Background: Recently, dyslipidaemia was observed in patients with coronavirus disease 2019 (COVID-19), especially in severe cases. This study aimed to explore the predictive value of blood lipid levels for COVID-19 severity. Methods: All patients with COVID-19 admitted to HwaMei Hospital, University of Chinese Academy of Sciences, from January 23 to April 20, 2020, were included in this retrospective study. General clinical characteristics and laboratory data (including blood lipid parameters) were obtained, and their predictive values for the severity were analysed. Results: In total, 142 consecutive patients with COVID-19 were included. The non-severe group included 125 cases, and 17 cases were included in the severe group. Total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein A1 (ApoA1) at baseline were signi cantly lower in the severe group. ApoA1 and interleukin-6 (IL-6) were recognized as independent risk factors for COVID-19 severity. ApoA1 had the highest area under the receiver operator characteristic curve (AUC) among all the single markers (AUC: 0.896, 95% CI: 0.834-0.941). Moreover, the risk model established using ApoA1 and IL-6 enhanced the predictive value (AUC: 0.977, 95% CI: 0.932-0.995). On the other hand, ApoA1 levels were elevated in the severe group during treatment, and there was no signi cant difference between the severe and non-severe groups during the recovery stage of the disease. Conclusion: The blood lipid pro le in severe COVID-19 patients is quite different from that in non-severe cases. Serum ApoA1 could severe as a good indictor to re ect the severity of COVID-19.

The beginning of the twenty-rst century has been marked by three distinct waves of zoonotic coronavirus outbreaks into the human population. The current pandemic COVID-19 (Coronavirus disease 2019) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With a rapid infection rate, it is a global threat endangering the livelihoods of millions worldwide. Currently, and despite the collaborative efforts of governments, researchers, and the pharmaceutical industries, there are no substantially signi cant treatment protocols for the disease. To address the need for such an immediate call of action, we leveraged the largest dataset of drug-induced transcriptomic perturbations, public SARS-CoV-2 transcriptomic datasets, and expression pro les from normal lung transcriptomes. Our unbiased systems biology approach not only shed light on previously unexplored molecular details of SARS-CoV-2 infection (e.g., interferon signaling, in ammation and ACE2 co-expression hallmarks in normal and infected lungs) but most importantly prioritized more than 50 repurposable drug candidates (e.g., Corticosteroids, Janus kinase and Bruton kinase inhibitors). Further clinical investigation of these FDA approved candidates as monotherapy or in combination with an antiviral regimen (e.g., Remdesivir) could lead to promising outcomes in COVID-19 patients.

Highlights d 11,394 proteins are quantified in autopsy samples from 7 organs in 19 COVID-19 patients d Elevated expression of cathepsin L1 is detected in the COVID-19 lung tissue d Dysregulation of angiogenesis, coagulation, and fibrosis is detected in multiple organs d Systemic metabolic dysregulation is detected in multiple organs

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...

Formalin-fixed, paraffin-embedded (FFPE), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here, we developed a pressure cycling technology (PCT)-SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PCa) and diffuse large B-cell lymphoma (DLBCL) samples. We show that the proteome patterns of FFPE PCa tissue samples and their analogous fresh-frozen (FF) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PCa tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored Abbreviations BPH, benign prostatic hyperplasia; CRYAB, crystallin alpha Bbetween 1 and 15 years in a biobank and show a high degree of the proteome pattern similarity between two types of histological regions in small FFPE samples, that is, punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of a certain degree of biological variations. Applying the method to two independent DLBCL cohorts, we identified myeloperoxidase, a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PCa and DLBCL have been discovered.