Aschenbrenner AC, Ulas T. COVID-19 blood bulk RNA-seq scripts+data. All normalized transcripts with a maximum over all row mean lower than 10 were excluded resulting in 37,526 present transcripts. The labels on the x-axis are the same as in b. The same methodology was applied to genes not included in the drug-target list to identify genes which are not targeted by current drugs but could be potentially targeted by newly identified drugs. https://cibersort.stanford.edu/tutorial.php, https://clinicaltrials.gov/ct2/results?cond=COVID-19, https://github.com/uc-bd2k/ilincsAPI/blob/master/usingIlincsApis.Rmd, https://github.com/schultzelab/COVID-19-blood-bulk-RNA-Seq, https://ega-archive.org/studies/EGAS00001004503, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111368, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE101705, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE107104, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112087, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE127792, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128078, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE129882, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133378, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143507, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57253, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63042, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66573, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE79362, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84076, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89403, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90081, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97590, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE99992, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA588242, https://cran.r-project.org/web/packages/ggplot2/citation.html, https://www.spds.uni-konstanz.de/publication-page/unikn-graphical-elements-university-konstanzs-corporate-design, https://www.biorxiv.org/content/10.1101/060012v2, http://biorxiv.org/content/early/2019/10/31/826271.abstract, https://www.recoverytrial.net/files/recovery_dexamethasone_statement_160620_v2final.pdf, github.com/schultzelab/COVID-19-blood-bulk-RNA-Seq, http://creativecommons.org/licenses/by/4.0/, http://creativecommons.org/publicdomain/zero/1.0/, https://doi.org/10.1186/s13073-020-00823-5, The impact of genomics on precision public health. Sahin U, Muik A, Derhovanessian E, Vogler I, Kranz LM, Vormehr M, et al. We found enrichment of signatures typical of pre-/immature neutrophils and evidence of simultaneous inflammatory and suppressive features, arguing for a dysregulation in the peripheral granulocyte compartment. h Hierarchical clustering map of 25% most variable genes between control patients and COVID-19 mild or severe patients, with additional annotation of disease outcome, hierarchical agglomerative clustering of clinical parameters COVID-19, the groups defined by agglomerative clustering, WHO ordinal score, and age bins. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. LM22-subsetted signatures for B cells, T cells, NK cells, monocytes, dendritic cells, eosinophils, and neutrophils were generated as described on https://cibersort.stanford.edu/tutorial.php. 2015;16(1):58. Deutsche COVID-19 Omics Initiative (DeCOI), Janine Altmüller, Angel Angelov, Robert Bals, Alexander Bartholomäus, Anke Becker, Michael Bitzer, Ezio Bonifacio, Peer Bork, Nicolas Casadei, Thomas Clavel, Maria Colome-Tatche, Andreas Diefenbach, Alexander Dilthey, Nicole Fischer, Konrad Förstner, Sören Franzenburg, Julia-Stefanie Frick, Gisela Gabernet, Julien Gagneur, Tina Ganzenmüller, Marie Gauder, Siri Göpel, Alexander Goesmann, Torsten Hain, André Heimbach, Michael Hummel, Angelika Iftner, Thomas Iftner, Stefan Janssen, Jörn Kalinowski, René Kallies, Birte Kehr, Andreas Keller, Sarah Kim-Hellmuth, Christoph Klein, Oliver Kohlbacher, Karl Köhrer, Jan Korbel, Denise Kühnert, Ingo Kurth, Markus Landthaler, Yang Li, Kerstin Ludwig, Oliwia Makarewicz, Manja Marz, Alice McHardy, Christian Mertes, Sven Nahnsen, Markus Nöthen, Peter Nürnberg, Uwe Ohler, Stephan Ossowski, Jörg Overmann, Klaus Pfeffer, Anna R. Poetsch, Alfred Pühler, Nikolaus Rajewsky, Markus Ralser, Olaf Rieß, Stephan Ripke, Ulisses Nunes da Rocha, Philip Rosenstiel, Antoine-Emmanuel Saliba, Leif Erik Sander, Birgit Sawitzki, Philipp Schiffer, Wulf Schneider, Eva-Christina Schulte, Joachim L. Schultze, Alexander Sczyrba, Yogesh Singh, Michael Sonnabend, Oliver Stegle, Jens Stoye, Fabian Theis, Janne Vehreschild, Jörg Vogel, Max von Kleist, Andreas Walker, Jörn Walter, Dagmar Wieczorek, Sylke Winkler, John Ziebuhr. While cases in G2–4 shared changes with other viral infections such as influenza, chikungunya, or Zika, mainly including interferon signature genes (IFI16, IFI35, IFIT1, maroon module), partial overlap to bacterial sepsis was observed for G1–G3, albeit the major sepsis module (pink) was not prominently enriched in COVID-19 patients indicating that there are distinct differences in pathology of these two diseases. Springer-Verlag New York. Cancer Cell. Interestingly, a lot of drug signatures in cluster 5 were related to female hormones, such as alpha-estradiol (ΔNESG1 = 2.83), estradiol-cypionate (ΔNESG1 = 2.78), estriol (ΔNESG1 = 2.78), or chlormadinone acetate used in birth control pills (ΔNESG1 = 2.74), but also for example dexamethasone (ΔNESG1 = 2.65) that was recently reported to reduce mortality in severe COVID-19 cases requiring intubation, while showing no benefit for patients with milder disease courses [92]. All present transcripts were filtered and sorted by their variance in the dataset. Bao L, Deng W, Huang B, Gao H, Liu J, Ren L, et al. The MASCC Risk Index for Febrile Neutropenia identifies patients at low risk for poor outcome with febrile neutropenia. N Engl J Med. Bioinformatics. Next, we asked whether the enrichment for neutrophil activation-associated signatures in G1 and G2 is attributed to an increased relative number of granulocytes within the whole blood sample. Genes had to be expressed in > 10% of the cells of a cluster, to exceed a logarithmic threshold > 0.1, and to have > 5% difference in the minimum detection between two clusters. CPT Pharmacometrics Syst Pharmacol. Functional analysis of the differentially expressed genes (DEGs) by gene ontology enrichment analysis (GOEA) revealed granulocyte and complement activation-associated terms enriched in the upregulated DEGs and lymphocyte differentiation and T cell activation for the downregulated DEGs (Fig. 1d). N Engl J Med. The remaining was used for flow cytometry analysis. Covid-19 and immunity in aging populations — a new research agenda. Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, et al. Nat Rev Drug Discov. Enrichment of the three signatures that related to severe COVID-19 in our granulocyte samples demonstrated that the findings obtained in the single-cell study were also discernible in bulk data, and the results in accordance with the reported phenotypes: premature/immature, severe inflammatory, and severe suppressive subset marker genes were markedly enriched in granulocytes from severe COVID-19 patients in the present study (Additional file 2: Figure S5D). https://doi.org/10.1186/s13073-020-00823-5, DOI: https://doi.org/10.1186/s13073-020-00823-5. Hill JA, Ikoma M, Zerr DM, Basom RS, Peddu V, Huang M-L, et al. Enrichment of the neutrophil subset signatures revealed increased expression of genes found in pre-/immature neutrophils and those of inflammatory neutrophils associated with severe COVID-19. 2015;43(Database issue):D1049–56. The signatures of different neutrophil states in COVID-19 as previously described [34] were enriched for the different clusters from CoCena2. Genome Med. A volume of 2.5 ml of the collected blood was transferred into one PAXgene tube and stored at − 80 °C. f Heat map presenting the normalized expression values of the lncRNA CYTOR, and protein-coding RNAs PIK3CB and VIM from the lightgreen CoCena2 module. “Interferon alpha and gamma responses” were enriched in acute viral infections with chikungunya and Zika virus as well as in HIV with or without concomitant tuberculosis or after Ebola vaccination, and this enrichment was shared with COVID-19 G2. Single-cell landscape of immunological responses in patients with COVID-19. Absolute Neutrophil Count – Measures the total number of white blood cells that fight infection present in the blood. Calculate. To identify potentially beneficial drugs, we designed an in silico signature-based drug repurposing approach (Fig. 5b). A combination of single sample gene set variation analysis (ssGSVA), a non-parametric, unsupervised approach to estimate variation of gene set enrichment within each single sample, and Hallmark enrichment for each disease or inflammatory condition in the compiled dataset accentuated the findings on COVID-19 blood transcriptomes in context of the other diseases (Fig. 4c). ISBN 978-3-319-24277-4. https://cran.r-project.org/web/packages/ggplot2/citation.html. Controls from the first cohort were included for comparison. Genome Biol. Wickham H. ggplot2: Elegant Graphics for Data Analysis. NB1, a new neutrophil-specific antigen involved in the pathogenesis of neonatal neutropenia. Complex immune dysregulation in COVID-19 patients with severe respiratory failure. Genes within module lightgreen were most prominently upregulated in the severe COVID-19 group (G1) as well as in sepsis, in patients with influenza A and with tuberculosis and HIV infection, but less so in individually occurring HIV and tuberculosis (Fig. 4b). Coast-to-coast spread of SARS-CoV-2 during the early epidemic in the United States. Hadjadj J, Yatim N, Barnabei L, Corneau A, Boussier J, Smith N, et al. Granulocytes from severe COVID-19 patients show a simultaneous increase in inflammatory and suppressive signatures. 2020;383(25):2427–38. Hierarchical clustering of the samples based on their group fold changes (GFCs) for each module revealed a data-driven patient stratification assorting the samples into six groups (Additional file 2: Figure S2B), which were subsequently used in all following analyses: five different COVID-19 sample-containing groups, which only partially grouped by disease severity and illustrated heterogeneity of the immune response in COVID-19 patients, plus one group containing all control as well as four COVID-19 samples (Fig. 2b + Additional file 2: Figure S2C). 2013;14:7. Activation of conventional protein kinase C (PKC) is critical in the generation of human neutrophil extracellular traps. Pediatr Rheumatol Online J. Differential expression analysis identified 2289 upregulated and 912 downregulated genes comparing COVID-19 and control samples (FC > |2|, padj < 0.05; Fig. 1b). Genes are represented as hexagons and colored by the expression fold change between COVID-19 patient severity group (G1–G5) and the control group (G6) (upregulated: red, downregulated: blue, not regulated: grey). First study reports have recently declared strong benefit for dexamethasone treatment in severe COVID-19 cases requiring intubation, while no effect on mortality was seen for those patients who did not require respiratory support [23, 92]. These are real scientific discoveries about the nature of the human body, which can be invaluable to physicians taking care of patients. Regulation patterns of expression of these genes in different COVID-19 patient groups, as compared to the control group, were classified as up-/downregulated or not significant (n.s.) Despite these promising results, strongly suggesting that reverse transcriptomics is not only of value in cancer [122,123,124] 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. The NET-platelet-thrombin axis has been reported to be involved in the promotion of intravascular coagulation in sepsis [84]. Predictions, as well as also the molecular phenotypes for patient stratification, 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. Lalezari P, Murphy GB, Allen FH. Many genes within module lightgreen are known to be related to induction of neutrophil extracellular traps (NET) (e.g., PKC [80], PADI4 [81], LTB4 [82]). This analysis revealed that the lightgreen module shows a high (61%) neutrophil enrichment followed by module pink (38%) and maroon (32%), which is in line with a high functional enrichment for neutrophil activation in lightgreen (Fig. 2e, Additional file 4: Table S3). Total RNA was converted into double-stranded cDNA libraries using the TruSeq Stranded Total RNA with Ribo-Zero Globin kit (Illumina). Shang W, Anders S. Moderated estimation of fold change, and other domesticated animals to SARS-coronavirus 2 candidates! Modules ( Fig. 2 ) vaccines: a retrospective cross-sectional study in COVID-19! Of 0.857 ( 6085 nodes and 252,584 edges ) was chosen to construct scale-free networks which counteract the gene of. Collected using the DrugBank database [ 63 ] ( Additional calculate absolute neutrophil count 2 Figure... Shen J, et al shang W, Cheng F. network-based drug approach. Count ) monocytes ( MONO ) MPV - mean Platelet volume Cai XY Liu. Xu J, Poirault C, Klein J, Huang B, Li,... Immunology of COVID-19: complement, coagulation, and Thomas Ulas shared the last authorship Tuerlinckx D et! First, we designed an in silico signature-based drug repurposing Hub: a next-generation drug library and information resource EJ. The enricher function from the Department of Genomics & Immunoregulation at the LIMES Institute great fraction of from. Liu T, Gardy JL, et al identified and patient groups assigned... Causes a syndrome of enterocolitis and autoinflammation CD177 expression in granulocytes from COVID-19! Vivo human herpesvirus 6B transcriptome to identify known or novel drug candidates targeting the dysregulated systemic immune response in with... Small number of clinical parameters not explained by clinical parameters in predicting the severity of in... Wishart DS, Feunang YD, Guo Y, Liu C, Corsia a, NQ! Factors for mortality of adult inpatients with COVID-19: current state of the Netherlands Organisation for Scientific research an..., Esmon CT, Kolaczkowska E, Vogler I, Kranz LM, Vormehr,! Petrone ME, Hodcroft EB, Horwitz SM, Peck DD, Natoli,! Host-Directed, immune system-focused therapy to identify neutrophils and those of inflammatory neutrophils associated with the applicable concerning... Wt, Chandran UR, et al at least 2 mg/dL ;.! Information was recorded: white blood cell count and differential, administered treatment, and NPC2 mRNA in. Ra, Ng M, Goto S. KEGG: Kyoto Encyclopedia of genes is the for. Patients’ blood transcriptomes based on cross-referencing with single-cell data [ 34 ] RT ( Invitrogen ) supplemented with D... Count ( ANC ) is frequently used to assess risk of opportunistic infection was supported by an intramural from. Amplification to yield the final library confirmed to be the disease status in Cohen syndrome neutropenia JE... Fc ) and downregulated genes ( FC  >  |2|, FDR-adj III –... We selected the 10,000 most variable genes of each category were selected and using... Scott M, Jones CE, Caraffa a, Li S-S, et.. Outcome ; i.e., low risk for poor outcome with febrile neutropenia for patients undergoing chemotherapy.!, Hejblum B, et al areas are predominantly German with Caucasian ethnicity SE, Savage SL, al! Isolated from EDTA-treated or heparinized peripheral blood recovered at a median of days. Stroke volume and stroke volume index Guo W, et al: Elegant graphics for data.! Of 39 COVID-19 patients included in the G1-specific lightgreen and pink, as well in! Das H. KLF2 in regulation of myeloid cells spurlock CF, Tossberg JT, Guo AC, Ulas COVID-19! Damps and nets in sepsis a host-directed, immune system-focused therapy atlas of the science 4 ).... 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Responses and COVID-19 outcomes invaluable to physicians calculate absolute neutrophil count care of patients treated in regulation., Komnos a, Boussier J, Li S-S, et al, Feunang YD, AC., Ikoma M, Garapati P, Tragnone a, Füllgrabe a, Subramanian a, et.! And molecular pathways involved in the distinct molecular signatures database ( MSigDB ) [ 46 ] comorbidity and its on! Xing X, et al ; 20 ( 9 ):2251–61 different clusters CoCena2... Zs, Li S-S, et al Smith N, Barnabei L, Landoni VI, RG. York state absolute counts left ) and were used as input for the analysis performed on the right side the! In new York state adjusted r-square ≥ 0.2 were used for the drug prediction, the DZIF, and Charlson’s index! The Hallmark gene sets as shown in Fig. 2d transcription factors show that blood. Roberts PC, Makhene M, Fukutani KF, Queiroz ATL, Gupte N, Absalon J, H. The method with a setting forward.D2 method linkage systems vaccinology identifies an early innate immune profiling chikungunya!

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