Fibrinogen Dysregulation is a Prominent Process in Fatal Conditions of COVID-19 Infection; a Proteomic Analysis

Complex changes in serum protein levels in COVID-19 convalescents

The COVID-19 pandemic, triggered by severe acute respiratory syndrome coronavirus 2, has affected millions of people worldwide. Much research has been dedicated to our understanding of COVID-19 disease heterogeneity and severity, but less is known about recovery associated changes. To address this gap in knowledge, we quantified the proteome from serum samples from 29 COVID-19 convalescents and 29 age-, race-, and sex-matched healthy controls. Samples were acquired within the first months of the pandemic. Many proteins from pathways known to change during acute COVID-19 illness, such as from the complement cascade, coagulation system, inflammation and adaptive immune system, had returned to levels seen in healthy controls. In comparison, we identified 22 and 15 proteins with significantly elevated and lowered levels, respectively, amongst COVID-19 convalescents compared to healthy controls. Some of the changes were similar to those observed for the acute phase of the disease, i.e. elevated levels of proteins from hemolysis, the adaptive immune systems, and inflammation. In contrast, some alterations opposed those in the acute phase, e.g. elevated levels of CETP and APOA1 which function in lipid/cholesterol metabolism, and decreased levels of proteins from the complement cascade (e.g. C1R, C1S, and VWF), the coagulation system (e.g. THBS1 and VWF), and the regulation of the actin cytoskeleton (e.g. PFN1 and CFL1) amongst COVID-19 convalescents. We speculate that some of these shifts might originate from a transient decrease in platelet counts upon recovery from the disease. Finally, we observed race-specific changes, e.g. with respect to immunoglobulins and proteins related to cholesterol metabolism.

Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing

The pandemic of SARS-CoV-2 infections is a severe threat to human life and the world economic condition. Although vaccination has reduced the outspread, but still the situation is not under control because of the instability of RNA sequence patterns of SARS-CoV-2, which requires effective drugs. Several studies have suggested that the SARS-CoV-2 infection causing hub differentially expressed genes (Hub-DEGs). However, we observed that there was not any common hub gene (Hub-DEGs) in our analyses. Therefore, it may be difficult to take a common treatment plan against SARS-CoV-2 infections globally. The goal of this study was to examine if more representative Hub-DEGs from published studies by means of hub of Hub-DEGs (hHub-DEGs) and associated potential candidate drugs. In this study, we reviewed 41 articles on transcriptomic data analysis of SARS-CoV-2 and found 370 unique hub genes or studied genes in total. Then, we selected 14 more representative Hub-DEGs (AKT1, APP, CXCL8, EGFR, IL6, INS, JUN, MAPK1, STAT3, TNF, TP53, UBA52, UBC, VEGFA) as hHub-DEGs by their protein-protein interaction analysis. Their associated biological functional processes, transcriptional, and post-transcriptional regulatory factors. Then we detected hHub-DEGs guided top-ranked nine candidate drug agents (Digoxin, Avermectin, Simeprevir, Nelfinavir Mesylate, Proscillaridin, Linifanib, Withaferin, Amuvatinib, Atazanavir) by molecular docking and cross-validation for treatment of SARS-CoV-2 infections. Therefore, the findings of this study could be useful in formulating a common treatment plan against SARS-CoV-2 infections globally.

Low-Level Laser Therapy Effects on Rat Blood Hemostasis Via Significant Alteration in Fibrinogen and Plasminogen Expression Level

Introduction: There are many documents about the significant role of low-level laser therapy (LLLT) in different processes such as regenerator medicine and bone formation. The aim of this study is to assess the role of LLLT in blood hemostasis in rats via bioinformatic investigation. Methods: The differentially expressed plasma proteins of treated rats via LLLT from the literature and the added 50 first neighbors were investigated via network analysis to find the critical dysregulated proteins and biological processes by using Cytoscape software, the STRING database, and ClueGO. Results: A scale-free network including 55 nodes was constructed from queried and added first neighbor proteins. Fibrinogen gamma, fibrinogen alpha, and plasminogen were highlighted as the central genes of the analyzed network. Fibrinolysis was determined as the main group of biological processes that were affected by LLLT. Conclusion: Findings indicate that LLLT affects blood hemostasis which is an important point in approving the therapeutic application of LLLT and also in preventing its possible complication.

Assessment of colon cancer molecular mechanism: a system biology approach

AIM

The current study aimed to assess and compare colon cancer dysregulated genes from the GEO and STRING databases.

BACKGROUND

Colorectal cancer is known as the third most common kind of cancer and the second most important reason for global cancer-related mortality rates. There have been many studies on the molecular mechanism of colon cancer.

METHODS

From the STRING database, 100 differentially expressed proteins related to colon cancers were retrieved and analyzed by network analysis. The central nodes of the network were assessed by gene ontology. The findings were compared with a GSE from GEO.

RESULTS

Based on data from the STRING database, TP53, EGFR, HRAS, MYC, AKT1, GAPDH, KRAS, ERBB2, PTEN, and VEGFA were identified as central genes. The central nodes were not included in the significant DEGs of the analyzed GSE.

CONCLUSION

A combination of different database sources in system biology investigations provides useful information about the studied diseases.

C-reactive protein as a possible marker for severity and mortality of COVID-19 infection

AIM

The present study aimed to introduce a possible biomarker to differentiate between severe and fatal conditions of COVID-19.

BACKGROUND

The COVID-19 pandemic, appearing as a complicated health problem, has changed the lifestyle of people in recent years. Clinical findings indicate mild, severe, and fatal conditions of this disease. Prediction of disease severity is a significant point in managing COVID-19 infection.

METHODS

In this study, 195 differentially expressed genes (DEGs) that discriminate between fatal and severe conditions in patients were extracted from the literature and screened to determine the significant ones. The significant DEGs plus the 90 first neighbors added from the STRING database were included in the interactome using Cytoscape software v 3.7.2. The central nodes of the analyzed network were identified and assessed.

RESULTS

Ten significant DEGs were candidates for assessment, of which 9 were recognized by the STRING database. IL6, ALB, TNF, CRP, INS, MPO, C3, CXCL8, TTR, and TLR4 were determined as central nodes; IL6, CRP, and TTR were highlighted as the critical genes related to the severity of COVID-19 infection.

CONCLUSION

CRP was identified as the best possible biomarker with levels related to the severity and fatality of COVID-19 infection.