scholarly journals Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data

2006 ◽  
Vol 7 (1) ◽  
Author(s):  
Ian B Jeffery ◽  
Desmond G Higgins ◽  
Aedín C Culhane
Keyword(s):  
Microarray Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Evaluation Of Methods

scholarly journals Transcriptome analyses and biofilm-forming characteristics of a clonal Pseudomonas aeruginosa from the cystic fibrosis lung

2008 ◽  
Vol 57 (12) ◽  
pp. 1454-1465 ◽  
Author(s):  
Jim Manos ◽  
Jonathan Arthur ◽  
Barbara Rose ◽  
Pholawat Tingpej ◽  
Carina Fung ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Pseudomonas Aeruginosa ◽  
Microarray Data ◽  
Reference Strain ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Strain Pao1 ◽  
Eastern Australia ◽  
Forming Characteristics ◽  
Insight Into

Transmissible Pseudomonas aeruginosa clones potentially pose a serious threat to cystic fibrosis (CF) patients. The AES-1 clone has been found to infect up to 40 % of patients in five CF centres in eastern Australia. Studies were carried out on clonal and non-clonal (NC) isolates from chronically infected CF patients, and the reference strain PAO1, to gain insight into the properties of AES-1. The transcriptomes of AES-1 and NC isolates, and of PAO1, grown planktonically and as a 72 h biofilm were compared using PAO1 microarrays. Microarray data were validated using real-time PCR. Overall, most differentially expressed genes were downregulated. AES-1 differentially expressed bacteriophage genes, novel motility genes, and virulence and quorum-sensing-related genes, compared with both PAO1 and NC. AES-1 but not NC biofilms significantly downregulated aerobic respiration genes compared with planktonic growth, suggesting enhanced anaerobic/microaerophilic growth by AES-1. Biofilm measurement showed that AES-1 formed significantly larger and thicker biofilms than NC or PAO1 isolates. This may be related to expression of the gene PA0729, encoding a biofilm-enhancing bacteriophage, identified by PCR in all AES-1 but few NC isolates (n=42). Links with the Liverpool epidemic strain included the presence of PA0729 and the absence of the bacteriophage gene cluster PA0632–PA0639. No common markers were found with the Manchester strain. No particular differentially expressed gene in AES-1 could definitively be ascribed a role in its infectivity, thus increasing the likelihood that AES-1 infectivity is multi-factorial and possibly involves novel genes. This study extends our understanding of the transcriptomic and genetic differences between clonal and NC strains of P. aeruginosa from CF lung.

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.

Identification of the Differentially Expressed Genes Involved in the Synergistic Neurotoxicity of an HIV Protease Inhibitor and Methamphetamine

2019 ◽  
Vol 17 (4) ◽  
pp. 290-303
Author(s):  
Sangsang Li ◽  
Yanfei Li ◽  
Bingpeng Deng ◽  
Jie Yan ◽  
Yong Wang
Keyword(s):  
Protease Inhibitor ◽  
Growth Inhibition ◽  
Quantitative Polymerase Chain Reaction ◽  
Differentially Expressed Gene ◽  
Antiretroviral Drugs ◽  
Differentially Expressed ◽  
Hiv Protease ◽  
Hiv Protease Inhibitor ◽  
Dopaminergic Cells ◽  
Immunodeficiency Syndrome

Background: The abuse of psychostimulants such as methamphetamine (METH) is common in human immunodeficiency virus (HIV)-infected individuals. Acquired immunodeficiency syndrome (AIDS) patients taking METH and antiretroviral drugs could suffer severe neurologic damage and cognitive impairment. Objective: To reveal the underlying neuropathologic mechanisms of an HIV protease inhibitor (PI) combined with METH, growth-inhibition tests of dopaminergic cells and RNA sequencing were performed. Methods: A combination of METH and PI caused more growth inhibition of dopaminergic cells than METH alone or a PI alone. Furthermore, we identified differentially expressed gene (DEG) patterns in the METH vs. untreated cells (1161 genes), PI vs. untreated cells (16 genes), METH-PI vs. PI (3959 genes), and METH-PI vs. METH groups (14 genes). Results: The DEGs in the METH-PI co-treatment group were verified in the brains of a mouse model using quantitative polymerase chain reaction and were involved mostly in the regulatory functions of cell proliferation and inflammation. Conclusion: Such identification of key regulatory genes could facilitate the study of their neuroprotective potential in the users of METH and PIs.

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 miRNA Expression Characterizes Histological Subtypes and Metastasis in Penile Squamous Cell Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1480
Author(s):  
Hiresh Ayoubian ◽  
Joana Heinzelmann ◽  
Sebastian Hölters ◽  
Oybek Khalmurzaev ◽  
Alexey Pryalukhin ◽  
...  
Keyword(s):  
Microarray Data ◽  
Expression Patterns ◽  
Hpv Infection ◽  
Differentially Expressed ◽  
Histological Subtypes ◽  
Specific Expression ◽  
Tissue Samples ◽  
Qrt Pcr ◽  
Differentially Expressed Mirnas ◽  
The Impact

Although microRNAs are described as promising biomarkers in many tumor types, little is known about their role in PSCC. Thus, we attempted to identify miRNAs involved in tumor development and metastasis in distinct histological subtypes considering the impact of HPV infection. In a first step, microarray analyses were performed on RNA from formalin-fixed, paraffin-embedded tumor (22), and normal (8) tissue samples. Microarray data were validated for selected miRNAs by qRT-PCR on an enlarged cohort, including 27 tumor and 18 normal tissues. We found 876 significantly differentially expressed miRNAs (p ≤ 0.01) between HPV-positive and HPV-negative tumor samples by microarray analysis. Although no significant differences were detected between normal and tumor tissue in the whole cohort, specific expression patterns occurred in distinct histological subtypes, such as HPV-negative usual PSCC (95 differentially expressed miRNAs, p ≤ 0.05) and HPV-positive basaloid/warty subtypes (247 differentially expressed miRNAs, p ≤ 0.05). Selected miRNAs were confirmed by qRT-PCR. Furthermore, microarray data revealed 118 miRNAs (p ≤ 0.01) that were significantly differentially expressed in metastatic versus non-metastatic usual PSCC. The lower expression levels for miR-137 and miR-328-3p in metastatic usual PSCC were validated by qRT-PCR. The results of this study confirmed that specific miRNAs could serve as potential diagnostic and prognostic markers in single PSCC subtypes and are associated with HPV-dependent pathways.

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