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Cholestatic liver disease (CLD) is a highly heterogeneous hepatobiliary disease with various causes. The purpose of this research was to explore the gene expression changes throughout the course of CLD revealing potential causative molecular mechanisms and therapeutic targets. We established two animal models of cholestasis: 3,5-diethoxycarbonyl-1,4-dihydrocollidine feeding for 2, 4 and 6 weeks and bile duct ligation for 14 days. Using these two models, we identified differentially expressed genes (DEGs) by RNA-Seq analysis and used the newly-found knowledge of DEGs in comprehensive bioinformatic analyses to investigate key molecular events. Sequencing results were confirmed by experimental verification. Our study detected overlapping DEGs in the two models, of these 568 genes were upregulated and 117 genes were downregulated. Gene Ontology analysis demonstrated that the upregulated genes were associated with the biological processes of cell adhesion, cell migration and cell motility, while the metabolic processes of various substances were enriched for the downregulated genes. Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that the upregulated pathways were mainly distributed in focal adhesion, ECM-receptor interaction and amoebiasis, while downregulated pathways focused on peroxisome proliferator-activated receptor signaling pathway, metabolic pathways and primary bile acid biosynthesis. These findings were further confirmed by protein–protein interaction network modeling. Hub genes Src, Pdgfb, Col15a1, Mmp9, Egfr were selected using centralities analyses and verified by qRT-PCR. We profiled a global mRNA landscape in CLD to promote a complete understanding of transcriptomic events of this disease, offering candidate biomarkers and therapeutic targets for the clinic. • Global gene expression profiles and dysregulated mRNAs are identified using RNA-Seq in CLD induced by DDC feeding and BDL. • Differential expression analysis of RNA-seq time course shows dynamic changes of each DEG in the process of DDC-induced CLD. • Enrichment analyses demonstrate the critical biological processes and signaling pathways in the development of CLD. • Protein-protein interaction network reveals the core sub-networks and top 5 hub genes involved in the course of CLD. [ABSTRACT FROM AUTHOR]

Abstract Background Mesenchymal stem cells (MSCs) are multipotent cells that can be widely used in stem cell therapy. However, few studies have revealed the potential mechanisms of the changes in aging MSC. Materials and methods In this study, microarray data GSE35955 was downloaded from the Gene Expression Omnibus database. Then limma package in R was used to filtrate differentially expressed genes (DEGs), Transcription factors (TFs) were predicted by DCGL package. After predicting TFs, protein-protein interaction (PPI) network and TF-mediated transcriptional regulation network were constructed. The functional and pathway enrichment analysis of screened DEGs, hub genes and TFs were conducted by the DAVID. Results Totally 156 up-regulated DEGs and 343 down-regulated DEGs were obtained. 6 hub genes (CTNNB1 , PPP2R1A , FYN , MAPK1 , PIK3C2A and EP300) were obtained from PPI network. 11 TFs (CREB1 , CUX1 , EGR1 , EP300 , FOXC1 , HSF2 , MEF2A , PLAU , SP1 , STAT1 and USF1) for DEGs were predicted and 2 highly scored co-expression relationships (EP300-PPP2R1A and STAT1 - FOXC1) were acquired from the TF-mediated transcriptional regulation network. Conclusions The discovery of the hub genes, TFs and pathways might contribute to the understanding of genetic and molecular functions of aging-related changes in MSC. Further validation studies on genes and TFs such as CTNNB1 , FYN , PPP2R1A , MAPK1 , EP300 and related biological processes and pathways, including adherens junction, DNA damage caused from oxidative stress, attribution of telomere, MSC differentiation and epigenetic regulation, are urgent for clinical prevention and treatment. Highlights • Identified 499 differentially expressed genes between elderly and middle-aged samples • Identified 6 hub genes through protein-protein interact network • Transcription factors of hub genes were identified from transcription factors meditated transcriptional regulation network. • Four hub genes and transcription factors (CTNNB1, FYN, PPP2R1A, MAPK1 and EP300) are associated with MSC aging. [ABSTRACT FROM AUTHOR]

Abstract SKT11, an important tumor suppressor, is a member of the serine/threonine kinase family and plays a crucial role in tumor invasion and metastasis by activated adenine monophosphate-activated protein kinase (AMPK) and AMPK-related kinase proteins. However, few studies have elaborated its regulations of development and metabolism of cartilage, as well as skeleton. This study was aimed to investigate the role of Stk11- knockout in chondrocyte by bioinformatics analysis. The gene expression profiles for Stk11 -knockout and wild-type mice were downloaded from the Gene Expression Omnibus (GEO) database. A total of 1104 differentially expressed genes (DEGs) were identified by Affymetrix Expression Console and Transcriptome Analysis Console (TAC) software, including 560 up-regulated and 544 down-regulated genes. The protein-protein interaction (PPI) networks were built by mapping DEGs into STRING, in which hub genes such as Fos , Pdgfrb , Pdgfra , Flt1/Vegfr1 , Smad3 , Mapk14, Twist and Aurkb were further identified. For the up-regulated genes, PI3K-AKT signaling pathway and Wnt signaling pathway were two main pathways in the KEGG analysis, and ossification and extracellular matrix organization were involved in the Gene Ontology (GO) analysis. On the other hand, the down-regulated genes were mainly involved in systemic lupus erythematosus and alcoholism pathways, and B cell receptor signaling pathway and immune system process biological processes. MiRNA-9, miRNA-134, miRNA-492, miRNA-224 and miRNA-142-5p were identified as key regulators in the miRNAs-DEG regulatory network. Additionally, OSF2/RUNX2 , and NFAT regulated DEGs collectively in the transcription factor regulatory network. The results of RT-PCR verified that the expression of hub genes, transcription factors and miRNAs in our experiment were basically consistent with the microarray hybridization. In this study, we provide an insight into the role of Stk11 in chondrocyte and identify novel genes related to Stk11. Highlights • A total of 1104 differentially expressed genes (DEGs) were identified in STK11-knockout mice. • PI3K-AKT, Wnt, systemic lupus erythematosus and alcoholism pathway were the main pathways in the KEGG analysis. • MiRNA-9, miRNA-374, miRNA-492, miRNA-224 and miRNA-142-5p were identified as key regulators. • OSF2/RUNX2, NFAT, and CEBP regulated DEGs collectively in the transcription factor regulatory network. [ABSTRACT FROM AUTHOR]

Necrotizing enterocolitis (NEC) is one of the most serious diseases that could threaten the life of neonates. However the current opinions about the pathogenesis or how to prevent or treat the disease are still ambiguous. The purpose of the present study was to identify the key genes of this disease and provide new insights into the mechanism of NEC. The gene expression data of GSE46619, including 5 specimens from NEC patients and 4 samples from surgical-control infants, were collected from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened with regard to NEC versus surgical-control group using Limma package in R software and Gene Ontology (GO) enrichment analysis and pathway enrichment analysis were conducted by means of Database for Annotation, Visualization and Integrated Discovery (DAVID) website subsequently. Furthermore the protein-protein interaction (PPI) network for DEGs was constructed using Cytoscape software and the most highly connected module was extracted using MCODE plugin from the PPI network. Moreover, the significantly enriched sub-pathways were identified using iSubpathwayMiner package in R software. A total of 2629 DEGs were screened out between NEC and control samples, including 367 up-regulated genes and 2262 down-regulated genes and they involved in different GO terms and pathways which may be associated with NEC onset and progression. PPI network and module analysis revealed that several genes were defined as hub genes including AGT, IL8 and KNG1. The sub-pathway analysis screened out 189 significantly enriched sub-pathways, including Tryptophan metabolism, Fatty acid metabolism, and Arachidonic acid metabolism. Genes in the corresponding sub-pathway, such as ACACB and CAT were regarded as critical genes in NEC. QRT-PCR was also conducted to identify the expression of the five key genes (AGT, IL8, KNG1, ACACB and CAT) in NEC samples. These findings have identified several hub genes (e.g., AGT, IL8, KNG1, ACACB and CAT) which were presumed to serve critical roles in NEC. [ABSTRACT FROM AUTHOR]

Osteonecrosis of the femoral head (ONFH) is a common orthopedic disease associated with high disability, and femoral neck fracture (FNF) is one of the most common reasons for traumatic ONFH. This study was designed to reveal the mechanisms underlying ONFH. Using fastx_toolkit and prinseq-lite tools, quality control was conducted for the sequencing data. The differentially expressed genes (DEGs, including both mRNAs and lncRNAs) between ONFH and FNF samples were identified using the edgeR package in R, and were then subjected to enrichment analysis using the BioCloud platform. Subsequently, protein-protein interaction (PPI) networks were constructed using Cytoscape software. After the target genes of DE-lncRNAs were predicted based on Spearman's rank correlation coefficient, lncRNA-gene coexpression network was visualized using the Cytoscape software. Furthermore, functional enrichment analysis was carried out for the target genes using the clusterprofiler package in R. Additionally, the key genes were detected by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 2965 DEGs were identified from the ONFH samples, including 602 DE-lncRNAs (such as downregulated FAM201A ). In the PPI networks, eight upregulated genes (including FGF2 , IGF1 , SOX9 , and COL2A1 ) and 11 downregulated genes were among the top 20 genes according to all of the scores, such as degree centrality, closeness centrality, and betweenness centrality scores. Functional enrichment analysis showed that IGF1 , SOX9 , and COL2A1 were significantly enriched during skeletal system development. Moreover, qRT-PCR experiments detected the upregulation of FGF2 and downregulation of FAM201A in ONFH samples. FGF2 and FAM201A were correlated with the development of ONFH. Besides, IGF1 , SOX9 , and COL2A1 might also affect the pathogenesis of ONFH. [ABSTRACT FROM AUTHOR]

This study aimed to explore crucial genes, transcription factors (TFs), and microRNAs (miRNAs) associated with the effects of curcumin against hepatocellular carcinoma (HCC). We downloaded data (GSE59713) from Gene Expression Omnibus to analyze differentially expressed genes (DEGs) between curcumin-treated and untreated HCC cell lines. Then, we identified the disease ontology (DO) and functional enrichment analysis of these DEGs and analyzed their protein–protein interactions (PPIs). Additionally, we constructed TF–target gene and miRNA–target gene regulatory networks and explored the potential functions of these DEGs. Finally, we detected the expression of CDKN1A , CTGF , LEF1 TF and MIR-19A regulated by curcumin in PLC/PRF/5 cells using RT-PCR. In total, 345 upregulated and 212 downregulated genes were identified. The main enriched pathway of upregulated genes was the TNF signaling pathway. The downregulated genes were significantly enriched in TGF-beta signaling pathway. In addition, most DEGs were significantly enriched in DO terms such as liver cirrhosis, hepatitis, hepatitis C and cholestasis (eg., CTGF). In the constructed PPI network, CDKN1A and CTGF were the key proteins. Moreover, LEF1, CDKN1A, and miR-19A that regulated CTGF were highlighted in the regulatory networks. Furthermore, the expression of CDKN1A , CTGF , LEF1 TF and miR-19A regulated by curcumin in PLC/PRF/5 cells was consistent with the aforementioned bioinformatics analysis results. To conclude, curcumin might exert its protective effects against HCC tumorigenesis by downregulating LEF1 and downregulating CTGF regulated by MIR-19A and upregulating CDKN1A expression. [ABSTRACT FROM AUTHOR]

In ovarian follicle development, estrogen acts as a regulatory molecule to mediate proliferation and differentiation of follicular cells. ERα (estrogen receptor α) exerts regulatory function classically by binding directly to the estrogen response element, recruiting co-factors and activating or repressing transcription in response to E2. In this study, we used ChIP-seq to map ERα-binding sites in ovaries of Hy-line Brown commercial hens at 45 d, 90 d and 160 d. In total, 24,886, 21,680 and 23,348 binding sites were identified in the ovaries of hens at 45 d, 90 d and 160 d, which are linked to 86, 83 and 74 genes, respectively. The PPI network contains 47 protein nodes and 164 interaction edges, among which, AKT1 (V-Akt Murine Thymoma Viral Oncogene Homolog 1) and ACTN2 (Actinin Alpha 2) with the highest weight in the network, followed by CREB1 (CAMP Responsive Element Binding Protein 1), and EPHA5 (EPH Receptor A5) were identified. These genes are likely related to sexual maturity in hens. This study also provides insight into the regulation of the ERα target gene networks and a reference for understanding ERα-regulated transcription. [ABSTRACT FROM AUTHOR]

• 5 hub genes involved in ferroptosis through a PPI. • These hub genes could distinguish T-ALL from normal individuals. • Silencing LCN2 promoted RSL3-induced ferroptosis in T-ALL. T-cell acute lymphoblastic leukemia (T-ALL) is a life-threatening malignancy and therapeutic toxicity remains a huge challenge for survival rates. A novel iron-dependent form of cell death, ferroptosis, shows potentials in cancer therapy. This study aimed to identify ferroptosis-associated hub genes within a proteinprotein interaction (PPI) network. We screened differential expressed genes (DEGs) in GSE46170 dataset and obtained ferroptosis-related genes from FerrDb database. Through overlapping between DEGs and ferroptosis-related genes, ferroptosis-associated DEGs were identified for further PPI network construction. Molecular complex detection (MCODE) algorithm in Cytoscape was employed to determine tightly connected protein clusters. Chord diagram of Gene Ontology (GO) was generated to reveal the potential biological process of hub genes. Through transfection with siRNA of lipocalin 2 (LCN2) into TALL cells, the regulatory role of LCN2 in ferroptosis was investigated. Venn diagram identified a total of 37 ferroptosis-associated DEGs between GSE46170 and ferroptosis-associated genes, which were mainly enriched in ferroptosis and necroptosis. Based on PPI network analysis, 5 hub genes (LCN2, LTF, HP, SLC40A1 and TFRC) were found. These hub genes were involved in iron ion transport and could distinguish T-ALL from normal individuals. Further experimental studies demonstrated that LCN2 was highly expressed in T-ALL, while silencing LCN2 promoted RSL3-induced ferroptotic cell death in T-ALL cells. This study identified novel ferroptosis-associated hub genes, which shed new insights into the underlying mechanism of ferroptosis in T-ALL and also provide promising therapeutic targets for T-ALL. [ABSTRACT FROM AUTHOR]

Background Ankylosing spondylitis, a systemic illness is a foundation of progressing joint swelling that for the most part influences the spine. However, it frequently causes aggravation in different joints far from the spine, and in addition organs, for example, the eyes, heart, lungs, and kidneys. It's an immune system ailment that may be activated by specific sorts of bacterial or viral diseases that initiate an invulnerable reaction that don't close off after the contamination is recuperated. The particular reason for ankylosing spondylitis is obscure, yet hereditary qualities assume a huge part in this condition. The rising apparatuses of network medicine offer a stage to investigate an unpredictable illness at framework level. In this study, we meant to recognize the key proteins and the biological regulator pathways including in AS and further investigating the molecular connectivity between these pathways by the topological examination of the Protein-protein communication (PPI) system. Results The extended network including of 93 nodes and have 199 interactions respectively scanned from STRING database and some separated small networks. 24 proteins with high BC at the threshold of 0.01 and 55 proteins with large degree at the threshold of 1 have been identified. CD4 with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from high BC proteins presents a clear and visual overview which shows all important regulatory pathways for AS and the crosstalk between them. Conclusion The finding of this research suggests that AS variation is orchestrated by an integrated PPI network centered on CD4 out of 93 nodes. Author summary Ankylosing spondylitis, a systemic disease is an establishment of advancing joint swelling that generally impacts the spine. Be that as it may, it as often as possible causes disturbance in various joints a long way from the spine, and what's more organs. It's a resistant framework affliction that might be actuated by particular sorts of bacterial or viral ailments that start an immune response that don't shut off after the pollution is recovered. The specific explanation behind AS is dark, yet innate qualities expect a colossal part in this condition. The rising devices of system solution offer a phase to examine an erratic ailment at structure level. In this study, we intended to perceive the key proteins and the natural controller pathways incorporating into AS. The finding of this research proposes that AS variety is organized by a coordinated PPI system focused on CD4. [ABSTRACT FROM AUTHOR]

This aim of this study was to explore novel biomarkers related to osteosarcoma. The mRNA expression profile GSE41293 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which included seven osteosarcoma and six control samples. After preprocessing, the FASTQ format reads of 13 samples were mapped to the reference sequences to screen for unique mapping reads. Differentially expressed genes (DEGs) were selected, which were then used for pathway and protein-protein interaction (PPI) network analyses. Moreover, the microarray data GSE63631 were downloaded from GEO database to verify our results. The percentages of unique mapping reads for osteosarcomas and control samples were both > 85%. A total of 6157 DEGs were identified between the two groups. DEGs that were upregulated were significantly enriched in 19 pathways, and those that were downregulated were enriched in 14 pathways. In the PPI network, DEGs such as SRC , ERBB2 , and CAV3 in cluster 1 were enriched in the pathway responsible for focal adhesions. The DEGs in cluster 2, such as CDK4 and CDK6 , were enriched in the cell cycle pathway. In GSE63631, DEGs were significantly enriched in focal adhesion pathway, which was in accordance with the result in GSE41293. Thus, the focal adhesion and cell cycle pathways may play important roles in osteosarcoma progression, and SRC , ERBB2 , CAV3 , CDK4 , and CDK6 may be used as critical biomarkers of osteosarcoma. [ABSTRACT FROM AUTHOR]

Abstract Pancreatic neuroendocrine tumors are relatively rare pancreatic neoplasms over the world. Investigations about molecular biology of PNETs are insufficient for nowadays. We aimed to explore the expression of messenger RNA and regulatory processes underlying pancreatic neuroendocrine tumors from different views. The expression profile of GSE73338 were downloaded, including samples with pancreatic neuroendocrine tumors. First, the Limma package was utilized to distinguish the differentially expressed messenger RNA. Gene Ontology classification and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed to explore the functions and pathways of target genes. In addition, we constructed a protein-protein interaction network. NEK2, UBE2C, TOP2A and PPP1R1A were revealed with continuous genomic alterations in higher tumor stage. 91 up-regulated and 36 down-regulated genes were identified to be differentially expressed in malignant PNETs. Locomotory behavior was significantly enriched for biological processes of metastasis PNETs. GCGR and GNAS were identified as the hub of proteins in the protein-protein interaction sub-network of malignant PNETs. We showed the gene expression differences in PNETs according to different clinicopathological aspects. NEK2, UBE2C, TOP2A are positively associated with high tumor grade, and PPP1R1A negatively. GCGR and GNAS are regarded as the hub of the PPI sub-network. CXCR4 may affect the progression of PNETs via the CXCR4-CXCL12-CXCR7 chemokine receptor axis. However, more studies are required. [ABSTRACT FROM AUTHOR]

Background Sarcoidosis is a multisystemic inflammatory and granulomatous disease that occurs in almost all populations and affects multiple organs. Meanwhile, its most common manifestation is pulmonary sarcoidosis. This study aimed to identify effective biomarkers for the diagnosis and therapy of pulmonary sarcoidosis. Methods GSE16538 was downloaded from Gene Expression Omnibus, including 6 pulmonary sarcoidosis samples and 6 normal lung samples. Then, differentially expressed genes (DEGs) were identified by limma package in R. After the expression values of the DEGs were extracted, hierarchical clustering analysis was performed for the DEGs using the pheatmap package in R. Subsequently, protein-protein interaction (PPI) pairs among the DEGs were searched by STRING or REACTOME databases, and then PPI networks were visualized by Cytoscape software. Using DAVID and KOBAS, functional and pathway enrichment analyses separately were performed for the DEGs involved in the PPI network. Results Total 208 DEGs were identified in pulmonary sarcoidosis samples, including 179 up-regulated genes and 29 down-regulated genes. Hierarchical clustering showed that the DEGs could clearly distinguish the pulmonary sarcoidosis samples from the normal lung samples. In the PPI network constructed by STRING database, CXCL9, STAT1, CCL5, CXCL11 and GBP1 had higher degrees and betweenness values, and could interact with each other. Functional enrichment showed that CXCL9 , CXCL11 and CCL5 were enriched in immune response. Moreover, STAT1 was enriched in pathways of chemokine signaling pathway and JAK-STAT signaling pathway. Conclusion CXCL9 , CXCL11 , STAT1 , CCL5 and GBP1 might be implicated in pulmonary sarcoidosis through interacting with each other. [ABSTRACT FROM AUTHOR]

[Display omitted] • Autophagy gene expression levels in 21 cancers investigated. • Protein-protein interaction network identified autophagy hub genes. • Significant involvement of autophagy process found in 8 cancers. • Drug-target network identified drugs for autophagy modulation. Identifying suitable deregulated targets in autophagy pathway is essential for developing autophagy modulating cancer therapies. With this aim, we systematically analyzed the expression levels of genes that contribute to the execution of autophagy in 21 cancers. Deregulated genes for 21 cancers were analyzed using the level 3 mRNA data from TCGAbiolinks. A total of 574 autophagy genes were mapped to the deregulated genes across 21 cancers. PPI network, cluster analysis, gene enrichment, gene ontology, KEGG pathway, patient survival, protein expression and cMap analysis were performed. Among the autophagy genes, 260 were upregulated, and 43 were downregulated across pan-cancer. The upregulated autophagy genes - CDKN2A and BIRC5 - were the most frequent signatures in cancers and could be universal cancer biomarkers. Significant involvement of autophagy process was found in 8 cancers (CHOL, HNSC, GBM, KICH, KIRC, KIRP, LIHC and SARC). Fifteen autophagy hub genes (ATP6V0C, BIRC5, HDAC1, IL4, ITGB1, ITGB4, MAPK3, mTOR, cMYC, PTK2, SRC, TCIRG1, TP63, TP73 and ULK1) were found to be linked with patients survival and also expressed in cancer patients tissue samples, making them as potential drug targets for these cancers. The deregulated autophagy genes were further used to identify drugs Losartan, BMS-345541, Embelin, Abexinostat, Panobinostat, Vorinostat, PD-184352, PP-1, XMD-1150, Triplotide, Doxorubicin and Ouabain, which could target one or more autophagy hub genes. Overall, our findings shed light on the most frequent cancer-associated autophagy genes, potential autophagy targets and molecules for cancer treatment. These findings can accelerate autophagy modulation in cancer therapy. [ABSTRACT FROM AUTHOR]

Myocardial infarction (MI) and transverse aortic constriction (TAC) are two models of cardiac hypertrophy. To study mechanisms of MI and TAC, GSE415 and GSE14267 were downloaded from Gene Expression Omnibus. GSE415 included left ventricle (LV) and intraventricular septum samples from mice that underwent MI, TAC or sham operation. GSE14267 included normal and MI samples from non-transgenic mice. Differentially expressed genes (DEGs) and microRNAs (miRNAs) were screened using limma package. Functional enrichment analysis was performed for DEGs using DAVID. Common DEGs of different groups were conducted for protein-protein interaction (PPI) analysis using STRING and visualized in PPI network by Cytoscape. Furthermore, targets were predicted for differentially expressed miRNAs using TarMir database. Totally, 277 DEGs, 31 common DEGs (e.g. SFRP2 ), 6 differentially expressed miRNAs (e.g. mmu-miR-448) and 1 miRNA-mRNA pair (mmu-miR-448 → SIM2 ) were screened out. DEGs were significantly enriched in biological processes related to muscle development and ion transportation. In the PPI network for common DEGs, LOX (degree = 7), POSTN (degree = 5), SPARC (degree = 4) and TIMP1 (degree = 3) were with higher degrees. In addition, they might function by interacting with each other (e.g. LOX - TIMP1 , LOX - POSTN , SPARC - TIMP1 and SPARC - POSTN ). In conclusion, LOX , POSTN , SPARC , TIMP1 and SFRP2 might affect MI and TAC.3 [ABSTRACT FROM AUTHOR]

• PG_0538, PG_0539, PG_0285, and PG_1797 are possible pharmacological targets. • PG_0539 may play a significant role in both resistance and virulence. • PG_0285 is a putative protein and topologically significant, hence requires further study to better understand P. gingivalis in polymicrobial interactions. • PG_1797 is the most indispensable protein in the network. The gram-negative bacteria Porphyromonas gingivalis (PG) is the most prevalent cause of periodontal diseases and multidrug-resistant (MDR) infections. Periodontitis and MDR infections are severe due to PG's ability to efflux antimicrobial and virulence factors. This gives rise to colonisation, biofilm development, evasion, and modulation of the host defence system. Despite extensive studies on the MDR efflux pump in other pathogens, little is known about the efflux pump and its association with the virulence factor in PG. Prolonged infection of PG leads to complete loss of teeth and other systemic diseases. This necessitates the development of new therapeutic interventions to prevent and control MDR. The study aims to identify the most indispensable proteins that regulate both resistance and virulence in PG, which could therefore be used as a target to fight against the MDR threat to antibiotics. We have adopted a hierarchical network-based approach to construct a protein interaction network. Firstly, individual networks of four major efflux pump proteins and two virulence regulatory proteins were constructed, followed by integrating them into one. The relationship between proteins was investigated using a combination of centrality scores, k-core network decomposition, and functional annotation, to computationally identify the indispensable proteins. Our study identified four topologically significant genes, PG_0538, PG_0539, PG_0285, and PG_1797, as potential pharmacological targets. PG_0539 and PG_1797 were identified to have significant associations between the efflux pump and virulence genes. This type of underpinning research may help in narrowing the drug spectrum used for treating periodontal diseases, and may also be exploited to look into antibiotic resistance and pathogenicity in bacteria other than PG. [ABSTRACT FROM AUTHOR]

Abstract: Elucidation of the determinants of the rate of protein sequence evolution is one of the great challenges in evolutionary biology. It has been proposed that housekeeping genes are evolutionarily slower than tissue specific genes. In the present communication, we have examined different determinants that influence the evolutionary rate variation in human housekeeping and tissue specific proteins present in protein–protein interaction network. Studies on yeast proteome, revealed a predominant role of protein connectivity in determining the rate of protein evolution. However, in human, we did not observe any significant influence of protein connectivity on its evolutionary rate. Rather, a significant impact of the proportion of protein''s interacting length (amount of protein interface involved in interaction with its partners), expression level and multifunctionality has been observed in determining the rate of protein evolution. We also observed that multi interface proteins are evolutionarily conserved between housekeeping and tissue specific genes and it has been found that the average number of biological processes they associated in these two sets of genes is similar. Moreover, single interface proteins in housekeeping genes evolve more slowly as compared to tissue specific genes owing to their involvement in different number of biological processes. Partial correlation analysis suggests that the relative importance of three individual factors in determining the evolutionary rate variation between housekeeping and tissue specific proteins is in the order of protein multifunctionality>protein expression level>interacting protein length. [Copyright &y& Elsevier]

• 11 overlapping genes associated with AAD were identified by integrative analysis. • Dysregulated gene expression plays an important role in AAD risk. • Extracellular matrix organization plays a vital role in AAD development. Aortic aneurysm and dissection (AAD) are a set of life-threatening diseases. This study aimed to investigate the genetic mechanisms of AAD by integrating transcriptome-wide association study (TWAS) and mRNA expression profile. The genome-wide association study (GWAS) summary data of AAD was obtained from the UK Biobank, which contains 452,264 White British individuals, including 1470 AAD patients. The TWAS analysis was performed by integrating expression quantitative trait loci (eQTL) data of aorta and the GWAS dataset of AAD using the FUSION software. The TWAS significant genes and differentially expressed genes (DEGs) identified by mRNA expression profile of aortic dissection were integrated to find common genes and biological process. For TWAS significant genes, protein–protein interaction (PPI) network analysis was further conducted based on STRING database. TWAS identified 423 genes with P < 0.05. After comparing the results of TWAS and mRNA expression profile, 11 overlapping genes (PDE8B, IKBKE, HMGA1, PKM, CHST1, DUS3L, S100A16, PTGS1, RAB38, PDLIM5, NOL6) and 15 common gene ontology (GO) terms (including extracellular matrix organization, external encapsulating structure organization, cell-substrate adhesion, actin filament-based process, focal adhesion, protein kinase activity) were identified. 9 hub genes of the TWAS results were identified via PPI network analysis, including RPS9, RPS18, RSRC1, DNAJC3, HBS1L, PRKCA, NCAM1, ITGB3, FTSJ3. Multiple candidate genes and biological processes associated with AAD were identified by the present integrative study of TWAS and mRNA expression profile. Further studies are needed to elucidate the genetic mechanisms of AAD. [ABSTRACT FROM AUTHOR]

• Primary chondrocytes were successfully isolated from the tibial plateau of OA patients. • A total of 1036 DEGs, including 593 upregulated and 443 downregulated genes. • The DEGs were significantly enriched in GO-BP terms such as response to the stimulus. • The DEGs were significantly enriched in pathways, including TNF signaling pathway. • PPI network identified UBC, FOS, IFIT1, CDK1, and ISG15 as the hub nodes. This study aimed to investigate the effect of 1,25-dihydroxy-vitamin D3 (1,25(OH)2D3) on primary chondrocytes cultured from patients with osteoarthritis (OA). Primary chondrocytes isolated from the tibial plateau of female OA patients were characterized by immunocytochemistry analysis. Using Cell Counting Kit-8 (CCK-8), cell viability was measured to select suitable 1,25(OH)2D3 concentrations for treating chondrocytes. RNA-sequencing was performed on primary chondrocytes treated with or without 1,25(OH)2D3. Differentially expressed genes (DEGs) as well as gene ontology (GO)-biological process (BP) and pathways affected by 1,25(OH)2D3 were identified. Protein-protein interaction (PPI) network was constructed, and the hub nodes in the PPI network were identified. qRT-PCR was conducted to confirm the expression levels of six DEGs. Positive collagen II staining confirmed the successful isolation of primary chondrocytes. CCK-8 assay showed maximal primary chondrocyte survival rate when treated with 10-5 μmol/L of 1,25(OH)2D3 for 72 h. RNA-sequencing results identified a total of 1036 DEGs, including 593 upregulated and 443 downregulated genes from 1,25(OH)2D3 treated and untreated cells. Further functional enrichment analyses showed the association of these DEGs with GO-BP terms such as response to the stimulus, cell proliferation, angiogenesis, and regulation of cell motility, and KEGG pathways, including TNF signaling pathway, IL-17 signaling pathway, cytokine-cytokine receptor interaction, and NF-kappa B signaling pathway. PPI network identified UBC, FOS, IFIT1, CDK1, and ISG15 as the hub nodes in the network. qRT-PCR results were in alignment with the results of RNA-sequencing. Our study might provide a theoretical basis for the use of vitamin D in treating OA. [ABSTRACT FROM AUTHOR]

Lung cancer is a malignant tumor with high mortality in both women and men. To study the mechanisms of smoking-induced lung cancer, we analyzed microarray of GSE4115. GSE4115 was downloaded from Gene Expression Omnibus including 78 and 85 bronchial epithelium tissue samples separately from smokers with and without lung cancer. Limma package in R was used to screen differentially expressed genes (DEGs). Hierarchical cluster analysis for DEGs was conducted using orange software and visualized by distance map. Using DAVID software, functional and pathway enrichment analyses separately were conducted for the DEGs. And protein-protein interaction (PPI) network was constructed using Cytoscape software. Then, the pathscores of enriched pathways were calculated. Besides, functional features were screened and optimized using the recursive feature elimination (RFE) method. Additionally, the support vector machine (SVM) method was used to train model. Total 1923 DEGs were identified between the two groups. Hierarchical cluster analysis indicated that there were differences in gene level between the two groups. And SVM analysis indicated that the five features had potential diagnostic value. Importantly, MAPK1 (degree=30), SRC (degree=29), SMAD4 (degree=23), EEF1A1 (degree=21), TRAF2 (degree=21) and PLCG1 (degree=20) had higher degrees in the PPI network of the DEGs. They might be involved in smoking-induced lung cancer by interacting with each other (e.g. MAPK1-SMAD4, SMAD4-EEF1A1 and SRC-PLCG1). MAPK1, SRC, SMAD4, EEF1A1, TRAF2 and PLCG1 might be responsible for the development of smoking-induced lung cancer., (Copyright © 2015 Elsevier B.V. All rights reserved.)

MicroRNAs (miRNAs) are a class of non-coding RNAs known to play important regulatory roles through targets, which can affect human cell proliferation, differentiation, and metabolism. Overlaps between different miRNA target prediction algorithms (MTPAs) are small, which limit the understanding of miRNA's biological functions. However, the overlaps increase on functional levels, such as Gene Ontology (GO), Protein-Protein Interaction Network (PPIN) and pathways. Here, we performed prioritization on existing predicted target sets for each miRNA by considering all the possible combinations of 7 functional levels. After analyzing the results of both single and multiple functional levels, we found that functional combination strategies including pathways and GO performed better in the prioritization of human miRNA target. The combination which performed best was "Pathway+GO BP+GO MF+GO CC+Target+PPIN". For the prioritized result of this combination, the valid target had top ranking, and our method performed better than the MTPAs after comparison adopting the validated ranking levels. Top genes in ranking lists generated by this strategy were either validated by experiments or share same functions with the corresponding miRNA/its validated genes in disease related biological processes., (© 2014.)