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Additional file 1. Additional tables and figures.

Supplementary material, approximately 10209 KB.

Background Host inflammation contributes to determine whether SARS-CoV-2 infection causes mild or life-threatening disease. Tools are needed for early risk assessment. Methods We studied in 111 COVID-19 patients prospectively followed at a single reference Hospital fifty-three potential biomarkers including alarmins, cytokines, adipocytokines and growth factors, humoral innate immune and neuroendocrine molecules and regulators of iron metabolism. Biomarkers at hospital admission together with age, degree of hypoxia, neutrophil to lymphocyte ratio (NLR), lactate dehydrogenase (LDH), C-reactive protein (CRP) and creatinine were analysed within a data-driven approach to classify patients with respect to survival and ICU outcomes. Classification and regression tree (CART) models were used to identify prognostic biomarkers. Results Among the fifty-three potential biomarkers, the classification tree analysis selected CXCL10 at hospital admission, in combination with NLR and time from onset, as the best predictor of ICU transfer (AUC [95% CI] = 0.8374 [0.6233–0.8435]), while it was selected alone to predict death (AUC [95% CI] = 0.7334 [0.7547–0.9201]). CXCL10 concentration abated in COVID-19 survivors after healing and discharge from the hospital. Conclusions CXCL10 results from a data-driven analysis, that accounts for presence of confounding factors, as the most robust predictive biomarker of patient outcome in COVID-19. Graphic abstract

Transcription factor dynamics is fundamental to determine the activation of accurate transcriptional programs and yet is heterogeneous at single-cell level, even between very similar cells. We asked how such heterogeneity emerges for the nuclear factorκB (NF-κB), whose dynamics have been reported to cover a wide spectrum of behaviors, including persistent, oscillatory and weak activation. We found that clonal populations of immortalized fibroblasts derived from a single mouse embryo (that can hence be considered quasi-identical) display robustly distinct dynamics upon tumor necrosis ɑ (TNF-ɑ) stimulation, which give rise to differences in the transcription of NF-κB targets. Notably, standard transcriptomic analyses indicate that the clones differ mostly in transcriptional programs related with development, but not in TNF-ɑ signaling. However, by combining transcriptomics data and simulations we show how the expression levels of genes coding for proteins of the signaling cascade determine the differences in early NF-κB activation; differences in the expression of IκBɑ determine differences in its persistence and oscillatory behavior. The same analysis predicts inter-clonal differences in the NF-κB response to IL-1β. We propose that small (less than twofold) differences at transcript level are a source of heterogeneity in TF dynamics within homogeneous cell populations, and all the more so among different cell types.Abstract Figure

Eukaryotic DNA is organized in nucleosomes, which package DNA and regulate its accessibility to transcription, replication, recombination and repair. Here, we show that in living cells nucleosomes protect DNA from high-energy radiation and reactive oxygen species. We combined sequence-based methods (ATAC-seq and BLISS) to determine the position of both nucleosomes and double strand breaks (DSBs) in the genome of nucleosome-rich malignant mesothelioma cells, and of the same cells partially depleted of nucleosomes. The results were replicated in the human MCF-7 breast carcinoma cell line. We found that, for each genomic sequence, the probability of DSB formation is directly proportional to the fraction of time it is nucleosome-free; DSBs accumulate distal from the nucleosome dyad axis. Nucleosome free regions and promoters of actively transcribed genes are more sensitive to DSB formation, and consequently to mutation. We argue that this may be true for a variety of chemical and physical DNA damaging agents.

Summary Nuclear factor (NF)-κB controls the transcriptional response to inflammatory signals by translocating into the nucleus, but we lack a single-cell characterization of the resulting transcription dynamics. Here we show that upon tumor necrosis factor (TNF)-α transcription of NF-κB target genes is heterogeneous in individual cells but results in an average nascent transcription profile that is prompt (i.e., occurs almost immediately) and sharp (i.e., increases and decreases rapidly) compared with NF-κB nuclear localization. Using an NF-κB-controlled MS2 reporter we show that the single-cell nascent transcription is more heterogeneous than NF-κB translocation dynamics, with a fraction of synchronized “first responders” that shape the average transcriptional profile and are more prone to respond to multiple TNF-α stimulations. A mathematical model combining NF-κB-mediated gene activation and a gene refractory state is able to reproduce these features. Our work shows how the expression of target genes induced by transcriptional activators can be heterogeneous across single cells and yet time resolved on average., Graphical Abstract, Highlights • Nascent transcription upon TNF-α is heterogeneous, with a subset of “first responders” • The average nascent transcription is prompt and sharper than NF-κB response • First responders do not depend on NF-κB dynamics and respond more to pulsed stimuli • A model including NF-κB and a gene refractory state reproduces these observations, Biological Sciences; Molecular Biology; Immunology; Biophysics; Systems Biology