A Vicious Cycle: In Severe and Critically Ill COVID-19 Patients.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, is one of the fastest-evolving viral diseases that has instigated a worldwide pandemic. Severe inflammatory syndrome and venous thrombosis are commonly noted in COVID-19 patients with severe and critical illness, contributing to the poor prognosis. Interleukin (IL)-6, a major complex inflammatory cytokine, is an independent factor in predicting the severity of COVID-19 disease in patients. IL-6 and tumor necrosis factor (TNF)-α participate in COVID-19-induced cytokine storm, causing endothelial cell damage and upregulation of plasminogen activator inhibitor-1 (PAI-1) levels. In addition, IL-6 and PAI-1 form a vicious cycle of inflammation and thrombosis, which may contribute to the poor prognosis of patients with severe COVID-19. Targeted inhibition of IL-6 and PAI-1 signal transduction appears to improve treatment outcomes in severely and critically ill COVID-19 patients suffering from cytokine storms and venous thrombosis. Motivated by studies highlighting the relationship between inflammatory cytokines and thrombosis in viral immunology, we provide an overview of the immunothrombosis and immunoinflammation vicious loop between IL-6 and PAI-1. Our goal is that understanding this ferocious circle will benefit critically ill patients with COVID-19 worldwide.

COVID-19 pandemic: A multidisciplinary perspective on the pathogenesis of a novel coronavirus from infection, immunity and pathological responses.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus2 (SARS-CoV-2), has spread to more than 200 countries and regions, having a huge impact on human health, hygiene, and economic activities. The epidemiological and clinical phenotypes of COVID-19 have increased since the onset of the epidemic era, and studies into its pathogenic mechanisms have played an essential role in clinical treatment, drug development, and prognosis prevention. This paper reviews the research progress on the pathogenesis of the novel coronavirus (SARS-CoV-2), focusing on the pathogenic characteristics, loci of action, and pathogenic mechanisms leading to immune response malfunction of SARS-CoV-2, as well as summarizing the pathological damage and pathological manifestations it causes. This will update researchers on the latest SARS-CoV-2 research and provide directions for future therapeutic drug development.

A Vicious Cycle: In Severe and Critically Ill COVID-19 Patients

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, is one of the fastest-evolving viral diseases that has instigated a worldwide pandemic. Severe inflammatory syndrome and venous thrombosis are commonly noted in COVID-19 patients with severe and critical illness, contributing to the poor prognosis. Interleukin (IL)-6, a major complex inflammatory cytokine, is an independent factor in predicting the severity of COVID-19 disease in patients. IL-6 and tumor necrosis factor (TNF)-α participate in COVID-19-induced cytokine storm, causing endothelial cell damage and upregulation of plasminogen activator inhibitor-1 (PAI-1) levels. In addition, IL-6 and PAI-1 form a vicious cycle of inflammation and thrombosis, which may contribute to the poor prognosis of patients with severe COVID-19. Targeted inhibition of IL-6 and PAI-1 signal transduction appears to improve treatment outcomes in severely and critically ill COVID-19 patients suffering from cytokine storms and venous thrombosis. Motivated by studies highlighting the relationship between inflammatory cytokines and thrombosis in viral immunology, we provide an overview of the immunothrombosis and immunoinflammation vicious loop between IL-6 and PAI-1. Our goal is that understanding this ferocious circle will benefit critically ill patients with COVID-19 worldwide.

A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires.

The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immune responses and the potential for future immunity. Analyses of TCR repertoires via deep learning (DL) methods have successfully diagnosed cancers and infectious diseases, including coronavirus disease 2019. However, few studies have used DL to analyze BCR repertoires. In this study, we collected IgG H chain Ab repertoires from 276 healthy control subjects and 326 patients with various infections. We then extracted a comprehensive feature set consisting of 10 subsets of repertoire-level features and 160 sequence-level features and tested whether these features can distinguish between infected individuals and healthy control subjects. Finally, we developed an ensemble DL model, namely, DL method for infection diagnosis (https://github.com/chenyuan0510/DeepID), and used this model to differentiate between the infected and healthy individuals. Four subsets of repertoire-level features and four sequence-level features were selected because of their excellent predictive performance. The DL method for infection diagnosis outperformed traditional machine learning methods in distinguishing between healthy and infected samples (area under the curve = 0.9883) and achieved a multiclassification accuracy of 0.9104. We also observed differences between the healthy and infected groups in V genes usage, clonal expansion, the complexity of reads within clone, the physical properties in the α region, and the local flexibility of the CDR3 amino acid sequence. Our results suggest that the Ab repertoire is a promising biomarker for the diagnosis of various infections.

RAPID: A Rep-Seq Dataset Analysis Platform With an Integrated Antibody Database.

The antibody repertoire is a critical component of the adaptive immune system and is believed to reflect an individual's immune history and current immune status. Delineating the antibody repertoire has advanced our understanding of humoral immunity, facilitated antibody discovery, and showed great potential for improving the diagnosis and treatment of disease. However, no tool to date has effectively integrated big Rep-seq data and prior knowledge of functional antibodies to elucidate the remarkably diverse antibody repertoire. We developed a Rep-seq dataset Analysis Platform with an Integrated antibody Database (RAPID; https://rapid.zzhlab.org/), a free and web-based tool that allows researchers to process and analyse Rep-seq datasets. RAPID consolidates 521 WHO-recognized therapeutic antibodies, 88,059 antigen- or disease-specific antibodies, and 306 million clones extracted from 2,449 human IGH Rep-seq datasets generated from individuals with 29 different health conditions. RAPID also integrates a standardized Rep-seq dataset analysis pipeline to enable users to upload and analyse their datasets. In the process, users can also select set of existing repertoires for comparison. RAPID automatically annotates clones based on integrated therapeutic and known antibodies, and users can easily query antibodies or repertoires based on sequence or optional keywords. With its powerful analysis functions and rich set of antibody and antibody repertoire information, RAPID will benefit researchers in adaptive immune studies.