Protein Interaction Network Construction Research Articles

Identification, Verification and Pathway Enrichment Analysis of Prognosis-Related Immune Genes in Patients With Hepatocellular Carcinoma.

Hepatocellular carcinoma is a common malignant tumor with poor prognosis, poor treatment effect, and lack of effective biomarkers. In this study, bioinformatics analysis of immune-related genes of hepatocellular carcinoma was used to construct a multi-gene combined marker that can predict the prognosis of patients. The RNA expression data of hepatocellular carcinoma were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the IMMPORT database. Differential analysis was performed by Wilcox test to obtain differentially expressed genes. Univariate Cox regression analysis, lasso regression analysis and multivariate Cox regression analysis were performed to establish a prognostic model of immune genes, a total of 5 genes (HDAC1, BIRC5, SPP1, STC2, NR6A1) were identified to construct the models. The expression levels of 5 genes in HCC tissues were significantly different from those in paracancerous tissues. The Kaplan-Meier survival curve showed that the risk score calculated according to the prognostic model was significantly related to the overall survival (OS) of HCC. The receiver operating characteristic (ROC) curve confirmed that the prognostic model had high accuracy. Independent prognostic analysis was performed to prove that the risk value can be used as an independent prognostic factor. Then, the gene expression data of hepatocellular carcinoma in the ICGC database was used as a validation data set for the verification of the above steps. In addition, we used the CIBERSORT software and TIMER database to conduct immune infiltration research, and the results showed that the five genes of the model and the risk score have a certain correlation with the content of immune cells. Moreover, through Gene Set Enrichment Analysis (GSEA) and the construction of protein interaction networks, we found that the p53-mediated signal transduction pathway is a potentially important signal pathway for hepatocellular carcinoma and is positively regulated by certain genes in the prognostic model. In conclusion, this study provides potential targets for predicting the prognosis and treatment of hepatocellular carcinoma patients, and also provides new ideas about the correlation between immune genes and potential pathways of hepatocellular carcinoma.

Comprehensive Analysis of mRNA Expression Profiling and Identification of Potential Diagnostic Biomarkers in Coronary Artery Disease

The aim of this study is to investigate mRNA expression profiling by RNA sequencing (RNA-seq) in patients with coronary artery disease (CAD) and validate differentially expressed genes (DEGs) as novel biomarkers for CAD. Transcriptome-wide mRNA expression analysis of peripheral blood mononuclear cells was performed in five CAD patients and five controls. Functional enrichment analyses, protein–protein interaction network construction, and hub gene selection were further conducted. Relative expression levels of hub genes were validated by quantitative reverse transcription PCR in larger cohorts. Spearman correlation test and multiple linear regression analysis were applied to examine the relationship between confounding factors with severity of coronary artery atherosclerosis. Receiver operating characteristic (ROC) curve analysis was adopted to identify potentially diagnostic biomarkers for CAD. A total of 527 upregulated and 653 downregulated mRNAs were identified as DEGs in CAD patients. The relative expression levels of beta-transducin repeat containing E3 ubiquitin protein ligase (BTRC), F-box and leucine-rich repeat protein 4 (FBXL4), ubiquitin conjugating enzyme E2 D2 (UBE2D2), and ankyrin repeat and SOCS box containing 1 (ASB1) were significantly different between two groups (all p ≤ 0.05). The severity of coronary artery atherosclerosis was negatively associated with the BTRC gene relative expression level (r = −0.323, p < 0.001) and positively with UBE2D2 (r = 0.285, p < 0.001). ROC analysis of BTRC and UBE2D2 genes showed that the areas under the curve were 0.782 (95% CI: 0.720–0.845, p < 0.001) and 0.753 (95% CI: 0.681–0.824, p < 0.001), respectively. We described the characteristics of mRNA expression in the peripheral blood of CAD patients and controls by RNA-seq. Combined with Spearman correlation analysis and ROC analyses, BTRC and UBE2D2 genes had significantly diagnostic values, which may have potential to act as novel diagnostic biomarkers and therapeutic targets for CAD.

Transcriptome analysis of triple negative breast cancer based on public database

Triple negative breast cancer (TNBC) is a pathological type with high malignancy in breast cancer. Because the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 appear histologically negative, the treatment effect of the existing drug is not ideal. Exploring the TNBC related pathological markers is one of hot spots in the research field of breast cancer. TNBC related gene sets are obtained from gene expression omnibus (GEO) database for transcriptome analysis, including principal component analysis, screening differentially expressed genes (DEGs), gene function enrichment analysis and signal pathway enrichment analysis, protein interaction network construction, weighted gene coexpression network analysis (WGCNA), etc. The results show that there are significantly differentially expressed genes (DEGs) between TNBC and non TNBC population. These DEGs are mainly enriched in the biological processes related to epidermal growth and differentiation, growth and development of mammary epithelial cells, and in molecular functions related to transcription factor binding and estrogen signal transduction pathways. The results of protein interaction network and weighted gene coexpression network analysis are integrated and TFF1, FOXA1, AGR2 and AGR3 are predicted as the key genes involved in the pathological phenotype of TNBC. The study of transcriptome analysis based on TNBC gene set in public database may provide certain theoretical research basis to reveal the potential biomarkers for pathogenesis of TNBC, and the derived disease diagnosis and therapeutic targets or molecular regulation mechanisms.

Integrated Multi-Omics Analysis of Mechanisms Underlying Yeast Ethanol Tolerance.

For yeast cells, tolerance to high levels of ethanol is vital both in their natural environment and in industrially relevant conditions. We recently genotyped experimentally evolved yeast strains adapted to high levels of ethanol and identified mutations linked to ethanol tolerance. In this study, by integrating genomic sequencing data with quantitative proteomics profiles from six evolved strains (data set identifier PXD006631) and construction of protein interaction networks, we elucidate exactly how the genotype and phenotype are related at the molecular level. Our multi-omics approach points to the rewiring of numerous metabolic pathways affected by genomic and proteomic level changes, from energy-producing and lipid pathways to differential regulation of transposons and proteins involved in cell cycle progression. One of the key differences is found in the energy-producing metabolism, where the ancestral yeast strain responds to ethanol by switching to respiration and employing the mitochondrial electron transport chain. In contrast, the ethanol-adapted strains appear to have returned back to energy production mainly via glycolysis and ethanol fermentation, as supported by genomic and proteomic level changes. This work is relevant for synthetic biology where systems need to function under stressful conditions, as well as for industry and in cancer biology, where it is important to understand how the genotype relates to the phenotype.

Screening of Atherosclerotic Druggable Targets from the Proteome Network of Differentially Expressed Genes.

Differently expressed genes of atherosclerotic sample analysis are helpful to sort the prominent genes that influence the plaque formation and progression. Scientific evidence-based protein-protein interaction network (PPIN) studies were used to find hub proteins in complex disease conditions. Druggable capacity is one of the important parameters to confirm as a successful drug target. Construction of protein interaction network and principal node analysis (PNA) on atherosclerotic data sets lead to screen the hub proteins. Furthermore, druggable property of protein pocket confirms the targetability of susceptible target candidates and for target selection. Differentially expressed genes are identified through GEO2R analyzer on data sets of various atherosclerotic samples. STRING database and Cytoscape are employed to construct PPIN. Targets were identified by PNA such as centrality measures and clustering algorithm. Gene Ontology enrichment also used as one of the screening parameters to filter the candidates related to atherosclerotic terms. Topological evaluation of target protein was successfully done by ITASSER and GROMACS, respectively. Grid-based principle of DoGSiteScorer is utilized for druggability analysis. Six proteins such as integrin alpha L (ITGAL), metallothionein 1F (MT1F), metallothionein 1X (MT1X), P-selectin glycoprotein ligand-1 (SELPLG), solute carrier family 30 A, zinc transporter protein (SLC30A1), and TNFSF13B are screened as potential biomarkers through network-based analysis. Among the six, ITGAL, SELPLG, SLC30A1, and TNSF13B are identified as better prioritized atherosclerotic targets through druggability efficiency.

Exploring the mechanism of aidi injection for lung cancer by network pharmacology approach and molecular docking validation.

Background. Aidi injection (ADI) is an effective Traditional Chinese medicine preparation widely used for lung cancer. However, the pharmacological mechanisms of ADI on lung cancer remain to be elucidated. Methods. A network pharmacology (NP)-based approach and the molecular docking validation were conducted to explore underlying mechanisms of ADI on lung cancer. The compounds and target genes were screened by Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (Batman-TCM) database. The STRING database was utilized for protein interaction network construction. The R package clusterProfiler was used for bioinformatics annotation of hub target genes. The gene expression analysis and survival analysis were performed based on The Cancer Genome Atlas (TCGA) database. The Autodock Vina was used for molecular docking validation. Results. A total of five key compounds with 324 putative target genes were screened out, and 14 hub target genes were identified for treating lung cancer. Six hub genes could influence the survival of non-small cell lung cancer (NSCLC) patients. Of these hub genes, the expression pattern of EGFR, MYC, PIK3CA, and SMAD3 were significantly higher in the LUSC, while PIK3CA and RELA expressed lower in the LUAD group and LUSC group, respectively. These six hub genes had good docking affinity with the key compounds of ADI. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that ADI may exert therapeutic effects on lung cancer by regulating critical pathways including the thyroid hormone signaling pathway, MAPK signaling pathway, and PI3K-Akt signaling pathway. Conclusions. The present study explored the potential pharmacological mechanisms of ADI on lung cancer, promoting the clinical application of ADI in treating lung cancer, and providing references for advanced researches.

CD3D has the Potential to be a Prognostic Factor for Endometrial Carcinoma and an Indicator of Tumor Immune Microenvironment Regulation: a Study based on TCGA Data Mining

Accumulating evidence shows that tumor immune microenvironment infiltration plays a crucial role in the occurrence and development of endometrial carcinoma (EC). At present, studying the regulation mechanism of the immune microenvironment in EC remains a challenge. We calculated the amount of immune and stromal components and the proportion of tumor-infiltrating immune lymphocytes via ESTIMATE, single-sample gene enrichment analysis (ssGSEA), and CIBERSORT computational methods. Differentially expressed genes were analyzed by protein–protein interaction network construction and univariate survival analysis. CD3D expression was negatively correlated with clinicopathological features, such as age, histological type, clinical grade, and pathological stage, but positively correlated with the survival of patients with EC. Logistic regression analysis suggested that CD3D expression could be used as a biomarker to predict EC occurrence. Gene set enrichment analysis revealed that CD3D was upregulated in the immune effect pathway. Interestingly, CD3D was downregulated in the EC pathway. EC was divided into three immune cell subsets by ssGSEA. Correlation analysis between ssGSEA and CIBERSORT showed that the infiltration level of CD8 + T cells and plasma cells in tumor-infiltrating lymphocytes was lower than that in the high-immune-score group (Immunity_H). Moreover, CD3D expression in Immunity_H was low, and the prognosis was even worse. This study demonstrated that low CD3D expression is an index of poor prognosis in patients with EC. Levels of CD3D in tumor microenvironment (TME) might be a useful indicator for predicting the prognosis of patients with EC patients.

Exploring the treatment of COVID-19 with Yinqiao powder based on network pharmacology.

Coronavirus disease 2019 (COVID‐19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). In China, Yinqiao powder is widely used to prevent and treat COVID‐19 patients with Weifen syndrome. In this study, the screening and verification of active ingredients, target selection and DisGeNET scoring, drug–ingredient–gene network construction, protein–protein interaction network construction, molecular docking and surface plasmon resonance (SPR) analysis, gene ontology (GO) functional analysis, gene tissue analysis, and kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were used to explore the active ingredients, targets, and potential mechanisms of Yinqiao powder in the treatment of COVID‐19. We also predicted the therapeutic effect of Yinqiao powder using TCM anti‐COVID‐19 (TCMATCOV). Yinqiao powder has a certain therapeutic effect on COVID‐19, with an intervention score of 20.16. Hesperetin, eriodictyol, luteolin, quercetin, and naringenin were the potentially effective active ingredients against COVID‐19. The hub‐proteins were interleukin‐6 (IL‐6), mitogen‐activated protein kinase 3 (MAPK3), tumor necrosis factor (TNF), and tumor protein P53 (TP53). The potential mechanisms of Yinqiao powder in the treatment of COVID‐19 are the TNF signaling pathway, T‐cell receptor signaling pathway, Toll‐like receptor signaling pathway, and MAPK signaling pathway. This study provides a new perspective for discovering potential drugs and mechanisms of COVID‐19.

Identification of key miRNA signature and pathways involved in multiple myeloma by integrated bioinformatics analysis

ABSTRACT Objectives: Multiple myeloma (MM) is one of the most common types of hematologic malignancy for which the underlying molecular mechanisms remain largely unclear. Dysregulated miRNA expression has been shown to be involved in MM tumorigenesis, progression and drug response. Therefore, a comprehensive analysis based on miRNA-level integrated strategy was performed. Methods: This study aimed to elucidate key miRNA signatures and pathways in MM by integrated bioinformatics analysis. Expression profiles GSE24371, GSE49261 and GSE54156 were obtained from the Gene Expression Omnibus database, and differentially expressed miRNAs (DEMirs) with p < 0.05 were identified. The target genes of these DEMirs were obtained from ENCORI database, and functional enrichment, subpathway enrichment and protein–protein interaction network construction were performed. The key target genes were identified by random walk algorithm and survival verification was performed. Results and discussion: First, six up-regulated and four down-regulated DEMirs shared between any two GSE data sets were identified. Second, target genes (DEMirTGs) by up-regulated and down-regulated DEMirs were obtained. Functional and subpathway enrichment analysis showed that these up-regulated DEMirs are consistently involved in the Wnt signaling pathway. Moreover, enrichment of the down-regulated DEMirs is mainly in the MAPK signaling pathway. Finally, a protein–protein interaction sub-network for these DEMirTGs was constructed, the correlations between the two key genes were identified and survival in MM was evaluated using multiple independent data sets. Conclusion: We identified miRNA signatures and key target genes that were closely related to MM biology, and these genes might serve as potential therapeutic targets for MM patients.

Network Pharmacology-Oriented Identification of Key Proteins and Signaling Pathways Targeted by Xihuang Pill in the Treatment of Breast Cancer.

PurposeThe compound traditional Chinese medicine Xihuang pill (XHP) has been adopted to treat breast cancer (BC) for centuries, but its specific mechanism of action is unclear.Materials and MethodsThe active ingredients and related targets of XHP were screened using the TCMSP and TCMID databases. GSE139038 was downloaded from the GEO database, and differentially expressed genes (DEGs) were analyzed. The intersection of targets and DEGs were chosen to build an ingredients–target genes network. Protein–protein interaction network construction and functional enrichment analysis of target genes were conducted.ResultsA PPI network of 37 targets was constructed, and seven core nodes (FOS, MYC, JUN, PPARG, MMP9, PTGS2, SERPINE1) were identified. Functional enrichment analysis revealed that the aforementioned targets were mainly enriched in the IL-17, toll-like receptor, and tumor necrosis factor signaling pathways, which are deeply involved in the inflammatory microenvironment of tumors.ConclusionThis network pharmacology study indicated that XHP can inhibit the development of BC by targeting a variety of proteins and signaling pathways involved in the inflammatory microenvironment.

Screening and identification of key regulatory connections and immune cell infiltration characteristics for lung transplant rejection using mucosal biopsies

This study aimed to explore key regulatory connections underlying lung transplant rejection. The differentially expressed genes (DEGs) between rejection and stable lung transplantation (LTx) samples were screened using R package limma, followed by functional enrichment analysis and protein–protein interaction network construction. Subsequently, a global triple network, including miRNAs, mRNAs, and transcription factors (TFs), was constructed. Furthermore, immune cell infiltration characteristics were analyzed to investigate the molecular immunology of lung transplant rejection. Finally, potential drug–target interactions were generated. In brief, 739 DEGs were found between rejection and stable LTx samples. PTPRC, IL-6, ITGAM, CD86, TLR8, TYROBP, CXCL10, ITGB2, and CCR5 were defined as hub genes. Eight TFs, including STAT1, SPIB, NFKB1, SPI1, STAT5A, RUNX1, VENTX, and BATF, and five miRNAs, including miR-335-5p, miR-26b-5p, miR-124-3p, miR-1-3p, and miR-155-5p, were involved in regulating hub genes. The immune cell infiltration analysis revealed higher proportions of activated memory CD4 T cells, follicular helper T cells, γδ T cells, monocytes, M1 and M2 macrophages, and eosinophils in rejection samples, besides lower proportions of resting memory CD4 T cells, regulatory T cells, activated NK cells, M0 macrophages, and resting mast cells. This study provided a comprehensive perspective of the molecular co-regulatory network underlying lung transplant rejection.

Genetic Expression and Mutation Profile Analysis in Different Pathologic Stages of Hepatocellular Carcinoma Patients

Background: The expression and mutation of multiple genes are involved in the complicated mechanism regarding the occurrence and development of hepatocellular carcinoma (HCC). The clinical pathological stage of HCC is closely linked to clinical prognosis of liver cancer. Methods: This study aims at analyzing the gene expression and mutation profile of different clinical pathological stages of HCC (stage I, II, III-IV), based on 367 HCC cases included in TCGA cohort. Findings: We identified a series of targeting genes with copy number variation (CNV), which is statistically associated with gene expression. For instance, compared with the normal group, CCNE 2 gene is highly expressed in the tumor group and specific stage I group, which are associated with three CNV types of single deletion, single gain, and amplification mutations. Protein interaction network construction and followed Molecular Complex Detection analysis indicated that the high expression of some cell cycle-related genes in HCC, such as TTK, CDC20, ASPM, is positively correlated with CNV. Non-synonymous mutations mainly existed in some genes, such as TTN, TP53, CTNNB1, MUC16, and ALB, however, we did not observe the association between the gene mutation frequency and the clinical pathological grade distribution. The rs121913396 and rs121913400 polymorphisms within the CTNNB1 gene were associated with the high expression of CTNNB1 protein, but not linked to the clinical prognosis of HCC. We performed the random forest and decision tree approaches for the modeling analysis and identified a group of genes related to different HCC pathological grades, such as the lowly expressed VIPR1, FAM99A, and GNA14 genes, or highly expressed CEP55, SEMA3F, and PRR11. Moreover, we conducted a principal component analysis (PCA) to obtain several genes associated with different pathological grades, including SLC27A5, ADAM17, SNRPA, SNRPD2, and ALDH2. Finally, we confirmed the highly expressed GAS2L3, SNRPA, SNRPD2 genes in the HCC tissues, for the first time, through a Chinese HLivH060PG02 cohort analysis. Interpretation: The identification of the targeting genes, including GAS2L3, SNRPA, SNRPD2, provides insight into the molecular mechanisms associated with different prognosis of HCC. Funding Statement: This work was supported by grants from the Innovation Team Development Plan of the Ministry of Education (IRT13085 to JY), National Nature Science Foundation of China (31670759 to JY, 31571380 to XG, 81572882 to ZY, 31701182 to CS), Excellent Talent Project of Tianjin Medical University (to JY), Tianjin Enterprise Science and Technology Commissioner Project (18JCTPJC59400 to XG). Declaration of Interests: The authors declare that they have no conflict of interest. Ethics Approval Statement: The use of human biological materials (Number: YB M-05-02) was approved by the Use Ethics Committee of Shanghai Outdo Biotech Company.

Circular RNA hsa_circ_0000376 Participates in Tumorigenesis of Breast Cancer by Targeting miR-1285-3p.

This study was designed to identify novel circular RNAs and the related regulatory axis to provide research targets for the diagnosis and treatment of breast cancer. The circular RNA expression microarray “GSE101123” related to breast cancer was downloaded from the Gene Expression Omnibus database. The differentially expressed circular RNAs between tumor and normal samples were screened using Limma package. The targeted microRNAs of the differentially expressed circular RNAs and the targeted messenger RNAs of the microRNAs were predicted using miRanda and miRWalk, respectively, and a circular RNAs–microRNAs–messenger RNAs network was constructed. Then, functional enrichment analysis, protein–protein interaction network construction, and drug–gene interaction analysis were conducted for the messenger RNAs. A total of 11 differentially expressed circular RNAs were identified between the breast cancer and normal samples, of which 3 were upregulated, while 8 were downregulated. The circular RNA–microRNA–messenger RNA network contained 1 circular RNA (hsa_circ_0000376), 2 microRNAs (miR-1285-3p and miR-1286), and 353 messenger RNAs. The protein–protein interaction network contained 150 nodes and 240 interactions. The hub genes in the protein–protein interaction network were all targeted messenger RNAs of miR-1285-3p that were significantly enriched in the ubiquitin–proteasome system, apoptosis, cell cycle arrest–related pathways, and cancer-related pathways involving SMAD specific E3 ubiquitin protein ligase 1, β-transducin repeat containing E3 ubiquitin protein ligase, tumor protein P53 among others. Twenty-two drugs were predicted to target 4 messenger RNAs, including tumor protein P53. A novel circular RNA, hsa_circ_0000376, was identified in breast cancer that may act as a sponge targeting miR-1285-3p expression which through its target genes, SMURF1, BTRC, and TP53, may further regulate tumorigenesis.

Integrated Genomic Analysis Revealed Associated Genes for Alzheimer's Disease in APOE4 Non-Carriers.

APOE4 is the strongest genetic risk factor for late-onset Alzheimer's disease (LOAD). LOAD patients carrying or not carrying APOE4 manifest distinct clinico-pathological characteristics. APOE4 has been shown to play a critical role in the pathogenesis of AD by affecting various aspects of pathological processes. However, the pathogenesis involved in LOAD not-carrying APOE4 remains elusive. We aimed to identify the associated genes involved in LOAD not-carrying APOE4. An integrated genomic analysis of datasets of genome-wide association study, genome-wide expression profiling and genome-wide linkage scan and protein-protein interaction network construction were applied to identify associated gene clusters in APOE4 non-carriers. The role of one of hub gene of an APOE4 non-carrier-associated gene cluster in tau phosphorylation was studied by knockdown and western blot. We identified 12 gene clusters associated with AD APOE4 non-carriers. The hub genes associated with AD in these clusters were MAPK8, POU2F1, XRCC1, PRKCG, EXOC6, VAMP4, SIRT1, MME, NOS1, ABCA1 and LDLR. The associated genes for APOE4 non-carriers were enriched in hereditary disorder, neurological disease and psychological disorders. Moreover, knockdown of PRKCG to reduce the expression of protein kinase Cγ isoform enhanced tau phosphorylation at Thr181 and Thr231 and the expression of glycogen synthase kinase 3β and cyclin-dependent kinase 5 in the presence of APOE3 but not APOE4. The study provides new insight into the mechanism of distinct pathogenesis of LOAD not carrying APOE4 and prompts the functional exploration of identified genes based on APOE genotypes.

A Web-Based Protocol for Interprotein Contact Prediction by Deep Learning.

Identifying residue-residue contacts in protein-protein interactions or complex is crucial for understanding protein and cell functions. DCA (direct-coupling analysis) methods shed some light on this, but they need many sequence homologs to yield accurate prediction. Inspired by the success of our deep-learning method for intraprotein contact prediction, we have developed RaptorX-ComplexContact, a web server for interprotein residue-residue contact prediction. Given a pair of interacting protein sequences, RaptorX-ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA) based on genomic distance and phylogeny information, respectively. Then, RaptorX-ComplexContact uses two deep convolutional residual neural networks (ResNet) to predict interprotein contacts from sequential features and coevolution information of paired MSAs. RaptorX-ComplexContact shall be useful for protein docking, protein-protein interaction prediction, and protein interaction network construction.

Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis.

Multiple myeloma (MM) is a common hematologic malignancy for which the underlying molecular mechanisms remain largely unclear. This study aimed to elucidate key candidate genes and pathways in MM by integrated bioinformatics analysis. Expression profiles GSE6477 and GSE47552 were obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) with p < .05 and [logFC] > 1 were identified. Functional enrichment, protein–protein interaction network construction and survival analyses were then performed. First, 51 upregulated and 78 downregulated DEGs shared between the two GSE datasets were identified. Second, functional enrichment analysis showed that these DEGs are mainly involved in the B cell receptor signaling pathway, hematopoietic cell lineage, and NF‐kappa B pathway. Moreover, interrelation analysis of immune system processes showed enrichment of the downregulated DEGs mainly in B cell differentiation, positive regulation of monocyte chemotaxis and positive regulation of T cell proliferation. Finally, the correlation between DEG expression and survival in MM was evaluated using the PrognoScan database. In conclusion, we identified key candidate genes that affect the outcomes of patients with MM, and these genes might serve as potential therapeutic targets.

PPICurator: A Tool for Extracting Comprehensive Protein-Protein Interaction Information.

Protein-protein interaction extraction through biological literature curation is widely employed for proteome analysis. There is a strong need for a tool that can assist researchers in extracting comprehensive PPI information through literature curation, which is critical in research on protein, for example, construction of protein interaction network, identification of protein signaling pathway, and discovery of meaningful protein interaction. However, most of current tools can only extract PPI relations. None of them are capable of extracting other important PPI information, such as interaction directions, effects, and functional annotations. To address these issues, this paper proposes PPICurator, a novel tool for extracting comprehensive PPI information with a variety of logic and syntax features based on a new support vector machine classifier. PPICurator provides a friendly web-based user interface. It is a platform that automates the extraction of comprehensive PPI information through literature, including PPI relations, as well as their confidential scores, interaction directions, effects, and functional annotations. Thus, PPICurator is more comprehensive than state-of-the-art tools. Moreover, it outperforms state-of-the-art tools in the accuracy of PPI relation extraction measured by F-score and recall on the widely used open datasets. PPICurator is available at https://ppicurator.hupo.org.cn.

Profiling of remote skeletal muscle gene changes resulting from stimulation of atopic dermatitis disease in NC/Nga mouse model.

Although atopic dermatitis (AD) is known to be a representative skin disorder, it also affects the systemic immune response. In a recent study, myoblasts were shown to be involved in the immune regulation, but the roles of muscle cells in AD are poorly understood. We aimed to identify the relationship between mitochondria and atopy by genome-wide analysis of skeletal muscles in mice. We induced AD-like symptoms using house dust mite (HDM) extract in NC/Nga mice. The transcriptional profiles of the untreated group and HDM-induced AD-like group were analyzed and compared using microarray, differentially expressed gene and functional pathway analyses, and protein interaction network construction. Our microarray analysis demonstrated that immune response-, calcium handling-, and mitochondrial metabolism-related genes were differentially expressed. In the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology pathway analyses, immune response pathways involved in cytokine interaction, nuclear factor-kappa B, and T-cell receptor signaling, calcium handling pathways, and mitochondria metabolism pathways involved in the citrate cycle were significantly upregulated. In protein interaction network analysis, chemokine family-, muscle contraction process-, and immune response-related genes were identified as hub genes with many interactions. In addition, mitochondrial pathways involved in calcium signaling, cardiac muscle contraction, tricarboxylic acid cycle, oxidation-reduction process, and calcium-mediated signaling were significantly stimulated in KEGG and Gene Ontology analyses. Our results provide a comprehensive understanding of the genome-wide transcriptional changes of HDM-induced AD-like symptoms and the indicated genes that could be used as AD clinical biomarkers.

Differentially Expressed Genes Associated with Primordial Follicle Formation and Transformation to Primary Follicles

Objective: Primordial follicle assembly and the transition from primordial follicle to primary follicle are critical processes in ovarian biology. These processes determine the size of the primordial follicle pool, which may limit the reproductive lifespan of a female. When the follicle pool is depleted, reproduction ceases and females enter menopause. In this study, we aimed to explore the role of differentially expressed genes (DEGs) in the processes of primordial follicle assembly and the transformation from primordial to primary follicle. Methods: The gene chip GSE9300 was downloaded from the Gene Expression Omnibus database. DEGs between ovaries on postnatal day 0 and ovaries on postnatal day 4, as well as ovaries on postnatal day 4 and ovaries cultured for 7 days from postnatal day 0 were identified. Gene ontology (GO), pathway enrichment analysis, and protein–protein interaction network construction were performed. Results: In total, 99 upregulated genes and 123 downregulated genes were identified in the process of primordial follicle assembly, and 101 upregulated genes and 98 downregulated genes were identified in the process of transformation from primordial to primary follicle. The GO analysis showed that response to estradiol was associated with two of the processes of assembly and subsequent development of primordial follicle, but the genes involved in response to estradiol between the two processes were significantly different. Furthermore, the two groups of DEGs were all associated with metabolic pathways. Conclusions: DEGs may provide new insights into the exploration of mechanisms related to primordial follicle assembly and subsequent development.

Identification of Protein Complexes by Using a Spatial and Temporal Active Protein Interaction Network.

The rapid development of proteomics and high-throughput technologies has produced a large amount of Protein-Protein Interaction (PPI) data, which makes it possible for considering dynamic properties of protein interaction networks (PINs) instead of static properties. Identification of protein complexes from dynamic PINs becomes a vital scientific problem for understanding cellular life in the post genome era. Up to now, plenty of models or methods have been proposed for the construction of dynamic PINs to identify protein complexes. However, most of the constructed dynamic PINs just focus on the temporal dynamic information and thus overlook the spatial dynamic information of the complex biological systems. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we propose a new model-based scheme for the construction of the Spatial and Temporal Active Protein Interaction Network (ST-APIN) by integrating time-course gene expression data and subcellular location information. To evaluate the efficiency of ST-APIN, the commonly used classical clustering algorithm MCL is adopted to identify protein complexes from ST-APIN and the other three dynamic PINs, NF-APIN, DPIN, and TC-PIN. The experimental results show that, the performance of MCL on ST-APIN outperforms those on the other three dynamic PINs in terms of matching with known complexes, sensitivity, specificity, and f-measure. Furthermore, we evaluate the identified protein complexes by Gene Ontology (GO) function enrichment analysis. The validation shows that the identified protein complexes from ST-APIN are more biologically significant. This study provides a general paradigm for constructing the ST-APINs, which is essential for further understanding of molecular systems and the biomedical mechanism of complex diseases.