Drug Contraindications in Comorbid Diseases: a Protein Interactome Perspective

Adverse drug reactions (ADRs) are leading causes of death and drug withdrawals and frequently co-occur with comorbidities. However, systematic studies on the effects of drugs in comorbidities are lacking. Drug interactions with the cellular protein-protein interaction (PPI) network give rise to ADRs. We selected 6 comorbid disease pairs, identified the drugs used in the treatment of the individual diseases A and B — 44 drugs in anxiety and depression, 128 in asthma and hypertension, 48 in chronic obstructive pulmonary disease and heart failure, 58 in type 2 diabetes and obesity, 58 in Parkinson′s disease and schizophrenia, and 84 in rheumatoid arthritis and osteoporosis — and categorized them based on whether they aggravate the comorbid condition. We constructed drug target networks (DTNs) and examined their enrichment among genes in disease A/B PPI networks, expressed across 53 tissues and involved in ≈1000 pathways. To pinpoint the biological features characterizing the DTNs, we performed principal component analysis and computed the Euclidean distance between DTN component scores and feature loading values. DTNs of disease A drugs not contraindicated in B were affiliated with proteins common to A/B networks or uniquely found in the B network, similarly regulated common pathways, and disease-B specific pathways and tissues. DTNs of disease A drugs contraindicated in B were affiliated with common proteins or those uniquely found in the A network, differentially regulated common pathways, and disease A-specific pathways and tissues. Hence, DTN enrichment in pathways, tissues, and PPI networks of comorbid diseases will help identify drugs contraindications in comorbidities.

Integrative Multi−Omics Analysis Reveals Candidate Biomarkers for Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common types of cancer worldwide. Due to the lack of early detection and treatment, the survival rate of OSCC remains poor and the incidence of OSCC has not decreased during the past decades. To explore potential biomarkers and therapeutic targets for OSCC, we analyzed differentially expressed genes (DEGs) associated with OSCC using RNA sequencing technology. Methylation−regulated and differentially expressed genes (MeDEGs) of OSCC were further identified via an integrative approach by examining publicly available methylomic datasets together with our transcriptomic data. Protein−protein interaction (PPI) networks of MeDEGs were constructed and highly connected hub MeDEGs were identified from these PPI networks. Subsequently, expression and survival analyses of hub genes were performed using The Cancer Genome Atlas (TCGA) database and the Gene Expression Profiling Interactive Analysis (GEPIA) online tool. A total of 56 upregulated MeDEGs and 170 downregulated MeDEGs were identified in OSCC. Eleven hub genes with high degree of connectivity were picked out from the PPI networks constructed by those MeDEGs. Among them, the expression level of four hub genes (CTLA4, CDSN, ACTN2, and MYH11) were found to be significantly changed in the head and neck squamous carcinoma (HNSC) patients. Three hypomethylated hub genes (CTLA4, GPR29, and TNFSF11) and one hypermethylated hub gene (ISL1) were found to be significantly associated with overall survival (OS) of HNSC patients. Therefore, these hub genes may serve as potential DNA methylation biomarkers and therapeutic targets of OSCC.

Exploration of the Role of Serine Proteinase Inhibitor A3 in Alcohol Dependence Using Gene Expression Omnibus Database

Background: Alcohol dependence is an overall health-related challenge; however, the specific mechanisms underlying alcohol dependence remain unclear. Serine proteinase inhibitor A3 (SERPINA3) plays crucial roles in multiple human diseases; however, its role in alcohol dependence clinical practice has not been confirmed.Methods: We screened Gene Expression Omnibus (GEO) expression profiles, and identified differentially expressed genes (DEGs). Protein-protein interaction (PPI) networks were generated using STRING and Cytoscape, and the key clustering module was identified using the MCODE plugin. SERPINA3-based target microRNA prediction was performed using online databases. Functional enrichment analysis was performed. Fifty-eight patients with alcohol dependence and 20 healthy controls were recruited. Clinical variables were collected and follow-up was conducted for 8 months for relapse.Results:SERPINA3 was identified as a DEG. ELANE and miR-137 were identified after PPI analysis. The enriched functions and pathways included acute inflammatory response, response to stress, immune response, and terpenoid backbone biosynthesis. SERPINA3 concentrations were significantly elevated in the alcohol dependence group than in healthy controls (P < 0.001). According to the median value of SERPINA3 expression level in alcohol dependence group, patients were divided into high SERPINA3 (≥2677.33 pg/ml, n = 29) and low SERPINA3 groups (<2677.33 pg/ml, n = 29). Binary logistic analysis indicated that IL-6 was statistically significant (P = 0.015) Kaplan-Meier survival analysis did not indicate any difference in event-free survival between patients with low and high SERPINA3 levels (P = 0.489) after 8 months of follow-up. Receiver characteristic curve analysis revealed that SERPINA3 had an area under the curve of 0.921 (P < 0.0001), with a sensitivity and specificity of 93.1 and 80.0%, respectively. Cox regression analysis revealed that aspartate transaminase level was a negative predictor of relapse (β = 0.003; hazard ratio = 1.003; P = 0.03).Conclusions:SERPINA3 level was remarkably elevated in patients with alcohol dependence than healthy controls, indicating that SERPINA3 is correlated with alcohol dependence. However, SERPINA3 may not be a potential predictive marker of relapse with patients in alcohol dependence.

Protein complexes detection based on node local properties and gene expression in PPI weighted networks

Background Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed. Most algorithms usually employ direct neighbors of nodes and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection. Result Based on this observation, we propose a new way by combining node resource allocation and gene expression information to weight protein network (NRAGE-WPN), in which protein complexes are detected based on core-attachment and second-order neighbors. Conclusions Through comparison with eleven methods in Yeast and Human PPI network, the experimental results demonstrate that this algorithm not only performs better than other methods on 75% in terms of f-measure+, but also can achieve an ideal overall performance in terms of a composite score consisting of five performance measures. This identification method is simple and can accurately identify more complexes.

Screening and analysis of RNAs associated with activated memory CD4 and CD8 T cells in liver cancer

Background Liver cancer is one of the most common malignant tumors in the world. T cell-mediated antitumor immune response is the basis of liver cancer immunotherapy. Objective To screen and analyze the RNAs associated with activated memory CD4 T cells and CD8 T cells in liver cancer. Methods ESTIMATE was used to calculate the stromal and immune scores of tumor samples, which were downloaded from The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) in high and low stromal and immune scores were screened, followed by functional enrichment of overlapped DEGs. We then conducted a survival analysis to identify immune-related prognostic indicators and constructed protein-protein interaction (PPI) networks and ceRNA networks. Finally, chemical small-molecule–target interaction pairs associated with liver cancer were screened. Results A total of 55,955 stromal-related DEGs and 1811 immune-related DEGs were obtained. The 1238 overlapped DEGs were enriched in 1457 biological process terms and 74 KEGG pathways. In addition, a total of 120 activated memory CD4 T cell-related genes and 309 CD8 T cell-related genes were identified. The survival analysis revealed that upregulated expression of T cell-related genes including EOMES, CST7, and CD5L indicated the favorable prognosis of liver cancer. EOMES was regulated by has-miR-23b-3p and has-miR-23b-3p was regulated by lncRNA AC104820.2 in the ceRNA network of activated memory CD4 T cell-related genes. In addition, EOMES was regulated by has-miR-23a-3p and has-miR-23a-3p was regulated by lncRNA AC000476.1 in the ceRNA network of CD8 T cells. Conclusion T cell-related RNAs EOMES, CST7, CD5L, has-miR-23b-3p, and has-miR-23a-3p may be associated with the prognosis of liver cancer. And the molecular characteristics of these T cell-related genes were plotted.

Integrating Network Pharmacology and Molecular Docking to Analyse the Potential Mechanism of action of Macleaya cordata (Willd.) R. Br. in the Treatment of Bovine Hoof Disease

Based on network pharmacological analysis and molecular docking techniques, the main components of M. cordata for the treatment of bovine relevant active compounds in M. cordata were searched for through previous research bases and literature databases, and then screened to identify candidate compounds based on physicochemical properties, pharmacokinetic parameters, bioavailability, and drug-like criteria. Target genes associated with hoof disease were obtained from the GeneCards database. Compound−target, compound−target−pathway−disease visualization networks, and protein−protein interaction (PPI) networks were constructed by Cytoscape. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in R language. Molecular docking analysis was done using AutoDockTools. The visual network analysis showed that four active compounds, sanguinarine, chelerythrine, allocryptopine and protopine, were associated with the 10 target genes/proteins (SRC, MAPK3, MTOR, ESR1, PIK3CA, BCL2L1, JAK2, GSK3B, MAPK1, and AR) obtained from the screen. The enrichment analysis indicated that the cAMP, PI3K-Akt, and ErbB signaling pathways may be key signaling pathways in network pharmacology. The molecular docking results showed that sanguinarine, chelerythrine, allocryptopine, and protopine bound well to MAPK3 and JAK2. A comprehensive bioinformatics-based network topology strategy and molecular docking study has elucidated the multi-component synergistic mechanism of action of M. cordata in the treatment of bovine hoof disease, offering the possibility of developing M. cordata as a new source of drugs for hoof disease treatment.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

BackgroundThe recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.MethodsRNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.ResultsBased on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19’s main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.ConclusionThis study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Understanding Gene Expression and Transcriptome Profiling of COVID-19: An Initiative Towards the Mapping of Protective Immunity Genes Against SARS-CoV-2 Infection

The COVID-19 pandemic has created an urgent situation throughout the globe. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability. The DEGs will help understand the disease’s potential underlying molecular mechanisms and genetic characteristics, including the regulatory genes associated with immune response elements and protective immunity. This study aimed to determine the DEGs in mild and severe COVID-19 patients versus healthy controls. The Agilent-085982 Arraystar human lncRNA V5 microarray GEO dataset (GSE164805 dataset) was used for this study. We used statistical tools to identify the DEGs. Our 15 human samples dataset was divided into three groups: mild, severe COVID-19 patients and healthy control volunteers. We compared our result with three other published gene expression studies of COVID-19 patients. Along with significant DEGs, we developed an interactome map, a protein-protein interaction (PPI) pattern, a cluster analysis of the PPI network, and pathway enrichment analysis. We also performed the same analyses with the top-ranked genes from the three other COVID-19 gene expression studies. We also identified differentially expressed lncRNA genes and constructed protein-coding DEG-lncRNA co-expression networks. We attempted to identify the regulatory genes related to immune response elements and protective immunity. We prioritized the most significant 29 protein-coding DEGs. Our analyses showed that several DEGs were involved in forming interactome maps, PPI networks, and cluster formation, similar to the results obtained using data from the protein-coding genes from other investigations. Interestingly we found six lncRNAs (TALAM1, DLEU2, and UICLM CASC18, SNHG20, and GNAS) involved in the protein-coding DEG-lncRNA network; which might be served as potential biomarkers for COVID-19 patients. We also identified three regulatory genes from our study and 44 regulatory genes from the other investigations related to immune response elements and protective immunity. We were able to map the regulatory genes associated with immune elements and identify the virogenomic responses involved in protective immunity against SARS-CoV-2 infection during COVID-19 development.

Comprehensive Analysis to Identify MAGEA3 Expression Correlated With Immune Infiltrates and Lymph Node Metastasis in Gastric Cancer

Gastric cancer (GC) is an aggressive malignant tumor and causes a significant number of deaths every year. With the coming of the age of cancer immunotherapy, search for a new target in gastric cancer may benefit more advanced patients. Melanoma-associated antigen-A3 (MAGEA3), one of the members of the cancer-testis antigen (CTA) family, was considered an important part of cancer immunotherapy. We evaluate the potential role of MAGEA3 in GC through the TCGA database. The result revealed that MAGEA3 is upregulated in GC and linked to poor OS and lymph node metastasis. MAGEA3 was also correlated with immune checkpoints, TMB, and affected the tumor immune microenvironment and the prognosis of GC through CIBERSORT, TIMER, and Kaplan-Meier plotter database analysis. In addition, GSEA-identified MAGEA3 is involved in the immune regulation of GC. Moreover, the protein-protein interaction (PPI) networks of MAGEA3 were constructed through STRING database and MAGEA3-correlated miRNAs were screened based on the joint analysis of multiple databases. In terms of experimental verification, we constructed pET21a (+)/MAGEA3 restructuring plasmids and transformed to Escherichia coli Rosetta. MAGEA3 protein was used as an antigen after being expressed and purified and can effectively detect the specific IgG in 93 GC patients’ serum specimens with 44.08% sensitivity and 92.54% specificity. Through further analysis, the positive rate of MAGEA3 was related to the stage and transfer number of lymph nodes. These results indicated that MAGEA3 is a novel biomarker and correlated with lymph node metastasis and immune infiltrates in GC, which could be a new target for immunotherapy.

Protein Function Prediction Based on PPI Networks: Network Reconstruction vs Edge Enrichment

Over the past decades, massive amounts of protein-protein interaction (PPI) data have been accumulated due to the advancement of high-throughput technologies, and but data quality issues (noise or incompleteness) of PPI have been still affecting protein function prediction accuracy based on PPI networks. Although two main strategies of network reconstruction and edge enrichment have been reported on the effectiveness of boosting the prediction performance in numerous literature studies, there still lack comparative studies of the performance differences between network reconstruction and edge enrichment. Inspired by the question, this study first uses three protein similarity metrics (local, global and sequence) for network reconstruction and edge enrichment in PPI networks, and then evaluates the performance differences of network reconstruction, edge enrichment and the original networks on two real PPI datasets. The experimental results demonstrate that edge enrichment work better than both network reconstruction and original networks. Moreover, for the edge enrichment of PPI networks, the sequence similarity outperformes both local and global similarity. In summary, our study can help biologists select suitable pre-processing schemes and achieve better protein function prediction for PPI networks.