ABSTRACT
BackgroundThe ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has limited treatment options partially due to our incomplete understanding of the molecular dysregulations of the COVID-19 patients. We aimed to generate a repository and data analysis tools to examine the modulated proteins underlying COVID-19 patients for the discovery of potential therapeutic targets and diagnostic biomarkers. MethodsWe built a web server containing proteomic expression data from COVID-19 patients with a toolset for user-friendly data analysis and visualization. The web resource covers expert-curated proteomic data from COVID-19 patients published before May 2022. The data were collected from ProteomeXchange and from select publications via PubMed searches and aggregated into a comprehensive dataset. Protein expression by disease subgroups across projects was compared by examining differentially expressed proteins. We also visualize differentially expressed pathways and proteins. Moreover, circulating proteins that differentiated severe cases were nominated as predictive biomarkers. FindingsWe built and maintain a web server COVIDpro (https://www.guomics.com/covidPro/) containing proteomics data generated by 41 original studies from 32 hospitals worldwide, with data from 3077 patients covering 19 types of clinical specimens, the majority from plasma and sera. 53 protein expression matrices were collected, for a total of 5434 samples and 14,403 unique proteins. Our analyses showed that the lipopolysaccharide-binding protein, as identified in the majority of the studies, was highly expressed in the blood samples of patients with severe disease. A panel of significantly dysregulated proteins was identified to separate patients with severe disease from non-severe disease. Classification of severe disease based on these proteomic signatures on five test sets reached a mean AUC of 0.87 and ACC of 0.80. InterpretationCOVIDpro is an online database with an integrated analysis toolkit. It is a unique and valuable resource for testing hypotheses and identifying proteins or pathways that could be targeted by new treatments of COVID-19 patients. FundingNational Key R&D Program of China: Key PDPM technologies (2021YFA1301602, 2021YFA1301601, 2021YFA1301603), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04), National Natural Science Foundation of China (81972492) and National Science Fund for Young Scholars (21904107), National Resource for Network Biology (NRNB) from the National Institute of General Medical Sciences (NIGMS-P41 GM103504) Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAlthough an increasing number of therapies against COVID-19 are being developed, they are still insufficient, especially with the rise of new variants of concern. This is partially due to our incomplete understanding of the diseases mechanisms. As data have been collected worldwide, several questions are now worth addressing via meta-analyses. Most COVID-19 drugs function by targeting or affecting proteins. Effectiveness and resistance to therapeutics can be effectively assessed via protein measurements. Empowered by mass spectrometry-based proteomics, protein expression has been characterized in a variety of patient specimens, including body fluids (e.g., serum, plasma, urea) and tissue (i.e., formalin-fixed and paraffin-embedded (FFPE)). We expert-curated proteomic expression data from COVID-19 patients published before May 2022, from the largest proteomic data repository ProteomeXhange as well as from literature search engines. Using this resource, a COVID-19 proteome meta-analysis could provide useful insights into the mechanisms of the disease and identify new potential drug targets. Added value of this studyWe integrated many published datasets from patients with COVID-19 from 11 nations, with over 3000 patients and more than 5434 proteome measurements. We collected these datasets in an online database, and generated a toolbox to easily explore, analyze, and visualize the data. Next, we used the database and its associated toolbox to identify new proteins of diagnostic and therapeutic value for COVID-19 treatment. In particular, we identified a set of significantly dysregulated proteins for distinguishing severe from non-severe patients using serum samples. Implications of all the available evidenceCOVIDpro will support the navigation and analysis of patterns of dysregulated proteins in various COVID-19 clinical specimens for identification and verification of protein biomarkers and potential therapeutic targets.
ABSTRACT
From early detection of variants of concern to vaccine and therapeutic design, pandemic preparedness depends on identifying viral mutations that escape the response of the host immune system. While experimental scans are useful for quantifying escape potential, they remain laborious and impractical for exploring the combinatorial space of mutations. Here we introduce a biologically grounded model to quantify the viral escape potential of mutations at scale. Our method - EVEscape - brings together fitness predictions from evolutionary models, structure-based features that assess antibody binding potential, and distances between mutated and wild-type residues. Unlike other models that predict variants of concern based on newly observed variants, EVEscape has no reliance on recent community prevalence, and is applicable before surveillance sequencing or experimental scans are broadly available. We validate EVEscape predictions against experimental data on H1N1, HIV and SARS-CoV-2, including data on immune escape. For SARS-CoV-2, we show that EVEscape anticipates mutation frequency, strain prevalence, and escape mutations. Drawing from GISAID, we provide continually updated escape predictions for all current strains of SARS-CoV-2.
ABSTRACT
In the global COVID-19 pandemic, there is a substantial need for effective, low-cost therapeutics. We investigated the potential effects of disulfiram on the incidence and outcomes of COVID-19 in an observational study in a large database of US Veterans Administration clinical records, the VA Corporate Data Warehouse (CDW). The study is motivated by the unique properties of disulfiram, which has been used as an anti-alcoholism drug since 1948, is non-toxic, easy to manufacture and inexpensive. Disulfiram reduces hyperinflammation in mammalian cells by inhibition of the gasdermin D pore. In a mouse model of sepsis, disulfiram reduced inflammatory cytokines and mortality. Disulfiram also is a low micromolar inhibitor of the Mpro and PLpro viral proteases of SARS-CoV-2. To investigate the potential effects of disulfiram on the incidence and severity of COVID-19, we carried out an epidemiological study in the CDW. The VA dataset used has 944,127 patients tested for SARS-Cov-2, 167,327 with a positive test, and 2,233 on disulfiram, of which 188 had a positive SARS-Cov-2 test. A multivariable Cox regression adjusted for age, gender, race/ethnicity, region, a diagnosis of alcohol use disorders, and Charlson comorbidity score revealed a reduced incidence of COVID-19 with disulfiram use with a hazard ratio of 0.66 and 95% confidence interval of 0.57 to 0.76 (P < 0.001). There were no deaths among the 188 SARS-Cov-2 positive patients treated with disulfiram. The expected number of deaths would have been 5-6 according to the 3% death rate among the untreated (P-value 0.03). Our finding of a lower hazard ratio and less severe outcomes for COVID-19 in patients treated with disulfiram compared to those not treated is a statistical association and does not prove any causative effect of disulfiram. However, the results of this study suggest that there is a pharmacological contribution to the reduced incidence and severity of COVID-19 with the use of disulfiram. Given the known anti-inflammatory and viral anti-protease effects of disulfiram, it is reasonable and urgent to initiate accelerated clinical trials to assess whether disulfiram reduces SARS-CoV-2 infection, disease severity and death. STRUCTURED OUTLINEO_ST_ABSImportanceC_ST_ABSIdentifying already approved medications with well characterized antiviral or anti-inflammatory properties supported by real world evidence as candidates for clinical trials for repurposing is an important strategy to manage the pandemic given the ongoing challenges with producing and administering vaccines, the emergence of more infectious viral mutants and the paucity of approved therapies. ObjectiveTo investigate the potential effects of disulfiram on the incidence and severity of COVID-19. DesignRetrospective cohort study from February 20, 2020 to February 1, 2021. SettingVeterans Health Administration. Veterans who had visited a VA primary care provider in the 18 months before their first SARS-CoV-2 test. Participants2,233 Veterans with at least one SARS-CoV-2 laboratory (positive or negative) test result on or after February 20, 2020 and at least one pharmacy record for disulfiram on or after February 20, 2019 and 941,894 Veterans without a pharmacy record for disulfiram. ExposureTreatment with disulfiram Main OutcomePositive test result for SARS-CoV-2 ResultsA multivariable Cox regression analysis adjusted for age, gender, race/ethnicity, region, diagnosis of an alcohol use disorder, and Charlson comorbidity score resulted in a reduced hazard of COVID-19 infection with disulfiram use, with a hazard ratio of 0.66 and 95% confidence interval of 0.57 to 0.76 (P < 0.001). Conclusions and RelevanceThe results of this study suggest that disulfiram use contributes to a reduced incidence of COVID-19. Given the known anti-inflammatory and anti-protease effects of disulfiram, its low cost, low side effects, and general availability, it is reasonable and urgent to initiate accelerated clinical trials to assess the effect of disulfiram on infection and the development of advanced disease.
ABSTRACT
SARS-CoV-2 causes acute respiratory distress that can progress to multiorgan failure and death in some patients. Although severe COVID-19 disease is linked to exuberant inflammation, how SARS-CoV-2 triggers inflammation is not understood. Monocytes are sentinel blood cells that sense invasive infection to form inflammasomes that activate caspase-1 and gasdermin D (GSDMD) pores, leading to inflammatory death (pyroptosis) and processing and release of IL-1 family cytokines, potent inflammatory mediators. Here we show that ~10% of blood monocytes in COVID-19 patients are dying and infected with SARS-CoV-2. Monocyte infection, which depends on antiviral antibodies, activates NLRP3 and AIM2 inflammasomes, caspase-1 and GSDMD cleavage and relocalization. Signs of pyroptosis (IL-1 family cytokines, LDH) in the plasma correlate with development of severe disease. Moreover, expression quantitative trait loci (eQTLs) linked to higher GSDMD expression increase the risk of severe COVID-19 disease (odds ratio, 1.3, p<0.005). These findings taken together suggest that antibody-mediated SARS-CoV-2 infection of monocytes triggers inflammation that contributes to severe COVID-19 disease pathogenesis. One sentence summaryAntibody-mediated SARS-CoV-2 infection of monocytes activates inflammation and cytokine release.
ABSTRACT
We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.
