Highlights d SARS-CoV2 infection elicits dynamic changes of circulating cells in the blood d Severe COVID-19 is characterized by increased metabolically active plasmablasts d Elevation of IFN-activated megakaryocytes and erythroid cells in severe COVID-19 d Cell-type-specific expression signatures are associated with a fatal COVID-19 outcome

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.

Background: Transcription factors (TFs) bind to specific DNA sequences, TF motifs, in cisregulatory sequences and control the expression of the diverse transcriptional programs encoded in the genome. The concerted action of TFs within the chromatin context enables precise temporal and spatial expression patterns. To understand how TFs control gene expression it is essential to model TF binding. TF motif information can help to interpret the exact role of individual regulatory elements, for instance to predict the functional impact of non-coding variants.Findings: Here we present GimmeMotifs, a comprehensive computational framework for TF motif analysis. Compared to the previously published version, this release adds a whole range of new functionality and analysis methods. It now includes tools for de novo motif discovery, motif scanning and sequence analysis, motif clustering, calculation of performance metrics and visualization. Included with GimmeMotifs is a non-redundant database of clustered motifs.Compared to other motif databases, this collection of motifs shows competitive performance in discriminating bound from unbound sequences. Using our de novo motif discovery pipeline we find large differences in performance between de novo motif finders on ChIP-seq data. Using an ensemble method such as implemented in GimmeMotifs will generally result in improved motif identification compared to a single motif finder. Finally, we demonstrate maelstrom, a new ensemble method that enables comparative analysis of TF motifs between multiple highthroughput sequencing experiments, such as ChIP-seq or ATAC-seq. Using a collection of ~200 H3K27ac ChIP-seq data sets we identify TFs that play a role in hematopoietic differentiation and lineage commitment. Conclusion:GimmeMotifs is a fully-featured and flexible framework for TF motif analysis. It contains both command-line tools as well as a Python API and is freely available at: https:// github.com/vanheeringen-lab/gimmemotifs.

Background: Decreased monocytic (m)HLA-DR expression is the most studied biomarker of sepsis-induced immunosuppression. To date, little is known about the relationship between sepsis characteristics, such as the site of infection, causative pathogen, or severity of disease, and mHLA-DR expression kinetics. Methods: We evaluated mHLA-DR expression kinetics in 241 septic shock patients with different primary sites of infection and pathogens. Furthermore, we used unsupervised clustering analysis to identify mHLA-DR trajectories and evaluated their association with outcome parameters. Results: No differences in mHLA-DR expression kinetics were found between groups of patients with different sites of infection (abdominal vs. respiratory, p = 0.13; abdominal vs. urinary tract, p = 0.53) and between pathogen categories (Gram-positive vs. Gram-negative, p = 0.54; Gram-positive vs. negative cultures, p = 0.84). The mHLA-DR expression kinetics differed between survivors and non-survivors (p < 0.001), with an increase over time in survivors only. Furthermore, we identified three mHLA-DR trajectories ('early improvers', 'delayed or non-improvers' and 'decliners'). The probability for adverse outcome (secondary infection or death) was higher in the delayed or nonimprovers and decliners vs. the early improvers (delayed or non-improvers log-rank p = 0.03, adjusted hazard ratio 2.0 [95% CI 1.0-4.0], p = 0.057 and decliners log-rank p = 0.01, adjusted hazard ratio 2.8 [95% CI 1.1-7.1], p = 0.03). Conclusion: Sites of primary infection or causative pathogens are not associated with mHLA-DR expression kinetics in septic shock patients. However, patients showing delayed or no improvement in or a declining mHLA-DR expression have a higher risk for adverse outcome compared with patients exhibiting a swift increase in mHLA-DR expression. Our study signifies that changes in mHLA-DR expression over time, and not absolute values or static measurements, are of clinical importance in septic shock patients.

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