Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Identification of Notch targets in human endothelial cells

ABSTRACT: Human umbilical vein endothelial cells (HUVECs) were transduced with either MIY-N1IC (Notch1 intracellular domain) or MIY vector control. The cells were sorted for YFP, and RNA was extracted using Trizol (Invitrogen) and analyzed by the Affymetrix Human Genome U133 Plus 2.0 Array. Results were analyzed using the GCRMA algorithm to identify genes with a minimum of 2-fold induction or reduction. This global gene expression study was used to identify Notch targets in the endothelium. Expression profiling of primary HUVECs transduced with N1IC. HUVEC primary cells were isolated, transduced with N1IC or vector (MIY) control in three biological replicates, and evaluated on Affymetrix Human Genome U133 Plus 2.0 arrays.

ORGANISM(S): Homo sapiens

SUBMITTER: Alex Chang

PROVIDER: E-GEOD-29850 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Notch initiates the endothelial-to-mesenchymal transition in the atrioventricular canal through autocrine activation of soluble guanylyl cyclase.

Chang Alex C Y AC Fu YangXin Y Garside Victoria C VC Niessen Kyle K Chang Linda L Fuller Megan M Setiadi Audi A Smrz Justin J Kyle Alastair A Minchinton Andrew A Marra Marco M Hoodless Pamela A PA Karsan Aly A

Developmental cell 20110801 2

The heart is the most common site of congenital defects, and valvuloseptal defects are the most common of the cardiac anomalies seen in the newborn. The process of endothelial-to-mesenchymal transition (EndMT) in the cardiac cushions is a required step during early valve development, and Notch signaling is required for this process. Here we show that Notch activation induces the transcription of both subunits of the soluble guanylyl cyclase (sGC) heterodimer, GUCY1A3 and GUCY1B3, which form the ...[more]

PMID: 21839921

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Project description:The needs for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering. In this work we establish proof-of-concept that whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,873 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being non-silent (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function. With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at www.sysbio.se/cenpk. Keywords: Two strains and two different carbon sources Two conditions (glucose and galactose) with two biological replicates for S. cerevisiae strains S288c and CEN.PK113-7D

Dataset Information

Identification of Notch targets in human endothelial cells

Publications

Notch initiates the endothelial-to-mesenchymal transition in the atrioventricular canal through autocrine activation of soluble guanylyl cyclase.

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