Microarray analysis of the fetal hippocampus in the Emx2 mutant

Unraveling a novel transcription factor code determining the human arterial-specific endothelial cell signature

Endothelial cells (ECs) lining arteries and veins have distinct molecular/functional signatures. The underlying regulatory mechanisms are incompletely understood. Here, we established a specific fingerprint of freshly isolated arterial and venous ECs from human umbilical cord comprising 64 arterial and 12 venous genes, representing distinct functions/pathways. Among the arterial genes were 8 transcription factors (TFs), including Notch target HEY2, the current "gold standard" determinant for arterial EC (aEC) specification. Culture abrogated differential gene expression in part due to gradual loss of canonical Notch activity and HEY2 expression. Notably, restoring HEY2 expression or Delta-like4-induced Notch signaling in cultured ECs only partially reinstated the aEC gene signature, whereas combined overexpression of the 8 TFs restored this fingerprint more robustly. Whereas some TFs stimulated few genes, others boosted a large proportion of arterial genes. Although there was some overlap and cross-regulation, the TFs largely complemented each other in regulating the aEC gene profile. Finally, overexpression of the 8 TFs in human umbilical vein ECs conveyed an arterial-like behavior upon their implantation in a Matrigel plug in vivo. Thus, our study shows that Notch signaling determines only part of the aEC signature and identifies additional novel and complementary transcriptional players in the complex regulation of human arteriovenous EC identity.

Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets

Background

Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality.

Methods

We report a comprehensive comparative evaluation of nine state-of-the art gene regulatory network inference methods encompassing the main algorithmic approaches (mutual information, correlation, partial correlation, random forests, support vector machines) using 38 simulated datasets and empirical serous papillary ovarian adenocarcinoma expression-microarray data. We then apply the best-performing method to infer normal and cancer networks. We assess the druggability of the proteins encoded by our predicted target genes using the CancerResource and PharmGKB webtools and databases.

Results

We observe large differences in the accuracy with which these methods predict the underlying gene regulatory network depending on features of the data, network size, topology, experiment type, and parameter settings. Applying the best-performing method (the supervised method SIRENE) to the serous papillary ovarian adenocarcinoma dataset, we infer and rank regulatory interactions, some previously reported and others novel. For selected novel interactions we propose testable mechanistic models linking gene regulation to cancer. Using network analysis and visualization, we uncover cross-regulation of angiogenesis-specific genes through three key transcription factors in normal and cancer conditions. Druggabilty analysis of proteins encoded by the 10 highest-confidence target genes, and by 15 genes with differential regulation in normal and cancer conditions, reveals 75% to be potential drug targets.

Conclusions

Our study represents a concrete application of gene regulatory network inference to ovarian cancer, demonstrating the complete cycle of computational systems biology research, from genome-scale data analysis via network inference, evaluation of methods, to the generation of novel testable hypotheses, their prioritization for experimental validation, and discovery of potential drug targets.

A review of current applications of mass spectrometry for neuroproteomics in epilepsy

The brain is unquestionably the most fascinating organ, and the hippocampus is crucial in memory storage and retrieval and plays an important role in stress response. In temporal lobe epilepsy (TLE), the seizure origin typically involves the hippocampal formation. Despite tremendous progress, current knowledge falls short of being able to explain its function. An emerging approach toward an improved understanding of the complex molecular mechanisms that underlie functions of the brain and hippocampus is neuroproteomics. Mass spectrometry has been widely used to analyze biological samples, and has evolved into an indispensable tool for proteomics research. In this review, we present a general overview of the application of mass spectrometry in proteomics, summarize neuroproteomics and systems biology-based discovery of protein biomarkers for epilepsy, discuss the methodology needed to explore the epileptic hippocampus proteome, and also focus on applications of ingenuity pathway analysis (IPA) in disease research. This neuroproteomics survey presents a framework for large-scale protein research in epilepsy that can be applied for immediate epileptic biomarker discovery and the far-reaching systems biology understanding of the protein regulatory networks. Ultimately, knowledge attained through neuroproteomics could lead to clinical diagnostics and therapeutics to lessen the burden of epilepsy on society.

Specific glial populations regulate hippocampal morphogenesis

The hippocampus plays an integral role in spatial navigation, learning and memory, and is a major site for adult neurogenesis. Critical to these functions is the proper organization of the hippocampus during development. Radial glia are known to regulate hippocampal formation, but their precise function in this process is yet to be defined. We find that in Nuclear Factor I b (Nfib)-deficient mice, a subpopulation of glia from the ammonic neuroepithelium of the hippocampus fail to develop. This results in severe morphological defects, including a failure of the hippocampal fissure, and subsequently the dentate gyrus, to form. As in wild-type mice, immature nestin-positive glia, which encompass all types of radial glia, populate the hippocampus in Nfib-deficient mice at embryonic day 15. However, these fail to mature into GLAST- and GFAP-positive glia, and the supragranular glial bundle is absent. In contrast, the fimbrial glial bundle forms, but alone is insufficient for proper hippocampal morphogenesis. Dentate granule neurons are present in the mutant hippocampus but their migration is aberrant, likely resulting from the lack of the complete radial glial scaffold usually provided by both glial bundles. These data demonstrate a role for Nfib in hippocampal fissure and dentate gyrus formation, and that distinct glial bundles are critical for correct hippocampal morphogenesis.

Regulatory pathway analysis by high-throughput in situ hybridization

Automated in situ hybridization enables the construction of comprehensive atlases of gene expression patterns in mammals. Such atlases can become Web-searchable digital expression maps of individual genes and thus offer an entryway to elucidate genetic interactions and signaling pathways. Towards this end, an atlas housing ∼1,000 spatial gene expression patterns of the midgestation mouse embryo was generated. Patterns were textually annotated using a controlled vocabulary comprising >90 anatomical features. Hierarchical clustering of annotations was carried out using distance scores calculated from the similarity between pairs of patterns across all anatomical structures. This process ordered hundreds of complex expression patterns into a matrix that reflects the embryonic architecture and the relatedness of patterns of expression. Clustering yielded 12 distinct groups of expression patterns. Because of the similarity of expression patterns within a group, members of each group may be components of regulatory cascades. We focused on the group containing Pax6, an evolutionary conserved transcriptional master mediator of development. Seventeen of the 82 genes in this group showed a change of expression in the developing neocortex of Pax6-deficient embryos. Electromobility shift assays were used to test for the presence of Pax6-paired domain binding sites. This led to the identification of 12 genes not previously known as potential targets of Pax6 regulation. These findings suggest that cluster analysis of annotated gene expression patterns obtained by automated in situ hybridization is a novel approach for identifying components of signaling cascades.

Signaling pathways drive biological processes with high specificity. Reductionist approaches such as mutagenesis provide one strategy to identity components of pathways. We used high throughput in situ hybridization to systematically map the spatiotemporal expression pattern of ∼1,000 developmental genes in the mouse embryo. The rich information collectively contained in these patterns was captured in annotation tables that were systematically mined using hierarchical clustering, resulting in 12 groups of genes with related expression patterns. We show that this process generates biologically meaningful, high-content information. The expression pattern of developmental master regulator Pax6 is found in a cluster together with that of 81 other genes. The paired DNA binding domain of Pax6 can bind to regulatory sequences in 14 of the 81 genes. We also found that the expression pattern of all these 14 genes is up- or downregulated in Pax6 mutant mice. These results emphasize that determining the expression pattern of many genes in a systematic way followed by an application of integrative tools leads to the identification of novel candidate components of signaling pathways. More generally, when complemented with appropriate data-mining strategies, transcriptome-scale in situ hybridization can be turned into a powerful instrument for systems biology.

Analysing the developing brain transcriptome with the GenePaint platform

We discuss technical means by which the complexity of gene and protein signalling cascades can be projected onto the complex structure of the mammalian brain. We argue that this requires both robotics and novel computational tools to register images of gene expression, annotate expression patterns and quantify gene expression. When sufficiently enriched and detailed, such gene expression/neuroanatomical atlases are hypothesis-generating tools and contain in themselves much of the information needed to investigate function in normal and genetically or otherwise modified brains. To be successful and useful, data-rich and comprehensive gene expression/neuroanatomical atlases have to be web accessible and structured in a way that allows the application of data exploration and mining tools.