1
|
Hing B, Mitchell SB, Filali Y, Eberle M, Hultman I, Matkovich M, Kasturirangan M, Johnson M, Wyche W, Jimenez A, Velamuri R, Ghumman M, Wickramasinghe H, Christian O, Srivastava S, Hultman R. Transcriptomic Evaluation of a Stress Vulnerability Network Using Single-Cell RNA Sequencing in Mouse Prefrontal Cortex. Biol Psychiatry 2024; 96:886-899. [PMID: 38866174 PMCID: PMC11524784 DOI: 10.1016/j.biopsych.2024.05.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 04/24/2024] [Accepted: 05/27/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND Increased vulnerability to stress is a major risk factor for several mood disorders, including major depressive disorder. Although cellular and molecular mechanisms associated with depressive behaviors following stress have been identified, little is known about the mechanisms that confer the vulnerability that predisposes individuals to future damage from chronic stress. METHODS We used multisite in vivo neurophysiology in freely behaving male and female C57BL/6 mice (n = 12) to measure electrical brain network activity previously identified as indicating a latent stress vulnerability brain state. We combined this neurophysiological approach with single-cell RNA sequencing of the prefrontal cortex to identify distinct transcriptomic differences between groups of mice with inherent high and low stress vulnerability. RESULTS We identified hundreds of differentially expressed genes (padjusted < .05) across 5 major cell types in animals with high and low stress vulnerability brain network activity. This unique analysis revealed that GABAergic (gamma-aminobutyric acidergic) neuron gene expression contributed most to the network activity of the stress vulnerability brain state. Upregulation of mitochondrial and metabolic pathways also distinguished high and low vulnerability brain states, especially in inhibitory neurons. Importantly, genes that were differentially regulated with vulnerability network activity significantly overlapped (above chance) with those identified by genome-wide association studies as having single nucleotide polymorphisms significantly associated with depression as well as genes more highly expressed in postmortem prefrontal cortex of patients with major depressive disorder. CONCLUSIONS This is the first study to identify cell types and genes involved in a latent stress vulnerability state in the brain.
Collapse
Affiliation(s)
- Benjamin Hing
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa
| | - Sara B Mitchell
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa; Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, Iowa
| | - Yassine Filali
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa; Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, Iowa
| | - Maureen Eberle
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa
| | - Ian Hultman
- Department of Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa
| | - Molly Matkovich
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa
| | | | - Micah Johnson
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa; Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, Iowa
| | - Whitney Wyche
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa
| | - Alli Jimenez
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa
| | - Radha Velamuri
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa
| | - Mahnoor Ghumman
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa
| | - Himali Wickramasinghe
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa
| | - Olivia Christian
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa
| | - Sanvesh Srivastava
- Department of Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa
| | - Rainbo Hultman
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, Iowa; Department of Psychiatry, University of Iowa, Iowa City, Iowa.
| |
Collapse
|
2
|
Bryers A, Hawkes CA, Parkin E, Dawson N. Progress towards understanding risk factor mechanisms in the development of autism spectrum disorders. Biochem Soc Trans 2024; 52:2047-2058. [PMID: 39221783 PMCID: PMC11555714 DOI: 10.1042/bst20231004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 08/09/2024] [Accepted: 08/20/2024] [Indexed: 09/04/2024]
Abstract
Autism spectrum disorders (ASD) are a heterogenous set of syndromes characterised by social impairment and cognitive symptoms. Currently, there are limited treatment options available to help people with ASD manage their symptoms. Understanding the biological mechanisms that result in ASD diagnosis and symptomatology is an essential step in developing new interventional strategies. Human genetic studies have identified common gene variants of small effect and rare risk genes and copy number variants (CNVs) that substantially increase the risk of developing ASD. Reverse translational studies using rodent models based on these genetic variants provide new insight into the biological basis of ASD. Here we review recent findings from three ASD associated CNV mouse models (16p11.2, 2p16.3 and 22q11.2 deletion) that show behavioural and cognitive phenotypes relevant to ASD. These models have identified disturbed excitation-inhibition neurotransmitter balance, evidenced by dysfunctional glutamate and GABA signalling, as a key aetiological mechanism. These models also provide emerging evidence for serotoninergic neurotransmitter system dysfunction, although more work is needed to clarify the nature of this. At the brain network level, prefrontal cortex (PFC) dysfunctional connectivity is also evident across these models, supporting disturbed PFC function as a key nexus in ASD aetiology. Overall, published data highlight the utility and valuable insight gained into ASD aetiology from preclinical CNV mouse models. These have identified key aetiological mechanisms that represent putative novel therapeutic targets for the treatment of ASD symptoms, making them useful translational models for future drug discovery, development and validation.
Collapse
Affiliation(s)
- Amelia Bryers
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster, UK
| | - Cheryl A. Hawkes
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster, UK
| | - Edward Parkin
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster, UK
| | - Neil Dawson
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster, UK
| |
Collapse
|
3
|
Alvino FG, Gini S, Minetti A, Pagani M, Sastre-Yagüe D, Barsotti N, De Guzman E, Schleifer C, Stuefer A, Kushan L, Montani C, Galbusera A, Papaleo F, Lombardo MV, Pasqualetti M, Bearden CE, Gozzi A. Synaptic-dependent developmental dysconnectivity in 22q11.2 deletion syndrome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.29.587339. [PMID: 38585897 PMCID: PMC10996624 DOI: 10.1101/2024.03.29.587339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Chromosome 22q11.2 deletion is among the strongest known genetic risk factors for neuropsychiatric disorders, including autism and schizophrenia. Brain imaging studies have reported disrupted large-scale functional connectivity in people with 22q11 deletion syndrome (22q11DS). However, the significance and biological determinants of these functional alterations remain unclear. Here, we use a cross-species design to investigate the developmental trajectory and neural underpinnings of brain dysconnectivity in 22q11DS. We find that LgDel mice, an established mouse model of 22q11DS, exhibit age-specific patterns of functional MRI (fMRI) dysconnectivity, with widespread fMRI hyper-connectivity in juvenile mice reverting to focal hippocampal hypoconnectivity over puberty. These fMRI connectivity alterations are mirrored by co-occurring developmental alterations in dendritic spine density, and are both transiently normalized by developmental GSK3β inhibition, suggesting a synaptic origin for this phenomenon. Notably, analogous hyper- to hypoconnectivity reconfiguration occurs also in human 22q11DS, where it affects hippocampal and cortical regions spatially enriched for synaptic genes that interact with GSK3β, and autism-relevant transcripts. Functional dysconnectivity in somatomotor components of this network is predictive of age-dependent social alterations in 22q11.2 deletion carriers. Taken together, these findings suggest that synaptic-related mechanisms underlie developmentally mediated functional dysconnectivity in 22q11DS.
Collapse
Affiliation(s)
- F G Alvino
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| | - S Gini
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - A Minetti
- Department of Biology, Unit of Cell and Developmental Biology, University of Pisa, Pisa, Italy
| | - M Pagani
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- IMT School for Advanced Studies, Lucca, Italy
| | - D Sastre-Yagüe
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - N Barsotti
- Centro per l'Integrazione della Strumentazione Scientifica dell'Universita di Pisa (CISUP), Pisa, Italy
| | - E De Guzman
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| | - C Schleifer
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, California
| | - A Stuefer
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - L Kushan
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, California
| | - C Montani
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| | - A Galbusera
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| | - F Papaleo
- Genetics of Cognition Laboratory, Neuroscience area, Istituto Italiano di Tecnologia, Genova, Italy
| | - M V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - M Pasqualetti
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- Centro per l'Integrazione della Strumentazione Scientifica dell'Universita di Pisa (CISUP), Pisa, Italy
| | - C E Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, California
| | - A Gozzi
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| |
Collapse
|
4
|
Hing B, Mitchell SB, Eberle M, Filali Y, Hultman I, Matkovich M, Kasturirangan M, Wyche W, Jimenez A, Velamuri R, Johnson M, Srivastava S, Hultman R. Single Cell Transcriptome of Stress Vulnerability Network in mouse Prefrontal Cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.14.540705. [PMID: 37662266 PMCID: PMC10473598 DOI: 10.1101/2023.05.14.540705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Increased vulnerability to stress is a major risk factor for the manifestation of several mood disorders, including major depressive disorder (MDD). Despite the status of MDD as a significant donor to global disability, the complex integration of genetic and environmental factors that contribute to the behavioral display of such disorders has made a thorough understanding of related etiology elusive. Recent developments suggest that a brain-wide network approach is needed, taking into account the complex interplay of cell types spanning multiple brain regions. Single cell RNA-sequencing technologies can provide transcriptomic profiling at the single-cell level across heterogenous samples. Furthermore, we have previously used local field potential oscillations and machine learning to identify an electrical brain network that is indicative of a predisposed vulnerability state. Thus, this study combined single cell RNA-sequencing (scRNA-Seq) with electrical brain network measures of the stress-vulnerable state, providing a unique opportunity to access the relationship between stress network activity and transcriptomic changes within individual cell types. We found especially high numbers of differentially expressed genes between animals with high and low stress vulnerability brain network activity in astrocytes and glutamatergic neurons but we estimated that vulnerability network activity depends most on GABAergic neurons. High vulnerability network activity included upregulation of microglia and mitochondrial and metabolic pathways, while lower vulnerability involved synaptic regulation. Genes that were differentially regulated with vulnerability network activity significantly overlapped with genes identified as having significant SNPs by human GWAS for depression. Taken together, these data provide the gene expression architecture of a previously uncharacterized stress vulnerability brain state, enabling new understanding and intervention of predisposition to stress susceptibility.
Collapse
|
5
|
Dopamine transporter silencing in the rat: systems-level alterations in striato-cerebellar and prefrontal-midbrain circuits. Mol Psychiatry 2022; 27:2329-2339. [PMID: 35246636 PMCID: PMC9126810 DOI: 10.1038/s41380-022-01471-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 01/15/2022] [Accepted: 02/02/2022] [Indexed: 12/12/2022]
Abstract
Silencing of dopamine transporter (DAT), a main controlling factor of dopaminergic signaling, results in biochemical and behavioral features characteristic for neuropsychiatric diseases with presumed hyperdopaminergia including schizophrenia, attention deficit hyperactivity disorder (ADHD), bipolar disorder, and obsessive-compulsive disorder (OCD). Investigation of DAT silencing thus provides a transdiagnostic approach towards a systems-level understanding of common underlying pathways. Using a high-field multimodal imaging approach and a highly sensitive cryogenic coil, we integrated structural, functional and metabolic investigations in tandem with behavioral assessments on a newly developed preclinical rat model, comparing DAT homozygous knockout (DAT-KO, N = 14), heterozygous knockout (N = 8) and wild-type male rats (N = 14). We identified spatially distributed structural and functional brain alterations encompassing motor, limbic and associative loops that demonstrated strong behavioral relevance and were highly consistent across imaging modalities. DAT-KO rats manifested pronounced volume loss in the dorsal striatum, negatively correlating with cerebellar volume increase. These alterations were associated with hyperlocomotion, repetitive behavior and loss of efficient functional small-world organization. Further, prefrontal and midbrain regions manifested opposite changes in functional connectivity and local network topology. These prefrontal disturbances were corroborated by elevated myo-inositol levels and increased volume. To conclude, our imaging genetics approach provides multimodal evidence for prefrontal-midbrain decoupling and striato-cerebellar neuroplastic compensation as two key features of constitutive DAT blockade, proposing them as transdiagnostic mechanisms of hyperdopaminergia. Thus, our study connects developmental DAT blockade to systems-level brain changes, underlying impaired action inhibition control and resulting in motor hyperactivity and compulsive-like features relevant for ADHD, schizophrenia and OCD.
Collapse
|