COVID-19: A Possible Contribution of the MAPK Pathway.

BACKGROUND: COVID-19 is characterized by an uncontrolled inflammatory response with high pro-inflammatory cytokine production through the activation of intracellular pathways, such as mitogen-activated protein kinase (MAPK). Viruses are able to exploit the MAPK pathway to their advantage; this pathway relevance to severe COVID-19 is poorly described. The aim of this study was to quantify biomarkers involved in the MAPK pathway and to clarify its possible role in affecting some COVID-19-related clinical features. METHODS: H-RAS, C-RAF, MAPK1, MAPK2, and ERK were quantified through ELISA, and genetic polymorphisms were evaluated through real-time PCR. RESULTS: We prospectively recruited 201 individuals (158 positive and 43 negative for SARS-CoV-2): 35 were male, and their median age was 65 years. MAPK-related biomarker levels were increased in SARS-CoV-2-positive participants (n = 89) compared to negative ones (n = 29). Dyspnea was reported by 48%; this symptom was associated with PBMC C-RAF levels in positive participants (p = 0.022) and type of ventilation (p = 0.031). The highest degree of ventilation was used by 8% for invasive ventilation and 41% for continuous positive airway pressure (CPAP). CONCLUSIONS: This is the first study that showed a possible contribution of MAPK-related biomarkers in affecting COVID-19 clinical features, and this may be relevant for identifying COVID-19 positive participants at risk of serious complications.

Evaluation of the Genes and Molecular Pathways Involved in Skin Lesions in Patients with COVID-19: Systems Biology and Bioinformatics Analysis Approach

Background: Coronavirus disease 2019 (COVID-19) was first identified in 2019 in Wuhan, China. Initially, although the number of COVID-19-infected individuals was very low, the infected cases increased as the virus spread worldwide. Skin manifestation is one of the symptoms observed in COVID-19 patients. Objectives: This study investigated the critical genes and molecular pathways involved in skin manifestations in COVID-19 patients through a biological system approach. Methods: In this study, the microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database and analyzed for identifying differentially expressed genes (DEGs). The enrichment analysis of DEGs was evaluated using the DAVID database. Afterward, protein-protein interaction (PPI) networks were constructed via the STRING database and visualized using Cytoscape software. The hub genes were recognized using the cytoHubba. The interaction of the microRNA (miRNA)-hub genes, transcription factor (TF)-hub genes, and drug-hub genes was also evaluated in this study. Results: After analysis, some genes with the highest degree of connectivity, which were involved in the pathogenesis of HELLP syndrome were identified, and they were known as hub genes. These genes are as follows: IFN-gamma CXCL1, CCL2, CCL3, TLR2, IL-1B, CXCL6, IL-6, CCL4, and CXCL2. has-mir-34a-5p, has-mir-20a-5p, and has-mir-27a-3p as miRNA, as well as RELA as TF had the most interaction with the hub genes. Conclusion: Finally, IL-6 and CXCL10 that were compared to the other hub genes had the highest interaction with other genes;therefore, their role in Shamgir's pathogenesis is significant. Targeting the cited genes would be a strategy to prevent symptom manifestation and better patient management.

Molecular cartooning with knowledge graphs.

Molecular "cartoons," such as pathway diagrams, provide a visual summary of biomedical research results and hypotheses. Their ubiquitous appearance within the literature indicates their universal application in mechanistic communication. A recent survey of pathway diagrams identified 64,643 pathway figures published between 1995 and 2019 with 1,112,551 mentions of 13,464 unique human genes participating in a wide variety of biological processes. Researchers generally create these diagrams using generic diagram editing software that does not itself embody any biomedical knowledge. Biomedical knowledge graphs (KGs) integrate and represent knowledge in a semantically consistent way, systematically capturing biomedical knowledge similar to that in molecular cartoons. KGs have the potential to provide context and precise details useful in drawing such figures. However, KGs cannot generally be translated directly into figures. They include substantial material irrelevant to the scientific point of a given figure and are often more detailed than is appropriate. How could KGs be used to facilitate the creation of molecular diagrams? Here we present a new approach towards cartoon image creation that utilizes the semantic structure of knowledge graphs to aid the production of molecular diagrams. We introduce a set of "semantic graphical actions" that select and transform the relational information between heterogeneous entities (e.g., genes, proteins, pathways, diseases) in a KG to produce diagram schematics that meet the scientific communication needs of the user. These semantic actions search, select, filter, transform, group, arrange, connect and extract relevant subgraphs from KGs based on meaning in biological terms, e.g., a protein upstream of a target in a pathway. To demonstrate the utility of this approach, we show how semantic graphical actions on KGs could have been used to produce three existing pathway diagrams in diverse biomedical domains: Down Syndrome, COVID-19, and neuroinflammation. Our focus is on recapitulating the semantic content of the figures, not the layout, glyphs, or other aesthetic aspects. Our results suggest that the use of KGs and semantic graphical actions to produce biomedical diagrams will reduce the effort required and improve the quality of this visual form of scientific communication.

Differences and Similarities between the Lung Transcriptomic Profiles of COVID-19, COPD, and IPF Patients: A Meta-Analysis Study of Pathophysiological Signaling Pathways.

Coronavirus disease 2019 (COVID-19) is a pandemic respiratory disease associated with high morbidity and mortality. Although many patients recover, long-term sequelae after infection have become increasingly recognized and concerning. Among other sequelae, the available data indicate that many patients who recover from COVID-19 could develop fibrotic abnormalities over time. To understand the basic pathophysiology underlying the development of long-term pulmonary fibrosis in COVID-19, as well as the higher mortality rates in patients with pre-existing lung diseases, we compared the transcriptomic fingerprints among patients with COVID-19, idiopathic pulmonary fibrosis (IPF), and chronic obstructive pulmonary disease (COPD) using interactomic analysis. Patients who died of COVID-19 shared some of the molecular biological processes triggered in patients with IPF, such as those related to immune response, airway remodeling, and wound healing, which could explain the radiological images seen in some patients after discharge. However, other aspects of this transcriptomic profile did not resemble the profile associated with irreversible fibrotic processes in IPF. Our mathematical approach instead showed that the molecular processes that were altered in COVID-19 patients more closely resembled those observed in COPD. These data indicate that patients with COPD, who have overcome COVID-19, might experience a faster decline in lung function that will undoubtedly affect global health.

Using bioinformatics and systems biology to discover common pathogenetic processes between sarcoidosis and COVID-19.

The coronavirus disease (COVID-19) pandemic caused by SARS-CoV-2 is ongoing. Individuals with sarcoidosis tend to develop severe COVID-19; however, the underlying pathological mechanisms remain elusive. To determine common transcriptional signatures and pathways between sarcoidosis and COVID-19, we investigated the whole-genome transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with COVID-19 and sarcoidosis and conducted bioinformatic analysis, including gene ontology and pathway enrichment, protein-protein interaction (PPI) network, and gene regulatory network (GRN) construction. We identified 33 abnormally expressed genes that were common between COVID-19 and sarcoidosis. Functional enrichment analysis showed that these differentially expressed genes were associated with cytokine production involved in the immune response and T cell cytokine production. We identified several hub genes from the PPI network encoded by the common genes. These hub genes have high diagnostic potential for COVID-19 and sarcoidosis and can be potential biomarkers. Moreover, GRN analysis identified important microRNAs and transcription factors that regulate the common genes. This study provides a novel characterization of the transcriptional signatures and biological processes commonly dysregulated in sarcoidosis and COVID-19 and identified several critical regulators and biomarkers. This study highlights a potential pathological association between COVID-19 and sarcoidosis, establishing a theoretical basis for future clinical trials.

Molecular pathways involved in COVID-19 and potential pathway-based therapeutic targets.

Deciphering the molecular downstream consequences of severe acute respiratory syndrome coronavirus (SARS-CoV)- 2 infection is important for a greater understanding of the disease and treatment planning. Furthermore, greater understanding of the underlying mechanisms of diagnostic and therapeutic strategies can help in the development of vaccines and drugs against COVID-19. At present, the molecular mechanisms of SARS-CoV-2 in the host cells are not sufficiently comprehended. Some of the mechanisms are proposed considering the existing similarities between SARS-CoV-2 and the other members of the ß-CoVs, and others are explained based on studies advanced in the structure and function of SARS-CoV-2. In this review, we endeavored to map the possible mechanisms of the host response following SARS-CoV-2 infection and surveyed current research conducted by in vitro, in vivo and human observations, as well as existing suggestions. We addressed the specific signaling events that can cause cytokine storm and demonstrated three forms of cell death signaling following virus infection, including apoptosis, pyroptosis, and necroptosis. Given the elicited signaling pathways, we introduced possible pathway-based therapeutic targets; ADAM17 was especially highlighted as one of the most important elements of several signaling pathways involved in the immunopathogenesis of COVID-19. We also provided the possible drug candidates against these targets. Moreover, the cytokine-cytokine receptor interaction pathway was found as one of the important cross-talk pathways through a pathway-pathway interaction analysis for SARS-CoV-2 infection.