Quantitative or qualitative differences in immunity may drive clinical severity in COVID-19. Although longitudinal studies to record the course of immunological changes are ample, they do not necessarily predict clinical progression at the time of hospital admission. Here we show, by a machine learning approach using serum pro-inflammatory, anti-inflammatory and anti-viral cytokine and anti-SARS-CoV-2 antibody measurements as input data, that COVID-19 patients cluster into three distinct immune phenotype groups. These immune-types, determined by unsupervised hierarchical clustering that is agnostic to severity, predict clinical course. The identified immune-types do not associate with disease duration at hospital admittance, but rather reflect variations in the nature and kinetics of individual patient’s immune response. Thus, our work provides an immune-type based scheme to stratify COVID-19 patients at hospital admittance into high and low risk clinical categories with distinct cytokine and antibody profiles that may guide personalized therapy.

Objectives Cytosolic DNA-sensing pathway stimulation prompts type I IFN (IFN-I) production, but its role in systemic IFN-I pathway activation in primary SS (pSS) is poorly studied. Here, we investigate the responsiveness of pSS monocytes and plasmacytoid dendritic cells (pDCs) to STING activation in relation to systemic IFN-I pathway activation and compare this to SLE. Methods Expression of DNA-sensing receptors cGAS, IFI16, ZBP-1 and DDX41, signalling molecules STING, TBK1 and IRF3, positive and negative STING regulators, and IFN-I-stimulated genes MxA, IFI44, IFI44L, IFIT1, and IFIT3 was analysed in whole blood, CD14+ monocytes, pDCs, and salivary glands by RT-PCR, monocyte RNA-sequencing data, flow cytometry and immunohistochemical staining. Peripheral blood mononuclear cells (PBMCs) from pSS, SLE and healthy controls (HC) were stimulated with STING-agonist 2’3’-cGAMP. STING phopshorylation (pSTING) and intracellular IFNα were evaluated using flow cytometry. Results STING activation induced a significantly higher proportion of IFNα-producing monocytes—but not pDCs—in both IFN-low and IFN-high pSS compared with HC PBMCs. Additionally, a trend towards more pSTING+ monocytes was observed in pSS and SLE, most pronounced in IFN-high patients. Positive STING regulators TRIM38, TRIM56, USP18 and SENP7 were significantly higher expressed in pSS than HC monocytes, while the dual-function STING regulator RNF26 was downregulated in pSS monocytes. STING was expressed in mononuclear infiltrates and ductal epithelium in pSS salivary glands. STING stimulation induced pSTING and IFNα in pSS and SLE pDCs. Conclusion pSS monocytes and pDCs are hyperresponsive to stimulation of the STING pathway, which was not restricted to patients with IFN-I pathway activation.

Quantitative or qualitative differences in immunity may drive and predict clinical severity in COVID-19. We therefore measured modules of serum pro-inflammatory, anti-inflammatory and anti-viral cytokines in combination with the anti-SARS-CoV-2 antibody response in COVID-19 patients admitted to tertiary care. Using machine learning and employing unsupervised hierarchical clustering, agnostic to severity, we identified three distinct immunotypes that were shown post-clustering to predict very different clinical courses such as clinical improvement or clinical deterioration. Immunotypes did not associate chronologically with disease duration but rather reflect variations in the nature and kinetics of individual patient's immune response. Here we demonstrate that immunophenotyping can stratify patients to high and low risk clinical subtypes, with distinct cytokine and antibody profiles, that can predict severity progression and guide personalized therapy.

Tear fluid forms a potential source for biomarker identification, and can be minimal invasively collected via Schirmer strips. The lack of knowledge on the processing of Schirmer strips however complicates the analysis and between-study comparisons. We studied two different pre-processing methods, specifically the use of punches of the strip versus elution of the strip in a buffer. Tear fluid filled Schirmer strips were collected from 5 healthy participants, and divided into two halves over the length of the strip. In either part, punches or eluates were obtained from 4 different locations, from the first part touching the eye (head) to the end, to assess the protein distribution along the strips. The levels of 92 inflammatory proteins were measured in the punches/eluates using proximity extension assays. The punch method yielded higher protein detectability compared to the elution method (76% vs 66%; p ≤ 0.001). Protein expression level was found to be slightly higher in the head of the strip, however, 3 out of 5 punches from the head failed quality control. Protein expression levels over the remaining parts of the strips were similar. Our study showed beneficial use of punches of any part of the strip except the head in future biomarker research.

Background Venom has evolved in parallel in multiple animals for the purpose of self-defense, prey capture or both. These venoms typically consist of highly complex mixtures of toxins: diverse bioactive peptides and/or proteins each with a specific pharmacological activity. Because of their specificity, they can be used as experimental tools to study cell mechanisms and develop novel medicines and drugs. It is therefore potentially valuable to explore the venoms of various animals to characterize their toxins and identify novel toxin-families. This study focuses on the annotation and exploration of the transcriptomes of six scorpion species from three different families. The transcriptomes were annotated with a custom-built automated pipeline, primarily consisting of Basic Local Alignment Search Tool searches against UniProt databases and filter steps based on transcript coverage. Results We annotated the transcriptomes of four scorpions from the family Buthidae, one from Iuridae and one from Diplocentridae using our annotation pipeline. We found that the four buthid scorpions primarily produce disulfide-bridged ion-channel targeting toxins, while the non-buthid scorpions have a higher abundance of non-disulfide-bridged toxins. Furthermore, analysis of the “unidentified” transcripts resulted in the discovery of six novel putative toxin families containing a total of 37 novel putative toxins. Additionally, 33 novel toxins in existing toxin-families were found. Lastly, 19 novel putative secreted proteins without toxin-like disulfide bonds were found. Conclusions We were able to assign most transcripts to a toxin family and classify the venom composition for all six scorpions. In addition to advancing our fundamental knowledge of scorpion venomics, this study may serve as a starting point for future research by facilitating the identification of the venom composition of scorpions and identifying novel putative toxin families. Electronic supplementary material The online version of this article (10.1186/s12864-019-6013-6) contains supplementary material, which is available to authorized users.