Functional organization of the transcriptome in human brain

Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells

Background

Recent work has revealed that a core group of transcription factors (TFs) regulates the key characteristics of embryonic stem (ES) cells: pluripotency and self-renewal. Current efforts focus on identifying genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these genes. To that end, we investigated the use of unsigned and signed network analysis to identify pluripotency and differentiation related genes.

Results

We show that signed networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted gene co-expression network analysis (WGCNA), we found a pluripotency module and a differentiation module, which are not identified in unsigned networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find that the pluripotency module is enriched with genes related to DNA damage repair and mitochondrial function in addition to transcriptional regulation. Using a connectivity measure of module membership, we not only identify known regulators of ES cells but also show that Mrpl15, Msh6, Nrf1, Nup133, Ppif, Rbpj, Sh3gl2, and Zfp39, among other genes, have important roles in maintaining ES cell pluripotency and self-renewal. We also report highly significant relationships between module membership and epigenetic modifications (histone modifications and promoter CpG methylation status), which are known to play a role in controlling gene expression during ES cell self-renewal and differentiation.

Conclusion

Our systems biologic re-analysis of gene expression, transcription factor binding, epigenetic and gene ontology data provides a novel integrative view of ES cell biology.

Network module detection: Affinity search technique with the multi-node topological overlap measure

Background

Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis.

Findings

We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering.

Conclusion

Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage:

Glioma-induced remodeling of the neurovascular unit

The normal BBB (blood-brain barrier) consists of a series of structures collectively known as neurovascular units, or NVU, that are composed of endothelial cells and astrocyte endfeet separated by a basal lamina at their interface. The integrity of the BBB and specifically endothelial tight junctions is maintained by interactions between these different components and the local microenvironment of the NVU. Central nervous system cancers such as gliomas disrupt the integrity of the BBB and this compromise is associated with increased tumor growth and invasion of the surrounding brain parenchyma. Because the relationship between glioma-induced BBB breakdown and glioma invasion remains poorly understood, and the host microenvironment can influence tumor cell migration, we used immunohistochemical techniques to characterize tumor associated BBB remodeling. Using an orthotopic xenograft model of glioma, we demonstrate that tumor cells induce specific changes in the composition of the basal lamina and in astrocytic components of the NVU. We suggest that these changes may be essential to understand the capacity of gliomas to regulate BBB integrity and as such, glioma invasion into brain parenchyma.

Roles of glial cells in synapse development

Brain function relies on communication among neurons via highly specialized contacts, the synapses, and synaptic dysfunction lies at the heart of age-, disease-, and injury-induced defects of the nervous system. For these reasons, the formation-and repair-of synaptic connections is a major focus of neuroscience research. In this review, I summarize recent evidence that synapse development is not a cell-autonomous process and that its distinct phases depend on assistance from the so-called glial cells. The results supporting this view concern synapses in the central nervous system as well as neuromuscular junctions and originate from experimental models ranging from cell cultures to living flies, worms, and mice. Peeking at the future, I will highlight recent technical advances that are likely to revolutionize our views on synapse-glia interactions in the developing, adult and diseased brain.

Prestige centrality-based functional outlier detection in gene expression analysis

MOTIVATION

Traditional gene expression analysis techniques capture an average gene expression state across sample replicates. However, the average signal across replicates will not capture activated gene networks in different states across replicates. For example, if a particular gene expression network is activated within a subset or all sample replicates, yet the activation state across the sample replicates differs by the specific genes activated in each replicate, the activation of this network will be washed out by averaging across replicates. This situation is likely to occur in single cell gene expression experiments or in noisy experimental settings where a small sub-population of cells contributes to the gene expression signature of interest.

RESULTS AND IMPLEMENTATION

In this light, we developed a novel network-based approach which considers gene expression within each replicate across its entire gene expression profile, and identifies outliers across replicates. The power of this method is demonstrated by its ability to enrich for distant metastasis related genes derived from noisy expression data of CD44+CD24-/low tumor initiating cells.

Collaborative models for translational neuroscience and rehabilitation research

Little formal research has been conducted on strategies to structure basic, preclinical, and clinical research to increase the likelihood of discovering efficacious interventions for patients with neurological diseases. How academic research is organized and funded by government agencies and foundations seems likely to affect the quality and rate of production of valued therapeutic agents. Few models for translational biomedical research, however, have been defined and no strategies have been compared. Given the narrow width of expertise and laboratory capacity of individual investigators, the complexity of identifying and manipulating mechanisms of disease components over time, and the demand for solutions from society, our continued reliance on funding therapeutic discovery through standalone investigators and projects seems counterproductive. Models are described for funding collaborations of basic and clinical scientists to work in iterative, adaptable, cross-disciplinary interactions around key progress-limiting questions. Problem-oriented collaborations require leadership, incentives, trust, ongoing assessment, and an efficient infrastructure that overcomes barriers. These models are as testable as the hypotheses that drive scientific research.

Functional and evolutionary insights into human brain development through global transcriptome analysis

Our understanding of the evolution, formation, and pathological disruption of human brain circuits is impeded by a lack of comprehensive data on the developing brain transcriptome. A whole-genome, exon-level expression analysis of 13 regions from left and right sides of the mid-fetal human brain revealed that 76% of genes are expressed, and 44% of these are differentially regulated. These data reveal a large number of specific gene expression and alternative splicing patterns, as well as coexpression networks, associated with distinct regions and neurodevelopmental processes. Of particular relevance to cognitive specializations, we have characterized the transcriptional landscapes of prefrontal cortex and perisylvian speech and language areas, which exhibit a population-level global expression symmetry. We show that differentially expressed genes are more frequently associated with human-specific evolution of putative cis-regulatory elements. These data provide a wealth of biological insights into the complex transcriptional and molecular underpinnings of human brain development and evolution.

Generic aspects of complexity in brain imaging data and other biological systems

A key challenge for systems neuroscience is the question of how to understand the complex network organization of the brain on the basis of neuroimaging data. Similar challenges exist in other specialist areas of systems biology because complex networks emerging from the interactions between multiple non-trivially interacting agents are found quite ubiquitously in nature, from protein interactomes to ecosystems. We suggest that one way forward for analysis of brain networks will be to quantify aspects of their organization which are likely to be generic properties of a broader class of biological systems. In this introductory review article we will highlight four important aspects of complex systems in general: fractality or scale-invariance; criticality; small-world and related topological attributes; and modularity. For each concept we will provide an accessible introduction, an illustrative data-based example of how it can be used to investigate aspects of brain organization in neuroimaging experiments, and a brief review of how this concept has been applied and developed in other fields of biomedical and physical science. The aim is to provide a didactic, focussed and user-friendly introduction to the concepts of complexity science for neuroscientists and neuroimagers.

A tool for identification of genes expressed in patterns of interest using the Allen Brain Atlas

MOTIVATION

Gene expression patterns can be useful in understanding the structural organization of the brain and the regulatory logic that governs its myriad cell types. A particularly rich source of spatial expression data is the Allen Brain Atlas (ABA), a comprehensive genome-wide in situ hybridization study of the adult mouse brain. Here, we present an open-source program, ALLENMINER, that searches the ABA for genes that are expressed, enriched, patterned or graded in a user-specified region of interest.

RESULTS

Regionally enriched genes identified by ALLENMINER accurately reflect the in situ data (95-99% concordance with manual curation) and compare with regional microarray studies as expected from previous comparisons (61-80% concordance). We demonstrate the utility of ALLENMINER by identifying genes that exhibit patterned expression in the caudoputamen and neocortex. We discuss general characteristics of gene expression in the mouse brain and the potential application of ALLENMINER to design strategies for specific genetic access to brain regions and cell types.

AVAILABILITY

ALLENMINER is freely available on the Internet at http://research.janelia.org/davis/allenminer.

WGCNA: an R package for weighted correlation network analysis

Background

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial.

Results

The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings.

Conclusion

The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

WGCNA: an R package for weighted correlation network analysis

BACKGROUND

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial.

RESULTS

The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings.

CONCLUSION

The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.