% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@ARTICLE{Aschenbrenner:178091,
author = {A. C. Aschenbrenner and M. Mouktaroudi and B. Krämer and
M. Oestreich and N. Antonakos and M. Nuesch-Germano and K.
Gkizeli and L. Bonaguro and N. Reusch and K. Baßler and M.
Saridaki and R. Knoll and T. Pecht and T. S. Kapellos and S.
Doulou and C. Kröger and M. Herbert and L. Holsten and A.
Horne and I. D. Gemünd and N. Rovina and S. Agrawal and K.
Dahm and M. van Uelft and A. Drews and L. Lenkeit and N.
Bruse and J. Gerretsen and J. Gierlich and M. Becker and K.
Händler and M. Kraut and H. Theis and S. Mengiste and E. De
Domenico and J. Schulte-Schrepping and L. Seep and J. Raabe
and C. Hoffmeister and M. ToVinh and V. Keitel and G. Rieke
and V. Talevi and D. Skowasch and N. A. Aziz and P. Pickkers
and F. L. van de Veerdonk and M. G. Netea and J. L. Schultze
and M. Kox and M. M. B. Breteler and J. Nattermann and A.
Koutsoukou and E. J. Giamarellos-Bourboulis and T. Ulas and
J. Altmüller and A. Angelov and R. Bals and A.
Bartholomäus and A. Becker and M. Bitzer and E. Bonifacio
and P. Bork and N. Casadei and T. Clavel and M.
Colome-Tatche and A. Diefenbach and A. Dilthey and N.
Fischer and K. Förstner and S. Franzenburg and J.-S. Frick
and G. Gabernet and J. Gagneur and T. Ganzenmüller and S.
Göpel and A. Goesmann and T. Hain and A. Heimbach and M.
Hummel and A. Iftner and T. Iftner and S. Janssen and J.
Kalinowski and R. Kallies and B. Kehr and A. Keller and S.
Kim-Hellmuth and C. Klein and O. Kohlbacher and K. Köhrer
and J. Korbel and D. Kühnert and I. Kurth and M. Landthaler
and Y. Li and K. Ludwig and O. Makarewicz and M. Marz and A.
McHardy and C. Mertes and M. Nöthen and P. Nürnberg and U.
Ohler and S. Ossowski and J. Overmann and K. Pfeffer and A.
R. Poetsch and A. Pühler and N. Rajewsky and M. Ralser and
O. Rieß and S. Ripke and U. Nunes da Rocha and P.
Rosenstiel and A.-E. Saliba and L. E. Sander and B. Sawitzki
and P. Schiffer and W. Schneider and E.-C. Schulte and J. L.
Schultze and A. Sczyrba and Y. Singh and M. Sonnabend and O.
Stegle$^*$ and J. Stoye and F. Theis and J. Vehreschild and
J. Vogel and M. von Kleist and A. Walker and J. Walter and
D. Wieczorek and S. Winkler and J. Ziebuhr},
collaboration = {G. C. O. Initiative},
title = {{D}isease severity-specific neutrophil signatures in blood
transcriptomes stratify {COVID}-19 patients.},
journal = {Genome medicine},
volume = {13},
number = {1},
issn = {1756-994X},
address = {London},
publisher = {BioMed Central},
reportid = {DKFZ-2021-03098},
pages = {7},
year = {2021},
abstract = {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.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.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.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.},
keywords = {Antiviral Agents: therapeutic use / COVID-19: drug therapy
/ COVID-19: pathology / COVID-19: virology / Case-Control
Studies / Down-Regulation / Drug Repositioning / Humans /
Neutrophils: cytology / Neutrophils: immunology /
Neutrophils: metabolism / Phenotype / Principal Component
Analysis / RNA: blood / RNA: chemistry / RNA: metabolism /
Sequence Analysis, RNA / Severity of Illness Index /
Transcriptome / Up-Regulation / Blood transcriptomics
(Other) / COVID-19 (Other) / Co-expression analysis (Other)
/ Drug repurposing (Other) / Granulocytes (Other) /
Molecular disease phenotypes (Other) / Neutrophils (Other) /
Stratification (Other) / Transcriptome (Other) / Antiviral
Agents (NLM Chemicals) / RNA (NLM Chemicals)},
cin = {B260},
ddc = {610},
cid = {I:(DE-He78)B260-20160331},
pnm = {899 - ohne Topic (POF4-899)},
pid = {G:(DE-HGF)POF4-899},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:33441124},
pmc = {pmc:PMC7805430},
doi = {10.1186/s13073-020-00823-5},
url = {https://inrepo02.dkfz.de/record/178091},
}
guest ::