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Defeng Pan,1,* Yufei Zhou,2,* Shengjue Xiao,1,* Yue Hu,3,* Chunyan Huan,1 Qi Wu,1 Xiaotong Wang,1 Qinyuan Pan,1 Jie Liu,1 Hong Zhu1 1Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China; 2Department of Cardiology, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, People’s Republic of China; 3Department of General Practice, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China*These authors contributed equally to this workCorrespondence: Defeng PanDepartment of Cardiology, The Affiliated Hospital of Xuzhou Medical University, 99 Huaihai West Road, Xuzhou, 221004, Jiangsu, People’s Republic of ChinaTel +86 516-83262017Email xzdefengpan@xzhmu.edu.cnIntroduction: Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, but the molecular mechanisms underlying AF are not known. We aimed to identify the pivotal genes and pathways involved in AF pathogenesis because they could become potential biomarkers and therapeutic targets of AF.Methods: The microarray datasets of GSE31821 and GSE41177 were downloaded from the Gene Expression Omnibus database. After combining the two datasets, differentially expressed genes (DEGs) were screened by the Limma package. MicroRNAs (miRNAs) confirmed experimentally to have an interaction with AF were screened through the miRTarBase database. Target genes of miRNAs were predicted using the miRNet database, and the intersection between DEGs and target genes of miRNAs, which were defined as common genes (CGs), were analyzed. Functional and pathway-enrichment analyses of DEGs and CGs were performed using the databases DAVID and KOBAS. Protein–protein interaction (PPI) network, miRNA- messenger(m) RNA network, and drug-gene network was visualized. Finally, reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) was used to validate the expression of hub genes in the miRNA–mRNA network.Results: Thirty-three CGs were acquired from the intersection of 65 DEGs from the integrated dataset and 9777 target genes of miRNAs. Fifteen “hub” genes were selected from the PPI network, and the miRNA-mRNA network, including 82 miRNAs and 9 target mRNAs, was constructed. Furthermore, with the validation by RT-qPCR, macrophage migration inhibitory factor (MIF), MYC proto-oncogene, bHLH transcription factor (MYC), inhibitor of differentiation 1 (ID1), and C-X-C Motif Chemokine Receptor 4 (CXCR4) were upregulated and superoxide Dismutase 2 (SOD2) was downregulated in patients with AF compared with healthy controls. We also found MIF, MYC, and ID1 were enriched in the transforming growth factor (TGF)-β and Hippo signaling pathway.Conclusion: We identified several pivotal genes and pathways involved in AF pathogenesis. MIF, MYC, and ID1 might participate in AF progression through the TGF-β and Hippo signaling pathways. Our study provided new insights into the mechanisms of action of AF.Keywords: atrial fibrillation, bioinformatics analysis, differentially expressed genes, pathways, protein–protein interaction network

The aim of this study is to develop an approach for predicting the functional effects of variants of non-coding genes which have great importance in human genetics. Non-coding genes have formed a very vital field of study since they have a high effect on diseases. However, little is known about non-coding genes compared to coding genes, and they are found in the body almost 9 times more than coding genes. This is a critical issue, and i t is very important to predict the effects of these genes, which are so abundant in the body and difficult to understand. This exhibits the motivation of the study described in the paper. For this purpose, an extensive literature review was first conducted, and possible datasets that could be used were examined. Then, using Python programming language, we developed a prediction model with high accuracy. After investigating how important non-coding gene variants are, and in what areas they can be used, we decided to use a functional interaction network from the deep learning models as the most suitable method. We used STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) which is a biological database and web resource of known and predicted protein-protein interactions. As a second step, we generated feature vectors. After checking the overlap of non-coding genes, we extracted three types of feature vectors. Identifying protein interaction network in Python, the outcome describes the interplay between the biomolecules encoded by genes. It allows to understand the complexities of cellular functions, and even predict potential therapeutics. As a last step, we implemented a deep learning model which included three fully connected (FC) layers, also known as dense layers, with dimensions 40, 10, and 2, respectively. Experimental results demonstrate that the proposed method captured high accuracy values.

Objective To investigate the relation between nicotinic acetylcholine receptor subunit (nAChR) genes and schizophrenia, and the relation between tag single nucleotide polymorphism (rs1317286, rs1044396, rs6494212, rs16969968, and rs684513) and schizophrenia in Han Chinese people.Methods The protein-protein interaction (PPI) network among nAChR protein and 350 proteins encoded by schizophrenia-related susceptibility genes was constructed through the String database to explore whether nAChR genes were associated with schizophrenia in these known databases. Then, five single nucleotide polymorphisms (SNPs) of CHRNA3 (rs1317286), CHRNA4 (rs1044396), CHRNA7 (rs6494212), and CHRNA5 (rs16969968, rs684513) were analyzed in a sample of 1,035 schizophrenic patients and 816 healthy controls. The interaction between the markers was analyzed using multifactor dimensionality reduction (MDR) software. Power analysis was performed using the Quanto program.Results There are no significant differences in genotype or allele distribution were identified between the patients and controls (p>0.05). The haplotypes constructed by four markers rs1317286, rs6494212, rs16969968, and rs684513 were not associated with schizophrenia either. However, a significant association between models made of rs1317286, rs1044396, rs6494212, and rs684513 and schizophrenia was revealed in interaction analysis (p

Mutations of the X-linked gene encoding methyl-CpG-binding protein 2 (MECP2) cause classical forms of Rett syndrome (RTT) in girls. A subset of patients who are recognized to have an overlapping neurological phenotype with RTT but are lacking a mutation in a gene that causes classical or atypical RTT can be described as having a ‘Rett-syndrome-like phenotype (RTT-L). Here, we report eight patients from our cohort diagnosed as having RTT-L who carry mutations in genes unrelated to RTT. We annotated the list of genes associated with RTT-L from our patient cohort, considered them in the light of peer-reviewed articles on the genetics of RTT-L, and constructed an integrated protein–protein interaction network (PPIN) consisting of 2871 interactions connecting 2192 neighboring proteins among RTT- and RTT-L-associated genes. Functional enrichment analysis of RTT and RTT-L genes identified a number of intuitive biological processes. We also identified transcription factors (TFs) whose binding sites are common across the set of RTT and RTT-L genes and appear as important regulatory motifs for them. Investigation of the most significant over-represented pathway analysis suggests that HDAC1 and CHD4 likely play a central role in the interactome between RTT and RTT-L genes.

11 p.-4 fig.-1 tab., The coronavirus disease 2019 pandemic accelerated drug/vaccine development processes, integrating scientists all over the globe to create therapeutic alternatives against this virus. In this work, we have collected information regarding proteins from SARS-CoV-2 and humans and how these proteins interact. We have also collected information from public databases on protein-drug interactions. We represent this data as networks that allow us to gain insights into protein-protein interactions between both organisms. With the collected data, we have obtained statistical metrics of the networks. This data analysis has allowed us to find relevant information on which proteins and drugs are the most relevant from the network pharmacology perspective. This method not only allows us to focus on viral proteins as the main targets for COVID-19 but also reveals that some human proteins could be also important in drug repurposing campaigns. As a result of the analysis of the SARS-CoV-2-human interactome, we have identified some old drugs, such as disulfiram, auranofin, gefitinib, suloctidil, and bromhexine as potential therapies for the treatment of COVID-19 deciphering their potential complex mechanism of action., This research was funded by the Agencia Nacional de Investigación y Desarrollo de Chile, grants No. COVID0199,REDES190074, and ACT210012, and the FONDECYT grant No.1220656, and also by the European Commission–NextGenerationEU (Regulation EU 2020/2094),through CSIC’s Global Health Platform (PTI Salud Global)

As a common and high-concentration heavy metal in the ocean, Cu can induce metal toxicity and significantly affect the metabolic function of marine organisms. Sepia esculenta is an important economic cephalopod found along the east coast of China, the growth, movement, and reproduction of which are all affected by heavy metals. Hitherto, the specific metabolic mechanism of heavy-metal exposure in S. esculenta is still unclear. In this study, we identified 1131 DEGs through transcriptome analysis of larval S. esculenta within 24 h of Cu exposure. GO and KEGG functional enrichment analysis results indicated that Cu exposure may affect purine metabolism, protein digestion and absorption, cholesterol metabolism, and other metabolic processes in S. esculenta larvae. It is worth noting that in this study we explore metabolic mechanism of Cu-exposed S. esculenta larvae through the comprehensive analysis of protein–protein interaction network and KEGG enrichment analysis for the first time and find 20 identified key and hub genes such as CYP7A1, CYP3A11, and ABCA1. Based on their expression, we preliminarily speculate that Cu exposure may inhibit multiple metabolic processes and induce metabolic disorders. Our results lay a foundation for further understanding the metabolic mechanism of S. esculenta against heavy metals and provide theoretical help for S. esculenta artificial breeding.

Background: The function and polarization of macrophages has a significant impact on the outcome of many diseases. Targeting tumor-associated macrophages (TAMs) is among the greatest challenges to solve because of the low in vitro reproducibility of the heterogeneous tumor microenvironment (TME). To create a more comprehensive model and to understand the inner workings of the macrophage and its dependence on extracellular signals driving polarization, we propose an in silico approach. Methods: A Boolean control network was built based on systematic manual curation of the scientific literature to model the early response events of macrophages by connecting extracellular signals (input) with gene transcription (output). The network consists of 106 nodes, classified as 9 input, 75 inner and 22 output nodes, that are connected by 217 edges. The direction and polarity of edges were manually verified and only included in the model if the literature plainly supported these parameters. Single or combinatory inhibitions were simulated mimicking therapeutic interventions, and output patterns were analyzed to interpret changes in polarization and cell function. Results: We show that inhibiting a single target is inadequate to modify an established polarization, and that in combination therapy, inhibiting numerous targets with individually small effects is frequently required. Our findings show the importance of JAK1, JAK3 and STAT6, and to a lesser extent STK4, Sp1 and Tyk2, in establishing an M1-like pro-inflammatory polarization, and NFAT5 in creating an anti-inflammatory M2-like phenotype. Conclusions: Here, we demonstrate a protein–protein interaction (PPI) network modeling the intracellular signalization driving macrophage polarization, offering the possibility of therapeutic repolarization and demonstrating evidence for multi-target methods.

SARS-CoV-2 is a novel coronavirus that replicates itself via interacting with the host proteins. As a result, identifying virus and host protein-protein interactions could help researchers better understand the virus disease transmission behavior and identify possible COVID-19 drugs. The International Committee on Virus Taxonomy has determined that nCoV is genetically 89% compared to the SARS-CoV epidemic in 2003. This paper focuses on assessing the host–pathogen protein interaction affinity of the coronavirus family, having 44 different variants. In light of these considerations, a GO-semantic scoring function is provided based on Gene Ontology (GO) graphs for determining the binding affinity of any two proteins at the organism level. Based on the availability of the GO annotation of the proteins, 11 viral variants, viz., SARS-CoV-2, SARS, MERS, Bat coronavirus HKU3, Bat coronavirus Rp3/2004, Bat coronavirus HKU5, Murine coronavirus, Bovine coronavirus, Rat coronavirus, Bat coronavirus HKU4, Bat coronavirus 133/2005, are considered from 44 viral variants. The fuzzy scoring function of the entire host–pathogen network has been processed with ~180 million potential interactions generated from 19,281 host proteins and around 242 viral proteins. ~4.5 million potential level one host–pathogen interactions are computed based on the estimated interaction affinity threshold. The resulting host–pathogen interactome is also validated with state-of-the-art experimental networks. The study has also been extended further toward the drug-repurposing study by analyzing the FDA-listed COVID drugs.

Bladder cancer (BC) is the most common malignant tumor of the urinary system and is associated with high morbidity and mortality; however, the molecular mechanism underlying its occurrence is not clear. In this study, the gene expression profile and related clinical information of GSE13507 were downloaded from the Gene Expression Omnibus (GEO) database. RNA sequencing (RNA-seq) expression data and related clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. Overlapping genes were identified by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). Then, we carried out functional enrichment analysis to understand the potential biological functions of these co-expressed genes. Finally, we performed a protein–protein interaction (PPI) analysis combined with survival analysis. Using the CytoHubba plug-in of Cytoscape, TROAP, CENPF, PRC1, AURKB, CCNB2, CDC20, TTK, CEP55, ASPM, and CDCA8 were identified as candidate central genes. According to the survival analysis, the high expression of TTK was related to the poor overall survival (OS) of patients with BC. TTK may also affect the bladder tumor microenvironment (TME) by affecting the number of immune cells. The expression level of TTK was verified by immunohistochemistry (IHC) and real-time quantitative polymerase chain reaction (RT-qPCR), and the tumor-promoting effect of TTK in BC cells was confirmed in vitro. Our results also identified the MSC-AS1/hsa-miR-664b-3p/TTK regulatory axis, which may also play an important role in the progression of BC, but further research is needed to verify this result. In summary, our results provide a new idea for accurate early diagnosis, clinical treatment, and prognosis of BC

The arterial aneurysm refers to localized dilation of blood vessel wall and is common in general population. The majority of aneurysm cases remains asymptomatic until a sudden rupture which is usually fatal and of extremely high mortality (~ 50–60%). Therefore, early diagnosis, prevention and management of aneurysm are in urgent need. Unfortunately, current understanding of disease driver genes of various aneurysm subtypes is still limited, and without appropriate biomarkers and drug targets no specialized drug has been developed for aneurysm treatment. In this research, aneurysm subtypes were analyzed based on protein–protein interaction network to better understand aneurysm pathogenesis. By measuring network-based proximity of aneurysm subtypes, we identified a relevant closest relationship between aortic aneurysm and aortic dissection. An improved random walk method was performed to prioritize candidate driver genes of each aneurysm subtype. Thereafter, transcriptomes of 6 human aneurysm subtypes were collected and differential expression genes were identified to further filter potential driver genes. Functional enrichment of above driver genes indicated a general role of ubiquitination and programmed cell death in aneurysm pathogenesis. Especially, we further observed participation of BCL-2-mediated apoptosis pathway and caspase-1 related pyroptosis in the development of cerebral aneurysm and aneurysmal subarachnoid hemorrhage in corresponding transcriptomes. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04513-w.

Uterine Corpus Endometrial Carcinoma (UCEC) is one of the most common malignancies of the female genital tract and there remains a major public health problem. Although significant progress has been made in explaining the progression of UCEC, it is still warranted that molecular mechanisms underlying the tumorigenesis of UCEC are to be elucidated. The aim of the current study was to investigate key modules and hub genes related to UCEC pathogenesis, and to explore potential biomarkers and therapeutic targets for UCEC. The RNA-seq dataset and corresponding clinical information for UCEC patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened between 23 paired UCEC tissues and adjacent non-cancerous tissues. Subsequently, the co-expression network of DEGs was determined via weighted gene co-expression network analysis (WGCNA). The Blue and Brown modules were identified to be significantly positively associated with neoplasm histologic grade. The highly connected genes of the two modules were then investigated as potential key factors related to tumor differentiation. Additionally, a protein-protein interaction (PPI) network for all genes in the two modules was constructed to obtain key modules and nodes. 10 genes were identified by both WGCNA and PPI analyses, and it was shown by Kaplan-Meier curve analysis that 6 out of the 10 genes were significantly negatively related to the 5-year overall survival (OS) in patients (AURKA, BUB1, CDCA8, DLGAP5, KIF2C, TPX2). Besides, according to the DEGs from the two modules, lncRNA-miRNA-mRNA and lncRNA-TF-mRNA networks were constructed to explore the molecular mechanism of UCEC-related lncRNAs. 3 lncRNAs were identified as being significantly negatively related to the 5-year OS (AC015849.16, DUXAP8 and DGCR5), with higher expression in UCEC tissues compared to non-tumor tissues. Finally, quantitative Real-time PCR was applied to validate the expression patterns of hub genes. Cell proliferation and colony formation assays, as well as cell cycle distribution and apoptosis analysis, were performed to test the effects of representative hub genes. Altogether, this study not only promotes our understanding of the molecular mechanisms for the pathogenesis of UCEC but also identifies several promising biomarkers in UCEC development, providing potential therapeutic targets for UCEC.

Background: DNA methylation is an important epigenetic modification, which plays an important role in regulating gene expression at the transcriptional level. In tumor research, it has been found that the change of DNA methylation leads to the abnormality of gene structure and function, which can provide early warning for tumorigenesis. Our study aims to explore the relationship between the occurrence and development of tumor and the level of DNA methylation. Moreover, this study will provide a set of prognostic biomarkers, which can more accurately predict the survival and health of patients after treatment.Methods: Datasets of bladder cancer patients and control samples were collected from TCGA database, differential analysis was employed to obtain genes with differential DNA methylation levels between tumor samples and normal samples. Then the protein-protein interaction network was constructed, and the potential tumor markers were further obtained by extracting Hub genes from subnet. Cox proportional hazard regression model and survival analysis were used to construct the prognostic model and screen out the prognostic markers of bladder cancer, so as to provide reference for tumor prognosis monitoring and improvement of treatment plan.Results: In this study, we found that DNA methylation was indeed related with the occurrence of bladder cancer. Genes with differential DNA methylation could serve as potential biomarkers for bladder cancer. Through univariate and multivariate Cox proportional hazard regression analysis, we concluded that FASLG and PRKCZ can be used as prognostic biomarkers for bladder cancer. Patients can be classified into high or low risk group by using this two-gene prognostic model. By detecting the methylation status of these genes, we can evaluate the survival of patients.Conclusion: The analysis in our study indicates that the methylation status of tumor-related genes can be used as prognostic biomarkers of bladder cancer.

Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein–protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein-protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein-protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease with high morbidity and mortality. The etiology of COPD is complex, and the pathogenesis mechanisms remain unclear. In this study, we used rat and human COPD gene expression data from our laboratory and the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between individuals with COPD and healthy individuals. Then, protein–protein interaction (PPI) networks were constructed, and hub genes were identified. Cytoscape was used to construct the co-expressed network and competitive endogenous RNA (ceRNA) networks. A total of 198 DEGs were identified, and a PPI network with 144 nodes and 355 edges was constructed. Twelve hub genes were identified by the cytoHubba plugin in Cytoscape. Of these genes, CCR3, CCL2, COL4A2, VWF, IL1RN, IL2RA, and CCL13 were related to inflammation or immunity, or tissue-specific expression in lung tissue, and their messenger RNA (mRNA) levels were validated by qRT-PCR. COL4A2, VWF, and IL1RN were further verified by the GEO dataset GSE76925, and the ceRNA network was constructed with Cytoscape. These three genes were consistent with COPD rat model data compared with control data, and their dysregulation direction was reversed when the COPD rat model was treated with effective-component compatibility of Bufei Yishen formula III. This bioinformatics analysis strategy may be useful for elucidating novel mechanisms underlying COPD. We pinpointed three key genes that may play a role in COPD pathogenesis and therapy, which deserved to be further studied.

Introduction Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease., This is a revision to https://zenodo.org/record/5598224#.Y3_PX-zMK3C and used for Supplemental Data to the revision of https://f1000research.com/articles/10-1111.

Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.

The tumor microenvironment (TME) has been shown to be involved in angiogenesis, tumor metastasis, and immune response, thereby affecting the treatment and prognosis of patients. This study aims to identify genes that are dysregulated in the TME of patients with colon adenocarcinoma (COAD) and to evaluate their prognostic value based on RNA omics data. We obtained 512 COAD samples from the Cancer Genome Atlas (TCGA) database and 579 COAD patients from the independent dataset (GSE39582) in the Gene Expression Omnibus (GEO) database. The immune/stromal/ESTIMATE score of each patient based on their gene expression was calculated using the ESTIMATE algorithm. Kaplan–Meier survival analysis, Cox regression analysis, gene functional enrichment analysis, and protein–protein interaction (PPI) network analysis were performed. We found that immune and stromal scores were significantly correlated with COAD patients’ overall survival (log rank p < 0.05). By comparing the high immune/stromal score group with the low score group, we identified 688 intersection differentially expressed genes (DEGs) from the TCGA dataset (663 upregulated and 25 downregulated). The functional enrichment analysis of intersection DEGs showed that they were mainly enriched in the immune process, cell migration, cell motility, Toll-like receptor signaling pathway, and PI3K-Akt signaling pathway. The hub genes were revealed by PPI network analysis. Through Kaplan–Meier and Cox analysis, four TME-related genes that were significantly related to the prognosis of COAD patients were verified in GSE39582. In addition, we uncovered the relationship between the four prognostic genes and immune cells in COAD. In conclusion, based on the RNA expression profiles of 1091 COAD patients, we screened four genes that can predict prognosis from the TME, which may serve as candidate prognostic biomarkers for COAD.

Intensive research efforts have been undertaken to slow human aging and therefore potentially delay the onset of age-related diseases. These efforts have generated an enormous amount of high-throughput data covering different levels in the physiologic hierarchy, e.g., genetic, epigenetic, transcriptomic, proteomic, and metabolomic, etc. We gathered 15 independent sources of information about genes potentially involved in human longevity and lifespan (N = 5836) and subjected them to various integrated analyses. Many of these genes were initially identified in non-human species, and we investigated their orthologs in three non-human species [i.e., mice (N = 967), fruit fly (N = 449), and worm (N = 411)] for further analysis. We characterized experimentally determined protein-protein interaction networks (PPIN) involving each species’ genes from 9 known protein databases and studied the enriched biological pathways among the individually constructed PPINs. We observed three important signaling pathways: FoxO signaling, mTOR signaling, and autophagy to be common and highly enriched in all four species (p-value ≤ 0.001). Our study implies that the interaction of proteins involved in the mechanistic target of rapamycin (mTOR) signaling pathway is somewhat limited to each species or that a “rewiring” of specific networks has taken place over time. To corroborate our findings, we repeated our analysis in 43 different human tissues. We investigated conserved modules in various tissue-specific PPINs of the longevity-associated genes based upon their protein expression. This analysis also revealed mTOR signaling as shared biological processes across four different human tissue-specific PPINs for liver, heart, skeletal muscle, and adipose tissue. Further, we explored our results’ translational potential by assessing the protein interactions with all the reported drugs and compounds that have been experimentally verified to promote longevity in the three-comparator species. We observed that the target proteins of the FDA-approved drug rapamycin (a known inhibitor of mTOR) were conserved across all four species. Drugs like melatonin and metformin exhibited shared targets with rapamycin in the human PPIN. The detailed information about the curated gene list, cross-species orthologs, PPIN, and pathways was assembled in an interactive data visualization portal using RStudio’s Shiny framework (https://agingnetwork.shinyapps.io/frontiers/).

Potassium ion ( K + ) is an essential macronutrient for plant growth and survival. K + involves several biochemical and physiological pathways and hence its regulation is essential for normal metabolism and cellular functions. K + homeostasis is maintained by many proteins in a coordinated manner. In barley at least fifteen proteins are directly involved in potassium transport. Interaction among these proteins forms a network along with forty-nine other proteins. We retrieved the potassium transporter interaction network from STRING and the network was analyzed for centrality measures using Cytoscape. Based on the centrality measures such as betweenness, bridging, closeness, degree, eccentricity, eigenvector, radiality and stress, the proteins MLOC_6793.2, MLOC_14891.1, MLOC_16944.1, MLOC_57408.2, and MLOC_63991.1 are found to be most important for the network. These five proteins interact among themselves and form a subgraph. MLOC_63991.1 is the most essential protein as it links other 4 proteins in the network. MLOC_63991.1 is a monomer with 769 amino acid sequences. This protein is predicted and described as potassium transporter.

Polymorphonuclear neutrophils (PMNs) are the most important determinants in the acute inflammatory response. Pathologically increased numbers of PMNs in the circulation or specific tissues (or both) lead to neutrophilia. However, the genes expressed and pathways involved in neutrophilia have yet to be elucidated. By analysis of three public microarray datasets related to neutrophilia (GSE64457, GSE54644, and GSE94923) and evaluation by gene ontology, pathway enrichment, protein–protein interaction networks, and hub genes analysis using multiple methods (DAVID, PATHER, Reactome, STRING, Reactome FI Plugin, and CytoHubba in Cytoscape), we identified the commonly up-regulated and down-regulated different expressed genes. We also discovered that multiple signaling pathways (IL-mediated, LPS-mediated, TNF-α, TLR cascades, MAPK, and PI3K-Akt) were involved in PMN regulation. Our findings suggest that the commonly expressed genes involved in regulation of multiple pathways were the underlying molecular mechanisms in the development of inflammatory, autoimmune, and hematologic diseases that share the common phenotypic characteristics of increased numbers of PMNs. Taken together, these data suggest that these genes are involved in the regulation of neutrophilia and that the corresponding gene products could serve as potential biomarkers and/or therapeutic targets for neutrophilia.

Introduction: Testis-specific gene antigen 10 (TSGA10) is a less-known gene, which is involved in the vague biological paths of different cancers. Here, we investigated the TSGA10 expression using different concentrations of glucose under hypoxia and also its interaction with the hypoxia-inducible factor 1 (HIF-1). Methods: The breast cancer MDA-MB-231 and MCF-7 cells were cultured with different concentrations of glucose (5.5, 11.0 and 25.0 mM) under normoxia/hypoxia for 24, 48, and 72 hours and examined for the HIF-1α expression and cell migration by Western blotting and scratch assays. The qPCR was employed to analyze the expression of TSGA10. Three-dimensional (3D) structure and the energy minimization of the interacting domain of TSGA10 were performed by MODELLER v9.17 and Swiss-PDB viewer v4.1.0/UCSF Chimera v1.11. The UCSF Chimera v1.13.1 and Hex 6.0 were used for the molecular docking simulation. The Cytoscape v3.7.1 and STRING v11.0 were used for protein-protein interaction (PPI) network analysis. The HIF-1a related hypoxia pathways were obtained from BioModels database and reconstructed in CellDesigner v4.4.2. Results: The increased expression of TSGA10 was found to be significantly associated with the reduced metastasis in the MDA-MB-231 cells, while an inverse relationship was seen between the TSGA10 mRNA level and cellular migration but not in the MCF-7 cells. The C-terminal domain of TSGA10 interacted with HIF-1α with high affinity, resulting in PPI network with 10 key nodes (HIF-1α, VEGFA, HSP90AA1, AKT1, ARNT, TP53, TSGA10, VHL, JUN, and EGFR). Conclusions: Collectively, TSGA10 functional expression alters under the hyper-/hypo-glycemia and hypoxia, which indicates its importance as a candidate bio-target for the cancer therapy.

Background Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells (HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use "omics" methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis. Aim To establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis. Methods We used microarrays, bioinformatics, protein-protein interaction (PPI) network, and sub-modules to investigate taurine-induced changes in gene expression in human HSCs (LX-2). Subsequently, all of the differentially expressed genes (DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software. Results A total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1 (ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase (MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway, estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21, TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis. Conclusion Taurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.

The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003. With this hypothesis, current work focuses on identifying the spreader nodes in the SARS-CoV-human protein–protein interaction network (PPIN) to find possible lineage with the disease propagation pattern of the current pandemic. Various PPIN characteristics like edge ratio, neighborhood density, and node weight have been explored for defining a new feature spreadability index by which spreader proteins and protein–protein interaction (in the form of network edges) are identified. Top spreader nodes with a high spreadability index have been validated by Susceptible-Infected-Susceptible (SIS) disease model, first using a synthetic PPIN followed by a SARS-CoV-human PPIN. The ranked edges highlight the path of entire disease propagation from SARS-CoV to human PPIN (up to level-2 neighborhood). The developed network attribute, spreadability index, and the generated SIS model, compared with the other network centrality-based methodologies, perform better than the existing state-of-art.

PurposeThe molecular mechanism underlying the carcinogenesis and development of lung squamous cell carcinoma (LUSC) has not been sufficiently elucidated. This analysis was performed to find pivotal genes and explore their prognostic roles in LUSC.MethodsA microarray dataset from GEO (GSE19188) and a TCGA-LUSC dataset were used to identify differentially co-expressed genes through Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We conducted functional enrichment analyses of differentially co-expressed genes and established a protein-protein interaction (PPI) network. Then, we identified the top 10 hub genes using the Maximal Clique Centrality (MCC) algorithm. We performed overall survival (OS) analysis of these hub genes among LUSC cases. GSEA analyses of survival-related hub genes were conducted. Ultimately, the GEO and The Human Protein Atlas (THPA) databases and immunohistochemistry (IHC) results from the real world were used to verify our findings.ResultsA list of 576 differentially co-expressed genes were selected. Functional enrichment analysis indicated that regulation of vasculature development, cell−cell junctions, actin binding and PPAR signaling pathways were mainly enriched. The top 10 hub genes were selected according to the ranking of MCC scores, and 5 genes were closely correlated with OS of LUSC. Additionally, GSEA analysis showed that spliceosome and cell adhesion molecules were associated with the expression of GNG11 and ADCY4, respectively. The GSE30219 and THPA databases and IHC results from the real world indicated that although GNG11 was not detected, ADCY4 was obviously downregulated in LUSC tissues at the mRNA and protein levels.ConclusionsThis analysis showed that survival-related hub genes are highly correlated to the tumorigenesis and development of LUSC. Additionally, ADCY4 is a candidate therapeutic and prognostic biomarker of LUSC.

BackgroundTumor necrosis factor-stimulated gene-6 (TSG-6) is a multifunctional, anti-inflammatory, and protective protein, while the association between TSG-6 and acute ischemic stroke (AIS) remains unclear in humans. This study aims to investigate the potential diagnostic and short-term prognosis predictive values of TSG-6 in non-cardioembolic AIS.MethodsA total of 134 non-cardioembolic AIS patients within 24 h after AIS onset and 40 control subjects were recruited. Using an AIS dataset from the Gene Expression Omnibus database and setting the median expression level of TNFAIP6 as the cutoff point, data were divided into TNFAIP6-high and TNFAIP6-low expression groups. Differently expressed genes (DEGs) were extracted to perform gene enrichment analysis and protein–protein interaction (PPI) network. Baseline data were analyzed in a four-group comparison plotted as plasma TSG-6 concentration median and 25th/75th percentiles. The correlative factors of 3-month outcome were evaluated by logistic regression. TSG-6 concentrations and TSG-6-to-interleukin-8 ratios were compared in a block design. A receiver-operating characteristic curve was used to analyze the detective value of TSG-6 and 3-month prognosis predictive values of TSG-6 and TSG-6-to-interleukin-8 ratio.ResultsNon-cardioembolic AIS patients had significantly higher plasma TSG-6 levels than control subjects (P P = 0.0184). Seven hundred and eighty-two DEGs might be both AIS-related and TNFAIP6-correlated genes, and 17 targets were deemed AIS-related being closely relevant to TNFAIP6. Interleukin-8 was selected for further study. The National Institutes of Health Stroke Scale and the Acute Stroke Registry and Analysis of Lausanne scores at admission, lesion volume, neutrophil count, neutrophil-to-lymphocyte ratio, and interleukin-8 level were positively correlated with TSG-6 level, respectively (P P P P = 0.017). In non-cardioembolic AIS, the cutoff values of TSG-6 concentration for detection and 3-month prognosis prediction and the TSG-6-to-interleukin-8 ratio for the 3-month prognosis prediction were 8.13 ng/ml [AUC, 0.774 (0.686–0.861); P P P ConclusionsPlasma TSG-6 concentration was a novel indicator for non-cardioembolic AIS diagnosis and 3-month prognosis. Elevated TSG-6-to-interleukin-8 ratio might suggest a 3-month favorable outcome.

Purpose Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value. Methods Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings. Results We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD. Conclusions Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

Fu-you formula (FY), a Traditional Chinese Medicine (TCM) formula composed of 12 herbs, as an in-hospital preparation, has been used treat to precocious puberty (PP) for decades. However, the lack of phytochemical characterization and mechanism of FY remains the main limitation for its spreading. In this study, we analyze the components and mechanisms of FY in PP, based on the integrated pharmacology. Investigated main constituents, targets, pathways of FY by using an integrative pharmacology, and recognized main constituents by HPLC-MS/MS. Then, observed the levels of Follicle-stimulating hormone (FSH), luteinizing hormone (LH), and estrogen (E2) in danazol-induced PP in Sprague–Dawley (SD) rats. Lastly, retrospective study analyzed the clinical data of 575 patients who were diagnosed PP, treated by the FY, and followed-up in our hospital from 2014–2020. The result that total of 116 important candidate targets were selected based on pharmacological analysis. Selected the top 10 values key targets such as the estrogen receptor alpha (ESR1) and insulin-like growth factor 1 (IGF1), were localized and the related gene functions were determined. Gene functions were associated with biological regulation, a cellular process, or signaling pathway, such as the Estrogen signaling pathway, MAPK signaling pathway and PI3K-Akt signaling pathway. By recognizing the five compounds related to the ESR1 and IGF1, which are Quercetin, kaempferol, Luteolin, Apigenin, and Emodin. The results of the molecular docking study further showed that the flavonoids had a strong binding affinity for ESR1 and IGF1 after docking into the crystal structure. The results showed that the FY could effectively reduce E2, LH, and FSH levels in SD rats. Furthermore, the results of the retrospective analysis of medical records showed that the FY could remarkably reduce E2 levels in girls with PP.

Disease-related gene prioritization is one of the most well-established pharmaceutical techniques used to identify genes that are important to a biological process relevant to a disease. In identifying these essential genes, the network diffusion (ND) approach is a widely used technique applied in gene prioritization. However, there is still a large number of candidate genes that need to be evaluated experimentally. Therefore, it would be of great value to develop a new strategy to improve the precision of the prioritization. Given the efficiency and simplicity of centrality measures in capturing a gene that might be important to the network structure, herein, we propose a technique that extends the scope of ND through a centrality measure to identify new disease-related genes. Five common centrality measures with different aspects were examined for integration in the traditional ND model. A total of 40 diseases were used to test our developed approach and to find new genes that might be related to a disease. Results indicated that the best measure to combine with the diffusion is closeness centrality. The novel candidate genes identified by the model for all 40 diseases were provided along with supporting evidence. In conclusion, the integration of network centrality in ND is a simple but effective technique to discover more precise disease-related genes, which is extremely useful for biomedical science.

The molecular mechanisms underlying the pathogenesis of COVID-19 have not been fully discovered. This study aims to decipher potentially hidden parts of the pathogenesis of COVID-19, potential novel drug targets, and identify potential drug candidates. Two gene expression profiles were analyzed, and overlapping differentially expressed genes (DEGs) were selected for which top enriched transcription factors and kinases were identified, and pathway analysis was performed. Protein-protein interaction (PPI) of DEGs was constructed, hub genes were identified, and module analysis was also performed. DGIdb database was used to identify drugs for the potential targets (hub genes and the most enriched transcription factors and kinases for DEGs). A drug-potential target network was constructed, and drugs were ranked according to the degree. L1000FDW was used to identify drugs that can reverse transcriptional profiles of COVID-19. We identified drugs currently in clinical trials, others predicted by different methods, and novel potential drug candidates Entrectinib, Omeprazole, and Exemestane for combating COVID-19. Besides the well-known pathogenic pathways, it was found that axon guidance is a potential pathogenic pathway. Sema7A, which may exacerbate hypercytokinemia, is considered a potential novel drug target. Another potential novel pathway is related to TINF2 overexpression, which may induce potential telomere dysfunction and damage DNA that may exacerbate lung fibrosis. This study identified new potential insights regarding COVID-19 pathogenesis and treatment, which might help us improve our understanding of the mechanisms of COVID-19.

Hereditary spastic paraplegia (HSP) is a diverse group of Mendelian genetic disorders affecting the upper motor neurons, specifically degeneration of their distal axons in the corticospinal tract. Currently, there are 80 genes or genomic loci (genomic regions for which the causative gene has not been identified) associated with HSP diagnosis. HSP is therefore genetically very heterogeneous. Finding treatments for the HSPs is a daunting task: a rare disease made rarer by so many causative genes and many potential mutations in those genes in individual patients. Personalized medicine through genetic correction may be possible, but impractical as a generalized treatment strategy. The ideal treatments would be small molecules that are effective for people with different causative mutations. This requires identification of disease-associated cell dysfunctions shared across genotypes despite the large number of HSP genes that suggest a wide diversity of molecular and cellular mechanisms. This review highlights the shared dysfunctional phenotypes in patient-derived cells from patients with different causative mutations and uses bioinformatic analyses of the HSP genes to identify novel cell functions as potential targets for future drug treatments for multiple genotypes.

High-mobility group protein 1 (HMGA1) participates in the processes of DNA transcription, replication, recombination, and repair. The HMGA1 gene is expressed abundantly during embryogenesis and is reactivated during carcinogenesis. HMGA1 gene expression has been associated with a high degree of malignancy, metastatic tendency, and poor survival in breast, colon, ovary, and pancreatic cancers. However, its prognostic significance in lung cancer remains unclear. Using publicly available data, HMGA1 was shown to be overexpressed in both small and non-small lung tumors, with higher expression compared to both the adjacent non-malignant lung tissues and non-tumor lung tissues of healthy individuals. Elevated HMGA1 expression could result from lowered HMGA1 methylation and was connected with some clinicopathological features like sex, age, and stage of the disease. The high HMGA1 expression level was connected with shorter overall and first progression survival time among lung adenocarcinoma patients, but not lung squamous cell carcinoma patients. HMGA1 could interact with proteins involved in cellular senescence and cell cycle control (TP53, RB1, RPS6KB1, and CDK1), transcription regulation (EP400 and HMGA2), chromatin assembly and remodeling (LMNB1), and cholesterol and isoprene biosynthesis (HMGCR and INSIG1). Taken together, HMGA1 overexpression could be an essential element of lung carcinogenesis and a prognostic feature in lung cancer.

Zhu1S is a thermosensitive genic male-sterile (TGMS) line of rice possessing outstanding combining ability and low critical temperature, which has been extensively utilized as a female parent in two-line hybrid ricebreeding. However, the fertility of Zhu 1S during hybrid seed production is frequently affected by high temperature, thus leading to its fertility alteration and aborted hybrid seed production. To understand its fertility conversion mechanism under high temperature, we employed transcriptomics analyses on the anthers of young panicles of Zhu 1S during the fertility alternation sensitivity stage under high (Zhu 1S-H) and low (Zhu 1S-L) temperatures. The results showed that a total of 1119 differentially expressed genes (DEGs) were identified between Zhu 1S-H and Zhu1S-L anthers, including 680 up-regulated and 439 down-regulated genes. Bioinformatics analysis of these DEGs revealed that the high temperature induction caused fertility-sterility conversion in Zhu1S, mainly by decreasing the mRNA abundances of important genes closely related to plant hormone and MAPK signal pathway and transcriptional regulation factors, thereby impeding the growth and development of the anther of Zhu 1S, which ultimately affected the fertility transition of Zhu 1S under high temperature. The protein–protein interaction network analysis indicates that transcription factor OsTIFY11C possibility plays a central role in the fertility transition of Zhu 1S under high temperature.The present studies offer a theoretical foundation for further research into the molecular mechanism underlying fertility conversion in TGMS line Zhu 1S.

Most cellular functions are carried out by a dynamic network of interacting proteins. An open question is whether the network properties of protein interactomes represent phenotypes under natural selection. One proposal is that protein interactomes have evolved to be resilient, such that they tend to maintain connectivity when proteins are removed from the network. This hypothesis predicts that interactome resilience should be maintained by natural selection during long-term experimental evolution. I tested this prediction by modeling the evolution of protein-protein interaction (PPI) networks in Lenski’s long-term evolution experiment with Escherichia coli (LTEE). In this test, I removed proteins affected by nonsense, insertion, deletion, and transposon mutations in evolved LTEE strains, and measured the resilience of the resulting networks. I compared the rate of change of network resilience in each LTEE population to the rate of change of network resilience for corresponding randomized networks. The evolved PPI networks are significantly more resilient than networks in which random proteins have been deleted.Moreover, the evolved networks are generally more resilient than networks in which the random deletion of proteins was restricted to those disrupted in LTEE. These results suggest that evolution in the LTEE has favored PPI networks that are, on average, more resilient than expected from the genetic variation across the evolved strains. My findings therefore support the hypothesis that selection maintains protein interactome resilience over evolutionary time.Significance StatementUnderstanding how protein-protein interaction (PPI) networks evolve is a central goal of evolutionary systems biology. One property that has been hypothesized to be important for PPI network evolution is resilience, which means that networks tend to maintain connectivity even after many nodes (proteins in this case) have been removed. This hypothesis predicts that PPI network resilience should be maintained during long-term experimental evolution. Consistent with this prediction, I found that the PPI networks that evolved over 50,000 generations of Lenski’s long-term evolution experiment with E. coli are more resilient than expected by chance.

Purpose: Progress related to the early detection and molecular targeted therapy of lung squamous cell carcinoma (LUSC) remains limited. The goal of our study was to identify key candidate indicators of LUSC.Methods: Three microarray datasets (GSE33532, GSE30219 and GSE19188) were applied to find differentially expressed genes (DEGs). Functional enrichment analyses of DEGs were carried out, and their protein-protein interaction (PPI) network was established. Hub genes were chosen from the PPI network according to their degree scores. Then, overall survival (OS) analyses of hub genes were carried out using Kaplan-Meier plotter, and their GSEA analyses were performed. Public databases were used to verify the expression patterns of CDK1 and CDC20. Furthermore, basic experiments were performed to verify our findings.Results: A total of 1,366 DEGs were identified, containing 669 downregulated and 697 upregulated DEGs. These DEGs were primarily enriched in cell cycle, chromosome centromeric region and nuclear division. Seventeen hub genes were selected from PPI network. Survival analyses demonstrated that CDK1 and CDC20 were closely associated with OS. GSEA analyses revealed that cell cycle, DNA replication, and mismatch repair were associated with CDK1 expression, while spliceosome, RNA degradation and cell cycle were correlated with CDC20 expression. Based on The Cancer Genome Atlas (TCGA) and The Human Protein Atlas (THPA) databases, CDK1 and CDC20 were upregulated in LUSC at the mRNA and protein levels. Moreover, basic experiments also supported the obvious upregulation of CDK1 and CDC20 in LUSC.Conclusion: Our study suggests and validates that CDK1 and CDC20 are potential therapeutic targets and prognostic biomarkers of LUSC.

Backgrounds Polycystic ovary syndrome affects 7% of women of reproductive ages. Poor-quality oocytes, along with lower cleavage and implantation rates, reduce fertilization. Objective This study aimed to determine crucial molecular mechanisms behind PCOS pathogenesis and repurpose new drug candidates interacting with them. To predict a more in-depth insight, we applied a novel bioinformatics approach to analyze interactions between the drug-related and PCOS proteins in PCOS patients. Methods The newest proteomics data was retrieved from 16 proteomics datasets and was used to construct the PCOS PPI network using Cytoscape. The topological network analysis determined hubs and bottlenecks. The MCODE Plugin was used to identify highly connected regions, and the associations between PCOS clusters and drug-related proteins were evaluated using the Chi-squared/Fisher's exact test. The crucial PPI hub-bottlenecks and the shared molecules (between the PCOS clusters and drug-related proteins) were then investigated for their drug-protein interactions with previously US FDA-approved drugs to predict new drug candidates. Results The PI3K/AKT pathway was significantly related to one PCOS subnetwork and most drugs (metformin, letrozole, pioglitazone, and spironolactone); moreover, VEGF, EGF, TGFB1, AGT, AMBP, and RBP4 were identified as the shared proteins between the PCOS subnetwork and the drugs. The shared top biochemical pathways between another PCOS subnetwork and rosiglitazone included metabolic pathways, carbon metabolism, and citrate cycle, while the shared proteins included HSPB1, HSPD1, ACO2, TALDO1, VDAC1, and MDH2. We proposed some new candidate medicines for further PCOS treatment investigations, such as copper and zinc compounds, reteplase, alteplase, gliclazide, Etc. Conclusion Some of the crucial molecules suggested by our model have already been experimentally reported as critical molecules in PCOS pathogenesis. Moreover, some repurposed medications have already shown beneficial effects on infertility treatment. These previous experimental reports confirm our suggestion for investigating our other repurposed drugs (in vitro and in vivo). Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s40199-021-00413-9.

El funcionament intern de les cèl·lules pot entendre’s com un conjunt d’interaccions entre biomolècules, formant una xarxa que coneixem amb el nom d’interactoma. Els fàrmacs i malalties poden considerar-se pertorbacions d’aquesta xarxa, modulant directament molècules específiques, però indirectament comunitats de molècules les interaccions de les quals es veuen afectades per la pertorbació. La medicina de xarxes busca representar i analitzar amb precisió les xarxes biològiques, per tal d’entendre millor les malalties i aconseguir tractaments més segurs i eficaços. En aquesta tesi, presento diverses eines in silico basades en la medicina de xarxes, pensades per estudiar els mecanismes moleculars de malalties i fàrmacs. Aquestes eines s’utilitzen en un ampli ventall d’aplicacions de la medicina de xarxes, com per exemple l’estudi de comorbiditats, endofenotips, efectes secundaris, reutilització i combinació de fàrmacs.

Background Coronary artery disease (CAD) is the leading cause of mortality worldwide. We aimed to screen out potential gene signatures and construct a diagnostic model for CAD. Method We downloaded two mRNA profiles, GSE66360 and GSE60993, and performed analyses of differential expression, gene ontology terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The STRING database was used to identify protein–protein interactions (PPI). PPI network visualization and screening out of key genes were performed using Cytoscape software. Finally, a diagnostic model was constructed. Results A total of 2127 differentially expressed genes (DEGs) were identified in GSE66360, and 527 DEGs in GSE60993. Of the 153 DEGs from both datasets that showed differential expression between CAD patients and controls, 471 biological process terms, 35 cellular component terms, 17 molecular function terms, and 49 KEGG pathways were significantly enriched. The top 20 key genes in the PPI network were identified, and a diagnostic model constructed from five optimal genes that could efficiently separate CAD patients from controls. Conclusion We identified several potential biomarkers for CAD and built a logistic regression model that will provide a valuable reference for future clinical diagnoses and guide therapeutic strategies.

The aim of this study was to identify genes and functional pathways associated with damaged cartilage tissues of knee using microarray analysis. The gene expression profile GSE129147 including including 10 knee cartilage tissues from damaged side and 10 knee nonweight-bearing healthy cartilage was downloaded and bioinformatics analysis was made. A total of 182 differentially-expressed genes including 123 up-regulated and 59 down-regulated genes were identified from the GSE129147 dataset. Gene ontology and pathway enrichment analysis confirmed that extracellular matrix organization, collagen catabolic process, antigen processing and presentation of peptide or polysaccharide antigen, and endocytic vesicle membrane were strongly associated with cartilage injury. Furthermore, 10 hub differentially-expressed genes with a higher connectivity degree in protein–protein interactions network were found such as POSTN, FBN1, LOX, insulin-like growth factor binding proteins3, C3AR1, MMP2, ITGAM, CDKN2A, COL1A1, COL5A1. These hub genes and pathways provide a new perspective for revealing the potential pathological mechanisms and therapy strategy of cartilage injury.

BackgroundDespite significant progress in surgical treatment of hypoplastic left heart syndrome (HLHS), its mortality and morbidity are still high. Little is known about the molecular abnormalities of the syndrome. In this study, we aimed to probe into hub genes and key pathways in the progression of the syndrome.MethodsDifferentially expressed genes (DEGs) were identified in left ventricle (LV) or right ventricle (RV) tissues between HLHS and controls using the GSE77798 dataset. Then, weighted gene co-expression network analysis (WGCNA) was performed and key modules were constructed for HLHS. Based on the genes in the key modules, protein–protein interaction networks were conducted, and hub genes and key pathways were screened. Finally, the GSE23959 dataset was used to validate hub genes between HLHS and controls.ResultsWe identified 88 and 41 DEGs in LV and RV tissues between HLHS and controls, respectively. DEGs in LV tissues of HLHS were distinctly involved in heart development, apoptotic signaling pathway and ECM receptor interaction. DEGs in RV tissues of HLHS were mainly enriched in BMP signaling pathway, regulation of cell development and regulation of blood pressure. A total of 16 co-expression network were constructed. Among them, black module (r = 0.79 andpvalue = 2e−04) and pink module (r = 0.84 andpvalue = 4e−05) had the most significant correlation with HLHS, indicating that the two modules could be the most relevant for HLHS progression. We identified five hub genes in the black module (includingFbn1,Itga8,Itga11,Itgb5andThbs2), and five hub genes (includingCblb,Ccl2,Edn1,Itgb3andMap2k1) in the pink module for HLHS. Their abnormal expression was verified in the GSE23959 dataset.ConclusionsOur findings revealed hub genes and key pathways for HLHS through WGCNA, which could play key roles in the molecular mechanism of HLHS.

Background: In the present study, we aimed to find an effective target for the treatment of tongue cancer using gene chip screening and signal pathway research. Methods: We used microarray screening and gene expression profile analyses to find important differentially expressed genes in tongue cancer. We constructed a protein-protein interaction network, and used enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes to screen for important genes. We then silenced the genes of interest in SCC154 cells to study the relationship with the Phosphatidylinositol 3-kinase/Akt signal pathway. Western blot analyses, the 3-(4,5Dimethylthiazol-yl)-2,5Dimethylthiazol-2-yl)-2,5diphenyltetrazolium bromide (MTT) test, and immunofluorescence assays were used to compare the expression levels of Phosphatidylinositol 3-kinase/Akt signal pathway-related proteins, cell viability, and cell proliferation ability in normal SCC154 cells, Si-RNA SCC154 cells, and gene-silenced SCC154 cells. The scratch test, Transwell test, and western blotting were used to determine migration, invasion, and carcinogenesis. Results: Using GSE9844, GSE13601, and GSE31056 gene chips, we identified 93 upregulated genes and 76 downregulated genes in tongue cancer. Using the protein-protein interaction network and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, we further identified 47 differentially expressed genes. Using Kaplan-Meier plotter online tools, we also identified 3 genes (SPP1, Recombinant Human Secreted Phosphoprotein 1; PLAU, plasminogen activator urinary; and APP, amyloid precursor protein). Compared with normal SCC154 cells and Si-RNA control SCC154 cells, the expressions of Phosphatidylinositol 3-kinase/Akt pathway proteins in si-SPP1 SCC154 cells were significantly decreased (*P < 0.05), and the protein activities and proliferation abilities were also significantly decreased (*P < 0.05), while the migration ability, invasion ability, and cancer forming ability were significantly increased (*P < 0.05). Conclusion: Inhibition of the SPP1 gene may have a therapeutic effect on tongue cancer, and could be an effective target for the treatment of this disorder.

Purpose: Detecting and diagnosing gastric cancer (GC) during its early period remains greatly difficult. Our analysis was performed to detect core genes correlated with GC and explore their prognostic values. Methods: Microarray datasets from the Gene Expression Omnibus (GEO) (GSE54129) and The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) datasets were applied for common differentially co-expressed genes using differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Functional enrichment analysis and protein–protein interaction (PPI) network analysis of differentially co-expressed genes were performed. We identified hub genes via the CytoHubba plugin. Prognostic values of hub genes were explored. Afterward, Gene Set Enrichment Analysis (GSEA) was used to analyze survival-related hub genes. Finally, the tumor-infiltrating immune cell (TIC) abundance profiles were estimated. Results: Sixty common differentially co-expressed genes were found. Functional enrichment analysis implied that cell–cell junction organization and cell adhesion molecules were primarily enriched. Hub genes were identified using the degree, edge percolated component (EPC), maximal clique centrality (MCC), and maximum neighborhood component (MNC) algorithms, and serpin family E member 1 (SERPINE1) was highly associated with the prognosis of GC patients. Moreover, GSEA demonstrated that extracellular matrix (ECM) receptor interactions and pathways in cancers were correlated with SERPINE1 expression. CIBERSORT analysis of the proportion of TICs suggested that CD8+ T cell and T-cell regulation were negatively associated with SERPINE1 expression, showing that SERPINE1 may inhibit the immune-dominant status of the tumor microenvironment (TME) in GC. Conclusions: Our analysis shows that SERPINE1 is closely correlated with the tumorigenesis and progression of GC. Furthermore, SERPINE1 acts as a candidate therapeutic target and prognostic biomarker of GC.

The Korean Hanwoo breed possesses a high capacity to accumulate intramuscular fat, which is measured as a marbling score in the beef industry. Unfortunately, the development of marbling is not completely understood and the identification of differentially expressed genes at an early age is required to better understand this trait. In this study, we took muscle samples from 12 Hanwoo steers at the age of 18 and 30 months. From the contrast between age and marbling score, we identified in total 1883 differentially expressed genes (FDR &lt, 0.05 and logarithm fold change &ge, 1.5) with 782 genes up-regulated and 1101 down-regulated. Differences in gene expression were higher between the ages x marbling groups rather than between high and low marbling groups. At 18 months of age, the genes SLC38A4, ABCA10, APOL6, and two novel genes (ENSBTAG00000015330 and ENSBTAG00000046041) were up-regulated in the high marbling group. From the protein&ndash, protein interaction network analysis, we identified unique networks when comparing marbling scores between different ages. Nineteen genes (AGT, SERPINE1, ADORA1, FOS, LEP, FOXO1, FOXO3, ADIPOQ, ITGA1, SDC1, SDC4, ITGB3, ITGB4, CXCL10, ACTG2, MX1, EDN1, ACTA2, and ESPL1) were identified to have an important role in marbling development. Further analyses are needed to better understand the role of these genes.

T cell‒mediated rejection (TCMR) and antibody‐mediated rejection (ABMR) are severe post‐transplantation complications for heart transplantation (HTx), whose molecular and immunological pathogenesis remains unclear. In the present study, the mRNA microarray data set GSE124897 containing 645 stable, 52 TCMR and 144 ABMR endomyocardial biopsies was obtained to screen for differentially expressed genes (DEGs) between rejected and stable HTx samples and to investigate immune cell infiltration. Functional enrichment analyses indicated roles of the DEGs primarily in immune‐related mechanisms. Protein‐protein interaction networks were then constructed, and ICAM1, CD44, HLA‐A and HLA‐B were identified as hub genes using the maximal clique centrality method. Immune cell infiltration analysis revealed differences in adaptive and innate immune cell populations between TCMR, ABMR and stable HTx samples. Additionally, hub gene expression levels significantly correlated with the degree and composition of immune cell infiltration in HTx rejection samples. Furthermore, drug‐gene interactions were constructed, and 12 FDA‐approved drugs were predicted to target hub genes. Finally, an external GSE2596 data set was used to validate the expression of the hub genes, and ROC curves indicated all four hub genes had promising diagnostic value for HTx rejection. This study provides a comprehensive perspective of molecular and immunological regulatory mechanisms underlying HTx rejection.

Glutathione peroxidases (GPxs) form a broad family of antioxidant proteins essential for maintaining redox homeostasis in eukaryotic cells. In this study, we used an integrative approach that combines bioinformatics, molecular biology, and biochemistry to investigate the role of GPxs in reactive oxygen species detoxification in the unicellular eukaryotic model organism Tetrahymena thermophila. Both phylogenetic and mechanistic empirical model analyses provided indications about the evolutionary relationships among the GPXs of Tetrahymena and the orthologous enzymes of phylogenetically related species. In-silico gene characterization and text mining were used to predict the functional relationships between GPxs and other physiologically-relevant processes. The GPx genes contain conserved transcriptional regulatory elements in the promoter region, which suggest that transcription is under tight control of specialized signaling pathways. The bioinformatic findings were next experimentally validated by studying the time course of gene transcription and enzymatic activity after copper (Cu) exposure. Results emphasize the role of GPxs in the detoxification pathways that, by complex regulation of GPx gene expression, enable Tethraymena to survive in high Cu concentrations and the associated redox environment.

Objective As a unique histological subtype of colorectal cancer (CRC), mucinous adenocarcinoma (MC) has a poor prognosis and responds poorly to treatment. Genes and markers related to MC have not been reported. Methods To identify biomarkers involved in development of MC compared with other common adenocarcinoma (AC) subtypes, four datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using GEO2R. A protein–protein interaction network was constructed. Functional annotation for DEGs was performed via DAVID, Metascape, and BiNGO. Significant modules and hub genes were identified using Cytoscape, and expression of hub genes and relationships between hub genes and MC were analyzed. Results The DEGs were mainly enriched in negative regulation of cell proliferation, bicarbonate transport, response to peptide hormone, cell–cell signaling, cell proliferation, and positive regulation of the canonical Wnt signaling pathway. The Venn diagram revealed eight significant hub genes: CXCL9, IDO1, MET, SNAI2, and ZEB2 were highly expressed in MC compared with AC, whereas AREG, TWIST1, and ZEB1 were expressed at a low level. AREG and MET might be significant biomarkers for MC. Conclusion The identified DEGs might help elucidate the pathogenesis of MC, identify potential targets, and improve treatment for CRC.

Background Pulmonary arterial hypertension (PAH) is a life-threatening condition. The aim of this study was to explore potential crucial genes and pathways associated with PAH based on integrative analyses of gene expression and to shed light on the identification of biomarker for PAH. Methods Gene expression profile of pulmonary tissues from 27 PAH patients and 22 normal controls were downloaded from public database (GSE53408 and GSE113439). After the identification of differentially expressed genes (DEGs), hub pathways and genes were identified based on the comprehensive evaluation of protein-protein interaction (PPI) network analysis, modular analysis and cytohubba’s analysis, and further validated in another PAH transcriptomic dataset (GSE33463). Potentially associated micro-RNAs (miRNAs) were also predicted. Results A total of 521 DEGs were found between PAH and normal controls, including 432 up-regulated DEGs and 89 down-regulated DEGs. Functional enrichment analysis showed that these DEGs were mainly enriched in mitotic cell cycle process, mitotic cell cycle and microtubule cytoskeleton organization. Moreover, five key genes (CDK1, SMC2, SMC4, KIF23, and CENPE) were identified and then further validated in another transcriptomic dataset associated with special phenotypes of PAH. Furthermore, these hub genes were mainly enriched in promoting mitotic cell cycle process, which may be closely associated with the pathogenesis of PAH. We also found that the predicted miRNAs targeting these hub genes were found to be enriched in TGF-β and Hippo signaling pathway. Conclusion These findings are expected to gain a further insight into the development of PAH and provide a promising index for the detection of PAH.

In any ‘omics study, the scale of analysis can dramatically affect the outcome. For instance, when clustering single-cell transcriptomes, is the analysis tuned to discover broad or specific cell types? Likewise, protein communities revealed from protein networks can vary widely in sizes depending on the method. Here, we use the concept of persistent homology, drawn from mathematical topology, to identify robust structures in data at all scales simultaneously. Application to mouse single-cell transcriptomes significantly expands the catalog of identified cell types, while analysis of SARS-COV-2 protein interactions suggests hijacking of WNT. The method, HiDeF, is available via Python and Cytoscape.

Objective Hepatocellular carcinoma (HCC) is a common cancer with a high mortality rate; the molecular mechanism involved in HCC remain unclear. We aimed to provide insight into HCC induced with HepG2 cells and identify genes and pathways associated with HCC, as well as potential therapeutic targets. Methods Dataset GSE72581 was downloaded from the Gene Expression Omnibus, including samples from mice injected in liver parenchyma with HepG2 cells, and from mice injected with cells from patient tumor explants. Differentially expressed genes (DEGs) between the two groups of mice were analyzed. Then, gene ontology and Kyoto Encyclopedia of Gene and Genomes pathway enrichment analyses were performed. The MCODE plug-in in Cytoscape was applied to create a protein–protein interaction (PPI) network of DEGs. Results We identified 1,405 DEGs (479 upregulated and 926 downregulated genes), which were enriched in complement and coagulation cascades, peroxisome proliferator-activated receptor signaling pathway, and extracellular matrix–receptor interaction. The top 4 modules and top 20 hub genes were identified from the PPI network, and associations with overall survival were determined using Kaplan–Meier analysis. Conclusion This preclinical study provided data on molecular targets in HCC that could be useful in the clinical treatment of HCC.

Two-component systems (TCS) in plants have evolved into a more complicated multi-step phosphorelay (MSP) pathway, which employs histidine kinases (HKs), histidine-containing phosphotransfer proteins (HPts), and response regulators (RRs) to regulate various aspects of plant growth and development. How plants perceive the external signals, then integrate and transduce the secondary signals specifically to the desired destination, is a fundamental characteristic of the MSP signaling network. The TCS elements involved in the MSP pathway and molecular mechanisms of signal transduction have been best understood in the model plant Arabidopsis thaliana. In this review, we focus on updated knowledge on TCS signal transduction in Arabidopsis. We first present a brief description of the TCS elements; then, the protein–protein interaction network is established. Finally, we discuss the possible molecular mechanisms involved in the specificity of the MSP signaling at the mRNA and protein levels.

Small intestinal neuroendocrine tumors (SI-NETs) remain the most common subset in gastrointestinal neuroendocrine tumors and featured by aggressiveness. However, the molecular feature of SI-NETs remains largely unclear with key genes and pathways yet to be identified. The gene expression profile GSE65286 was retrieved for analysis. Artificial neural networks (ANNs) were constructed for the hub genes network models. A total of 613 differentially expressed genes (DEGs) were identified between normal (N) and primary tumor (T) groups, whereas 61 DEGs were identified between T and liver metastases (LM) groups. The top Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for the DEGs of N versus T were fat digestion and absorption pathway. For T versus LM the top KEGG pathways were complement and coagulation. In gene set enrichment analysis (GSEA), five gene sets, including Notch signaling, inflammatory response, coagulation, KRAS signaling, and allograft rejection were significantly enriched in the T group. The hub genes in the DEGs of T versus LM included albumin, fibrinogen gamma chain, alpha 2-HS glycoprotein, transferrin and GC, vitamin D binding protein. A distinct correlational alteration of hub genes was observed between T and LM groups. In ANN analysis, ALB and TF were the top predictors of metastasis. Moreover, the expression of ALB≤ showed the highest support to T whereas ALB>15.97 supports LM. TF≤7.54 showed the highest negative correlation to the T. This bioinformatics analysis provided insights on potential key pathways and genes networks involved in SI-NETs and established an ANN-based hub gene model for metastatic prediction.