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Rajan A, Fame RM. Brain development and bioenergetic changes. Neurobiol Dis 2024; 199:106550. [PMID: 38849103 DOI: 10.1016/j.nbd.2024.106550] [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: 04/15/2024] [Revised: 05/29/2024] [Accepted: 06/01/2024] [Indexed: 06/09/2024] Open
Abstract
Bioenergetics describe the biochemical processes responsible for energy supply in organisms. When these changes become dysregulated in brain development, multiple neurodevelopmental diseases can occur, implicating bioenergetics as key regulators of neural development. Historically, the discovery of disease processes affecting individual stages of brain development has revealed critical roles that bioenergetics play in generating the nervous system. Bioenergetic-dependent neurodevelopmental disorders include neural tube closure defects, microcephaly, intellectual disability, autism spectrum disorders, epilepsy, mTORopathies, and oncogenic processes. Developmental timing and cell-type specificity of these changes determine the long-term effects of bioenergetic disease mechanisms on brain form and function. Here, we discuss key metabolic regulators of neural progenitor specification, neuronal differentiation (neurogenesis), and gliogenesis. In general, transitions between glycolysis and oxidative phosphorylation are regulated in early brain development and in oncogenesis, and reactive oxygen species (ROS) and mitochondrial maturity play key roles later in differentiation. We also discuss how bioenergetics interface with the developmental regulation of other key neural elements, including the cerebrospinal fluid brain environment. While questions remain about the interplay between bioenergetics and brain development, this review integrates the current state of known key intersections between these processes in health and disease.
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Affiliation(s)
- Arjun Rajan
- Developmental Biology Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - Ryann M Fame
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
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Chen S, Sun S, Cai M, Zhou Z, Ma Y, Zhou Z, Wang F, Liu J, Song W, Liu Y, Huang K, Yang Q, Guo Y. A metabolome-wide Mendelian randomization study prioritizes causal circulating metabolites for reproductive disorders including primary ovarian insufficiency, polycystic ovary syndrome, and abnormal spermatozoa. J Ovarian Res 2024; 17:166. [PMID: 39143642 PMCID: PMC11325614 DOI: 10.1186/s13048-024-01486-1] [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] [Accepted: 07/27/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Accumulating studies have highlighted the significant role of circulating metabolomics in the etiology of reproductive system disorders. However, the causal effects between genetically determined metabolites (GDMs) and reproductive diseases, including primary ovarian insufficiency (POI), polycystic ovary syndrome (PCOS), and abnormal spermatozoa (AS), still await thorough clarification. METHODS With the currently most comprehensive genome-wide association studies (GWAS) data of metabolomics, systematic two-sample Mendelian randomization (MR) analyses were conducted to disclose causal associations between 1,091 blood metabolites and 309 metabolite ratios with reproductive disorders. The inverse-variance weighted (IVW) method served as the primary analysis approach, and multiple effective MR methods were employed as complementary analyses including MR-Egger, weighted median, constrained maximum likelihood (cML-MA), contamination mixture method, robust adjusted profile score (MR-RAPS), and debiased inverse-variance weighted method. Heterogeneity and pleiotropy were assessed via MR-Egger intercept and Cochran's Q statistical analysis. Outliers were detected by Radial MR and MR-PRESSO methods. External replication and metabolic pathway analysis were also conducted. RESULTS Potential causal associations of 63 GDMs with POI were unearthed, and five metabolites with strong causal links to POI were emphasized. Two metabolic pathways related to the pathogenesis of POI were pinpointed. Suggestive causal effects of 70 GDMs on PCOS were detected, among which 7 metabolites stood out for strong causality with elevated PCOS risk. Four metabolic pathways associated with PCOS mechanisms were recognized. For AS, 64 GDMs as potential predictive biomarkers were identified, particularly highlighting two metabolites for their strong causal connections with AS. Three pathways underneath the AS mechanism were identified. Multiple assessments were conducted to further confirm the reliability and robustness of our causal inferences. CONCLUSION By extensively assessing the causal implications of circulating GDMs on reproductive system disorders, our study underscores the intricate and pivotal role of metabolomics in reproductive ill-health, laying a theoretical foundation for clinical strategies from metabolic insights.
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Affiliation(s)
- Shuang Chen
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Shihao Sun
- Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Mingshu Cai
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Zhaokai Zhou
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Yuan Ma
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Zihan Zhou
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Fang Wang
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Jinhao Liu
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Wenyan Song
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Yu Liu
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Kai Huang
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Qingling Yang
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
| | - Yihong Guo
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
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Xiao X, Xue P, Yan X, Li Y, Shi Y, Qin H, Qin D, Cao W, Zou Z, Wang L, Jin R, Li Y, Yao J, Li J. Exploring the Bidirectional Effects of Gut Microbiota and Short-Chain Fatty Acids on Urticaria Subtypes Through Mendelian Randomization and Mediation Analysis. Clin Cosmet Investig Dermatol 2024; 17:1827-1839. [PMID: 39155883 PMCID: PMC11328851 DOI: 10.2147/ccid.s474422] [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: 04/18/2024] [Accepted: 08/08/2024] [Indexed: 08/20/2024]
Abstract
Background Emerging evidence links gut microbiota and their by-products, notably short-chain fatty acids (SCFAs), to urticaria. This study employs multiple Mendelian Randomization (MR) analyses to unravel the complex interactions among gut microbiota, SCFAs, and different subtypes of urticaria, aiming to elucidate the underlying mechanisms and enhance future clinical research. Methods We analyzed published genome-wide association study (GWAS) summary statistics to identify associations between gut microbiota and three common subtypes of urticaria: spontaneous, dermatographic, and temperature-triggered. Initial two-sample and reverse MR analyses explored the causality in these relationships. Subsequent multivariate MR analyses investigated the role of SCFAs in modulating these interactions, with multiple sensitivity analyses to ensure robustness. Findings Specific taxa were differently associated with various urticaria subtypes. From microbiota to urticaria: one taxon was negatively associated with dermatographic urticaria; seven taxa were negatively associated and four positively associated with temperature-triggered urticaria; four taxa were negatively associated and six positively associated with spontaneous urticaria. Conversely, from urticaria to microbiota: five taxa were negatively associated with dermatographic urticaria; four were negatively and two positively associated with temperature-triggered urticaria; and two were negatively associated with spontaneous urticaria. These associations were observed at a nominal significance level (P < 0.05). After applying Bonferroni correction for multiple testing, these associations did not reach statistical significance. The observed trends, however, provide insights into potential microbiota-urticaria interactions. Multivariate MR analyses elucidated the role of SCFAs, particularly acetate, which plays a crucial role in modulating immune response. Adjusting for acetate revealed direct effects of Actinobacteria, Bifidobacteriales, and Bifidobacteriaceae on spontaneous urticaria, with corresponding mediation effects of -22%, -24.9%, and -24.9% respectively. Similarly, adjustments for Alcaligenaceae and Betaproteobacteria indicated significant negative effects of acetate on dermatographic and spontaneous urticaria, with mediation effects of -21.7% and -23.7%, respectively. Conclusion This study confirms the interconnected roles of gut microbiota, SCFAs, and urticaria. It highlights SCFAs' potential mediating role in influencing urticaria through microbiota, providing insights for future therapeutic strategies.
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Affiliation(s)
- Xianjun Xiao
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Peiwen Xue
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Xiangyun Yan
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Yanqiu Li
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Yunzhou Shi
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Haiyan Qin
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Di Qin
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Wei Cao
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Zihao Zou
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Lu Wang
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Rongjiang Jin
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Ying Li
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Junpeng Yao
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Juan Li
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
- Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCM, Chengdu, Sichuan, 611135, People’s Republic of China
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Lin Y, Zhang Y, Wang S, Cao L, Zhao R, Ma X, Yang Q, Zhang L, Yang Q. Pharmacological targets of SGLT2 inhibition on prostate cancer mediated by circulating metabolites: a drug-target Mendelian randomization study. Front Pharmacol 2024; 15:1443045. [PMID: 39166104 PMCID: PMC11333260 DOI: 10.3389/fphar.2024.1443045] [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: 06/03/2024] [Accepted: 07/19/2024] [Indexed: 08/22/2024] Open
Abstract
Background The relationship between sodium-glucose cotransporter 2 (SGLT2) inhibitors and prostate cancer is still unknown. Although these inhibitors can influence tumor glycolysis, the underlying mechanism requires further exploration. Methods A two-sample two-step MR was used to determine 1) causal effects of SGLT2 inhibition on prostate cancer; 2) causal effects of 1,400 circulating metabolites or metabolite ratios on prostate cancer; and 3) mediation effects of these circulating metabolites. Genetic proxies for SGLT2 inhibition were identified as variants in the SLC5A2 gene and glycated hemoglobin level (HbA1c). Additionally, positive control analysis on type 2 diabetes mellitus (T2DM) was conducted to test the selection of genetic proxies. Phenome Wide Association Study (PheWAS) and MR-PheWAS analysis were used to explore potential treatable diseases and adverse outcomes of SGLT2 inhibitors. Results Genetically predicted SGLT2 inhibition (per 1 SD decrement in HbA1c) was associated with reduced risk of T2DM [odds ratio (OR) = 0.66 (95% CI 0.53, 0.82), P = 1.57 × 10-4]; prostate cancer [0.34 (0.23, 0.49), P = 2.21 × 10-8] and prostate-specific antigen [0.26 (0.08, 0.81), P = 2.07 × 10-2]. The effect of SGLT2 inhibition on prostate cancer was mediated by uridine level, with a mediated proportion of 9.34% of the total effect. In MR-PheWAS, 65 traits were found to be associated with SLGT2 inhibitors (P < 1.78 × 10-5), and among them, 13 were related to diabetes. Conclusion Our study suggested that SGLT2 inhibition could lower prostate cancer risk through uridine mediation. More mechanistic and clinical research is necessary to explore how uridine mediates the link between SGLT2 inhibition and prostate cancer.
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Affiliation(s)
- Yilong Lin
- Department of Breast Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- School of Medicine, Xiamen University, Xiamen, China
| | - Yue Zhang
- Department of Hematology, Xiangya Hospital, Xiangya School of Medicine, Central South University, Changsha, China
| | - Songsong Wang
- School of Medicine, Xiamen University, Xiamen, China
| | - Lin Cao
- The First Clinical College of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China
| | - Ruidan Zhao
- Department of Breast Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xilai Ma
- School of Medicine, Xiamen University, Xiamen, China
| | - Qiaolu Yang
- Department of Breast Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Liyi Zhang
- Department of Breast Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Qingmo Yang
- Department of Breast Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, China
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Yang X, Li Y, Mei T, Duan J, Yan X, McNaughton LR, He Z. Genome-wide association study of exercise-induced skeletal muscle hypertrophy and the construction of predictive model. Physiol Genomics 2024; 56:578-589. [PMID: 38881426 DOI: 10.1152/physiolgenomics.00019.2024] [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: 02/11/2024] [Revised: 05/21/2024] [Accepted: 06/10/2024] [Indexed: 06/18/2024] Open
Abstract
The aim of the current study was to investigate interindividual differences in muscle thickness of the rectus femoris (MTRF) following 12 wk of resistance training (RT) or high-intensity interval training (HIIT) to explore the genetic architecture underlying skeletal muscle hypertrophy and to construct predictive models. We conducted musculoskeletal ultrasound assessments of the MTRF response in 440 physically inactive adults after the 12-wk exercise period. A genome-wide association study was used to identify variants associated with the MTRF response, separately for RT and HIIT. Using the polygenic predictor score (PPS), we estimated the genetic contribution to exercise-induced hypertrophy. Predictive models for the MTRF response were constructed using random forest (RF), support vector mac (SVM), and generalized linear model (GLM) in 10 cross-validated approaches. MTRF increased significantly after both RT (8.8%, P < 0.05) and HIIT (5.3%, P < 0.05), but with considerable interindividual differences (RT: -13.5 to 38.4%, HIIT: -14.2 to 30.7%). Eleven lead single-nucleotide polymorphisms in RT and eight lead single-nucleotide polymorphisms in HIIT were identified at a significance level of P < 1 × 10-5. The PPS was associated with the MTRF response, explaining 47.2% of the variation in response to RT and 38.3% of the variation in response to HIIT. Notably, the GLM and SVM predictive models exhibited superior performance compared with RF models (P < 0.05), and the GLM demonstrated optimal performance with an area under curve of 0.809 (95% confidence interval: 0.669-0.949). Factors such as PPS, baseline MTRF, and exercise protocol exerted influence on the MTRF response to exercise, with PPS being the primary contributor. The GLM and SVM predictive model, incorporating both genetic and phenotypic factors, emerged as promising tools for predicting exercise-induced skeletal muscle hypertrophy.NEW & NOTEWORTHY The interindividual variability induced muscle hypertrophy by resistance training (RT) or high-intensity interval training (HIIT) and the associated genetic architecture remain uncertain. We identified genetic variants that underlie RT- or HIIT-induced muscle hypertrophy and established them as pivotal factors influencing the response regardless of the training type. The genetic-phenotype predictive model developed has the potential to identify nonresponders or individuals with low responsiveness before engaging in exercise training.
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Affiliation(s)
- Xiaolin Yang
- China Institute of Sport and Health Science, Beijing Sport University, Beijing, China
- Key Laboratory for Performance Training and Recovery of General Administration of Sport, Beijing Sport University, Beijing, China
| | - Yanchun Li
- China Institute of Sport and Health Science, Beijing Sport University, Beijing, China
- Key Laboratory for Performance Training and Recovery of General Administration of Sport, Beijing Sport University, Beijing, China
| | - Tao Mei
- China Institute of Sport and Health Science, Beijing Sport University, Beijing, China
- Key Laboratory for Performance Training and Recovery of General Administration of Sport, Beijing Sport University, Beijing, China
| | - Jiayan Duan
- China Institute of Sport and Health Science, Beijing Sport University, Beijing, China
| | - Xu Yan
- Institute for Health and Sport, Victoria University, Melbourne, Victoria, Australia
- Regenerative Medicine and Stem Cells Program, Australian Institute for Musculoskeletal Science, St Albans, Victoria, Australia
| | - Lars Robert McNaughton
- Sport Performance, Exercise and Nutrition Research Group, Department of Sport and Physical Activity, Edge Hill University, Ormskirk, United Kingdom
| | - Zihong He
- Biology Center, China Institute of Sport Science, Beijing, China
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Jiang L, Shen J, Darst BF, Haiman CA, Mancuso N, Conti DV. Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data. Genet Epidemiol 2024. [PMID: 38887957 DOI: 10.1002/gepi.22577] [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: 09/27/2023] [Revised: 04/04/2024] [Accepted: 05/15/2024] [Indexed: 06/20/2024]
Abstract
Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.
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Affiliation(s)
- Lai Jiang
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Jiayi Shen
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Burcu F Darst
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA
| | - David V Conti
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA
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Duan A, Qiu Y, Song B, Tao Y, Wang M, Yin Z, Xie M, Chen Z, Wang Z, Sun X. Metabolome-Wide Mendelian Randomization Assessing the Causal Role of Serum and Cerebrospinal Metabolites in Traumatic Brain Injury. Biomedicines 2024; 12:1178. [PMID: 38927385 PMCID: PMC11201266 DOI: 10.3390/biomedicines12061178] [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: 03/26/2024] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 06/28/2024] Open
Abstract
Previous studies have identified metabolites as biomarkers or potential therapeutic targets for traumatic brain injury (TBI). However, the causal association between them remains unknown. Therefore, we investigated the causal effect of serum metabolites and cerebrospinal fluid (CSF) metabolites on TBI susceptibility through Mendelian randomization (MR). Genetic variants related to metabolites and TBI were extracted from a corresponding genome-wide association study (GWAS). Causal effects were estimated through the inverse variance weighted approach, supplemented by a weighted median, weight mode, and the MR-Egger test. In addition, sensitivity analyses were further performed to evaluate the stability of the MR results, including the MR-Egger intercept, leave-one-out analysis, Cochrane's Q-test, and the MR-PRESSO global test. Metabolic pathway analysis was applied to uncover the underlying pathways of the significant metabolites in TBI. In blood metabolites, substances such as 4-acetaminophen sulfate and kynurenine showed positive links, whereas beta-hydroxyisovalerate and creatinine exhibited negative correlations. CSF metabolites such as N-formylanthranilic acid were positively related, while kynurenate showed negative associations. The metabolic pathway analysis highlighted the potential biological pathways involved in TBI. Of these 16 serum metabolites, 11 CSF metabolites and metabolic pathways may serve as useful circulating biomarkers in clinical screening and prevention, and may be candidate molecules for the exploration of mechanisms and drug targets.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Zhong Wang
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou 215006, China; (A.D.); (Y.Q.)
| | - Xiaoou Sun
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou 215006, China; (A.D.); (Y.Q.)
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Zhou Z, Leng H. Deciphering the causal relationship between plasma and cerebrospinal fluid metabolites and glioblastoma multiforme: a Mendelian Randomization study. Aging (Albany NY) 2024; 16:8306-8319. [PMID: 38742944 PMCID: PMC11131984 DOI: 10.18632/aging.205818] [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: 12/26/2023] [Accepted: 04/10/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Glioblastoma Multiforme (GBM) is one of the most aggressive and fatal brain cancers. The study of metabolites could be crucial for understanding GBM's biology and reveal new treatment strategies. METHODS The GWAS data for GBM were sourced from the FinnGen database. A total of 1400 plasma metabolites were collected from the GWAS Catalog dataset. The cerebrospinal fluid (CSF) metabolites data were collected from subsets of participants in the WADRC and WRAP studies. We utilized the inverse variance weighting (IVW) method as the primary tool to explore the causal relationship between metabolites in plasma and CSF and glioblastoma, ensuring the exclusion of instances with horizontal pleiotropy. Additionally, four supplementary analytical methods were applied to reinforce our findings. Aberrant results were identified and omitted based on the outcomes of the leave-one-out sensitivity analysis. Conclusively, a reverse Mendelian Randomization analysis was also conducted to further substantiate our results. RESULTS The study identified 69 plasma metabolites associated with GBM. Of these, 40 metabolites demonstrated a significant positive causal relationship with GBM, while 29 exhibited a significant negative causal association. Notably, Trimethylamine N-oxide (TMAO) levels in plasma, not CSF, were found to be a significant exposure factor for GBM (OR = 3.1627, 95% CI = (1.6347, 6.1189), P = 0.0006). The study did not find a reverse causal relationship between GBM and plasma TMAO levels. CONCLUSIONS This research has identified 69 plasma metabolites potentially associated with the incidence of GBM, among which TMAO stands out as a promising candidate for an early detectable biomarker for GBM.
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Affiliation(s)
- Zhiwei Zhou
- Department of Neurosurgery, Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City), Changde, Hunan 415003, People’s Republic of China
| | - Haibin Leng
- Department of Neurosurgery, Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City), Changde, Hunan 415003, People’s Republic of China
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Lu T, Chen Y, Yoshiji S, Ilboudo Y, Forgetta V, Zhou S, Greenwood CMT. Circulating Metabolite Abundances Associated With Risks of Bipolar Disorder, Schizophrenia, and Depression: A Mendelian Randomization Study. Biol Psychiatry 2024:S0006-3223(24)01285-X. [PMID: 38705554 DOI: 10.1016/j.biopsych.2024.04.016] [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: 08/30/2023] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/07/2024]
Abstract
BACKGROUND Preventive measures and treatments for psychiatric disorders are limited. Circulating metabolites are potential candidates for biomarker and therapeutic target identification, given their measurability and essential roles in biological processes. METHODS Leveraging large-scale genome-wide association studies, we conducted Mendelian randomization analyses to assess the associations between circulating metabolite abundances and the risks of bipolar disorder, schizophrenia, and depression. Genetic instruments were selected for 94 metabolites measured in the Canadian Longitudinal Study on Aging cohort (N = 8299). We repeated Mendelian randomization analyses based on the UK Biobank, INTERVAL, and EPIC (European Prospective Investigation into Cancer)-Norfolk studies. RESULTS After validating Mendelian randomization assumptions and colocalization evidence, we found that a 1 SD increase in genetically predicted circulating abundances of eicosapentaenoate and docosapentaenoate was associated with odds ratios of 0.72 (95% CI, 0.65-0.79) and 0.63 (95% CI, 0.55-0.72), respectively, for bipolar disorder. Genetically increased Ω-3 unsaturated fatty acids abundance and Ω-3-to-total fatty acids ratio, as well as genetically decreased Ω-6-to-Ω-3 ratio, were negatively associated with the risk of bipolar disorder in the UK Biobank. Genetically increased circulating abundances of 3 N-acetyl-amino acids were associated with an increased risk of schizophrenia with a maximum odds ratio of 1.31 (95% CI, 1.18-1.44) per 1 SD increase. Furthermore, a 1 SD increase in genetically predicted circulating abundance of hypotaurine was associated with an odds ratio of 0.85 (95% CI, 0.78-0.93) for depression. CONCLUSIONS The biological mechanisms that underlie Ω-3 unsaturated fatty acids, NAT8-catalyzed N-acetyl-amino acids, and hypotaurine warrant exploration to identify new biomarkers and potential therapeutic targets.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada; Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.
| | - Yiheng Chen
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada; Five Prime Sciences Inc., Montréal, Québec, Canada; Department of Human Genetics, McGill University, Montréal, Québec, Canada
| | - Satoshi Yoshiji
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada; Department of Human Genetics, McGill University, Montréal, Québec, Canada; Kyoto-McGill International Collaborative Program in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Japan Society for the Promotion of Science, Tokyo, Japan
| | - Yann Ilboudo
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada
| | | | - Sirui Zhou
- Department of Human Genetics, McGill University, Montréal, Québec, Canada; McGill Genome Centre, McGill University, Montréal, Québec, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada; Department of Human Genetics, McGill University, Montréal, Québec, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada; Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada.
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10
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Meier L, Bruginski E, Marafiga JR, Caus LB, Pasquetti MV, Calcagnotto ME, Campos FR. Hippocampal metabolic profile during epileptogenesis in the pilocarpine model of epilepsy. Biomed Chromatogr 2024; 38:e5820. [PMID: 38154955 DOI: 10.1002/bmc.5820] [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: 11/11/2023] [Revised: 12/02/2023] [Accepted: 12/07/2023] [Indexed: 12/30/2023]
Abstract
Temporal lobe epilepsy (TLE) is a common form of refractory epilepsy in adulthood. The metabolic profile of epileptogenesis is still poorly investigated. Elucidation of such a metabolic profile using animal models of epilepsy could help identify new metabolites and pathways involved in the mechanisms of epileptogenesis process. In this study, we evaluated the metabolic profile during the epileptogenesis periods. Using a pilocarpine model of epilepsy, we analyzed the global metabolic profile of hippocampal extracts by untargeted metabolomics based on ultra-performance liquid chromatography-high-resolution mass spectrometry, at three time points (3 h, 1 week, and 2 weeks) after status epilepticus (SE) induction. We demonstrated that epileptogenesis periods presented different hippocampal metabolic profiles, including alterations of metabolic pathways of amino acids and lipid metabolism. Six putative metabolites (tryptophan, N-acetylornithine, N-acetyl-L-aspartate, glutamine, adenosine, and cholesterol) showed significant different levels during epileptogenesis compared to their respective controls. These putative metabolites could be associated with the imbalance of neurotransmitters, mitochondrial dysfunction, and cell loss observed during both epileptogenesis and epilepsy. With these findings, we provided an overview of hippocampal metabolic profiles during different stages of epileptogenesis that could help investigate pathways and respective metabolites as predictive tools in epilepsy.
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Affiliation(s)
- Letícia Meier
- Biosciences and Mass Spectrometry Laboratory, Department of Pharmacy, Universidade Federal do Paraná, Curitiba, PR, Brazil
- Graduate Program in Pharmaceutical Science, Universidade Federal do Paraná, Curitiba, PR, Brazil
| | - Estevan Bruginski
- Biosciences and Mass Spectrometry Laboratory, Department of Pharmacy, Universidade Federal do Paraná, Curitiba, PR, Brazil
- Graduate Program in Pharmaceutical Science, Universidade Federal do Paraná, Curitiba, PR, Brazil
| | - Joseane Righes Marafiga
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory (NNNESP Lab.), Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Graduate Program in Biological Science: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Letícia Barbieri Caus
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory (NNNESP Lab.), Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Mayara Vendramin Pasquetti
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory (NNNESP Lab.), Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Maria Elisa Calcagnotto
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory (NNNESP Lab.), Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Graduate Program in Biological Science: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Francinete Ramos Campos
- Biosciences and Mass Spectrometry Laboratory, Department of Pharmacy, Universidade Federal do Paraná, Curitiba, PR, Brazil
- Graduate Program in Pharmaceutical Science, Universidade Federal do Paraná, Curitiba, PR, Brazil
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11
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Wang DH, Fujita Y, Dono A, Rodriguez Armendariz AG, Shah M, Putluri N, Pichardo-Rojas PS, Patel CB, Zhu JJ, Huse JT, Parker Kerrigan BC, Lang FF, Esquenazi Y, Ballester LY. The genomic alterations in glioblastoma influence the levels of CSF metabolites. Acta Neuropathol Commun 2024; 12:13. [PMID: 38243318 PMCID: PMC10799404 DOI: 10.1186/s40478-024-01722-1] [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: 10/30/2023] [Accepted: 12/31/2023] [Indexed: 01/21/2024] Open
Abstract
Cerebrospinal fluid (CSF) analysis is underutilized in patients with glioblastoma (GBM), partly due to a lack of studies demonstrating the clinical utility of CSF biomarkers. While some studies show the utility of CSF cell-free DNA analysis, studies analyzing CSF metabolites in patients with glioblastoma are limited. Diffuse gliomas have altered cellular metabolism. For example, mutations in isocitrate dehydrogenase enzymes (e.g., IDH1 and IDH2) are common in diffuse gliomas and lead to increased levels of D-2-hydroxyglutarate in CSF. However, there is a poor understanding of changes CSF metabolites in GBM patients. In this study, we performed targeted metabolomic analysis of CSF from n = 31 patients with GBM and n = 13 individuals with non-neoplastic conditions (controls), by mass spectrometry. Hierarchical clustering and sparse partial least square-discriminant analysis (sPLS-DA) revealed differences in CSF metabolites between GBM and control CSF, including metabolites associated with fatty acid oxidation and the gut microbiome (i.e., carnitine, 2-methylbutyrylcarnitine, shikimate, aminobutanal, uridine, N-acetylputrescine, and farnesyl diphosphate). In addition, we identified differences in CSF metabolites in GBM patients based on the presence/absence of TP53 or PTEN mutations, consistent with the idea that different mutations have different effects on tumor metabolism. In summary, our results increase the understanding of CSF metabolites in patients with diffuse gliomas and highlight several metabolites that could be informative biomarkers in patients with GBM.
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Affiliation(s)
- Daniel H Wang
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 2130 W. Holcombe Blvd., Suite 910, Houston, TX, 77030, USA
| | - Yoko Fujita
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 6400 Fannin St., Suite 2800, Houston, TX, 77030, USA
| | - Antonio Dono
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 6400 Fannin St., Suite 2800, Houston, TX, 77030, USA
| | - Ana G Rodriguez Armendariz
- Escuela de Medicina y Ciencias de la Salud, Tecnológico de Monterrey, Av. Ignacio Morones Prieto 3000, Sertoma, Monterrey, N.L, 64710, Mexico
| | - Mauli Shah
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 2130 W. Holcombe Blvd., Suite 910, Houston, TX, 77030, USA
| | - Nagireddy Putluri
- Advanced Technology Core, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA
| | - Pavel S Pichardo-Rojas
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 6400 Fannin St., Suite 2800, Houston, TX, 77030, USA
| | - Chirag B Patel
- Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1002, BSRB S5.8116b, Houston, TX, 77030, USA
| | - Jay-Jiguang Zhu
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 6400 Fannin St., Suite 2800, Houston, TX, 77030, USA
- Memorial Hermann Hospital-Texas Medical Center, Houston, TX, 77030, USA
| | - Jason T Huse
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 2130 W. Holcombe Blvd., Suite 910, Houston, TX, 77030, USA
- Department of Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Brittany C Parker Kerrigan
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, 1400 Holcombe Blvd., Room FC7.2000, Unit 442, Houston, TX, 77030, USA
| | - Frederick F Lang
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, 1400 Holcombe Blvd., Room FC7.2000, Unit 442, Houston, TX, 77030, USA
| | - Yoshua Esquenazi
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 6400 Fannin St., Suite 2800, Houston, TX, 77030, USA
- Memorial Hermann Hospital-Texas Medical Center, Houston, TX, 77030, USA
- Center for Precision Health, McGovern Medical School, The University of Texas Health Science Center at Houston, 7000 Fannin St., Suite 600, Houston, TX, 77030, USA
| | - Leomar Y Ballester
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 2130 W. Holcombe Blvd., Suite 910, Houston, TX, 77030, USA.
- Department of Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
- Neuropathology and Molecular Genetic Pathology, Department of Pathology, Department of Translational Molecular Pathology, 1515 Holcombe Blvd, Unit 85, Houston, TX, 77030, USA.
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12
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de Souza HMR, Pereira TTP, de Sá HC, Alves MA, Garrett R, Canuto GAB. Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens. Metabolites 2024; 14:36. [PMID: 38248839 PMCID: PMC10819689 DOI: 10.3390/metabo14010036] [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: 11/24/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 01/23/2024] Open
Abstract
This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.
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Affiliation(s)
- Hygor M. R. de Souza
- Instituto de Química, Universidade Federal do Rio de Janeiro, LabMeta—LADETEC, Rio de Janeiro 21941-598, Brazil;
| | - Tássia T. P. Pereira
- Departamento de Genética, Ecologia e Evolucao, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil;
- Departamento de Biodiversidade, Evolução e Meio Ambiente, Universidade Federal de Ouro Preto, Ouro Preto 35400-000, Brazil
| | - Hanna C. de Sá
- Departamento de Química Analítica, Instituto de Química, Universidade Federal da Bahia, Salvador 40170-115, Brazil;
| | - Marina A. Alves
- Instituto de Pesquisa de Produtos Naturais Walter Mors, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-599, Brazil;
| | - Rafael Garrett
- Instituto de Química, Universidade Federal do Rio de Janeiro, LabMeta—LADETEC, Rio de Janeiro 21941-598, Brazil;
- Department of Laboratory Medicine, Boston Children’s Hospital—Harvard Medical School, Boston, MA 02115, USA
| | - Gisele A. B. Canuto
- Departamento de Química Analítica, Instituto de Química, Universidade Federal da Bahia, Salvador 40170-115, Brazil;
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13
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Petrova B, Lacey TE, Culhane AJ, Cui J, Raskin A, Misra A, Lehtinen MK, Kanarek N. Metabolomics of Mouse Embryonic CSF Following Maternal Immune Activation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.06.570507. [PMID: 38105934 PMCID: PMC10723469 DOI: 10.1101/2023.12.06.570507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The cerebrospinal fluid (CSF) serves various roles in the developing central nervous system (CNS), from neurogenesis to lifelong cognitive functions. Changes in CSF composition due to inflammation can impact brain function. We recently identified an abnormal cytokine signature in embryonic CSF (eCSF) following maternal immune activation (MIA), a mouse model of autism spectrum disorder (ASD). We hypothesized that MIA leads to other alterations in eCSF composition and employed untargeted metabolomics to profile changes in the eCSF metabolome in mice after inducing MIA with polyI:C. We report these data here as a resource, include a comprehensive MS1 and MS2 reference dataset, and present additional datasets comparing two mouse strains (CD-1 and C57Bl/6) and two developmental time points (E12.5 and E14.5). Targeted metabolomics further validated changes upon MIA. We show a significant elevation of glucocorticoids and kynurenine pathway related metabolites. Both pathways are relevant for suppressing inflammation or could be informative as disease biomarkers. Our resource should inform future mechanistic studies regarding the etiology of MIA neuropathology and roles and contributions of eCSF metabolites to brain development.
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14
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Vallianatou T, Nilsson A, Bjärterot P, Shariatgorji R, Slijkhuis N, Aerts JT, Jansson ET, Svenningsson P, Andrén PE. Rapid Metabolic Profiling of 1 μL Crude Cerebrospinal Fluid by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Can Differentiate De Novo Parkinson's Disease. Anal Chem 2023; 95:18352-18360. [PMID: 38059473 PMCID: PMC10733901 DOI: 10.1021/acs.analchem.3c02900] [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: 07/03/2023] [Revised: 11/10/2023] [Accepted: 11/14/2023] [Indexed: 12/08/2023]
Abstract
Parkinson's disease (PD) is a highly prevalent neurodegenerative disorder affecting the motor system. However, the correct diagnosis of PD and atypical parkinsonism may be difficult with high clinical uncertainty. There is an urgent need to identify reliable biomarkers using high-throughput, molecular-specific methods to improve current diagnostics. Here, we present a matrix-assisted laser desorption/ionization mass spectrometry imaging method that requires minimal sample preparation and only 1 μL of crude cerebrospinal fluid (CSF). The method enables analysis of hundreds of samples in a single experiment while simultaneously detecting numerous metabolites with subppm mass accuracy. To test the method, we analyzed CSF samples from 12 de novo PD patients (that is, newly diagnosed and previously untreated) and 12 age-matched controls. Within the identified molecules, we found neurotransmitters and their metabolites such as γ-aminobutyric acid, 3-methoxytyramine, homovanillic acid, serotonin, histamine, amino acids, and metabolic intermediates. Limits of detection were estimated for multiple neurotransmitters with high linearity (R2 > 0.99) and sensitivity (as low as 16 pg/μL). Application of multivariate classification led to a highly significant (P < 0.001) model of PD prediction with a 100% classification rate, which was further thoroughly validated with a permutation test and univariate analysis. Molecules related to the neuromelanin pathway were found to be significantly increased in the PD group, indicated by their elevated relative intensities compared to the control group. Our method enables rapid detection of PD-related biomarkers in low sample volumes and could serve as a valuable tool in the development of robust PD diagnostics.
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Affiliation(s)
- Theodosia Vallianatou
- Department
of Pharmaceutical Biosciences, Spatial Mass Spectrometry, Science
for Life Laboratory, Uppsala University, Uppsala SE-75124, Sweden
| | - Anna Nilsson
- Department
of Pharmaceutical Biosciences, Spatial Mass Spectrometry, Science
for Life Laboratory, Uppsala University, Uppsala SE-75124, Sweden
| | - Patrik Bjärterot
- Department
of Pharmaceutical Biosciences, Spatial Mass Spectrometry, Science
for Life Laboratory, Uppsala University, Uppsala SE-75124, Sweden
| | - Reza Shariatgorji
- Department
of Pharmaceutical Biosciences, Spatial Mass Spectrometry, Science
for Life Laboratory, Uppsala University, Uppsala SE-75124, Sweden
| | - Nuria Slijkhuis
- Department
of Pharmaceutical Biosciences, Spatial Mass Spectrometry, Science
for Life Laboratory, Uppsala University, Uppsala SE-75124, Sweden
| | - Jordan T. Aerts
- Department
of Pharmaceutical Biosciences, Spatial Mass Spectrometry, Science
for Life Laboratory, Uppsala University, Uppsala SE-75124, Sweden
| | - Erik T. Jansson
- Department
of Pharmaceutical Biosciences, Spatial Mass Spectrometry, Science
for Life Laboratory, Uppsala University, Uppsala SE-75124, Sweden
| | - Per Svenningsson
- Department
of Clinical Neuroscience, Karolinska Institute, Stockholm SE-17177, Sweden
| | - Per E. Andrén
- Department
of Pharmaceutical Biosciences, Spatial Mass Spectrometry, Science
for Life Laboratory, Uppsala University, Uppsala SE-75124, Sweden
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15
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Wang Z, Zhu H, Xiong W. Advances in mass spectrometry-based multi-scale metabolomic methodologies and their applications in biological and clinical investigations. Sci Bull (Beijing) 2023; 68:2268-2284. [PMID: 37666722 DOI: 10.1016/j.scib.2023.08.047] [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: 06/15/2023] [Revised: 07/25/2023] [Accepted: 08/22/2023] [Indexed: 09/06/2023]
Abstract
Metabolomics is a nascent field of inquiry that emerged in the late 20th century. It encompasses the comprehensive profiling of metabolites across a spectrum of organisms, ranging from bacteria and cells to tissues. The rapid evolution of analytical methods and data analysis has greatly accelerated progress in this dynamic discipline over recent decades. Sophisticated techniques such as liquid chromatograph mass spectrometry (MS), gas chromatograph MS, capillary electrophoresis MS, and nuclear magnetic resonance serve as the cornerstone of metabolomic analysis. Building upon these methods, a plethora of modifications and combinations have emerged to propel the advancement of metabolomics. Despite this progress, scrutinizing metabolism at the single-cell or single-organelle level remains an arduous task over the decades. Some of the most thrilling advancements, such as single-cell and single-organelle metabolic profiling techniques, offer profound insights into the intricate mechanisms within cells and organelles. This allows for a comprehensive study of metabolic heterogeneity and its pivotal role in multiple biological processes. The progress made in MS imaging has enabled high-resolution in situ metabolic profiling of tissue sections and even individual cells. Spatial reconstruction techniques enable the direct representation of metabolic distribution and alteration in three-dimensional space. The application of novel metabolomic techniques has led to significant breakthroughs in biological and clinical studies, including the discovery of novel metabolic pathways, determination of cell fate in differentiation, anti-aging intervention through modulating metabolism, metabolomics-based clinicopathologic analysis, and surgical decision-making based on on-site intraoperative metabolic analysis. This review presents a comprehensive overview of both conventional and innovative metabolomic techniques, highlighting their applications in groundbreaking biological and clinical studies.
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Affiliation(s)
- Ziyi Wang
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Hongying Zhu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China; Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; CAS Key Laboratory of Brain Function and Disease, Hefei 230026, China; Anhui Province Key Laboratory of Biomedical Aging Research, Hefei 230026, China.
| | - Wei Xiong
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China; Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; CAS Key Laboratory of Brain Function and Disease, Hefei 230026, China; Anhui Province Key Laboratory of Biomedical Aging Research, Hefei 230026, China.
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16
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Dong R, Lu Q, Kang H, Suridjan I, Kollmorgen G, Wild N, Deming Y, Van Hulle CA, Anderson RM, Zetterberg H, Blennow K, Carlsson CM, Asthana S, Johnson SC, Engelman CD. CSF metabolites associated with biomarkers of Alzheimer's disease pathology. Front Aging Neurosci 2023; 15:1214932. [PMID: 37719875 PMCID: PMC10499619 DOI: 10.3389/fnagi.2023.1214932] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 07/17/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction Metabolomics technology facilitates studying associations between small molecules and disease processes. Correlating metabolites in cerebrospinal fluid (CSF) with Alzheimer's disease (AD) CSF biomarkers may elucidate additional changes that are associated with early AD pathology and enhance our knowledge of the disease. Methods The relative abundance of untargeted metabolites was assessed in 161 individuals from the Wisconsin Registry for Alzheimer's Prevention. A metabolome-wide association study (MWAS) was conducted between 269 CSF metabolites and protein biomarkers reflecting brain amyloidosis, tau pathology, neuronal and synaptic degeneration, and astrocyte or microglial activation and neuroinflammation. Linear mixed-effects regression analyses were performed with random intercepts for sample relatedness and repeated measurements and fixed effects for age, sex, and years of education. The metabolome-wide significance was determined by a false discovery rate threshold of 0.05. The significant metabolites were replicated in 154 independent individuals from then Wisconsin Alzheimer's Disease Research Center. Mendelian randomization was performed using genome-wide significant single nucleotide polymorphisms from a CSF metabolites genome-wide association study. Results Metabolome-wide association study results showed several significantly associated metabolites for all the biomarkers except Aβ42/40 and IL-6. Genetic variants associated with metabolites and Mendelian randomization analysis provided evidence for a causal association of metabolites for soluble triggering receptor expressed on myeloid cells 2 (sTREM2), amyloid β (Aβ40), α-synuclein, total tau, phosphorylated tau, and neurogranin, for example, palmitoyl sphingomyelin (d18:1/16:0) for sTREM2, and erythritol for Aβ40 and α-synuclein. Discussion This study provides evidence that CSF metabolites are associated with AD-related pathology, and many of these associations may be causal.
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Affiliation(s)
- Ruocheng Dong
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Hyunseung Kang
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | | | | | | | - Yuetiva Deming
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Carol A. Van Hulle
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Rozalyn M. Anderson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Geriatrics Research Education and Clinical Center, Middleton VA Hospital, Madison, WI, United States
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, United Kingdom
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, Hong Kong SAR, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Cynthia M. Carlsson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Geriatrics Research Education and Clinical Center, Middleton VA Hospital, Madison, WI, United States
| | - Sanjay Asthana
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Geriatrics Research Education and Clinical Center, Middleton VA Hospital, Madison, WI, United States
| | - Sterling C. Johnson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Geriatrics Research Education and Clinical Center, Middleton VA Hospital, Madison, WI, United States
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Corinne D. Engelman
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
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17
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Wang C, Western D, Yang C, Ali M, Wang L, Gorijala P, Timsina J, Ruiz A, Pastor P, Fernandez M, Panyard D, Engelman C, Deming Y, Boada M, Cano A, García-González P, Graff-Radford N, Mori H, Lee JH, Perrin R, Sung YJ, Cruchaga C. Unique genetic architecture of CSF and brain metabolites pinpoints the novel targets for the traits of human wellness. RESEARCH SQUARE 2023:rs.3.rs-2923409. [PMID: 37333177 PMCID: PMC10274943 DOI: 10.21203/rs.3.rs-2923409/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Brain metabolism perturbation can contribute to traits and diseases. We conducted the first large-scale CSF and brain genome-wide association studies, which identified 219 independent associations (59.8% novel) for 144 CSF metabolites and 36 independent associations (55.6% novel) for 34 brain metabolites. Most of the novel signals (97.7% and 70.0% in CSF and brain) were tissue specific. We also integrated MWAS-FUSION approaches with Mendelian Randomization and colocalization to identify causal metabolites for 27 brain and human wellness phenotypes and identified eight metabolites to be causal for eight traits (11 relationships). Low mannose level was causal to bipolar disorder and as dietary supplement it may provide therapeutic benefits. Low galactosylglycerol level was found causal to Parkinson's Disease (PD). Our study expanded the knowledge of MQTL in central nervous system, provided insights into human wellness, and successfully demonstrates the utility of combined statistical approaches to inform interventions.
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Affiliation(s)
| | - Dan Western
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | | | | | - Lihua Wang
- Washington University School of Medicine
| | | | | | | | - Pau Pastor
- University Hospital Germans Trias i Pujol
| | | | | | | | | | - Merce Boada
- Research Center and Memory Clinic of Fundació ACE, Institut Català de Neurociències Aplicades-UIC, Barcelona
| | - Amanda Cano
- Research Center and Memory Clinic, ACE Alzheimer Center Barcelona. Universitat Internacional de Catalunya, Spain
| | | | | | - Hiroshi Mori
- Department of Clinical Neuroscience, Faculty of medicine
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Wittrahm R, Takalo M, Kuulasmaa T, Mäkinen PM, Mäkinen P, Končarević S, Fartzdinov V, Selzer S, Kokkola T, Antikainen L, Martiskainen H, Kemppainen S, Marttinen M, Jeskanen H, Rostalski H, Rahunen E, Kivipelto M, Ngandu T, Natunen T, Lambert JC, Tanzi RE, Kim DY, Rauramaa T, Herukka SK, Soininen H, Laakso M, Pike I, Leinonen V, Haapasalo A, Hiltunen M. Protective Alzheimer's disease-associated APP A673T variant predominantly decreases sAPPβ levels in cerebrospinal fluid and 2D/3D cell culture models. Neurobiol Dis 2023; 182:106140. [PMID: 37120095 DOI: 10.1016/j.nbd.2023.106140] [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: 02/23/2023] [Revised: 04/23/2023] [Accepted: 04/25/2023] [Indexed: 05/01/2023] Open
Abstract
The rare A673T variant was the first variant found within the amyloid precursor protein (APP) gene conferring protection against Alzheimer's disease (AD). Thereafter, different studies have discovered that the carriers of the APP A673T variant show reduced levels of amyloid beta (Aβ) in the plasma and better cognitive performance at high age. Here, we analyzed cerebrospinal fluid (CSF) and plasma of APP A673T carriers and control individuals using a mass spectrometry-based proteomics approach to identify differentially regulated targets in an unbiased manner. Furthermore, the APP A673T variant was introduced into 2D and 3D neuronal cell culture models together with the pathogenic APP Swedish and London mutations. Consequently, we now report for the first time the protective effects of the APP A673T variant against AD-related alterations in the CSF, plasma, and brain biopsy samples from the frontal cortex. The CSF levels of soluble APPβ (sAPPβ) and Aβ42 were significantly decreased on average 9-26% among three APP A673T carriers as compared to three well-matched controls not carrying the protective variant. Consistent with these CSF findings, immunohistochemical assessment of cortical biopsy samples from the same APP A673T carriers did not reveal Aβ, phospho-tau, or p62 pathologies. We identified differentially regulated targets involved in protein phosphorylation, inflammation, and mitochondrial function in the CSF and plasma samples of APP A673T carriers. Some of the identified targets showed inverse levels in AD brain tissue with respect to increased AD-associated neurofibrillary pathology. In 2D and 3D neuronal cell culture models expressing APP with the Swedish and London mutations, the introduction of the APP A673T variant resulted in lower sAPPβ levels. Concomitantly, the levels of sAPPα were increased, while decreased levels of CTFβ and Aβ42 were detected in some of these models. Our findings emphasize the important role of APP-derived peptides in the pathogenesis of AD and demonstrate the effectiveness of the protective APP A673T variant to shift APP processing towards the non-amyloidogenic pathway in vitro even in the presence of two pathogenic mutations.
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Affiliation(s)
- Rebekka Wittrahm
- Institute of Biomedicine, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Mari Takalo
- Institute of Biomedicine, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Teemu Kuulasmaa
- Institute of Biomedicine, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Petra M Mäkinen
- Institute of Biomedicine, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Petri Mäkinen
- A.I. Virtanen Institute for Molecular Sciences, 70211 Kuopio, Finland.
| | | | | | - Stefan Selzer
- Proteome Sciences GmbH & Co. KG, 60438 Frankfurt, Germany.
| | - Tarja Kokkola
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70210 Kuopio, Finland.
| | - Leila Antikainen
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70210 Kuopio, Finland.
| | - Henna Martiskainen
- Institute of Biomedicine, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Susanna Kemppainen
- Institute of Biomedicine, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Mikael Marttinen
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.
| | - Heli Jeskanen
- Institute of Biomedicine, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Hannah Rostalski
- A.I. Virtanen Institute for Molecular Sciences, 70211 Kuopio, Finland.
| | - Eija Rahunen
- Institute of Biomedicine, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Miia Kivipelto
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland; Division of Clinical Geriatrics, Department of Neurobiology, Center for Alzheimer Research, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; The Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, United Kingdom; Theme Aging, Karolinska University Hospital, Stockholm, Sweden; Institute of Public Health and Clinical Nutrition, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Tiia Ngandu
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland; Division of Clinical Geriatrics, Department of Neurobiology, Center for Alzheimer Research, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Teemu Natunen
- Institute of Biomedicine, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Jean-Charles Lambert
- U1167, University of Lille, Inserm, Institut Pasteur de Lille, F-59000 Lille, France.
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
| | - Doo Yeon Kim
- Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
| | - Tuomas Rauramaa
- Department of Pathology, Kuopio University Hospital, 70211 Kuopio, Finland; Unit of Pathology, Institute of Clinical Medicine, University of Eastern Finland, 70210 Kuopio, Finland.
| | - Sanna-Kaisa Herukka
- Department of Neurology, University of Eastern Finland, 70210 Kuopio, Finland; NeuroCenter, Neurology, Kuopio University Hospital, Kuopio, Finland.
| | - Hilkka Soininen
- Department of Neurology, University of Eastern Finland, 70210 Kuopio, Finland.
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70210 Kuopio, Finland; Department of Medicine, Kuopio University Hospital, 70210 Kuopio, Finland.
| | - Ian Pike
- Proteome Sciences plc, Hamilton House, London, WC1H 9BB, UK.
| | - Ville Leinonen
- Department of Neurosurgery, Kuopio University Hospital, and Institute of Clinical Medicine, Unit of Neurosurgery, University of Eastern Finland, Kuopio, Finland.
| | | | - Mikko Hiltunen
- Institute of Biomedicine, University of Eastern Finland, 70211 Kuopio, Finland.
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Molecular Landscape of Tourette's Disorder. Int J Mol Sci 2023; 24:ijms24021428. [PMID: 36674940 PMCID: PMC9865021 DOI: 10.3390/ijms24021428] [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: 11/28/2022] [Revised: 12/29/2022] [Accepted: 01/01/2023] [Indexed: 01/12/2023] Open
Abstract
Tourette's disorder (TD) is a highly heritable childhood-onset neurodevelopmental disorder and is caused by a complex interplay of multiple genetic and environmental factors. Yet, the molecular mechanisms underlying the disorder remain largely elusive. In this study, we used the available omics data to compile a list of TD candidate genes, and we subsequently conducted tissue/cell type specificity and functional enrichment analyses of this list. Using genomic data, we also investigated genetic sharing between TD and blood and cerebrospinal fluid (CSF) metabolite levels. Lastly, we built a molecular landscape of TD through integrating the results from these analyses with an extensive literature search to identify the interactions between the TD candidate genes/proteins and metabolites. We found evidence for an enriched expression of the TD candidate genes in four brain regions and the pituitary. The functional enrichment analyses implicated two pathways ('cAMP-mediated signaling' and 'Endocannabinoid Neuronal Synapse Pathway') and multiple biological functions related to brain development and synaptic transmission in TD etiology. Furthermore, we found genetic sharing between TD and the blood and CSF levels of 39 metabolites. The landscape of TD not only provides insights into the (altered) molecular processes that underlie the disease but, through the identification of potential drug targets (such as FLT3, NAALAD2, CX3CL1-CX3CR1, OPRM1, and HRH2), it also yields clues for developing novel TD treatments.
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Panyard DJ, Deming YK, Darst BF, Van Hulle CA, Zetterberg H, Blennow K, Kollmorgen G, Suridjan I, Carlsson CM, Johnson SC, Asthana S, Engelman CD, Lu Q. Liver-Specific Polygenic Risk Score Is Associated with Alzheimer's Disease Diagnosis. J Alzheimers Dis 2023; 92:395-409. [PMID: 36744333 PMCID: PMC10050104 DOI: 10.3233/jad-220599] [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] [Indexed: 02/05/2023]
Abstract
BACKGROUND Our understanding of the pathophysiology underlying Alzheimer's disease (AD) has benefited from genomic analyses, including those that leverage polygenic risk score (PRS) models of disease. The use of functional annotation has been able to improve the power of genomic models. OBJECTIVE We sought to leverage genomic functional annotations to build tissue-specific AD PRS models and study their relationship with AD and its biomarkers. METHODS We built 13 tissue-specific AD PRS and studied the scores' relationships with AD diagnosis, cerebrospinal fluid (CSF) amyloid, CSF tau, and other CSF biomarkers in two longitudinal cohort studies of AD. RESULTS The AD PRS model that was most predictive of AD diagnosis (even without APOE) was the liver AD PRS: n = 1,115; odds ratio = 2.15 (1.67-2.78), p = 3.62×10-9. The liver AD PRS was also statistically significantly associated with cerebrospinal fluid biomarker evidence of amyloid-β (Aβ42:Aβ40 ratio, p = 3.53×10-6) and the phosphorylated tau:amyloid-β ratio (p = 1.45×10-5). CONCLUSION These findings provide further evidence of the role of the liver-functional genome in AD and the benefits of incorporating functional annotation into genomic research.
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Affiliation(s)
- Daniel J. Panyard
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Yuetiva K. Deming
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Burcu F. Darst
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States of America
| | - Carol A. Van Hulle
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | | | - Cynthia M. Carlsson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sanjay Asthana
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Corinne D. Engelman
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI 53726, United States of America
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53706, United States of America
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Xu Y, Vasiljevic E, Deming YK, Jonaitis EM, Koscik RL, Van Hulle CA, Lu Q, Carboni M, Kollmorgen G, Wild N, Carlsson CM, Johnson SC, Zetterberg H, Blennow K, Engelman CD. Effect of Pathway-Specific Polygenic Risk Scores for Alzheimer's Disease (AD) on Rate of Change in Cognitive Function and AD-Related Biomarkers Among Asymptomatic Individuals. J Alzheimers Dis 2023; 94:1587-1605. [PMID: 37482996 PMCID: PMC10468904 DOI: 10.3233/jad-230097] [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] [Indexed: 07/25/2023]
Abstract
BACKGROUND Genetic scores for late-onset Alzheimer's disease (LOAD) have been associated with preclinical cognitive decline and biomarker variations. Compared with an overall polygenic risk score (PRS), a pathway-specific PRS (p-PRS) may be more appropriate in predicting a specific biomarker or cognitive component underlying LOAD pathology earlier in the lifespan. OBJECTIVE In this study, we leveraged longitudinal data from the Wisconsin Registry for Alzheimer's Prevention and explored changing patterns in cognition and biomarkers at various age points along six biological pathways. METHODS PRS and p-PRSs with and without APOE were constructed separately based on the significant SNPs associated with LOAD in a recent genome-wide association study meta-analysis and compared to APOE alone. We used a linear mixed-effects model to assess the association between PRS/p-PRSs and cognitive trajectories among 1,175 individuals. We also applied the model to the outcomes of cerebrospinal fluid biomarkers in a subset. Replication analyses were performed in an independent sample. RESULTS We found p-PRSs and the overall PRS can predict preclinical changes in cognition and biomarkers. The effects of PRS/p-PRSs on rate of change in cognition, amyloid-β, and tau outcomes are dependent on age and appear earlier in the lifespan when APOE is included in these risk scores compared to when APOE is excluded. CONCLUSION In addition to APOE, the p-PRSs can predict age-dependent changes in amyloid-β, tau, and cognition. Once validated, they could be used to identify individuals with an elevated genetic risk of accumulating amyloid-β and tau, long before the onset of clinical symptoms.
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Affiliation(s)
- Yuexuan Xu
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, WI, USA
| | - Eva Vasiljevic
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, WI, USA
- Center for Demography of Health and Aging, University of Wisconsin-Madison, WI, USA
| | - Yuetiva K. Deming
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, WI, USA
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, WI, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, WI, USA
| | - Erin M. Jonaitis
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, WI, USA
| | - Rebecca L. Koscik
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, WI, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, WI, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, WI, USA
| | - Carol A. Van Hulle
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, WI, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, WI, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, WI, USA
| | | | | | | | - Cynthia M. Carlsson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, WI, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, WI, USA
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA Hospital, Madison, WI, USA
| | - Sterling C. Johnson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, WI, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, WI, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Corinne D. Engelman
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, WI, USA
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22
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Lee IH, Smith MR, Yazdani A, Sandhu S, Walker DI, Mandl KD, Jones DP, Kong SW. Comprehensive characterization of putative genetic influences on plasma metabolome in a pediatric cohort. Hum Genomics 2022; 16:67. [PMID: 36482414 PMCID: PMC9730628 DOI: 10.1186/s40246-022-00440-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype-metabotype associations. However, these associations have not been characterized in children. RESULTS We conducted the largest genome by metabolome-wide association study to date of children (N = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h2) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h2 (> 0.8) for 15.9% of features and low h2 (< 0.2) for most of features (62.0%). The features with high h2 were enriched for amino acid and nucleic acid metabolism, while carbohydrate and lipid concentrations showed low h2. For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p < 3.5 × 10-12 (= 5 × 10-8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; and ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride (m/z 781.7483, retention time (RT) 89.3 s); CALN1 and Tridecanol (m/z 283.2741, RT 27.6). A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for dADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network. CONCLUSION Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene-environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene-environment interaction toward healthy aging trajectories.
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Affiliation(s)
- In-Hee Lee
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA
| | - Matthew Ryan Smith
- grid.189967.80000 0001 0941 6502Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, GA 30602 USA ,grid.414026.50000 0004 0419 4084Atlanta Department of Veterans Affairs Medical Center, Decatur, GA 30033 USA
| | - Azam Yazdani
- grid.38142.3c000000041936754XCenter of Perioperative Genetics and Genomics, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Sumiti Sandhu
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA
| | - Douglas I. Walker
- grid.59734.3c0000 0001 0670 2351Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Kenneth D. Mandl
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA ,grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115 USA ,grid.38142.3c000000041936754XDepartment of Pediatrics, Harvard Medical School, Boston, MA 02115 USA
| | - Dean P. Jones
- grid.189967.80000 0001 0941 6502Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, GA 30602 USA
| | - Sek Won Kong
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA ,grid.38142.3c000000041936754XDepartment of Pediatrics, Harvard Medical School, Boston, MA 02115 USA
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23
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Moore A, Busch MP, Dziewulska K, Francis RO, Hod EA, Zimring JC, D’Alessandro A, Page GP. Genome-wide metabolite quantitative trait loci analysis (mQTL) in red blood cells from volunteer blood donors. J Biol Chem 2022; 298:102706. [PMID: 36395887 PMCID: PMC9763692 DOI: 10.1016/j.jbc.2022.102706] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
The red blood cell (RBC)-Omics study, part of the larger NHLBI-funded Recipient Epidemiology and Donor Evaluation Study (REDS-III), aims to understand the genetic contribution to blood donor RBC characteristics. Previous work identified donor demographic, behavioral, genetic, and metabolic underpinnings to blood donation, storage, and (to a lesser extent) transfusion outcomes, but none have yet linked the genetic and metabolic bodies of work. We performed a genome-wide association (GWA) analysis using RBC-Omics study participants with generated untargeted metabolomics data to identify metabolite quantitative trait loci in RBCs. We performed GWA analyses of 382 metabolites in 243 individuals imputed using the 1000 Genomes Project phase 3 all-ancestry reference panel. Analyses were conducted using ProbABEL and adjusted for sex, age, donation center, number of whole blood donations in the past 2 years, and first 10 principal components of ancestry. Our results identified 423 independent genetic loci associated with 132 metabolites (p < 5×10-8). Potentially novel locus-metabolite associations were identified for the region encoding heme transporter FLVCR1 and choline and for lysophosphatidylcholine acetyltransferase LPCAT3 and lysophosphatidylserine 16.0, 18.0, 18.1, and 18.2; these associations are supported by published rare disease and mouse studies. We also confirmed previous metabolite GWA results for associations, including N(6)-methyl-L-lysine and protein PYROXD2 and various carnitines and transporter SLC22A16. Association between pyruvate levels and G6PD polymorphisms was validated in an independent cohort and novel murine models of G6PD deficiency (African and Mediterranean variants). We demonstrate that it is possible to perform metabolomics-scale GWA analyses with a modest, trans-ancestry sample size.
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Affiliation(s)
- Amy Moore
- Division of Biostatistics and Epidemiology, RTI International, Atlanta, Georgia, USA
| | | | - Karolina Dziewulska
- Department of Pathology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Richard O. Francis
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, USA
| | - Eldad A. Hod
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, USA
| | - James C. Zimring
- Department of Pathology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Angelo D’Alessandro
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, USA,Department of Biochemistry and Molecular Genetics, University of Colorado Denver – Anschutz Medical Campus, Aurora, CO, USA,For correspondence: Grier P. Page; Angelo D’Alessandro
| | - Grier P. Page
- Division of Biostatistics and Epidemiology, RTI International, Atlanta, Georgia, USA,For correspondence: Grier P. Page; Angelo D’Alessandro
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24
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Beck T, Rowlands T, Shorter T, Brookes AJ. GWAS Central: an expanding resource for finding and visualising genotype and phenotype data from genome-wide association studies. Nucleic Acids Res 2022; 51:D986-D993. [PMID: 36350644 PMCID: PMC9825503 DOI: 10.1093/nar/gkac1017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/18/2022] [Accepted: 10/20/2022] [Indexed: 11/10/2022] Open
Abstract
The GWAS Central resource gathers and curates extensive summary-level genome-wide association study (GWAS) data and puts a range of user-friendly but powerful website tools for the comparison and visualisation of GWAS data at the fingertips of researchers. Through our continued efforts to harmonise and import data received from GWAS authors and consortia, and data sets actively collected from public sources, the database now contains over 72.5 million P-values for over 5000 studies testing over 7.4 million unique genetic markers investigating over 1700 unique phenotypes. Here, we describe an update to integrate this extensive data collection with mouse disease model data to support insights into the functional impact of human genetic variation. GWAS Central has expanded to include mouse gene-phenotype associations observed during mouse gene knockout screens. To allow similar cross-species phenotypes to be compared, terms from mammalian and human phenotype ontologies have been mapped. New interactive interfaces to find, correlate and view human and mouse genotype-phenotype associations are included in the website toolkit. Additionally, the integrated browser for interrogating multiple association data sets has been updated and a GA4GH Beacon API endpoint has been added for discovering variants tested in GWAS. The GWAS Central resource is accessible at https://www.gwascentral.org/.
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Affiliation(s)
- Tim Beck
- To whom correspondence should be addressed. Tel: +44 116 204 4732; Fax: +44 116 252 3378;
| | - Thomas Rowlands
- Department of Genetics and Genome Biology, University of Leicester, Leicester, LE1 7RH, UK
| | - Tom Shorter
- Department of Genetics and Genome Biology, University of Leicester, Leicester, LE1 7RH, UK
| | - Anthony J Brookes
- Department of Genetics and Genome Biology, University of Leicester, Leicester, LE1 7RH, UK,Health Data Research UK (HDR UK), London, UK
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Panyard DJ, Yu B, Snyder MP. The metabolomics of human aging: Advances, challenges, and opportunities. SCIENCE ADVANCES 2022; 8:eadd6155. [PMID: 36260671 PMCID: PMC9581477 DOI: 10.1126/sciadv.add6155] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
As the global population becomes older, understanding the impact of aging on health and disease becomes paramount. Recent advancements in multiomic technology have allowed for the high-throughput molecular characterization of aging at the population level. Metabolomics studies that analyze the small molecules in the body can provide biological information across a diversity of aging processes. Here, we review the growing body of population-scale metabolomics research on aging in humans, identifying the major trends in the field, implicated biological pathways, and how these pathways relate to health and aging. We conclude by assessing the main challenges in the research to date, opportunities for advancing the field, and the outlook for precision health applications.
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Affiliation(s)
- Daniel J. Panyard
- Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
- Corresponding author. (D.J.P.); (M.P.S.)
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Michael P. Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
- Corresponding author. (D.J.P.); (M.P.S.)
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26
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Keep RF, Jones HC, Drewes LR. Advances in brain barriers and brain fluids research in 2021: great progress in a time of adversity. Fluids Barriers CNS 2022; 19:48. [PMID: 35681151 PMCID: PMC9178944 DOI: 10.1186/s12987-022-00343-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 11/10/2022] Open
Abstract
This editorial highlights advances in brain barrier and brain fluid research in 2021. It covers research on components of the blood–brain barrier, neurovascular unit and brain fluid systems; how brain barriers and brain fluid systems are impacted by neurological disorders and their role in disease progression; and advances in strategies for treating such disorders.
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Affiliation(s)
- Richard F Keep
- Department of Neurosurgery, University of Michigan, R5018 BSRB, 109 Zina Pitcher Place, Ann Arbor, MI, 48109-2200, USA.
| | | | - Lester R Drewes
- Department of Biomedical Sciences, University of Minnesota Medical School Duluth, Duluth, MN, 55812, USA
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27
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[64Cu]Cu-Albumin Clearance Imaging to Evaluate Lymphatic Efflux of Cerebrospinal Space Fluid in Mouse Model. Nucl Med Mol Imaging 2022; 56:137-146. [DOI: 10.1007/s13139-022-00746-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 10/18/2022] Open
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28
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Olarini A, Ernst M, Gürdeniz G, Kim M, Brustad N, Bønnelykke K, Cohen A, Hougaard D, Lasky-Su J, Bisgaard H, Chawes B, Rasmussen MA. Vertical Transfer of Metabolites Detectable from Newborn's Dried Blood Spot Samples Using UPLC-MS: A Chemometric Study. Metabolites 2022; 12:94. [PMID: 35208170 PMCID: PMC8879569 DOI: 10.3390/metabo12020094] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/14/2022] [Accepted: 01/17/2022] [Indexed: 02/04/2023] Open
Abstract
The pregnancy period and first days of a newborn's life is an important time window to ensure a healthy development of the baby. This is also the time when the mother and her baby are exposed to the same environmental conditions and intake of nutrients, which can be determined by assessing the blood metabolome. For this purpose, dried blood spots (DBS) of newborns are a valuable sampling technique to characterize what happens during this important mother-child time window. We used metabolomics profiles from DBS of newborns (age 2-3 days) and maternal plasma samples at gestation week 24 and postpartum week 1 from n=664 mother-child pairs of the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC2010) cohort, to study the vertical mother-child transfer of metabolites. Further, we investigated how persistent the metabolites are from the newborn and up to 6 months, 18 months, and 6 years of age. Two hundred seventy two metabolites from UPLC-MS (Ultra Performance Liquid Chromatography-Mass Spectrometry) analysis of DBS and maternal plasma were analyzed using correlation analysis. A total of 11 metabolites exhibited evidence of transfer (R>0.3), including tryptophan betaine, ergothioneine, cotinine, theobromine, paraxanthine, and N6-methyllysine. Of these, 7 were also found to show persistence in their levels in the child from birth to age 6 years. In conclusion, this study documents vertical transfer of environmental and food-derived metabolites from mother to child and tracking of those metabolites through childhood, which may be of importance for the child's later health and disease.
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Affiliation(s)
- Alessandra Olarini
- Section of Chemometrics and Analytical Technologies, Department of Food Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg C, Denmark;
| | - Madeleine Ernst
- Section for Clinical Mass Spectrometry, Department of Congenital Disorders, Danish Center for Neonatal Screening, Statens Serum Institut, 2300 Copenhagen, Denmark; (M.E.); (A.C.); (D.H.)
| | - Gözde Gürdeniz
- COPSAC—Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 2820 Gentofte, Denmark; (G.G.); (M.K.); (N.B.); (K.B.); (H.B.)
| | - Min Kim
- COPSAC—Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 2820 Gentofte, Denmark; (G.G.); (M.K.); (N.B.); (K.B.); (H.B.)
| | - Nicklas Brustad
- COPSAC—Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 2820 Gentofte, Denmark; (G.G.); (M.K.); (N.B.); (K.B.); (H.B.)
| | - Klaus Bønnelykke
- COPSAC—Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 2820 Gentofte, Denmark; (G.G.); (M.K.); (N.B.); (K.B.); (H.B.)
| | - Arieh Cohen
- Section for Clinical Mass Spectrometry, Department of Congenital Disorders, Danish Center for Neonatal Screening, Statens Serum Institut, 2300 Copenhagen, Denmark; (M.E.); (A.C.); (D.H.)
| | - David Hougaard
- Section for Clinical Mass Spectrometry, Department of Congenital Disorders, Danish Center for Neonatal Screening, Statens Serum Institut, 2300 Copenhagen, Denmark; (M.E.); (A.C.); (D.H.)
| | - Jessica Lasky-Su
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA;
| | - Hans Bisgaard
- COPSAC—Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 2820 Gentofte, Denmark; (G.G.); (M.K.); (N.B.); (K.B.); (H.B.)
| | - Bo Chawes
- COPSAC—Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 2820 Gentofte, Denmark; (G.G.); (M.K.); (N.B.); (K.B.); (H.B.)
| | - Morten Arendt Rasmussen
- Section of Chemometrics and Analytical Technologies, Department of Food Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg C, Denmark;
- COPSAC—Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 2820 Gentofte, Denmark; (G.G.); (M.K.); (N.B.); (K.B.); (H.B.)
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29
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Lee JW, Profant M, Wang C. Metabolic Sex Dimorphism of the Brain at the Gene, Cell, and Tissue Level. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 208:212-220. [PMID: 35017210 DOI: 10.4049/jimmunol.2100853] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/09/2021] [Indexed: 12/21/2022]
Abstract
The palpable observation in the sex bias of disease prevalence in the CNS has fascinated scientists for several generations. Brain sex dimorphism has been visualized by imaging and analytical tools at the tissue, cellular, and molecular levels. Recent work highlighted the specificity of such sex bias in the brain and its subregions, offering a unique lens through which disease pathogenesis can be investigated. The brain is the largest consumer of energy in the body and provides a unique metabolic environment for diverse lineages of cells. Immune cells are increasingly recognized as an integral part of brain physiology, and their function depends on metabolic homeostasis. This review focuses on metabolic sex dimorphism in brain tissue, resident, and infiltrating immune cells. In this context, we highlight the relevance of recent advances in metabolomics and RNA sequencing technologies at the single cell resolution and the development of novel computational approaches.
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Affiliation(s)
- Jun Won Lee
- Biological Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada; and
| | - Martin Profant
- Biological Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada; and.,Department of Immunology, University of Toronto, Toronto, Ontario, Canada
| | - Chao Wang
- Biological Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada; and .,Department of Immunology, University of Toronto, Toronto, Ontario, Canada
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Pang Z, Chong J, Zhou G, de Lima Morais DA, Chang L, Barrette M, Gauthier C, Jacques PÉ, Li S, Xia J. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res 2021; 49:W388-W396. [PMID: 34019663 PMCID: PMC8265181 DOI: 10.1093/nar/gkab382] [Citation(s) in RCA: 2116] [Impact Index Per Article: 705.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/17/2021] [Accepted: 04/27/2021] [Indexed: 12/31/2022] Open
Abstract
Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.
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Affiliation(s)
- Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jasmine Chong
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | | | - Le Chang
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Michel Barrette
- Centre de Calcul Scientifique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Carol Gauthier
- Centre de Calcul Scientifique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Pierre-Étienne Jacques
- Centre de Calcul Scientifique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Département de Biologie, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Department of Animal Science, McGill University, Montreal, Quebec, Canada
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