scholarly journals Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human Monocytes Infected by Listeria monocytogenes

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Wufeng Fan ◽  
Yuhan Zhou ◽  
Hao Li
Keyword(s):  
Listeria Monocytogenes ◽  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Interaction Network ◽  
Principal Component ◽  
Centrality Measures ◽  
Network Centrality ◽  
Human Monocytes ◽  
Pathway Interaction ◽  
Pathway Interaction Network

In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM) based on pathway interaction network (PIN) which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA) was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs), and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways) with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection.

scholarly journals Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways

2020 ◽  
Author(s):  
Jun Wang ◽  
Ziying Yang ◽  
Carlotta Domeniconi ◽  
Xiangliang Zhang ◽  
Guoxian Yu
Keyword(s):  
Heterogeneous Network ◽  
Characteristic Curve ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Driver Genes ◽  
Cancer Data ◽  
Pathway Interaction ◽  
Target Disease ◽  
Driver Pathway ◽  
Pathway Interaction Network

Abstract Discovering driver pathways is an essential step to uncover the molecular mechanism underlying cancer and to explore precise treatments for cancer patients. However, due to the difficulties of mapping genes to pathways and the limited knowledge about pathway interactions, most previous work focus on identifying individual pathways. In practice, two (or even more) pathways interplay and often cooperatively trigger cancer. In this study, we proposed a new approach called CDPathway to discover cooperative driver pathways. First, CDPathway introduces a driver impact quantification function to quantify the driver weight of each gene. CDPathway assumes that genes with larger weights contribute more to the occurrence of the target disease and identifies them as candidate driver genes. Next, it constructs a heterogeneous network composed of genes, miRNAs and pathways nodes based on the known intra(inter)-relations between them and assigns the quantified driver weights to gene–pathway and gene–miRNA relational edges. To transfer driver impacts of genes to pathway interaction pairs, CDPathway collaboratively factorizes the weighted adjacency matrices of the heterogeneous network to explore the latent relations between genes, miRNAs and pathways. After this, it reconstructs the pathway interaction network and identifies the pathway pairs with maximal interactive and driver weights as cooperative driver pathways. Experimental results on the breast, uterine corpus endometrial carcinoma and ovarian cancer data from The Cancer Genome Atlas show that CDPathway can effectively identify candidate driver genes [area under the receiver operating characteristic curve (AUROC) of $\geq $0.9] and reconstruct the pathway interaction network (AUROC of>0.9), and it uncovers much more known (potential) driver genes than other competitive methods. In addition, CDPathway identifies 150% more driver pathways and 60% more potential cooperative driver pathways than the competing methods. The code of CDPathway is available at http://mlda.swu.edu.cn/codes.php?name=CDPathway.

scholarly journals Predicting the Mechanism of the Analgesic Property of Yanhusuo Based on Network Pharmacology

2019 ◽  
Vol 14 (10) ◽  
pp. 1934578X1988307
Author(s):  
Wen-Ping Xiao ◽  
Yan-Fang Yang ◽  
He-Zhen Wu ◽  
Yi-yi Xiong
Keyword(s):  
Molecular Docking ◽  
Metabolic Pathways ◽  
Interaction Network ◽  
Binding Activity ◽  
Network Pharmacology ◽  
Active Components ◽  
Network Diagram ◽  
Pathway Interaction ◽  
Pathway Interaction Network ◽  
Pathway Analyses

Yanhusuo (Corydalis Rhizoma) extracts are widely used for the treatment of pain and inflammation. The effects of Yanhusuo in pain assays were assessed in a few studies. However, there are few studies on its analgesic mechanism. In this paper, network pharmacology was used to explore the analgesic components of Yanhusuo and its analgesic mechanism. The active components of Yanhusuo were screened by TCMSP database, combined with literature data. PharmMapper and GeneCards databases were used for screening the analgesic targets of the components. The protein interaction network diagram was drawn by String database and Cytoscape software, the gene ontology and KEGG pathway analyses of the target were performed by DAVID database, and the component–target–pathway interaction network diagram was further drawn by Cytoscape3.6.1 software. System Dock Web Site verified the molecular docking among components and targets. Finally, an interaction network of the component–target–pathway of Yanhusuo was constructed, and the functions and pathways were analyzed for preliminarily investigating the mechanism of Yanhusuo in analgesia. The results showed that the active components of analgesic in Yanhusuo were Corynoline, 13-methylpalmatrubine, dehydrocorydaline, saulatine, 2,3,9,10-tetramethoxy-13-methyl-5,6-dihydroisoquinolino[2,1-b]isoquinolin-8-on-e, and Capaurine. The mechanisms were involved in metabolic pathways, PI3k-Akt signaling pathway, pathways in cancer, and so on. The top 3 targets were NOS3, glucose-6-phosphate dehydrogenase, and glucose-6-phosphate isomerase in components-target-pathways network, and they were all enriched in metabolic pathways. Meanwhile the molecular docking showed that there was a high binding activity between the 6 components and the important target proteins, as a further certification for the subsequent network analysis. This study reveals the relationship of the components, targets, and pathways of active components in Yanhusuo, and provides new ideas and methods for further research on the analgesic mechanism of Yanhusuo.

Molecular characterization, biological function, tumor microenvironment association and clinical significance of m6A regulators in lung adenocarcinoma

2020 ◽  
Author(s):  
Yin Li ◽  
Jie Gu ◽  
Fengkai Xu ◽  
Qiaoliang Zhu ◽  
Yiwei Chen ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Lung Adenocarcinoma ◽  
Web Application ◽  
Molecular Mechanisms ◽  
Treatment Strategies ◽  
Interaction Network ◽  
Pathway Interaction ◽  
Clinical Benefits ◽  
Pathway Interaction Network ◽  
Scoring Tool

Abstract N6-methyladenosine (m6A) modification can regulate a variety of biological processes. However, the implications of m6A modification in lung adenocarcinoma (LUAD) remain largely unknown. Here, we systematically evaluated the m6A modification features in more than 2400 LUAD samples by analyzing the multi-omics features of 23 m6A regulators. We depicted the genetic variation features of m6A regulators, and found mutations of FTO and YTHDF3 were linked to worse overall survival. Many m6A regulators were aberrantly expressed in tumors, among which FTO, IGF2BP3, YTHDF1 and RBM15 showed consistent alteration features across 11 independent cohorts. Besides, the regulator-pathway interaction network demonstrated that m6A modification was associated with various biological pathways, including immune-related pathways. The correlation between m6A regulators and tumor microenvironment was also assessed. We found that LRPPRC was negatively correlated with most tumor-infiltrating immune cells. On the other hand, we established a scoring tool named m6Sig, which was positively correlated with PD-L1 expression and could reflect both the tumor microenvironment characterization and prognosis of LUAD patients. Comparison of CNV between high and low m6Sig groups revealed differences on chromosome 7. Application of m6Sig on an anti-PD-L1 immunotherapy cohort confirmed that the high m6Sig group demonstrated therapeutic advantages and clinical benefits. Our study indicated that m6A modification is involved in many aspects of LUAD and contributes to tumor microenvironment formation. A better understanding of m6A modification will provide more insights into the molecular mechanisms of LUAD and facilitate developing more effective personalized treatment strategies. A web application was built along with this study (http://www.bioinfo-zs.com/luadexpress/).

scholarly journals Subcellular Proteomics to Understand Promotive Effect of Plant-Derived Smoke Solution on Soybean Root

Proteomes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 39
Author(s):  
Yusuke Murashita ◽  
Takumi Nishiuchi ◽  
Shafiq Ur Rehman ◽  
Setsuko Komatsu
Keyword(s):  
Root Growth ◽  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Principal Component ◽  
Immunoblot Analysis ◽  
Plasma Membrane Atpase ◽  
Soybean Root ◽  
Promotive Effect ◽  
Membrane Atpase ◽  
Subcellular Proteomics

Plant-derived smoke solution enhances soybean root growth; however, its mechanism is not clearly understood. Subcellular proteomics techniques were used for underlying roles of plant-derived smoke solution on soybean root growth. The fractions of membrane and nucleus were purified and evaluated for purity. ATPase and histone were enriched in the fractions of membrane and nucleus, respectively. Principal component analysis of proteomic results indicated that the plant-derived smoke solution affected the proteins in the membrane and nucleus. The proteins in the membrane and nucleus mainly increased and decreased, respectively, by the treatment of plant-derived smoke solution compared with control. In the proteins in the plasma membrane, ATPase increased, which was confirmed by immunoblot analysis, and ATP contents increased through the treatment of plant-derived smoke solution. Additionally, although the nuclear proteins mainly decreased, the expression of RNA polymerase II was up-regulated through the treatment of plant-derived smoke solution. These results indicate that plant-derived smoke solution enhanced soybean root growth through the transcriptional promotion with RNA polymerase II expression and the energy production with ATPase accumulation.

scholarly journals Ssl2/TFIIH function in transcription start site scanning by RNA Polymerase II in Saccharomyces cerevisiae

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Tingting Zhao ◽  
Irina O Vvedenskaya ◽  
William KM Lai ◽  
Shrabani Basu ◽  
B Franklin Pugh ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Core Promoter ◽  
Interaction Network ◽  
Transcription Start ◽  
Pol Ii ◽  
Transcription Start Sites ◽  
Scanning Rate ◽  
Residue Interaction

In Saccharomyces cerevisiae, RNA Polymerase II (Pol II) selects transcription start sites (TSS) by a unidirectional scanning process. During scanning, a preinitiation complex (PIC) assembled at an upstream core promoter initiates at select positions within a window ~40-120 basepairs downstream. Several lines of evidence indicate that Ssl2, the yeast homolog of XPB and an essential and conserved subunit of the general transcription factor (GTF) TFIIH, drives scanning through its DNA-dependent ATPase activity, therefore potentially controlling both scanning rate and scanning extent (processivity). To address questions of how Ssl2 functions in promoter scanning and interacts with other initiation activities, we leveraged distinct initiation-sensitive reporters to identify novel ssl2 alleles. These ssl2 alleles, many of which alter residues conserved from yeast to human, confer either upstream or downstream TSS shifts at the model promoter ADH1 and genome-wide. Specifically, tested ssl2 alleles alter TSS selection by increasing or narrowing the distribution of TSSs used at individual promoters. Genetic interactions of ssl2 alleles with other initiation factors are consistent with ssl2 allele classes functioning through increasing or decreasing scanning processivity but not necessarily scanning rate. These alleles underpin a residue interaction network that likely modulates Ssl2 activity and TFIIH function in promoter scanning. We propose that the outcome of promoter scanning is determined by two functional networks, the first being Pol II activity and factors that modulate it to determine initiation efficiency within a scanning window, and the second being Ssl2/TFIIH and factors that modulate scanning processivity to determine the width of the scanning widow.

scholarly journals Histone modifications at the transcription start site of non-coding RNA revealed regulatory patterns of activation in Caenorhabditis elegans embryo

2020 ◽  
Author(s):  
Megan A. Bandeira ◽  
Max E. Boeck
Keyword(s):  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Transcription Start Site ◽  
Histone Modifications ◽  
Principal Component ◽  
Integrated Analysis ◽  
Regulation Of Transcription ◽  
Start Site ◽  
Transcription Start ◽  
Non Coding Rna

AbstractHistone modifications play an essential role in regulating recruitment of RNA polymerase II and through this regulation of transcription itself. Which modifications are essential for regulating the transcription of non-coding RNA (ncRNA) species and how these patterns differ between the different types of ncRNA remains less studied compared to mRNA. We performed a principal component analysis (PCA) of histone modifications patterns surrounding the transcription start site (TSS) of ncRNA in an attempt to understand how histone modifications predict polymerase recruitment and transcription of ncRNA in early C. elegans development We found that our first PCA axis was a better predictor of polymerase recruitment and expression than any single histone modification for ncRNA and miRNA. This indicates an integrated analysis of many histone modifications is essential for predicting expression based on histone modifications and that each ncRNA species have unique regulation of RNA polymerase recruitment through histone modifications.

scholarly journals Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis

2018 ◽  
Author(s):  
Denise Thiel ◽  
Nataša Djurdjevac Conrad ◽  
Ria X Peschutter ◽  
Heike Siebert ◽  
Annalisa Marsico
Keyword(s):  
Transcriptional Regulation ◽  
Rna Polymerase Ii ◽  
Expression Analysis ◽  
Unknown Function ◽  
Interaction Network ◽  
Chromatin Interaction ◽  
Centrality Measures ◽  
Regulatory Modules ◽  
Chromatin Interactions ◽  
State Models

AbstractBackgroundAlthough several studies have provided insights into the role of long non-coding RNAs (lncRNAs), the majority of them has unknown function. Recent evidence has shown the importance of both lncR-NAs and chromatin interactions in transcriptional regulation. Although network-based methods, mainly exploiting gene-lncRNA co-expression, have been applied to characterize lncRNA of unknown function by means of ‘guilt-by-association’ strategies, no method exists which combines co-expression analysis with 3D chromatin interaction data.ResultsTo better understand the function of chromatin interactions in the context of lncRNA-mediated gene regulation, we have developed a multi-step graph analysis approach to examine the RNA polymerase II ChIA-PET chromatin interaction network in the K562 human cell line. We have annotated the network with gene and lncRNA coordinates, and chromatin states from the ENCODE project. We used centrality measures, as well as an adaptation of our previously developed Markov State Models (MSM) clustering method, to gain a better understanding of lncRNAs in transcriptional regulation. The novelty of our approach resides into the detection of fuzzy regulatory modules based on network properties and their optimization based on co-expression analysis between genes and gene-lncRNA pairs. This results in our method returning morebona fideregulatory modules than other state-of-the art approaches for clustering on graphs.ConclusionsInterestingly, we find that lncRNA network hubs tend to be significantly enriched in disease association, positional conservation and enhancer-like functions. We validated regulatory functions for well known lncRNAs, such as MALAT1 and the enhancer-like lncRNA FALEC. In addition, by investigating the modular structure of bigger components we show that we can propose regulatory functional mechanisms for uncharacterized lncRNAs, such FLJ37453, RP11442N24 B.1 and LINC00910.

scholarly journals The use of gene interaction networks to improve the identification of cancer driver genes

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2568 ◽  
Author(s):  
Emilie Ramsahai ◽  
Kheston Walkins ◽  
Vrijesh Tripathi ◽  
Melford John
Keyword(s):  
Interaction Network ◽  
Gene Interaction ◽  
Driver Genes ◽  
Genomic Libraries ◽  
Cancer Driver ◽  
Gene Interaction Networks ◽  
Combining Data ◽  
Pathway Interaction ◽  
Cancer Driver Genes ◽  
Pathway Interaction Network

Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.

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