A database provides information on the relative abundances and locations of human proteins that are predicted to be secreted.

The drivers of critical coronavirus disease 2019 (COVID-19) remain unknown. Given major confounding factors such as age and comorbidities, true mediators of this condition have remained elusive. We used a multi-omics analysis combined with artificial intelligence in a young patient cohort where major comorbidities were excluded at the onset. The cohort included 47 “critical” (in the intensive care unit under mechanical ventilation) and 25 “non-critical” (in a non-critical care ward) patients with COVID-19 and 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cell proteomics, cytokine profiling, and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing, and structural causal modeling were used. Patients with critical COVID-19 were characterized by exacerbated inflammation, perturbed lymphoid and myeloid compartments, increased coagulation, and viral cell biology. Among differentially expressed genes, we observed up-regulation of the metalloproteaseADAM9. This gene signature was validated in a second independent cohort of 81 critical and 73 recovered patients with COVID-19 and was further confirmed at the transcriptional and protein level and by proteolytic activity. Ex vivo ADAM9 inhibition decreased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, cohort of individuals with COVID-19, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. We further identifiedADAM9as a driver of disease severity and a candidate therapeutic target.

We used a multiplexed suspension bead array immunoassay to map GPCR-receptor activity–modifying protein (RAMP) complexes.

Background: Emerging data support detectable immune responses for months after severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection and vaccination, but it is not yet established to what degree and for how long protection against reinfection lasts.Methods: We investigated SARS‐CoV‐2‐specific humoral and cellular immune responses more than 8 months post‐asymptomatic, mild and severe infection in a cohort of 1884 healthcare workers (HCW) and 51 hospitalized COVID‐19 patients. Possible protection against SARS‐CoV‐2 reinfection was analyzed by a weekly 3‐month polymerase chain reaction (PCR) screening of 252 HCW that had seroconverted 7 months prior to start of screening and 48 HCW that had remained seronegative at multiple time points.Results: All COVID‐19 patients and 96% (355/370) of HCW who were anti‐spike IgG positive at inclusion remained anti‐spike IgG positive at the 8‐month follow‐up. Circulating SARS‐CoV‐2‐specific memory T cell responses were detected in 88% (45/51) of COVID‐19 patients and in 63% (233/370) of seropositive HCW. The cumulative incidence of PCR‐confirmed SARS‐CoV‐2 infection was 1% (3/252) among anti‐spike IgG positive HCW (0.13 cases per 100 weeks at risk) compared to 23% (11/48) among anti‐spike IgG negative HCW (2.78 cases per 100 weeks at risk), resulting in a protective effect of 95.2% (95% CI 81.9%–99.1%).Conclusions: The vast majority of anti‐spike IgG positive individuals remain anti‐spike IgG positive for at least 8 months regardless of initial COVID‐19 disease severity. The presence of anti‐spike IgG antibodies is associated with a substantially reduced risk of reinfection up to 9 months following asymptomatic to mild COVID‐19.

The human blood proteome provides a holistic readout of health states through the assessment of thousands of circulating proteins. In this study, we present a pan-disease resource to enable the study of diverse disease phenotypes within a harmonized proteomics dataset. By profiling protein concentrations across 59 diseases and healthy cohorts, we identified proteins associated with age, sex, and body mass index, as well as disease-specific signatures. This study highlights shared and distinct protein patterns across conditions, demonstrating the power of a unified proteomics approach to uncover biological insights. The dataset, covering 8262 individuals and up to 5416 proteins, serves as an online resource for exploring disease-specific protein profiles and advancing precision medicine research.