scholarly journals Similarities in Gene Expression Profiles duringIn VitroAging of Primary Human Embryonic Lung and Foreskin Fibroblasts

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Shiva Marthandan ◽  
Steffen Priebe ◽  
Mario Baumgart ◽  
Marco Groth ◽  
Alessandro Cellerino ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Replicative Senescence ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Cellular Aging ◽  
Embryonic Lung ◽  
Human Embryonic Lung ◽  
Time Points

Replicative senescence is of fundamental importance for the process of cellular aging, since it is a property of most of our somatic cells. Here, we elucidated this process by comparing gene expression changes, measured by RNA-seq, in fibroblasts originating from two different tissues, embryonic lung (MRC-5) and foreskin (HFF), at five different time points during their transition into senescence. Although the expression patterns of both fibroblast cell lines can be clearly distinguished, the similar differential expression of an ensemble of genes was found to correlate well with their transition into senescence, with only a minority of genes being cell line specific. Clustering-based approaches further revealed common signatures between the cell lines. Investigation of the mRNA expression levels at various time points during the lifespan of either of the fibroblasts resulted in a number of monotonically up- and downregulated genes which clearly showed a novel strong link to aging and senescence related processes which might be functional. In terms of expression profiles of differentially expressed genes with age, common genes identified here have the potential to rule the transition into senescence of embryonic lung and foreskin fibroblasts irrespective of their different cellular origin.

scholarly journals Differential gene expression patterns in vein regions susceptible versus resistant to neointimal hyperplasia

2018 ◽  
Vol 50 (8) ◽  
pp. 615-627
Author(s):  
Sun Hyung Kwon ◽  
Li Li ◽  
Christi M. Terry ◽  
Yan-Ting Shiu ◽  
Philip J. Moos ◽  
...  
Keyword(s):  
Gene Expression ◽  
Vein Graft ◽  
Expression Profiles ◽  
Neointimal Hyperplasia ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Yield Potential ◽  
Microarray Gene Expression ◽  
Graft Placement ◽  
Time Points

Arteriovenous hemodialysis graft (AVG) stenosis results in thrombosis and AVG failure, but prevention of stenosis has been unsuccessful due in large part to our limited understanding of the molecular processes involved in neointimal hyperplasia (NH) formation. AVG stenosis develops chiefly as a consequence of highly localized NH formation in the vein-graft anastomosis region. Surprisingly, the vein region just downstream of the vein-graft anastomosis (herein termed proximal vein region) is relatively resistant to NH. We hypothesized that the gene expression profiles of the NH-prone and NH-resistant regions will be different from each other after graft placement, and analysis of their genomic profiles may yield potential therapeutic targets to prevent AVG stenosis. To test this, we evaluated the vein-graft anastomosis (NH-prone) and proximal vein (NH-resistant) regions in a porcine model of AVG stenosis with a porcine microarray. Gene expression changes in these two distinct vein regions, relative to the gene expression in unoperated control veins, were examined at early (5 days) and later (14 days) time points following graft placement. Global genomic changes were much greater in the NH-prone region than in the NH-resistant region at both time points. In the NH-prone region, genes related to regulation of cell proliferation and osteo-/chondrogenic vascular remodeling were most enriched among the significantly upregulated genes, and genes related to smooth muscle phenotype were significantly downregulated. These results provide insights into the spatial and temporal genomic modulation underlying NH formation in AVG and suggest potential therapeutic strategies to prevent and/or limit AVG stenosis.

Abstract P744: Gene Transcript Clusters Distinguish Time-Dependent Expression Patterns in Monocytes, Neutrophils and Whole Blood After Ischemic Stroke Injury

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Paulina Carmona-Mora ◽  
Glen C Jickling ◽  
Xinhua Zhan ◽  
Marisa Hakoupian ◽  
Heather Hull ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ischemic Stroke ◽  
Whole Blood ◽  
Temporal Dynamics ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Time Dependent ◽  
Gene Transcript ◽  
Time Points

Introduction: After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. We previously showed that peripheral blood cells display different gene expression profiles after IS and these transcriptional programs reflect the changes in immune processes in response to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of the changes of molecular and cellular pathways involved in acute brain injury. Methods: We analyzed the transcriptomic profiles of 33 IS patients in isolated monocytes, neutrophils and whole blood. RNA-sequencing was performed on all the stroke samples as well as 12 controls with vascular risk factors (diabetes and/or hypertension and/or hypercholesterolemia). To identify differentially expressed genes, subjects were split into time points (TPs) from stroke onset (TP1= 0-24 h; TP2= 24-48 h; and TP3= > 48 h), and controls were assigned TP0. A linear regression model including time and the interaction of diagnosis x TP with cutoff of p<0.02 and fold-change>|1.2| was used. Time dependent changes were analyzed using artificial neural networks to identify clusters of genes that behave in a similar way across TPs. Results: Unique patterns of temporal expression were distinguished for the three sample types. These include genes not expressed in TP0 that peak only within the first 24 h, others that peak or decrease in TP2 and TP3, and more complex patterns. Genes that peak at TP1 in monocytes and neutrophils are related to cell adhesion and leukocyte differentiation/migration, respectively. Early peaks in whole blood occur in genes related to transcriptional regulation. In monocytes, interleukin pathways are enriched across all TPs, whereas there is a trend of suppression after 24 h in neutrophils. The inflammasome pathway is enriched in the earlier TPs in neutrophils, while not enriched in monocytes until over 48 hours. Conclusion: Our analyses on gene expression dynamics and cluster patterns allow identification of key genes and pathways at different time points following ischemic injury that are valuable as IS biomarkers and may be possible treatment targets.

scholarly journals Molecular Characterization of Putative Chordoma Cell Lines

Sarcoma ◽  
2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Silke Brüderlein ◽  
Joshua B. Sommer ◽  
Paul S. Meltzer ◽  
Sufeng Li ◽  
Takuya Osada ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Line ◽  
Cell Lines ◽  
Copy Number ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Cancer Cell Line ◽  
Copy Number Variations ◽  
Genetic Changes

Immortal tumor cell lines are an important model system for cancer research, however, misidentification and cross-contamination of cell lines are a common problem. Seven chordoma cell lines are reported in the literature, but none has been characterized in detail. We analyzed gene expression patterns and genomic copy number variations in five putative chordoma cell lines (U-CH1, CCL3, CCL4, GB60, and CM319). We also created a new chordoma cell line, U-CH2, and provided genotypes for cell lines for identity confirmation. Our analyses revealed that CCL3, CCL4, and GB60 are not chordoma cell lines, and that CM319 is a cancer cell line possibly derived from chordoma, but lacking expression of key chordoma biomarkers. U-CH1 and U-CH2 both have gene expression profiles, copy number aberrations, and morphology consistent with chordoma tumors. These cell lines also harbor genetic changes, such as loss of p16, MTAP, or PTEN, that make them potentially useful models for studying mechanisms of chordoma pathogenesis and for evaluating targeted therapies.

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

scholarly journals Comprehensive analysis of the gene expression profiles in human gastric cancer cell lines

Oncogene ◽  
2002 ◽  
Vol 21 (42) ◽  
pp. 6549-6556 ◽  
Author(s):  
Jiafu Ji ◽  
Xin Chen ◽  
Suet Yi Leung ◽  
Jen-Tsan A Chi ◽  
Kent Man Chu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gastric Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Comprehensive Analysis ◽  
Human Gastric Cancer ◽  
Gastric Cancer Cell Lines

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

CFTR ΔF508 mutation has minimal effect on the gene expression profile of differentiated human airway epithelia

2005 ◽  
Vol 289 (4) ◽  
pp. L545-L553 ◽  
Author(s):  
Joseph Zabner ◽  
Todd E. Scheetz ◽  
Hakeem G. Almabrazi ◽  
Thomas L. Casavant ◽  
Jian Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cystic Fibrosis ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Primary Cultures ◽  
Gene Expression Profiles ◽  
Filter Method ◽  
Tissue Destruction ◽  
Airway Epithelia

Cystic fibrosis (CF) is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial chloride channel regulated by phosphorylation. Most of the disease-associated morbidity is the consequence of chronic lung infection with progressive tissue destruction. As an approach to investigate the cellular effects of CFTR mutations, we used large-scale microarray hybridization to contrast the gene expression profiles of well-differentiated primary cultures of human CF and non-CF airway epithelia grown under resting culture conditions. We surveyed the expression profiles for 10 non-CF and 10 ΔF508 homozygote samples. Of the 22,283 genes represented on the Affymetrix U133A GeneChip, we found evidence of significant changes in expression in 24 genes by two-sample t-test ( P < 0.00001). A second, three-filter method of comparative analysis found no significant differences between the groups. The levels of CFTR mRNA were comparable in both groups. There were no significant differences in the gene expression patterns between male and female CF specimens. There were 18 genes with significant increases and 6 genes with decreases in CF relative to non-CF samples. Although the function of many of the differentially expressed genes is unknown, one transcript that was elevated in CF, the KCl cotransporter (KCC4), is a candidate for further study. Overall, the results indicate that CFTR dysfunction has little direct impact on airway epithelial gene expression in samples grown under these conditions.

scholarly journals Comparative transcriptomics identifies differences in the regulation of the floral transition between Arabidopsis and Brassica rapa cultivars

2020 ◽  
Author(s):  
Alexander Calderwood ◽  
Jo Hepworth ◽  
Shannon Woodhouse ◽  
Lorelei Bilham ◽  
D. Marc Jones ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brassica Rapa ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Detailed Comparison ◽  
Developmental Time ◽  
Floral Transition ◽  
Gene Regulatory

AbstractThe timing of the floral transition affects reproduction and yield, however its regulation in crops remains poorly understood. Here, we use RNA-Seq to determine and compare gene expression dynamics through the floral transition in the model species Arabidopsis thaliana and the closely related crop Brassica rapa. A direct comparison of gene expression over time between species shows little similarity, which could lead to the inference that different gene regulatory networks are at play. However, these differences can be largely resolved by synchronisation, through curve registration, of gene expression profiles. We find that different registration functions are required for different genes, indicating that there is no common ‘developmental time’ to which Arabidopsis and B. rapa can be mapped through gene expression. Instead, the expression patterns of different genes progress at different rates. We find that co-regulated genes show similar changes in synchronisation between species, suggesting that similar gene regulatory sub-network structures may be active with different wiring between them. A detailed comparison of the regulation of the floral transition between Arabidopsis and B. rapa, and between two B. rapa accessions reveals different modes of regulation of the key floral integrator SOC1, and that the floral transition in the B. rapa accessions is triggered by different pathways, even when grown under the same environmental conditions. Our study adds to the mechanistic understanding of the regulatory network of flowering time in rapid cycling B. rapa under long days and highlights the importance of registration methods for the comparison of developmental gene expression data.

Spatial Transcriptomics analysis of uterine gene expression in enhancer of Zeste homolog 2 (Ezh2) conditional knockout mice

2021 ◽  
Author(s):  
Ana M Mesa ◽  
Jiude Mao ◽  
Theresa I Medrano ◽  
Nathan J Bivens ◽  
Alexander Jurkevich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Epithelial Cells ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Reproductive Organs ◽  
Conditional Knockout ◽  
Estrogen Signaling ◽  
Uterine Epithelial Cell ◽  
Stromal Tissue

Abstract Histone proteins undergo various modifications that alter chromatin structure, including addition of methyl groups. Enhancer of homolog 2 (EZH2), is a histone methyltransferase that methylates lysine residue 27, and thereby, suppresses gene expression. EZH2 plays integral role in the uterus and other reproductive organs. We have previously shown that conditional deletion of uterine EZH2 results in increased proliferation of luminal and glandular epithelial cells, and RNAseq analyses reveal several uterine transcriptomic changes in Ezh2 conditional (c) knockout (KO) mice that can affect estrogen signaling pathways. To pinpoint the origin of such gene expression changes, we used the recently developed spatial transcriptomics (ST) method with the hypotheses that Ezh2cKO mice would predominantly demonstrate changes in epithelial cells and/or ablation of this gene would disrupt normal epithelial/stromal gene expression patterns. Uteri were collected from ovariectomized adult WT and Ezh2cKO mice and analyzed by ST. Asb4, Cxcl14, Dio2, and Igfbp5 were increased, Sult1d1, Mt3, and Lcn2 were reduced in Ezh2cKO uterine epithelium vs. WT epithelium. For Ezh2cKO uterine stroma, differentially expressed key hub genes included Cald1, Fbln1, Myh11, Acta2, and Tagln. Conditional loss of uterine Ezh2 also appears to shift the balance of gene expression profiles in epithelial vs. stromal tissue toward uterine epithelial cell and gland development and proliferation, consistent with uterine gland hyperplasia in these mice. Current findings provide further insight into how EZH2 may selectively affect uterine epithelial and stromal compartments. Additionally, these transcriptome data might provide the mechanistic understanding and valuable biomarkers for human endometrial disorders with epigenetic underpinnings.

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

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