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Geng Q, Xu Y, Hu Y, Wang L, Wang Y, Fan Z, Kong D. Progress in the Application of Organoids-On-A-Chip in Diseases. Organogenesis 2024; 20:2386727. [PMID: 39126669 PMCID: PMC11318694 DOI: 10.1080/15476278.2024.2386727] [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: 03/05/2024] [Revised: 07/12/2024] [Accepted: 07/26/2024] [Indexed: 08/12/2024] Open
Abstract
With the rapid development of the field of life sciences, traditional 2D cell culture and animal models have long been unable to meet the urgent needs of modern biomedical research and new drug development. Establishing a new generation of experimental models and research models is of great significance for deeply understanding human health and disease processes, and developing effective treatment measures. As is well known, long research and development cycles, high risks, and high costs are the "three mountains" facing the development of new drugs today. Organoids and organ-on-chips technology can highly simulate and reproduce the human physiological environment and complex reactions in vitro, greatly improving the accuracy of drug clinical efficacy prediction, reducing drug development costs, and avoiding the defects of drug testing animal models. Therefore, organ-on-chips have enormous potential in medical diagnosis and treatment.
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Affiliation(s)
- Qiao Geng
- Chinese Medicine Modernization and Big Data Research Center, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Yanyan Xu
- Department of Anoenterology, Nanjing Hospital of Chinese Medicine affiliated to Nanjing University of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Yang Hu
- Chinese Medicine Modernization and Big Data Research Center, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Lu Wang
- Department of colorectal surgery, The Second Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yi Wang
- Department of colorectal surgery, The Second Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhimin Fan
- Department of colorectal surgery, The Second Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Desong Kong
- Chinese Medicine Modernization and Big Data Research Center, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
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2
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Opsasnick LA, Zhao W, Schmitz LL, Ratliff SM, Faul JD, Zhou X, Needham BL, Smith JA. Epigenome-wide association study of long-term psychosocial stress in older adults. Epigenetics 2024; 19:2323907. [PMID: 38431869 PMCID: PMC10913704 DOI: 10.1080/15592294.2024.2323907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/21/2024] [Indexed: 03/05/2024] Open
Abstract
Long-term psychosocial stress is strongly associated with negative physical and mental health outcomes, as well as adverse health behaviours; however, little is known about the role that stress plays on the epigenome. One proposed mechanism by which stress affects DNA methylation is through health behaviours. We conducted an epigenome-wide association study (EWAS) of cumulative psychosocial stress (n = 2,689) from the Health and Retirement Study (mean age = 70.4 years), assessing DNA methylation (Illumina Infinium HumanMethylationEPIC Beadchip) at 789,656 CpG sites. For identified CpG sites, we conducted a formal mediation analysis to examine whether smoking, alcohol use, physical activity, and body mass index (BMI) mediate the relationship between stress and DNA methylation. Nine CpG sites were associated with psychosocial stress (all p < 9E-07; FDR q < 0.10). Additionally, health behaviours and/or BMI mediated 9.4% to 21.8% of the relationship between stress and methylation at eight of the nine CpGs. Several of the identified CpGs were in or near genes associated with cardiometabolic traits, psychosocial disorders, inflammation, and smoking. These findings support our hypothesis that psychosocial stress is associated with DNA methylation across the epigenome. Furthermore, specific health behaviours mediate only a modest percentage of this relationship, providing evidence that other mechanisms may link stress and DNA methylation.
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Affiliation(s)
- Lauren A. Opsasnick
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Lauren L. Schmitz
- Robert M. La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Scott M. Ratliff
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jessica D. Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Xiang Zhou
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Belinda L. Needham
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
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3
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Ziemann M, Abeysooriya M, Bora A, Lamon S, Kasu MS, Norris MW, Wong YT, Craig JM. Direction-aware functional class scoring enrichment analysis of infinium DNA methylation data. Epigenetics 2024; 19:2375022. [PMID: 38967555 PMCID: PMC11229754 DOI: 10.1080/15592294.2024.2375022] [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: 02/22/2024] [Accepted: 06/26/2024] [Indexed: 07/06/2024] Open
Abstract
Infinium Methylation BeadChip arrays remain one of the most popular platforms for epigenome-wide association studies, but tools for downstream pathway analysis have their limitations. Functional class scoring (FCS) is a group of pathway enrichment techniques that involve the ranking of genes and evaluation of their collective regulation in biological systems, but the implementations described for Infinium methylation array data do not retain direction information, which is important for mechanistic understanding of genomic regulation. Here, we evaluate several candidate FCS methods that retain directional information. According to simulation results, the best-performing method involves the mean aggregation of probe limma t-statistics by gene followed by a rank-ANOVA enrichment test using the mitch package. This method, which we call 'LAM,' outperformed an existing over-representation analysis method in simulations, and showed higher sensitivity and robustness in an analysis of real lung tumour-normal paired datasets. Using matched RNA-seq data, we examine the relationship of methylation differences at promoters and gene bodies with RNA expression at the level of pathways in lung cancer. To demonstrate the utility of our approach, we apply it to three other contexts where public data were available. First, we examine the differential pathway methylation associated with chronological age. Second, we investigate pathway methylation differences in infants conceived with in vitro fertilization. Lastly, we analyse differential pathway methylation in 19 disease states, identifying hundreds of novel associations. These results show LAM is a powerful method for the detection of differential pathway methylation complementing existing methods. A reproducible vignette is provided to illustrate how to implement this method.
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Affiliation(s)
- Mark Ziemann
- Bioinformatics Working Group, Burnet Institute, Melbourne, Australia
- School of Life and Environmental Sciences, Deakin University, Geelong, Australia
| | - Mandhri Abeysooriya
- School of Life and Environmental Sciences, Deakin University, Geelong, Australia
- School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition, Deakin University, Geelong, Australia
| | - Anusuiya Bora
- Bioinformatics Working Group, Burnet Institute, Melbourne, Australia
- School of Life and Environmental Sciences, Deakin University, Geelong, Australia
| | - Séverine Lamon
- School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition, Deakin University, Geelong, Australia
| | - Mary Sravya Kasu
- School of Life and Environmental Sciences, Deakin University, Geelong, Australia
| | - Mitchell W. Norris
- School of Life and Environmental Sciences, Deakin University, Geelong, Australia
| | - Yen Ting Wong
- School of Medicine, Deakin University, Geelong, Australia
- Murdoch Children’s Research Institute, Melbourne, Australia
| | - Jeffrey M. Craig
- School of Medicine, Deakin University, Geelong, Australia
- Murdoch Children’s Research Institute, Melbourne, Australia
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4
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Li W, Ballard J, Zhao Y, Long Q. Knowledge-guided learning methods for integrative analysis of multi-omics data. Comput Struct Biotechnol J 2024; 23:1945-1950. [PMID: 38736693 PMCID: PMC11087912 DOI: 10.1016/j.csbj.2024.04.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 05/14/2024] Open
Abstract
Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.
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Affiliation(s)
- Wenrui Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, 19104, PA, USA
| | - Jenna Ballard
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, 19104, PA, USA
| | - Yize Zhao
- Department of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, 06510, CT, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, 19104, PA, USA
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5
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Wang G, Zhang H, Shao M, Tian M, Feng H, Li Q, Cao C. Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases. Comput Struct Biotechnol J 2024; 23:2478-2486. [PMID: 38952424 PMCID: PMC11215961 DOI: 10.1016/j.csbj.2024.05.050] [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: 02/01/2024] [Revised: 05/31/2024] [Accepted: 05/31/2024] [Indexed: 07/03/2024] Open
Abstract
Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L 0 +L 1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.
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Affiliation(s)
- Guishen Wang
- College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
| | - Hangchen Zhang
- College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
| | - Mengting Shao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Min Tian
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Hui Feng
- College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
| | - Qiaoling Li
- Department of Cardiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Chen Cao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
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6
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Hagenauer MH, Sannah Y, Hebda-Bauer EK, Rhoads C, O'Connor AM, Flandreau E, Watson SJ, Akil H. Resource: A curated database of brain-related functional gene sets (Brain.GMT). MethodsX 2024; 13:102788. [PMID: 39049932 PMCID: PMC11267058 DOI: 10.1016/j.mex.2024.102788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 05/31/2024] [Indexed: 07/27/2024] Open
Abstract
Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation. •We compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT").•Brain.GMT can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret neuroscience transcriptional profiling results from three species (rat, mouse, human).•Although Brain.GMT is still undergoing development, it substantially improved the interpretation of differential expression results within our initial use cases.
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Affiliation(s)
- Megan H. Hagenauer
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yusra Sannah
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Cosette Rhoads
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
- National Institutes of Health, Bethesda, MD 20892, USA
| | - Angela M. O'Connor
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Stanley J. Watson
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Huda Akil
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
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7
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Melton HJ, Zhang Z, Deng HW, Wu L, Wu C. MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations. Epigenetics 2024; 19:2370542. [PMID: 38963888 PMCID: PMC11225927 DOI: 10.1080/15592294.2024.2370542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 06/12/2024] [Indexed: 07/06/2024] Open
Abstract
Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.
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Affiliation(s)
- Hunter J. Melton
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Zichen Zhang
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hong-Wen Deng
- Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA
| | - Lang Wu
- Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA
| | - Chong Wu
- Cancer Epidemiology Division, University of Hawaii Cancer Center, Honolulu, HI, USA
- Institute for Data Science in Oncology, The UT MD Anderson Cancer Center
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8
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Li M, Yuan Y, Hou Z, Hao S, Jin L, Wang B. Human brain organoid: trends, evolution, and remaining challenges. Neural Regen Res 2024; 19:2387-2399. [PMID: 38526275 PMCID: PMC11090441 DOI: 10.4103/1673-5374.390972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/26/2023] [Accepted: 10/28/2023] [Indexed: 03/26/2024] Open
Abstract
Advanced brain organoids provide promising platforms for deciphering the cellular and molecular processes of human neural development and diseases. Although various studies and reviews have described developments and advancements in brain organoids, few studies have comprehensively summarized and analyzed the global trends in this area of neuroscience. To identify and further facilitate the development of cerebral organoids, we utilized bibliometrics and visualization methods to analyze the global trends and evolution of brain organoids in the last 10 years. First, annual publications, countries/regions, organizations, journals, authors, co-citations, and keywords relating to brain organoids were identified. The hotspots in this field were also systematically identified. Subsequently, current applications for brain organoids in neuroscience, including human neural development, neural disorders, infectious diseases, regenerative medicine, drug discovery, and toxicity assessment studies, are comprehensively discussed. Towards that end, several considerations regarding the current challenges in brain organoid research and future strategies to advance neuroscience will be presented to further promote their application in neurological research.
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Affiliation(s)
- Minghui Li
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, China
- Southwest Hospital/Southwest Eye Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yuhan Yuan
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, China
| | - Zongkun Hou
- School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, Guizhou Province, China
| | - Shilei Hao
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, China
| | - Liang Jin
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, China
| | - Bochu Wang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, China
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9
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Alves VC, Carro E, Figueiro-Silva J. Unveiling DNA methylation in Alzheimer's disease: a review of array-based human brain studies. Neural Regen Res 2024; 19:2365-2376. [PMID: 38526273 PMCID: PMC11090417 DOI: 10.4103/1673-5374.393106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 12/05/2023] [Indexed: 03/26/2024] Open
Abstract
The intricacies of Alzheimer's disease pathogenesis are being increasingly illuminated by the exploration of epigenetic mechanisms, particularly DNA methylation. This review comprehensively surveys recent human-centered studies that investigate whole genome DNA methylation in Alzheimer's disease neuropathology. The examination of various brain regions reveals distinctive DNA methylation patterns that associate with the Braak stage and Alzheimer's disease progression. The entorhinal cortex emerges as a focal point due to its early histological alterations and subsequent impact on downstream regions like the hippocampus. Notably, ANK1 hypermethylation, a protein implicated in neurofibrillary tangle formation, was recurrently identified in the entorhinal cortex. Further, the middle temporal gyrus and prefrontal cortex were shown to exhibit significant hypermethylation of genes like HOXA3, RHBDF2, and MCF2L, potentially influencing neuroinflammatory processes. The complex role of BIN1 in late-onset Alzheimer's disease is underscored by its association with altered methylation patterns. Despite the disparities across studies, these findings highlight the intricate interplay between epigenetic modifications and Alzheimer's disease pathology. Future research efforts should address methodological variations, incorporate diverse cohorts, and consider environmental factors to unravel the nuanced epigenetic landscape underlying Alzheimer's disease progression.
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Affiliation(s)
- Victoria Cunha Alves
- Neurodegenerative Diseases Group, Hospital Universitario 12 de Octubre Research Institute (imas12), Madrid, Spain
- Network Center for Biomedical Research, Neurodegenerative Diseases (CIBERNED), Madrid, Spain
- PhD Program in Neuroscience, Autonoma de Madrid University, Madrid, Spain
- Neurotraumatology and Subarachnoid Hemorrhage Group, Hospital Universitario 12 de Octubre Research Institute (imas12), Madrid, Spain
| | - Eva Carro
- Network Center for Biomedical Research, Neurodegenerative Diseases (CIBERNED), Madrid, Spain
- Neurobiology of Alzheimer's Disease Unit, Functional Unit for Research Into Chronic Diseases, Instituto de Salud Carlos III, Madrid, Spain
| | - Joana Figueiro-Silva
- Institute of Medical Genetics, University of Zurich, Zurich, Switzerland
- Department of Molecular Life Science, University of Zurich, Zurich, Switzerland
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10
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Favaretto E, Bedani F, Brancati GE, De Berardis D, Giovannini S, Scarcella L, Martiadis V, Martini A, Pampaloni I, Perugi G, Pessina E, Raffone F, Ressico F, Cattaneo CI. Synthesising 30 years of clinical experience and scientific insight on affective temperaments in psychiatric disorders: State of the art. J Affect Disord 2024; 362:406-415. [PMID: 38972642 DOI: 10.1016/j.jad.2024.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/17/2024] [Accepted: 07/04/2024] [Indexed: 07/09/2024]
Abstract
The concept of affective temperament has been extensively discussed throughout the history of psychopathology and represents a cornerstone in the study of mood disorders. This review aims to trace the evolution of the concept of affective temperaments (ATs) from Kraepelin's seminal work to the present day. In the 1980s, Akiskal redefined Kraepelin's concept of affective temperaments (ATs) by integrating the five recognized ATs into the broader framework of the soft bipolar spectrum. This conceptualization viewed ATs as non-pathological predispositions underlying psychiatric disorders, particularly mood disorders. Epidemiological and clinical studies have validated the existence of the five ATs. Furthermore, evidence suggests that ATs may serve as precursors to various psychiatric disorders and influence clinical dimensions such as disease course, psychopathology, and treatment adherence. Additionally, ATs appear to play a significant role in moderating phenomena such as suicide risk and stress coping. Incorporating an evaluation of temperamental bases of disorders into the multidimensional psychiatric diagnostic process could enhance treatment optimization and prognosis estimation.
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Affiliation(s)
- E Favaretto
- Department of Addiction, South Tyrol Health Care, Bressanone, Italy.
| | - F Bedani
- Mercy University Hospital, Cork, IRELAND
| | | | - D De Berardis
- Department of Psychiatry, Azienda Sanitaria Locale 4, Teramo, ITALY.
| | - S Giovannini
- Department of Addiction, South Tyrol Health Care, Bressanone, Italy
| | - L Scarcella
- Department of Psychiatry, South Tyrol Health Care, Bressanone, Italy.
| | - V Martiadis
- Department of Mental Health, Asl Napoli 1 Centro, Naples, Italy
| | - A Martini
- Department of Mental Health, ASL CN2 Alba - Bra, Italy
| | - I Pampaloni
- National OCD and BDD Unit, South West London and St Georges NHS Trust, London, United Kingdom.
| | - G Perugi
- Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy.
| | - E Pessina
- Department of Mental Health, ASL CN2 Alba - Bra, Italy
| | - F Raffone
- Department of Mental Health, Asl Napoli 1 Centro, Naples, Italy
| | - F Ressico
- Outpatient Unit Department of Mental Health Novara, Borgomanero, Italy
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11
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Reid BM. Early life stress and iron metabolism in developmental psychoneuroimmunology. Brain Behav Immun Health 2024; 40:100824. [PMID: 39161875 PMCID: PMC11331713 DOI: 10.1016/j.bbih.2024.100824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 06/03/2024] [Accepted: 07/15/2024] [Indexed: 08/21/2024] Open
Abstract
An estimated 250 million children face adverse health outcomes from early life exposure to severe or chronic social, economic, and nutritional adversity, highlighting/emphasizing the pressing concern about the link between ELS and long-term implications on mental and physical health. There is significant overlap between populations experiencing high levels of chronic stress and those experiencing iron deficiency, spotlighting the potential role of iron as a key mediator in this association. Iron, an essential micronutrient for brain development and immune function, is often depleted in stress conditions. Iron deficiency among the most common nutrient deficiencies in the world. Fetal and infant iron status may thus serve as a crucial intermediary between early chronic psychological stress and subsequent immune system changes to impact neurodevelopment. The review presents a hypothesized pathway between early life stress (ELS), iron deficiency, and neurodevelopment through the hypothalamic-pituitary-adrenocortical (HPA) axis and the IL-6-hepcidin axis. This hypothesis is derived from (1) evidence that stress impacts iron status (2) long-term neurodevelopmental outcomes that are shared by ELS and iron deficiency exposure, and (3) possible mechanisms for how iron may mediate the relation between ELS and iron deficiency through alterations in the developing immune system. The article concludes by proposing future research directions, emphasizing the need for rigorous studies to elucidate how stress and iron metabolism interact to modify the developing immune system. Understanding these mechanisms could open new avenues for improving human health and neurodevelopment for women and children globally, making it a timely and vital area of study in psychoneuroimmunology research.
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Affiliation(s)
- Brie M. Reid
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, USA
- Center for Behavioral and Preventive Medicine, The Miriam Hospital, USA
- Department of Psychology, Department of Health Sciences, Northeastern University, USA
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Lengvenyte A, Cognasse F, Hamzeh-Cognasse H, Sénèque M, Strumila R, Olié E, Courtet P. Baseline circulating biomarkers, their changes, and subsequent suicidal ideation and depression severity at 6 months: A prospective analysis in patients with mood disorders. Psychoneuroendocrinology 2024; 168:107119. [PMID: 39003840 DOI: 10.1016/j.psyneuen.2024.107119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 06/19/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND Identifying circulating biomarkers associated with prospective suicidal ideation (SI) and depression could help better understand the dynamics of these phenomena and identify people in need of intense care. In this study, we investigated the associations between baseline peripheral biomarkers implicated in neuroplasticity, vascular homeostasis and inflammation, and prospective SI and depression severity during 6 months of follow-up in patients with mood disorders. METHODS 149 patients underwent a psychiatric evaluation and gave blood to measure 32 plasma soluble proteins. At follow-up, SI incidence over six months was measured with the Columbia Suicide Severity Rating Scale, and depressive symptoms were assessed with the Inventory for Depressive Symptomatology. Ninety-six patients provided repeated blood samples. Statistical analyses included Spearman partial correlation and Elastic Net regression, followed by the covariate-adjusted regression models. RESULTS 51.4 % (N = 71) of patients reported SI during follow-up. After adjustment for covariates, higher baseline levels of interferon-γ were associated with SI occurrence during follow-up. Higher baseline interferon-γ and lower orexin-A were associated with increased depression severity, and atypical and anxious, but not melancholic, symptoms. There was also a tendency for associations of elevated baseline levels of interferon-γ, interleukin-1β, and lower plasma serotonin levels with SI at the six-month follow-up time point. Meanwhile, reduction in transforming growth factor- β1 (TGF-β1) plasma concentration correlated with atypical symptoms reduction. CONCLUSION We identified interferon-γ and orexin-A as potential predictive biomarkers of SI and depression, whereas TGF-β1 was identified as a possible target of atypical symptoms.
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Affiliation(s)
- Aiste Lengvenyte
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France; Faculty of Medicine, Institute of Clinical Medicine, Psychiatric Clinic, Vilnius University, Vilnius, Lithuania.
| | - Fabrice Cognasse
- Université Jean Monnet, Mines Saint-Étienne, INSERM, U 1059 Sainbiose, Saint-Étienne, France; Etablissement Français du Sang Auvergne-Rhône-Alpes, Saint-Étienne, France
| | - Hind Hamzeh-Cognasse
- Université Jean Monnet, Mines Saint-Étienne, INSERM, U 1059 Sainbiose, Saint-Étienne, France
| | - Maude Sénèque
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Robertas Strumila
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France; Faculty of Medicine, Institute of Clinical Medicine, Psychiatric Clinic, Vilnius University, Vilnius, Lithuania
| | - Emilie Olié
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Philippe Courtet
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
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Wang R, Su Y, O'Donnell K, Caron J, Meaney M, Meng X, Li Y. Differential interactions between gene expressions and stressors across the lifespan in major depressive disorder. J Affect Disord 2024; 362:688-697. [PMID: 39029669 DOI: 10.1016/j.jad.2024.07.069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 07/05/2024] [Accepted: 07/14/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND Both genetic predispositions and exposures to stressors have collectively contributed to the development of major depressive disorder (MDD). To deep dive into their roles in MDD, our study aimed to examine which susceptible gene expression interacts with various dimensions of stressors in the MDD risk among a large population cohort. METHODS Data analyzed were from a longitudinal community-based cohort from Southwest Montreal, Canada (N = 1083). Latent profile models were used to identify distinct patterns of stressors for the study cohort. A transcriptome-wide association study (TWAS) method was performed to examine the interactive effects of three dimensions of stressors (threat, deprivation, and cumulative lifetime stress) and gene expression on the MDD risk in a total of 48 tissues from GTEx. Additional analyses were also conducted to further explore and specify these associations including colocalization, and fine-mapping analyses, in addition to enrichment analysis investigations based on TWAS. RESULTS We identified 3321 genes linked to MDD at the nominal p-value <0.05 and found that different patterns of stressors can amplify the genetic susceptibility to MDD. We also observed specific genes and pathways that interacted with deprivation and cumulative lifetime stressors, particularly in specific brain tissues including basal ganglia, prefrontal cortex, brain amygdala, brain cerebellum, brain cortex, and the whole blood. Colocalization analysis also identified these genes as having a high probability of sharing MDD causal variants. LIMITATIONS The study cohort was composed exclusively of individuals of Caucasians, which restricts the generalizability of the findings to other ethnic population groups. CONCLUSIONS The findings of the study unveiled significant interactions between potential tissue-specific gene expression × stressors in the MDD risk and shed light on the intricate etiological attributes of gene expression and specific stressors across the lifespan in MDD. These genetic and environmental attributes in MDD corroborate the vulnerability-stress theory and direct future stress research to have a closer examination of genetic predisposition and potential involvements of omics studies to specify the intricate relationships between genes and stressful environments.
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Affiliation(s)
- Ruiyang Wang
- Department of Financial and Risk Engineering, New York University, NY, NYC, USA; Department of Psychiatry, McGill University, Montreal, QC, Canada; Douglas Research Centre, Montreal, QC, Canada
| | - Yingying Su
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Kieran O'Donnell
- Department of Psychiatry, McGill University, Montreal, QC, Canada; Douglas Research Centre, Montreal, QC, Canada; Yale Child Study Center, Department of Obstetrics Gynecology & Reproductive Sciences, Yale School of Medicine, Yale University, New Haven, CT, USA; Child & Brain Development Program, CIFAR, Toronto, ON, Canada
| | - Jean Caron
- Department of Psychiatry, McGill University, Montreal, QC, Canada; Douglas Research Centre, Montreal, QC, Canada
| | - Michael Meaney
- Department of Psychiatry, McGill University, Montreal, QC, Canada; Douglas Research Centre, Montreal, QC, Canada
| | - Xiangfei Meng
- Department of Psychiatry, McGill University, Montreal, QC, Canada; Douglas Research Centre, Montreal, QC, Canada.
| | - Yue Li
- School of Computer Science, McGill University, Montreal, QC, Canada.
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Guo X, Chen Y, Huang H, Liu Y, Kong L, Chen L, Lyu H, Gao T, Lai J, Zhang D, Hu S. Serum signature of antibodies to Toxoplasma gondii, rubella virus, and cytomegalovirus in females with bipolar disorder: A cross-sectional study. J Affect Disord 2024; 361:82-90. [PMID: 38844171 DOI: 10.1016/j.jad.2024.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND AND AIM Immunity alterations have been observed in bipolar disorder (BD). However, whether serum positivity of antibodies to Toxoplasma gondii (T gondii), rubella, and cytomegalovirus (CMV) shared clinical relevance with BD, remains controversial. This study aimed to investigate this association. METHODS Antibody seropositivity of IgM and IgG to T gondii, rubella virus, and CMV of females with BD and controls was extracted based on medical records from January 2018 to January 2023. Family history, type of BD, onset age, and psychotic symptom history were also collected. RESULTS 585 individuals with BD and 800 healthy controls were involved. Individuals with BD revealed a lower positive rate of T gondii IgG in the 10-20 aged group (OR = 0.10), and a higher positive rate of rubella IgG in the 10-20 (OR = 5.44) and 20-30 aged group (OR = 3.15). BD with family history preferred a higher positive rate of T gondii IgG (OR = 24.00). Type-I BD owned a decreased positive rate of rubella IgG (OR = 0.37) and an elevated positive rate of CMV IgG (OR = 2.12) compared to type-II BD, while BD with early onset showed contrast results compared to BD without early onset (Rubella IgG, OR = 2.54; CMV IgG, OR = 0.26). BD with psychotic symptom history displayed a lower positive rate of rubella IgG (OR = 0.50). LIMITATIONS Absence of male evidence and control of socioeconomic status and environmental exposure. CONCLUSIONS Differential antibody seropositive rates of T gondii, rubella, and cytomegalovirus in BD were observed.
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Affiliation(s)
- Xiaonan Guo
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
| | - Yiqing Chen
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
| | - Huimin Huang
- Department of Psychiatry, The Third Affiliated Hospital of Wenzhou Medical University, 325800, Wenzhou, Zhejiang, China.
| | - Yifeng Liu
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.
| | - Lingzhuo Kong
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
| | - Lizichen Chen
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
| | - Hailong Lyu
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
| | | | - Jianbo Lai
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; Zhejiang Key Laboratory of Precision Psychiatry, Hangzhou 310003, China; Brain Research Institute of Zhejiang University, Hangzhou 310058, China; Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou 310003, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University School of Medicine, Hangzhou 310058, China.
| | - Dan Zhang
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.
| | - Shaohua Hu
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; Zhejiang Key Laboratory of Precision Psychiatry, Hangzhou 310003, China; Brain Research Institute of Zhejiang University, Hangzhou 310058, China; Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou 310003, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University School of Medicine, Hangzhou 310058, China; Department of Psychology and Behavioral Sciences, Graduate School, Zhejiang University, Hangzhou 310058, China; Nanhu Brain-computer Interface Institute, Hangzhou 311100, China.
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Park M, Shin JE, Yee J, Ahn YM, Joo EJ. Gene-gene interaction analysis for age at onset of bipolar disorder in a Korean population. J Affect Disord 2024; 361:97-103. [PMID: 38834091 DOI: 10.1016/j.jad.2024.05.152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 05/24/2024] [Accepted: 05/28/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND Multiple genes might interact to determine the age at onset of bipolar disorder. We investigated gene-gene interactions related to age at onset of bipolar disorder in the Korean population, using genome-wide association study (GWAS) data. METHODS The study population consisted of 303 patients with bipolar disorder. First, the top 1000 significant single-nucleotide polymorphisms (SNPs) associated with age at onset of bipolar disorder were selected through single SNP analysis by simple linear regression. Subsequently, the QMDR method was used to find gene-gene interactions. RESULTS The best 10 SNPs from simple regression were located in chromosome 1, 2, 3, 10, 11, 14, 19, and 21. Only five SNPs were found in several genes, such as FOXN3, KIAA1217, OPCML, CAMSAP2, and PTPRS. On QMDR analyses, five pairs of SNPs showed significant interactions with a CVC exceeding 1/5 in a two-locus model. The best interaction was found for the pair of rs60830549 and rs12952733 (CVC = 1/5, P < 1E-07). In three-locus models, four combinations of SNPs showed significant associations with age at onset, with a CVC of >1/5. The best three-locus combination was rs60830549, rs12952733, and rs12952733 (CVC = 2/5, P < 1E-6). The SNPs showing significant interactions were located in the KIAA1217, RBFOX3, SDK2, CYP19A1, NTM, SMYD3, and RBFOX1 genes. CONCLUSIONS Our analysis confirmed genetic interactions influencing the age of onset for bipolar disorder and identified several potential candidate genes. Further exploration of the functions of these promising genes, which may have multiple roles within the neuronal network, is necessary.
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Affiliation(s)
- Mira Park
- Department of Preventive Medicine, School of Medicine, Eulji University, Daejeon, Republic of Korea
| | - Ji-Eun Shin
- Department of Biomedical Informatics, School of Medicine, Konyang University, Daejeon, Republic of Korea
| | - Jaeyong Yee
- Department of Physiology and Biophysics, School of Medicine, Eulji University, Daejeon, Republic of Korea
| | - Yong Min Ahn
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Eun-Jeong Joo
- Department of Psychiatry, Uijeongbu Eulji Medical Center, Eulji University, Gyeonggi, Republic of Korea; Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon, Republic of Korea.
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Tume CE, Chick SL, Holmans PA, Rees E, O’Donovan MC, Cameron D, Bray NJ. Genetic Implication of Specific Glutamatergic Neurons of the Prefrontal Cortex in the Pathophysiology of Schizophrenia. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100345. [PMID: 39099730 PMCID: PMC11295574 DOI: 10.1016/j.bpsgos.2024.100345] [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: 02/14/2024] [Revised: 05/03/2024] [Accepted: 05/19/2024] [Indexed: 08/06/2024] Open
Abstract
Background The prefrontal cortex (PFC) has been strongly implicated in the pathophysiology of schizophrenia. Here, we combined high-resolution single-nuclei RNA sequencing data from the human PFC with large-scale genomic data for schizophrenia to identify constituent cell populations likely to mediate genetic liability to the disorder. Methods Gene expression specificity values were calculated from a single-nuclei RNA sequencing dataset comprising 84 cell populations from the human PFC, spanning gestation to adulthood. Enrichment of schizophrenia common variant liability and burden of rare protein-truncating coding variants were tested in genes with high expression specificity for each cell type. We also explored schizophrenia common variant associations in relation to gene expression across the developmental trajectory of implicated neurons. Results Common risk variation for schizophrenia was prominently enriched in genes with high expression specificity for a population of mature layer 4 glutamatergic neurons emerging in infancy. Common variant liability to schizophrenia increased along the developmental trajectory of this neuronal population. Fine-mapped genes at schizophrenia genome-wide association study risk loci had significantly higher expression specificity than other genes in these neurons and in a population of layer 5/6 glutamatergic neurons. People with schizophrenia had a higher rate of rare protein-truncating coding variants in genes expressed by cells of the PFC than control individuals, but no cell population was significantly enriched above this background rate. Conclusions We identified a population of layer 4 glutamatergic PFC neurons likely to be particularly affected by common variant genetic risk for schizophrenia, which may contribute to disturbances in thalamocortical connectivity in the condition.
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Affiliation(s)
- Claire E. Tume
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Sophie L. Chick
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Peter A. Holmans
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Elliott Rees
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Michael C. O’Donovan
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Darren Cameron
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Nicholas J. Bray
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
- Neuroscience & Mental Health Innovation Institute, Cardiff University, Cardiff, Wales, United Kingdom
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Jonsson L, Song J, Joas E, Pålsson E, Landén M. Polygenic scores for psychiatric disorders associate with year of first bipolar disorder diagnosis: A register-based study between 1972 and 2016. Psychiatry Res 2024; 339:116081. [PMID: 38996631 DOI: 10.1016/j.psychres.2024.116081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 07/05/2024] [Accepted: 07/06/2024] [Indexed: 07/14/2024]
Abstract
The diagnostic criteria of bipolar disorder (BD) have changed over time. To test if these changes are reflected in the polygenic profile in BD, we studied the association between first BD diagnosis year (during 1972-2016) and polygenic scores (PGS) for psychiatric disorders in BD patients (N = 3,818). We found significant associations between diagnosis year and PGS for BD, depression, and attention deficit hyperactivity disorder (ADHD). The PGS remained largely stable over time in BD type 1, while changes were observed in BD type 2. These findings bear significance not only for genetic research but also for clinical practise, as shifts in patient characteristics can influence treatment response.
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Affiliation(s)
- Lina Jonsson
- Department of psychiatry and neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Blå stråket 15, Gothenburg 413 45, Sweden.
| | - Jie Song
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Mental Health Center and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Erik Joas
- Department of psychiatry and neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Blå stråket 15, Gothenburg 413 45, Sweden
| | - Erik Pålsson
- Department of psychiatry and neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Blå stråket 15, Gothenburg 413 45, Sweden
| | - Mikael Landén
- Department of psychiatry and neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Blå stråket 15, Gothenburg 413 45, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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18
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Kolesnikova TO, Demin KA, Costa FV, de Abreu MS, Kalueff AV. Zebrafish models for studying cognitive enhancers. Neurosci Biobehav Rev 2024; 164:105797. [PMID: 38971515 DOI: 10.1016/j.neubiorev.2024.105797] [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: 05/08/2024] [Revised: 06/16/2024] [Accepted: 07/03/2024] [Indexed: 07/08/2024]
Abstract
Cognitive decline is commonly seen both in normal aging and in neurodegenerative and neuropsychiatric diseases. Various experimental animal models represent a valuable tool to study brain cognitive processes and their deficits. Equally important is the search for novel drugs to treat cognitive deficits and improve cognitions. Complementing rodent and clinical findings, studies utilizing zebrafish (Danio rerio) are rapidly gaining popularity in translational cognitive research and neuroactive drug screening. Here, we discuss the value of zebrafish models and assays for screening nootropic (cognitive enhancer) drugs and the discovery of novel nootropics. We also discuss the existing challenges, and outline future directions of research in this field.
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Affiliation(s)
| | - Konstantin A Demin
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia; Institute of Experimental Medicine, Almazov National Medical Research Centre, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia
| | - Fabiano V Costa
- Neurobiology Program, Sirius University of Science and Technology, Sochi, Russia
| | - Murilo S de Abreu
- Graduate Program in Health Sciences, Federal University of Health Sciences of Porto Alegre, Porto Alegre, Brazil; West Caspian University, Baku, Azerbaijan.
| | - Allan V Kalueff
- Neurobiology Program, Sirius University of Science and Technology, Sochi, Russia; Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia; Institute of Experimental Medicine, Almazov National Medical Research Centre, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia; Suzhou Key Laboratory on Neurobiology and Cell Signaling, Department of Biological Sciences, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, China.
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Blose BA, Silverstein SM, Stuart KV, Keane PA, Khawaja AP, Wagner SK. Association between polygenic risk for schizophrenia and retinal morphology: A cross-sectional analysis of the United Kingdom Biobank. Psychiatry Res 2024; 339:116106. [PMID: 39079374 DOI: 10.1016/j.psychres.2024.116106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 07/17/2024] [Accepted: 07/24/2024] [Indexed: 08/15/2024]
Abstract
We examined the relationship between genetic risk for schizophrenia (SZ), using polygenic risk scores (PRSs), and retinal morphological alterations. Retinal structural and vascular indices derived from optical coherence tomography (OCT) and color fundus photography (CFP) and PRSs for SZ were analyzed in N = 35,024 individuals from the prospective cohort study, United Kingdom Biobank (UKB). Results indicated that macular ganglion cell-inner plexiform layer (mGC-IPL) thickness was significantly inversely related to PRS for SZ, and this relationship was strongest within higher PRS quintiles and independent of potential confounders and age. PRS, however, was unrelated to retinal vascular characteristics, with the exception of venular tortuosity, and other retinal structural indices (macular retinal nerve fiber layer [mRNFL], inner nuclear layer [INL], cup-to-disc ratio [CDR]). Additionally, the association between greater PRS and reduced mGC-IPL thickness was only significant for participants in the 40-49 and 50-59 age groups, not those in the 60-69 age group. These findings suggest that mGC-IPL thinning is associated with a genetic predisposition to SZ and may reflect neurodevelopmental and/or neurodegenerative processes inherent to SZ. Retinal microvasculature alterations, however, may be secondary consequences of SZ and do not appear to be associated with a genetic predisposition to SZ.
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Affiliation(s)
- Brittany A Blose
- Department of Psychology, University of Rochester, Rochester, NY, United States; Department of Psychiatry, University of Rochester Medical Center, Rochester, New York, United States
| | - Steven M Silverstein
- Department of Psychiatry, University of Rochester Medical Center, Rochester, New York, United States; Department of Ophthalmology, University of Rochester Medical Center, Rochester, New York, United States; Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, United States; Center for Visual Science, University of Rochester, Rochester, New York, United States.
| | - Kelsey V Stuart
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom; Institute of Ophthalmology, University College London, London, United Kingdom; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Pearse A Keane
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom; Institute of Ophthalmology, University College London, London, United Kingdom; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Anthony P Khawaja
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom; Institute of Ophthalmology, University College London, London, United Kingdom; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Siegfried K Wagner
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom; Institute of Ophthalmology, University College London, London, United Kingdom; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
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Segura-Ortiz A, García-Nieto J, Aldana-Montes JF, Navas-Delgado I. Multi-objective context-guided consensus of a massive array of techniques for the inference of Gene Regulatory Networks. Comput Biol Med 2024; 179:108850. [PMID: 39013340 DOI: 10.1016/j.compbiomed.2024.108850] [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: 02/08/2024] [Revised: 07/03/2024] [Accepted: 07/03/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND AND OBJECTIVE Gene Regulatory Network (GRN) inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand. METHODS MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network inference. Friedman's statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance. RESULTS MO-GENECI's Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner. CONCLUSIONS MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.
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Affiliation(s)
- Adrián Segura-Ortiz
- Department de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain.
| | - José García-Nieto
- Department de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
| | - José F Aldana-Montes
- Department de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
| | - Ismael Navas-Delgado
- Department de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
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González-Peñas J, Alloza C, Brouwer R, Díaz-Caneja CM, Costas J, González-Lois N, Gallego AG, de Hoyos L, Gurriarán X, Andreu-Bernabeu Á, Romero-García R, Fañanás L, Bobes J, González-Pinto A, Crespo-Facorro B, Martorell L, Arrojo M, Vilella E, Gutiérrez-Zotes A, Perez-Rando M, Moltó MD, Buimer E, van Haren N, Cahn W, O'Donovan M, Kahn RS, Arango C, Pol HH, Janssen J, Schnack H. Accelerated Cortical Thinning in Schizophrenia Is Associated With Rare and Common Predisposing Variation to Schizophrenia and Neurodevelopmental Disorders. Biol Psychiatry 2024; 96:376-389. [PMID: 38521159 DOI: 10.1016/j.biopsych.2024.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Schizophrenia is a highly heritable disorder characterized by increased cortical thinning throughout the life span. Studies have reported a shared genetic basis between schizophrenia and cortical thickness. However, no genes whose expression is related to abnormal cortical thinning in schizophrenia have been identified. METHODS We conducted linear mixed models to estimate the rates of accelerated cortical thinning across 68 regions from the Desikan-Killiany atlas in individuals with schizophrenia compared with healthy control participants from a large longitudinal sample (ncases = 169 and ncontrols = 298, ages 16-70 years). We studied the correlation between gene expression data from the Allen Human Brain Atlas and accelerated thinning estimates across cortical regions. Finally, we explored the functional and genetic underpinnings of the genes that contribute most to accelerated thinning. RESULTS We found a global pattern of accelerated cortical thinning in individuals with schizophrenia compared with healthy control participants. Genes underexpressed in cortical regions that exhibit this accelerated thinning were downregulated in several psychiatric disorders and were enriched for both common and rare disrupting variation for schizophrenia and neurodevelopmental disorders. In contrast, none of these enrichments were observed for baseline cross-sectional cortical thickness differences. CONCLUSIONS Our findings suggest that accelerated cortical thinning, rather than cortical thickness alone, serves as an informative phenotype for neurodevelopmental disruptions in schizophrenia. We highlight the genetic and transcriptomic correlates of this accelerated cortical thinning, emphasizing the need for future longitudinal studies to elucidate the role of genetic variation and the temporal-spatial dynamics of gene expression in brain development and aging in schizophrenia.
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Affiliation(s)
- Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain.
| | - Clara Alloza
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain
| | - Rachel Brouwer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain; School of Medicine, Universidad Complutense, Madrid, Spain
| | - Javier Costas
- Instituto de Investigación Sanitària de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde, Santiago de Compostela, Galicia, Spain
| | - Noemí González-Lois
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain
| | - Ana Guil Gallego
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain
| | - Lucía de Hoyos
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain
| | - Xaquín Gurriarán
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain
| | - Álvaro Andreu-Bernabeu
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain
| | - Rafael Romero-García
- Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla, HUVR/CSIC/Universidad de Sevilla/CIBERSAM, Instituto de Salud Carlos III, Sevilla, Spain; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Lourdes Fañanás
- CIBERSAM, Madrid, Spain; Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Julio Bobes
- CIBERSAM, Madrid, Spain; Faculty of Medicine and Health Sciences-Psychiatry, Universidad de Oviedo, Instituto de Investigación Sanitaria del Principado de Asturias, Instituto de Neurociencias del Principado de Asturias, Oviedo, Spain
| | - Ana González-Pinto
- CIBERSAM, Madrid, Spain; BIOARABA Health Research Institute, Organización Sanitaria Integrada Araba, University Hospital, University of the Basque Country, Vitoria, Spain
| | - Benedicto Crespo-Facorro
- CIBERSAM, Madrid, Spain; Hospital Universitario Virgen del Rocío, Department of Psychiatry, Universidad de Sevilla, Sevilla, Spain
| | - Lourdes Martorell
- CIBERSAM, Madrid, Spain; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-Centres de Recerca de Catalunya, Universitat Rovira i Virgili, Reus, Spain
| | - Manuel Arrojo
- Instituto de Investigación Sanitària de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde, Santiago de Compostela, Galicia, Spain
| | - Elisabet Vilella
- CIBERSAM, Madrid, Spain; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-Centres de Recerca de Catalunya, Universitat Rovira i Virgili, Reus, Spain
| | - Alfonso Gutiérrez-Zotes
- CIBERSAM, Madrid, Spain; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-Centres de Recerca de Catalunya, Universitat Rovira i Virgili, Reus, Spain
| | - Marta Perez-Rando
- Fundación Investigación Hospital Clínico de València, Fundación Investigación Hospital Clínico de Valencia, València, Spain; Unidad de Neurobiología, Instituto de Biotecnología y Biomedicina, Universitat de València, València, Spain
| | - María Dolores Moltó
- CIBERSAM, Madrid, Spain; Unidad de Neurobiología, Instituto de Biotecnología y Biomedicina, Universitat de València, València, Spain; Department of Genetics, Universitat de València, Campus of Burjassot, València, Spain
| | - Elizabeth Buimer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Neeltje van Haren
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - Wiepke Cahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Altrecht Mental Health Institute, Altrecht Science, Utrecht, the Netherlands
| | - Michael O'Donovan
- Medical Research Council for Neuropsychiatric Genetics and Genomics and Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - René S Kahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain; School of Medicine, Universidad Complutense, Madrid, Spain
| | - Hilleke Hulshoff Pol
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Joost Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain; Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hugo Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
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22
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Drobotenko MI, Lyasota OM, Hernandez-Caceres JL, Labrada RR, Svidlov AA, Dorohova АA, Baryshev MG, Nechipurenko YD, Pérez LV, Dzhimak SS. Abnormal open states patterns in the ATXN2 DNA sequence depends on the CAG repeats length. Int J Biol Macromol 2024; 276:133849. [PMID: 39004246 DOI: 10.1016/j.ijbiomac.2024.133849] [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: 01/17/2024] [Revised: 05/04/2024] [Accepted: 07/11/2024] [Indexed: 07/16/2024]
Abstract
Hereditary ataxias are one of the «anticipation diseases» types. Spinocerebral ataxia type 2 occurs when the number of CAG repeats in the coding region of the ATXN2 gene exceeds 34 or more. In healthy people, the CAG repeat region in the ATXN2 gene usually consists of 22-23 CAG trinucleotides. Mutations that increase the length of CAG repeats can cause severe neurodegenerative and neuromuscular disorders known as trinucleotide repeat expansion diseases. The mechanisms causing such diseases are associated with non-canonical configurations that can be formed in the CAG repeat region during replication, transcription or repair. This makes it relevant to study the zones of open states that arise in the region of CAG repeats under torque. The purpose of this work is to study, using mathematical modeling, zones of open states in the region of CAG repeats of the ATXN2 gene, caused by torque. It has been established that the torque effect on the 1st exon of the ATXN2 gene, in addition to the formation of open states in the promoter region, can lead to the formation of additional various sizes open states zones in the CAG repeats region. Moreover, the frequency of additional large zones genesis increases with increasing number of CAG repeats. The inverse of this frequency correlates with the dependence of the disease onset average age on the CAG repeats length. The obtained results will allow us to get closer to understanding the genetic mechanisms that cause trinucleotide repeat diseases.
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Affiliation(s)
- Mikhail I Drobotenko
- Department of Radiophysics and Nanothechnology, Kuban State University, 350040 Krasnodar, Russian Federation
| | - Oksana M Lyasota
- Laboratory of Problems of Stable Isotope Spreading in Living Systems, Southern Scientific Center of the Russian Academy of Sciences, 344006 Rostov-on-Don, Russian Federation
| | | | | | - Alexandr A Svidlov
- Laboratory of Problems of Stable Isotope Spreading in Living Systems, Southern Scientific Center of the Russian Academy of Sciences, 344006 Rostov-on-Don, Russian Federation
| | - Аnna A Dorohova
- Department of Radiophysics and Nanothechnology, Kuban State University, 350040 Krasnodar, Russian Federation; Laboratory of Problems of Stable Isotope Spreading in Living Systems, Southern Scientific Center of the Russian Academy of Sciences, 344006 Rostov-on-Don, Russian Federation
| | - Mikhail G Baryshev
- Laboratory of Problems of Stable Isotope Spreading in Living Systems, Southern Scientific Center of the Russian Academy of Sciences, 344006 Rostov-on-Don, Russian Federation
| | - Yury D Nechipurenko
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russian Federation
| | | | - Stepan S Dzhimak
- Department of Radiophysics and Nanothechnology, Kuban State University, 350040 Krasnodar, Russian Federation; Laboratory of Problems of Stable Isotope Spreading in Living Systems, Southern Scientific Center of the Russian Academy of Sciences, 344006 Rostov-on-Don, Russian Federation.
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23
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Sullivan PF, Yao S, Hjerling-Leffler J. Schizophrenia genomics: genetic complexity and functional insights. Nat Rev Neurosci 2024; 25:611-624. [PMID: 39030273 DOI: 10.1038/s41583-024-00837-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/04/2024] [Indexed: 07/21/2024]
Abstract
Determining the causes of schizophrenia has been a notoriously intractable problem, resistant to a multitude of investigative approaches over centuries. In recent decades, genomic studies have delivered hundreds of robust findings that implicate nearly 300 common genetic variants (via genome-wide association studies) and more than 20 rare variants (via whole-exome sequencing and copy number variant studies) as risk factors for schizophrenia. In parallel, functional genomic and neurobiological studies have provided exceptionally detailed information about the cellular composition of the brain and its interconnections in neurotypical individuals and, increasingly, in those with schizophrenia. Taken together, these results suggest unexpected complexity in the mechanisms that drive schizophrenia, pointing to the involvement of ensembles of genes (polygenicity) rather than single-gene causation. In this Review, we describe what we now know about the genetics of schizophrenia and consider the neurobiological implications of this information.
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Affiliation(s)
- Patrick F Sullivan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jens Hjerling-Leffler
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
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24
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Merner AR, Trotter PM, Ginn LA, Bach J, Freedberg KJ, Soda T, Storch EA, Pereira S, Lázaro-Muñoz G. Psychiatric polygenic risk scores: Experience, hope for utility, and concerns among child and adolescent psychiatrists. Psychiatry Res 2024; 339:116080. [PMID: 39002500 PMCID: PMC11321910 DOI: 10.1016/j.psychres.2024.116080] [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: 03/15/2024] [Revised: 06/26/2024] [Accepted: 07/05/2024] [Indexed: 07/15/2024]
Abstract
Recent advances in psychiatric genetics have enabled the use of polygenic risk scores (PRS) to estimate genetic risk for psychiatric disorders. However, the potential use of PRS in child and adolescent psychiatry has raised concerns. This study provides an in-depth examination of attitudes among child and adolescent psychiatrists (CAP) regarding the use of PRS in psychiatry. We conducted semi-structured interviews with U.S.-based CAP (n = 29) who possess expertise in genetics. The majority of CAP indicated that PRS have limited clinical utility in their current form and are not ready for clinical implementation. Most clinicians stated that nothing would motivate them to generate PRS at present; however, some exceptions were noted (e.g., parent/family request). Clinicians spoke to challenges related to ordering, interpreting, and explaining PRS to patients and families. CAP raised concerns regarding the potential for this information to be misinterpreted or misused by patients, families, clinicians, and outside entities such as insurance companies. Finally, some CAP noted that PRS may lead to increased stigmatization of psychiatric disorders, and at the extreme, could be used to support eugenics. As PRS testing increases, it will be critical to examine CAP and other stakeholders' views to ensure responsible implementation of this technology.
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Affiliation(s)
- Amanda R Merner
- Center for Bioethics, Harvard Medical School, Boston, MA 02115, United States
| | - Page M Trotter
- Center for Medical Ethics & Health Policy at Baylor College of Medicine, United States
| | - Lauren A Ginn
- Center for Medical Ethics & Health Policy at Baylor College of Medicine, United States; Department of Biosciences, Rice University, Houston, Texas, United States
| | - Jason Bach
- University of Pennsylvania Law School, Philadelphia, Pennsylvania, United States
| | | | - Takahiro Soda
- Department of Psychiatry, University of Florida, Gainesville, Florida, United States; Center for Autism and Neurodevelopment, University of Florida, Gainesville, Florida, United States
| | - Eric A Storch
- Department of Psychiatry & Behavioral Sciences, Baylor College of Medicine, Houston, Texas, United States
| | - Stacey Pereira
- Center for Medical Ethics & Health Policy at Baylor College of Medicine, United States
| | - Gabriel Lázaro-Muñoz
- Center for Bioethics, Harvard Medical School, Boston, MA 02115, United States; Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, United States.
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25
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Noble PA, Pozhitkov A, Singh K, Woods E, Liu C, Levin M, Javan G, Wan J, Abouhashem AS, Mathew-Steiner SS, Sen CK. Unraveling the Enigma of Organismal Death: Insights, Implications, and Unexplored Frontiers. Physiology (Bethesda) 2024; 39:0. [PMID: 38624244 DOI: 10.1152/physiol.00004.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/21/2024] [Accepted: 04/11/2024] [Indexed: 04/17/2024] Open
Abstract
Significant knowledge gaps exist regarding the responses of cells, tissues, and organs to organismal death. Examining the survival mechanisms influenced by metabolism and environment, this research has the potential to transform regenerative medicine, redefine legal death, and provide insights into life's physiological limits, paralleling inquiries in embryogenesis.
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Affiliation(s)
- Peter A Noble
- Department of Microbiology, University of Alabama Birmingham, Birmingham, Alabama, United States
| | - Alexander Pozhitkov
- Division of Research Informatics, Beckman Research Institute, City of Hope, Duarte, California, United States
| | - Kanhaiya Singh
- Department of Surgery, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Erik Woods
- Ossium Health, Indianapolis, Indiana, United States
| | - Chunyu Liu
- Institute for Human Performance, Upstate Medical University, Syracuse, New York, United States
| | - Michael Levin
- Department of Biology, Tufts University, Medford, Massachusetts, United States
| | - Gulnaz Javan
- Department of Physical and Forensic Sciences, Alabama State University, Montgomery, Alabama, United States
| | - Jun Wan
- Department of Surgery, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Ahmed Safwat Abouhashem
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, United States
| | - Shomita S Mathew-Steiner
- Department of Surgery, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Chandan K Sen
- Department of Surgery, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
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26
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Francia M, Bot M, Boltz T, De la Hoz JF, Boks M, Kahn RS, Ophoff RA. Fibroblasts as an in vitro model of circadian genetic and genomic studies. Mamm Genome 2024; 35:432-444. [PMID: 38960898 PMCID: PMC11329553 DOI: 10.1007/s00335-024-10050-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 06/25/2024] [Indexed: 07/05/2024]
Abstract
Bipolar disorder (BD) is a heritable disorder characterized by shifts in mood that manifest in manic or depressive episodes. Clinical studies have identified abnormalities of the circadian system in BD patients as a hallmark of underlying pathophysiology. Fibroblasts are a well-established in vitro model for measuring circadian patterns. We set out to examine the underlying genetic architecture of circadian rhythm in fibroblasts, with the goal to assess its contribution to the polygenic nature of BD disease risk. We collected, from primary cell lines of 6 healthy individuals, temporal genomic features over a 48 h period from transcriptomic data (RNA-seq) and open chromatin data (ATAC-seq). The RNA-seq data showed that only a limited number of genes, primarily the known core clock genes such as ARNTL, CRY1, PER3, NR1D2 and TEF display circadian patterns of expression consistently across cell cultures. The ATAC-seq data identified that distinct transcription factor families, like those with the basic helix-loop-helix motif, were associated with regions that were increasing in accessibility over time. Whereas known glucocorticoid receptor target motifs were identified in those regions that were decreasing in accessibility. Further evaluation of these regions using stratified linkage disequilibrium score regression analysis failed to identify a significant presence of them in the known genetic architecture of BD, and other psychiatric disorders or neurobehavioral traits in which the circadian rhythm is affected. In this study, we characterize the biological pathways that are activated in this in vitro circadian model, evaluating the relevance of these processes in the context of the genetic architecture of BD and other disorders, highlighting its limitations and future applications for circadian genomic studies.
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Affiliation(s)
- Marcelo Francia
- Interdepartmental Program for Neuroscience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
| | - Merel Bot
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
| | - Toni Boltz
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Juan F De la Hoz
- Bioinformatics Interdepartamental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Marco Boks
- Department Psychiatry, Brain Center University Medical Center Utrecht, University Utrecht, Utrecht, The Netherlands
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Roel A Ophoff
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
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27
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Breithaupt L, Holsen LM, Ji C, Hu J, Petterway F, Rosa-Caldwell M, Nilsson IA, Thomas JJ, Williams KA, Boutin R, Slattery M, Bulik CM, Arnold SE, Lawson EA, Misra M, Eddy KT. Identification of State Markers in Anorexia Nervosa: Replication and Extension of Inflammation-Associated Biomarkers Using Multiplex Profiling. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100332. [PMID: 38989135 PMCID: PMC11233894 DOI: 10.1016/j.bpsgos.2024.100332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 03/12/2024] [Accepted: 04/01/2024] [Indexed: 07/12/2024] Open
Abstract
Background Proteomics offers potential for detecting and monitoring anorexia nervosa (AN) and its variant, atypical AN (atyp-AN). However, research has been limited by small protein panels, a focus on adult AN, and lack of replication. Methods In this study, we performed Olink multiplex profiling of 92 inflammation-related proteins in females with AN/atyp-AN (n = 64), all of whom were ≤90% of expected body weight, and age-matched healthy control individuals (n = 44). Results Five proteins differed significantly between the primary AN/atyp-AN group and the healthy control group (lower levels: HGF, IL-18R1, TRANCE; higher levels: CCL23, LIF-R). The expression levels of 3 proteins (lower IL-18R1, TRANCE; higher LIF-R) were uniquely disrupted in participants with AN in our primary model. No unique expression levels emerged for atyp-AN. In the total sample, 12 proteins (ADA, CD5, CD6, CXCL1, FGF-21, HGF, IL-12B, IL18, IL-18R1, SIRT2, TNFSF14, TRANCE) were positively correlated with body mass index and 5 proteins (CCL11, FGF-19, IL8, LIF-R, OPG) were negatively correlated with body mass index in our primary models. Conclusions Our results replicate the results of a previous study that demonstrated a dysregulated inflammatory status in AN and extend those results to atyp-AN. Of the 17 proteins correlated with body mass index, 11 were replicated from a previous study that used similar methods, highlighting the promise of inflammatory protein expression levels as biomarkers of AN disease monitoring. Our findings underscore the complexity of AN and atyp-AN by highlighting the inability of the identified proteins to differentiate between these 2 subtypes, thereby emphasizing the heterogeneous nature of these disorders.
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Affiliation(s)
- Lauren Breithaupt
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
- Eating Disorders Clinical and Research Program, Massachusetts General Hospital, Boston, Massachusetts
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, Massachusetts
| | - Laura M. Holsen
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, Massachusetts
- Division of Women’s Health, Departments of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Chunni Ji
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, Massachusetts
- Division of Women’s Health, Departments of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jie Hu
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Department of Anesthesia, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Felicia Petterway
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, Massachusetts
- Neuroendocrine Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Megan Rosa-Caldwell
- Department of Neurology, Beth Israel Deaconess Hospital, Boston, Massachusetts
- Department of Neurology, Harvard Medical School, Boston, Massachusetts
| | - Ida A.K. Nilsson
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
- Centre for Eating Disorders Innovation, Karolinska Institutet, Stockholm, Sweden
| | - Jennifer J. Thomas
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
- Eating Disorders Clinical and Research Program, Massachusetts General Hospital, Boston, Massachusetts
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, Massachusetts
| | - Kyle A. Williams
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
- Pediatric Neuropsychiatry and Immunology Program, Massachusetts General Hospital, Boston, Massachusetts
| | - Regine Boutin
- Neuroendocrine Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Meghan Slattery
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, Massachusetts
- Neuroendocrine Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Cynthia M. Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Steven E. Arnold
- Department of Neurology, Harvard Medical School, Boston, Massachusetts
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Elizabeth A. Lawson
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, Massachusetts
- Neuroendocrine Unit, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Madhusmita Misra
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, Massachusetts
- Neuroendocrine Unit, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Neuroendocrine Unit, Massachusetts General Children’s Hospital, Boston, Massachusetts
| | - Kamryn T. Eddy
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
- Eating Disorders Clinical and Research Program, Massachusetts General Hospital, Boston, Massachusetts
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, Massachusetts
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Mitjans M, Papiol S, Fatjó-Vilas M, González-Peñas J, Acosta-Díez M, Zafrilla-López M, Costas J, Arango C, Vilella E, Martorell L, Moltó MD, Bobes J, Crespo-Facorro B, González-Pinto A, Fañanás L, Rosa A, Arias B. Shared vulnerability and sex-dependent polygenic burden in psychotic disorders. Eur Neuropsychopharmacol 2024; 86:49-54. [PMID: 38941950 DOI: 10.1016/j.euroneuro.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 04/21/2024] [Accepted: 04/27/2024] [Indexed: 06/30/2024]
Abstract
Evidence suggests a remarkable shared genetic susceptibility between psychiatric disorders. However, sex-dependent differences have been less studied. We explored the contribution of schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) polygenic scores (PGSs) on the risk for psychotic disorders and whether sex-dependent differences exist (CIBERSAM sample: 1826 patients and 1372 controls). All PGSs were significantly associated with psychosis. Sex-stratified analyses showed that the variance explained in psychotic disorders risk was significantly higher in males than in females for all PGSs. Our results confirm the shared genetic architecture across psychotic disorders and demonstrate sex-dependent differences in the vulnerability to psychotic disorders.
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Affiliation(s)
- Marina Mitjans
- Departament Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Institut de Recerca Sant Joan de Déu (IRSJD), Esplugues de Llobregat, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
| | - Sergi Papiol
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany; Max Planck Institute of Psychiatry, Munich, Germany; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Mar Fatjó-Vilas
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; Departament Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense de Madrid, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Miriam Acosta-Díez
- Departament Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Marina Zafrilla-López
- Departament Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Javier Costas
- Psychiatric Genetics group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Santiago de Compostela, Galicia, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense de Madrid, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Elisabet Vilella
- Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-CERCA, Universitat Rovira i Virgili, Reus, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Lourdes Martorell
- Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-CERCA, Universitat Rovira i Virgili, Reus, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - M Dolores Moltó
- INCLIVA Biomedical Research Institute, Fundación Investigación Hospital Clínico de Valencia; Department of Genetics, Universitat de València, Valencia, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Julio Bobes
- Department of Psychiatry, Universidad de Oviedo, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Neurociencias del Principado de Asturias (INEUROPA), Servicio de Salud del Principado de Asturias (SESPA) Oviedo, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Benedicto Crespo-Facorro
- University Hospital Virgen del Rocio/IBiS/CSIC-Department of Psychiatry, School of Medicine, University of Sevilla, Sevilla, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Ana González-Pinto
- BIOARABA Health Research Institute, OSI Araba, University Hospital, University of the Basque Country, Vitoria, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Lourdes Fañanás
- Departament Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Araceli Rosa
- Departament Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Bárbara Arias
- Departament Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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Fu Y, Xie GM, Liu RQ, Xie JL, Zhang J, Zhang J. From aberrant neurodevelopment to neurodegeneration: Insights into the hub gene associated with autism and alzheimer's disease. Brain Res 2024; 1838:148992. [PMID: 38729333 DOI: 10.1016/j.brainres.2024.148992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 03/31/2024] [Accepted: 05/07/2024] [Indexed: 05/12/2024]
Affiliation(s)
- Yu Fu
- Research Center for Translational Medicine at East Hospital, School of Medicine, Tongji University, Shanghai 200010, China
| | - Guang-Ming Xie
- Research Center for Translational Medicine at East Hospital, School of Medicine, Tongji University, Shanghai 200010, China
| | - Rong-Qi Liu
- Research Center for Translational Medicine at East Hospital, School of Life Science and Technology, Tongji University, Shanghai 200010, China
| | - Jun-Ling Xie
- Research Center for Translational Medicine at East Hospital, School of Medicine, Tongji University, Shanghai 200010, China
| | - Jing Zhang
- Research Center for Translational Medicine at East Hospital, School of Life Science and Technology, Tongji University, Shanghai 200010, China.
| | - Jun Zhang
- Research Center for Translational Medicine at East Hospital, School of Medicine, Tongji University, Shanghai 200010, China; Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai 200092, China.
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Zhu S, He J, Yin L, Zhou J, Lian J, Ren Y, Zhang X, Yuan J, Wang G, Li X. Matrix metalloproteinases targeting in prostate cancer. Urol Oncol 2024; 42:275-287. [PMID: 38806387 DOI: 10.1016/j.urolonc.2024.05.002] [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: 02/06/2024] [Revised: 04/07/2024] [Accepted: 05/06/2024] [Indexed: 05/30/2024]
Abstract
Prostate cancer (PCa) is one of the most common tumors affecting men all over the world. PCa has brought a huge health burden to men around the world, especially for elderly men, but its pathogenesis is unclear. In prostate cancer, epigenetic inheritance plays an important role in the development, progression, and metastasis of the disease. An important role in cancer invasion and metastasis is played by matrix metalloproteinases (MMPs), zinc-dependent proteases that break down extracellular matrix. We review two important forms of epigenetic modification and the role of matrix metalloproteinases in tumor regulation, both of which may be of significant value as novel biomarkers for early diagnosis and prognosis monitoring. The author considers that both mechanisms have promising therapeutic applications for therapeutic agent research in prostate cancer, but that efforts should be made to mitigate or eliminate the side effects of drug therapy in order to maximize quality of life of patients. The understanding of epigenetic modification, MMPs, and their inhibitors in the functional regulation of prostate cancer is gradually advancing, it will provide a new technical means for the prevention of prostate cancer, early diagnosis, androgen-independent prostate cancer treatment, and drug research.
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Affiliation(s)
- Shuying Zhu
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, PR China
| | - Jing He
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, PR China
| | - Liliang Yin
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, PR China
| | - Jiawei Zhou
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, PR China
| | - Jiayi Lian
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, PR China
| | - Yanli Ren
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China
| | - Xinling Zhang
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, PR China
| | - Jinghua Yuan
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, PR China
| | - Gang Wang
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, PR China
| | - Xiaoping Li
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, PR China.
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Song J, Pasman JA, Johansson V, Kuja-Halkola R, Harder A, Karlsson R, Lu Y, Kowalec K, Pedersen NL, Cannon TD, Hultman CM, Sullivan PF. Polygenic Risk Scores and Twin Concordance for Schizophrenia and Bipolar Disorder. JAMA Psychiatry 2024:2822688. [PMID: 39196586 DOI: 10.1001/jamapsychiatry.2024.2406] [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] [Indexed: 08/29/2024]
Abstract
Importance Schizophrenia and bipolar disorder are highly heritable psychiatric disorders with strong genetic and phenotypic overlap. Twin and molecular methods can be leveraged to predict the shared genetic liability to these disorders. Objective To investigate whether twin concordance for psychosis depends on the level of polygenic risk score (PRS) for psychosis and zygosity and compare PRS from cases and controls from several large samples and estimate the twin heritability of psychosis. Design, Setting, and Participants In this case-control study, psychosis PRS were generated from a genome-wide association study (GWAS) combining schizophrenia and bipolar disorder into a single psychosis phenotype and compared between cases and controls from the Schizophrenia and Bipolar Twin Study in Sweden (STAR) project. Further tests were conducted to ascertain if twin concordance for psychosis depended on the mean PRS for psychosis. Structural equation modeling was used to estimate heritability. This study constituted an analysis of existing clinical and population datasets with genotype and/or twin data. Included were twins from the STAR cohort and from the Swedish Twin Registry. Data were collected during the 2006 to 2013 period and analyzed from March 2023 to June 2024. Exposures PRS for psychosis based on the most recent GWAS of combined schizophrenia/bipolar disorder. Main Outcomes and Measures Psychosis case status was assessed by clinical interviews and/or Swedish National Register data. Results The final cohort comprised 87 pairs of twins with 1 or both affected and 59 unaffected pairs from the STAR project (for a total of 292 twins) as well as 443 pairs with 1 or both affected and 20 913 unaffected pairs from the Swedish Twin Registry. Among the 292 twins (mean [SD] birth year, 1960 [10.8] years; 158 female [54.1%]; 134 male [45.9%]), 134 were monozygotic twins, and 158 were dyzygotic twins. PRS for psychosis was higher in cases than in controls and associated with twin concordance for psychosis (1-SD increase in PRS, odds ratio [OR], 2.12; 95% CI, 1.23-3.87 on case status in monozygotic twins and OR, 2.74; 95% CI, 1.56-5.30 in dizygotic twins). The association between PRS for psychosis and concordance was not modified by zygosity. The twin heritability was estimated at 0.73 (95% CI, 0.30-1.00), which overlapped with the estimate in the full Swedish Twin Registry (0.69; 95% CI, 0.43-0.85). Conclusions and Relevance In this case-control study, using the natural experiment of twins, results suggest that twins with greater inherited liability for psychosis were more likely to have an affected co-twin. Results from twin and molecular designs largely aligned. Even as illness vulnerability is not solely genetic, PRS carried predictive power for psychosis even in a modest sample size.
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Affiliation(s)
- Jie Song
- Mental Health Center and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Joëlle A Pasman
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Viktoria Johansson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Sciences, Psychiatry, Umeå University, Umeå, Sweden
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Harder
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kaarina Kowalec
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- College of Pharmacy, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Christina M Hultman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Departments of Genetics and Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill
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Tokuoka SM, Hamano F, Kobayashi A, Adachi S, Andou T, Natsume T, Oda Y. Plasma proteomics and lipidomics facilitate elucidation of the link between Alzheimer's disease development and vessel wall fragility. Sci Rep 2024; 14:19901. [PMID: 39191863 DOI: 10.1038/s41598-024-71097-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 08/23/2024] [Indexed: 08/29/2024] Open
Abstract
Proximity Extension Assay (PEA) and mass spectrometry (MS) methodologies were utilized for the proteomic and lipidomic characterization of plasma specimens from patients who developed Alzheimer's disease. Proteomics was performed by both PEA and Liquid Chromatography (LC)/MS in this study, but all the more because LC/MS generally tends to be biased towards proteins with high expression and high variability, generating hypotheses proved challenging. Consequently, attempt was made to interpret the results from the PEA data. There were 150 significantly variable proteins and 68 lipids among 1000 proteins and 400 lipids. Pathway analysis was performed for both total and variable proteins measured to reduce bias, and it appeared that vascular fragility was related to AD. Furthermore, a multitude of lipid-associated proteins exhibited statistical changes. In certain instances, the function of individual proteins affected the factors associated with them, whereas others demonstrated trends contrary to anticipated outcomes. These trends seem indicative of diverse feedback mechanisms that provide homeostatic equilibrium. The degree of unsaturation of fatty acids, correlated with cardiovascular risk, warrants specific attention. Certain bile acids exhibited the potential to cause vascular endothelial damage. Contemplating these discoveries in tandem with previously documented phenomena, subtle shifts in homeostatic functions seem to be linked to the fragility of vascular endothelial cells. This is evidenced by the slow and chronic evolution of Alzheimer's disease from preclinical stages to its manifestation.
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Affiliation(s)
- Suzumi M Tokuoka
- Department of Lipidomics, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan
| | - Fumie Hamano
- Department of Lipidomics, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan
- Life Sciences Core Facility, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Ayako Kobayashi
- Department of Lipidomics, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan
| | - Shungo Adachi
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology, 2-3-26 Aoumi, Koto-ku, Tokyo, 135-0064, Japan
| | - Tomohiro Andou
- Axcelead Drug Discovery Partners, Inc., 2-26-1 Muraoka-Higashi, Fujisawa, Kanagawa, 251-0012, Japan
| | - Tohru Natsume
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology, 2-3-26 Aoumi, Koto-ku, Tokyo, 135-0064, Japan
| | - Yoshiya Oda
- Department of Lipidomics, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan.
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Whitton AE, Cooper JA, Merchant JT, Treadway MT, Lewandowski KE. Using Computational Phenotyping to Identify Divergent Strategies for Effort Allocation Across the Psychosis Spectrum. Schizophr Bull 2024; 50:1127-1136. [PMID: 38498838 PMCID: PMC11348999 DOI: 10.1093/schbul/sbae024] [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] [Indexed: 03/20/2024]
Abstract
BACKGROUND AND HYPOTHESIS Disturbances in effort-cost decision-making have been highlighted as a potential transdiagnostic process underpinning negative symptoms in individuals with schizophrenia. However, recent studies using computational phenotyping show that individuals employ a range of strategies to allocate effort, and use of different strategies is associated with unique clinical and cognitive characteristics. Building on prior work in schizophrenia, this study evaluated whether effort allocation strategies differed in individuals with distinct psychotic disorders. STUDY DESIGN We applied computational modeling to effort-cost decision-making data obtained from individuals with psychotic disorders (n = 190) who performed the Effort Expenditure for Rewards Task. The sample included 91 individuals with schizophrenia/schizoaffective disorder, 90 individuals with psychotic bipolar disorder, and 52 controls. STUDY RESULTS Different effort allocation strategies were observed both across and within different disorders. Relative to individuals with psychotic bipolar disorder, a greater proportion of individuals with schizophrenia/schizoaffective disorder did not use reward value or probability information to guide effort allocation. Furthermore, across disorders, different effort allocation strategies were associated with specific clinical and cognitive features. Those who did not use reward value or probability information to guide effort allocation had more severe positive and negative symptoms, and poorer cognitive and community functioning. In contrast, those who only used reward value information showed a trend toward more severe positive symptoms. CONCLUSIONS These findings indicate that similar deficits in effort-cost decision-making may arise from different computational mechanisms across the psychosis spectrum.
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Affiliation(s)
- Alexis E Whitton
- Black Dog Institute, University of New South Wales, Sydney, NSW, Australia
| | - Jessica A Cooper
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Jaisal T Merchant
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA
| | - Michael T Treadway
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Kathryn E Lewandowski
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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Cameron D, Vinh NN, Prapaiwongs P, Perry EA, Walters JTR, Li M, O’Donovan MC, Bray NJ. Genetic Implication of Prenatal GABAergic and Cholinergic Neuron Development in Susceptibility to Schizophrenia. Schizophr Bull 2024; 50:1171-1184. [PMID: 38869145 PMCID: PMC11349020 DOI: 10.1093/schbul/sbae083] [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] [Indexed: 06/14/2024]
Abstract
BACKGROUND The ganglionic eminences (GE) are fetal-specific structures that give rise to gamma-aminobutyric acid (GABA)- and acetylcholine-releasing neurons of the forebrain. Given the evidence for GABAergic, cholinergic, and neurodevelopmental disturbances in schizophrenia, we tested the potential involvement of GE neuron development in mediating genetic risk for the condition. STUDY DESIGN We combined data from a recent large-scale genome-wide association study of schizophrenia with single-cell RNA sequencing data from the human GE to test the enrichment of schizophrenia risk variation in genes with high expression specificity for developing GE cell populations. We additionally performed the single nuclei Assay for Transposase-Accessible Chromatin with Sequencing (snATAC-Seq) to map potential regulatory genomic regions operating in individual cell populations of the human GE, using these to test for enrichment of schizophrenia common genetic variant liability and to functionally annotate non-coding variants-associated with the disorder. STUDY RESULTS Schizophrenia common variant liability was enriched in genes with high expression specificity for developing neuron populations that are predicted to form dopamine D1 and D2 receptor-expressing GABAergic medium spiny neurons of the striatum, cortical somatostatin-positive GABAergic interneurons, calretinin-positive GABAergic neurons, and cholinergic neurons. Consistent with these findings, schizophrenia genetic risk was concentrated in predicted regulatory genomic sequence mapped in developing neuronal populations of the GE. CONCLUSIONS Our study implicates prenatal development of specific populations of GABAergic and cholinergic neurons in later susceptibility to schizophrenia, and provides a map of predicted regulatory genomic elements operating in cells of the GE.
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Affiliation(s)
- Darren Cameron
- Division of Psychological Medicine and Clinical Neurosciences, Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, Cardiff, UK
| | - Ngoc-Nga Vinh
- Division of Psychological Medicine and Clinical Neurosciences, Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, Cardiff, UK
| | - Parinda Prapaiwongs
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, UK
| | - Elizabeth A Perry
- Division of Psychological Medicine and Clinical Neurosciences, Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, Cardiff, UK
| | - James T R Walters
- Division of Psychological Medicine and Clinical Neurosciences, Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, Cardiff, UK
| | - Meng Li
- Division of Psychological Medicine and Clinical Neurosciences, Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, Cardiff, UK
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, UK
| | - Michael C O’Donovan
- Division of Psychological Medicine and Clinical Neurosciences, Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, Cardiff, UK
| | - Nicholas J Bray
- Division of Psychological Medicine and Clinical Neurosciences, Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, Cardiff, UK
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, UK
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Song Y, Li L, Jiang Y, Peng B, Jiang H, Chao Z, Chang X. Multitrait Genetic Analysis Identifies Novel Pleiotropic Loci for Depression and Schizophrenia in East Asians. Schizophr Bull 2024:sbae145. [PMID: 39190819 DOI: 10.1093/schbul/sbae145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
BACKGROUND AND HYPOTHESIS While genetic correlations, pleiotropic loci, and shared genetic mechanisms of psychiatric disorders have been extensively studied in European populations, the investigation of these factors in East Asian populations has been relatively limited. STUDY DESIGN To identify novel pleiotropic risk loci for depression and schizophrenia (SCZ) in East Asians. We utilized the most comprehensive dataset available for East Asians and quantified the genetic overlap between depression, SCZ, and their related traits via a multitrait genome-wide association study. Global and local genetic correlations were estimated by LDSC and ρ-HESS. Pleiotropic loci were identified by the multitrait analysis of GWAS (MTAG). STUDY RESULTS Besides the significant correlation between depression and SCZ, our analysis revealed genetic correlations between depression and obesity-related traits, such as weight, BMI, T2D, and HDL. In SCZ, significant correlations were detected with HDL, heart diseases and use of various medications. Conventional meta-analysis of depression and SCZ identified a novel locus at 1q25.2 in East Asians. Further multitrait analysis of depression, SCZ and related traits identified ten novel pleiotropic loci for depression, and four for SCZ. CONCLUSIONS Our findings demonstrate shared genetic underpinnings between depression and SCZ in East Asians, as well as their associated traits, providing novel candidate genes for the identification and prioritization of therapeutic targets specific to this population.
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Affiliation(s)
- Yingchao Song
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Linzehao Li
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Yue Jiang
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Bichen Peng
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Hengxuan Jiang
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Zhen Chao
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Xiao Chang
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
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Xiao Z, Zheng N, Chen H, Yang Z, Wang R, Liang Z. Identifying novel proteins underlying bipolar disorder via integrating pQTLs of the plasma, CSF, and brain with GWAS summary data. Transl Psychiatry 2024; 14:344. [PMID: 39191728 DOI: 10.1038/s41398-024-03056-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 08/19/2024] [Accepted: 08/21/2024] [Indexed: 08/29/2024] Open
Abstract
Bipolar disorder (BD) presents a significant challenge due to its chronic and relapsing nature, with its underlying pathogenesis remaining elusive. This study employs Mendelian randomization (MR), a widely recognized genetic approach, to unveil intricate causal associations between proteins and BD, leveraging protein quantitative trait loci (pQTL) as key exposures. We integrate pQTL data from brain, cerebrospinal fluid (CSF), and plasma with genome-wide association study (GWAS) findings of BD within a comprehensive systems analysis framework. Our analyses, including two-sample MR, Steiger filtering, and Bayesian colocalization, reveal noteworthy associations. Elevated levels of AGRP, FRZB, and IL36A in CSF exhibit significant associations with increased BD_ALL risk, while heightened levels of CTSF and LRP8 in CSF, and FLRT3 in plasma, correlate with decreased BD_ALL risk. Specifically for Bipolar I disorder (BD_I), increased CSF AGRP levels are significantly linked to heightened BD_I risk, whereas elevated CSF levels of CTSF and LRP8, and plasma FLRT3, are associated with reduced BD_I risk. Notably, genes linked to BD-related proteins demonstrate substantial enrichment in functional pathways such as "antigen processing and presentation," "metabolic regulation," and "regulation of myeloid cell differentiation." In conclusion, our findings provide beneficial evidence to support the potential causal relationship between IL36A, AGRP, FRZB, LRP8 in cerebrospinal fluid, and FLRT3 in plasma, and BD and BD_I, providing insights for future mechanistic studies and therapeutic development.
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Affiliation(s)
- Zhehao Xiao
- Fujian Medical University Union Hospital, Fuzhou, China
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Nan Zheng
- Fujian Medical University Union Hospital, Fuzhou, China
- Department of Anesthesiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Haodong Chen
- Fujian Medical University Union Hospital, Fuzhou, China
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zhelun Yang
- Fujian Medical University Union Hospital, Fuzhou, China
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Rui Wang
- Fujian Medical University Union Hospital, Fuzhou, China.
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China.
| | - Zeyan Liang
- Fujian Medical University Union Hospital, Fuzhou, China.
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China.
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Xie Y, Zhao Y, Zhou Y, Jiang Y, Zhang Y, Du J, Cai M, Fu J, Liu H. Shared Genetic Architecture Among Gastrointestinal Diseases, Schizophrenia, and Brain Subcortical Volumes. Schizophr Bull 2024; 50:1243-1254. [PMID: 38973257 PMCID: PMC11349026 DOI: 10.1093/schbul/sbae099] [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] [Indexed: 07/09/2024]
Abstract
BACKGROUND AND HYPOTHESIS The gut-brain axis plays important roles in both gastrointestinal diseases (GI diseases) and schizophrenia (SCZ). Moreover, both GI diseases and SCZ exhibit notable abnormalities in brain subcortical volumes. However, the genetic mechanisms underlying the comorbidity of these diseases and the shared alterations in brain subcortical volumes remain unclear. STUDY DESIGN Using the genome-wide association studies data of SCZ, 14 brain subcortical volumes, and 8 GI diseases, the global polygenic overlap and local genetic correlations were identified, as well as the shared genetic variants among those phenotypes. Furthermore, we conducted multi-trait colocalization analyses to bolster our findings. Functional annotations, cell-type enrichment, and protein-protein interaction (PPI) analyses were carried out to reveal the critical etiology and pathology mechanisms. STUDY RESULTS The global polygenic overlap and local genetic correlations informed the close relationships between SCZ and both GI diseases and brain subcortical volumes. Moreover, 84 unique lead-shared variants were identified. The associated genes were linked to vital biological processes within the immune system. Additionally, significant correlations were observed with key immune cells and the PPI analysis identified several histone-associated hub genes. These findings highlighted the pivotal roles played by the immune system for both SCZ and GI diseases, along with the shared alterations in brain subcortical volumes. CONCLUSIONS These findings revealed the shared genetic architecture contributing to SCZ and GI diseases, as well as their shared alterations in brain subcortical volumes. These insights have substantial implications for the concurrent development of intervention and therapy targets for these diseases.
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Affiliation(s)
- Yingying Xie
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yao Zhao
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujing Zhou
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yurong Jiang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujie Zhang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jiaojiao Du
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Mengjing Cai
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jilian Fu
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Huaigui Liu
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
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Lu ZA, Ploner A, Birgegård A, Bulik CM, Bergen SE. Shared Genetic Architecture Between Schizophrenia and Anorexia Nervosa: A Cross-trait Genome-Wide Analysis. Schizophr Bull 2024; 50:1255-1265. [PMID: 38848516 PMCID: PMC11349005 DOI: 10.1093/schbul/sbae087] [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] [Indexed: 06/09/2024]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SCZ) and anorexia nervosa (AN) are 2 severe and highly heterogeneous disorders showing substantial familial co-aggregation. Genetic factors play a significant role in both disorders, but the shared genetic etiology between them is yet to be investigated. STUDY DESIGN Using summary statistics from recent large genome-wide association studies on SCZ (Ncases = 53 386) and AN (Ncases = 16 992), a 2-sample Mendelian randomization analysis was conducted to explore the causal relationship between SCZ and AN. MiXeR was employed to quantify their polygenic overlap. A conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was adopted to identify loci jointly associated with both disorders. Functional annotation and enrichment analyses were performed on the shared loci. STUDY RESULTS We observed a cross-trait genetic enrichment, a suggestive bidirectional causal relationship, and a considerable polygenic overlap (Dice coefficient = 62.2%) between SCZ and AN. The proportion of variants with concordant effect directions among all shared variants was 69.9%. Leveraging overlapping genetic associations, we identified 6 novel loci for AN and 33 novel loci for SCZ at condFDR <0.01. At conjFDR <0.05, we identified 10 loci jointly associated with both disorders, implicating multiple genes highly expressed in the cerebellum and pituitary and involved in synapse organization. Particularly, high expression of the shared genes was observed in the hippocampus in adolescence and orbitofrontal cortex during infancy. CONCLUSIONS This study provides novel insights into the relationship between SCZ and AN by revealing a shared genetic component and offers a window into their complex etiology.
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Affiliation(s)
- Zheng-An Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Alexander Ploner
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Andreas Birgegård
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Cynthia M Bulik
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sarah E Bergen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Zhang C, Yang Z, Li X, Zhao L, Guo W, Deng W, Wang Q, Hu X, Li M, Sham PC, Xiao X, Li T. Unraveling NEK4 as a Potential Drug Target in Schizophrenia and Bipolar I Disorder: A Proteomic and Genomic Approach. Schizophr Bull 2024; 50:1185-1196. [PMID: 38869147 PMCID: PMC11349004 DOI: 10.1093/schbul/sbae094] [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] [Indexed: 06/14/2024]
Abstract
BACKGROUND AND HYPOTHESIS Investigating the shared brain protein and genetic components of schizophrenia (SCZ) and bipolar I disorder (BD-I) presents a unique opportunity to understand the underlying pathophysiological processes and pinpoint potential drug targets. STUDY DESIGN To identify overlapping susceptibility brain proteins in SCZ and BD-I, we carried out proteome-wide association studies (PWAS) and Mendelian Randomization (MR) by integrating human brain protein quantitative trait loci with large-scale genome-wide association studies for both disorders. We utilized transcriptome-wide association studies (TWAS) to determine the consistency of mRNA-protein dysregulation in both disorders. We applied pleiotropy-informed conditional false discovery rate (pleioFDR) analysis to identify common risk genetic loci for SCZ and BD-I. Additionally, we performed a cell-type-specific analysis in the human brain to detect risk genes notably enriched in distinct brain cell types. The impact of risk gene overexpression on dendritic arborization and axon length in neurons was also examined. STUDY RESULTS Our PWAS identified 42 proteins associated with SCZ and 14 with BD-I, among which NEK4, HARS2, SUGP1, and DUS2 were common to both conditions. TWAS and MR analysis verified the significant risk gene NEK4 for both SCZ and BD-I. PleioFDR analysis further supported genetic risk loci associated with NEK4 for both conditions. The cell-type specificity analysis revealed that NEK4 is expressed on the surface of glutamatergic neurons, and its overexpression enhances dendritic arborization and axon length in cultured primary neurons. CONCLUSIONS These findings underscore a shared genetic origin for SCZ and BD-I, offering novel insights for potential therapeutic target identification.
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Affiliation(s)
- Chengcheng Zhang
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Nanhu Brain-Computer Interface Institute, Hangzhou, China
| | - ZhiHui Yang
- Yunnan Key Laboratory of Animal Models and Human Disease Mechanisms, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Key Laboratory of Genetic Evolution and Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Xiaojing Li
- Department of Neurobiology, Affiliated Mental Health Center and Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wanjun Guo
- Department of Neurobiology, Affiliated Mental Health Center and Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Wei Deng
- Department of Neurobiology, Affiliated Mental Health Center and Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xun Hu
- The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ming Li
- Yunnan Key Laboratory of Animal Models and Human Disease Mechanisms, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Key Laboratory of Genetic Evolution and Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Pak Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Xiao Xiao
- Yunnan Key Laboratory of Animal Models and Human Disease Mechanisms, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Key Laboratory of Genetic Evolution and Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Tao Li
- Nanhu Brain-Computer Interface Institute, Hangzhou, China
- Department of Neurobiology, Affiliated Mental Health Center and Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China
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Yuan K, Longchamps RJ, Pardiñas AF, Yu M, Chen TT, Lin SC, Chen Y, Lam M, Liu R, Xia Y, Guo Z, Shi W, Shen C, Daly MJ, Neale BM, Feng YCA, Lin YF, Chen CY, O'Donovan MC, Ge T, Huang H. Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases. Nat Genet 2024:10.1038/s41588-024-01870-z. [PMID: 39187616 DOI: 10.1038/s41588-024-01870-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 07/15/2024] [Indexed: 08/28/2024]
Abstract
Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestry has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping. SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and linkage disequilibrium patterns, accounts for multiple causal variants in a genomic region and can be applied to GWAS summary statistics. We comprehensively assessed the performance of SuSiEx using simulations. We further showed that SuSiEx improves the fine-mapping of a range of quantitative traits available in both the UK Biobank and Taiwan Biobank, and improves the fine-mapping of schizophrenia-associated loci by integrating GWAS across East Asian and European ancestries.
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Affiliation(s)
- Kai Yuan
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ryan J Longchamps
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Antonio F Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Mingrui Yu
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Tzu-Ting Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Shu-Chin Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Yu Chen
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Max Lam
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Human Genetics, Genome Institute of Singapore, Singapore, Singapore
- Division of Psychiatry Research, the Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Research Division Institute of Mental Health Singapore, Singapore, Singapore
| | - Ruize Liu
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Yan Xia
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Zhenglin Guo
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wenzhao Shi
- Digital Health China Technologies Corp. Ltd, Beijing, China
| | - Chengguo Shen
- Digital Health China Technologies Corp. Ltd, Beijing, China
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yen-Chen A Feng
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
- Department of Public Health & Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | | | - Michael C O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Tian Ge
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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Wang S, Dan YL, Yang Y, Tian Y. The shared genetic etiology of antisocial behavior and psychiatric disorders: Insights from pleiotropy and causality analysis. J Affect Disord 2024:S0165-0327(24)01383-1. [PMID: 39187189 DOI: 10.1016/j.jad.2024.08.149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 08/11/2024] [Accepted: 08/23/2024] [Indexed: 08/28/2024]
Abstract
BACKGROUND Antisocial behavior (ASB) infringes on the rights of others and significantly disrupts social order. Studies have shown that ASB is phenotypically associated with various psychiatric disorders. However, these studies often neglected the importance of genetic foundations. METHODS This study utilized genome-wide association studies and pleiotropy analysis to explore the genetic correlation between ASB and psychiatric disorders. Linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) methods were employed to assess genetic correlations, and the PLACO method was used for pleiotropy analysis. Functional annotation and biological pathway analysis of identified pleiotropic genes were performed using enrichment analysis. Furthermore, Mendelian randomization (MR) analysis was conducted to validate these causal relationships. RESULTS LDSC and HDL analysis showed that significant positive genetic correlations were between ASB and attention deficit hyperactivity disorder (ADHD), schizophrenia (SCZ), major depressive disorder (MDD), and post-traumatic stress disorder (PTSD). Multiple potential pleiotropic genetic loci were identified, particularly the FOXP2 and MDFIC genes located at the 7q31.1 locus. Enrichment analysis showed that these pleiotropic genes are highly expressed in several brain regions (such as the hypothalamus, cerebellar hemisphere, cortex, and amygdala) and immune-related cells. MR analysis further confirmed the causal effects ADHD, SCZ, and MDD on ASB risk. CONCLUSION This study reveals significant genetic correlations and potential causal mechanisms between ASB and various psychiatric disorders. The MR analysis confirmed the causal effects of psychiatric disorders on ASB. These findings deepen our understanding of the genetic architecture of psychiatric disorders and ASB.
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Affiliation(s)
- Shaoyang Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University. Hefei, China
| | - Yi-Lin Dan
- Collaborative Innovation Center of Bone and Immunology between Sihong Hospital and Soochow University, Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu, China; Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Soochow University, Suzhou, Jiangsu, China
| | - Yiqun Yang
- Collaborative Innovation Center of Bone and Immunology between Sihong Hospital and Soochow University, Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu, China; Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Soochow University, Suzhou, Jiangsu, China
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University. Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230022, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center. Hefei 230088, China; Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China.
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42
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Xu Y, He C, Fan J, Zhou Y, Cheng C, Meng R, Cui Y, Li W, Gamazon ER, Zhou D. A multi-modal framework improves prediction of tissue-specific gene expression from a surrogate tissue. EBioMedicine 2024; 107:105305. [PMID: 39180788 DOI: 10.1016/j.ebiom.2024.105305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 08/08/2024] [Accepted: 08/09/2024] [Indexed: 08/26/2024] Open
Abstract
BACKGROUND Tissue-specific analysis of the transcriptome is critical to elucidating the molecular basis of complex traits, but central tissues are often not accessible. We propose a methodology, Multi-mOdal-based framework to bridge the Transcriptome between PEripheral and Central tissues (MOTPEC). METHODS Multi-modal regulatory elements in peripheral blood are incorporated as features for gene expression prediction in 48 central tissues. To demonstrate the utility, we apply it to the identification of BMI-associated genes and compare the tissue-specific results with those derived directly from surrogate blood. FINDINGS MOTPEC models demonstrate superior performance compared with both baseline models in blood and existing models across the 48 central tissues. We identify a set of BMI-associated genes using the central tissue MOTPEC-predicted transcriptome data. The MOTPEC-based differential gene expression (DGE) analysis of BMI in the central tissues (including brain caudate basal ganglia and visceral omentum adipose tissue) identifies 378 genes overlapping the results from a TWAS of BMI, while only 162 overlapping genes are identified using gene expression in blood. Cellular perturbation analysis further supports the utility of MOTPEC for identifying trait-associated gene sets and narrowing the effect size divergence between peripheral blood and central tissues. INTERPRETATION The MOTPEC framework improves the gene expression prediction accuracy for central tissues and enhances the identification of tissue-specific trait-associated genes. FUNDING This research is supported by the National Natural Science Foundation of China 82204118 (D.Z.), the seed funding of the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province (2020E10004), the National Institutes of Health (NIH) Genomic Innovator Award R35HG010718 (E.R.G.), NIH/NHGRI R01HG011138 (E.R.G.), NIH/NIA R56AG068026 (E.R.G.), NIH Office of the Director U24OD035523 (E.R.G.), and NIH/NIGMS R01GM140287 (E.R.G.).
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Affiliation(s)
- Yue Xu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Chunfeng He
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jiayao Fan
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yuan Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Chunxiao Cheng
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Ran Meng
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ya Cui
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Wei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Eric R Gamazon
- Vanderbit Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Data Science Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Dan Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China.
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Schleifer CH, Chang SE, Amir CM, O'Hora KP, Fung H, Kang JWD, Kushan-Wells L, Daly E, Di Fabio F, Frascarelli M, Gudbrandsen M, Kates WR, Murphy D, Addington J, Anticevic A, Cadenhead KS, Cannon TD, Cornblatt BA, Keshavan M, Mathalon DH, Perkins DO, Stone W, Walker E, Woods SW, Uddin LQ, Kumar K, Hoftman GD, Bearden CE. Unique functional neuroimaging signatures of genetic versus clinical high risk for psychosis. Biol Psychiatry 2024:S0006-3223(24)01538-5. [PMID: 39181389 DOI: 10.1016/j.biopsych.2024.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 08/05/2024] [Accepted: 08/08/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND 22q11.2 Deletion Syndrome (22qDel) is a copy number variant (CNV) associated with psychosis and other neurodevelopmental disorders. Adolescents at clinical high risk for psychosis (CHR) are identified based on the presence of subthreshold psychosis symptoms. Whether common neural substrates underlie these distinct high-risk populations is unknown. We compared functional brain measures in 22qDel and CHR cohorts and mapped results to biological pathways. METHODS We analyzed two large multi-site cohorts with resting-state functional MRI (rs-fMRI): 1) 22qDel (n=164, 47% female) and typically developing (TD) controls (n=134, 56% female); 2) CHR individuals (n=244, 41% female) and TD controls (n=151, 46% female) from the North American Prodrome Longitudinal Study-2. We computed global brain connectivity (GBC), local connectivity (LC), and brain signal variability (BSV) across cortical regions, testing case-control differences for 22qDel and CHR separately. Group difference maps were related to published brain maps using autocorrelation-preserving permutation. RESULTS BSV, LC, and GBC are significantly disrupted in 22qDel compared with TD controls (False Discovery Rate q<0.05). Spatial maps of BSV and LC differences are highly correlated with each other, unlike GBC. In CHR, only LC is significantly altered versus controls, with a different spatial pattern compared to 22qDel. Group differences map onto biological gradients, with 22qDel effects strongest in regions with high predicted blood flow and metabolism. CONCLUSION 22qDel and CHR exhibit divergent effects on fMRI temporal variability and multi-scale functional connectivity. In 22qDel, strong and convergent disruptions in BSV and LC not seen in CHR individuals suggest distinct functional brain alterations.
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Affiliation(s)
- Charles H Schleifer
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Sarah E Chang
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Carolyn M Amir
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Kathleen P O'Hora
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Hoki Fung
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Jee Won D Kang
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Leila Kushan-Wells
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Eileen Daly
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, UK
| | - Fabio Di Fabio
- Department of Human Neurosciences, Sapienza University, Rome, Italy
| | | | - Maria Gudbrandsen
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, UK; Centre for Psychological Research (CREW), School of Psychology, University of Roehampton, London, UK
| | - Wendy R Kates
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Declan Murphy
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, UK
| | - Jean Addington
- Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Alan Anticevic
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | | | - Tyrone D Cannon
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - Barbara A Cornblatt
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, and San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - William Stone
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Elaine Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Scott W Woods
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Kuldeep Kumar
- Centre de Recherche du CHU Sainte-Justine, University of Montreal, Montreal, Canada
| | - Gil D Hoftman
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA; Department of Psychology, University of California, Los Angeles, CA, USA.
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Johnston KG, Grieco SF, Nie Q, Theis FJ, Xu X. Small data methods in omics: the power of one. Nat Methods 2024:10.1038/s41592-024-02390-8. [PMID: 39174710 DOI: 10.1038/s41592-024-02390-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 07/24/2024] [Indexed: 08/24/2024]
Abstract
Over the last decade, biology has begun utilizing 'big data' approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with 'small data' approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.
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Affiliation(s)
- Kevin G Johnston
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Steven F Grieco
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA, USA
- Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA.
| | - Fabian J Theis
- Helmholtz Center Munich-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, Technical University of Munich, Munich, Germany.
| | - Xiangmin Xu
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA, USA.
- Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA, USA.
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45
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Jiang Y, Rex DA, Schuster D, Neely BA, Rosano GL, Volkmar N, Momenzadeh A, Peters-Clarke TM, Egbert SB, Kreimer S, Doud EH, Crook OM, Yadav AK, Vanuopadath M, Hegeman AD, Mayta M, Duboff AG, Riley NM, Moritz RL, Meyer JG. Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry. ACS MEASUREMENT SCIENCE AU 2024; 4:338-417. [PMID: 39193565 PMCID: PMC11348894 DOI: 10.1021/acsmeasuresciau.3c00068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 05/03/2024] [Accepted: 05/03/2024] [Indexed: 08/29/2024]
Abstract
Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.
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Affiliation(s)
- Yuming Jiang
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Devasahayam Arokia
Balaya Rex
- Center for
Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India
| | - Dina Schuster
- Department
of Biology, Institute of Molecular Systems
Biology, ETH Zurich, Zurich 8093, Switzerland
- Department
of Biology, Institute of Molecular Biology
and Biophysics, ETH Zurich, Zurich 8093, Switzerland
- Laboratory
of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institute, Villigen 5232, Switzerland
| | - Benjamin A. Neely
- Chemical
Sciences Division, National Institute of
Standards and Technology, NIST, Charleston, South Carolina 29412, United States
| | - Germán L. Rosano
- Mass
Spectrometry
Unit, Institute of Molecular and Cellular
Biology of Rosario, Rosario, 2000 Argentina
| | - Norbert Volkmar
- Department
of Biology, Institute of Molecular Systems
Biology, ETH Zurich, Zurich 8093, Switzerland
| | - Amanda Momenzadeh
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Trenton M. Peters-Clarke
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California, 94158, United States
| | - Susan B. Egbert
- Department
of Chemistry, University of Manitoba, Winnipeg, Manitoba, R3T 2N2 Canada
| | - Simion Kreimer
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Emma H. Doud
- Center
for Proteome Analysis, Indiana University
School of Medicine, Indianapolis, Indiana, 46202-3082, United States
| | - Oliver M. Crook
- Oxford
Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, United
Kingdom
| | - Amit Kumar Yadav
- Translational
Health Science and Technology Institute, NCR Biotech Science Cluster 3rd Milestone Faridabad-Gurgaon
Expressway, Faridabad, Haryana 121001, India
| | | | - Adrian D. Hegeman
- Departments
of Horticultural Science and Plant and Microbial Biology, University of Minnesota, Twin Cities, Minnesota 55108, United States
| | - Martín
L. Mayta
- School
of Medicine and Health Sciences, Center for Health Sciences Research, Universidad Adventista del Plata, Libertador San Martin 3103, Argentina
- Molecular
Biology Department, School of Pharmacy and Biochemistry, Universidad Nacional de Rosario, Rosario 2000, Argentina
| | - Anna G. Duboff
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Nicholas M. Riley
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Robert L. Moritz
- Institute
for Systems biology, Seattle, Washington 98109, United States
| | - Jesse G. Meyer
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
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Etkin A, Powell J, Savitz AJ. Opportunities for use of neuroimaging in de-risking drug development and improving clinical outcomes in psychiatry: an industry perspective. Neuropsychopharmacology 2024:10.1038/s41386-024-01970-8. [PMID: 39169213 DOI: 10.1038/s41386-024-01970-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/30/2024] [Accepted: 08/14/2024] [Indexed: 08/23/2024]
Abstract
Neuroimaging, across positron emission tomography (PET), electroencephalography (EEG), and magnetic resonance imaging (MRI), has been a mainstay of clinical neuroscience research for decades, yet has penetrated little into psychiatric drug development beyond often underpowered phase 1 studies, or into clinical care. Simultaneously, there is a pressing need to improve the probability of success in drug development, increase mechanistic diversity, and enhance clinical efficacy. These goals can be achieved by leveraging neuroimaging in a precision psychiatry framework, wherein effects of drugs on the brain are measured early in clinical development to understand dosing and indication, and then in later-stage trials to identify likely drug responders and enrich clinical trials, ultimately improving clinical outcomes. Here we examine the key variables important for success in using neuroimaging for precision psychiatry from the lens of biotechnology and pharmaceutical companies developing and deploying new drugs in psychiatry. We argue that there are clear paths for incorporating different neuroimaging modalities to de-risk subsequent development phases in the near to intermediate term, culminating in use of select neuroimaging modalities in clinical care for prescription of new precision drugs. Better outcomes through neuroimaging biomarkers, however, require a wholesale commitment to a precision psychiatry approach and will necessitate a cultural shift to align biopharma and clinical care in psychiatry to a precision orientation already routine in other areas of medicine.
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Affiliation(s)
- Amit Etkin
- Alto Neuroscience Inc., Los Altos, CA, 94022, USA.
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94304, USA.
| | | | - Adam J Savitz
- Alto Neuroscience Inc., Los Altos, CA, 94022, USA
- Department of Psychiatry, Weill Cornell Medical College, New York, NY, 10021, USA
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47
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Wang H, Zhao Q, Zhang Y, Ma J, Lei M, Zhang Z, Xue H, Liu J, Sun Z, Xu J, Zhai Y, Wang Y, Cai M, Zhu W, Liu F. Shared genetic architecture of cortical thickness alterations in major depressive disorder and schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111121. [PMID: 39154931 DOI: 10.1016/j.pnpbp.2024.111121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/29/2024] [Accepted: 08/15/2024] [Indexed: 08/20/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) and schizophrenia (SCZ) are heritable brain disorders characterized by alterations in cortical thickness. However, the shared genetic basis for cortical thickness changes in these disorders remains unclear. METHODS We conducted a systematic literature search on cortical thickness in MDD and SCZ through PubMed and Web of Science. A coordinate-based meta-analysis was performed to identify cortical thickness changes. Additionally, utilizing summary statistics from the largest genome-wide association studies for depression (Ncase = 268,615, Ncontrol = 667,123) and SCZ (Ncase = 53,386, Ncontrol = 77,258), we explored shared genomic loci using conjunctional false discovery rate (conjFDR) analysis. Transcriptome-neuroimaging association analysis was then employed to identify shared genes associated with cortical thickness alterations, and enrichment analysis was finally carried out to elucidate the biological significance of these genes. RESULTS Our search yielded 34 MDD (Ncase = 1621, Ncontrol = 1507) and 19 SCZ (Ncase = 1170, Ncontrol = 1043) neuroimaging studies for cortical thickness meta-analysis. Specific alterations in the left supplementary motor area were observed in MDD, while SCZ exhibited widespread reductions in various brain regions, particularly in the frontal and temporal areas. The conjFDR approach identified 357 genomic loci jointly associated with MDD and SCZ. Within these loci, 55 genes were found to be associated with cortical thickness alterations in both disorders. Enrichment analysis revealed their involvement in nervous system development, apoptosis, and cell communication. CONCLUSION This study revealed the shared genetic architecture underlying cortical thickness alterations in MDD and SCZ, providing insights into common neurobiological pathways. The identified genes and pathways may serve as potential transdiagnostic markers, informing precision medicine approaches in psychiatric care.
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Affiliation(s)
- He Wang
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Qiyu Zhao
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yijing Zhang
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Juanwei Ma
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Minghuan Lei
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Zhihui Zhang
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Hui Xue
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jiawei Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Zuhao Sun
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jinglei Xu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Ying Zhai
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Ying Wang
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Mengjing Cai
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China; Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou 450000, China.
| | - Wenshuang Zhu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
| | - Feng Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China.
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Gezsi A, Van der Auwera S, Mäkinen H, Eszlari N, Hullam G, Nagy T, Bonk S, González-Colom R, Gonda X, Garvert L, Paajanen T, Gal Z, Kirchner K, Millinghoffer A, Schmidt CO, Bolgar B, Roca J, Cano I, Kuokkanen M, Antal P, Juhasz G. Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories. Nat Commun 2024; 15:7190. [PMID: 39168988 PMCID: PMC11339304 DOI: 10.1038/s41467-024-51467-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 08/07/2024] [Indexed: 08/23/2024] Open
Abstract
The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.
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Grants
- This research has been conducted using the UK Biobank Resource under Application Number 1602. Linked health data Copyright © 2019, NHS England. Re-used with the permission of the UK Biobank. All rights reserved. This study was supported by the Hungarian National Research, Development, and Innovation Office 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERA PerMed (ERAPERMED2019-108); the Hungarian National Research, Development, and Innovation Office (K 143391, K 139330, PD 146014, and PD 134449 grants); the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); and the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, under the TKP2021-EGA funding scheme (TKP2021-EGA-25 and TKP2021-EGA-02). Supported by the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory.
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Affiliation(s)
- Andras Gezsi
- Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics, Budapest, Hungary
| | - Sandra Van der Auwera
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Centre for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Hannu Mäkinen
- Department of Public Health and Welfare, Population Health Unit, Public Health Research Team, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Nora Eszlari
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Gabor Hullam
- Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics, Budapest, Hungary
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
| | - Tamas Nagy
- Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics, Budapest, Hungary
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Sarah Bonk
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Rubèn González-Colom
- Clínic Barcelona, Fundació de Recerca Clinic Barcelona - Institut d'Investigacions Biomèdiques August Pi i Sunyer (FRCB-IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Xenia Gonda
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Linda Garvert
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Teemu Paajanen
- Department of Public Health and Welfare, Population Health Unit, Public Health Research Team, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Zsofia Gal
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Kevin Kirchner
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | | | - Carsten O Schmidt
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Bence Bolgar
- Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics, Budapest, Hungary
| | - Josep Roca
- Clínic Barcelona, Fundació de Recerca Clinic Barcelona - Institut d'Investigacions Biomèdiques August Pi i Sunyer (FRCB-IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Isaac Cano
- Clínic Barcelona, Fundació de Recerca Clinic Barcelona - Institut d'Investigacions Biomèdiques August Pi i Sunyer (FRCB-IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Mikko Kuokkanen
- Department of Public Health and Welfare, Population Health Unit, Public Health Research Team, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine at University of Texas Rio Grande Valley, Brownsville, TX, USA
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Peter Antal
- Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics, Budapest, Hungary
| | - Gabriella Juhasz
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary.
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary.
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Mäki-Marttunen T, Blackwell KT, Akkouh I, Shadrin A, Valstad M, Elvsåshagen T, Linne ML, Djurovic S, Einevoll GT, Andreassen OA. Genetic mechanisms for impaired synaptic plasticity in schizophrenia revealed by computational modeling. Proc Natl Acad Sci U S A 2024; 121:e2312511121. [PMID: 39141354 PMCID: PMC11348150 DOI: 10.1073/pnas.2312511121] [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: 07/21/2023] [Accepted: 03/23/2024] [Indexed: 08/15/2024] Open
Abstract
Schizophrenia phenotypes are suggestive of impaired cortical plasticity in the disease, but the mechanisms of these deficits are unknown. Genomic association studies have implicated a large number of genes that regulate neuromodulation and plasticity, indicating that the plasticity deficits have a genetic origin. Here, we used biochemically detailed computational modeling of postsynaptic plasticity to investigate how schizophrenia-associated genes regulate long-term potentiation (LTP) and depression (LTD). We combined our model with data from postmortem RNA expression studies (CommonMind gene-expression datasets) to assess the consequences of altered expression of plasticity-regulating genes for the amplitude of LTP and LTD. Our results show that the expression alterations observed post mortem, especially those in the anterior cingulate cortex, lead to impaired protein kinase A (PKA)-pathway-mediated LTP in synapses containing GluR1 receptors. We validated these findings using a genotyped electroencephalogram (EEG) dataset where polygenic risk scores for synaptic and ion channel-encoding genes as well as modulation of visual evoked potentials were determined for 286 healthy controls. Our results provide a possible genetic mechanism for plasticity impairments in schizophrenia, which can lead to improved understanding and, ultimately, treatment of the disorder.
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Affiliation(s)
- Tuomo Mäki-Marttunen
- Biomedicine, Faculty of Medicine and Health Technology, Tampere University, Tampere33720, Finland
- Department of Biosciences, University of Oslo, Oslo0371, Norway
| | - Kim T. Blackwell
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA52242
| | - Ibrahim Akkouh
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo0450, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo0450, Norway
| | - Alexey Shadrin
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo0450, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo0450, Norway
| | - Mathias Valstad
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo0456, Norway
| | - Torbjørn Elvsåshagen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo0450, Norway
- Department of Neurology, Oslo University Hospital, Oslo0450, Norway
| | - Marja-Leena Linne
- Biomedicine, Faculty of Medicine and Health Technology, Tampere University, Tampere33720, Finland
| | - Srdjan Djurovic
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo0450, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo0450, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo0450, Norway
| | - Gaute T. Einevoll
- Department of Physics, Norwegian University of Life Sciences, Ås1433, Norway
- Department of Physics, University of Oslo, Oslo0316, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo0450, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo0450, Norway
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Méndez-Albelo NM, Sandoval SO, Xu Z, Zhao X. An in-depth review of the function of RNA-binding protein FXR1 in neurodevelopment. Cell Tissue Res 2024:10.1007/s00441-024-03912-8. [PMID: 39155323 DOI: 10.1007/s00441-024-03912-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2024] [Accepted: 07/30/2024] [Indexed: 08/20/2024]
Abstract
FMR1 autosomal homolog 1 (FXR1) is an RNA-binding protein that belongs to the Fragile X-related protein (FXR) family. FXR1 is critical for development, as its loss of function is intolerant in humans and results in neonatal death in mice. Although FXR1 is expressed widely including the brain, functional studies on FXR1 have been mostly performed in cancer cells. Limited studies have demonstrated the importance of FXR1 in the brain. In this review, we will focus on the roles of FXR1 in brain development and pathogenesis of brain disorders. We will summarize the current knowledge in FXR1 in the context of neural biology, including structural features, isoform diversity and nomenclature, expression patterns, post-translational modifications, regulatory mechanisms, and molecular functions. Overall, FXR1 emerges as an important regulator of RNA metabolism in the brain, with strong implications in neurodevelopmental and psychiatric disorders.
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Affiliation(s)
- Natasha M Méndez-Albelo
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Neuroscience, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Molecular Cellular Pharmacology Training Program, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Soraya O Sandoval
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Neuroscience, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Zhiyan Xu
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Neuroscience, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Graduate Program in Cellular and Molecular Biology, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Xinyu Zhao
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Neuroscience, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA.
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