Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients.
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Aschenbrenner Anna C, Mouktaroudi Maria, Krämer Benjamin, Oestreich Marie, Antonakos Nikolaos, Nuesch-Germano Melanie, Gkizeli Konstantina, Bonaguro Lorenzo, Reusch Nico, Baßler Kevin, Saridaki Maria, Knoll Rainer, Pecht Tal, Kapellos Theodore S, Doulou Sarandia, Kröger Charlotte, Herbert Miriam, Holsten Lisa, Horne Arik, Gemünd Ioanna D, Rovina Nikoletta, Agrawal Shobhit, Dahm Kilian, van Uelft Martina, Drews Anna, Lenkeit Lena, Bruse Niklas, Gerretsen Jelle, Gierlich Jannik, Becker Matthias, Händler Kristian, Kraut Michael, Theis Heidi, Mengiste Simachew, De Domenico Elena, Schulte-Schrepping Jonas, Seep Lea, Raabe Jan, Hoffmeister Christoph, ToVinh Michael, Keitel Verena, Rieke Gereon, Talevi Valentina, Skowasch Dirk, Aziz N Ahmad, Pickkers Peter, van de Veerdonk Frank L, Netea Mihai G, Schultze Joachim L, Kox Matthijs, Breteler Monique M B, Nattermann Jacob, Koutsoukou Antonia, Giamarellos-Bourboulis Evangelos J, Ulas Thomas, German COVID-19 Omics Initiative (DeCOI)
Аннотация
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.