107104, GSE112087, GSE127792, GSE128078, GSE129882, GSE133378, GSE143507, GSE57253, GSE63042, GSE66573, GSE79362, GSE84076, GSE89403, GSE90081, GSE97590, GSE99992 and the Rhineland study) were downloaded and aligned with STAR.
    STAR
    suggested: (STAR, RRID:SCR_015899)
    The modules were analyzed for enriched immune cell markers as provided by CIBERSORT and BD Rhapsody and those that showed neutrophil enrichment were screened for genes representative of different neutrophil subtypes as recently described (42).
    CIBERSORT
    suggested: (CIBERSORT, RRID:SCR_016955)
    Gene set enrichment analysis (GSVA): The GSVA R package (v1.34.0) (103) was used to test the enrichment of neutrophil signatures (42) in the normalized gene expression table.
    Gene set enrichment analysis
    suggested: (Gene Set Enrichment Analysis, RRID:SCR_003199)
    The gsva method was used for the run and data were visualized in a heat map with the pheatmap (v1.0.12) package.
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    Classification of the drugs was performed based on the ATC code, as well as additional research on the drugs action.
    ATC
    suggested: None
    Drug target genes were identified using the DrugBank database (104) (Table S6).
    DrugBank
    suggested: (DrugBank, RRID:SCR_002700)
    Drug prediction: To identify drugs, which reverse the gene expression signature observed in the comparisons of the COVID-19-specific clusters compared to the control cluster, the drug prediction databases iLINCS (http://www.ilincs.org/ilincs/) and CLUE (https://clue.io/) were accessed.
    https://clue.io/
    suggested: (CMap, RRID:SCR_016204)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Despite these promising results, strongly suggesting that reverse transcriptomics is not only of value in cancer (79–81) but might also be used to identify drugs targeting the immune pathophysiology in COVID-19, we would also like to point out current limitations of our findings that need to be addressed in future studies. Predictions will further benefit from and focused by validation studies in independent COVID-19 patient cohorts, which is to be fostered by a central database for COVID-19 patients’ blood transcriptome data. Nevertheless, we used samples from different countries, illustrating the generalizability. Furthermore, the molecularly derived and prioritized drug candidates presented here might be tested in very recently introduced pre-clinical models (86) prior to starting clinical trials. Irrespective of the current shortcomings, we favor such drug candidate identification, since it is based on interrogation of molecular data directly derived from patients’ immune cells involved in the ongoing processes in the disease and therefore may increase the likelihood of a beneficial effect in patients. Collectively, we provide first evidence for whole blood transcriptomics to potentially become a valuable tool for distinguishing COVID-19 from other infections in cases for which pathogen detection might be difficult, for monitoring and potentially predicting outcome of the disease, to further dissect molecular phenotypes of COVID-19, particularly of the host’s immune syste...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04394416RecruitingTrial of Imatinib for Hospitalized Adults With COVID-19
    NCT04357613Not yet recruitingIMATINIB IN COVID-19 DISEASE IN AGED PATIENTS.
    NCT04346147RecruitingClinical Trial to Evaluate Efficacy of 3 Types of Treatment …
    NCT04356495RecruitingTrial of COVID-19 Outpatient Treatment in Individuals With R…
    NCT04348071WithdrawnSafety and Efficacy of Ruxolitinib for COVID-19
    NCT04355793AvailableExpanded Access Program of Ruxolitinib for the Emergency Tre…
    NCT04377620RecruitingAssessment of Efficacy and Safety of Ruxolitinib in Particip…
    NCT04338802Not yet recruitingEfficacy and Safety of Nintedanib in the Treatment of Pulmon…

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    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.

  4. Version published to 10.1101/2020.07.07.20148395v1 on medRxiv