1The COVID-19 pandemic is spreading globally with high disparity in the 2 susceptibility of the disease severity. Identification of the key underlying factors for 3 this disparity is highly warranted. Here we describe constructing a proteomic risk 4 score based on 20 blood proteomic biomarkers which predict the progression to 5 severe COVID-19. We demonstrate that in our own cohort of 990 individuals without 6 infection, this proteomic risk score is positively associated with proinflammatory 7 cytokines mainly among older, but not younger, individuals. We further discovered 8 that a core set of gut microbiota could accurately predict the above proteomic 9 biomarkers among 301 individuals using a machine learning model, and that these gut 10 microbiota features are highly correlated with proinflammatory cytokines in another 11 set of 366 individuals. Fecal metabolomic analysis suggested potential amino 12 acid-related pathways linking gut microbiota to inflammation. This study suggests 13 that gut microbiota may underlie the predisposition of normal individuals to severe : medRxiv preprint ( Figure S1). Gut microbiota data were collected and measured during a follow-up 107 visit of the cohort participants, with a cross-sectional subset of the individuals (n=132) 108 having blood proteomic data at the same time point as the stool collection and another 109 independent prospective subset of the individuals (n=169) having proteomic data at a 110 next follow-up visit ~3 years later than the stool collection. 111 112 Among the cross-sectional subset, using a machine learning-based method: 113 LightGBM and a very conservative and strict tenfold cross-validation strategy, we 114 identified 20 top predictive operational taxonomic units (OTUs), and this subset of 115 core OTUs explained an average 21.5% of the PRS variation (mean out-of-sample 116 R 2 =0.215 across ten cross-validations). The list of these core OTUs along with their 117 taxonomic classification is provided inTable S3. These OTUs were mainly assigned 118 to Bacteroides genus, Streptococcus genus, Lactobacillus genus, Ruminococcaceae 119 family, Lachnospiraceae family and Clostridiales order.120 121To test the verification of the core OTUs, the Pearson correlation analysis showed the 122 coefficient between the core OTUs-predicted PRS and actual PRS reached 0.59 123 (p<0.001), substantially outperforming the predictive capacity of other demographic 124 characteristics and laboratory tests including age, BMI, sex, blood pressure and blood 125 lipids (Pearson's r =0.154, p=0.087) ( Figure 3A). Additionally, we used co-inertia 126 analysis (CIA) to further test co-variance between the 20 identified core OTUs and 20 127 predictive proteomic biomarkers of severe COVID-19, outputting a RV coefficient 128 (ranged from 0 to 1) to quantify the closeness. The results indicated a close 129 association of these OTUs with the proteomic biomarkers (RV=0.12, p<0.05) (Figure 130 S3A). When replicating this analysis stratified by age, significant association was 131 observed...

The utility of the urinary proteome in infectious diseases remains unclear. Here we analyzed the proteome and metabolome of urine and serum samples from patients with COVID-19 and healthy controls. Our data show that urinary proteins effectively classify COVID-19 by severity. We detect 197 cytokines and their receptors in urine but only 124 in serum using TMT-based proteomics. Decrease of urinary ESCRT complex proteins correlates with active SARS-CoV-2 replication. Downregulation of urinary CXCL14 in severe COVID-19 cases positively correlates with blood lymphocyte counts. Integrative multiomic analysis suggests that innate immune activation and inflammation triggered renal injuries in patients with COVID-19. COVID-19-associated modulation of the urinary proteome offers unique insights into the pathogenesis of this disease. This study demonstrates the added value of including the urinary proteome in a suite of multiomic analytes in evaluating the immune pathobiology and clinical course of COVID-19 and, potentially, other infectious diseases.

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Background: The number of patients with pneumonia stemming from the 2019 novel coronavirus (COVID-19) infection has increased rapidly. However, the clinical characteristics of discharged patients remain little known. Here, we attempt to describe the clinical characteristics and treatment experiences of discharged cases from Taizhou, China.Methods: A total of 60 patients with COVID-19-infected pneumonia who were discharged from Taizhou

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