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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.

Additional file 1: Table S1. Data on explained variance by the latent variables PC1 to PC4 from the principal component analyses for the macrocycle ensemble (1 to 6: all) and for individual macrocycles for the different charge states. Solvents considered are mixed all solvents, water, DMSO and chloroform. Table S2. Percentages of conformers with between 0 and 3 IMHB in different media and charge state. Table S3. Cluster populations for compounds 1 to 7 for neutral (n) and charged (c) species in water (W) and chloroform (C). Table S4. Results of Homoscedasticity Test with post Hoc Tukey HSD Test. Figure S1. Correlation between 2D TPSA and 3D PSA values for 10,000 randomly selected compounds from the Aldrich Market Select catalogue. Figure S2. Torsion angle distribution profiles expressed as sinus and cosinus distributions of the original 32-dimensional space after transformation. Figure S3. Conformer maps for macrocycles 1 to 6 in solvent DMSO, color-coded by binned raw conformer relative energies with thresholds of 6 and 10 kcal mol-1 (conformers with relative energies higher than 100 kcal mol-1 were filtered out). Figure S4. Conformer maps for macrocycles 1 to 6 in solvent CHCl3, color-coded by binned raw conformer relative energies with thresholds of 6 and 10 kcal mol-1 (conformers with relative energies higher than 100 kcal mol-1 were filtered out). Figure S5. Histograms of the energy distributions of the MD snapshots for macrocycles 1 to 7 in solvent water. Figure S6. Conformer ensembles for charged compound 1 in solvent CHCL3. Figure S7. Ensembles for compound 2 in water generated by a simulated annealing protocol; a) and c) show the neutral, b) and d) the charged state. Figure S8. Density maps for distributions of accumulated conformers derived from five MD simulations at 300 K with five different starting coordinates in solvent water (orange), DMSO (blue), and CHCl3 (pink), for neutral compounds 1 to 6. Figure S9. Maps of latent torsional space accessed by post-minimized MD snapshots (5 starting conformers, 300 K 100 ns each) for neutral compounds in SPC water in purple, overlaid with conformers created by BEST (left column), PMM (middle) and CONF (right) in orange for macrocycles 1 to 7 (see row labels). Figure S10. Maps of latent torsional space accessed by post-minimized MD snapshots (5 starting conformers, 300 K 100 ns each) for charged compounds 1 to 7 in SPC water in purple, overlaid with conformers created by BEST (left column), PMM (middle) and CONF (right) in orange. Note that compound 3 is missing since it is N-methylated. Figure S11. Maps of latent torsional space accessed by post-minimized MD snapshots (5 starting conformers, 300 K 100 ns each) for neutral compounds 1 to 7 in CHCl3 in purple, overlaid with conformers created by BEST (left column), PMM (middle) and CONF (right) in orange. Figure S12. Maps of latent torsional space accessed by a) raw MD snapshots (5 starting conformers, 300 K 100 ns each, solvent water) for neutral compound 1, b) raw (non-minimized) conformations by BEST, c) raw conformations by PMM, d) raw conformations by CON. Figure S13. 16 plots providing the complete conformer space mapping in 32 latent dimensions for 1, neutral state in water. Figure S14. Dependence of total variance (trace) on the number of principal components considered; a) traces for minimized MD (blue) and BEST (green), both projected on the map created from all raw conformers, and the accumulated variance (cum. Proportion, red), showing that the curves cross at the point of about 90% of explained variance; b) traces for minimized MD (pink) and BEST (blue), with dashed lines for normal distributed noise (mean=0, sd=2*pi/180) generated for each sample for each dihedral angle. Figure S15. Histograms of 3D-PSA distributions for the MD snapshots (5 starting conformers, 300 K 100 ns each) of compounds 1 to 7; left column shows neutral compounds in water, center column charged compounds in water, right column neutral compounds in CHCl3. For N-methylated compound three there is no charged species.

Additional file 2: Supplementary Figures S1-S8.