scholarly journals Prius: From Differentiated Genes to Affected Pathways

2016 ◽  
Author(s):  
Shailesh Patil ◽  
Bharath Venkatesh ◽  
Randeep Singh
Keyword(s):  
Gene Expression ◽  
Pathway Analysis ◽  
Cohort Analysis ◽  
Variant Calling ◽  
Interaction Network ◽  
R Package ◽  
Pathway Data ◽  
Number Of Genes ◽  
Differential Behaviour ◽  
Analytical Tools

AbstractExpression analysis and variant calling workflows are employed to identify genes that either exhibit a differential behaviour or have a significant functional impact of mutations. This is always followed by pathway analysis which provides greater insights and simplifies explanation of observed phenotype. The current techniques used towards this purpose have some serious limitations. Only a small number of genes which satisfy certain thresholds are used for pathway analysis. All the shortlisted genes are treated as equal ignoring the differences in p-values and fold changes. These genes are treated as independent entities and interactions among them are ignored for statistical pathway analysis. Hence, there is serious disconnect between the techniques employed and networked nature of the data. Various Pathway data ases have great degree of discordance on structure of pathway graphs. Many of the pathways are still far from complete. Current algorithms do not take into account this uncertainty. In this paper, we propose a theoretical framework Prius to overcome many limitations of current techniques. Prius perturbs the gene expression fold changes through interaction network and generates an ordered list of affected pathways. Thus, it integrates the networked nature of the data and provides facility to weigh each gene differently and in the process we do away with the need of arbitrary cut-offs. This framework is designed to be modular and provides the researchers with flexibility to plug analytical tools of their choice for every component. We also demonstrate effectiveness of our approach for personalized and cohort analysis of cancer gene expression samples with PageRank as one of the modules in the framework. The R package for Prius is available on GitHub.

Identification of Candidate Genes and Pathways in Nonsegmental Vitiligo Using Integrated Bioinformatics Methods

Dermatology ◽  
2020 ◽  
pp. 1-9
Author(s):  
Baoyi Liu ◽  
Yongyi Xie ◽  
Zhouwei Wu
Keyword(s):  
Gene Expression ◽  
Interaction Network ◽  
Unknown Origin ◽  
Cell Receptor ◽  
R Package ◽  
Peroxisome Proliferator ◽  
Hub Genes ◽  
Peroxisome Proliferator Activated Receptor ◽  
Lesional Skin ◽  
Functional Changes

<b><i>Background:</i></b> Nonsegmental vitiligo (NSV) is an acquired depigmentation disorder of unknown origin. Enormous interests focus on finding novel biomarkers and pathways responsible for NSV. <b><i>Methods:</i></b> The gene expression level was obtained by integrating microarray datasets (GSE65127 and GSE75819) from the Gene Expression Omnibus database using the sva R package. Differentially expressed genes (DEGs) between each group were identified by the limma R package. The interaction network was constructed using STRING, and significant modules coupled with hub genes were identified by cytoHubba and molecular complex detection. Pathway analyses were conducted using generally applicable gene set enrichment and further visualized in R environment. <b><i>Results:</i></b> A total of 102 DEGs between vitiligo lesional skin and healthy skin, 14 lesion-specific genes, and 29 predisposing genes were identified from the integrated dataset. Except for the anticipated decrease in melanogenesis, three major functional changes were identified, including oxidative phosphorylation, p53, and peroxisome proliferator-activated receptor (PPAR) signaling in lesional skin. <i>PPARG</i>, <i>MUC1</i>, <i>S100A8</i>, and <i>S100A9</i> were identified as key hub genes involved in the pathogenesis of vitiligo. Besides, upregulation of the T cell receptor signaling pathway was considered to be associated with susceptibility of the skin in NSV patients. <b><i>Conclusion:</i></b> Our study reveals several potential pathways and related genes involved in NSV using integrated bioinformatics methods. It might provide references for targeted strategies for NSV.

scholarly journals Investigation of gene expression and genetic simultaneous control associated with erectile dysfunction and diabetes

2021 ◽  
Vol 67 (3) ◽  
pp. 195-200
Author(s):  
Elham Kazemi ◽  
Javaad Zargooshi ◽  
Marzieh Kaboudi ◽  
Fereshteh Izadi ◽  
Hamid-Reza Mohammadi Motlagh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Erectile Dysfunction ◽  
Betweenness Centrality ◽  
Linear Models ◽  
Interaction Network ◽  
R Package ◽  
Normal Mucosa ◽  
Gene Expression Omnibus ◽  
Diabetic Rats ◽  
Simultaneous Control

Diabetes can cause some diseases or abnormalities. One of the disorders caused by diabetes may be erectile dysfunction (ED). ED is sexual dysfunction characterized by the inability to establish or maintain an erect penis during sexual activity and is a complication of men with chronic type 2 diabetes. These processes, disorders and diseases are highly influenced by the genetics of individuals. In this study, the relationship between genes and diabetes and ED has been explored by a system biology approach. For this purpose, the samples from ten control and diabetic-ED rats were collected. After a search in Gene Expression Omnibus (GEO), series with accession number GSE2457 comprising of 5 normal and 5 diabetic-ED rats were selected. Raw CEL files of these samples were normalized with robust multi-array average (RMA) expression measure method by using the linear models for microarray data (LIMMA) R package. The extracted probe IDs were transformed into 10451 unique and validated official gene symbols. Then, differentially expressed genes (DEGs) were identified between control and normal mucosa by employing the LIMMA R package. DEGs were classified by utilizing KEGG to underlying pathways by Enrichr. The expression values of DEGs were used to construct a gene regulatory network (GRN), by the GENEI3 R package. To analyze the topology of constructed GRNs, betweenness centrality was calculated. Genes with higher betweenness centrality scores were then identified, through the CytoNCA. We then took the commonality of DEGs genes and high-top ranking genes from CytoNCA via a predicted interaction network using GeneMANIA as the most likely important genes in erectile dysfunction. Among the 374 DEGs studied, 146 DEGs showed up-regulation and 228 DEGs displayed down-regulation expression in diabetic-ED rats. According to the Volcano plot, the dpp4, LOC102553868, Ndufa412, Oxct1, Atp2b3 and Zfp91 gene down-regulated and Lpl, Retsat, B4galt1 and Pdk4 genes up-regulated in ED and diabetic rats. Furthermore, genes like dpp4 acted as hubs in the inferred GRN.

scholarly journals Genetic expression and mutation profile analysis in different pathologic stages of hepatocellular carcinoma patients

2020 ◽  
Author(s):  
Xingjie Gao ◽  
Chunyan Zhao ◽  
Xiaoteng Cui ◽  
Nan Zhang ◽  
Yuanyuan Ren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Molecular Mechanisms ◽  
Cohort Analysis ◽  
Interaction Network ◽  
Principal Component ◽  
High Expression ◽  
Clinical Prognosis ◽  
Synonymous Mutations ◽  
Mutation Profile

Abstract 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. 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.Results: We identified a series of targeting genes with copy number variation (CNV), which is statistically associated with gene expression. For instance, compared withthe normal group, CCNE2 gene is highly expressed in the tumor group and specificstage I group, which are associated withthree 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, andALB, however, we did not observe the association between thegene mutation frequency and the clinical pathological grade distribution. The rs121913396 and rs121913400 polymorphisms withintheCTNNB1 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 approachesfor 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.Conclusions: The identification of the targeting genes, including GAS2L3, SNRPA, SNRPD2, provides insight into the molecular mechanisms associated with different prognosis of HCC.

A Review of Pathway Databases and Related Methods Analysis

2020 ◽  
Vol 15 (5) ◽  
pp. 379-395
Author(s):  
Ali Ghulam ◽  
Xiujuan Lei ◽  
Min Guo ◽  
Chen Bian
Keyword(s):  
Pathway Analysis ◽  
Biological Pathways ◽  
Human Diseases ◽  
Computational Tools ◽  
Essential Information ◽  
Pathway Data ◽  
Pathways Analysis ◽  
High Level ◽  
Analytical Tools ◽  
Pathway Databases

Pathway analysis integrates most of the computational tools for the investigation of high-level and complex human diseases. In the field of bioinformatics research, biological pathways analysis is an important part of systems biology. The molecular complexities of biological pathways are difficult to understand in human diseases, which can be explored through pathway analysis. In this review, we describe essential information related to pathway databases and their mechanisms, algorithms and methods. In the pathway database analysis, we present a brief introduction on how to gain knowledge from fundamental pathway data in regard to specific human pathways and how to use pathway databases and pathway analysis to predict diseases during an experiment. We also provide detailed information related to computational tools that are used in complex pathway data analysis, the roles of these tools in the bioinformatics field and how to store the pathway data. We illustrate various methodological difficulties that are faced during pathway analysis. The main ideas and techniques for the pathway-based examination approaches are presented. We provide the list of pathway databases and analytical tools. This review will serve as a helpful manual for pathway analysis databases.

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

2020 ◽  
Vol 26 (29) ◽  
pp. 3619-3630
Author(s):  
Saumya Choudhary ◽  
Dibyabhaba Pradhan ◽  
Noor S. Khan ◽  
Harpreet Singh ◽  
George Thomas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Gene ◽  
Therapeutic Targets ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Keratinocyte Differentiation ◽  
Hub Genes ◽  
Lesional Skin ◽  
Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

scholarly journals Transcriptome Analysis of Hypertrophic Heart Tissues from Murine Transverse Aortic Constriction and Human Aortic Stenosis Reveals Key Genes and Transcription Factors Involved in Cardiac Remodeling Induced by Mechanical Stress

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Peng Yu ◽  
Baoli Zhang ◽  
Ming Liu ◽  
Ying Yu ◽  
Ji Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Aortic Stenosis ◽  
Mechanical Stress ◽  
Cardiac Remodeling ◽  
Interaction Network ◽  
Cytokine Signaling ◽  
Transverse Aortic Constriction ◽  
Aortic Constriction ◽  
Genomic Changes

Background. Mechanical stress-induced cardiac remodeling that results in heart failure is characterized by transcriptional reprogramming of gene expression. However, a systematic study of genomic changes involved in this process has not been performed to date. To investigate the genomic changes and underlying mechanism of cardiac remodeling, we collected and analyzed DNA microarray data for murine transverse aortic constriction (TAC) and human aortic stenosis (AS) from the Gene Expression Omnibus database and the European Bioinformatics Institute. Methods and Results. The differential expression genes (DEGs) across the datasets were merged. The Venn diagrams showed that the number of intersections for early and late cardiac remodeling was 74 and 16, respectively. Gene ontology and protein–protein interaction network analysis showed that metabolic changes, cell differentiation and growth, cell cycling, and collagen fibril organization accounted for a great portion of the DEGs in the TAC model, while in AS patients’ immune system signaling and cytokine signaling displayed the most significant changes. The intersections between the TAC model and AS patients were few. Nevertheless, the DEGs of the two species shared some common regulatory transcription factors (TFs), including SP1, CEBPB, PPARG, and NFKB1, when the heart was challenged by applied mechanical stress. Conclusions. This study unravels the complex transcriptome profiles of the heart tissues and highlighting the candidate genes involved in cardiac remodeling induced by mechanical stress may usher in a new era of precision diagnostics and treatment in patients with cardiac remodeling.

scholarly journals Identification of potential biomarkers for abdominal pain in IBS patients by bioinformatics approach

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhongyuan Lin ◽  
Yimin Wang ◽  
Shiqing Lin ◽  
Decheng Liu ◽  
Guohui Mo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Irritable Bowel Syndrome ◽  
Abdominal Pain ◽  
Gastrointestinal Disease ◽  
Rectal Mucosa ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Irritable Bowel ◽  
Potential Biomarkers

Abstract Background Irritable bowel syndrome (IBS) is the most common functional gastrointestinal disease characterized by chronic abdominal discomfort and pain. The mechanisms of abdominal pain, as a relevant symptom, in IBS are still unclear. We aimed to explore the key genes and neurobiological changes specially involved in abdominal pain in IBS. Methods Gene expression data (GSE36701) was downloaded from Gene Expression Omnibus database. Fifty-three rectal mucosa samples from 27 irritable bowel syndrome with diarrhea (IBS-D) patients and 40 samples from 21 healthy volunteers as controls were included. Differentially expressed genes (DEGs) between two groups were identified using the GEO2R online tool. Functional enrichment analysis of DEGs was performed on the DAVID database. Then a protein–protein interaction network was constructed and visualized using STRING database and Cytoscape. Results The microarray analysis demonstrated a subset of genes (CCKBR, CCL13, ACPP, BDKRB2, GRPR, SLC1A2, NPFF, P2RX4, TRPA1, CCKBR, TLX2, MRGPRX3, PAX2, CXCR1) specially involved in pain transmission. Among these genes, we identified GRPR, NPFF and TRPA1 genes as potential biomarkers for irritating abdominal pain of IBS patients. Conclusions Overexpression of certain pain-related genes (GRPR, NPFF and TRPA1) may contribute to chronic visceral hypersensitivity, therefore be partly responsible for recurrent abdominal pain or discomfort in IBS patients. Several synapses modification and biological process of psychological distress may be risk factors of IBS.

scholarly journals Identification of glycolysis related pathways in pancreatic adenocarcinoma and liver hepatocellular carcinoma based on TCGA and GEO datasets

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ji Li ◽  
Chen Zhu ◽  
Peipei Yue ◽  
Tianyu Zheng ◽  
Yan Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Energy Metabolism ◽  
Pancreatic Adenocarcinoma ◽  
Digestive System ◽  
Prognostic Significance ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
System Structure ◽  
Liver Hepatocellular Carcinoma

Abstract Background Abnormal energy metabolism is one of the characteristics of tumor cells, and it is also a research hotspot in recent years. Due to the complexity of digestive system structure, the frequency of tumor is relatively high. We aim to clarify the prognostic significance of energy metabolism in digestive system tumors and the underlying mechanisms. Methods Gene set variance analysis (GSVA) R package was used to establish the metabolic score, and the score was used to represent the metabolic level. The relationship between the metabolism and prognosis of digestive system tumors was explored using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Volcano plots and gene ontology (GO) analyze were used to show different genes and different functions enriched between different glycolysis levels, and GSEA was used to analyze the pathway enrichment. Nomogram was constructed by R package based on gene characteristics and clinical parameters. qPCR and Western Blot were applied to analyze gene expression. All statistical analyses were conducted using SPSS, GraphPad Prism 7, and R software. All validated experiments were performed three times independently. Results High glycolysis metabolism score was significantly associated with poor prognosis in pancreatic adenocarcinoma (PAAD) and liver hepatocellular carcinoma (LIHC). The STAT3 (signal transducer and activator of transcription 3) and YAP1 (Yes1-associated transcriptional regulator) pathways were the most critical signaling pathways in glycolysis modulation in PAAD and LIHC, respectively. Interestingly, elevated glycolysis levels could also enhance STAT3 and YAP1 activity in PAAD and LIHC cells, respectively, forming a positive feedback loop. Conclusions Our results may provide new insights into the indispensable role of glycolysis metabolism in digestive system tumors and guide the direction of future metabolism–signaling target combined therapy.

scholarly journals Stress-induced differential gene expression in cardiac tissue

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ana Elisa T. S. de Carvalho ◽  
Marco A. Cordeiro ◽  
Luana S. Rodrigues ◽  
Daniela Ortolani ◽  
Regina C. Spadari
Keyword(s):  
Gene Expression ◽  
Stress Response ◽  
Left Ventricle ◽  
Differential Gene Expression ◽  
Cardiac Tissue ◽  
Stressful Situation ◽  
Adaptive Mechanism ◽  
Number Of Genes ◽  
Differential Gene ◽  
Shock Stress

AbstractThe stress response is adaptive and aims to guarantee survival. However, the persistence of a stressor can culminate in pathology. Catecholamines released as part of the stress response over activate beta adrenoceptors (β-AR) in the heart. Whether and how stress affects the expression of components of the intracellular environment in the heart is still, however, unknown. This paper used microarray to analyze the gene expression in the left ventricle wall of rats submitted to foot shock stress, treated or not treated with the selective β2-AR antagonist ICI118,551 (ICI), compared to those of non-stressed rats also treated or not with ICI, respectively. The main findings were that stress induces changes in gene expression in the heart and that β2-AR plays a role in this process. The vast majority of genes disregulated by stress were exclusive for only one of the comparisons, indicating that, in the same stressful situation, the profile of gene expression in the heart is substantially different when the β2-AR is active or when it is blocked. Stress induced alterations in the expression of such a large number of genes seems to be part of stress-induced adaptive mechanism.

scholarly journals Potential treatment for chronic myeloid leukemia using microRNA: in silico comparison between plants and human microRNAs in targeting BCR-ABL1 gene

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Syarifah Faezah Syed Mohamad ◽  
Marjanu Hikmah Elias
Keyword(s):  
Targeted Therapy ◽  
Chronic Myeloid Leukemia ◽  
Tyrosine Kinase ◽  
Pathway Analysis ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
In Silico ◽  
Myeloid Leukemia ◽  
Interaction Network ◽  
Folding Energy

Abstract Background Chronic myeloid leukemia (CML) is a myeloproliferative disorder characterized by the expression of the BCR-ABL1 fusion gene. Tyrosine kinase inhibitors (TKI) are used to treat CML, but mutations in the tyrosine kinase domain contribute to CML chemo-resistance. Therefore, finding alternative molecular-targeted therapy is important for the comprehensive treatment of CML. MicroRNAs (miRNA) are small non-coding regulatory RNAs which suppress the expression of their target genes by binding to the 3′ untranslated region (3′UTR) of the target mRNA. Hypothetically, the miRNA-mRNA interaction would suppress BCR-ABL1 expression and consequently reduce and inhibit CML cell proliferation. Thus, our objective was to determine the target interaction of human and plant miRNAs targeting the 3′UTR region of BCR-ABL1 in terms of miRNA binding conformity, protein interaction network, and pathways using in silico analysis. The 3′UTR sequence of BCR-ABL1 is obtained from Ensembl Genome Browser while the binding conformity was determined using the PsRNATarget Analysis Server, RNA22, Target Rank Server, and DIANA TOOLS. Protein-protein interaction network and pathway analysis are determined using STRING, Cytoscape, and KEGG pathway analysis. Results Five plants and five human miRNAs show strong binding conformity with 3′UTR of BCR-ABL1. The strongest binding conformity was shown by Oryza sativa’s Osa-miR1858a and osa-miR1858b with −24.4 kcal/mol folding energy and a p value of 0.0077. Meanwhile, in human miRNA, the hsa-miR-891a-3p shows the highest miTG score of 0.99 with −12 kcal/mol folding energy and a p value of 0.037. Apart from ABL1, osa-miR1858a/osa-miR1858b and hsa-miR891a-3p also target other 720 and 645 genes, respectively. The interaction network of Osa-miR1858a/osa-miR1858b and hsa-miR891a-3p identifies nineteen and twelve ABL1’s immediate neighboring proteins, respectively. The pathways analysis focuses on the RAS, MAPK, CML, and hematopoietic cell lineage pathway. Conclusion Both plant and human miRNAs tested in this study could be a potential therapeutic prospect in CML treatment, but thermodynamically, osa-miR1858a/osa-miR1858b binding to ABL1 is more favorable. However, it is important to carry out more research in vitro and in vivo and clinical studies to assess its efficacy as a targeted therapy for CML. Graphical abstract

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