Temporal Effects of Impact on Articular Cartilage Cell Death, Gene Expression, Matrix Biochemistry, and Biomechanics

2008 ◽  
Vol 36 (5) ◽  
pp. 780-792 ◽  
Author(s):  
Roman M. Natoli ◽  
C. Corey Scott ◽  
Kyriacos A. Athanasiou
Keyword(s):  
Gene Expression ◽  
Cell Death ◽  
Articular Cartilage ◽  
Cartilage Cell ◽  
Temporal Effects ◽  
Gene Expression Matrix ◽  
Cell Death Gene ◽  
Expression Matrix ◽  
Articular Cartilage Cell

The gene expression matrix: Towards the extraction of genetic network architectures

1997 ◽  
Vol 30 (3) ◽  
pp. 1815-1824 ◽  
Author(s):  
Roland Somogyi ◽  
Stefanie Fuhrman ◽  
Manor Askenazi ◽  
Andy Wuensche
Keyword(s):  
Gene Expression ◽  
Genetic Network ◽  
Network Architectures ◽  
Gene Expression Matrix ◽  
Expression Matrix

Development of Immortalized Human Articular Cartilage Cell Lines

1993 ◽  
pp. 267-272 ◽  
Author(s):  
G. Verbruggen ◽  
A. M. Malfait ◽  
K. F. Almgvist ◽  
E. M. Veys ◽  
S. Thenet ◽  
...  
Keyword(s):  
Articular Cartilage ◽  
Cell Lines ◽  
Human Articular Cartilage ◽  
Cartilage Cell ◽  
Articular Cartilage Cell

Panax ginseng C.A. Meyer modulates the levels of MMP3 in S12 murine articular cartilage cell line

2009 ◽  
Vol 124 (3) ◽  
pp. 397-403 ◽  
Author(s):  
Joon-Shik Shin ◽  
Namhee Park ◽  
Jehyeon Ra ◽  
Yangseok Kim ◽  
Minkyu Shin ◽  
...  
Keyword(s):  
Articular Cartilage ◽  
Cell Line ◽  
Panax Ginseng ◽  
Cartilage Cell ◽  
Articular Cartilage Cell

Cell death gene expression profile: Role of RIPK2 in dengue virus-mediated apoptosis

2011 ◽  
Vol 156 (1-2) ◽  
pp. 25-34 ◽  
Author(s):  
Atthapan Morchang ◽  
Umpa Yasamut ◽  
Janjuree Netsawang ◽  
Sansanee Noisakran ◽  
Wiyada Wongwiwat ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Death ◽  
Dengue Virus ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Cell Death Gene

scholarly journals RecBic: a fast and accurate algorithm recognizing trend-preserving biclusters

2020 ◽  
Vol 36 (20) ◽  
pp. 5054-5060
Author(s):  
Xiangyu Liu ◽  
Di Li ◽  
Juntao Liu ◽  
Zhengchang Su ◽  
Guojun Li
Keyword(s):  
Gene Expression ◽  
Biological Data ◽  
Supplementary Information ◽  
Gene Expression Matrix ◽  
Real Gene ◽  
Powerful Approach ◽  
Number Of Genes ◽  
Functional Patterns ◽  
Robustness To Noise ◽  
Expression Matrix

Abstract Motivation Biclustering has emerged as a powerful approach to identifying functional patterns in complex biological data. However, existing tools are limited by their accuracy and efficiency to recognize various kinds of complex biclusters submerged in ever large datasets. We introduce a novel fast and highly accurate algorithm RecBic to identify various forms of complex biclusters in gene expression datasets. Results We designed RecBic to identify various trend-preserving biclusters, particularly, those with narrow shapes, i.e. clusters where the number of genes is larger than the number of conditions/samples. Given a gene expression matrix, RecBic starts with a column seed, and grows it into a full-sized bicluster by simply repetitively comparing real numbers. When tested on simulated datasets in which the elements of implanted trend-preserving biclusters and those of the background matrix have the same distribution, RecBic was able to identify the implanted biclusters in a nearly perfect manner, outperforming all the compared salient tools in terms of accuracy and robustness to noise and overlaps between the clusters. Moreover, RecBic also showed superiority in identifying functionally related genes in real gene expression datasets. Availability and implementation Code, sample input data and usage instructions are available at the following websites. Code: https://github.com/holyzews/RecBic/tree/master/RecBic/. Data: http://doi.org/10.5281/zenodo.3842717. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Single-cell RNA-seq data reveals TNBC tumor heterogeneity through characterizing subclone compositions and proportions

2019 ◽  
Author(s):  
Weida Wang ◽  
Jinyuan Xu ◽  
Shuyuan Wang ◽  
Peng Xia ◽  
Li Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Tumor Heterogeneity ◽  
Single Cells ◽  
Rna Seq ◽  
Biological Functions ◽  
Gene Markers ◽  
Gene Expression Matrix ◽  
Deconvolution Algorithm ◽  
Expression Matrix

AbstractUnderstanding subclonal architecture and their biological functions poses one of the key challenges to deeply portray and investigative the cause of triple-negative breast cancer (TNBC). Here we combine single-cell and bulk sequencing data to analyze tumor heterogeneity through characterizing subclone compositions and proportions. Based on sing-cell RNA-seq data (GSE118389) we identified five distinct cell subpopulations and characterized their biological functions based on their gene markers. According to the results of functional annotation, we found that C1 and C2 are related to immune functions, while C5 is related to programmed cell death. Then based on subclonal basis gene expression matrix, we applied deconvolution algorithm on TCGA tissue RNA-seq data and observed that microenvironment is diverse among TNBC subclones, especially C1 is closely related to T cells. What’s more, we also found that high C5 proportions would led to poor survival outcome, log-rank test p-value and HR [95%CI] for five years overall survival in GSE96058 dataset were 0.0158 and 2.557 [1.160-5.636]. Collectively, our analysis reveals both intra-tumor and inter-tumor heterogeneity and their association with subclonal microenvironment in TNBC (subclone compositions and proportions), and uncovers the organic combination of subclones dictating poor outcomes in this disease.HighlightsWe applied deconvolution algorithm on subclonal basis gene expression matrix to link single cells and bulk tissue together.

scholarly journals A Cut/cohesin axis alters the chromatin landscape to facilitate neuroblast death

2018 ◽  
Author(s):  
Richa Arya ◽  
Seda Gyonjyan ◽  
Katherine Harding ◽  
Tatevik Sarkissian ◽  
Ying Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Cell Death ◽  
Nervous System ◽  
Neural Stem Cells ◽  
Open Chromatin ◽  
Chromatin Conformation ◽  
Precise Control ◽  
Cell Death Gene ◽  
Cell Death Genes

AbstractPrecise control of cell death in the nervous system is essential for development. Spatial and temporal factors activate the death of Drosophila neural stem cells (neuroblasts) by controlling the transcription of multiple cell death genes through a shared enhancer, enh1. The activity of enh1 is controlled by abdominalA and Notch, but additional inputs are needed for proper specificity. Here we show that the Cut DNA binding protein is required for neuroblast death, acting downstream of enh1. In the nervous system, Cut promotes an open chromatin conformation in the cell death gene locus, allowing cell death gene expression in response to abdominalA. We demonstrate a temporal increase in global H3K27me3 levels in neuroblasts, which is enhanced by cut knockdown. Furthermore, cut regulates the expression of the cohesin subunit Stromalin in the nervous system. The cohesin components Stromalin and NippedB are required for neuroblast death, and knockdown of Stromalin increases repressive histone modifications in neuroblasts. Thus Cut and cohesin regulate apoptosis in the developing nervous system by altering the chromatin landscape.Summary statementCut regulates the programmed death of neural stem cells by altering cohesin levels and promoting a more open chromatin conformation to allow cell death gene expression.

Sign in / Sign up

Export Citation Format

Share Document