Identification of Cell-Cycle Phases Using Neural Network and Steerable Filter Features

2006 ◽  
pp. 702-709
Author(s):  
Xiaodong Yang ◽  
Houqiang Li ◽  
Xiaobo Zhou ◽  
Stephen T. C. Wong
Keyword(s):  
Neural Network ◽  
Cell Cycle ◽  
Cell Cycle Phases ◽  
Steerable Filter

Hydrogen peroxide and quercetin induced changes on cell viability, apoptosis and oxidative stress in HepG2 cells

2020 ◽  
Vol 01 ◽  
Author(s):  
Ayşe Mine Yılmaz ◽  
Gökhan Biçim ◽  
Kübra Toprak ◽  
Betül Karademir Yılmaz ◽  
Irina Milisav ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Cell Cycle ◽  
Hydrogen Peroxide ◽  
Cell Viability ◽  
Hepg2 Cells ◽  
Proteasome Activity ◽  
Cell Cycle Phases ◽  
Induced Changes ◽  
And Oxidative Stress ◽  
Quercetin Treatment

Background: Different cellular responses influence the progress of cancer. In this study, we have investigated the effect of hydrogen peroxide and quercetin induced changes on cell viability, apoptosis and oxidative stress in human hepatocellular carcinoma (HepG2) cells. Methods: The effects of hydrogen peroxide and quercetin on cell viability, cell cycle phases and oxidative stress related cellular changes were investigated. Cell viability was assessed by WST-1 assay. Apoptosis rate, cell cycle phase changes and oxidative stress were measured by flow cytometry. Protein expressions of p21, p27, p53, NF-Kβ-p50 and proteasome activity were determined by Western blot and fluorometry, respectively. Results: Hydrogen peroxide and quercetin treatment resulted in decreased cell viability and increased apoptosis in HepG2 cells. Proteasome activity was increased by hydrogen peroxide but decreased by quercetin treatment. Conclusion: Both agents resulted in decreased p53 protein expression and increased cell death by different mechanisms regarding proteostasis and cell cycle phases.

miRNA-mediated Drug Repurposing Unveiled Potential Candidate Drugs for Prolactinoma Treatment

2021 ◽  
Author(s):  
Büşra Aydin ◽  
Sema Arslan ◽  
Fatih Bayraklı ◽  
Betül Karademir ◽  
Kazim Yalcin Arga
Keyword(s):  
Cell Cycle ◽  
Data Integration ◽  
Cell Viability ◽  
Drug Repurposing ◽  
Serum Prolactin ◽  
Drug Candidates ◽  
Mirna Level ◽  
Level Data ◽  
Cell Cycle Phases

Introduction: Prolactinomas, also called lactotroph adenomas, are the most encountered type of hormone-secreting pituitary neuroendocrine tumors (PitNET) in the clinic. The preferred first-line therapy is a medical treatment with dopamine agonists (DA), mainly cabergoline, to reduce serum prolactin levels, tumor volume, and mass effect. However, in some cases, patients have displayed DA-resistance with aggressive tumor behavior or are faced with recurrence after drug withdrawal. Also, currently used therapeutics have notorious side effects and impair the life quality of the patients. Methods: Since the amalgamation of clinical and laboratory data besides tumor histopathogenesis and transcriptional regulatory features of the tumor emerge to exhibit essential roles in the behavior and progression of prolactinomas, in this work, we integrated mRNA and microRNA (miRNA) level transcriptome data that exploit disease-specific signatures in addition to biological and pharmacological data to elucidate a rational prioritization of pathways and drugs in prolactinoma. Results: We identified eight drug candidates through drug repurposing based on mRNA-miRNA level data integration and evaluated their potential through in vitro assays in the MMQ cell line. Seven re-purposed drugs including 5-flourocytosine, nortriptyline, neratinib, puromycin, taxifolin, vorinostat, and zileuton were proposed as potential drug candidates for the treatment of prolactinoma. We further hypothesized possible mechanisms of drug action on MMQ cell viability through analyzing PI3K/Akt signaling pathway and cell cycle arrest via flow cytometry and western blotting. Discussion: We presented the transcriptomic landscape of prolactinoma through miRNA and mRNA level data integration and proposed repurposed drug candidates based on this integration. We validated our findings through testing cell viability, cell cycle phases, and PI3K/Akt protein expressions. Effects of the drugs on cell cycle phases and inhibition of PI3K/Akt pathway by all drugs gave us promising output for further studies using these drugs in the treatment of prolactinoma. This is the first study that reports miRNA-mediated repurposed drugs for prolactinoma treatment via in vitro experiments.

Q-FIHC: Quantification of fluorescence immunohistochemistry to analysep63 isoforms and cell cycle phases in human limbal stem cells

2006 ◽  
Vol 69 (12) ◽  
pp. 983-991 ◽  
Author(s):  
Enzo Di Iorio ◽  
Vanessa Barbaro ◽  
Stefano Ferrari ◽  
Claudio Ortolani ◽  
Michele De Luca ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Stem Cells ◽  
Limbal Stem Cells ◽  
Cell Cycle Phases ◽  
Fluorescence Immunohistochemistry

scholarly journals Continued initiation of DNA synthesis in arginine-deprived Chinese hamster ovary cells.

1977 ◽  
Vol 73 (1) ◽  
pp. 200-205 ◽  
Author(s):  
A S Weissfeld ◽  
H Rouse
Keyword(s):  
Cell Cycle ◽  
Dna Synthesis ◽  
Chinese Hamster Ovary ◽  
Cho Cells ◽  
Chinese Hamster Ovary Cells ◽  
Chinese Hamster ◽  
Cell Multiplication ◽  
Ovary Cells ◽  
Cell Cycle Phases

When exponentially growing CHO cells were deprived of arginine (Arg), cell multiplication ceased after 12 h, but initiation of DNA synthesis continued: after 48 h of starvation with continuous [3H]thymidine exposure, 85% of the population had incorporated label, as detected autoradiographically. Consideration of the distribution of exponential cells in the various cell cycle phases leads to a calculation that most cells in G1 at the time that Arg was removed, as well as those in S, engaged in some DNA synthesis during starvation. In contrast, isoleucine (Ile)-starved cells did not initiate DNA synthesis, as has been reported by others. Experiments with cells synchronized by mitotic selection confirmed this difference in Arg- and Ile- deprived behavior, but also showed that cells which underwent the mitosis leads to G1 transition during Arg starvation remained arrested in G1 (G0?). The results suggest that Arg-deprived cells continue to maintain some proliferative function(s) while Ile-deprived cells do not.

scholarly journals Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

2017 ◽  
Author(s):  
Anthony Szedlak ◽  
Spencer Sims ◽  
Nicholas Smith ◽  
Giovanni Paternostro ◽  
Carlo Piermarocchi
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Cell Cycle ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Expression Data ◽  
Time Series Gene Expression ◽  
Human Cervical Cancer

AbstractModern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.Author SummaryCell cycle – the process in which a parent cell replicates its DNA and divides into two daughter cells – is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the “edge of chaos” is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

scholarly journals Analysis of cell cycle phases and progression in cultured mammalian cells

Methods ◽  
2007 ◽  
Vol 41 (2) ◽  
pp. 143-150 ◽  
Author(s):  
Christoph Schorl ◽  
John M. Sedivy
Keyword(s):  
Cell Cycle ◽  
Mammalian Cells ◽  
Cell Cycle Phases
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