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Hu T, Dai Q, Epstein MP, Yang J. Proteome-wide association studies using summary proteomic data identified 23 risk genes of Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.28.24305044. [PMID: 38585769 PMCID: PMC10996749 DOI: 10.1101/2024.03.28.24305044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Characterizing the genetic mechanisms underlying Alzheimer's disease (AD) dementia is crucial for developing new therapeutics. Proteome-wide association study (PWAS) integrating proteomics data with genome-wide association study (GWAS) summary data was shown as a powerful tool for detecting risk genes. The identified PWAS risk genes can be interpretated as having genetic effects mediated through the genetically regulated protein abundances. Existing PWAS analyses of AD often rely on the availability of individual-level proteomics and genetics data of a reference cohort. Leveraging summary-level protein quantitative trait loci (pQTL) reference data of multiple relevant tissues is expected to improve PWAS findings for studying AD. Here, we applied our recently developed OTTERS tool to conduct PWAS of AD dementia, by leveraging summary-level pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, and multiple statistical methods. For each target protein, imputation models of the protein abundance with genetic predictors were trained from summary-level pQTL data, estimating a set of pQTL weights for considered genetic predictors. PWAS p-values were obtained by integrating GWAS summary data of AD dementia with estimated pQTL weights. PWAS p-values from multiple statistical methods were combined by the aggregated Cauchy association test to yield one omnibus PWAS p-value for the target protein. We identified significant PWAS risk genes through omnibus PWAS p-values and analyzed their protein-protein interactions using STRING. Their potential causal effects were assessed by the probabilistic Mendelian randomization (PMR-Egger). As a result, we identified a total of 23 significant PWAS risk genes for AD dementia in brain, CSF, and plasma tissues, including 7 novel findings. We showed that 15 of these risk genes were interconnected within a protein-protein interaction network involving the well-known AD risk gene of APOE and 5 novel findings, and enriched in immune functions and lipids pathways including positive regulation of immune system process, positive regulation of macrophage proliferation, humoral immune response, and high-density lipoprotein particle clearance. Existing biological evidence was found to relate our novel findings with AD. We validated the mediated causal effects of 14 risk genes (60.8%). In conclusion, we identified both known and novel PWAS risk genes, providing novel insights into the genetic mechanisms in brain, CSF, and plasma tissues, and targeted therapeutics development of AD dementia. Our study also demonstrated the effectiveness of integrating public available summary-level pQTL data with GWAS summary data for mapping risk genes of complex human diseases.
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Affiliation(s)
- Tingyang Hu
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Qile Dai
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA, 30322, USA
| | - Michael P. Epstein
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
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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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Plasma Phospho-Tau-181 as a Diagnostic Aid in Alzheimer’s Disease. Biomedicines 2022; 10:biomedicines10081879. [PMID: 36009425 PMCID: PMC9405617 DOI: 10.3390/biomedicines10081879] [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: 04/26/2022] [Revised: 07/20/2022] [Accepted: 07/27/2022] [Indexed: 11/16/2022] Open
Abstract
Cerebrospinal fluid (CSF) biomarkers remain the gold standard for fluid-biomarker-based diagnosis of Alzheimer’s disease (AD) during life. Plasma biomarkers avoid lumbar puncture and allow repeated sampling. Changes of plasma phospho-tau-181 in AD are of comparable magnitude and seem to parallel the changes in CSF, may occur in preclinical or predementia stages of the disease, and may differentiate AD from other causes of dementia with adequate accuracy. Plasma phospho-tau-181 may offer a useful alternative to CSF phospho-tau determination, but work still has to be done concerning the optimal method of determination with the highest combination of sensitivity and specificity and cost-effect parameters.
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The Role of Cerebrospinal Fluid Biomarkers in Dementia and Other Related Neurodegenerative Disorders. Brain Sci 2022; 12:brainsci12050627. [PMID: 35625013 PMCID: PMC9139857 DOI: 10.3390/brainsci12050627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 05/03/2022] [Indexed: 01/10/2023] Open
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