Molecular Insights into the MAPK Cascade during Viral Infection: Potential Crosstalk between HCQ and HCQ Analogues

Network pharmacology and molecular docking to elucidate the common mechanism of hydroxychloroquine treatment in lupus nephritis and IgA nephropathy

OBJECTIVE

Hydroxychloroquine (HCQ), characterized by a broad effect on immune regulation, has been widely used in the treatment of autoimmune glomerulonephritis such as lupus nephritis (LN) and immunoglobulin A nephropathy (IgAN). The current research investigates whether HCQ plays a role in the treatment of LN and IgAN through common mechanisms since the pathogenesis of both LN and IgAN is closely related to immune complex deposition, complement activation, and ultimately inflammation.

METHODS

Seventy-two common targets were obtained related to the common mechanism of HCQ treatment of LN and IgAN. Targets associated with LN and IgAN were collected based on DisGeNET, GeneCards, and OMIM databases. Possible HCQ targets were obtained from the PubChem database and PharmMapper databases. The overlapping targets of HCQ ingredients, IgAN, and LN were discovered via the Venn 2.1.0 online platform. Through the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. Cytoscape (v3.9.1) was used to build a protein-protein interaction (PPI) network. Molecular docking was performed by using AutoDockTools 1.5.6 software and PyMol software to match the binding activity between HCQ and the 10 core targets.

RESULTS

The results showed that core targets (including MMP 2, PPARG, IL-2, MAPK14, MMP 9, and SRC), three signaling pathways (including the PI3K-Akt, AGE-RAGE, and MAPK), and cell differentiation (including Th1, Th2, and Th17) might be related to the body's immunity and inflammation. These results suggested that HCQ might act on targets and pathways involved in inflammation and immune regulation to exert a common effect on the treatment of LN and IgAN.

CONCLUSIONS

The current study provided new evidence for the protective mechanism and clinical utility of HCQ against LN and IgAN.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology

PURPOSE

Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations.

METHODS

To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins.

RESULTS

We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences.

CONCLUSION

Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

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.

Baicalein Inhibits α-Melanocyte-stimulating Hormone-stimulated Melanogenesis via p38 Mitogen-activated Protein Kinase Pathway in B16F10 Mouse Melanoma Cells

Excessive UVB exposure causes development of both malignant and non-malignant melanoma via the secretion of α-melanocyte-stimulating hormone (α-MSH). We investigated whether baicalein (5,6,7-trihydroxyflavone) could inhibit α-MSH-stimulated melanogenesis. Baicalein prevented UVB- and α-MSH-induced melanin production and attenuated α-MSH-stimulated tyrosinase (monophenol monooxygenase) activity, and expression of tyrosinase and tyrosine-related protein-2. In addition, baicalein prevented melanogenesis and pigmentation via the p38 mitogen-activated protein kinases signaling pathway. These findings suggest that baicalein represents a natural compound for attenuating melanogenesis.

Molecular crosstalk between COVID-19 and Alzheimer's disease using microarray and RNA-seq datasets: A system biology approach

Objective

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The clinical and epidemiological analysis reported the association between SARS-CoV-2 and neurological diseases. Among neurological diseases, Alzheimer's disease (AD) has developed as a crucial comorbidity of SARS-CoV-2. This study aimed to understand the common transcriptional signatures between SARS-CoV-2 and AD.

Materials and methods

System biology approaches were used to compare the datasets of AD and COVID-19 to identify the genetic association. For this, we have integrated three human whole transcriptomic datasets for COVID-19 and five microarray datasets for AD. We have identified differentially expressed genes for all the datasets and constructed a protein-protein interaction (PPI) network. Hub genes were identified from the PPI network, and hub genes-associated regulatory molecules (transcription factors and miRNAs) were identified for further validation.

Results

A total of 9,500 differentially expressed genes (DEGs) were identified for AD and 7,000 DEGs for COVID-19. Gene ontology analysis resulted in 37 molecular functions, 79 cellular components, and 129 biological processes were found to be commonly enriched in AD and COVID-19. We identified 26 hub genes which includes AKT1, ALB, BDNF, CD4, CDH1, DLG4, EGF, EGFR, FN1, GAPDH, INS, ITGB1, ACTB, SRC, TP53, CDC42, RUNX2, HSPA8, PSMD2, GFAP, VAMP2, MAPK8, CAV1, GNB1, RBX1, and ITGA2B. Specific miRNA targets associated with Alzheimer's disease and COVID-19 were identified through miRNA target prediction. In addition, we found hub genes-transcription factor and hub genes-drugs interaction. We also performed pathway analysis for the hub genes and found that several cell signaling pathways are enriched, such as PI3K-AKT, Neurotrophin, Rap1, Ras, and JAK-STAT.

Conclusion

Our results suggest that the identified hub genes could be diagnostic biomarkers and potential therapeutic drug targets for COVID-19 patients with AD comorbidity.

Protein kinase double-stranded RNA-dependent (PKR) in antiviral defence in fish and mammals

The interferon-inducible double-stranded RNA-dependent protein kinase (PKR) is one of the key antiviral arms of the innate immune system. Upon binding of viral double stranded RNA, a viral Pattern Associated Molecular Pattern (PAMP), PKR gets activated and phosphorylates the eukaryotic initiation factor 2α (eIF2α) resulting in a protein shut-down that limits viral replication. Since its discovery in the mid-seventies, PKR has been shown to be involved in multiple important cellular processes including apoptosis, proinflammatory and innate immune responses. Viral subversion mechanisms of PKR underline its importance in the antiviral response of the host. PKR activation pathways and its mechanisms of action were previously identified and characterised mostly in mammalian models. However, fish Pkr and fish-specific paralogue Z-DNA-dependent protein kinase (Pkz) also play key role in antiviral defence. This review gives an update on the current knowledge on fish Pkr/Pkz, their conditions of activation and their implication in the immune responses to viruses, in comparison to their mammalian counterparts.

Shared miRNA landscapes of COVID-19 and neurodegeneration confirm neuroinflammation as an important overlapping feature

Introduction

Development and worsening of most common neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis, have been associated with COVID-19 However, the mechanisms associated with neurological symptoms in COVID-19 patients and neurodegenerative sequelae are not clear. The interplay between gene expression and metabolite production in CNS is driven by miRNAs. These small non-coding molecules are dysregulated in most common neurodegenerative diseases and COVID-19.

Methods

We have performed a thorough literature screening and database mining to search for shared miRNA landscapes of SARS-CoV-2 infection and neurodegeneration. Differentially expressed miRNAs in COVID-19 patients were searched using PubMed, while differentially expressed miRNAs in patients with five most common neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, and multiple sclerosis) were searched using the Human microRNA Disease Database. Target genes of the overlapping miRNAs, identified with the miRTarBase, were used for the pathway enrichment analysis performed with Kyoto Encyclopedia of Genes and Genomes and Reactome.

Results

In total, 98 common miRNAs were found. Additionally, two of them (hsa-miR-34a and hsa-miR-132) were highlighted as promising biomarkers of neurodegeneration, as they are dysregulated in all five most common neurodegenerative diseases and COVID-19. Additionally, hsa-miR-155 was upregulated in four COVID-19 studies and found to be dysregulated in neurodegeneration processes as well. Screening for miRNA targets identified 746 unique genes with strong evidence for interaction. Target enrichment analysis highlighted most significant KEGG and Reactome pathways being involved in signaling, cancer, transcription and infection. However, the more specific identified pathways confirmed neuroinflammation as being the most important shared feature.

Discussion

Our pathway based approach has identified overlapping miRNAs in COVID-19 and neurodegenerative diseases that may have a valuable potential for neurodegeneration prediction in COVID-19 patients. Additionally, identified miRNAs can be further explored as potential drug targets or agents to modify signaling in shared pathways.

Graphical Abstract

Evaluation of both expression and serum protein levels of caspase-8 and mitogen-activated protein kinase 1 genes in patients with different severities of COVID-19 infection

AIM

The current study aimed to evaluate the effects of caspase-8 (CASP8) and mitogen-activated protein kinase 1 (MAPK1) gene expression levels and their products on preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

METHODS

A total of 40 patients (men, 15 [37.5%]; women, 25 [62.5%]) with COVID-19 infection were included in the current study. The patients were divided into four main groups based on disease severity: mild (n = 7), moderate (n = 10), severe (n = 14), and critical (n = 9). Individuals aged < 18 years and pregnant women were excluded. Patients were classified according to the World Health Organization (WHO) classification system (WHO/2019-nCoV/clinical/2021.1).

RESULTS

Considering all groups, statistically significant differences were detected among all groups for both CASP8^2-ΔΔCt (p = 0.006) and MAPK1 2^-ΔΔCt values (p = 0.015). Moreover, statistically significant differences were detected between mild and moderate (p = 0.013), moderate and critical (p = 0.018), and severe and critical (p = 0.023) groups for lymphocytes.

CONCLUSION

The CASP8/MAPK1 expression levels and/or its products are essential in preventing injury caused by COVID-19 infection. They play crucial roles in maintaining cellular homeostasis and viability. Furthermore, CASP8/MAPK1 levels can provide information about disease severity.

Design and in silico investigation of novel Maraviroc analogues as dual inhibition of CCR-5/SARS-CoV-2 Mpro

A sudden increase in life-threatening COVID-19 infections around the world inflicts global crisis and emotional trauma. In current study two druggable targets, namely SARS-COV-2 M^pro and CCR-5 were selected due to their significant nature in the viral life cycle and cytokine molecular storm respectively. The systematic drug repurposing strategy has been utilized to recognize inhibitory mechanism through extensive in silico investigation of novel Maraviroc analogues as promising inhibitors against SARS-CoV-2 M^pro and CCR-5. The dual inhibition specificity approach implemented in present study using molecular docking, molecular dynamics (MD), principal component analysis (PCA), free energy landscape (FEL) and MM/PBSA binding energy studies. The proposed Maraviroc analogues obtained from in silico investigation could be easily synthesized and constructive in developing significant drug against COVID-19 pandemic, with essentiality of their in vivo/in vitro evaluation to affirm the conclusions of this study. This will further fortify the concept of single drug targeting dual inhibition mechanism for treatment of COVID-19 infection and complications.

Inflammatory pathways in COVID‐19: Mechanism and therapeutic interventions

The 2019 coronavirus disease (COVID‐19) pandemic has become a global crisis. In the immunopathogenesis of COVID‐19, SARS‐CoV‐2 infection induces an excessive inflammatory response in patients, causing an inflammatory cytokine storm in severe cases. Cytokine storm leads to acute respiratory distress syndrome, pulmonary and other multiorgan failure, which is an important cause of COVID‐19 progression and even death. Among them, activation of inflammatory pathways is a major factor in generating cytokine storms and causing dysregulated immune responses, which is closely related to the severity of viral infection. Therefore, elucidation of the inflammatory signaling pathway of SARS‐CoV‐2 is important in providing otential therapeutic targets and treatment strategies against COVID‐19. Here, we discuss the major inflammatory pathways in the pathogenesis of COVID‐19, including induction, function, and downstream signaling, as well as existing and potential interventions targeting these cytokines or related signaling pathways. We believe that a comprehensive understanding of the regulatory pathways of COVID‐19 immune dysregulation and inflammation will help develop better clinical therapy strategies to effectively control inflammatory diseases, such as COVID‐19.

Differing coronavirus genres alter shared host signaling pathways upon viral infection

Coronaviruses are important viral pathogens across a range of animal species including humans. They have a high potential for cross-species transmission as evidenced by the emergence of COVID-19 and may be the origin of future pandemics. There is therefore an urgent need to study coronaviruses in depth and to identify new therapeutic targets. This study shows that distant coronaviruses such as Alpha-, Beta-, and Deltacoronaviruses can share common host immune associated pathways and genes. Differentially expressed genes (DEGs) in the transcription profile of epithelial cell lines infected with swine acute diarrhea syndrome, severe acute respiratory syndrome coronavirus 2, or porcine deltacoronavirus, showed that DEGs within 10 common immune associated pathways were upregulated upon infection. Twenty Three pathways and 21 DEGs across 10 immune response associated pathways were shared by these viruses. These 21 DEGs can serve as focused targets for therapeutics against newly emerging coronaviruses. We were able to show that even though there is a positive correlation between PDCoV and SARS-CoV-2 infections, these viruses could be using different strategies for efficient replication in their cells from their natural hosts. To the best of our knowledge, this is the first report of comparative host transcriptome analysis across distant coronavirus genres.

Plant Source Derived Compound Exhibited In Silico Inhibition of Membrane Glycoprotein In SARS-CoV-2: Paving the Way to Discover a New Class of Compound For Treatment of COVID-19

SARS-CoV-2 is the virus responsible for causing COVID-19 disease in humans, creating the recent pandemic across the world, where lower production of Type I Interferon (IFN-I) is associated with the deadly form of the disease. Membrane protein or SARS-CoV-2 M proteins are known to be the major reason behind the lower production of human IFN-I by suppressing the expression of IFNβ and Interferon Stimulated Genes. In this study, 7,832 compounds from 32 medicinal plants of India possessing traditional knowledge linkage with pneumonia-like disease treatment, were screened against the Homology-Modelled structure of SARS-CoV-2 M protein with the objective of identifying some active phytochemicals as inhibitors. The entire study was carried out using different modules of Schrodinger Suite 2020-3. During the docking of the phytochemicals against the SARS-CoV-2 M protein, a compound, ZIN1722 from Zingiber officinale showed the best binding affinity with the receptor with a Glide Docking Score of −5.752 and Glide gscore of −5.789. In order to study the binding stability, the complex between the SARS-CoV-2 M protein and ZIN1722 was subjected to 50 ns Molecular Dynamics simulation using Desmond module of Schrodinger suite 2020-3, during which the receptor-ligand complex showed substantial stability after 32 ns of MD Simulation. The molecule ZIN1722 also showed promising results during ADME-Tox analysis performed using Swiss ADME and pkCSM. With all the findings of this extensive computational study, the compound ZIN1722 is proposed as a potential inhibitor to the SARS-CoV-2 M protein, which may subsequently prevent the immunosuppression mechanism in the human body during the SARS-CoV-2 virus infection. Further studies based on this work would pave the way towards the identification of an effective therapeutic regime for the treatment and management of SARS-CoV-2 infection in a precise and sustainable manner.

Computational anti-COVID-19 drug design: progress and challenges

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

The Role of Macrophages in the Development of Acute and Chronic Inflammatory Lung Diseases

Macrophages play an important role in the innate and adaptive immune responses of organ systems, including the lungs, to particles and pathogens. Cumulative results show that macrophages contribute to the development and progression of acute or chronic inflammatory responses through the secretion of inflammatory cytokines/chemokines and the activation of transcription factors in the pathogenesis of inflammatory lung diseases, such as acute lung injury (ALI), acute respiratory distress syndrome (ARDS), ARDS related to COVID-19 (coronavirus disease 2019, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)), allergic asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF). This review summarizes the functions of macrophages and their associated underlying mechanisms in the development of ALI, ARDS, COVID-19-related ARDS, allergic asthma, COPD, and IPF and briefly introduces the acute and chronic experimental animal models. Thus, this review suggests an effective therapeutic approach that focuses on the regulation of macrophage function in the context of inflammatory lung diseases.

Computational analysis revealed miRNAs produced by Chikungunya virus target genes associated with antiviral immune responses and cell cycle regulation

Chikungunya virus (CHIKV) that causes chikungunya fever, is an alphavirus that belongs to the Togaviridae family containing a single-stranded RNA genome. Mosquitoes of the Aedes species act as the vectors for this virus and can be found in the blood, which can be passed from an infected person to a mosquito through mosquito bites. CHIKV has drawn much attention recently because of its potential of causing an epidemic. As the detailed mechanism of its pathogenesis inside the host system is still lacking, in this in silico research we have hypothesized that CHIKV might create miRNAs, which would target the genes associated with host cellular regulatory pathways, thereby providing the virus with prolonged refuge. Using bioinformatics approaches we found several putative miRNAs produced by CHIKV. Then we predicted the genes of the host targeted by these miRNAs. Functional enrichment analysis of these targeted genes shows the involvement of several biological pathways regulating antiviral immune stimulation, cellular proliferation, and cell cycle, thereby provide themselves with prolonged refuge and facilitate their pathogenesis, which in turn may lead to disease conditions. Finally, we analyzed a publicly available microarray dataset (GSE49985) to determine the altered expression levels of the targeted genes and found genes associated with pathways such as cell differentiation, phagocytosis, T-cell activation, response to cytokine, autophagy, Toll-like receptor signaling, RIG-I like receptor signaling and apoptosis. Our finding presents novel miRNAs and their targeted genes, which upon experimental validation could facilitate in developing new therapeutics to combat CHIKV infection and minimize CHIKV mediated diseases.