Dataset Information

A multi-resource data integration approach: identification of candidate genes regulating cell proliferation during neocortical development.

ABSTRACT: Neurons of the mammalian neocortex are produced by proliferating cells located in the ventricular zone (VZ) lining the lateral ventricles. This is a complex and sequential process, requiring precise control of cell cycle progression, fate commitment and differentiation. We have analyzed publicly available databases from mouse and human to identify candidate genes that are potentially involved in regulating early neocortical development and neurogenesis. We used a mouse in situ hybridization dataset (The Allen Institute for Brain Science) to identify 13 genes (Cdon, Celsr1, Dbi, E2f5, Eomes, Hmgn2, Neurog2, Notch1, Pcnt, Sox3, Ssrp1, Tead2, Tgif2) with high correlation of expression in the proliferating cells of the VZ of the neocortex at early stages of development (E15.5). We generated a similar human brain network using microarray and RNA-seq data (BrainSpan Atlas) and identified 407 genes with high expression in the developing human VZ and subventricular zone (SVZ) at 8-9 post-conception weeks. Seven of the human genes were also present in the mouse VZ network. The human and mouse networks were extended using available genetic and proteomic datasets through GeneMANIA. A gene ontology search of the mouse and human networks indicated that many of the genes are involved in the cell cycle, DNA replication, mitosis and transcriptional regulation. The reported involvement of Cdon, Celsr1, Dbi, Eomes, Neurog2, Notch1, Pcnt, Sox3, Tead2, and Tgif2 in neural development or diseases resulting from the disruption of neurogenesis validates these candidate genes. Taken together, our knowledge-based discovery method has validated the involvement of many genes already known to be involved in neocortical development and extended the potential number of genes by 100's, many of which are involved in functions related to cell proliferation but others of which are potential candidates for involvement in the regulation of neocortical development.

SUBMITTER: Vied CM

PROVIDER: S-EPMC4139594 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A multi-resource data integration approach: identification of candidate genes regulating cell proliferation during neocortical development.

Vied Cynthia M CM Freudenberg Florian F Wang Yuting Y Raposo Alexandre A S F AA Feng David D Nowakowski Richard S RS

Frontiers in neuroscience 20140821

Neurons of the mammalian neocortex are produced by proliferating cells located in the ventricular zone (VZ) lining the lateral ventricles. This is a complex and sequential process, requiring precise control of cell cycle progression, fate commitment and differentiation. We have analyzed publicly available databases from mouse and human to identify candidate genes that are potentially involved in regulating early neocortical development and neurogenesis. We used a mouse in situ hybridization data ...[more]

PMID: 25191221

Similar Datasets

Project description:The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks--graphs with multiple node and edge types--for accomplishing both tasks. First we constructed a network with 18 node types--genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections--and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as influential mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data integration across multiple domains.

Dataset Information

A multi-resource data integration approach: identification of candidate genes regulating cell proliferation during neocortical development.

Publications

A multi-resource data integration approach: identification of candidate genes regulating cell proliferation during neocortical development.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets