Neurogenomics: at the intersection of neurobiology and genome sciences

Interfacing Neural Network Components and Nucleic Acids

Translating neural activity into nucleic acid modifications in a controlled manner harbors unique advantages for basic neurobiology and bioengineering. It would allow for a new generation of biological computers that store output in ultra-compact and long-lived DNA and enable the investigation of animal nervous systems at unprecedented scales. Furthermore, by exploiting the ability of DNA to precisely influence neuronal activity and structure, it could be possible to more effectively create cellular therapy approaches for psychiatric diseases that are currently difficult to treat.

A genes eye view of ontogeny: de novo assembly and profiling of the Gryllus rubens transcriptome

Crickets (Orthoptera:Gryllidae) are widely used model organisms for developmental, evolutionary, neurobiological and behavioural research. Here, we developed a de novo transcriptome from pooled RNA-seq Illumina data spanning seven stages in the life cycle of Gryllus rubens. Approximately 705 Mbp of data was assembled and filtered to form 27 312 transcripts. We were able to annotate 52% of our transcripts using BLAST and assign at least one gene ontology term to 41%. Pooled samples from three different ontogenetic stages were used for transcriptomic profiling revealing patterns of differential gene expression that highlight processes in the different life stages. Embryonic and early instar development was enriched for ecdysteroid metabolism, cytochrome P450s and glutathione production. Late instar development was enriched for regulation of gene expression and many of the genes highly expressed during this stage were involved in conserved developmental signalling pathways suggesting that these developmental pathways are active beyond embryonic development. Adults were enriched for fat transport (mostly relating to egg production) and production of octopamine, an important neurohormone. We also identified genes involved in conserved developmental pathways (Hedgehog, Hippo, Wnt, JAK/STAT, TGF-beta, Notch, and MEK/ERK). This is the first transcriptome spanning ontogeny in Gryllus rubens and a valuable resource for future work on development and evolution in Orthoptera.

Neurogenomics: Challenges and opportunities for Ghana

The application of genomic tools and technologies has shown the potential to help improve healthcare and our understanding of disease mechanisms. While genomic tools are increasingly being applied to research on infectious diseases, malaria and neglected tropical diseases in Africa, an area that has seen little application of genomic approaches on this continent is neuroscience. In this article, we examined the prospects of developing neurogenomics research and its clinical use in Ghana, one of the African countries actively involved in genomics research. We noted that established international research funding sources and foundations in genomic research such as H3ABioNet nodes established at a couple of research centres in Ghana provide excellent platforms for extending the usage of genomic tools and techniques to neuroscience-related research areas. However, existing challenges such as the (i) lack of degree programmes in neuroscience, genomics and bioinformatics; (ii) low availability of infrastructure and appropriately-trained scientists; and (iii) lack of local research funding opportunities, need to be addressed. To promote and safeguard the long-term sustainability of neurogenomics research in the country, the impact of the existing challenges and possible ways of addressing them have been discussed.

Neurogenomics: An opportunity to integrate neuroscience, genomics and bioinformatics research in Africa

Modern genomic approaches have made enormous contributions to improving our understanding of the function, development and evolution of the nervous system, and the diversity within and between species. However, most of these research advances have been recorded in countries with advanced scientific resources and funding support systems. On the contrary, little is known about, for example, the possible interplay between different genes, non-coding elements and environmental factors in modulating neurological diseases among populations in low-income countries, including many African countries. The unique ancestry of African populations suggests that improved inclusion of these populations in neuroscience-related genomic studies would significantly help to identify novel factors that might shape the future of neuroscience research and neurological healthcare. This perspective is strongly supported by the recent identification that diseased individuals and their kindred from specific sub-Saharan African populations lack common neurological disease-associated genetic mutations. This indicates that there may be population-specific causes of neurological diseases, necessitating further investigations into the contribution of additional, presently-unknown genomic factors. Here, we discuss how the development of neurogenomics research in Africa would help to elucidate disease-related genomic variants, and also provide a good basis to develop more effective therapies. Furthermore, neurogenomics would harness African scientists' expertise in neuroscience, genomics and bioinformatics to extend our understanding of the neural basis of behaviour, development and evolution.

Computational modeling of drug response with applications to neuroscience

The development of novel high-throughput technologies has opened up the opportunity to deeply characterize patient tissues at various molecular levels and has given rise to a paradigm shift in medicine towards personalized therapies. Computational analysis plays a pivotal role in integrating the various genome data and understanding the cellular response to a drug. Based on that data, molecular models can be constructed that incorporate the known downstream effects of drug-targeted receptor molecules and that predict optimal therapy decisions. In this article, we describe the different steps in the conceptual framework of computational modeling. We review resources that hold information on molecular pathways that build the basis for constructing the model interaction maps, highlight network analysis concepts that have been helpful in identifying predictive disease patterns, and introduce the basic concepts of kinetic modeling. Finally, we illustrate this framework with selected studies related to the modeling of important target pathways affected by drugs.

Single cell genomics of the brain: focus on neuronal diversity and neuropsychiatric diseases

Single cell genomics has made increasingly significant contributions to our understanding of the role that somatic genome variations play in human neuronal diversity and brain diseases. Studying intercellular genome and epigenome variations has provided new clues to the delineation of molecular mechanisms that regulate development, function and plasticity of the human central nervous system (CNS). It has been shown that changes of genomic content and epigenetic profiling at single cell level are involved in the pathogenesis of neuropsychiatric diseases (schizophrenia, mental retardation (intellectual/leaning disability), autism, Alzheimer's disease etc.). Additionally, several brain diseases were found to be associated with genome and chromosome instability (copy number variations, aneuploidy) variably affecting cell populations of the human CNS. The present review focuses on the latest advances of single cell genomics, which have led to a better understanding of molecular mechanisms of neuronal diversity and neuropsychiatric diseases, in the light of dynamically developing fields of systems biology and "omics".

Morphology and behaviour: functional links in development and evolution

Development and evolution of animal behaviour and morphology are frequently addressed independently, as reflected in the dichotomy of disciplines dedicated to their study distinguishing object of study (morphology versus behaviour) and perspective (ultimate versus proximate). Although traits are known to develop and evolve semi-independently, they are matched together in development and evolution to produce a unique functional phenotype. Here I highlight similarities shared by both traits, such as the decisive role played by the environment for their ontogeny. Considering the widespread developmental and functional entanglement between both traits, many cases of adaptive evolution are better understood when proximate and ultimate explanations are integrated. A field integrating these perspectives is evolutionary developmental biology (evo-devo), which studies the developmental basis of phenotypic diversity. Ultimate aspects in evo-devo studies--which have mostly focused on morphological traits--could become more apparent when behaviour, 'the integrator of form and function', is integrated into the same framework of analysis. Integrating a trait such as behaviour at a different level in the biological hierarchy will help to better understand not only how behavioural diversity is produced, but also how levels are connected to produce functional phenotypes and how these evolve. A possible framework to accommodate and compare form and function at different levels of the biological hierarchy is outlined. At the end, some methodological issues are discussed.

Behavior, brain, and genome in genomic disorders: finding the correspondences

OBJECTIVE

Within the last decade or so, there has been an acceleration of research attempting to connect specific genetic lesions to the patterns of brain structure and activation. This article comments on observations that have been made based on these recent data and discusses their importance for the field of investigations into developmental disorders.

METHODS

In making these observations, the authors focus on one specific genomic lesion, the well-studied, yet still incompletely understood, 22q11.2 deletion syndrome.

RESULTS

The authors demonstrate the degree of variability in the phenotype that occurs at both the brain and behavioral levels of genomic disorders and describe how this variability is, on close inspection, represented at the genomic level.

CONCLUSION

The authors emphasize the importance of combining genetic/genomic analyses and neuroimaging for research and for future clinical diagnostic purposes and for the purposes of developing individualized, patient-tailored treatment and remediation approaches.

Automated pipeline for atlas-based annotation of gene expression patterns: application to postnatal day 7 mouse brain

Massive amounts of image data have been collected and continue to be generated for representing cellular gene expression throughout the mouse brain. Critical to exploiting this key effort of the post-genomic era is the ability to place these data into a common spatial reference that enables rapid interactive queries, analysis, data sharing, and visualization. In this paper, we present a set of automated protocols for generating and annotating gene expression patterns suitable for the establishment of a database. The steps include imaging tissue slices, detecting cellular gene expression levels, spatial registration with an atlas, and textual annotation. Using high-throughput in situ hybridization to generate serial sets of tissues displaying gene expression, this process was applied toward the establishment of a database representing over 200 genes in the postnatal day 7 mouse brain. These data using this protocol are now well-suited for interactive comparisons, analysis, queries, and visualization.

2D-PCR: a method of mapping DNA in tissue sections

A novel approach was developed for mapping the location of target DNA in tissue sections. The method combines a high-density, multi-well plate with an innovative single-tube procedure to directly extract, amplify, and detect the DNA in parallel while maintaining the two-dimensional (2D) architecture of the tissue. A 2D map of the gene glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was created from a tissue section and shown to correlate with the spatial area of the sample. It is anticipated that this approach may be easily adapted to assess the status of multiple genes within tissue sections, yielding a molecular map that directly correlates with the histology of the sample. This will provide investigators with a new tool to interrogate the molecular heterogeneity of tissue specimens.

Clustering of spatial gene expression patterns in the mouse brain and comparison with classical neuroanatomy

Spatial gene expression profiles provide a novel means of exploring the structural organization of the brain. Computational analysis of these patterns is made possible by genome-scale mapping of the C57BL/6J mouse brain in the Allen Brain Atlas. Here we describe methodology used to explore the spatial structure of gene expression patterns across a set of 3041 genes chosen on the basis of consistency across experimental observations (N=2). The analysis was performed on smoothed, co-registered 3D expression volumes for each gene obtained by aggregating cellular resolution image data. Following dimensionality and noise reduction, voxels were clustered according to similarity of expression across the gene set. We illustrate the resulting parcellations of the mouse brain for different numbers of clusters (K) and quantitatively compare these parcellations with a classically-defined anatomical reference atlas at different levels of granularity, revealing a high degree of correspondence. These observations suggest that spatial localization of gene expression offers substantial promise in connecting knowledge at the molecular level with higher-level information about brain organization.

Analysis of multiplex gene expression maps obtained by voxelation

BACKGROUND

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions.

RESULTS

To analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in cortex and corpus callosum.

CONCLUSION

The experimental results confirm the hypothesis that genes with similar gene expression maps might have similar gene functions. The voxelation data takes into account the location information of gene expression level in mouse brain, which is novel in related research. The proposed approach can potentially be used to predict gene functions and provide helpful suggestions to biologists.

The importance of brain banks for molecular neuropathological research: The New South Wales Tissue Resource Centre experience

New developments in molecular neuropathology have evoked increased demands for postmortem human brain tissue. The New South Wales Tissue Resource Centre (TRC) at The University of Sydney has grown from a small tissue collection into one of the leading international brain banking facilities, which operates with best practice and quality control protocols. The focus of this tissue collection is on schizophrenia and allied disorders, alcohol use disorders and controls. This review highlights changes in TRC operational procedures dictated by modern neuroscience, and provides examples of applications of modern molecular techniques to study the neuropathogenesis of many different brain disorders.

Domain-specific data sharing in neuroscience: what do we have to learn from each other?

Molecular biology and genomics have made notable strides in the sharing of primary data and resources. In other domains of neuroscience research, however, there has been resistance to adopting formalized strategies for data exchange, archiving, and availability. In this article, we discuss how neuroscience domains might follow the lead of molecular biology on what has been successful and what has failed in active data sharing. This considers not only the technical challenges but also the sociological concerns in making it possible. Though, not a pain-free process, with increased data availability, scientists from multiple fields can enjoy greater opportunity for novel discoveries about the brain in health and disease.

The epidemiology of autism spectrum disorders

Autism spectrum disorders (ASDs) are complex, lifelong, neurodevelopmental conditions of largely unknown cause. They are much more common than previously believed, second in frequency only to mental retardation among the serious developmental disorders. Although a heritable component has been demonstrated in ASD etiology, putative risk genes have yet to be identified. Environmental risk factors may also play a role, perhaps via complex gene-environment interactions, but no specific exposures with significant population effects are known. A number of endogenous biomarkers associated with autism risk have been investigated, and these may help identify significant biologic pathways that, in turn, will aid in the discovery of specific genes and exposures. Future epidemiologic research should focus on expanding population-based descriptive data on ASDs, exploring candidate risk factors in large well-designed studies incorporating both genetic and environmental exposure data and addressing possible etiologic heterogeneity in studies that can stratify case groups and consider alternate endophenotypes.

Neuroinformatics for genome-wide 3D gene expression mapping in the mouse brain

Large scale gene expression studies in the mammalian brain offer the promise of understanding the topology, networks and ultimately the function of its complex anatomy, opening previously unexplored avenues in neuroscience. High-throughput methods permit genome-wide searches to discover genes that are uniquely expressed in brain circuits and regions that control behavior. Previous gene expression mapping studies in model organisms have employed situ hybridization (ISH), a technique that uses labeled nucleic acid probes to bind to specific mRNA transcripts in tissue sections. A key requirement for this effort is the development of fast and robust algorithms for anatomically mapping and quantifying gene expression for ISH. We describe a neuroinformatics pipeline for automatically mapping expression profiles of ISH data and its use to produce the first genomic scale 3-D mapping of gene expression in a mammalian brain. The pipeline is fully automated and adaptable to other organisms and tissues. Our automated study of over 20,000 genes indicates that at least 78.8 percent are expressed at some level in the adult C56BL/6J mouse brain. In addition to providing a platform for genomic scale search, high-resolution images and visualization tools for expression analysis are available at the Allen Brain Atlas web site (http://www.brain-map.org).

A genome-scale map of expression for a mouse brain section obtained using voxelation

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases. We have reconstructed two-dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 mm3. Good reliability of the microarray results were confirmed using multiple replicates, subsequent quantitative RT-PCR voxelation, mass spectrometry voxelation, and publicly available in situ hybridization data. Known and novel genes were identified with expression patterns localized to defined substructures within the brain. In addition, genes with unexpected patterns were identified, and cluster analysis identified a set of genes with a gradient of dorsal/ventral expression not restricted to known anatomical boundaries. The genome-scale maps of gene expression obtained using voxelation will be a valuable tool for the neuroscience community.

Study of gene function based on spatial co-expression in a high-resolution mouse brain atlas

Background

The Allen Brain Atlas (ABA) project systematically profiles three-dimensional high-resolution gene expression in postnatal mouse brains for thousands of genes. By unveiling gene behaviors at both the cellular and molecular levels, ABA is becoming a unique and comprehensive neuroscience data source for decoding enigmatic biological processes in the brain. Given the unprecedented volume and complexity of the in situ hybridization image data, data mining in this area is extremely challenging. Currently, the ABA database mainly serves as an online reference for visual inspection of individual genes; the underlying rich information of this large data set is yet to be explored by novel computational tools. In this proof-of-concept study, we studied the hypothesis that genes sharing similar three-dimensional expression profiles in the mouse brain are likely to share similar biological functions.

Results

In order to address the pattern comparison challenge when analyzing the ABA database, we developed a robust image filtering method, dubbed histogram-row-column (HRC) algorithm. We demonstrated how the HRC algorithm offers the sensitivity of identifying a manageable number of gene pairs based on automatic pattern searching from an original large brain image collection. This tool enables us to quickly identify genes of similar in situ hybridization patterns in a semi-automatic fashion and consequently allows us to discover several gene expression patterns with expression neighborhoods containing genes of similar functional categories.

Conclusion

Given a query brain image, HRC is a fully automated algorithm that is able to quickly mine vast number of brain images and identify a manageable subset of genes that potentially shares similar spatial co-distribution patterns for further visual inspection. A three-dimensional in situ hybridization pattern, if statistically significant, could serve as a fingerprint of certain gene function. Databases such as ABA provide valuable data source for characterizing brain-related gene functions when armed with powerful image querying tools like HRC.

Statistical analysis of genomic protein family and domain controlled annotations for functional investigation of classified gene lists

Background

The increasing protein family and domain based annotations constitute important information to understand protein functions and gain insight into relations among their codifying genes. To allow analyzing of gene proteomic annotations, we implemented novel modules within GFINDer, a Web system we previously developed that dynamically aggregates functional and phenotypic annotations of user-uploaded gene lists and allows performing their statistical analysis and mining.

Results

Exploiting protein information in Pfam and InterPro databanks, we developed and added in GFINDer original modules specifically devoted to the exploration and analysis of functional signatures of gene protein products. They allow annotating numerous user-classified nucleotide sequence identifiers with controlled information on related protein families, domains and functional sites, classifying them according to such protein annotation categories, and statistically analyzing the obtained classifications. In particular, when uploaded nucleotide sequence identifiers are subdivided in classes, the Statistics Protein Families&Domains module allows estimating relevance of Pfam or InterPro controlled annotations for the uploaded genes by highlighting protein signatures significantly more represented within user-defined classes of genes. In addition, the Logistic Regression module allows identifying protein functional signatures that better explain the considered gene classification.

Conclusion

Novel GFINDer modules provide genomic protein family and domain analyses supporting better functional interpretation of gene classes, for instance defined through statistical and clustering analyses of gene expression results from microarray experiments. They can hence help understanding fundamental biological processes and complex cellular mechanisms influenced by protein domain composition, and contribute to unveil new biomedical knowledge about the codifying genes.

Spatial mapping of protein abundances in the mouse brain by voxelation integrated with high-throughput liquid chromatography-mass spectrometry

Temporally and spatially resolved mapping of protein abundance patterns within the mammalian brain is of significant interest for understanding brain function and molecular etiologies of neurodegenerative diseases; however, such imaging efforts have been greatly challenged by complexity of the proteome, throughput and sensitivity of applied analytical methodologies, and accurate quantitation of protein abundances across the brain. Here, we describe a methodology for comprehensive spatial proteome mapping that addresses these challenges by employing voxelation integrated with automated microscale sample processing, high-throughput liquid chromatography (LC) system coupled with high-resolution Fourier transform ion cyclotron resonance (FTICR) mass spectrometer, and a "universal" stable isotope labeled reference sample approach for robust quantitation. We applied this methodology as a proof-of-concept trial for the analysis of protein distribution within a single coronal slice of a C57BL/6J mouse brain. For relative quantitation of the protein abundances across the slice, an 18O-isotopically labeled reference sample, derived from a whole control coronal slice from another mouse, was spiked into each voxel sample, and stable isotopic intensity ratios were used to obtain measures of relative protein abundances. In total, we generated maps of protein abundance patterns for 1028 proteins. The significant agreement of the protein distributions with previously reported data supports the validity of this methodology, which opens new opportunities for studying the spatial brain proteome and its dynamics during the course of disease progression and other important biological and associated health aspects in a discovery-driven fashion.

Genome-wide atlas of gene expression in the adult mouse brain

Molecular approaches to understanding the functional circuitry of the nervous system promise new insights into the relationship between genes, brain and behaviour. The cellular diversity of the brain necessitates a cellular resolution approach towards understanding the functional genomics of the nervous system. We describe here an anatomically comprehensive digital atlas containing the expression patterns of approximately 20,000 genes in the adult mouse brain. Data were generated using automated high-throughput procedures for in situ hybridization and data acquisition, and are publicly accessible online. Newly developed image-based informatics tools allow global genome-scale structural analysis and cross-correlation, as well as identification of regionally enriched genes. Unbiased fine-resolution analysis has identified highly specific cellular markers as well as extensive evidence of cellular heterogeneity not evident in classical neuroanatomical atlases. This highly standardized atlas provides an open, primary data resource for a wide variety of further studies concerning brain organization and function.

SynDB: a Synapse protein DataBase based on synapse ontology

A synapse is the junction across which a nerve impulse passes from an axon terminal to a neuron, muscle cell or gland cell. The functions and building molecules of the synapse are essential to almost all neurobiological processes. To describe synaptic structures and functions, we have developed Synapse Ontology (SynO), a hierarchical representation that includes 177 terms with hundreds of synonyms and branches up to eight levels deep. associated 125 additional protein keywords and 109 InterPro domains with these SynO terms. Using a combination of automated keyword searches, domain searches and manual curation, we collected 14,000 non-redundant synapse-related proteins, including 3000 in human. We extensively annotated the proteins with information about sequence, structure, function, expression, pathways, interactions and disease associations and with hyperlinks to external databases. The data are stored and presented in the Synapse protein DataBase (SynDB, http://syndb.cbi.pku.edu.cn). SynDB can be interactively browsed by SynO, Gene Ontology (GO), domain families, species, chromosomal locations or Tribe-MCL clusters. It can also be searched by text (including Boolean operators) or by sequence similarity. SynDB is the most comprehensive database to date for synaptic proteins.

Case-control studies in the genomic era: a clinician's guide

The goal of case-control association studies is to find genetic variants in the human genome that influence common traits. The Human Genome and HapMap projects have added fresh impetus to this goal by cataloguing the raw genetic data behind human DNA variation. Studies that associate these genetic variants with phenotype improve both molecular diagnostics and drug discovery and offer clinicians important opportunities to improve care of patients. In this review I focus on case-control studies, which are the most widely used design and expected to be the most powerful. I also address the problem of case-control non-replication, which is widespread despite enormous effort and use of resources. Important causes of non-replication include inadequate statistical power to detect small and moderate effects, phenotype heterogeneity, population stratification, publication bias, and multiple comparison testing.

Visualization, reconstruction, and integration of neuronal structures in digital brain atlases

Brain atlases are used in neuroanatomy to define the spatial layout of neuronal structures. Their digital variant can serve as a database and common reference frame for integrating data from different biological experiments. This article presents an overview of methods for three-dimensional visualization of neuroanatomical image data, reconstructing neuronal structures from image data, creating digital brain atlases, and registering data in an atlas. This enables analysis of spatial relations between individual structures imaged in different experiments as well as between these structures and the atlas.

The problem of neuronal cell types: a physiological genomics approach

Neural circuits within the vertebrate brain are composed of highly diverse cell types. The exact extent of this diversity is a matter of continuing debate. For example, do cortical interneurons comprise a few, dozens or >100 distinct cell types? Recently, several groups have used microarrays to measure genome-wide gene expression profiles for specific neuronal cell types. These methods can offer an objective basis for neuronal classification. In this review, we argue that this approach should now be carried out more broadly and that it should be coupled to large-scale efforts to generate mouse driver lines in which tools for genetic manipulation, such as the Cre recombinase, are expressed in identified cell types within the brain. This would enable neuroscientists to begin to investigate more systematically the roles of specific genes in establishing particular cellular phenotypes, and also the roles of particular cell types within brain circuits. This review is part of the TINS special issue on The Neural Substrates of Cognition.

Identification of novel autism candidate regions through analysis of reported cytogenetic abnormalities associated with autism

The identification of the candidate genes for autism through linkage and association studies has proven to be a difficult enterprise. An alternative approach is the analysis of cytogenetic abnormalities associated with autism. We present a review of all studies to date that relate patients with cytogenetic abnormalities to the autism phenotype. A literature survey of the Medline and Pubmed databases was performed, using multiple keyword searches. Additional searches through cited references and abstracts from the major genetic conferences from 2000 onwards completed the search. The quality of the phenotype (i.e. of the autism spectrum diagnosis) was rated for each included case. Available specific probe and marker information was used to define optimally the boundaries of the cytogenetic abnormalities. In case of recurrent deletions or duplications on chromosome 15 and 22, the positions of the low copy repeats that are thought to mediate these rearrangements were used to define the most likely boundaries of the implicated 'Cytogenetic Regions Of Interest' (CROIs). If no molecular data were available, the sequence position of the relevant chromosome bands was used to obtain the approximate molecular boundaries of the CROI. The findings of the current review indicate: (1) several regions of overlap between CROIs and known loci of significant linkage and/or association findings, and (2) additional regions of overlap among multiple CROIs at the same locus. Whereas the first finding confirms previous linkage/association findings, the latter may represent novel, not previously identified regions containing genes that contribute to autism. This analysis not only has confirmed the presence of several known autism risk regions but has also revealed additional previously unidentified loci, including 2q37, 5p15, 11q25, 16q22.3, 17p11.2, 18q21.1, 18q23, 22q11.2, 22q13.3 and Xp22.2-p22.3.

Comprehensive expression atlas of fibroblast growth factors and their receptors generated by a novel robotic in situ hybridization platform

A recently developed robotic platform termed "Genepaint" can carry out large-scale nonradioactive in situ hybridization (ISH) on tissue sections. We report a series of experiments that validate this novel platform. Signal-to-noise ratio and mRNA detection limits were comparable to traditional ISH procedures, and hybridization was transcript-specific, even in cases in which probes could have hybridized to several transcripts of a multigene family. We established an atlas of expression patterns of fibroblast growth factors (Fgfs) and their receptors (Fgfrs) for the embryonic day 14.5 mouse embryo. This atlas provides a comprehensive overview of previously known as well as novel sites of expression for this important family of signaling molecules. The Fgf/Fgfr atlas was integrated into the transcriptome database (www.genepaint.org), where individual Fgf and Fgfr expression patterns can be interactively viewed at cellular resolution and where sites of expressions can be retrieved using an anatomy-based search.

Psychiatry as a clinical neuroscience discipline

One of the fundamental insights emerging from contemporary neuroscience is that mental illnesses are brain disorders. In contrast to classic neurological illnesses that involve discrete brain lesions, mental disorders need to be addressed as disorders of distributed brain systems with symptoms forged by developmental and social experiences. While genomics will be important for revealing risk, and cellular neuroscience should provide targets for novel treatments for these disorders, it is most likely that the tools of systems neuroscience will yield the biomarkers needed to revolutionize psychiatric diagnosis and treatment. This essay considers the discoveries that will be necessary over the next two decades to translate the promise of modern neuroscience into strategies for prevention and cures of mental disorders. To deliver on this spectacular new potential, clinical neuroscience must be integrated into the discipline of psychiatry, thereby transforming current psychiatric training, tools, and practices.

A digital atlas to characterize the mouse brain transcriptome

Massive amounts of data are being generated in an effort to represent for the brain the expression of all genes at cellular resolution. Critical to exploiting this effort is the ability to place these data into a common frame of reference. Here we have developed a computational method for annotating gene expression patterns in the context of a digital atlas to facilitate custom user queries and comparisons of this type of data. This procedure has been applied to 200 genes in the postnatal mouse brain. As an illustration of utility, we identify candidate genes that may be related to Parkinson disease by using the expression of a dopamine transporter in the substantia nigra as a search query pattern. In addition, we discover that transcription factor Rorb is down-regulated in the barrelless mutant relative to control mice by quantitative comparison of expression patterns in layer IV somatosensory cortex. The semi-automated annotation method developed here is applicable to a broad spectrum of complex tissues and data modalities.

The mammalian brain is a complex organ with hundreds of functional parts. Describing when and where genes are expressed in the brain is thus a potentially powerful method for understanding the function of gene products. In recent years, several mammalian genomes including those of human and mouse have been characterized. There are now efforts around the world that aim to determine the expression patterns for all genes in the mouse brain. To search these expression data readily, they must be placed into an atlas. The authors propose a new method for bringing such genetic data into a common spatial framework so that one can perform spatial searches and comparisons of gene expression patterns. To create this atlas, the authors developed a series of maps of the brain using a graphical modeling method called subdivision. These maps were deformed to match the shape of tissue sections, and genetic activity information was associated with the appropriate coordinates on the map. After placing 200 genes into the context of this atlas, the authors illustrate its application in discovering genes potentially involved in diseases and brain development.

Genomic characterization of Alzheimer’s disease and genotype-related phenotypic analysis of biological markers in dementia

More than 180 genes distributed across the human genome are potentially involved in the pathogenesis of Alzheimer’s disease (AD). The AD population shows a higher genetic variation rate than the control population. Significant differences in allelic distribution and frequency exist when AD-related polygenic clusters are compared with other forms of dementia, indicating that the genetic component in neurodegenerative dementia differs from that of other CNS disorders. The characterization of AD genotype-related phenotypic profiles reveals substantial differences in biological markers among AD clusters associated with different genes and/or allelic combinations. AD and dementia with vascular component (DVC) are the most prevalent forms of dementia. Both clinical entities share many similarities, but they differ in their major phenotypic and genotypic profiles, as revealed by structural and functional genomics studies. Comparative phenotypic studies have identified significant differences in 25% of more than 100 parametric variables, including anthropometric values, cardiovascular function, blood pressure, lipid metabolism, uric acid metabolism, peripheral calcium homeostasis, liver function, alkaline phosphatase, lactate dehydrogenase, red and white blood cells, regional brain atrophy, and brain blood flow velocity. Functional genomic studies incorporating apolipoprotein E (APOE)-related changes in biological markers extended the difference between AD and DVC by up to 57%. Structural genomic studies with AD-related genes, including APP, MAPT, APOE, PS1, PS2, A2M, ACE, AGT, cFOS, and PRNP, demonstrate different genetic profiles in AD and DVC, with an absolute genetic variation rate in the range of 30–80%, depending upon genes and genetic clusters. The relative polymorphic variation in genetic clusters integrated by two, three or four genes associated with AD ranges from 1 to 3%. The main phenotypic differences in AD are genotype dependent, indicating a powerful influence of polygenic factors on the AD phenotypic profile. All these genotypic and phenotypic variations bring about important consequences for the pharmacogenomics of AD.