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Nakamura T, Yoshihara T, Tanegashima C, Kadota M, Kobayashi Y, Honda K, Ishiwata M, Ueda J, Hara T, Nakanishi M, Takumi T, Itohara S, Kuraku S, Asano M, Kasahara T, Nakajima K, Tsuboi T, Takata A, Kato T. Transcriptomic dysregulation and autistic-like behaviors in Kmt2c haploinsufficient mice rescued by an LSD1 inhibitor. Mol Psychiatry 2024; 29:2888-2904. [PMID: 38528071 PMCID: PMC11420081 DOI: 10.1038/s41380-024-02479-8] [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: 01/05/2024] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 03/27/2024]
Abstract
Recent studies have consistently demonstrated that the regulation of chromatin and gene transcription plays a pivotal role in the pathogenesis of neurodevelopmental disorders. Among many genes involved in these pathways, KMT2C, encoding one of the six known histone H3 lysine 4 (H3K4) methyltransferases in humans and rodents, was identified as a gene whose heterozygous loss-of-function variants are causally associated with autism spectrum disorder (ASD) and the Kleefstra syndrome phenotypic spectrum. However, little is known about how KMT2C haploinsufficiency causes neurodevelopmental deficits and how these conditions can be treated. To address this, we developed and analyzed genetically engineered mice with a heterozygous frameshift mutation of Kmt2c (Kmt2c+/fs mice) as a disease model with high etiological validity. In a series of behavioral analyses, the mutant mice exhibit autistic-like behaviors such as impairments in sociality, flexibility, and working memory, demonstrating their face validity as an ASD model. To investigate the molecular basis of the observed abnormalities, we performed a transcriptomic analysis of their bulk adult brains and found that ASD risk genes were specifically enriched in the upregulated differentially expressed genes (DEGs), whereas KMT2C peaks detected by ChIP-seq were significantly co-localized with the downregulated genes, suggesting an important role of putative indirect effects of Kmt2c haploinsufficiency. We further performed single-cell RNA sequencing of newborn mouse brains to obtain cell type-resolved insights at an earlier stage. By integrating findings from ASD exome sequencing, genome-wide association, and postmortem brain studies to characterize DEGs in each cell cluster, we found strong ASD-associated transcriptomic changes in radial glia and immature neurons with no obvious bias toward upregulated or downregulated DEGs. On the other hand, there was no significant gross change in the cellular composition. Lastly, we explored potential therapeutic agents and demonstrate that vafidemstat, a lysine-specific histone demethylase 1 (LSD1) inhibitor that was effective in other models of neuropsychiatric/neurodevelopmental disorders, ameliorates impairments in sociality but not working memory in adult Kmt2c+/fs mice. Intriguingly, the administration of vafidemstat was shown to alter the vast majority of DEGs in the direction to normalize the transcriptomic abnormalities in the mutant mice (94.3 and 82.5% of the significant upregulated and downregulated DEGs, respectively, P < 2.2 × 10-16, binomial test), which could be the molecular mechanism underlying the behavioral rescuing. In summary, our study expands the repertoire of ASD models with high etiological and face validity, elucidates the cell-type resolved molecular alterations due to Kmt2c haploinsufficiency, and demonstrates the efficacy of an LSD1 inhibitor that might be generalizable to multiple categories of psychiatric disorders along with a better understanding of its presumed mechanisms of action.
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Affiliation(s)
- Takumi Nakamura
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, Saitama, Japan
- Department of Psychiatry and Behavioral Science, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Center for Brain Science, Saitama, Japan
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Toru Yoshihara
- Institute of Laboratory Animals, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Chiharu Tanegashima
- Laboratory for Phyloinformatics, RIKEN Center for Biosystems Dynamics Research, Hyogo, Japan
| | - Mitsutaka Kadota
- Laboratory for Phyloinformatics, RIKEN Center for Biosystems Dynamics Research, Hyogo, Japan
| | - Yuki Kobayashi
- Laboratory for Behavioral Genetics, RIKEN Center for Brain Science, Saitama, Japan
| | - Kurara Honda
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, Saitama, Japan
| | - Mizuho Ishiwata
- Department of Psychiatry and Behavioral Science, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Center for Brain Science, Saitama, Japan
| | - Junko Ueda
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, Saitama, Japan
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Center for Brain Science, Saitama, Japan
| | - Tomonori Hara
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, Saitama, Japan
- Department of Organ Anatomy, Tohoku University Graduate School of Medicine, Miyagi, Japan
| | - Moe Nakanishi
- Laboratory for Mental Biology, RIKEN Center for Brain Science, Saitama, Japan
- Laboratory for Molecular Mechanism of Brain Development, RIKEN Center for Brain Science, Saitama, Japan
| | - Toru Takumi
- Laboratory for Mental Biology, RIKEN Center for Brain Science, Saitama, Japan
- Department of Physiology and Cell Biology, Kobe University School of Medicine, Hyogo, Japan
| | - Shigeyoshi Itohara
- Laboratory for Behavioral Genetics, RIKEN Center for Brain Science, Saitama, Japan
| | - Shigehiro Kuraku
- Laboratory for Phyloinformatics, RIKEN Center for Biosystems Dynamics Research, Hyogo, Japan
- Molecular Life History Laboratory, Department of Genomics and Evolutionary Biology, National Institute of Genetics, Shizuoka, Japan
- Department of Genetics, SOKENDAI (Graduate University for Advanced Studies), Shizuoka, Japan
| | - Masahide Asano
- Institute of Laboratory Animals, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Takaoki Kasahara
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Center for Brain Science, Saitama, Japan
- Institute of Biology and Environmental Sciences, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany
| | - Kazuo Nakajima
- Department of Psychiatry and Behavioral Science, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Center for Brain Science, Saitama, Japan
- Department of Physiology, Teikyo University School of Medicine, Tokyo, Japan
| | - Takashi Tsuboi
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Atsushi Takata
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, Saitama, Japan.
- Research Institute for Diseases of Old Age, Juntendo University Graduate School of Medicine, Tokyo, Japan.
| | - Tadafumi Kato
- Department of Psychiatry and Behavioral Science, Juntendo University Graduate School of Medicine, Tokyo, Japan.
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Center for Brain Science, Saitama, Japan.
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Wang Z, Wang Y, Yan J, Wei Y, Zhang Y, Wang X, Leng X. Analysis of cuproptosis-related genes in Ulcerative colitis and immunological characterization based on machine learning. Front Med (Lausanne) 2023; 10:1115500. [PMID: 37529244 PMCID: PMC10389668 DOI: 10.3389/fmed.2023.1115500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 07/03/2023] [Indexed: 08/03/2023] Open
Abstract
Cuproptosis is a novel form of cell death, mediated by protein lipid acylation and highly associated with mitochondrial metabolism, which is regulated in the cell. Ulcerative colitis (UC) is a chronic inflammatory bowel disease that recurs frequently, and its incidence is increasing worldwide every year. Currently, a growing number of studies have shown that cuproptosis-related genes (CRGs) play a crucial role in the development and progression of a variety of tumors. However, the regulatory role of CRGs in UC has not been fully elucidated. Firstly, we identified differentially expressed genes in UC, Likewise, CRGs expression profiles and immunological profiles were evaluated. Using 75 UC samples, we typed UC based on the expression profiles of CRGs, followed by correlative immune cell infiltration analysis. Using the weighted gene co-expression network analysis (WGCNA) methodology, the cluster's differentially expressed genes (DEGs) were produced. Then, the performances of extreme gradient boosting models (XGB), support vector machine models (SVM), random forest models (RF), and generalized linear models (GLM) were constructed and predicted. Finally, the effectiveness of the best machine learning model was evaluated using five external datasets, receiver operating characteristic curve (ROC), the area under the curve of ROC (AUC), a calibration curve, a nomogram, and a decision curve analysis (DCA). A total of 13 CRGs were identified as significantly different in UC and control samples. Two subtypes were identified in UC based on CRGs expression profiles. Immune cell infiltration analysis of subtypes showed significant differences between immune cells of different subtypes. WGCNA results showed a total of 8 modules with significant differences between subtypes, with the turquoise module being the most specific. The machine learning results showed satisfactory performance of the XGB model (AUC = 0.981). Finally, the construction of the final 5-gene-based XGB model, validated by the calibration curve, nomogram, decision curve analysis, and five external datasets (GSE11223: AUC = 0.987; GSE38713: AUC = 0.815; GSE53306: AUC = 0.946; GSE94648: AUC = 0.809; GSE87466: AUC = 0.981), also proved to predict subtypes of UC with accuracy. Our research presents a trustworthy model that can predict the likelihood of developing UC and methodically outlines the complex relationship between CRGs and UC.
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Affiliation(s)
- Zhengyan Wang
- Changchun University of Chinese Medicine, Changchun, China
| | - Ying Wang
- The Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China
| | - Jing Yan
- Changchun University of Chinese Medicine, Changchun, China
| | - Yuchi Wei
- Changchun University of Chinese Medicine, Changchun, China
| | - Yinzhen Zhang
- Changchun University of Chinese Medicine, Changchun, China
| | - Xukai Wang
- Department of Orthopedics, The Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China
| | - Xiangyang Leng
- Changchun University of Chinese Medicine, Changchun, China
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Gastrin-releasing peptide regulates fear learning under stressed conditions via activation of the amygdalostriatal transition area. Mol Psychiatry 2022; 27:1694-1703. [PMID: 34997193 DOI: 10.1038/s41380-021-01408-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 11/16/2021] [Accepted: 11/25/2021] [Indexed: 11/08/2022]
Abstract
The amygdala, a critical brain region responsible for emotional behavior, is crucially involved in the regulation of the effects of stress on emotional behavior. In the mammalian forebrain, gastrin-releasing peptide (GRP), a 27-amino-acid mammalian neuropeptide, which is a homolog of the 14-amino-acid amidated amphibian peptide bombesin, is highly expressed in the amygdala. The levels of GRP are markedly increased in the amygdala after acute stress; therefore, it is known as a stress-activated modulator. To determine the role of GRP in emotional behavior under stress, we conducted some behavioral and biochemical experiments with GRP-knockout (KO) mice. GRP-KO mice exhibited a longer freezing response than wild-type (WT) littermates in both contextual and auditory fear (also known as threat) conditioning tests only when they were subjected to acute restraint stress 20 min before the conditioning. To identify the critical neural circuits associated with the regulation of emotional memory by GRP, we conducted Arc/Arg3.1-reporter mapping in the amygdala with an Arc-Venus reporter transgenic mouse line. In the amygdalostriatal transition area (AST) and the lateral side of the basal nuclei, fear conditioning after restraint stress increased neuronal activity significantly in WT mice, and GRP KO was found to negate this potentiation only in the AST. These results indicate that the GRP-activated neurons in the AST are likely to suppress excessive fear expression through the regulation of downstream circuits related to fear learning following acute stress.
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