A review of COVID-19 biomarkers and drug targets: resources and tools

COVIDpro: Database for Mining Protein Dysregulation in Patients with COVID-19

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 still has limited treatment options. Our understanding of the molecular dysregulations that occur in response to infection remains incomplete. We developed a web application COVIDpro (https://www.guomics.com/covidPro/) that includes proteomics data obtained from 41 original studies conducted in 32 hospitals worldwide, involving 3077 patients and covering 19 types of clinical specimens, predominantly plasma and serum. The data set encompasses 53 protein expression matrices, comprising a total of 5434 samples and 14,403 unique proteins. We identified a panel of proteins that exhibit significant dysregulation, enabling the classification of COVID-19 patients into severe and non-severe disease categories. The proteomic signatures achieved promising results in distinguishing severe cases, with a mean area under the curve of 0.87 and accuracy of 0.80 across five independent test sets. COVIDpro serves as a valuable resource for testing hypotheses and exploring potential targets for novel treatments in COVID-19 patients.

Preparation of noninfectious scRNAseq samples from SARS-CoV-2-infected epithelial cells

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS coronavirus 2 (SARS-CoV-2) virus. Direct assessment, detection, and quantitative analysis using high throughput methods like single-cell RNA sequencing (scRNAseq) is imperative to understanding the host response to SARS-CoV-2. One barrier to studying SARS-CoV-2 in the laboratory setting is the requirement to process virus-infected cell cultures, and potentially infectious materials derived therefrom, under Biosafety Level 3 (BSL-3) containment. However, there are only 190 BSL3 laboratory facilities registered with the U.S. Federal Select Agent Program, as of 2020, and only a subset of these are outfitted with the equipment needed to perform high-throughput molecular assays. Here, we describe a method for preparing non-hazardous RNA samples from SARS-CoV-2 infected cells, that enables scRNAseq analyses to be conducted safely in a BSL2 facility-thereby making molecular assays of SARS-CoV-2 cells accessible to a much larger community of researchers. Briefly, we infected African green monkey kidney epithelial cells (Vero-E6) with SARS-CoV-2 for 96 hours, trypsin-dissociated the cells, and inactivated them with methanol-acetone in a single-cell suspension. Fixed cells were tested for the presence of infectious SARS-CoV-2 virions using the Tissue Culture Infectious Dose Assay (TCID50), and also tested for viability using flow cytometry. We then tested the dissociation and methanol-acetone inactivation method on primary human lung epithelial cells that had been differentiated on an air-liquid interface. Finally, we performed scRNAseq quality control analysis on the resulting cell populations to evaluate the effects of our virus inactivation and sample preparation protocol on the quality of the cDNA produced. We found that methanol-acetone inactivated SARS-CoV-2, fixed the lung epithelial cells, and could be used to obtain noninfectious, high-quality cDNA libraries. This methodology makes investigating SARS-CoV-2, and related high-containment RNA viruses at a single-cell level more accessible to an expanded community of researchers.

COVIDpro: Database for mining protein dysregulation in patients with COVID-19

Background

The 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.

Methods

We 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.

Findings

We 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.

Interpretation

COVIDpro 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.

Funding

National 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 context

Evidence before this study: Although 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 disease’s 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 study: We 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 evidence: COVIDpro 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.

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape. This article is protected by copyright. All rights reserved.

Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort

The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements for the first 72 h after hospitalization. Clinical and biochemical variables from 1039 confirmed COVID-19 patients framed on the “COVID Data Save Lives” were grouped in 24-h blocks to perform a longitudinal k-means clustering algorithm to the trajectories. The final solution of the three clusters showed a strong association with different clinical severity outcomes (OR for death: Cluster A reference, Cluster B 12.83 CI: 6.11−30.54, and Cluster C 14.29 CI: 6.66−34.43; OR for ventilation: Cluster-B 2.22 CI: 1.64−3.01, and Cluster-C 1.71 CI: 1.08−2.76), improving the AUC of the models in terms of age, sex, oxygen concentration, and the Charlson Comorbidities Index (0.810 vs. 0.871 with p < 0.001 and 0.749 vs. 0.807 with p < 0.001, respectively). Patient diagnoses and prognoses remarkably diverged between the three clusters obtained, evidencing that data-driven technologies devised for the screening, analysis, prediction, and tracking of patients play a key role in the application of individualized management of the COVID-19 pandemics.

Significance of Cardiac Troponins as an Identification Tool in COVID-19 Patients Using Biosensors: An Update

Coronavirus disease 2019 (COVID-19) has rapidly developed as a global health emergency. Respiratory diseases are significant causes of morbidity and mortality in these patients with a spectrum of different diseases, from asymptomatic subclinical infection to the progression of severe pneumonia and subsequent acute respiratory distress syndrome. Individuals with cardiovascular disease are more likely to become infected with SARS-CoV-2 and develop severe symptoms. Hence, patients with underlying cardiovascular disease mortality rate are over three times. Furthermore, note that patients with a history of cardiovascular disease are more likely to have higher cardiac biomarkers, especially cardiac troponins, than infected patients, especially those with severe disease, making these patients more susceptible to cardiac damage caused by SARS-2-CoV. Biomarkers are important in decision-making to facilitate the efficient allocation of resources. Viral replication in the heart muscle can lead to a cascade of inflammatory processes that lead to fibrosis and, ultimately, cardiac necrosis. Elevated troponin may indicate damage to the heart muscle and may predict death. After the first Chinese analysis, increased cardiac troponin value was observed in a significant proportion of patients, suggesting that myocardial damage is a possible pathogenic mechanism leading to severe disease and death. However, the prognostic performance of troponin and whether its value is affected by different comorbidities present in COVID-19 patients are not known. This review aimed to assess the diagnostic value of troponin to offer insight into pathophysiological mechanisms and reported new assessment methods, including new biosensors for troponin in patients with COVID-19.

The Role of Interleukin-8 in Lung Inflammation and Injury: Implications for the Management of COVID-19 and Hyperinflammatory Acute Respiratory Distress Syndrome

Severe Acute Respiratory Syndrome Coronavirus—2 (SARS CoV-2) has resulted in the global spread of Coronavirus Disease 2019 (COVID-19) and an increase in complications including Acute Respiratory Distress Syndrome (ARDS). Due to the lack of therapeutic options for Acute Respiratory Distress Syndrome, recent attention has focused on differentiating hyper- and hypo-inflammatory phenotypes of ARDS to help define effective therapeutic strategies. Interleukin 8 (IL-8) is a pro-inflammatory cytokine that has a role in neutrophil activation and has been identified within the pathogenesis and progression of this disease. The aim of this review is to highlight the role of IL-8 as a biomarker and prognostic factor in modulating the hyperinflammatory response in ARDS. The crucial role of IL-8 in lung inflammation and disease pathogenesis might suggest IL-8 as a possible new therapeutic target to efficiently modulate the hyperinflammatory response in ARDS.

Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review

The outbreak of the COVID-19 pandemic is unarguably the biggest catastrophe of the 21st century, probably the most significant global crisis after the second world war. The rapid spreading capability of the virus has compelled the world population to maintain strict preventive measures. The outrage of the virus has rampaged through the healthcare sector tremendously. This pandemic created a huge demand for necessary healthcare equipment, medicines along with the requirement for advanced robotics and artificial intelligence-based applications. The intelligent robot systems have great potential to render service in diagnosis, risk assessment, monitoring, telehealthcare, disinfection, and several other operations during this pandemic which has helped reduce the workload of the frontline workers remarkably. The long-awaited vaccine discovery of this deadly virus has also been greatly accelerated with AI-empowered tools. In addition to that, many robotics and Robotics Process Automation platforms have substantially facilitated the distribution of the vaccine in many arrangements pertaining to it. These forefront technologies have also aided in giving comfort to the people dealing with less addressed mental health complicacies. This paper investigates the use of robotics and artificial intelligence-based technologies and their applications in healthcare to fight against the COVID-19 pandemic. A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is conducted to accumulate such literature, and an extensive review on 147 selected records is performed.

Role of miRNAs as biomarkers of COVID-19: a scoping review of the status and future directions for research in this field

Aim: miRNAs are potential biomarkers of several diseases. This review aimed to identify the miRNAs that could serve as biomarkers of COVID-19. Materials & methods: A literature search of nine databases was carried out for studies published before 13 June 2021 that described dysregulated miRNAs in cells or animals infected by SARS-CoV-2 or in patients with COVID-19. Two independent reviewers selected the studies and extracted data; disagreements were resolved by a third reviewer. Results: Twenty studies were included in this scoping review; results suggested that miR-21-5p, miR-146a, miR-126-3p, miR-144 and miR-155 are the most important dysregulated miRNAs that could serve as biomarkers for diagnosing and indicating the severity of COVID-19. miRNAs appear to play key roles in viral replication, proliferation of infected cells, immune response, inflammation and cardiovascular dysfunction. Conclusion: This review provides insights into the role of miRNAs as biomarkers in COVID-19 and the current status and future directions for research in this field.

Genome-Wide Profiling Reveals Alternative Polyadenylation of Innate Immune-Related mRNA in Patients With COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has caused many deaths worldwide. To date, the mechanism of viral immune escape remains unclear, which is a great obstacle to developing effective clinical treatment. RNA processing mechanisms, including alternative polyadenylation (APA) and alternative splicing (AS), are crucial in the regulation of most human genes in many types of infectious diseases. Because the role of APA and AS in response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remains unknown, we performed de novo identification of dynamic APA sites using a public dataset of human peripheral blood mononuclear cell (PBMC) RNA-Seq data in COVID-19 patients. We found that genes with APA were enriched in innate immunity -related gene ontology categories such as neutrophil activation, regulation of the MAPK cascade and cytokine production, response to interferon-gamma and the innate immune response. We also reported genome-wide AS events and enriched viral transcription-related categories upon SARS-CoV-2 infection. Interestingly, we found that APA events may give better predictions than AS in COVID-19 patients, suggesting that APA could act as a potential therapeutic target and novel biomarker in those patients. Our study is the first to annotate genes with APA and AS in COVID-19 patients and highlights the roles of APA variation in SARS-CoV-2 infection.

Single-cell transcriptome identifies FCGR3B upregulated subtype of alveolar macrophages in patients with critical COVID-19

Understanding host cell heterogeneity is critical for unraveling disease mechanism. Utilizing large-scale single-cell transcriptomics, we analyzed multiple tissue specimens from patients with life-threatening COVID-19 pneumonia, compared with healthy controls. We identified a subtype of monocyte-derived alveolar macrophages (MoAMs) where genes associated with severe COVID-19 comorbidities are significantly upregulated in bronchoalveolar lavage fluid of critical cases. FCGR3B consistently demarcated MoAM subset in different samples from severe COVID-19 cohorts and in CCL3L1-upregulated cells from nasopharyngeal swabs. In silico findings were validated by upregulation of FCGR3B in nasopharyngeal swabs of severe ICU COVID-19 cases, particularly in older patients and those with comorbidities. Additional lines of evidence from transcriptomic data and in vivo of severe COVID-19 cases suggest that FCGR3B may identify a specific subtype of MoAM in patients with severe COVID-19 that may present a novel biomarker for screening and prognosis, as well as a potential therapeutic target.

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Association of MoAM subtype with severe COVID-19 cases presented with comorbidities

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Upregulated FCGR3B in CCL3L1 positive MoAM cells in severe COVID-19 cases

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Upregulated FCGR3B within MoAM subtype as a potential marker for COVID-19 severity

Main Chemotypes of SARS-CoV-2 Reproduction Inhibitors

The COVID-19 pandemic has forced scientists all over the world to focus their effort on searching for targeted drugs for coronavirus chemotherapy. The present review is an attempt to systematize low-molecular-weight compounds, including well-known pharmaceuticals and natural substances that have exhibited high anti-coronavirus activity, not in terms of action on their targets, but in terms of their structural type.