Network reconstruction using knock-out and over-expression data

2013 ◽  
Author(s):  
David Hayden ◽  
Ye Yuan ◽  
Jorge Goncalves
Keyword(s):  
Network Reconstruction ◽  
Expression Data ◽  
Knock Out ◽  
Over Expression

Gene co-expression network reconstruction: a review on computational methods for inferring functional information from plant-based expression data

2017 ◽  
Vol 11 (2) ◽  
pp. 71-86 ◽  
Author(s):  
Abbasali Emamjomeh ◽  
Elham Saboori Robat ◽  
Javad Zahiri ◽  
Mahmood Solouki ◽  
Pegah Khosravi
Keyword(s):  
Computational Methods ◽  
Network Reconstruction ◽  
Expression Data ◽  
Functional Information

scholarly journals NAMPT Over-Expression Recapitulates the BRAF Inhibitor Resistant Phenotype Plasticity in Melanoma

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3855
Author(s):  
Valentina Audrito ◽  
Vincenzo Gianluca Messana ◽  
Enrico Moiso ◽  
Nicoletta Vitale ◽  
Francesca Arruga ◽  
...  
Keyword(s):  
Side Population ◽  
Braf Inhibitor ◽  
The Cancer Genome Atlas ◽  
Nicotinamide Phosphoribosyltransferase ◽  
Knock Out ◽  
Mesenchymal Transition ◽  
Over Expression ◽  
Resistant Phenotype ◽  
Phenotype Plasticity

Serine–threonine protein kinase B-RAF (BRAF)-mutated metastatic melanoma (MM) is a highly aggressive type of skin cancer. Treatment of MM patients using BRAF/MEK inhibitors (BRAFi/MEKi) eventually leads to drug resistance, limiting any clinical benefit. Herein, we demonstrated that the nicotinamide adenine dinucleotide (NAD)-biosynthetic enzyme nicotinamide phosphoribosyltransferase (NAMPT) is a driving factor in BRAFi resistance development. Using stable and inducible NAMPT over-expression systems, we showed that forced NAMPT expression in MM BRAF-mutated cell lines led to increased energy production, MAPK activation, colony-formation capacity, and enhance tumorigenicity in vivo. Moreover, NAMPT over-expressing cells switched toward an invasive/mesenchymal phenotype, up-regulating expression of ZEB1 and TWIST, two transcription factors driving the epithelial to mesenchymal transition (EMT) process. Consistently, within the NAMPT-overexpressing cell line variants, we observed an increased percentage of a rare, drug-effluxing stem cell-like side population (SP) of cells, paralleled by up-regulation of ABCC1/MRP1 expression and CD133-positive cells. The direct correlation between NAMPT expression and gene set enrichments involving metastasis, invasiveness and mesenchymal/stemness properties were verified also in melanoma patients by analyzing The Cancer Genome Atlas (TCGA) datasets. On the other hand, CRISPR/Cas9 full knock-out NAMPT BRAFi-resistant MM cells are not viable, while inducible partial silencing drastically reduces tumor growth and aggressiveness. Overall, this work revealed that NAMPT over-expression is both necessary and sufficient to recapitulate the BRAFi-resistant phenotype plasticity.

scholarly journals Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection

2011 ◽  
Vol 1 (1) ◽  
pp. 27 ◽  
Author(s):  
Konstantina Dimitrakopoulou ◽  
Charalampos Tsimpouris ◽  
George Papadopoulos ◽  
Claudia Pommerenke ◽  
Esther Wilk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Influenza A ◽  
Gene Network ◽  
Network Reconstruction ◽  
Expression Data ◽  
H1n1 Infection ◽  
Influenza A H1n1 ◽  
Gene Network Reconstruction

ICN: a normalization method for gene expression data considering the over-expression of informative genes

2016 ◽  
Vol 12 (10) ◽  
pp. 3057-3066 ◽  
Author(s):  
Lixin Cheng ◽  
Xuan Wang ◽  
Pak-Kan Wong ◽  
Kwan-Yeung Lee ◽  
Le Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Normalization Method ◽  
Expression Data ◽  
Over Expression ◽  
Global Increase ◽  
Microarray Studies

The global increase of gene expression has been frequently established in cancer microarray studies.

scholarly journals Addressing confounding artifacts in reconstruction of gene co-expression networks

2017 ◽  
Author(s):  
Princy Parsana ◽  
Claire Ruberman ◽  
Andrew E. Jaffe ◽  
Michael C. Schatz ◽  
Alexis Battle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Network Inference ◽  
Principal Component ◽  
Network Reconstruction ◽  
Common Interest ◽  
Expression Data ◽  
Reconstruction Algorithms ◽  
Wide Range ◽  
False Discoveries

AbstractBackgroundGene co-expression networks capture diverse biological relationships between genes, and are important tools in predicting gene function and understanding disease mechanisms. Functional interactions between genes have not been fully characterized for most organisms, and therefore reconstruction of gene co-expression networks has been of common interest in a variety of settings. However, methods routinely used for reconstruction of gene co-expression networks do not account for confounding artifacts known to affect high dimensional gene expression measurements.ResultsIn this study, we show that artifacts such as batch effects in gene expression data confound commonly used network reconstruction algorithms. Both theoretically and empirically, we demonstrate that removing the effects of top principal components from gene expression measurements prior to network inference can reduce false discoveries, especially when well annotated technical covariates are not available. Using expression data from the GTEx project in multiple tissues and hundreds of individuals, we show that this latent factor residualization approach often reduces false discoveries in the reconstructed networks.ConclusionNetwork reconstruction is susceptible to confounders that affect measurements of gene expression. Even controlling for major individual known technical covariates fails to fully eliminate confounding variation from the data. In studies where a wide range of annotated technical factors are measured and available, correcting gene expression data with multiple covariates can also improve network reconstruction, but such extensive annotations are not always available. Our study shows that principal component correction, which does not depend on study design or annotation of all relevant confounders, removes patterns of artifactual variation and improves network reconstruction in both simulated data, and gene expression data from GTEx project. We have implemented our PC correction approach in the Bioconductor package sva which can be used prior to network reconstruction with a range of methods.

scholarly journals A novel method of gene regulatory network structure inference from gene knock-out expression data

2019 ◽  
Vol 24 (4) ◽  
pp. 446-455 ◽  
Author(s):  
Xiang Chen ◽  
Min Li ◽  
Ruiqing Zheng ◽  
Siyu Zhao ◽  
Fang-Xiang Wu ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Network Structure ◽  
Regulatory Network ◽  
Expression Data ◽  
Knock Out ◽  
Novel Method ◽  
Gene Regulatory

Heparanase Activation of the Coagulation System in a Mice Sepsis Model

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 378-378
Author(s):  
Israel Henig ◽  
Elena Axelman ◽  
Benjamin Brenner ◽  
Israel Vlodavsky ◽  
Yona Nadir
Keyword(s):  
Tumor Metastasis ◽  
Conflicts Of Interest ◽  
Factor Xa ◽  
Factor Vii ◽  
Coagulation System ◽  
Knock Out ◽  
Over Expression ◽  
Plasma Factor ◽  
Knock Out Mice ◽  
D Dimer

Abstract Abstract 378 Background: Heparanase that is abundant in platelets and neutrophils is implicated in cell invasion, tumor metastasis and angiogenesis. Recently, we demonstrated that heparanase is directly involved in the regulation of the hemostatic system. Heparanase was shown to form a complex and enhance tissue factor (TF) activity, resulting in increased factor Xa production (Nadir et al, Haematologica, 2010). In the present study, the effect of heparanase on sepsis was investigated. Methods: ICR mice, heparanase knock-out mice and heparanase over-expression mice were studied. Sepsis was induced by intra-peritoneal injection of lipopolysaccharides (LPS). Four hours after injection, blood was obtained via temple plexus puncture followed by mice sacrificing. Levels of thrombin-antithrombin (TAT), D-Dimer and IL-6 were tested using ELISA. Plasma factor VII levels were evaluated using the Western blot analysis and fibrinogen levels in the lung, kidney and spleen were estimated by immunostaining. Results: Intra-peritoneal injection of heparanase (0.5μg/mg) compared to PBS injection, increased the TAT (p<0.05) and D-Dimer (p<0.05) levels, which were similar to the ones induced by LPS (5μg/mg), although the IL-6 levels did not rise and mice did not show signs of illness. Moreover, heparanase injection half an hour following LPS administration resulted in reduced levels of TAT, D-Dimer, fibrinogen and of factor VII (p<0.001), increased levels of IL-1 (p<0.001) and profuse blood leak from puncture, compatible with severe disseminated intravascular coagulation (DIC). Similarly, injection of LPS to heparanase knock-out mice led to lower levels of TAT (p<0.001), D-Dimer (p<0.001) and IL-6 (p<0.001) compared to control mice, while injection of LPS to heparanase over-expression mice resulted in DIC. Conclusions: Heparanase is found to induce activation of the coagulation system without inflammatory response. It also contributes to laboratory enhancement of sepsis resulting in DIC. Inhibitors of heparanase may potentially attenuate morbidity and mortality in sepsis. Disclosures: No relevant conflicts of interest to declare.

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