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Microglia maintain homeostasis in the brain, but whether aberrant microglial activation can cause neurodegeneration remains controversial. Here, we use transcriptome profiling to demonstrate that deficiency in frontotemporal dementia (FTD) gene progranulin (Grn) leads to an age-dependent, progressive upregulation of lysosomal and innate immunity genes, increased complement production, and enhanced synaptic pruning in microglia. During aging, Grn(-/-) mice show profound microglia infiltration and preferential elimination of inhibitory synapses in the ventral thalamus, which lead to hyperexcitability in the thalamocortical circuits and obsessive-compulsive disorder (OCD)-like grooming behaviors. Remarkably, deleting C1qa gene significantly reduces synaptic pruning by Grn(-/-) microglia and mitigates neurodegeneration, behavioral phenotypes, and premature mortality in Grn(-/-) mice. Together, our results uncover a previously unrecognized role of progranulin in suppressing aberrant microglia activation during aging. These results represent an important conceptual advance that complement activation and microglia-mediated synaptic pruning are major drivers, rather than consequences, of neurodegeneration caused by progranulin deficiency., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Proper neurological function in humans requires precise control of levels of the epigenetic regulator methyl CpG-binding protein 2 (MeCP2). MeCP2 protein levels are low in fetal brains, where the predominant MECP2 transcripts have an unusually long 3' untranslated region (UTR). Here, we show that miR-483-5p, an intragenic microRNA of the imprinted IGF2, regulates MeCP2 levels through a human-specific binding site in the MECP2 long 3' UTR. We demonstrate the inverse correlation of miR-483-5p and MeCP2 levels in developing human brains and fibroblasts from Beckwith-Wiedemann syndrome patients. Importantly, expression of miR-483-5p rescues abnormal dendritic spine phenotype of neurons overexpressing human MeCP2. In addition, miR-483-5p modulates the levels of proteins of the MeCP2-interacting corepressor complexes, including HDAC4 and TBL1X. These data provide insight into the role of miR-483-5p in regulating the levels of MeCP2 and interacting proteins during human fetal development.

Understanding human-specific patterns of brain gene expression and regulation can provide key insights into human brain evolution and speciation. Here, we use next-generation sequencing, and Illumina and Affymetrix microarray platforms, to compare the transcriptome of human, chimpanzee, and macaque telencephalon. Our analysis reveals a predominance of genes differentially expressed within human frontal lobe and a striking increase in transcriptional complexity specific to the human lineage in the frontal lobe. In contrast, caudate nucleus gene expression is highly conserved. We also identify gene coexpression signatures related to either neuronal processes or neuropsychiatric diseases, including a human-specific module with CLOCK as its hub gene and another module enriched for neuronal morphological processes and genes coexpressed with FOXP2, a gene important for language evolution. These data demonstrate that transcriptional networks have undergone evolutionary remodeling even within a given brain region, providing a window through which to view the foundation of uniquely human cognitive capacities., (Copyright © 2012 Elsevier Inc. All rights reserved.)

In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix) while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables). Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1) preservation of cholesterol biosynthesis pathway in mouse tissues, 2) comparison of human and chimpanzee brain networks, 3) preservation of selected KEGG pathways between human and chimpanzee brain networks, 4) sex differences in human cortical networks, 5) sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation.

Background: Since human brain tissue is often unavailable for transcriptional profiling studies, blood expression data is frequently used as a substitute. The underlying hypothesis in such studies is that genes expressed in brain tissue leave a transcriptional footprint in blood. We tested this hypothesis by relating three human brain expression data sets (from cortex, cerebellum and caudate nucleus) to two large human blood expression data sets (comprised of 1463 individuals)., Results: We found mean expression levels were weakly correlated between the brain and blood data (r range: [0.24,0.32]). Further, we tested whether co-expression relationships were preserved between the three brain regions and blood. Only a handful of brain co-expression modules showed strong evidence of preservation and these modules could be combined into a single large blood module. We also identified highly connected intramodular "hub" genes inside preserved modules. These preserved intramodular hub genes had the following properties: first, their expression levels tended to be significantly more heritable than those from non-preserved intramodular hub genes (p < 10⁻⁹⁰); second, they had highly significant positive correlations with the following cluster of differentiation genes: CD58, CD47, CD48, CD53 and CD164; third, a significant number of them were known to be involved in infection mechanisms, post-transcriptional and post-translational modification and other basic processes., Conclusions: Overall, we find transcriptome organization is poorly preserved between brain and blood. However, the subset of preserved co-expression relationships characterized here may aid future efforts to identify blood biomarkers for neurological and neuropsychiatric diseases when brain tissue samples are unavailable.

Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA), is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel's membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results., (Copyright 2010 Elsevier Inc. All rights reserved.)

The enormous complexity of the human brain ultimately derives from a finite set of molecular instructions encoded in the human genome. These instructions can be directly studied by exploring the organization of the brain's transcriptome through systematic analysis of gene coexpression relationships. We analyzed gene coexpression relationships in microarray data generated from specific human brain regions and identified modules of coexpressed genes that correspond to neurons, oligodendrocytes, astrocytes and microglia. These modules provide an initial description of the transcriptional programs that distinguish the major cell classes of the human brain and indicate that cell type-specific information can be obtained from whole brain tissue without isolating homogeneous populations of cells. Other modules corresponded to additional cell types, organelles, synaptic function, gender differences and the subventricular neurogenic niche. We found that subventricular zone astrocytes, which are thought to function as neural stem cells in adults, have a distinct gene expression pattern relative to protoplasmic astrocytes. Our findings provide a new foundation for neurogenetic inquiries by revealing a robust and previously unrecognized organization to the human brain transcriptome.

Mutations in FOXP2, a member of the forkhead family of transcription factor genes, are the only known cause of developmental speech and language disorders in humans. To date, there are no known targets of human FOXP2 in the nervous system. The identification of FOXP2 targets in the developing human brain, therefore, provides a unique tool with which to explore the development of human language and speech. Here, we define FOXP2 targets in human basal ganglia (BG) and inferior frontal cortex (IFC) by use of chromatin immunoprecipitation followed by microarray analysis (ChIP-chip) and validate the functional regulation of targets in vitro. ChIP-chip identified 285 FOXP2 targets in fetal human brain; statistically significant overlap of targets in BG and IFC indicates a core set of 34 transcriptional targets of FOXP2. We identified targets specific to IFC or BG that were not observed in lung, suggesting important regional and tissue differences in FOXP2 activity. Many target genes are known to play critical roles in specific aspects of central nervous system patterning or development, such as neurite outgrowth, as well as plasticity. Subsets of the FOXP2 transcriptional targets are either under positive selection in humans or differentially expressed between human and chimpanzee brain. This is the first ChIP-chip study to use human brain tissue, making the FOXP2-target genes identified in these studies important to understanding the pathways regulating speech and language in the developing human brain. These data provide the first insight into the functional network of genes directly regulated by FOXP2 in human brain and by evolutionary comparisons, highlighting genes likely to be involved in the development of human higher-order cognitive processes.

Comparisons of gene expression between human and non-human primate brains have identified hundreds of differentially expressed genes, yet translating these lists into key functional distinctions between species has proved difficult. Here we provide a more integrated view of human brain evolution by examining the large-scale organization of gene coexpression networks in human and chimpanzee brains. We identify modules of coexpressed genes that correspond to discrete brain regions and quantify their conservation between the species. Module conservation in cerebral cortex is significantly weaker than module conservation in subcortical brain regions, revealing a striking gradient that parallels known evolutionary hierarchies. We introduce a method for identifying species-specific network connections and demonstrate how differential network connectivity can be used to identify key drivers of evolutionary change. By integrating our results with comparative genomic sequence data and estimates of protein sequence divergence rates, we confirm a number of network predictions and validate these findings. Our results provide insights into the molecular bases of primate brain organization and demonstrate the general utility of weighted gene coexpression network analysis.

Several recent microarray studies have compared gene-expression patterns n humans, chimpanzees and other non-human primates to identify evolutionary changes that contribute to the distinctive cognitive and behavioural characteristics of humans. These studies support the surprising conclusion that the evolution of the human brain involved an upregulation of gene expression relative to non-human primates, a finding that could be relevant to understanding human cerebral physiology and function. These results show how genetic and genomic methods can shed light on the basis of human neural and cognitive specializations, and have important implications for neuroscience, anthropology and medicine.

Blood vessels in the CNS form a specialized and critical structure, the blood-brain barrier (BBB). We present a resource to understand the molecular mechanisms that regulate BBB function in health and dysfunction during disease. Using endothelial cell enrichment and RNA sequencing, we analyzed the gene expression of endothelial cells in mice, comparing brain endothelial cells with peripheral endothelial cells. We also assessed the regulation of CNS endothelial gene expression in models of stroke, multiple sclerosis, traumatic brain injury and seizure, each having profound BBB disruption. We found that although each is caused by a distinct trigger, they exhibit strikingly similar endothelial gene expression changes during BBB disruption, comprising a core BBB dysfunction module that shifts the CNS endothelial cells into a peripheral endothelial cell-like state. The identification of a common pathway for BBB dysfunction suggests that targeting therapeutic agents to limit it may be effective across multiple neurological disorders.

It is widely assumed that cells must be physically isolated to study their molecular profiles. However, intact tissue samples naturally exhibit variation in cellular composition, which drives covariation of cell-class-specific molecular features. By analyzing transcriptional covariation in 7,221 intact CNS samples from 840 neurotypical individuals, representing billions of cells, we reveal the core transcriptional identities of major CNS cell classes in humans. By modeling intact CNS transcriptomes as a function of variation in cellular composition, we identify cell-class-specific transcriptional differences in Alzheimer's disease, among brain regions, and between species. Among these, we show that PMP2 is expressed by human but not mouse astrocytes and significantly increases mouse astrocyte size upon ectopic expression in vivo, causing them to more closely resemble their human counterparts. Our work is available as an online resource ( http://oldhamlab.ctec.ucsf.edu/ ) and provides a generalizable strategy for determining the core molecular features of cellular identity in intact biological systems.

Background: Since human brain tissue is often unavailable for transcriptional profiling studies, blood expression data is frequently used as a substitute. The underlying hypothesis in such studies is that genes expressed in brain tissue leave a transcriptional footprint in blood. We tested this hypothesis by relating three human brain expression data sets (from cortex, cerebellum and caudate nucleus) to two large human blood expression data sets (comprised of 1463 individuals). Results: We found mean expression levels were weakly correlated between the brain and blood data (r range: [0.24,0.32]). Further, we tested whether co-expression relationships were preserved between the three brain regions and blood. Only a handful of brain co-expression modules showed strong evidence of preservation and these modules could be combined into a single large blood module. We also identified highly connected intramodular "hub" genes inside preserved modules. These preserved intramodular hub genes had the following properties: first, their expression levels tended to be significantly more heritable than those from non-preserved intramodular hub genes (p < 10-90); second, they had highly significant positive correlations with the following cluster of differentiation genes: CD58, CD47, CD48, CD53 and CD164; third, a significant number of them were known to be involved in infection mechanisms, post-transcriptional and post-translational modification and other basic processes. Conclusions: Overall, we find transcriptome organization is poorly preserved between brain and blood. However, the subset of preserved co-expression relationships characterized here may aid future efforts to identify blood biomarkers for neurological and neuropsychiatric diseases when brain tissue samples are unavailable. [ABSTRACT FROM AUTHOR]