CXCL8, CCL2, and CMV Seropositivity as New Prognostic Factors for a Severe COVID-19 Course

Circulating TNF-RII, IP-10 and HGF are associated with severity of COVID-19 in oncologic patients

The COVID-19 patients showed hyperinflammatory response depending on the severity of the disease but little have been reported about this response in oncologic patients that also were infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Sixty-five circulating cytokines/chemokines were quantified in 15 oncologic patients, just after SARS-CoV-2 infection and fourteen days later, and their levels were compared in patients who required hospitalisation by COVID-19 versus non-hospitalised patients. A higher median age of 72 years (range 61-83) in oncologic patients after SARS-CoV-2 infection was associated with hospitalisation requirement by COVID-19 versus a median age of 49 years (20-75) observed in the non-hospitalised oncologic patients (p = 0.008). Moreover, oncologic patients at metastatic stage or with lung cancer were significantly associated with hospitalisation by COVID-19 (p = 0.044). None of these hospitalised patients required ICU treatment. Higher basal levels of tumour necrosis factor receptor II (TNF-RII), interferon-γ (IFNγ)-induced protein 10 (IP-10) and hepatocyte growth factor (HGF) in plasma were significantly observed in oncologic patients who required hospitalisation by COVID-19. Higher TNF-RII, IP-10 and HGF levels after the SARS-CoV-2 infection in oncologic patients could be used as biomarkers of COVID-19 severity associated with hospitalisation requirements.

Identification of key gene expression associated with quality of life after recovery from COVID-19

Post-acute sequelae of COVID-19 (PASC) is a persistent complication of severe acute respiratory syndrome coronavirus 2 infection that includes symptoms, such as fatigue, cognitive impairment, and respiratory distress. These symptoms severely affect the quality of life of patients after their recovery from COVID-19. In this study, a group of machine learning algorithms analyzed the whole blood RNA-seq data from patients with different PASC levels. The purpose of this analysis was to identify the gene markers associated with PASC and the special expression patterns for different PASC levels. By comparing the quality of life of patients after the acute phase of COVID-19 and before the disease, samples in the dataset were divided into three groups, namely, "Better," "The Same," and "Worse." Each patient was represented by the expression levels of 58,929 genes. The machine learning-based workflow included six feature-ranking algorithms, incremental feature selection (IFS), and four classification algorithms. The feature ranking algorithms were in charge of assessing feature importance, whereas IFS with classification algorithms were used to extract essential genes and to construct efficient classifiers and classification rules. The expression of top genes in the results was associated with the immune response to viral infection, which is supported by the published literature. For example, patients with low CCDC18 expression and high CPED1 expression had good quality of life, whereas those with low CDC16 expression had poor quality of life.

The Complement System and C4b-Binding Protein: A Focus on the Promise of C4BPα as a Biomarker to Predict Clopidogrel Resistance

The complement system plays a dual role in the body, either as a first-line defense barrier when balanced between activation and inhibition or as a potential driver of complement-associated injury or diseases when unbalanced or over-activated. C4b-binding protein (C4BP) was the first circulating complement regulatory protein identified and it functions as an important complement inhibitor. C4BP can suppress the over-activation of complement components and prevent the complement system from attacking the host cells through the binding of complement cleavage products C4b and C3b, working in concert as a cofactor for factor I in the degradation of C4b and C3b, and consequently preventing or reducing the assembly of C3 convertase and C5 convertase, respectively. C4BP, particularly C4BP α-chain (C4BPα), exerts its unique inhibitory effects on complement activation and opsonization, systemic inflammation, and platelet activation and aggregation. It has long been acknowledged that crosstalk or interplay exists between the complement system and platelets. Our unpublished preliminary data suggest that circulating C4BPα exerts its antiplatelet effects through inhibition of both complement activity levels and complement-induced platelet reactivity. Plasma C4BPα levels appear to be significantly higher in patients sensitive to, rather than resistant to, clopidogrel, and we suggest that a plasma C4BPα measurement could be used to predict clopidogrel resistance in the clinical settings.

Comprehensive bioinformatics analysis and systems biology approaches to identify the interplay between COVID-19 and pericarditis

Background

Increasing evidence indicating that coronavirus disease 2019 (COVID-19) increased the incidence and related risks of pericarditis and whether COVID-19 vaccine is related to pericarditis has triggered research and discussion. However, mechanisms behind the link between COVID-19 and pericarditis are still unknown. The objective of this study was to further elucidate the molecular mechanisms of COVID-19 with pericarditis at the gene level using bioinformatics analysis.

Methods

Genes associated with COVID-19 and pericarditis were collected from databases using limited screening criteria and intersected to identify the common genes of COVID-19 and pericarditis. Subsequently, gene ontology, pathway enrichment, protein-protein interaction, and immune infiltration analyses were conducted. Finally, TF-gene, gene-miRNA, gene-disease, protein-chemical, and protein-drug interaction networks were constructed based on hub gene identification.

Results

A total of 313 common genes were selected, and enrichment analyses were performed to determine their biological functions and signaling pathways. Eight hub genes (IL-1β, CD8A, IL-10, CD4, IL-6, TLR4, CCL2, and PTPRC) were identified using the protein-protein interaction network, and immune infiltration analysis was then carried out to examine the functional relationship between the eight hub genes and immune cells as well as changes in immune cells in disease. Transcription factors, miRNAs, diseases, chemicals, and drugs with high correlation with hub genes were predicted using bioinformatics analysis.

Conclusions

This study revealed a common gene interaction network between COVID-19 and pericarditis. The screened functional pathways, hub genes, potential compounds, and drugs provided new insights for further research on COVID-19 associated with pericarditis.

VEGFR and DPP-IV as Markers of Severe COVID-19 and Predictors of ICU Admission

The pathophysiology of the severe course of COVID-19 is multifactorial and not entirely elucidated. However, it is well known that the hyperinflammatory response and cytokine storm are paramount events leading to further complications. In this paper, we investigated the vascular response in the pathophysiology of severe COVID-19 and aimed to identify novel biomarkers predictive of ICU admission. The study group consisted of 210 patients diagnosed with COVID-19 (age range: 18-93; mean ± SD: 57.78 ± 14.16), while the control group consisted of 80 healthy individuals. We assessed the plasma concentrations of various vascular factors using the Luminex technique. Then, we isolated RNA from blood mononuclear cells and performed a bioinformatics analysis investigating various processes related to vascular response, inflammation and angiogenesis. Our results confirmed that severe COVID-19 is associated with vWF/ADAMTS 13 imbalance. High plasma concentrations of VEGFR and low DPP-IV may be potential predictors of ICU admission. SARS-CoV-2 infection impairs angiogenesis, hinders the generation of nitric oxide, and thus impedes vasodilation. The hypercoagulable state develops mainly in the early stages of the disease, which may contribute to the well-established complications of COVID-19.

The Effect of SARS-CoV-2 Spike Protein RBD-Epitope on Immunometabolic State and Functional Performance of Cultured Primary Cardiomyocytes Subjected to Hypoxia and Reoxygenation

Cardio complications such as arrhythmias and myocardial damage are common in COVID-19 patients. SARS-CoV-2 interacts with the cardiovascular system primarily via the ACE2 receptor. Cardiomyocyte damage in SARS-CoV-2 infection may stem from inflammation, hypoxia-reoxygenation injury, and direct toxicity; however, the precise mechanisms are unclear. In this study, we simulated hypoxia-reoxygenation conditions commonly seen in SARS-CoV-2-infected patients and studied the impact of the SARS-CoV-2 spike protein RBD-epitope on primary rat cardiomyocytes to gain insight into the potential mechanisms underlying COVID-19-related cardiac complications. Cell metabolic activity was evaluated with PrestoBlueTM. Gene expression of proinflammatory markers was measured by qRT-PCR and their secretion was quantified by Luminex assay. Cardiomyocyte contractility was analysed using the Myocyter plugin of ImageJ. Mitochondrial respiration was determined through Seahorse Mito Stress Test. In hypoxia-reoxygenation conditions, treatment of the SARS-CoV-2 spike RBD-epitope reduced the metabolic activity of primary cardiomyocytes, upregulated Il1β and Cxcl1 expression, and elevated GM-CSF and CCL2 cytokines secretion. Contraction time increased, while amplitude and beating frequency decreased. Acute treatment with a virus RBD-epitope inhibited mitochondrial respiration and lowered ATP production. Under ischaemia-reperfusion, the SARS-CoV-2 RBD-epitope induces cardiomyocyte injury linked to impaired mitochondrial activity.

Graphical Learning and Causal Inference for Drug Repurposing

Gene expression profiles that connect drug perturbations, disease gene expression signatures, and clinical data are important for discovering potential drug repurposing indications. However, the current approach to gene expression reversal has several limitations. First, most methods focus on validating the reversal expression of individual genes. Second, there is a lack of causal approaches for identifying drug repurposing candidates. Third, few methods for passing and summarizing information on a graph have been used for drug repurposing analysis, with classical network propagation and gene set enrichment analysis being the most common. Fourth, there is a lack of graph-valued association analysis, with current approaches using real-valued association analysis one gene at a time to reverse abnormal gene expressions to normal gene expressions. To overcome these limitations, we propose a novel causal inference and graph neural network (GNN)-based framework for identifying drug repurposing candidates. We formulated a causal network as a continuous constrained optimization problem and developed a new algorithm for reconstructing large-scale causal networks of up to 1,000 nodes. We conducted large-scale simulations that demonstrated good false positive and false negative rates. To aggregate and summarize information on both nodes and structure from the spatial domain of the causal network, we used directed acyclic graph neural networks (DAGNN). We also developed a new method for graph regression in which both dependent and independent variables are graphs. We used graph regression to measure the degree to which drugs reverse altered gene expressions of disease to normal levels and to select potential drug repurposing candidates. To illustrate the application of our proposed methods for drug repurposing, we applied them to phase I and II L1000 connectivity map perturbational profiles from the Broad Institute LINCS, which consist of gene-expression profiles for thousands of perturbagens at a variety of time points, doses, and cell lines, as well as disease gene expression data under-expressed and over-expressed in response to SARS-CoV-2.

Impact of the MCP-1-2518A>G polymorphism on COVID-19 severity in the Iranian population: A case-control study

As a result of SARS-CoV-2 infection, the host's immune system is disrupted, and chemokines and cytokines are intensified to eliminate the virus, resulting in cytokine storm syndrome and acute respiratory distress syndrome (ARDS). Patients with COVID-19 have been observed to have elevated levels of MCP-1, a chemokine associated with the severity of the disease. In some diseases, polymorphisms in the regulatory region of the MCP-1 gene correspond to serum levels and disease severity. An attempt was made in this study to assess the relationship between MCP-1 G-2518A and serum MCP-1 levels in Iranian COVID-19 patients and the severity of the disease. In this study, patients were randomly sampled from outpatients on the first day of diagnosis and from inpatients on the first day of their hospitalization. Patients were classified into the outpatient (without symptoms or with mild symptoms) and inpatient (with moderate, severe, and critical symptoms) groups. The serum level of MCP-1 was measured by ELISA and the frequency of MCP-1 G-2518A gene polymorphism genotypes in COVID-19 patients was checked by the RFLP-PCR method. Participants with COVID-19 infection had a higher rate of underlying diseases, such as diabetes, high blood pressure, kidney disease, and cardiovascular disease than the control group (P-value < 0.001). Also, the frequency of these factors in inpatients was significantly higher compared to outpatients (P-value < 0.001). Additionally, the level of MCP-1 in serum was significantly different with an average of 11.90 in comparison to 2.98 in the control group (P-value, 0.05), which is attributed to elevated serum levels among patients in hospitals with an average of 11.72 in comparison to 2.98 in the control group. Compared with outpatients, inpatients had a higher frequency of the G allele of the MCP-1-2518 polymorphism (P-value < 0.05), while a notable difference was observed in the serum level of MCP-1 in COVID-19 patients with the MCP-1-2518 AA genotype in the whole group in comparison to the control group (P-value: 0.024). Totally, the results showed that a high frequency of the G allele is related to hospitalization and poor outcome in COVID-19 cases.

Plasma Proteomic Variables Related to COVID-19 Severity: An Untargeted nLC-MS/MS Investigation

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection leads to a wide range of clinical manifestations and determines the need for personalized and precision medicine. To better understand the biological determinants of this heterogeneity, we explored the plasma proteome of 43 COVID-19 patients with different outcomes by an untargeted liquid chromatography-mass spectrometry approach. The comparison between asymptomatic or pauci-symptomatic subjects (MILDs), and hospitalised patients in need of oxygen support therapy (SEVEREs) highlighted 29 proteins emerged as differentially expressed: 12 overexpressed in MILDs and 17 in SEVEREs. Moreover, a supervised analysis based on a decision-tree recognised three proteins (Fetuin-A, Ig lambda-2chain-C-region, Vitronectin) that are able to robustly discriminate between the two classes independently from the infection stage. In silico functional annotation of the 29 deregulated proteins pinpointed several functions possibly related to the severity; no pathway was associated exclusively to MILDs, while several only to SEVEREs, and some associated to both MILDs and SEVEREs; SARS-CoV-2 signalling pathway was significantly enriched by proteins up-expressed in SEVEREs (SAA1/2, CRP, HP, LRG1) and in MILDs (GSN, HRG). In conclusion, our analysis could provide key information for 'proteomically' defining possible upstream mechanisms and mediators triggering or limiting the domino effect of the immune-related response and characterizing severe exacerbations.

COVID-19, SARS-CoV-2 Vaccination, and Human Herpesviruses Infections

There are several human herpesviruses. A common characteristic of infection by these viruses is latency, by which the virus assumes a non-replicative state, subverting the attentions of the host's immune response. In immunocompetent hosts, herpesviruses are immunologically controlled, although periodic virus shedding can occur. In situations where immunological control is lost, herpesviruses can reactivate and produce clinically apparent disease. It is now becoming apparent that COVID-19 or exposure to COVID-19 vaccines can exert several effects on the immune system. The pandemic of COVID-19 shows no sign of abating, with new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants continuing to evolve. Several COVID-19 vaccines have been developed, and much of the world's population has either experienced COVID-19 or been vaccinated against it. There are an increasing number of reports of associations between herpesvirus infections or reactivations and COVID-19 or COVID-19 vaccination. For instance, a positive cytomegalovirus serostatus may indicate a greater likelihood of severe COVID-19, and herpes simplex virus reactivation may be linked to increased mortality. Epstein-Barr virus reactivation appears to be associated with post-acute sequelae of COVID-19. Finally, herpes zoster has been reported to be associated with COVID-19 vaccination. This brief narrative review will provide several insights into associations between herpesvirus infections or reactivations and COVID-19 or SARS-CoV-2 vaccination.