Cell-type-specific neuroanatomy of cliques of autism-related genes in the mouse brain

Two cliques of genes identified computationally for their high co-expression in the mouse brain according to the Allen Brain Atlas, and for their enrichment in genes related to autism spectrum disorder (ASD), have recently been shown to be highly co-expressed in the cerebellar cortex, compared to what could be expected by chance. Moreover, the expression of these cliques of genes is not homogeneous across the cerebellar cortex, and it has been noted that their expression pattern seems to highlight the granular layer. However, this observation was only made by eye, and recent advances in computational neuroanatomy allow to rank cell types in the mouse brain (characterized by their transcriptome profiles) according to the similarity between their spatial density profiles and the spatial expression profiles of the cliques. We establish by Monte Carlo simulation that with probability at least 99%, the expression profiles of the two cliques are more similar to the density profile of granule cells than 99% of the expression of cliques containing the same number of genes (Purkinje cells also score above 99% in one of the cliques). Thresholding the expression profiles shows that the signal is more intense in the granular layer. Finally, we work out pairs of cell types whose combined expression profiles are more similar to the expression profiles of the cliques than any single cell type. These pairs predominantly consist of one cortical pyramidal cell and one cerebellar cell (which can be either a granule cell or a Purkinje cell).

[1]  Partha P. Mitra,et al.  Computational neuroanatomy and gene expression: Optimal sets of marker genes for brain regions , 2012, 2012 46th Annual Conference on Information Sciences and Systems (CISS).

[2]  Allan R. Jones,et al.  A mesoscale connectome of the mouse brain , 2014, Nature.

[3]  Ken Sugino,et al.  Transcriptional and Electrophysiological Maturation of Neocortical Fast-Spiking GABAergic Interneurons , 2009, The Journal of Neuroscience.

[4]  Michael Hawrylycz,et al.  NeuroBlast: a 3D spatial homology search tool for gene expression , 2007, BMC Neuroscience.

[5]  Lydia Ng,et al.  Clustering of spatial gene expression patterns in the mouse brain and comparison with classical neuroanatomy. , 2010, Methods.

[6]  G. Allan Johnson,et al.  Digital Atlasing and Standardization in the Mouse Brain , 2011, PLoS Comput. Biol..

[7]  D. Cory-Slechta,et al.  Cerebellar Purkinje cell p75 neurotrophin receptor and autistic behavior , 2014, Translational Psychiatry.

[8]  Catherine Lord,et al.  Epidemiology: How common is autism? , 2011, Nature.

[9]  Allan R. Jones,et al.  An anatomically comprehensive atlas of the adult human brain transcriptome , 2012, Nature.

[10]  John Morris,et al.  Multi-scale correlation structure of gene expression in the brain , 2011, Neural Networks.

[11]  James A. Eddy,et al.  Cell type-specific genes show striking and distinct patterns of spatial expression in the mouse brain , 2013, Proceedings of the National Academy of Sciences.

[12]  Kenny Q. Ye,et al.  De Novo Gene Disruptions in Children on the Autistic Spectrum , 2012, Neuron.

[13]  Sacha B. Nelson,et al.  A Quantitative Comparison of Cell-Type-Specific Microarray Gene Expression Profiling Methods in the Mouse Brain , 2011, PloS one.

[14]  S. Hasnain,et al.  Array-Based Comparative Genomic Hybridization , 2007 .

[15]  P. Greengard,et al.  A Translational Profiling Approach for the Molecular Characterization of CNS Cell Types , 2008, Cell.

[16]  Paola Arlotta,et al.  Neuronal Subtype-Specific Genes that Control Corticospinal Motor Neuron Development In Vivo , 2005, Neuron.

[17]  Sharmila Banerjee-Basu,et al.  Animal model integration to AutDB, a genetic database for autism , 2011, BMC Medical Genomics.

[18]  S. Nelson,et al.  Molecular taxonomy of major neuronal classes in the adult mouse forebrain , 2006, Nature Neuroscience.

[19]  Michael F. Walker,et al.  De novo mutations revealed by whole-exome sequencing are strongly associated with autism , 2012, Nature.

[20]  Bradley P. Coe,et al.  Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations , 2012, Nature.

[21]  J. Price.  :Allen Reference Atlas: A Digital Color Brain Atlas of the C57BL/6J Male Mouse , 2008 .

[22]  Partha P. Mitra,et al.  Brain Gene Expression Analysis: a MATLAB toolbox for the analysis of brain-wide gene-expression data , 2012 .

[23]  D. Amaral,et al.  Neuroanatomy of autism , 2008, Trends in Neurosciences.

[24]  A. Zimmerman,et al.  Neuroglial activation and neuroinflammation in the brain of patients with autism , 2005, Annals of neurology.

[25]  Allan R. Jones,et al.  Genome-wide atlas of gene expression in the adult mouse brain , 2007, Nature.

[26]  Edouard Henrion,et al.  A Population Genetic Approach to Mapping Neurological Disorder Genes Using Deep Resequencing , 2011, PLoS genetics.

[27]  Y. Xing,et al.  A Transcriptome Database for Astrocytes, Neurons, and Oligodendrocytes: A New Resource for Understanding Brain Development and Function , 2008, The Journal of Neuroscience.

[28]  Allan R. Jones,et al.  An anatomic gene expression atlas of the adult mouse brain , 2009, Nature Neuroscience.

[29]  Julie Daniels,et al.  The epidemiology of autism spectrum disorders. , 2007, Annual review of public health.

[30]  Edwin H. Cook,et al.  Copy-number variations associated with neuropsychiatric conditions , 2008, Nature.

[31]  Partha P. Mitra,et al.  Computational neuroanatomy and co-expression of genes in the adult mouse brain, analysis tools for the Allen Brain Atlas , 2013, Quantitative Biology.

[32]  Oliver von Ahsen,et al.  Global Transcriptome Analysis of Genetically Identified Neurons in the Adult Cortex , 2006, The Journal of Neuroscience.

[33]  Sharmila Banerjee-Basu,et al.  AutDB: a gene reference resource for autism research , 2008, Nucleic Acids Res..

[34]  Evan T. Geller,et al.  Patterns and rates of exonic de novo mutations in autism spectrum disorders , 2012, Nature.

[35]  Shuiwang Ji,et al.  Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns , 2014, BMC Bioinformatics.

[36]  T. Kemper,et al.  Regional Alterations in Purkinje Cell Density in Patients with Autism , 2014, PloS one.

[37]  S. Horvath,et al.  Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology , 2011, Nature.

[38]  Z. Modrušan,et al.  Deconvolution of Blood Microarray Data Identifies Cellular Activation Patterns in Systemic Lupus Erythematosus , 2009, PloS one.

[39]  Ling Lin,et al.  Cell type-specific gene expression of midbrain dopaminergic neurons reveals molecules involved in their vulnerability and protection. , 2005, Human molecular genetics.

[40]  Michael Wigler,et al.  Rare De Novo Variants Associated with Autism Implicate a Large Functional Network of Genes Involved in Formation and Function of Synapses , 2011, Neuron.

[41]  J. Hohmann,et al.  Insights from spatially mapped gene expression in the mouse brain. , 2007, Human molecular genetics.

[42]  Aleksandra Badura,et al.  The Cerebellum, Sensitive Periods, and Autism , 2014, Neuron.

[43]  Thomas Bourgeron,et al.  Mapping autism risk loci using genetic linkage and chromosomal rearrangements , 2007, Nature Genetics.

[44]  Hong Wei Dong,et al.  Allen reference atlas : a digital color brain atlas of the C57Black/6J male mouse , 2008 .

[45]  Paul Pavlidis,et al.  Neuron-Enriched Gene Expression Patterns are Regionally Anti-Correlated with Oligodendrocyte-Enriched Patterns in the Adult Mouse and Human Brain , 2013, Front. Neurosci..

[46]  Brian B. Avants,et al.  Neuroinformatics for Genome-Wide 3-D Gene Expression Mapping in the Mouse Brain , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[47]  Margaret A. Pericak-Vance,et al.  Individual common variants exert weak effects on the risk for autism spectrum disorders , 2012, Human molecular genetics.

[48]  Boris Yamrom,et al.  Rare De Novo and Transmitted Copy-Number Variation in Autistic Spectrum Disorders , 2011, Neuron.

[49]  S. Nelson,et al.  Cell-type–based model explaining coexpression patterns of genes in the brain , 2011, Proceedings of the National Academy of Sciences.

[50]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[51]  R Redon,et al.  Array-based comparative genomic hybridisation identifies high frequency of cryptic chromosomal rearrangements in patients with syndromic autism spectrum disorders , 2006, Journal of Medical Genetics.

[52]  Michael Hawrylycz,et al.  Quantitative methods for genome-scale analysis of in situ hybridization and correlation with microarray data , 2008, Genome Biology.

[53]  Partha P. Mitra,et al.  Co-expression Profiling of Autism Genes in the Mouse Brain , 2013, PLoS Comput. Biol..