Ahmed Mahfouz

Genome-wide coexpression of steroid receptors in the mouse brain: Identifying signaling pathways and functionally coordinated regions

Significance Steroid hormones coordinate the activity of many brain regions by binding to nuclear receptors that act as transcription factors. This study uses genome-wide correlation of gene expression in the mouse brain to discover (i) brain regions that respond in a similar manner to particular steroids, (ii) signaling pathways that are used in a steroid receptor and brain region-specific manner, and (iii) potential target genes and relationships between groups of target genes. The data constitute a rich repository for the research community to support further new insights in neuroendocrine relationships and to develop novel ways to manipulate brain activity in research or clinical settings. Steroid receptors are pleiotropic transcription factors that coordinate adaptation to different physiological states. An important target organ is the brain, but even though their effects are well studied in specific regions, brain-wide steroid receptor targets and mediators remain largely unknown due to the complexity of the brain. Here, we tested the idea that novel aspects of steroid action can be identified through spatial correlation of steroid receptors with genome-wide mRNA expression across different regions in the mouse brain. First, we observed significant coexpression of six nuclear receptors (NRs) [androgen receptor (Ar), estrogen receptor alpha (Esr1), estrogen receptor beta (Esr2), glucocorticoid receptor (Gr), mineralocorticoid receptor (Mr), and progesterone receptor (Pgr)] with sets of steroid target genes that were identified in single brain regions. These coexpression relationships were also present in distinct other brain regions, suggestive of as yet unidentified coordinate regulation of brain regions by, for example, glucocorticoids and estrogens. Second, coexpression of a set of 62 known NR coregulators and the six steroid receptors in 12 nonoverlapping mouse brain regions revealed selective downstream pathways, such as Pak6 as a mediator for the effects of Ar and Gr on dopaminergic transmission. Third, Magel2 and Irs4 were identified and validated as strongly responsive targets to the estrogen diethylstilbestrol in the mouse hypothalamus. The brain- and genome-wide correlations of mRNA expression levels of six steroid receptors that we provide constitute a rich resource for further predictions and understanding of brain modulation by steroid hormones.

Visualizing the spatial gene expression organization in the brain through non-linear similarity embeddings

The Allen Brain Atlases enable the study of spatially resolved, genome-wide gene expression patterns across the mammalian brain. Several explorative studies have applied linear dimensionality reduction methods such as Principal Component Analysis (PCA) and classical Multi-Dimensional Scaling (cMDS) to gain insight into the spatial organization of these expression patterns. In this paper, we describe a non-linear embedding technique called Barnes-Hut Stochastic Neighbor Embedding (BH-SNE) that emphasizes the local similarity structure of high-dimensional data points. By applying BH-SNE to the gene expression data from the Allen Brain Atlases, we demonstrate the consistency of the 2D, non-linear embedding of the sagittal and coronal mouse brain atlases, and across 6 human brains. In addition, we quantitatively show that BH-SNE maps are superior in their separation of neuroanatomical regions in comparison to PCA and cMDS. Finally, we assess the effect of higher-order principal components on the global structure of the BH-SNE similarity maps. Based on our observations, we conclude that BH-SNE maps with or without prior dimensionality reduction (based on PCA) provide comprehensive and intuitive insights in both the local and global spatial transcriptome structure of the human and mouse Allen Brain Atlases.

Shared Pathways Among Autism Candidate Genes Determined by Co-expression Network Analysis of the Developing Human Brain Transcriptome

Autism spectrum disorder (ASD) is a neurodevelopmental syndrome known to have a significant but complex genetic etiology. Hundreds of diverse genes have been implicated in ASD; yet understanding how many genes, each with disparate function, can all be linked to a single clinical phenotype remains unclear. We hypothesized that understanding functional relationships between autism candidate genes during normal human brain development may provide convergent mechanistic insight into the genetic heterogeneity of ASD. We analyzed the co-expression relationships of 455 genes previously implicated in autism using the BrainSpan human transcriptome database, across 16 anatomical brain regions spanning prenatal life through adulthood. We discovered modules of ASD candidate genes with biologically relevant temporal co-expression dynamics, which were enriched for functional ontologies related to synaptogenesis, apoptosis, and GABA-ergic neurons. Furthermore, we also constructed co-expression networks from the entire transcriptome and found that ASD candidate genes were enriched in modules related to mitochondrial function, protein translation, and ubiquitination. Hub genes central to these ASD-enriched modules were further identified, and their functions supported these ontological findings. Overall, our multi-dimensional co-expression analysis of ASD candidate genes in the normal developing human brain suggests the heterogeneous set of ASD candidates share transcriptional networks related to synapse formation and elimination, protein turnover, and mitochondrial function.

Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex

The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale) to chromatin compartment interactions (i.e. large-scale).

Gene co-expression analysis identifies brain regions and cell types involved in migraine pathophysiology: a GWAS-based study using the Allen Human Brain Atlas

Migraine is a common disabling neurovascular brain disorder typically characterised by attacks of severe headache and associated with autonomic and neurological symptoms. Migraine is caused by an interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified over a dozen genetic loci associated with migraine. Here, we integrated migraine GWAS data with high-resolution spatial gene expression data of normal adult brains from the Allen Human Brain Atlas to identify specific brain regions and molecular pathways that are possibly involved in migraine pathophysiology. To this end, we used two complementary methods. In GWAS data from 23,285 migraine cases and 95,425 controls, we first studied modules of co-expressed genes that were calculated based on human brain expression data for enrichment of genes that showed association with migraine. Enrichment of a migraine GWAS signal was found for five modules that suggest involvement in migraine pathophysiology of: (i) neurotransmission, protein catabolism and mitochondria in the cortex; (ii) transcription regulation in the cortex and cerebellum; and (iii) oligodendrocytes and mitochondria in subcortical areas. Second, we used the high-confidence genes from the migraine GWAS as a basis to construct local migraine-related co-expression gene networks. Signatures of all brain regions and pathways that were prominent in the first method also surfaced in the second method, thus providing support that these brain regions and pathways are indeed involved in migraine pathophysiology.

NeuroD factors discriminate mineralocorticoid from glucocorticoid receptor DNA binding in the male rat brain

In the limbic brain, mineralocorticoid receptors (MRs) and glucocorticoid receptors (GRs) both function as receptors for the naturally occurring glucocorticoids (corticosterone/cortisol) but mediate distinct effects on cellular physiology via transcriptional mechanisms. The transcriptional basis for specificity of these MR- vs GR-mediated effects is unknown. To address this conundrum, we have identified the extent of MR/GR DNA-binding selectivity in the rat hippocampus using chromatin immunoprecipitation followed by sequencing. We found 918 and 1450 nonoverlapping binding sites for MR and GR, respectively. Furthermore, 475 loci were co-occupied by MR and GR. De novo motif analysis resulted in a similar binding motif for both receptors at 100% of the target loci, which matched the known glucocorticoid response element (GRE). In addition, the Atoh/NeuroD consensus sequence was found in co-occurrence with all MR-specific binding sites but was absent for GR-specific or MR-GR overlapping sites. Basic helix-loop-helix family members Neurod1, Neurod2, and Neurod6 showed hippocampal expression and were hypothesized to bind the Atoh motif. Neurod2 was detected at rat hippocampal MR binding sites but not at GR-exclusive sites. All three NeuroD transcription factors acted as DNA-binding-dependent coactivators for both MR and GR in reporter assays in heterologous HEK293 cells, likely via indirect interactions with the receptors. In conclusion, a NeuroD family member binding to an additional motif near the GRE seems to drive specificity for MR over GR binding at hippocampal binding sites.

Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics

The use of genome-wide data in cancer research, for the identification of groups of patients with similar molecular characteristics, has become a standard approach for applications in therapy-response, prognosis-prediction, and drug-development. To progress in these applications, the trend is to move from single genome-wide measurements in a single cancer-type towards measuring several different molecular characteristics across multiple cancer-types. Although current approaches shed light on molecular characteristics of various cancer-types, detailed relationships between patients within cancer clusters are unclear. We propose a novel multi-omic integration approach that exploits the joint behavior of the different molecular characteristics, supports visual exploration of the data by a two-dimensional landscape, and inspection of the contribution of the different genome-wide data-types. We integrated 4,434 samples across 19 cancer-types, derived from TCGA, containing gene expression, DNA-methylation, copy-number variation and microRNA expression data. Cluster analysis revealed 18 clusters, where three clusters showed a complex collection of cancer-types, squamous-cell-carcinoma, colorectal cancers, and a novel grouping of kidney-cancers. Sixty-four samples were identified outside their tissue-of-origin cluster. Known and novel patient subgroups were detected for Acute Myeloid Leukemia’s, and breast cancers. Quantification of the contributions of the different molecular types showed that substructures are driven by specific (combinations of) molecular characteristics.

BrainScope : Interactive visual exploration of the spatial and temporal human brain transcriptome

Abstract Spatial and temporal brain transcriptomics has recently emerged as an invaluable data source for molecular neuroscience. The complexity of such data poses considerable challenges for analysis and visualization. We present BrainScope: a web portal for fast, interactive visual exploration of the Allen Atlases of the adult and developing human brain transcriptome. Through a novel methodology to explore high-dimensional data (dual t-SNE), BrainScope enables the linked, all-in-one visualization of genes and samples across the whole brain and genome, and across developmental stages. We show that densities in t-SNE scatter plots of the spatial samples coincide with anatomical regions, and that densities in t-SNE scatter plots of the genes represent gene co-expression modules that are significantly enriched for biological functions. We also show that the topography of the gene t-SNE maps reflect brain region-specific gene functions, enabling hypothesis and data driven research. We demonstrate the discovery potential of BrainScope through three examples: (i) analysis of cell type specific gene sets, (ii) analysis of a set of stable gene co-expression modules across the adult human donors and (iii) analysis of the evolution of co-expression of oligodendrocyte specific genes over developmental stages. BrainScope is publicly accessible at www.brainscope.nl.

Continuous infusion of manganese improves contrast and reduces side effects in manganese-enhanced magnetic resonance imaging studies

ABSTRACT The ability to administer systemically high doses of manganese as contrast agent while circumventing its toxicity is of particular interest for exploratory MRI studies of the brain. Administering low doses either repeatedly or continuously over time has been shown to enable the acquisition of satisfactory MRI images of the mouse brain without apparent side effects. Here we have systematically compared the obtained MRI contrast and recorded potential systemic side effects such as stress response and muscle strength impairment in relation to the achieved contrast. We show in mice that administering MnCl2 via osmotic infusion pumps allows for a side‐effect free delivery of a high cumulative dose of manganese chloride (480 mg/kg bodyweight in 8 days). High contrast in MRI was achieved while we did not observe the weight loss or distress seen in other studies where mice received manganese via fractionated intraperitoneal injections of lower doses of manganese. As the normal daily conduct of the mice was not affected, this new manganese delivery method might be of particular use to study brain activity over several days. This may facilitate the phenotyping of new transgenic mouse models, the study of chronic disease models and the monitoring of changes in brain activity in long‐term behavioral studies. HIGHLIGHTSManganese doses up to 480mg/kg bodyweight can be administered in mice without acute side effects.Manganese was injected intraperitoneally or infused via a subcutaneous pump over 8 days.Achieved MRI contrast and subtle side effects have been monitored and compared.Via infusion, higher doses with no side effects and good MRI contrast are achieved.

Brain transcriptome atlases: a computational perspective

The immense complexity of the mammalian brain is largely reflected in the underlying molecular signatures of its billions of cells. Brain transcriptome atlases provide valuable insights into gene expression patterns across different brain areas throughout the course of development. Such atlases allow researchers to probe the molecular mechanisms which define neuronal identities, neuroanatomy, and patterns of connectivity. Despite the immense effort put into generating such atlases, to answer fundamental questions in neuroscience, an even greater effort is needed to develop methods to probe the resulting high-dimensional multivariate data. We provide a comprehensive overview of the various computational methods used to analyze brain transcriptome atlases.