Rapid Selection of Differentially Expressed Genes in TNFα-activated Endothelial Cells

Abstract Background: Kawasaki disease (KD) leads to coronary artery damage and the etiology of KD is unknown. The present study was designed to explore the differentially expressed genes (DEGs) in KD serum-induced human coronary artery endothelial cells (HCAECs) by RNA-sequence (RNA-seq). Methods: HCAECs were stimulated with serum (15% (v/v)), which were collected from 20 healthy children and 20 KD patients, for 24 hours. DEGs were then detected and analyzed by RNA-seq and bioinformatics analysis. Results: The expression of SMAD1, SMAD6, CD34, CXCL1, PITX2, and APLN was validated by qPCR. 102 genes, 59 up-regulated and 43 down-regulated genes, were significantly differentially expressed in KD groups. GO enrichment analysis showed that DEGs were enriched in cellular response to cytokines, cytokine-mediated signaling pathway, and regulation of immune cells migration and chemotaxis. KEGG signaling pathway analysis showed that DEGs were mainly involved in cytokine−cytokine receptor interaction, chemokine signaling pathway, and TGF−β signaling pathway. Besides, the mRNA expression levels of SMAD1, SMAD6, CD34, CXCL1, and APLN in the KD group were signiﬁcantly up-regulated compared with the normal group, whilePITX2 was significantly down-regulated. Conclusion: 102 DEGs in KD serum-induced HCAECs were identified, and six new targets were proposed as potential indicators of KD.

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Differentially expressed genes in human endometrial endothelial cells derived from eutopic endometrium of patients with endometriosis compared with those from patients without endometriosis

Human Reproduction ◽

10.1093/humrep/dem266 ◽

2007 ◽

Vol 22 (12) ◽

pp. 3159-3169 ◽

Cited By ~ 34

Author(s):

G. Sha ◽

D. Wu ◽

L. Zhang ◽

X. Chen ◽

M. Lei ◽

...

Keyword(s):

Endothelial Cells ◽

Differentially Expressed Genes ◽

Differentially Expressed ◽

Eutopic Endometrium

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MicroArray Data Simulator For Improved Selection of Differentially Expressed Genes

Cancer Biology & Therapy ◽

10.4161/cbt.2.4.431 ◽

2003 ◽

Vol 2 (4) ◽

pp. 383-391 ◽

Cited By ~ 30

Author(s):

Sunil Singhal ◽

Chris G. Kyvernitis ◽

Steven W. Johnson ◽

Larry R. Kaiser ◽

Michael N. Liebman ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Microarray Data ◽

Differentially Expressed ◽

Selection Of

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A THEORETICAL ANALYSIS OF THE SELECTION OF DIFFERENTIALLY EXPRESSED GENES

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720005001211 ◽

2005 ◽

Vol 03 (03) ◽

pp. 627-643 ◽

Cited By ~ 4

Author(s):

SACH MUKHERJEE ◽

STEPHEN J. ROBERTS

Keyword(s):

Theoretical Analysis ◽

Differentially Expressed Genes ◽

Gene Selection ◽

Small Sample ◽

Ranking Function ◽

Differentially Expressed ◽

Computationally Efficient ◽

Small Sample Sizes ◽

Selection Algorithms ◽

Selection Of

A great deal of recent research has focused on the challenging task of selecting differentially expressed genes from microarray data ("gene selection"). Numerous gene selection algorithms have been proposed in the literature, but it is often unclear exactly how these algorithms respond to conditions like small sample sizes or differing variances. Choosing an appropriate algorithm can therefore be difficult in many cases. In this paper we propose a theoretical analysis of gene selection, in which the probability of successfully selecting differentially expressed genes, using a given ranking function, is explicitly calculated in terms of population parameters. The theory developed is applicable to any ranking function which has a known sampling distribution, or one which can be approximated analytically. In contrast to methods based on simulation, the approach presented here is computationally efficient and can be used to examine the behavior of gene selection algorithms under a wide variety of conditions, even when the number of genes involved runs into the tens of thousands. The utility of our approach is illustrated by comparing three widely-used gene selection methods.

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Intertwining Threshold Settings, Biological Data and Database Knowledge to Optimize the Selection of Differentially Expressed Genes from Microarray

PLoS ONE ◽

10.1371/journal.pone.0013518 ◽

2010 ◽

Vol 5 (10) ◽

pp. e13518 ◽

Cited By ~ 7

Author(s):

Paul Chuchana ◽

Philippe Holzmuller ◽

Frederic Vezilier ◽

David Berthier ◽

Isabelle Chantal ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Biological Data ◽

Differentially Expressed ◽

Selection Of

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Long Non-coding RNAs in Traumatic Brain Injury Accelerated Fracture Healing

Frontiers in Surgery ◽

10.3389/fsurg.2021.663377 ◽

2021 ◽

Vol 8 ◽

Author(s):

Guoning Guo ◽

Yajun Gou ◽

Xingyu Jiang ◽

Shuhong Wang ◽

Ruilie Wang ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Endothelial Cells ◽

Brain Injury ◽

B Cells ◽

Fracture Healing ◽

Differentially Expressed Genes ◽

Differentially Expressed ◽

Cellular Behavior ◽

Cellular Behaviors ◽

Non Coding Rnas

It is commonly observed that patients with bone fracture concomitant with traumatic brain injury (TBI) had significantly increased fracture healing, but the underlying mechanisms were not fully revealed. Long non-coding RNAs (lncRNAs) are known to play complicated roles in bone homeostasis, but their role in TBI accelerated fracture was rarely reported. The present study was designed to determine the role of lncRNAs in TBI accelerated fracture via transcriptome sequencing and further bioinformatics analyses. Blood samples from three fracture-only patients, three fracture concomitant with TBI patients, and three healthy controls were harvested and were subsequently subjected to transcriptome lncRNA sequencing. Differentially expressed genes were identified, and pathway enrichment was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. High-dimensional data visualization by self-organizing map (SOM) machine learning was applied to further interpret the data. An xCell method was then used to predict cellular behavior in all samples based on gene expression profiles, and an lncRNA–cell interaction network was generated. A total of 874 differentially expressed genes were identified, of which about 26% were lncRNAs. Those identified lncRNAs were mainly enriched on TBI-related and damage repair-related pathways. SOM analyses revealed that those differentially expressed lncRNAs could be divided into three major module implications and were mainly enriched on transcriptional regulation and immune-related signal pathways, which promote us to further explore cellular behaviors based on differentially expressed lncRNAs. We have predicted that basophils, CD8+ T effector memory cells, B cells, and naïve B cells were significantly downregulated, while microvascular endothelial cells were predicted to be significantly upregulated in the Fr/TBI group, was the lowest and highest, respectively. ENSG00000278905, ENSG00000240980, ENSG00000255670, and ENSG00000196634 were the most differentially expressed lncRNAs related to all changes of cellular behavior. The present study has revealed for the first time that several critical lncRNAs may participate in TBI accelerated fracture potentially via regulating cellular behaviors of basophils, cytotoxic T cells, B cells, and endothelial cells.

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Analysis of Differentially Expressed Genes in Endothelial Cells Following Tumor Cell Adhesion, and the Role of PRKAA2 and miR-124-3p

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.604038 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yan Pan ◽

Marhaba Abdureyim ◽

Qing Yao ◽

Xuejun Li

Keyword(s):

Endothelial Cells ◽

Cell Adhesion ◽

Tumor Cell ◽

Tumor Cells ◽

Differentially Expressed Genes ◽

Differentially Expressed ◽

Expression Level ◽

Luciferase Reporter ◽

Tumor Cell Adhesion ◽

Tumor Endothelium

Tumor cell adhesion to the endothelium is one pattern of tumor–endothelium interaction and a key step during tumor metastasis. Endothelium integrity is an important barrier to prevent tumor invasion and metastasis. Changes in endothelial cells (ECs) due to tumor cell adhesion provide important signaling mechanisms for the angiogenesis and metastasis of tumor cells. However, the changes happened in endothelial cells when tumor–endothelium interactions are still unclear. In this study, we used Affymetrix Gene Chip Human Transcriptome Array 2.0. and quantitative real-time PCR (qPCR) to clarify the detailed gene alteration in endothelial cells adhered by prostate tumor cells PC-3M. A total of 504 differentially expressed mRNAs and 444 lncRNAs were obtained through chip data analysis. Gene Ontology (GO) function analysis showed that differentially expressed genes (DEGs) mainly mediated gland development and DNA replication at the biological level; at the cell component level, they were mainly involved in the mitochondrial inner membrane; and at the molecular function level, DEGs were mainly enriched in ATPase activity and catalytic activity. Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway analysis showed that the DEGs mainly regulated pathways in cancer, cell cycle, pyrimidine metabolism, and the mTOR signaling pathway. Then, we constructed a protein–protein interaction functional network and mRNA–lncRNA interaction network using Cytoscape v3.7.2. to identify core genes, mRNAs, and lncRNAs. The miRNAs targeted by the core mRNA PRKAA2 were predicted using databases (miRDB, RNA22, and Targetscan). The qPCR results showed that miR-124-3p, the predicted target miRNA of PRKAA2, was significantly downregulated in endothelial cells adhered by PC-3M. With a dual luciferase reporter assay, the binding of miR-124-3p with PRKAA2 3’UTR was confirmed. Additionally, by using the knockdown lentiviral vectors of miR-124-3p to downregulate the miR-124-3p expression level in endothelial cells, we found that the expression level of PRKAA2 increased accordingly. Taken together, the adhesion of tumor cells had a significant effect on mRNAs and lncRNAs in the endothelial cells, in which PRKAA2 is a notable changed molecule and miR-124-3p could regulate its expression and function in endothelial cells.

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Bioinformatics Study on the Response of Human Endothelial Cells to Different Strains of Staphylococcus Aureus

10.21203/rs.3.rs-100257/v1 ◽

2020 ◽

Author(s):

Tian-ao Xie ◽

Ke-ying Fang ◽

Wen-chao Cao ◽

Jie Lv ◽

Jia-xin Chen ◽

...

Keyword(s):

Gene Expression ◽

Staphylococcus Aureus ◽

Endothelial Cells ◽

Differentially Expressed Genes ◽

Expression Patterns ◽

Differentially Expressed ◽

Specific Gene ◽

Human Endothelial Cells ◽

Different Strains ◽

Differential Genes

Abstract BackgroundStaphylococcus aureus-induced bacteremia has an impact on human health due to its high mortality rate of 20–30%. To better study the invasion process of staphylococcus aureus, we conducted a study in human endothelial cells to try to find a link between the infection process and bacteremia at the molecular level.MethodsIn this study, the datasets GSE13736, GSE82036 were analyzed using R software to identify differentially expressed genes. Only the infection samples of four different strains had differential gene expression compared to the control samples. Then the GO analysis and KEGG analysis were conducted to construct a protein-protein interaction (PPI) network which shows the interaction and influence relationship between these differential genes. Finally, the central gene of the selected CytoHubba plug-in was verified using GraphPad Prism 8.ResultsThere were 421 differential genes in the Strain 6850, including 64 up-regulated and 357 down-regulated; There were 319 differential genes in the Strain 8325-4, including 14 up-regulated and 305 down-regulated. There were 90 differential genes in the Strain K70058396, including 12 up-regulated and 78 down-regulated. There were 876 differential genes in the Strain K1801/10, accompanied by 261 up-regulated and 615 down-regulated. An analysis of GO and KEGG revealed that these differentially expressed genes were significantly enriched in pathways associated with immune response and cytokines; Verification of the hub gene can provide a molecular basis for studying the relationship between invasive endothelial infection and bacteremia.ConclusionsWe found specific gene expression patterns in endothelial cells in response to infection with Strain K70058396, and these central genes and their expression products (RSAD2, DDX58, IFITT3, and IFIH1) play a key role in this process of infection.

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