scholarly journals Ion Channel and Ubiquitin Differential Expression during Erythromycin-Induced Anhidrosis in Foals

Animals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3379
Author(s):  
Laura Patterson Rosa ◽  
Martha F. Mallicote ◽  
Robert J. MacKay ◽  
Samantha A. Brooks
Keyword(s):  
Ion Channel ◽  
Multiple Testing ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Biologically Relevant ◽  
Sweat Production ◽  
Gene Transcripts ◽  
Skin Biopsies ◽  
Pre Treatment ◽  
Gland Function

Macrolide drugs are the treatment of choice for Rhodococcus equi infections, despite severe side-effects temporary anhidrosis as a. To better understand the molecular biology leading to macrolide induced anhidrosis, we performed skin biopsies and Quantitative Intradermal Terbutaline Sweat Tests (QITSTs) in six healthy pony-cross foals for three different timepoints during erythromycin administration—pre-treatment (baseline), during anhidrosis and post-recovery. RNA sequencing of biopsies followed by differential gene expression analysis compared both pre and post normal sweating timepoints to the erythromycin induced anhidrosis episode. After Bonferroni correction for multiple testing, 132 gene transcripts were significantly differentially expressed during the anhidrotic timepoint. Gene ontology analysis of the full differentially expressed gene set identified over-represented biological functions for ubiquitination and ion-channel function, both biologically relevant to sweat production. These same mechanisms were previously implicated in heritable equine idiopathic anhidrosis and sweat gland function and their involvement in macrolide-induced temporary anhidrosis warrants further investigation.

scholarly journals Epigenetic and Transcriptional Alterations in Human Adipose Tissue of Polycystic Ovary Syndrome

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Milana Kokosar ◽  
Anna Benrick ◽  
Alexander Perfilyev ◽  
Romina Fornes ◽  
Emma Nilsson ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Adipose Tissue ◽  
Polycystic Ovary Syndrome ◽  
Multiple Testing ◽  
Polycystic Ovary ◽  
Differentially Expressed Gene ◽  
Human Adipose Tissue ◽  
Differentially Expressed ◽  
Ovary Syndrome ◽  
Unique Genes

Abstract Genetic and epigenetic factors may predispose women to polycystic ovary syndrome (PCOS), a common heritable disorder of unclear etiology. Here we investigated differences in genome-wide gene expression and DNA methylation in adipose tissue from 64 women with PCOS and 30 controls. In total, 1720 unique genes were differentially expressed (Q < 0.05). Six out of twenty selected genes with largest expression difference (CYP1B1, GPT), genes linked to PCOS (RAB5B) or type 2 diabetes (PPARG, SVEP1), and methylation (DMAP1) were replicated in a separate case-control study. In total, 63,213 sites (P < 0.05) and 440 sites (Q < 0.15) were differently methylated. Thirty differentially expressed genes had corresponding changes in 33 different DNA methylation sites. Moreover, a total number of 1913 pairs of differentially expressed “gene-CpG” probes were significantly correlated after correction for multiple testing and corresponded with 349 unique genes. In conclusion, we identified a large number of genes and pathways that are affected in adipose tissue from women with PCOS. We also identified specific DNA methylation pathways that may affect mRNA expression. Together, these novel findings show that women with PCOS have multiple transcriptional and epigenetic changes in adipose tissue that are relevant for development of the disease.

scholarly journals The Comparison of Gene Expression from Multiple cDNA Libraries

2000 ◽  
Vol 10 (12) ◽  
pp. 2055-2061
Author(s):  
Dov J Stekel ◽  
Yoav Git ◽  
Francesco Falciani
Keyword(s):  
Gene Expression ◽  
Quantitative Measure ◽  
Cell Types ◽  
Differentially Expressed ◽  
Cdna Libraries ◽  
Cancer Genome Anatomy Project ◽  
Test Statistic ◽  
Biologically Relevant ◽  
Degree Of Belief ◽  
Gene Transcripts

We describe a method for comparing the abundance of gene transcripts in cDNA libraries. This method allows for the comparison of gene expression in any number of libraries, in a single statistical analysis, to identify differentially expressed genes. Such genes may be of potential biological or pharmaceutical relevance. The formula that we derive is essentially the entropy of a partitioning of genes among cDNA libraries. This work goes beyond previously published analyses, which can either compare only two libraries, or identify a single outlier in a group of libraries. This work also addresses the problem of false positives associated with repeating the test on many thousands of genes. A randomization procedure is described that provides a quantitative measure of the degree of belief in the results; the results are further verified by considering a theoretically derived large deviations rate for the test statistic. As an example, the analysis is applied to four prostate cancer libraries from the Cancer Genome Anatomy Project. The analysis identifies biologically relevant genes that are differentially expressed in the different tumor cell types.

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.

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