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Polakkattil BK, Vellichirammal NN, Nair IV, Nair CM, Banerjee M. Methylome-wide and meQTL analysis helps to distinguish treatment response from non-response and pathogenesis markers in schizophrenia. Front Psychiatry 2024; 15:1297760. [PMID: 38516266 PMCID: PMC10954811 DOI: 10.3389/fpsyt.2024.1297760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 02/06/2024] [Indexed: 03/23/2024] Open
Abstract
Schizophrenia is a complex condition with entwined genetic and epigenetic risk factors, posing a challenge to disentangle the intermixed pathological and therapeutic epigenetic signatures. To resolve this, we performed 850K methylome-wide and 700K genome-wide studies on the same set of schizophrenia patients by stratifying them into responders, non-responders, and drug-naïve patients. The key genes that signified the response were followed up using real-time gene expression studies to understand the effect of antipsychotics at the gene transcription level. The study primarily implicates hypermethylation in therapeutic response and hypomethylation in the drug-non-responsive state. Several differentially methylated sites and regions colocalized with the schizophrenia genome-wide association study (GWAS) risk genes and variants, supporting the convoluted gene-environment association. Gene ontology and protein-protein interaction (PPI) network analyses revealed distinct patterns that differentiated the treatment response from drug resistance. The study highlights the strong involvement of several processes related to nervous system development, cell adhesion, and signaling in the antipsychotic response. The ability of antipsychotic medications to alter the pathology by modulating gene expression or methylation patterns is evident from the general increase in the gene expression of response markers and histone modifiers and the decrease in class II human leukocyte antigen (HLA) genes following treatment with varying concentrations of medications like clozapine, olanzapine, risperidone, and haloperidol. The study indicates a directional overlap of methylation markers between pathogenesis and therapeutic response, thereby suggesting a careful distinction of methylation markers of pathogenesis from treatment response. In addition, there is a need to understand the trade-off between genetic and epigenetic observations. It is suggested that methylomic changes brought about by drugs need careful evaluation for their positive effects on pathogenesis, course of disease progression, symptom severity, side effects, and refractoriness.
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Affiliation(s)
- Binithamol K. Polakkattil
- Human Molecular Genetics Laboratory, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India
- Research Center, University of Kerala, Thiruvananthapuram, Kerala, India
| | - Neetha N. Vellichirammal
- Human Molecular Genetics Laboratory, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India
| | - Indu V. Nair
- Mental Health Centre, Thiruvananthapuram, Kerala, India
| | | | - Moinak Banerjee
- Human Molecular Genetics Laboratory, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India
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Han Y, Huang C, Pan Y, Gu X. Single Cell Sequencing Technology and Its Application in Alzheimer's Disease. J Alzheimers Dis 2024; 97:1033-1050. [PMID: 38217599 DOI: 10.3233/jad-230861] [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] [Indexed: 01/15/2024]
Abstract
Alzheimer's disease (AD) involves degeneration of cells in the brain. Due to insidious onset and slow progression, AD is often not diagnosed until it gets progressed to a more severe stage. The diagnosis and treatment of AD has been a challenge. In recent years, high-throughput sequencing technologies have exhibited advantages in exploring the pathogenesis of diseases. However, the types of cells of the central nervous system are complex and traditional bulk sequencing cannot reflect their heterogeneity. Single-cell sequencing technology enables study at the individual cell level and has an irreplaceable advantage in the study of complex diseases. In recent years, this field has expanded rapidly and several types of single-cell sequencing technologies have emerged, including transcriptomics, epigenomics, genomics and proteomics. This review article provides an overview of these single-cell sequencing technologies and their application in AD.
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Affiliation(s)
- Yuru Han
- Shanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- School of Pharmacy, Shanghai University of Medicine & Health Sciences, Shanghai, China
- School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Congying Huang
- Shanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- School of Pharmacy, Shanghai University of Medicine & Health Sciences, Shanghai, China
- School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yuhui Pan
- Center for Disease Control and Prevention of Harbin, Harbin, China
| | - Xuefeng Gu
- Shanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- School of Pharmacy, Shanghai University of Medicine & Health Sciences, Shanghai, China
- School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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Galantamine Based Novel Acetylcholinesterase Enzyme Inhibitors: A Molecular Modeling Design Approach. Molecules 2023; 28:molecules28031035. [PMID: 36770702 PMCID: PMC9919016 DOI: 10.3390/molecules28031035] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 12/31/2022] [Accepted: 01/09/2023] [Indexed: 01/22/2023] Open
Abstract
Acetylcholinesterase (AChE) enzymes play an essential role in the development of Alzheimer's disease (AD). Its excessive activity causes several neuronal problems, particularly psychopathies and neuronal cell death. A bioactive pose on the hAChE B site of the human acetylcholinesterase (hAChE) enzyme employed in this investigation, which was obtained from the Protein Data Bank (PDB ID 4EY6), allowed for the prediction of the binding affinity and free binding energy between the protein and the ligand. Virtual screening was performed to obtain structures similar to Galantamine (GNT) with potential hAChE activity. The top 200 hit compounds were prioritized through the use of filters in ZincPharmer, with special features related to the pharmacophore. Critical analyses were carried out, such as hierarchical clustering analysis (HCA), ADME/Tox predictions, molecular docking, molecular simulation studies, synthetic accessibility (SA), lipophilicity, water solubility, and hot spots to confirm the stable binding of the two promising molecules (ZINC16951574-LMQC2, and ZINC08342556-LMQC5). The metabolism prediction, with metabolites M3-2, which is formed by Glutathionation reaction (Phase II), M1-2, and M2-2 formed from the reaction of S-oxidation and Aliphatic hydroxylation (Phase I), were both reactive but with no side effects. Theoretical synthetic routes and prediction of synthetic accessibility for the most promising compounds are also proposed. In conclusion, this study shows that in silico modeling can be used to create new drug candidate inhibitors for hAChE. The compounds ZINC16951574-LMQC2, and ZINC08342556-LMQC5 are particularly promising for oral administration because they have a favorable drug-likeness profile, excellent lipid solubility, high bioavailability, and adequate pharmacokinetics.
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Epigenetic Changes in Prion and Prion-like Neurodegenerative Diseases: Recent Advances, Potential as Biomarkers, and Future Perspectives. Int J Mol Sci 2022; 23:ijms232012609. [PMID: 36293477 PMCID: PMC9604074 DOI: 10.3390/ijms232012609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/09/2022] [Accepted: 10/18/2022] [Indexed: 12/01/2022] Open
Abstract
Prion diseases are transmissible spongiform encephalopathies (TSEs) caused by a conformational conversion of the native cellular prion protein (PrPC) to an abnormal, infectious isoform called PrPSc. Amyotrophic lateral sclerosis, Alzheimer’s, Parkinson’s, and Huntington’s diseases are also known as prion-like diseases because they share common features with prion diseases, including protein misfolding and aggregation, as well as the spread of these misfolded proteins into different brain regions. Increasing evidence proposes the involvement of epigenetic mechanisms, namely DNA methylation, post-translational modifications of histones, and microRNA-mediated post-transcriptional gene regulation in the pathogenesis of prion-like diseases. Little is known about the role of epigenetic modifications in prion diseases, but recent findings also point to a potential regulatory role of epigenetic mechanisms in the pathology of these diseases. This review highlights recent findings on epigenetic modifications in TSEs and prion-like diseases and discusses the potential role of such mechanisms in disease pathology and their use as potential biomarkers.
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C. Silva T, Zhang W, Young JI, Gomez L, Schmidt MA, Varma A, Chen XS, Martin ER, Wang L. Distinct sex-specific DNA methylation differences in Alzheimer's disease. Alzheimers Res Ther 2022; 14:133. [PMID: 36109771 PMCID: PMC9479371 DOI: 10.1186/s13195-022-01070-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 08/30/2022] [Indexed: 01/17/2023]
Abstract
BACKGROUND Sex is increasingly recognized as a significant factor contributing to the biological and clinical heterogeneity in AD. There is also growing evidence for the prominent role of DNA methylation (DNAm) in Alzheimer's disease (AD). METHODS We studied sex-specific DNA methylation differences in the blood samples of AD subjects compared to cognitively normal subjects, by performing sex-specific meta-analyses of two large blood-based epigenome-wide association studies (ADNI and AIBL), which included DNA methylation data for a total of 1284 whole blood samples (632 females and 652 males). Within each dataset, we used two complementary analytical strategies, a sex-stratified analysis that examined methylation to AD associations in male and female samples separately, and a methylation-by-sex interaction analysis that compared the magnitude of these associations between different sexes. After adjusting for age, estimated immune cell type proportions, batch effects, and correcting for inflation, the inverse-variance fixed-effects meta-analysis model was used to identify the most consistent DNAm differences across datasets. In addition, we also evaluated the performance of the sex-specific methylation-based risk prediction models for AD diagnosis using an independent external dataset. RESULTS In the sex-stratified analysis, we identified 2 CpGs, mapped to the PRRC2A and RPS8 genes, significantly associated with AD in females at a 5% false discovery rate, and an additional 25 significant CpGs (21 in females, 4 in males) at P-value < 1×10-5. In methylation-by-sex interaction analysis, we identified 5 significant CpGs at P-value < 10-5. Out-of-sample validations using the AddNeuroMed dataset showed in females, the best logistic prediction model included age, estimated immune cell-type proportions, and methylation risk scores (MRS) computed from 9 of the 23 CpGs identified in AD vs. CN analysis that are also available in AddNeuroMed dataset (AUC = 0.74, 95% CI: 0.65-0.83). In males, the best logistic prediction model included only age and MRS computed from 2 of the 5 CpGs identified in methylation-by-sex interaction analysis that are also available in the AddNeuroMed dataset (AUC = 0.70, 95% CI: 0.56-0.82). CONCLUSIONS Overall, our results show that the DNA methylation differences in AD are largely distinct between males and females. Our best-performing sex-specific methylation-based prediction model in females performed better than that for males and additionally included estimated cell-type proportions. The significant discriminatory classification of AD samples with our methylation-based prediction models demonstrates that sex-specific DNA methylation could be a predictive biomarker for AD. As sex is a strong factor underlying phenotypic variability in AD, the results of our study are particularly relevant for a better understanding of the epigenetic architecture that underlie AD and for promoting precision medicine in AD.
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Affiliation(s)
- Tiago C. Silva
- grid.26790.3a0000 0004 1936 8606Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, 1120 NW 14th Street, Miami, FL 33136 USA
| | - Wei Zhang
- grid.26790.3a0000 0004 1936 8606Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, 1120 NW 14th Street, Miami, FL 33136 USA
| | - Juan I. Young
- grid.26790.3a0000 0004 1936 8606Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136 USA
| | - Lissette Gomez
- grid.26790.3a0000 0004 1936 8606John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136 USA
| | - Michael A. Schmidt
- grid.26790.3a0000 0004 1936 8606Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136 USA
| | - Achintya Varma
- grid.26790.3a0000 0004 1936 8606John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136 USA
| | - X. Steven Chen
- grid.26790.3a0000 0004 1936 8606Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, 1120 NW 14th Street, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL 33136 USA
| | - Eden R. Martin
- grid.26790.3a0000 0004 1936 8606Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136 USA
| | - Lily Wang
- grid.26790.3a0000 0004 1936 8606Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, 1120 NW 14th Street, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL 33136 USA
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Inaba Y, Iwamoto S, Nakayama K. Genome-wide DNA methylation status of Mongolians exhibits signs of cellular stress response related to their nomadic lifestyle. J Physiol Anthropol 2022; 41:30. [PMID: 35986394 PMCID: PMC9388360 DOI: 10.1186/s40101-022-00305-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 08/10/2022] [Indexed: 11/29/2022] Open
Abstract
Background Epigenetics is crucial for connecting environmental stresses with physiological responses in humans. Mongolia, where nomadic livestock pastoralism has been the primal livelihood, has a higher prevalence of various chronic diseases than the surrounding East Asian regions, which are more suitable for crop farming. The genes related to dietary stress and pathogenesis of related disorders may have varying epigenetic statuses among the human populations with diverse dietary cultures. Hence, to understand such epigenetic differences, we conducted a comparative analysis of genome-wide DNA methylation of Mongolians and crop-farming East Asians. Methods Genome-wide DNA methylation status of peripheral blood cells (PBCs) from 23 Mongolian adults and 24 Thai adults was determined using the Infinium Human Methylation 450K arrays and analyzed in combination with previously published 450K data of 20 Japanese and 8 Chinese adults. CpG sites/regions differentially methylated between Mongolians and crop-farming East Asians were detected using a linear model adjusted for sex, age, ethnicity, and immune cell heterogeneity on RnBeads software. Results Of the quality-controlled 389,454 autosomal CpG sites, 223 CpG sites were significantly differentially methylated among Mongolians and the four crop farming East Asian populations (false discovery rate < 0.05). Analyses focused on gene promoter regions revealed that PM20D1 (peptidase M20 domain containing 1), which is involved in mitochondrial uncoupling and various processes, including cellular protection from reactive oxygen species (ROS) and thermogenesis, was the top differentially methylated gene. Moreover, gene ontology enrichment analysis revealed that biological processes related to ROS metabolism were overrepresented among the top 1% differentially methylated genes. The promoter regions of these genes were generally hypermethylated in Mongolians, suggesting that the metabolic pathway detoxifying ROS might be globally suppressed in Mongolians, resulting in the high susceptibility of this population to various chronic diseases. Conclusions This study showed a significantly diverse DNA methylation status among Mongolians and crop-farming East Asians. Further, we found an association between the differentially methylated genes and various metabolic and neurodegenerative diseases. Knowledge of the epigenetic regulators might help in proper understanding, treatment, and control of such disorders, and physiological adaptation in the future. Supplementary Information The online version contains supplementary material available at 10.1186/s40101-022-00305-0.
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C. Silva T, Young JI, Zhang L, Gomez L, Schmidt MA, Varma A, Chen XS, Martin ER, Wang L. Cross-tissue analysis of blood and brain epigenome-wide association studies in Alzheimer's disease. Nat Commun 2022; 13:4852. [PMID: 35982059 PMCID: PMC9388493 DOI: 10.1038/s41467-022-32475-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 08/01/2022] [Indexed: 01/17/2023] Open
Abstract
To better understand DNA methylation in Alzheimer's disease (AD) from both mechanistic and biomarker perspectives, we performed an epigenome-wide meta-analysis of blood DNA methylation in two large independent blood-based studies in AD, the ADNI and AIBL studies, and identified 5 CpGs, mapped to the SPIDR, CDH6 genes, and intergenic regions, that are significantly associated with AD diagnosis. A cross-tissue analysis that combined these blood DNA methylation datasets with four brain methylation datasets prioritized 97 CpGs and 10 genomic regions that are significantly associated with both AD neuropathology and AD diagnosis. An out-of-sample validation using the AddNeuroMed dataset showed the best performing logistic regression model includes age, sex, immune cell type proportions, and methylation risk score based on prioritized CpGs in cross-tissue analysis (AUC = 0.696, 95% CI: 0.616 - 0.770, P-value = 2.78 × 10-5). Our study offers new insights into epigenetics in AD and provides a valuable resource for future AD biomarker discovery.
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Affiliation(s)
- Tiago C. Silva
- grid.26790.3a0000 0004 1936 8606Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136 USA
| | - Juan I. Young
- grid.26790.3a0000 0004 1936 8606Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136 USA
| | - Lanyu Zhang
- grid.26790.3a0000 0004 1936 8606Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136 USA
| | - Lissette Gomez
- grid.26790.3a0000 0004 1936 8606John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136 USA
| | - Michael A. Schmidt
- grid.26790.3a0000 0004 1936 8606John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136 USA
| | - Achintya Varma
- grid.26790.3a0000 0004 1936 8606John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136 USA
| | - X. Steven Chen
- grid.26790.3a0000 0004 1936 8606Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL 33136 USA
| | - Eden R. Martin
- grid.26790.3a0000 0004 1936 8606Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136 USA
| | - Lily Wang
- grid.26790.3a0000 0004 1936 8606Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136 USA ,grid.26790.3a0000 0004 1936 8606Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL 33136 USA
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Villa C, Stoccoro A. Epigenetic Peripheral Biomarkers for Early Diagnosis of Alzheimer’s Disease. Genes (Basel) 2022; 13:genes13081308. [PMID: 35893045 PMCID: PMC9332601 DOI: 10.3390/genes13081308] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/20/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and represents the leading cause of cognitive impairment and dementia in older individuals throughout the world. The main hallmarks of AD include brain atrophy, extracellular deposition of insoluble amyloid-β (Aβ) plaques, and the intracellular aggregation of protein tau in neurofibrillary tangles. These pathological modifications start many years prior to clinical manifestations of disease and the spectrum of AD progresses along a continuum from preclinical to clinical phases. Therefore, identifying specific biomarkers for detecting AD at early stages greatly improves clinical management. However, stable and non-invasive biomarkers are not currently available for the early detection of the disease. In the search for more reliable biomarkers, epigenetic mechanisms, able to mediate the interaction between the genome and the environment, are emerging as important players in AD pathogenesis. Herein, we discuss altered epigenetic signatures in blood as potential peripheral biomarkers for the early detection of AD in order to help diagnosis and improve therapy.
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Affiliation(s)
- Chiara Villa
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy
- Correspondence: ; Tel.: +39-02-6448-8138
| | - Andrea Stoccoro
- Department of Translational Research and of New Surgical and Medical Technologies, Medical School, University of Pisa, 56126 Pisa, Italy;
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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Coto-Vílchez C, Martínez-Magaña JJ, Mora-Villalobos L, Valerio D, Genis-Mendoza AD, Silverman JM, Nicolini H, Raventós H, Chavarria-Soley G. Genome-wide DNA methylation profiling in nonagenarians suggests an effect of PM20D1 in late onset Alzheimer's disease. CNS Spectr 2021; 28:1-27. [PMID: 34911598 DOI: 10.1017/s109285292100105x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
AbstractBackgroundThe aim of this study is to identify differentially methylated regions (DMRs) in the genomes of a sample of cognitively healthy individuals and a sample of individuals with LOAD, all of them nonagenarians from Costa Rica.MethodsIn this study, we compared whole blood DNA methylation profiles of 32 individuals: 21 cognitively healthy and 11 with LOAD, using the Infinium MethylationEPIC BeadChip. First, we calculated the epigenetic age of the participants based on Horvath’s epigenetic clock. DMRcate and Bumphunter were used to identify DMRs. After in silico and knowledge-based filtering of the DMRs, we performed a methylation quantitative loci (mQTL) analysis (rs708727 and rs960603).ResultsOn average, the epigenetic age was 73 years in both groups, which represents a difference of over 20 years between epigenetic and chronological age in both affected and unaffected individuals. Methylation analysis revealed 11 DMRs between groups, which contain six genes and two pseudogenes. These genes are involved in cell cycle regulation, embryogenesis, synthesis of ceramides, and migration of interneurons to the cerebral cortex. One of the six genes is PM20D1, for which altered expression has been reported in LOAD. After genotyping previously reported mQTL SNPs for the gene, we found that average methylation in the PM20D1 DMR differs between genotypes for rs708727, but not for rs960603.ConclusionsThis work supports the possible role of PM20D1 in protection against AD, by showing differential methylation in blood of affected and unaffected nonagenarians. Our results also support the influence of genetic factors on PM20D1 methylation levels.
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Li QS, Vasanthakumar A, Davis JW, Idler KB, Nho K, Waring JF, Saykin AJ. Association of peripheral blood DNA methylation level with Alzheimer's disease progression. Clin Epigenetics 2021; 13:191. [PMID: 34654479 PMCID: PMC8518178 DOI: 10.1186/s13148-021-01179-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 09/29/2021] [Indexed: 12/11/2022] Open
Abstract
Background Identifying biomarkers associated with Alzheimer’s disease (AD) progression may enable patient enrichment and improve clinical trial designs. Epigenome-wide association studies have revealed correlations between DNA methylation at cytosine-phosphate-guanine (CpG) sites and AD pathology and diagnosis. Here, we report relationships between peripheral blood DNA methylation profiles measured using Infinium® MethylationEPIC BeadChip and AD progression in participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Results The rate of cognitive decline from initial DNA sampling visit to subsequent visits was estimated by the slopes of the modified Preclinical Alzheimer Cognitive Composite (mPACC; mPACCdigit and mPACCtrailsB) and Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) plots using robust linear regression in cognitively normal (CN) participants and patients with mild cognitive impairment (MCI), respectively. In addition, diagnosis conversion status was assessed using a dichotomized endpoint. Two CpG sites were significantly associated with the slope of mPACC in CN participants (P < 5.79 × 10−8 [Bonferroni correction threshold]); cg00386386 was associated with the slope of mPACCdigit, and cg09422696 annotated to RP11-661A12.5 was associated with the slope of CDR-SB. No significant CpG sites associated with diagnosis conversion status were identified. Genes involved in cognition and learning were enriched. A total of 19, 13, and 5 differentially methylated regions (DMRs) associated with the slopes of mPACCtrailsB, mPACCdigit, and CDR-SB, respectively, were identified by both comb-p and DMRcate algorithms; these included DMRs annotated to HOXA4. Furthermore, 5 and 19 DMRs were associated with conversion status in CN and MCI participants, respectively. The most significant DMR was annotated to the AD-associated gene PM20D1 (chr1: 205,818,956 to 205,820,014 [13 probes], Sidak-corrected P = 7.74 × 10−24), which was associated with both the slope of CDR-SB and the MCI conversion status. Conclusion Candidate CpG sites and regions in peripheral blood were identified as associated with the rate of cognitive decline in participants in the ADNI cohort. While we did not identify a single CpG site with sufficient clinical utility to be used by itself due to the observed effect size, a biosignature composed of DNA methylation changes may have utility as a prognostic biomarker for AD progression. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-021-01179-2.
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Affiliation(s)
- Qingqin S Li
- Neuroscience, Janssen Research and Development, LLC, 1125 Trenton-Harbourton Road, Titusville, NJ, 08560, USA.
| | | | - Justin W Davis
- Genomics Research Center, AbbVie, North Chicago, IL, USA
| | | | - Kwangsik Nho
- Indiana Alzheimer's Disease Research Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
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12
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Castro de Moura M, Davalos V, Planas-Serra L, Alvarez-Errico D, Arribas C, Ruiz M, Aguilera-Albesa S, Troya J, Valencia-Ramos J, Vélez-Santamaria V, Rodríguez-Palmero A, Villar-Garcia J, Horcajada JP, Albu S, Casasnovas C, Rull A, Reverte L, Dietl B, Dalmau D, Arranz MJ, Llucià-Carol L, Planas AM, Pérez-Tur J, Fernandez-Cadenas I, Villares P, Tenorio J, Colobran R, Martin-Nalda A, Soler-Palacin P, Vidal F, Pujol A, Esteller M. Epigenome-wide association study of COVID-19 severity with respiratory failure. EBioMedicine 2021; 66:103339. [PMID: 33867313 PMCID: PMC8047083 DOI: 10.1016/j.ebiom.2021.103339] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 03/16/2021] [Accepted: 03/26/2021] [Indexed: 12/20/2022] Open
Abstract
Background Patients infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the coronavirus disease 2019 (COVID-19), exhibit a wide spectrum of disease behaviour. Since DNA methylation has been implicated in the regulation of viral infections and the immune system, we performed an epigenome-wide association study (EWAS) to identify candidate loci regulated by this epigenetic mark that could be involved in the onset of COVID-19 in patients without comorbidities. Methods Peripheral blood samples were obtained from 407 confirmed COVID-19 patients ≤ 61 years of age and without comorbidities, 194 (47.7%) of whom had mild symptomatology that did not involve hospitalization and 213 (52.3%) had a severe clinical course that required respiratory support. The set of cases was divided into discovery (n = 207) and validation (n = 200) cohorts, balanced for age and sex of individuals. We analysed the DNA methylation status of 850,000 CpG sites in these patients. Findings The DNA methylation status of 44 CpG sites was associated with the clinical severity of COVID-19. Of these loci, 23 (52.3%) were located in 20 annotated coding genes. These genes, such as the inflammasome component Absent in Melanoma 2 (AIM2) and the Major Histocompatibility Complex, class I C (HLA-C) candidates, were mainly involved in the response of interferon to viral infection. We used the EWAS-identified sites to establish a DNA methylation signature (EPICOVID) that is associated with the severity of the disease. Interpretation We identified DNA methylation sites as epigenetic susceptibility loci for respiratory failure in COVID-19 patients. These candidate biomarkers, combined with other clinical, cellular and genetic factors, could be useful in the clinical stratification and management of patients infected with the SARS-CoV-2. Funding The Unstoppable campaign of the Josep Carreras Leukaemia Foundation, the Cellex Foundation and the CERCA Programme/Generalitat de Catalunya.
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Affiliation(s)
- Manuel Castro de Moura
- Josep Carreras Leukaemia Research Institute (IJC), 08916 Badalona, Barcelona, Catalonia, Spain
| | - Veronica Davalos
- Josep Carreras Leukaemia Research Institute (IJC), 08916 Badalona, Barcelona, Catalonia, Spain
| | - Laura Planas-Serra
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Catalonia, Spain
| | - Damiana Alvarez-Errico
- Josep Carreras Leukaemia Research Institute (IJC), 08916 Badalona, Barcelona, Catalonia, Spain
| | - Carles Arribas
- Josep Carreras Leukaemia Research Institute (IJC), 08916 Badalona, Barcelona, Catalonia, Spain
| | - Montserrat Ruiz
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Catalonia, Spain
| | | | - Jesús Troya
- Infanta Leonor University Hospital, Madrid, Spain
| | | | - Valentina Vélez-Santamaria
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Catalonia, Spain; Bellvitge University Hospital, L'Hospitalet de Llobregat, Barcelona, Catalonia, Spain
| | - Agustí Rodríguez-Palmero
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Catalonia, Spain; University Hospital Germans Trias i Pujol, Badalona, Barcelona, Catalonia, Spain
| | - Judit Villar-Garcia
- Hospital del Mar - IMIM Biomedical Research Institute, Barcelona, Catalonia, Spain
| | - Juan P Horcajada
- Hospital del Mar - IMIM Biomedical Research Institute, Barcelona, Catalonia, Spain
| | - Sergiu Albu
- Institut Guttmann Foundation, Badalona, Barcelona, Catalonia, Spain
| | - Carlos Casasnovas
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Catalonia, Spain; Bellvitge University Hospital, L'Hospitalet de Llobregat, Barcelona, Catalonia, Spain
| | - Anna Rull
- Hospital Universitari de Tarragona Joan XXIII, IISPV, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain
| | - Laia Reverte
- Hospital Universitari de Tarragona Joan XXIII, IISPV, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain
| | - Beatriz Dietl
- Servei de malalties infeccioses Hospital Universitari MutuaTerrassa, Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - David Dalmau
- MutuaTerrassa Research and Innovation Foundation, HIV/AIDS Unit Hospital Universitari MutuaTerrassa, University of Barcelona, Barcelona, Catalonia, Spain
| | - Maria J Arranz
- Fundaciò Docència i Recerca Mutua Terrassa i Hospital Universitari Mutua Terrassa, Barcelona, Catalonia, Spain
| | - Laia Llucià-Carol
- Stroke Pharmacogenomics and Genetics Group, Sant Pau Institute of Research, Sant Pau Hospital, Barcelona, Catalonia, Spain
| | - Anna M Planas
- Department of Brain Ischemia and Neurodegeneration, Institut d'Investigacions Biomèdiques de Barcelona (IIBB), Consejo Superior de Investigaciones Científicas (CSIC), Area of Neurosciences, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
| | - Jordi Pérez-Tur
- Institut de Biomedicina de València-CSIC, CIBERNED, Unitat Mixta de Neurologia i Genètica, IIS La Fe, Vallencia, Spain
| | - Israel Fernandez-Cadenas
- Stroke Pharmacogenomics and Genetics Group, Sant Pau Institute of Research, Sant Pau Hospital, Barcelona, Catalonia, Spain
| | - Paula Villares
- Internal Medicine Department, Hospital HM Sanchinarro, HM Hospitales, Madrid, Spain
| | - Jair Tenorio
- INGEMM-Instituto de Genética Médica y Molecular, Hospital Universitario La Paz, Madrid, Spain; Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Madrid, Spain
| | - Roger Colobran
- Immunology Division, Genetics Department, Hospital Universitari Vall d'Hebron, Vall d'Hebron Research Institute, Vall d'Hebron Barcelona Hospital Campus, UAB, Barcelona, Catalonia, Spain
| | - Andrea Martin-Nalda
- Pediatric Infectious Diseases and Immunodeficiencies Unit, Hospital Universitari Vall d'Hebron, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Catalonia, Spain
| | - Pere Soler-Palacin
- Pediatric Infectious Diseases and Immunodeficiencies Unit, Hospital Universitari Vall d'Hebron, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Catalonia, Spain
| | - Francesc Vidal
- Hospital Universitari de Tarragona Joan XXIII, IISPV, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain
| | - Aurora Pujol
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Catalonia, Spain; Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Madrid, Spain; Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
| | - Manel Esteller
- Josep Carreras Leukaemia Research Institute (IJC), 08916 Badalona, Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red de Cancer (CIBERONC), Spain; Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain; Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), Catalonia, Spain.
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Wang Q, Chen Y, Readhead B, Chen K, Su Y, Reiman EM, Dudley JT. Longitudinal data in peripheral blood confirm that PM20D1 is a quantitative trait locus (QTL) for Alzheimer's disease and implicate its dynamic role in disease progression. Clin Epigenetics 2020; 12:189. [PMID: 33298155 PMCID: PMC7724832 DOI: 10.1186/s13148-020-00984-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 11/18/2020] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND While Alzheimer's disease (AD) remains one of the most challenging diseases to tackle, genome-wide genetic/epigenetic studies reveal many disease-associated risk loci, which sheds new light onto disease heritability, provides novel insights to understand its underlying mechanism and potentially offers easily measurable biomarkers for early diagnosis and intervention. METHODS We analyzed whole-genome DNA methylation data collected from peripheral blood in a cohort (n = 649) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and compared the DNA methylation level at baseline among participants diagnosed with AD (n = 87), mild cognitive impairment (MCI, n = 175) and normal controls (n = 162), to identify differentially methylated regions (DMRs). We also leveraged up to 4 years of longitudinal DNA methylation data, sampled at approximately 1 year intervals to model alterations in methylation levels at DMRs to delineate methylation changes associated with aging and disease progression, by linear mixed-effects (LME) modeling for the unchanged diagnosis groups (AD, MCI and control, respectively) and U-shape testing for those with changed diagnosis (converters). RESULTS When compared with controls, patients with MCI consistently displayed promoter hypomethylation at methylation QTL (mQTL) gene locus PM20D1. This promoter hypomethylation was even more prominent in patients with mild to moderate AD. This is in stark contrast with previously reported hypermethylation in hippocampal and frontal cortex brain tissues in patients with advanced-stage AD at this locus. From longitudinal data, we show that initial promoter hypomethylation of PM20D1 during MCI and early stage AD is reversed to eventual promoter hypermethylation in late stage AD, which helps to complete a fuller picture of methylation dynamics. We also confirm this observation in an independent cohort from the Religious Orders Study and Memory and Aging Project (ROSMAP) Study using DNA methylation and gene expression data from brain tissues as neuropathological staging (Braak score) advances. CONCLUSIONS Our results confirm that PM20D1 is an mQTL in AD and demonstrate that it plays a dynamic role at different stages of the disease. Further in-depth study is thus warranted to fully decipher its role in the evolution of AD and potentially explore its utility as a blood-based biomarker for AD.
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Affiliation(s)
- Qi Wang
- grid.215654.10000 0001 2151 2636ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ USA
| | - Yinghua Chen
- grid.418204.b0000 0004 0406 4925Banner Alzheimer’s Institute, Phoenix, AZ USA
| | - Benjamin Readhead
- grid.215654.10000 0001 2151 2636ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ USA
| | - Kewei Chen
- grid.418204.b0000 0004 0406 4925Banner Alzheimer’s Institute, Phoenix, AZ USA
| | - Yi Su
- grid.418204.b0000 0004 0406 4925Banner Alzheimer’s Institute, Phoenix, AZ USA
| | - Eric M. Reiman
- grid.215654.10000 0001 2151 2636ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ USA ,grid.418204.b0000 0004 0406 4925Banner Alzheimer’s Institute, Phoenix, AZ USA
| | - Joel T. Dudley
- grid.215654.10000 0001 2151 2636ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ USA ,grid.59734.3c0000 0001 0670 2351Icahn School of Medicine at Mount Sinai, New York, NY USA
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Kim JT, Jedrychowski MP, Wei W, Fernandez D, Fischer CR, Banik SM, Spiegelman BM, Long JZ. A Plasma Protein Network Regulates PM20D1 and N-Acyl Amino Acid Bioactivity. Cell Chem Biol 2020; 27:1130-1139.e4. [PMID: 32402239 PMCID: PMC7502524 DOI: 10.1016/j.chembiol.2020.04.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 04/15/2020] [Accepted: 04/22/2020] [Indexed: 02/06/2023]
Abstract
N-acyl amino acids are a family of cold-inducible circulating lipids that stimulate thermogenesis. Their biosynthesis is mediated by a secreted enzyme called PM20D1. The extracellular mechanisms that regulate PM20D1 or N-acyl amino acid activity in the complex environment of blood plasma remains unknown. Using quantitative proteomics, here we show that PM20D1 circulates in tight association with both low- and high-density lipoproteins. Lipoprotein particles are powerful co-activators of PM20D1 activity in vitro and N-acyl amino acid biosynthesis in vivo. We also identify serum albumin as a physiologic N-acyl amino acid carrier, which spatially segregates N-acyl amino acids away from their sites of production, confers resistance to hydrolytic degradation, and establishes an equilibrium between thermogenic "free" versus inactive "bound" fractions. These data establish lipoprotein particles as principal extracellular sites of N-acyl amino acid biosynthesis and identify a lipoprotein-albumin network that regulates the activity of a circulating thermogenic lipid family.
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Affiliation(s)
- Joon T Kim
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Mark P Jedrychowski
- Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Wei Wei
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biology, Stanford University, Stanford, CA 94305, USA; Stanford ChEM-H, Stanford University, Stanford, CA 94305, USA
| | | | - Curt R Fischer
- Stanford ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Steven M Banik
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA; Stanford ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Bruce M Spiegelman
- Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Jonathan Z Long
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford ChEM-H, Stanford University, Stanford, CA 94305, USA.
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de la Rocha C, Zaina S, Lund G. Is Any Cardiovascular Disease-Specific DNA Methylation Biomarker Within Reach? Curr Atheroscler Rep 2020; 22:62. [DOI: 10.1007/s11883-020-00875-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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