Transcriptional and Cellular Signatures of Morphometric Similarity Remodeling in Major Depressive Disorder

Little is known about how major depressive disorder (MDD)-related anatomical endophenotypes are driven by transcriptomic profiles. Here, we examined a link between brain-wide gene expression and morphometric similarity (MS) remodeling in two MDD samples. MDD exhibited replicable abnormal MS patterns compared to healthy controls. Using spatially-comprehensive cortical gene expression data, we further identified two types of transcriptional signatures of MS remodeling: i) gene specificity, in which closely linked transcriptionally upregulated genes from postmortem samples in MDD, but not in other brain disorders, were spatially correlated with MDD MS remodeling; and ii) ontological enrichment, which identified reliable neurobiologically-relevant ontology terms and pathways previously described in MDD. Finally, we assigned transcriptional signatures to cell-types, which specified microglia and neurons as contributing most to the transcriptomic relationship of MS remodeling in MDD. Collectively, combined gene transcripts and connectome topology provided insight into how microscale genetic molecular mechanisms cause mesoscale morphometric abnormalities in MDD.

[1]  Erick Jorge Canales-Rodríguez,et al.  Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders. , 2020, JAMA psychiatry.

[2]  D. Geschwind,et al.  Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders , 2020, Nature Communications.

[3]  J. Ragoussis,et al.  Single-nucleus transcriptomics of the prefrontal cortex in major depressive disorder implicates oligodendrocyte precursor cells and excitatory neurons , 2020, Nature Neuroscience.

[4]  Ju Wang,et al.  Analyzing the genes and pathways related to major depressive disorder via a systems biology approach , 2019, Brain and behavior.

[5]  Peter B. Jones,et al.  Schizotypy-Related Magnetization of Cortex in Healthy Adolescence Is Colocated With Expression of Schizophrenia-Related Genes , 2019, Biological Psychiatry.

[6]  Yong Xu,et al.  The rise and fall of MRI studies in major depressive disorder , 2019, Translational Psychiatry.

[7]  M. P. van den Heuvel,et al.  Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder , 2019, Molecular Psychiatry.

[8]  Fan Yang,et al.  Astrocyte, a Promising Target for Mood Disorder Interventions , 2019, Front. Mol. Neurosci..

[9]  E. Bullmore,et al.  Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes , 2018, Proceedings of the National Academy of Sciences.

[10]  Alireza Hadj Khodabakhshi,et al.  Metascape provides a biologist-oriented resource for the analysis of systems-level datasets , 2019, Nature Communications.

[11]  Garry D. Honey,et al.  Patients with autism spectrum disorders display reproducible functional connectivity alterations , 2019, Science Translational Medicine.

[12]  Ben D. Fulcher,et al.  Bridging the Gap between Connectome and Transcriptome , 2019, Trends in Cognitive Sciences.

[13]  Annie W Shieh,et al.  Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder , 2018, Science.

[14]  Ben D. Fulcher,et al.  A practical guide to linking brain-wide gene expression and neuroimaging data , 2018, NeuroImage.

[15]  M. Dylan Tisdall,et al.  Quantitative assessment of structural image quality , 2018, NeuroImage.

[16]  Thomas W. Mühleisen,et al.  Integration of transcriptomic and cytoarchitectonic data implicates a role for MAOA and TAC1 in the limbic-cortical network , 2018, Brain Structure and Function.

[17]  Warren W. Kretzschmar,et al.  Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression , 2017, Nature Genetics.

[18]  anonymous In Review , 2018 .

[19]  Peter B. Jones,et al.  Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation , 2017, Neuron.

[20]  Leon French,et al.  Transcriptomic characterization of MRI contrast with focus on the T1-w/T2-w ratio in the cerebral cortex , 2017, NeuroImage.

[21]  Edward T. Bullmore,et al.  Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism , 2017, bioRxiv.

[22]  Alan C. Evans,et al.  Gene networks show associations with seed region connectivity , 2017, Human brain mapping.

[23]  Peter B. Jones,et al.  Adolescent Tuning of Association Cortex in Human Structural Brain Networks , 2017, bioRxiv.

[24]  René S. Kahn,et al.  Connectome Disconnectivity and Cortical Gene Expression in Patients With Schizophrenia , 2017, Biological Psychiatry.

[25]  Anders M. Dale,et al.  Genetic and environmental influences on cortical mean diffusivity , 2017, NeuroImage.

[26]  E. Sibille,et al.  Decrease in somatostatin‐positive cell density in the amygdala of females with major depression , 2017, Depression and anxiety.

[27]  Chad J. Donahue,et al.  Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: A Quantitative Comparison with Tracers in the Monkey , 2016, The Journal of Neuroscience.

[28]  Nick C Fox,et al.  Analysis of shared heritability in common disorders of the brain , 2018, Science.

[29]  S. Horvath,et al.  Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap , 2016, Science.

[30]  Zonglei Zhen,et al.  Genetic Variation in S100B Modulates Neural Processing of Visual Scenes in Han Chinese , 2016, Cerebral cortex.

[31]  Claus C. Hilgetag,et al.  Principles of ipsilateral and contralateral cortico-cortical connectivity in the mouse , 2015, Brain Structure and Function.

[32]  D. Geschwind,et al.  Correspondence between Resting-State Activity and Brain Gene Expression , 2015, Neuron.

[33]  Allan R. Jones,et al.  Canonical Genetic Signatures of the Adult Human Brain , 2015, Nature Neuroscience.

[34]  R. Yirmiya,et al.  Depression as a Microglial Disease , 2015, Trends in Neurosciences.

[35]  H. Barbas General cortical and special prefrontal connections: principles from structure to function. , 2015, Annual review of neuroscience.

[36]  M. Rietschel,et al.  Correlated gene expression supports synchronous activity in brain networks , 2015, Science.

[37]  P. Fox,et al.  Identification of a common neurobiological substrate for mental illness. , 2015, JAMA psychiatry.

[38]  E. Sibille,et al.  Somatostatin, neuronal vulnerability and behavioral emotionality , 2014, Molecular Psychiatry.

[39]  L. Williams,et al.  Abnormal Structural Networks Characterize Major Depressive Disorder: A Connectome Analysis , 2014, Biological Psychiatry.

[40]  E. Bullmore,et al.  The hubs of the human connectome are generally implicated in the anatomy of brain disorders , 2014, Brain : a journal of neurology.

[41]  Etienne Sibille,et al.  Why Are Cortical GABA Neurons Relevant to Internal Focus in Depression? A cross-level model linking cellular, biochemical, and neural network findings , 2014, Molecular Psychiatry.

[42]  J. P. Hamilton,et al.  Anomalous Gray Matter Structural Networks in Major Depressive Disorder , 2013, Biological Psychiatry.

[43]  J. Nabekura,et al.  Microglia: actively surveying and shaping neuronal circuit structure and function , 2013, Trends in Neurosciences.

[44]  真田 昌 骨髄異形成症候群のgenome-wide analysis , 2013 .

[45]  E. Bullmore,et al.  Imaging structural co-variance between human brain regions , 2013, Nature Reviews Neuroscience.

[46]  R. Mechoulam,et al.  The endocannabinoid system and the brain. , 2013, Annual review of psychology.

[47]  Peter B. Jones,et al.  Adult mental health disorders and their age at onset , 2013, British Journal of Psychiatry.

[48]  G. Aghajanian,et al.  Synaptic Dysfunction in Depression: Potential Therapeutic Targets , 2012, Science.

[49]  P. Castillo,et al.  Endocannabinoid Signaling and Synaptic Function , 2012, Neuron.

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

[51]  G. Rajkowska,et al.  Decreased Expression of Synapse-Related Genes and Loss of Synapses in Major Depressive Disorder , 2012, Nature Medicine.

[52]  T. Freund,et al.  Multiple functions of endocannabinoid signaling in the brain. , 2012, Annual review of neuroscience.

[53]  Allan R. Jones,et al.  Large-Scale Cellular-Resolution Gene Profiling in Human Neocortex Reveals Species-Specific Molecular Signatures , 2012, Cell.

[54]  Yong He,et al.  Topologically Convergent and Divergent Structural Connectivity Patterns between Patients with Remitted Geriatric Depression and Amnestic Mild Cognitive Impairment , 2012, The Journal of Neuroscience.

[55]  Line Harder Clemmensen,et al.  Effects of network resolution on topological properties of human neocortex , 2012, NeuroImage.

[56]  G. Tseng,et al.  Molecular evidence for BDNF- and GABA-related dysfunctions in the amygdala of female subjects with Major Depression , 2011, Molecular Psychiatry.

[57]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[58]  M. Furey,et al.  Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression , 2008, Brain Structure and Function.

[59]  A. Simon,et al.  Somatostatinergic systems in brain: Networks and functions , 2008, Molecular and Cellular Endocrinology.

[60]  A. Bilkei-Gorzo,et al.  Diminished Anxiety- and Depression-Related Behaviors in Mice with Selective Deletion of the Tac1 Gene , 2002, The Journal of Neuroscience.

[61]  H. Abdi Partial least squares regression and projection on latent structure regression (PLS Regression) , 2010 .

[62]  C. Economo,et al.  Atlas of Cytoarchitectonics of the Adult Human Cerebral Cortex , 2008 .