Genomics

Dataset Information

0

Expression data from wild type and calreticulin deficient murine embryonic stem cells


ABSTRACT: Primordial genomic challenge compromises embryonic development and survival, and surveillance of deployed transcriptional programs may provide an early opportunity to forecast phenotype abnormalities. Here, comparisons between wild-type and calreticulin-ablated embryonic stem cells revealed transcriptome shifts precipitated by calreticulin loss. Bioinformatic analysis identified down and up-regulation in 1187 and 418 genes, respectively. Cardiovascular development precedes other organogenic programs, and examination of cardiogenic genes revealed a map of calreticulin-calibrated expression profiles that encompass the developmental regulators, Ccnd1, Ccnd2 and Notch1. Interrogation of primary function in the resolved network forecasted abnormalities during myocardial development. Whole embryo magnetic resonance imaging, verified by pathoanatomical analysis, diagnosed prominent ventricular septal defect. Correlation clustering and network resolution of probesets associated with protein folding/chaperoning and calcium handling demonstrated 14 and 19 genes, respectively, modulated by calreticulin deficiency. Calreticulin deletion provoked ontological re-prioritization of gene expression, molecular transport and protein trafficking that translated into multiple subcellular functional outcomes. Individual stem cell-derived cardiomyocytes lacking calreticulin demonstrated a disorganized contractile apparatus with mitochondrial paucity and architectural aberrations. Thus, bioinformatic deconvolution of primordial embryonic stem cell transcriptomes enables predictive phenotyping of defective developmental networks that coalesce from complex systems biology hierarchies. Keywords: Comparison of embryonic stem cell genomes between wild type and calreticulin knockouts

ORGANISM(S): Mus musculus

PROVIDER: GSE13805 | GEO | 2009/12/02

SECONDARY ACCESSION(S): PRJNA110571

REPOSITORIES: GEO

Similar Datasets

2022-02-04 | MSV000088780 | MassIVE
2013-03-21 | E-MTAB-1038 | biostudies-arrayexpress
2018-08-02 | MSV000082680 | GNPS
2022-10-19 | GSE196552 | GEO
2022-10-19 | GSE196551 | GEO
2016-08-22 | GSE80983 | GEO
2010-11-16 | E-GEOD-23322 | biostudies-arrayexpress
2018-06-28 | E-MTAB-6851 | biostudies-arrayexpress
2016-08-22 | GSE81175 | GEO
| PRJNA110571 | ENA