scholarly journals Transcriptomic analysis of tobacco-flavored E-cigarette and menthol-flavored E-cigarette exposure in the human middle ear

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jae-Jun Song ◽  
Yoon Young Go ◽  
Jong Kyou Lee ◽  
Bum Sang Lee ◽  
Su-Kyoung Park ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Middle Ear ◽  
Differentially Expressed ◽  
Heme Oxygenase 1 ◽  
Bhlh Transcription Factor ◽  
Prostaglandin Endoperoxide ◽  
Cellular Processes ◽  
Prostaglandin Endoperoxide Synthase ◽  
Human Middle Ear

AbstractElectronic cigarettes (e-cigarettes) are the most widely used electronic nicotine delivery systems and are designed to imitate smoking and aid in smoking cessation. Although the number of e-cigarette users is increasing rapidly, especially among young adults and adolescents, the potential health impacts and biologic effects of e-cigarettes still need to be elucidated. Our previous study demonstrated the cytotoxic effects of electronic liquids (e-liquids) in a human middle ear epithelial cell (HMEEC-1) line, which were affected by the manufacturer and flavoring agents regardless of the presence of nicotine. In this study, we aimed to evaluate the gene expression profile and identify potential molecular modulator genes and pathways in HMEEC-1 exposed to two different e-liquids (tobacco- and menthol-flavored). HMEEC-1 was exposed to e-liquids, and RNA sequencing, functional analysis, and pathway analysis were conducted to identify the resultant transcriptomic changes. A total of 843 genes were differentially expressed following exposure to the tobacco-flavored e-liquid, among which 262 genes were upregulated and 581 were downregulated. Upon exposure to the menthol-flavored e-liquid, a total of 589 genes were differentially expressed, among which 228 genes were upregulated and 361 were downregulated. Among the signaling pathways associated with the differentially expressed genes mediated by tobacco-flavored e-liquid exposure, several key molecular genes were identified, including IL6 (interleukin 6), PTGS2 (prostaglandin-endoperoxide synthase 2), CXCL8 (C-X-C motif chemokine ligand 8), JUN (Jun proto-oncogene), FOS (Fos proto-oncogene), and TP53 (tumor protein 53). Under menthol-flavored e-liquid treatment, MMP9 (matrix metallopeptidase 9), PTGS2 (prostaglandin-endoperoxide synthase 2), MYC (MYC proto-oncogene, bHLH transcription factor), HMOX1 (heme oxygenase 1), NOS3 (nitric oxide synthase 3), and CAV1 (caveolin 1) were predicted as key genes. In addition, we identified related cellular processes, including inflammatory responses, oxidative stress and carcinogenesis, under exposure to tobacco- and menthol-flavored e-liquids. We identified differentially expressed genes and related cellular processes and gene signaling pathways after e-cigarette exposure in human middle ear cells. These findings may provide useful evidence for understanding the effect of e-cigarette exposure.

scholarly journals AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1037.2-1038
Author(s):  
X. Sun ◽  
S. X. Zhang ◽  
S. Song ◽  
T. Kong ◽  
C. Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Shanxi Province ◽  
Receptor Interaction ◽  
Term Therapy ◽  
Pathway Enrichment Analysis ◽  
Key Genes

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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.

scholarly journals Transcriptomic analysis of PPARα-dependent alterations during cardiac hypertrophy

2008 ◽  
Vol 36 (1) ◽  
pp. 15-23 ◽  
Author(s):  
Pascal J. H. Smeets ◽  
Heleen M. de Vogel-van den Bosch ◽  
Peter H. M. Willemsen ◽  
Alphons P. Stassen ◽  
Torik Ayoubi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Lipid Metabolism ◽  
Cardiac Hypertrophy ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Left Ventricular ◽  
Transcriptomic Analysis ◽  
Differentially Expressed ◽  
Wild Type

Peroxisome proliferator-activated receptor (PPAR)α regulates lipid metabolism at the transcriptional level and modulates the expression of genes involved in inflammation, cell proliferation, and differentiation. Although PPARα has been shown to mitigate cardiac hypertrophy, knowledge about underlying mechanisms and the nature of signaling pathways involved is fragmentary and incomplete. The aim of this study was to identify the processes and signaling pathways regulated by PPARα in hearts challenged by a chronic pressure overload by means of whole genome transcriptomic analysis. PPARα−/− and wild-type mice were subjected to transverse aortic constriction (TAC) for 28 days, and left ventricular gene expression profile was determined with Affymetrix GeneChip Mouse Genome 430 2.0 arrays containing >45,000 probe sets. In unchallenged hearts, the mere lack of PPARα resulted in 821 differentially expressed genes, many of which are related to lipid metabolism and immune response. TAC resulted in a more pronounced cardiac hypertrophy and more extensive changes in gene expression (1,910 and 312 differentially expressed genes, respectively) in PPARα−/− mice than in wild-type mice. Many of the hypertrophy-related genes were related to development, signal transduction, actin filament organization, and collagen synthesis. Compared with wild-type hypertrophied hearts, PPARα−/− hypertrophied hearts revealed enrichment of gene clusters related to extracellular matrix remodeling, immune response, oxidative stress, and inflammatory signaling pathways. The present study therefore demonstrates that, in addition to lipid metabolism, PPARα is an important modulator of immune and inflammatory response in cardiac muscle.

scholarly journals Maternal Hyperglycemia Induces Changes in Gene Expression and Morphology in Mouse Placentas

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Molly Eckmann ◽  
Quanhu Sheng ◽  
Scott Baldwin H ◽  
Rolanda L. Lister
Keyword(s):  
Differentially Expressed Genes ◽  
Glycogen Content ◽  
Vascular Development ◽  
The United States ◽  
Differentially Expressed ◽  
Collagen Deposition ◽  
Pregestational Diabetes ◽  
Cellular Processes ◽  
Placental Dysfunction ◽  
Maternal Hyperglycemia

Background: Pregestational diabetes complicates one million pregnancies in the United States and is associated with placental dysfunction. Placental dysfunction can manifest as stillbirth, spontaneous abortions, fetal growth restriction, and preeclampsia in the mother. However, the underlying mechanisms of placental dysfunction are not well understood. Objective: We hypothesize that maternal hyperglycemia disrupts cellular processes important for normal vascular development and function. Study Design: Hyperglycemia, defined as a non-fasting glucose concentration of >250 mg/dL was induced in eight-week-old female CD1 mice by injecting a one-time intraperitoneal dose of 150mg/kg streptozotocin. Control mice received an equal volume of normal saline. Hyperglycemic and control females were mated with CD-1 males. At Embryonic Day 17.5, the pregnant mice were euthanized. Sixty-eight placentas were harvested from the six euglycemic dams and twenty-six placentas were harvested from three hyperglycemic dams. RNA was extracted from homogenized placental tissue (N=12/group; 2-4 placentas per litter of each group). Total RNA was prepared and sequenced. Differentially expressed genes that were >2-fold change was considered significant. Placentas (9-20/group) were fixed in paraffin wax and sectioned at 6 µm. Cross-sectional areas of placental zones were evaluated using slides stained for hematoxylin and eosin, glycogen, collagen, proliferation and apoptosis. Quantification of staining intensity and percent positive nuclei was done using Leica Image Hub Data software. Data were compared between the control and experimental group using t-tests. Values of p < 0.05 were considered to be statistically significant. Results: The average maternal blood glucose concentrations for control and diabetic dams were 112+/-24 and 473+/- 47 respectively (p<0.0001). A higher rate of resorptions was noted in the hyperglycemia exposed placentas compared to euglycemic exposed placentas (24% vs 7%; p=0.04). A total of 24 RNA libraries (12/group) were prepared. Placentas from hyperglycemic pregnancies exhibited 1374 differentially expressed genes (DEGs). The 10 most significantly differentially expressed genes are Filip 1, Prom 2, Fam 78a, Pde4d, Pou3f1, Kcnk5, Dusp4, Cxcr4, Slc6a4 and D430019H16Rik. Their corresponding biologic functions are related to chemotaxis, ossification, cellular and vascular development. Histologically, we found that hyperglycemia exposed placentas demonstrated increased proliferation, apoptosis, and glycogen content and decreased collagen deposition. Conclusion: There was a higher rate of resorptions in the pregnancies of hyperglycemic dams. Pregestational diabetes resulted in significant changes in placental morphology, including increased glycogen content in the spongiotrophoblast, decreased collagen deposition, increased apoptosis and proliferation in the junction zone. Maternal diabetes causes widespread disruption in multiple cellular processes important for normal vascular development and sets the platform for placenta dysfunction.

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