1
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Phadke RA, Brack A, Fournier LA, Kruzich E, Sha M, Picard I, Johnson C, Stroumbakis D, Salgado M, Yeung C, Escude Velasco B, Liu YY, Cruz-Martín A. The schizophrenia risk gene C4 induces pathological synaptic loss by impairing AMPAR trafficking. Mol Psychiatry 2024:10.1038/s41380-024-02701-7. [PMID: 39227431 DOI: 10.1038/s41380-024-02701-7] [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: 11/08/2023] [Revised: 08/15/2024] [Accepted: 08/16/2024] [Indexed: 09/05/2024]
Abstract
Neuroimmune interactions play a significant role in regulating synaptic plasticity in both the healthy and diseased brain. The complement pathway, an extracellular proteolytic cascade, exemplifies these interactions. Its activation triggers microglia-dependent synaptic elimination via the complement receptor 3 (CR3). Current models of pathological complement activity in the brain propose that accelerated synaptic loss resulting from overexpression of C4 (C4-OE), a gene associated with schizophrenia, follows this pathway. Here, we report that C4-mediated cortical hypoconnectivity is CR3-independent. Instead, C4-OE triggers impaired GluR1 trafficking through an intracellular mechanism involving the endosomal protein SNX27, resulting in pathological synaptic loss. Moreover, C4 circuit alterations in the prefrontal cortex, a brain region associated with neuropsychiatric disorders, were rescued by increasing neuronal levels of SNX27, which we identify as an interacting partner of this neuroimmune protein. Our results link excessive complement activity to an intracellular endo-lysosomal trafficking pathway altering synaptic plasticity.
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Affiliation(s)
- Rhushikesh A Phadke
- Molecular Biology, Cell Biology & Biochemistry Program, Boston University, Boston, MA, USA
| | - Alison Brack
- Molecular Biology, Cell Biology & Biochemistry Program, Boston University, Boston, MA, USA
| | - Luke A Fournier
- Neurobiology Section in the Department of Biology, Boston University, Boston, MA, USA
| | - Ezra Kruzich
- Neurobiology Section in the Department of Biology, Boston University, Boston, MA, USA
| | - Mingqi Sha
- Neurobiology Section in the Department of Biology, Boston University, Boston, MA, USA
| | - Ines Picard
- Neurobiology Section in the Department of Biology, Boston University, Boston, MA, USA
| | - Connor Johnson
- Neurobiology Section in the Department of Biology, Boston University, Boston, MA, USA
| | - Dimitri Stroumbakis
- Neurobiology Section in the Department of Biology, Boston University, Boston, MA, USA
| | - Maria Salgado
- Neurobiology Section in the Department of Biology, Boston University, Boston, MA, USA
| | - Charlotte Yeung
- Neurobiology Section in the Department of Biology, Boston University, Boston, MA, USA
| | - Berta Escude Velasco
- Neurobiology Section in the Department of Biology, Boston University, Boston, MA, USA
| | - Yen Yu Liu
- Neurobiology Section in the Department of Biology, Boston University, Boston, MA, USA
| | - Alberto Cruz-Martín
- Molecular Biology, Cell Biology & Biochemistry Program, Boston University, Boston, MA, USA.
- Neurobiology Section in the Department of Biology, Boston University, Boston, MA, USA.
- Department of Anesthesiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- NeuroTechnology Center (NTC), University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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2
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Reed ER, Chandler KB, Lopez P, Costello CE, Andersen SL, Perls TT, Li M, Bae H, Soerensen M, Monti S, Sebastiani P. Cross-platform proteomics signatures of extreme old age. GeroScience 2024:10.1007/s11357-024-01286-x. [PMID: 39048883 DOI: 10.1007/s11357-024-01286-x] [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: 04/10/2024] [Accepted: 07/10/2024] [Indexed: 07/27/2024] Open
Abstract
In previous work, we used a SomaLogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood generated in an independent set. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals. The comparison with blood transcriptomics also highlights a possible role for neutrophil degranulation in aging.
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Affiliation(s)
- Eric R Reed
- Data Intensive Study Center, Tufts University, Boston, MA, USA
| | - Kevin B Chandler
- Center for Biomedical Mass Spectrometry, Department of Biochemistry and Cell Biology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Cellular and Molecular Medicine, Florida International University, Miami, FL, USA
| | - Prisma Lopez
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
| | - Catherine E Costello
- Center for Biomedical Mass Spectrometry, Department of Biochemistry and Cell Biology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Stacy L Andersen
- Geriatric Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Thomas T Perls
- Geriatric Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Mengze Li
- Division of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Harold Bae
- Biostatistics Program, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Mette Soerensen
- Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Stefano Monti
- Division of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Paola Sebastiani
- Data Intensive Study Center, Tufts University, Boston, MA, USA.
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
- Department of Medicine, School of Medicine, Tufts University, Boston, MA, USA.
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3
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Salminen A, Kaarniranta K, Kauppinen A. Tissue fibroblasts are versatile immune regulators: An evaluation of their impact on the aging process. Ageing Res Rev 2024; 97:102296. [PMID: 38588867 DOI: 10.1016/j.arr.2024.102296] [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: 12/18/2023] [Revised: 03/26/2024] [Accepted: 04/03/2024] [Indexed: 04/10/2024]
Abstract
Fibroblasts are abundant stromal cells which not only control the integrity of extracellular matrix (ECM) but also act as immune regulators. It is known that the structural cells within tissues can establish an organ-specific immunity expressing many immune-related genes and closely interact with immune cells. In fact, fibroblasts can modify their immune properties to display both pro-inflammatory and immunosuppressive activities in a context-dependent manner. After acute insults, fibroblasts promote tissue inflammation although they concurrently recruit immunosuppressive cells to enhance the resolution of inflammation. In chronic pathological states, tissue fibroblasts, especially senescent fibroblasts, can display many pro-inflammatory and immunosuppressive properties and stimulate the activities of different immunosuppressive cells. In return, immunosuppressive cells, such as M2 macrophages and myeloid-derived suppressor cells (MDSC), evoke an excessive conversion of fibroblasts into myofibroblasts, thus aggravating the severity of tissue fibrosis. Single-cell transcriptome studies on fibroblasts isolated from aged tissues have confirmed that tissue fibroblasts express many genes coding for cytokines, chemokines, and complement factors, whereas they lose some fibrogenic properties. The versatile immune properties of fibroblasts and their close cooperation with immune cells indicate that tissue fibroblasts have a crucial role in the aging process and age-related diseases.
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Affiliation(s)
- Antero Salminen
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, P.O. Box 1627, Kuopio FI-70211, Finland.
| | - Kai Kaarniranta
- Department of Ophthalmology, Institute of Clinical Medicine, University of Eastern Finland, P.O. Box 1627, Kuopio FI-70211, Finland; Department of Ophthalmology, Kuopio University Hospital, P.O. Box 100, KYS FI-70029, Finland
| | - Anu Kauppinen
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, P.O. Box 1627, Kuopio FI-70211, Finland
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4
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Reed ER, Chandler KB, Lopez P, Costello CE, Andersen SL, Perls TT, Li M, Bae H, Soerensen M, Monti S, Sebastiani P. Cross-platform proteomics signatures of extreme old age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588876. [PMID: 38645061 PMCID: PMC11030369 DOI: 10.1101/2024.04.10.588876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
In previous work we used a Somalogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals.
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Affiliation(s)
- Eric R Reed
- Data Intensive Study Center, Tufts University, Boston, MA, USA
| | - Kevin B Chandler
- Center for Biomedical Mass Spectrometry, Department of Biochemistry and Cell Biology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Cellular and Molecular Medicine, Florida International University, Miami, FL, USA
| | - Prisma Lopez
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
| | - Catherine E Costello
- Center for Biomedical Mass Spectrometry, Department of Biochemistry and Cell Biology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Stacy L Andersen
- Geriatric Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Thomas T Perls
- Geriatric Section, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Mengze Li
- Division of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Harold Bae
- Biostatistics Program, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Mette Soerensen
- Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Stefano Monti
- Division of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Paola Sebastiani
- Data Intensive Study Center, Tufts University, Boston, MA, USA
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
- Department of Medicine, School of Medicine, Tufts University, Boston, MA, USA
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5
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Bafei SEC, Shen C. Biomarkers selection and mathematical modeling in biological age estimation. NPJ AGING 2023; 9:13. [PMID: 37393295 DOI: 10.1038/s41514-023-00110-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 05/08/2023] [Indexed: 07/03/2023]
Abstract
Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.
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Affiliation(s)
- Solim Essomandan Clémence Bafei
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China
| | - Chong Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.
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6
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The Complement System, Aging, and Aging-Related Diseases. Int J Mol Sci 2022; 23:ijms23158689. [PMID: 35955822 PMCID: PMC9369321 DOI: 10.3390/ijms23158689] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/03/2022] [Accepted: 08/03/2022] [Indexed: 12/10/2022] Open
Abstract
The complement system is a part of the immune system and consists of multiple complement components with biological functions such as defense against pathogens and immunomodulation. The complement system has three activation pathways: the classical pathway, the lectin pathway, and the alternative pathway. Increasing evidence indicates that the complement system plays a role in aging. Complement plays a role in inflammatory processes, metabolism, apoptosis, mitochondrial function, and Wnt signaling pathways. In addition, the complement system plays a significant role in aging-related diseases, including Alzheimer’s disease, age-related macular degeneration, and osteoarthritis. However, the effect of complement on aging and aging-related diseases is still unclear. Thus, a better understanding of the potential relationship between complement, aging, and aging-related diseases will provide molecular targets for treating aging, while focusing on the balance of complement in during treatment. Inhibition of a single component does not result in a good outcome. In this review, we discussed the research progress and effects of complement in aging and aging-related diseases.
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7
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Cao X, Yang G, Jin X, He L, Li X, Zheng Z, Liu Z, Wu C. A Machine Learning-Based Aging Measure Among Middle-Aged and Older Chinese Adults: The China Health and Retirement Longitudinal Study. Front Med (Lausanne) 2021; 8:698851. [PMID: 34926482 PMCID: PMC8671693 DOI: 10.3389/fmed.2021.698851] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 10/28/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Biological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population. Materials and methods: We used data from 9,771 middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave of the China Health and Retirement Longitudinal Study and followed until 2018. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure-Klemera and Doubal method-BA (KDM-BA) we previously developed-with physical disability and mortality, respectively. Results: The Gradient Boosting Regression model performed the best, resulting in an ML-BA with an R-squared value of 0.270 and an MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1 to 7% (per 1-year increment in ML-BA, all P < 0.001), independent of CA. These associations were generally comparable to that of KDM-BA. Conclusion: This study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and may help facilitate preventive and geroprotector intervention studies.
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Affiliation(s)
- Xingqi Cao
- Department of Big Data in Health Science, School of Public Health and Center for Clinical Big Data and Analytics, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Guanglai Yang
- Global Health Research Center, Duke Kunshan University, Kunshan, China
| | - Xurui Jin
- Global Health Research Center, Duke Kunshan University, Kunshan, China.,MindRank AI ltd., Hangzhou, China
| | - Liu He
- Department of Big Data in Health Science, School of Public Health and Center for Clinical Big Data and Analytics, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Li
- Department of Big Data in Health Science, School of Public Health and Center for Clinical Big Data and Analytics, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhoutao Zheng
- Department of Big Data in Health Science, School of Public Health and Center for Clinical Big Data and Analytics, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zuyun Liu
- Department of Big Data in Health Science, School of Public Health and Center for Clinical Big Data and Analytics, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chenkai Wu
- Global Health Research Center, Duke Kunshan University, Kunshan, China
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8
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Gao L, Zhang J, Ran X, Jia X, Xing Y, Dai T, Song W, Wu Z, Sun W, Shan D. Urinary Proteomics for Noninvasive Prenatal Screening of Trisomy 21: New Biomarker Candidates. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2021; 25:738-744. [PMID: 34714146 DOI: 10.1089/omi.2021.0154] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Trisomy 21 is a common birth defect in humans. Screening for trisomy 21 is one of the most important tasks in routine prenatal care and robust noninvasive diagnostics are needed in clinical practice. Urinary proteomics offers a new research platform for diagnostics innovation in this context. We report here new biomarker candidates using urinary proteomics profiling. Specifically, we used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze the proteomes of urine samples from 19 pregnant women (aged 28-44 years) carrying fetuses with trisomy 21 and 22 healthy pregnant women (aged 27-42 years) carrying fetuses with normal karyotype. We identified more than 50 differentially expressed proteins between the trisomy 21 group and healthy group, and most of these proteins were associated with the embryonic development. Importantly, tissue inhibitor of metalloproteinases 2 (TIMP2) and lysosomal-associated membrane protein 2 (LAMP2) were further selected as potential urinary protein biomarkers. We found that the combination of TIMP2 and LAMP2 could differentiate fetuses with trisomy 21 from healthy controls with a sensitivity of 74%, a specificity of 82%, and an area under the receiver operating characteristic curves (AUC) value of 0.82 (95% confidence interval, 0.69-0.95). We conclude that TIMP2 and LAMP2 offer promise as biomarker candidates and warrant further clinical research in larger study samples. These findings further our understanding of the pathological processes involved in fetal trisomy 21 and are poised to accelerate the development of new noninvasive potential biomarkers for trisomy 21 prenatal screening.
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Affiliation(s)
- Lei Gao
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Jian Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Xiaoju Ran
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.,School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Xue Jia
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Yiyi Xing
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Tianyi Dai
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Wei Song
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Ze Wu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Wei Sun
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.,School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Dan Shan
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China
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9
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Yang H, Liu D, Zhao C, Feng B, Lu W, Yang X, Xu M, Zhou W, Jing H, Yang J. Mendelian randomization integrating GWAS and eQTL data revealed genes pleiotropically associated with major depressive disorder. Transl Psychiatry 2021; 11:225. [PMID: 33866329 PMCID: PMC8053199 DOI: 10.1038/s41398-021-01348-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 03/19/2021] [Accepted: 03/31/2021] [Indexed: 01/21/2023] Open
Abstract
Previous genome-wide association studies (GWAS) have identified potential genetic variants associated with the risk of major depressive disorder (MDD), but the underlying biological interpretation remains largely unknown. We aimed to prioritize genes that were pleiotropically or potentially causally associated with MDD. We applied the summary data-based Mendelian randomization (SMR) method integrating GWAS and gene expression quantitative trait loci (eQTL) data in 13 brain regions to identify genes that were pleiotropically associated with MDD. In addition, we repeated the analysis by using the meta-analyzed version of the eQTL summary data in the brain (brain-eMeta). We identified multiple significant genes across different brain regions that may be involved in the pathogenesis of MDD. The prime-specific gene BTN3A2 (corresponding probe: ENSG00000186470.9) was the top hit showing pleiotropic association with MDD in 9 of the 13 brain regions and in brain-eMeta, after correction for multiple testing. Many of the identified genes are located in the human major histocompatibility complex (MHC) region on chromosome 6 and are mainly involved in the immune response. Our SMR analysis indicated that multiple genes showed pleiotropic association with MDD across the brain regions. These findings provided important leads to a better understanding of the mechanism of MDD and revealed potential therapeutic targets for the prevention and effective treatment of MDD.
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Affiliation(s)
- Huarong Yang
- grid.452244.1Department of Neurology, The Second Affiliated Hospital of Guizhou Medical University, Kaili, Guizhou China
| | - Di Liu
- grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Chuntao Zhao
- grid.239573.90000 0000 9025 8099Brain Tumor Center, Cancer & Blood Diseases Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH USA
| | - Bowen Feng
- grid.267455.70000 0004 1936 9596Odette School of Business, University of Windsor, Windsor, ON Canada
| | - Wenjin Lu
- grid.83440.3b0000000121901201Department of Mathematics, University College London, London, UK
| | - Xiaohan Yang
- grid.24696.3f0000 0004 0369 153XDepartment of Health Management and Policy, School of Public Health, Capital Medical University, Beijing, China
| | - Minglu Xu
- grid.24696.3f0000 0004 0369 153XDepartment of Health Management and Policy, School of Public Health, Capital Medical University, Beijing, China
| | - Weizhu Zhou
- grid.415680.e0000 0000 9549 5392Department of Epidemiology and Health Statistics, School of Public Health, Shenyang Medical College, Shenyang, China
| | - Huiquan Jing
- Department of Health Management and Policy, School of Public Health, Capital Medical University, Beijing, China.
| | - Jingyun Yang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA. .,Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA.
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