scholarly journals Aniridia-related Keratopathy Relevant Cell Signaling Pathways in Human Fetal Corneas

2021 ◽  
Author(s):  
Marta Sloniecka ◽  
Andre Vicente ◽  
Berit Bystrom ◽  
Jingxia Liu ◽  
Fatima Pedrosa-Domellof
Keyword(s):  
Gene Expression ◽  
Cell Signaling ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Sonic Hedgehog ◽  
Cell Signaling Pathway ◽  
Human Adult ◽  
Pathway Activation ◽  
Normal Human ◽  
Cell Signaling Pathways

Background: To study aniridia-related keratopathy (ARK) relevant cell signaling pathways (Notch1, Wnt/β-catenin, Sonic hedgehog (SHH) and mTOR) in normal human fetal corneas in comparison with normal human adult corneas. Results: 20 wg fetal and normal adult corneas showed similar staining patterns for Notch1, however 10-11 wg fetal corneas showed increased presence of Notch1. Numb and Dlk1 had an enhanced presence in the fetal corneas compared to the adult corneas. Fetal corneas showed stronger immunolabeling with antibodies against β-catenin, Wnt5a and Wnt7a, Gli1, Hes1, p-rpS6, and mTOR when compared to the adult corneas. Gene expression of Notch1, Wnt5A, Wnt7A, β-catenin, Hes1, mTOR and rps6 was higher in the 9-12 wg fetal corneas when compared to adult corneas. Conclusions: The cell signaling pathway differences found between human fetal and adult corneas were similar to those previously found in ARK corneas with the exception of Notch1. Analogous profiles of cell signaling pathway activation between human fetal corneas and ARK corneas suggests that there is a less differentiated host milieu in ARK.

scholarly journals Patterns of cell signaling pathway activation that characterize mammary development

Development ◽  
2008 ◽  
Vol 135 (14) ◽  
pp. 2403-2413 ◽  
Author(s):  
E. R. Andrechek ◽  
S. Mori ◽  
R. E. Rempel ◽  
J. T. Chang ◽  
J. R. Nevins
Keyword(s):  
Cell Signaling ◽  
Signaling Pathway ◽  
Mammary Development ◽  
Cell Signaling Pathway ◽  
Pathway Activation

scholarly journals Five patterns of cell signaling pathways associated with cell behavior

2020 ◽  
Author(s):  
Yuji Takeda ◽  
Kazuharu Kawano ◽  
Rui Ma ◽  
Shinichi Saitoh ◽  
Hironobu Asao
Keyword(s):  
Cell Signaling ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Cell Behavior ◽  
General Strategy ◽  
Signal Transducers ◽  
Cell Signaling Pathway ◽  
Flow Cytometric Data ◽  
Phosphorylation Level ◽  
A Cell

AbstractCell signaling pathway is complex systems. Here, we present a concept for a new approach to analyze cell signaling pathway associated with cell behavior. In theoretically, cell behavior is recognized by energy and fluctuation. In this study, we measured phosphorylation level of signal transducers in a cell and fluctuation of the phosphorylation level in the cell population using flow cytometry. Flow cytometric data of mean fluorescence intensity (MFI) and coefficient variation (CV) were considered to the energy and the fluctuation, respectively. Topologically, the changes of MFI and CV were categorized into five patterns (we tentatively named as attractive, subsequent, passive, counter, and negative arbiter). In this study, we clarified the relationship between the cell behavior and the five patterns. Furthermore, combining the five patterns can define the signaling pathways, such as simple activated signal, oscillating signal, regulatory signal, robust signal, or homeostatic signal. These observations provide a proof of concept for general strategy to use the five patterns for connection between cell signaling pathway and cell behavior.

Identification of distinctive patterns in cell signaling pathways in glioblastoma multiforme subtypes using gene expression TCGA data sets.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23162-e23162
Author(s):  
Konstantin Volyanskyy ◽  
Minghao Zhong ◽  
Payal Keswarpu ◽  
John T Fallon ◽  
Michael Paul Fanucchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Signaling ◽  
Signaling Pathways ◽  
Normal Tissue ◽  
Gene Selection ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Cell Signaling Pathways

e23162 Background: Cancer is characterized by a variety of heterogeneous genomic and transcriptomic patterns involving highly complex signaling biological pathways. The problem of identification of the factors driving tumor progression becomes even more challenging due to intricate interaction mechanisms between these pathways. Using novel approaches in machine learning, we demonstrate the ability to quantitatively describe characteristic signaling patterns in cancer based on transcriptomic data Methods: We used RNASeq data from 20531 genes in 174 samples of GBM from The Cancer Genome Atlas including 5 major histological subtypes – Classical, G-CIMP, Mesenchymal, Neural, and Proneural, anddeveloped predictive computational framework for molecular subtype differentiation from normal tissue relying on variance based gene selection and random forest algorithm. Results: We obtained a few key findings – (1) genes from cell signaling pathways alone differentiate each subtype from normal tissue with 100% accuracy; (2) predictive genes are specific to each subtype; (3) inferred pathway interactions are also specific to each subtype; (4) typically most of the predictive genes involved in signaling are down-regulated in tumor compared to normal tissue (MAPT, PRKCG, PDE2A, RYR2, ATP1B1, GRN1, GNAO1), however, in each subtype we observed a smaller subset of predictive genes which are highly up-regulated in tumor (ID3, FN1, JAG1, F2R, COL4A1, EDAR, CDK2, CDK4, MFNG, BIRC5, CCNB2). We detected and quantitatively evaluated characteristic signaling pathway involvement across the GBM subtypes for MAPK, RAP1, RAS, Notch, PI3K-Akt, mTOR, FoxO, Jak-STAT, Wnt, cAMP, and Calcium Signaling, providing a unique approximation for each subtype signaling profile. Conclusions: In this study, we identified gene expression profiles and associated signaling pathways for distinguishing GBM Multiforme subtypes from normal tissue. We observed and described a dense complex picture of interacting signaling pathways. The detected interactions may provide clinical insights and could be used to identify potential therapeutic targets, however, more research is needed to confirm this.

Comparison of HCV-associated gene expression and cell signaling pathways in cells with or without HCV replicon and in replicon-cured cells

2009 ◽  
Vol 45 (5) ◽  
pp. 523-536 ◽  
Author(s):  
Yuki Nishimura-Sakurai ◽  
Naoya Sakamoto ◽  
Kaoru Mogushi ◽  
Satoshi Nagaie ◽  
Mina Nakagawa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Signaling ◽  
Signaling Pathways ◽  
Cell Signaling Pathways ◽  
In Cells

scholarly journals Identification of hub genes and small-molecule compounds in medulloblastoma by integrated bioinformatic analyses

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8670
Author(s):  
Zhendong Liu ◽  
Ruotian Zhang ◽  
Zhenying Sun ◽  
Jiawei Yao ◽  
Penglei Yao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Cell Signaling ◽  
Signaling Pathway ◽  
Small Molecule ◽  
Pathway Analysis ◽  
Trichostatin A ◽  
Hub Genes ◽  
Cell Signaling Pathway ◽  
Small Molecule Drugs

Background Medulloblastoma (MB) is the most common intracranial malignant tumor in children. The genes and pathways involved in the pathogenesis of MB are relatively unknown. We aimed to identify potential biomarkers and small-molecule drugs for MB. Methods Gene expression profile data sets were obtained from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were identified using the Limma package in R. Functional annotation, and cell signaling pathway analysis of DEGs was carried out using DAVID and Kobas. A protein-protein interaction network was generated using STRING. Potential small-molecule drugs were identified using CMap. Result We identified 104 DEGs (29 upregulated; 75 downregulated). Gene ontology analysis showed enrichment in the mitotic cell cycle, cell cycle, spindle, and DNA binding. Cell signaling pathway analysis identified cell cycle, HIF-1 signaling pathway, and phospholipase D signaling pathway as key pathways. SYN1, CNTN2, FAIM2, MT3, and SH3GL2 were the prominent hub genes and their expression level were verified by RT-qPCR. Vorinostat, resveratrol, trichostatin A, pyrvinium, and prochlorperazine were identified as potential drugs for MB. The five hub genes may be targets for diagnosis and treatment of MB, and the small-molecule compounds are promising drugs for effective treatment of MB. Conclusion In this study we obtained five hub genes of MB, SYN1, CNTN2, FAIM2, MT3, and SH3GL2 were confirmed as hub genes. Meanwhile, Vorinostat, resveratrol, trichostatin A, pyrvinium, and prochlorperazine were identified as potential drugs for MB.

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