scholarly journals Data MiningMycobacterium tuberculosisPathogenic Gene Transcription Factors and Their Regulatory Network Nodes

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Guangxin Yuan ◽  
Yu Bai ◽  
Yuhang Zhang ◽  
Guangyu Xu
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Expression Profile ◽  
Regulatory Network ◽  
Regulatory Element ◽  
Single Gene ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Profile Data ◽  
Extracellular Region

Tuberculosis (TB) is one of the deadliest infectious diseases worldwide. InMycobacterium tuberculosis, changes in gene expression are highly variable and involve many genes, so traditional single-gene screening ofM. tuberculosistargets has been unable to meet the needs of clinical diagnosis. In this study, using the National Center for Biotechnology Information (NCBI) GEO Datasets, whole blood gene expression profile data were obtained in patients with active pulmonary tuberculosis. Linear model-experience Bayesian statistics using the Limma package in R combined witht-tests were applied for nonspecific filtration of the expression profile data, and the differentially expressed human genes were determined. Using DAVID and KEGG, the functional analysis of differentially expressed genes (GO analysis) and the analysis of signaling pathways were performed. Based on the differentially expressed gene, the transcriptional regulatory element databases (TRED) were integrated to construct theM. tuberculosispathogenic gene regulatory network, and the correlation of the network genes with disease was analyzed with the DAVID online annotation tool. It was predicted that IL-6, JUN, and TP53, along with transcription factors SRC, TNF, and MAPK14, could regulate the immune response, with their function being extracellular region activity and protein binding during infection withM. tuberculosis.

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.

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Gene Sets ◽  
Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

scholarly journals Transcriptome analyses and virus induced gene silencing identify genes in the Rpp4-mediated Asian soybean rust resistance pathway

2013 ◽  
Vol 40 (10) ◽  
pp. 1029 ◽  
Author(s):  
Aguida M. A. P. Morales ◽  
Jamie A. O'Rourke ◽  
Martijn van de Mortel ◽  
Katherine T. Scheider ◽  
Timothy J. Bancroft ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Silencing ◽  
Differentially Expressed ◽  
Soybean Rust ◽  
Phakopsora Pachyrhizi ◽  
R Gene ◽  
Virus Induced Gene Silencing ◽  
Asian Soybean Rust ◽  
Defence Responses

Rpp4 (Resistance to Phakopsora pachyrhizi 4) confers resistance to Phakopsora pachyrhizi Sydow, the causal agent of Asian soybean rust (ASR). By combining expression profiling and virus induced gene silencing (VIGS), we are developing a genetic framework for Rpp4-mediated resistance. We measured gene expression in mock-inoculated and P. pachyrhizi-infected leaves of resistant soybean accession PI459025B (Rpp4) and the susceptible cultivar (Williams 82) across a 12-day time course. Unexpectedly, two biphasic responses were identified. In the incompatible reaction, genes induced at 12 h after infection (hai) were not differentially expressed at 24 hai, but were induced at 72 hai. In contrast, genes repressed at 12 hai were not differentially expressed from 24 to 144 hai, but were repressed 216 hai and later. To differentiate between basal and resistance-gene (R-gene) mediated defence responses, we compared gene expression in Rpp4-silenced and empty vector-treated PI459025B plants 14 days after infection (dai) with P. pachyrhizi. This identified genes, including transcription factors, whose differential expression is dependent upon Rpp4. To identify differentially expressed genes conserved across multiple P. pachyrhizi resistance pathways, Rpp4 expression datasets were compared with microarray data previously generated for Rpp2 and Rpp3-mediated defence responses. Fourteen transcription factors common to all resistant and susceptible responses were identified, as well as fourteen transcription factors unique to R-gene-mediated resistance responses. These genes are targets for future P. pachyrhizi resistance research.

Regional variations in ABC transporter expression along the mouse intestinal tract

2004 ◽  
Vol 17 (1) ◽  
pp. 11-20 ◽  
Author(s):  
David M. Mutch ◽  
Pascale Anderle ◽  
Muriel Fiaux ◽  
Robert Mansourian ◽  
Karine Vidal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Abc Transporter ◽  
Expression Profile ◽  
Abc Transporters ◽  
Intestinal Tract ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Assessment Model ◽  
Differentially Expressed ◽  
Distal Intestine

The ATP-binding cassette (ABC) family of proteins comprise a group of membrane transporters involved in the transport of a wide variety of compounds, such as xenobiotics, vitamins, lipids, amino acids, and carbohydrates. Determining their regional expression patterns along the intestinal tract will further characterize their transport functions in the gut. The mRNA expression levels of murine ABC transporters in the duodenum, jejunum, ileum, and colon were examined using the Affymetrix MuU74v2 GeneChip set. Eight ABC transporters (Abcb2, Abcb3, Abcb9, Abcc3, Abcc6, Abcd1, Abcg5, and Abcg8) displayed significant differential gene expression along the intestinal tract, as determined by two statistical models (a global error assessment model and a classic ANOVA, both with a P < 0.01). Concordance with semiquantitative real-time PCR was high. Analyzing the promoters of the differentially expressed ABC transporters did not identify common transcriptional motifs between family members or with other genes; however, the expression profile for Abcb9 was highly correlated with fibulin-1, and both genes share a common complex promoter model involving the NFκB, zinc binding protein factor (ZBPF), GC-box factors SP1/GC (SP1F), and early growth response factor (EGRF) transcription binding motifs. The cellular location of another of the differentially expressed ABC transporters, Abcc3, was examined by immunohistochemistry. Staining revealed that the protein is consistently expressed in the basolateral compartment of enterocytes along the anterior-posterior axis of the intestine. Furthermore, the intensity of the staining pattern is concordant with the expression profile. This agrees with previous findings in which the mRNA, protein, and transport function of Abcc3 were increased in the rat distal intestine. These data reveal regional differences in gene expression profiles along the intestinal tract and demonstrate that a complete understanding of intestinal ABC transporter function can only be achieved by examining the physiologically distinct regions of the gut.

scholarly journals Discriminative feature of cells characterizes cell populations of interest by a small subset of genes

2021 ◽  
Author(s):  
Takeru Fujii ◽  
Kazumitsu Maehara ◽  
Masatoshi Fujita ◽  
Yasuyuki Ohkawa
Keyword(s):  
Gene Expression ◽  
Statistical Methods ◽  
Cell Population ◽  
Differentially Expressed Gene ◽  
Adaptive Lasso ◽  
Differentially Expressed ◽  
Small Subset ◽  
Specific Gene ◽  
Large Sample Size ◽  
Discriminative Feature

ABSTRACTStatistical methods for detecting differences in individual gene expression are indispensable for understanding cell types. However, conventional statistical methods have faced difficulties associated with the inflation of P-values because of both the large sample size and selection bias introduced by exploratory data analysis such as single-cell transcriptomics. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using differentially expressed gene-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for the discrimination of a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data, and that DFC enabled to characterize the muscle satellite cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement differentially expressed gene-based methods for interpreting large data sets.

scholarly journals Gene Expression Profiles of Papillary Thyroid Microcarcinoma

2017 ◽  
Vol 102 (1-2) ◽  
pp. 39-46 ◽  
Author(s):  
Woo Young Kim ◽  
Jae Bok Lee ◽  
Seung Pil Jung ◽  
Hoon Yub Kim ◽  
Sang Uk Woo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Differentially Expressed Genes ◽  
Expression Profile ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Papillary Thyroid Microcarcinoma ◽  
Papillary Thyroid ◽  
Normal Thyroid ◽  
Thyroid Microcarcinoma

The objective was to identify gene expression profile of papillary thyroid microcarcinoma. To help improve diagnosis of papillary thyroid microcarcinoma, we performed gene expression profiling and compared it to pair normal thyroid tissues. We performed microarray analysis with 6 papillary thyroid microcarcinoma and 6 pair normal thyroid tissues. Differentially expressed genes were selected using paired t test, linear models for microarray data, and significance analysis of microarrays. Real-time quantitative reverse transcription–polymerase chain reaction was used to validate the representative 10 genes (MET, TIMP1, QPCT, PROS1, LRP4, SDC4, CITED1, DPP4, LRRK2, RUNX2). We identified 91 differentially expressed genes (84 upregulated and 7 downregulated) in the gene expression profile and validated 10 genes of the profile. We identified a significant genetic difference between papillary thyroid microcarcinoma and normal tissue by 10 upregulated genes greater than 2-fold (P &lt; 0.05).

scholarly journals A Comparison of Gene Expression Changes in the Blood of Individuals Consuming Diets Supplemented with Olives, Nuts or Long-Chain Omega-3 Fatty Acids

Nutrients ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3765
Author(s):  
Virginie Bottero ◽  
Judith A. Potashkin
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Fatty Acids ◽  
Transcription Factors ◽  
Mediterranean Diet ◽  
Differentially Expressed ◽  
Omega 3 Fatty Acids ◽  
Omega 3 ◽  
The Mediterranean ◽  
Long Chain

Background: The Mediterranean diet, which is rich in olive oil, nuts, and fish, is considered healthy and may reduce the risk of chronic diseases. Methods: Here, we compared the transcriptome from the blood of subjects with diets supplemented with olives, nuts, or long-chain omega-3 fatty acids and identified the genes differentially expressed. The dietary genes obtained were subjected to network analysis to determine the main pathways, as well as the transcription factors and microRNA interaction networks to elucidate their regulation. Finally, a gene-associated disease interaction network was performed. Results: We identified several genes whose expression is altered after the intake of components of the Mediterranean diets compared to controls. These genes were associated with infection and inflammation. Transcription factors and miRNAs were identified as potential regulators of the dietary genes. Interestingly, caspase 1 and sialophorin are differentially expressed in the opposite direction after the intake of supplements compared to Alzheimer’s disease patients. In addition, ten transcription factors were identified that regulated gene expression in supplemented diets, mild cognitive impairment, and Alzheimer’s disease. Conclusions: We identified genes whose expression is altered after the intake of the supplements as well as the transcription factors and miRNAs involved in their regulation. These genes are associated with schizophrenia, neoplasms, and rheumatic arthritis, suggesting that the Mediterranean diet may be beneficial in reducing these diseases. In addition, the results suggest that the Mediterranean diet may also be beneficial in reducing the risk of dementia.

scholarly journals Identification of Differentially Expressed Genes and miRNAs Associated with Esophageal Squamous Cell Carcinoma by Integrated Analysis of Microarray Data

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Lemeng Zhang ◽  
Jianhua Chen ◽  
Tianli Cheng ◽  
Hua Yang ◽  
Changqie Pan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Survival Analysis ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Regulatory Network ◽  
Differentially Expressed ◽  
Pathway Enrichment

To identify candidate key genes and miRNAs associated with esophageal squamous cell carcinoma (ESCC) development and prognosis, the gene expression profiles and miRNA microarray data including GSE20347, GSE38129, GSE23400, and GSE55856 were downloaded from the Gene Expression Omnibus (GEO) database. Clinical and survival data were retrieved from The Cancer Genome Atlas (TCGA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes (DEGs) was analyzed via DAVID, while the DEG-associated protein-protein interaction network (PPI) was constructed using the STRING database. Additionally, the miRNA target gene regulatory network and miRNA coregulatory network were constructed, using the Cytoscape software. Survival analysis and prognostic model construction were performed via the survival (version 2.42-6) and rbsurv R packages, respectively. The results showed a total of 2575, 2111, and 1205 DEGs, and 226 differentially expressed miRNAs (DEMs) were identified. Pathway enrichment analyses revealed that DEGs were mainly enriched in 36 pathways, such as the proteasome, p53, and beta-alanine metabolism pathways. Furthermore, 448 nodes and 1144 interactions were identified in the PPI network, with MYC having the highest random walk score. In addition, 7 DEMs in the microarray data, including miR-196a, miR-21, miR-205, miR-194, miR-103, miR-223, and miR-375, were found in the regulatory network. Moreover, several reported disease-related miRNAs, including miR-198a, miR-103, miR-223, miR-21, miR-194, and miR-375, were found to have common target genes with other DEMs. Survival analysis revealed that 85 DEMs were related to prognosis, among which hsa-miR-1248, hsa-miR-1291, hsa-miR-421, and hsa-miR-7-5p were used for a prognostic survival model. Taken together, this study revealed the important roles of DEGs and DEMs in ESCC development, as well as DEMs in the prognosis of ESCC. This will provide potential therapeutic targets and prognostic predictors for ESCC.

scholarly journals Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?

Biomolecules ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 850 ◽  
Author(s):  
Mehran Piran ◽  
Reza Karbalaei ◽  
Mehrdad Piran ◽  
Jehad Aldahdooh ◽  
Mehdi Mirzaie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Expression Profile ◽  
Signaling Network ◽  
Differentially Expressed ◽  
Network Activity ◽  
Gene Products ◽  
Expression Data ◽  
Gene Pairs

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.

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