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Li W, Ballard J, Zhao Y, Long Q. Knowledge-guided learning methods for integrative analysis of multi-omics data. Comput Struct Biotechnol J 2024; 23:1945-1950. [PMID: 38736693 PMCID: PMC11087912 DOI: 10.1016/j.csbj.2024.04.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 05/14/2024] Open
Abstract
Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.
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Affiliation(s)
- Wenrui Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, 19104, PA, USA
| | - Jenna Ballard
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, 19104, PA, USA
| | - Yize Zhao
- Department of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, 06510, CT, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, 19104, PA, USA
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Lindner K, Gavin AC. Isoform- and cell-state-specific APOE homeostasis and function. Neural Regen Res 2024; 19:2456-2466. [PMID: 38526282 PMCID: PMC11090418 DOI: 10.4103/nrr.nrr-d-23-01470] [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: 08/31/2023] [Revised: 11/17/2023] [Accepted: 12/26/2023] [Indexed: 03/26/2024] Open
Abstract
Apolipoprotein E is the major lipid transporter in the brain and an important player in neuron-astrocyte metabolic coupling. It ensures the survival of neurons under stressful conditions and hyperactivity by nourishing and detoxifying them. Apolipoprotein E polymorphism, combined with environmental stresses and/or age-related alterations, influences the risk of developing late-onset Alzheimer's disease. In this review, we discuss our current knowledge of how apolipoprotein E homeostasis, i.e. its synthesis, secretion, degradation, and lipidation, is affected in Alzheimer's disease.
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Affiliation(s)
- Karina Lindner
- Department of Cell Physiology and Metabolism, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Diabetes Center, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Anne-Claude Gavin
- Department of Cell Physiology and Metabolism, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Diabetes Center, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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Eteleeb AM, Novotny BC, Tarraga CS, Sohn C, Dhungel E, Brase L, Nallapu A, Buss J, Farias F, Bergmann K, Bradley J, Norton J, Gentsch J, Wang F, Davis AA, Morris JC, Karch CM, Perrin RJ, Benitez BA, Harari O. Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer's disease. PLoS Biol 2024; 22:e3002607. [PMID: 38687811 PMCID: PMC11086901 DOI: 10.1371/journal.pbio.3002607] [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: 09/06/2023] [Revised: 05/10/2024] [Accepted: 03/28/2024] [Indexed: 05/02/2024] Open
Abstract
Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple human AD cohorts. We discovered 4 unique multimodal molecular profiles, one of them showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles. We found this molecular profile to be present in multiple affected cortical regions associated with higher Braak tau scores and significant dysregulation of synapse-related genes, endocytosis, phagosome, and mTOR signaling pathways altered in AD early and late stages. AD cross-omics data integration with transcriptomic data from an SNCA mouse model revealed an overlapping signature. Furthermore, we leveraged single-nuclei RNA-seq data to identify distinct cell-types that most likely mediate molecular profiles. Lastly, we identified that the multimodal clusters uncovered cerebrospinal fluid biomarkers poised to monitor AD progression and possibly cognition. Our cross-omics analyses provide novel critical molecular insights into AD.
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Affiliation(s)
- Abdallah M. Eteleeb
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
| | - Brenna C. Novotny
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Carolina Soriano Tarraga
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Christopher Sohn
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Eliza Dhungel
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Logan Brase
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Aasritha Nallapu
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Jared Buss
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Fabiana Farias
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Kristy Bergmann
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Joseph Bradley
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Joanne Norton
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Jen Gentsch
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Fengxian Wang
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Albert A. Davis
- Department of Neurology, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
| | - John C. Morris
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
- Department of Neurology, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
| | - Celeste M. Karch
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
| | - Richard J. Perrin
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
- Department of Neurology, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
- Department of Pathology and Immunology, Washington University, St. Louis, Missouri, United States of America
| | - Bruno A. Benitez
- Department of Neurology and Neuroscience, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - Oscar Harari
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
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Wang YL, Zhu MY, Yuan ZF, Ren XY, Guo XT, Hua Y, Xu L, Zhao CY, Jiang LH, Zhang X, Sheng GX, Jiang PF, Zhao ZY, Gao F. Proteomic profiling of cerebrospinal fluid in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease. World J Pediatr 2024; 20:259-271. [PMID: 36507981 PMCID: PMC10957615 DOI: 10.1007/s12519-022-00661-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 11/15/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) is an autoimmune demyelinating disorder of the central nervous system. METHODS Extracted proteins from 34 cerebrospinal fluid (CSF) samples [patients with MOGAD (MOG group, n = 12); healthy controls (HC group, n = 12); patients with MOG seronegative and metagenomics next-generation sequencing-negative inflammatory neurological diseases (IND group, n = 10)] were processed and subjected to label-free quantitative proteomics. Supervised partial least squares-discriminant analysis (PLS-DA) and orthogonal PLS-DA (O-PLS-DA) models were also performed based on proteomics data. Functional analysis of differentially expressed proteins (DEPs) was performed using Gene Ontology, InterPro, and Kyoto Encyclopedia Genes and Genomes. An enzyme-linked immunosorbent assay was used to determine the complement levels in serum from patients with MOGAD. RESULTS Four hundred and twenty-nine DEPs (149 upregulated and 280 downregulated proteins) were identified in the MOG group compared to the HC group according to the P value and fold change (FC). Using the O-PLS-DA model, 872 differentially abundant proteins were identified with variable importance projection (VIP) scores > 1. Five proteins (gamma-glutamyl hydrolase, cathepsin F, interalpha-trypsin inhibitor heavy chain 5, latent transforming growth factor beta-binding protein 4 and leukocyte-associated immunoglobulin-like receptor 1) overlapping between the top 30 DEPs with top-ranked P value and FC and top 30 proteins in PLS-DA VIP lists were acquired. Functional analysis revealed that the dysregulated proteins in the MOG group were primarily involved in complement and coagulation cascades, cell adhesion, axon guidance, and glycosphingolipid biosynthesis compared to the HC group. CONCLUSION The proteomic alterations in CSF samples from children with MOGAD identified in the current study might provide opportunities for developing novel biomarker candidates.
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Affiliation(s)
- Yi-Long Wang
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Meng-Ying Zhu
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Zhe-Feng Yuan
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Xiao-Yan Ren
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Xiao-Tong Guo
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Yi Hua
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Lu Xu
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Cong-Ying Zhao
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Li-Hua Jiang
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Xin Zhang
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Guo-Xia Sheng
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Pei-Fang Jiang
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Zheng-Yan Zhao
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China.
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China.
| | - Feng Gao
- Department of Neurology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China.
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China.
- Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou 310052, China.
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Feng Q, Lu Y, Zhang R, Li Y, Zhao Z, Zhou H. Identification of differentially expressed exosome proteins in serum as potential biomarkers for cognitive impairments in cerebral small vessel disease. Neurosci Lett 2024; 822:137631. [PMID: 38211879 DOI: 10.1016/j.neulet.2024.137631] [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: 08/29/2023] [Revised: 12/25/2023] [Accepted: 01/08/2024] [Indexed: 01/13/2024]
Abstract
BACKGROUND Cognitive impairment arising from cerebral small vessel disease (CSVD) represents a critical subtype of vascular cognitive impairments (VCI) and is the primary cause of vascular dementia. However, identifying reliable clinical and laboratory indicators for this disease remain elusive. We hypothesize that plasma exosome proteins hold the potential to serve as biomarkers for the onset of cognitive dysfunction associated with cerebrovascular diseases. METHODS We employed TMT-based proteomics to discern variations in serum exosome proteomes between individuals with cognitive impairments due to CSVD and healthy volunteers. RESULTS Each group comprised 18 subjects, and through differential expression analysis, we identified 22 down-regulated and 8 up-regulated proteins between the two groups. Our research revealed 30 differentially expressed plasma exosome proteins, including histone, proteasome, clusterin and coagulation factor XIII, in individuals with cognitive impairments caused by CSVD. CONCLUSION The 30 differentially expressed plasma exosome proteins identified in our study are promising as biomarkers for diagnosing cognitive impairments resulting from CSVD. These findings may help us better understand the underlying pathological mechanisms involved in the diseases.
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Affiliation(s)
- Qian Feng
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Yanjing Lu
- Department of Neurology, Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Ruyang Zhang
- Department of Neurology, Suzhou Wuzhong People's Hospital, Suzhou, China
| | - Yifan Li
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Zhong Zhao
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
| | - Hua Zhou
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
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Goddard TR, Brookes KJ, Sharma R, Moemeni A, Rajkumar AP. Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science. Cells 2024; 13:223. [PMID: 38334615 PMCID: PMC10854541 DOI: 10.3390/cells13030223] [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: 12/14/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/10/2024] Open
Abstract
Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into disease pathology. Variants within SNCA, GBA, APOE, SNCB, and MAPT have been shown to be associated with DLB in repeated genomic studies. Transcriptomic analysis, conducted predominantly on candidate genes, has identified signatures of synuclein aggregation, protein degradation, amyloid deposition, neuroinflammation, mitochondrial dysfunction, and the upregulation of heat-shock proteins in DLB. Yet, the understanding of DLB molecular pathology is incomplete. This precipitates the current clinical position whereby there are no available disease-modifying treatments or blood-based diagnostic biomarkers. Data science methods have the potential to improve disease understanding, optimising therapeutic intervention and drug development, to reduce disease burden. Genomic prediction will facilitate the early identification of cases and the timely application of future disease-modifying treatments. Transcript-level analyses across the entire transcriptome and machine learning analysis of multi-omic data will uncover novel signatures that may provide clues to DLB pathology and improve drug development. This review will discuss the current genomic and transcriptomic understanding of DLB, highlight gaps in the literature, and describe data science methods that may advance the field.
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Affiliation(s)
- Thomas R. Goddard
- Mental Health and Clinical Neurosciences Academic Unit, Institute of Mental Health, School of Medicine, University of Nottingham, Nottingham NG7 2TU, UK
| | - Keeley J. Brookes
- Department of Biosciences, School of Science & Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
| | - Riddhi Sharma
- Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham NG7 2RD, UK
- UK Health Security Agency, Radiation Effects Department, Radiation Protection Science Division, Harwell Science Campus, Didcot, Oxfordshire OX11 0RQ, UK
| | - Armaghan Moemeni
- School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
| | - Anto P. Rajkumar
- Mental Health and Clinical Neurosciences Academic Unit, Institute of Mental Health, School of Medicine, University of Nottingham, Nottingham NG7 2TU, UK
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Liu Y, Tan Y, Zhang Z, Yi M, Zhu L, Peng W. The interaction between ageing and Alzheimer's disease: insights from the hallmarks of ageing. Transl Neurodegener 2024; 13:7. [PMID: 38254235 PMCID: PMC10804662 DOI: 10.1186/s40035-024-00397-x] [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: 09/13/2023] [Revised: 12/31/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024] Open
Abstract
Ageing is a crucial risk factor for Alzheimer's disease (AD) and is characterised by systemic changes in both intracellular and extracellular microenvironments that affect the entire body instead of a single organ. Understanding the specific mechanisms underlying the role of ageing in disease development can facilitate the treatment of ageing-related diseases, such as AD. Signs of brain ageing have been observed in both AD patients and animal models. Alleviating the pathological changes caused by brain ageing can dramatically ameliorate the amyloid beta- and tau-induced neuropathological and memory impairments, indicating that ageing plays a crucial role in the pathophysiological process of AD. In this review, we summarize the impact of several age-related factors on AD and propose that preventing pathological changes caused by brain ageing is a promising strategy for improving cognitive health.
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Affiliation(s)
- Yuqing Liu
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
- National Clinical Research Center for Metabolic Diseases, Changsha, 410011, People's Republic of China
| | - Yejun Tan
- School of Mathematics, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA
| | - Zheyu Zhang
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
- National Clinical Research Center for Metabolic Diseases, Changsha, 410011, People's Republic of China
| | - Min Yi
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
- National Clinical Research Center for Metabolic Diseases, Changsha, 410011, People's Republic of China
| | - Lemei Zhu
- Academician Workstation, Changsha Medical University, Changsha, 410219, People's Republic of China
| | - Weijun Peng
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China.
- National Clinical Research Center for Metabolic Diseases, Changsha, 410011, People's Republic of China.
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Xie L, Raj Y, Varathan P, He B, Yu M, Nho K, Salama P, Saykin AJ, Yan J. Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer's Disease. J Alzheimers Dis 2024; 99:715-727. [PMID: 38728189 DOI: 10.3233/jad-240098] [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: 05/12/2024]
Abstract
Background There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD. Objective The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions. Methods We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins. Results When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation. Conclusions Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.
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Affiliation(s)
- Linhui Xie
- Department of Electrical and Computer Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Yash Raj
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Pradeep Varathan
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Bing He
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Meichen Yu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Paul Salama
- Department of Electrical and Computer Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Jingwen Yan
- Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
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Jia X, Chen S, Hou X, Zhuang Q, Tan N, Zhang M, Wang J, Xing X, Xiao Y. Development and Validation of Serum Markers as Noninvasive Diagnostic Methods for Achalasia. Clin Transl Gastroenterol 2024; 15:e00651. [PMID: 37787436 PMCID: PMC10810595 DOI: 10.14309/ctg.0000000000000651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/25/2023] [Indexed: 10/04/2023] Open
Abstract
INTRODUCTION Currently, the diagnosis of achalasia mainly relies on invasive or radioactive examinations. This study aimed to develop a noninvasive diagnostic method for achalasia based on specific serum markers. METHODS Serum levels of profilin-1, galectin-10, immunoglobulin heavy variable 3-9, vasodilator-stimulated phosphoprotein, and transgelin-2 were measured in patients with achalasia and controls by enzyme-linked immunosorbent assay. The diagnostic values and thresholds were determined by the receiver operating characteristic curve analysis. Then, patients with dysphagia were prospectively enrolled to validate the ability of these molecules for achalasia diagnosing. RESULTS A total of 142 patients with achalasia and 50 nonachalasia controls (healthy volunteers and patients with reflux esophagitis) were retrospectively included. The serum levels of profilin-1, galectin-10, and transgelin-2 in patients with achalasia were significantly higher than those in healthy volunteers and patients with reflux esophagitis ( P all < 0.001). Profilin-1, galectin-10, and transgelin-2 were of good performance in diagnosing achalasia, with optimal thresholds of 2,171.2, 33.9, and 1,630.6 pg/mL, respectively. Second, 40 patients with dysphagia were prospectively enrolled to the validation of achalasia. For profilin-1, the positive predictive value, negative predictive value, sensitivity, and specificity were 100.0%, 64.5%, 45.0%, and 100.0%, respectively. The figures for transgelin-2 were 65.5%, 90.9%, 95.0%, and 50.0%. When both increased, the positive predictive value reached to 100.0%. When both indexes were normal, the negative predictive value was 100.0%. DISCUSSION Profilin-1 and transgelin-2 were promising biomarkers for achalasia diagnosis and performed better in combination. Further multicenter studies are necessary to verify their application as preliminary screening tools for achalasia.
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Affiliation(s)
- Xingyu Jia
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangdong, China
| | - Songfeng Chen
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangdong, China
| | - Xun Hou
- Gastrointestinal Surgery Center, The First Affiliated Hospital of Sun Yat-sen University, Guangdong, China
| | - Qianjun Zhuang
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangdong, China
| | - Niandi Tan
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangdong, China
| | - Mengyu Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangdong, China
| | - Jinhui Wang
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangdong, China
| | - Xiangbin Xing
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangdong, China
| | - Yinglian Xiao
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangdong, China
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Chamoso-Sanchez D, Rabadán Pérez F, Argente J, Barbas C, Martos-Moreno GA, Rupérez FJ. Identifying subgroups of childhood obesity by using multiplatform metabotyping. Front Mol Biosci 2023; 10:1301996. [PMID: 38174068 PMCID: PMC10761426 DOI: 10.3389/fmolb.2023.1301996] [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: 09/25/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
Introduction: Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals. Methods: This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features. Results and Discussion: Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios.
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Affiliation(s)
- David Chamoso-Sanchez
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Spain
| | | | - Jesús Argente
- Department of Pediatrics and Pediatric Endocrinology, Hospital Infantil Universitario Niño Jesús, Instituto de Investigación Sanitaria La Princesa, Universidad Autónoma de Madrid, Madrid, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- IMDEA Food Institute, Madrid, Spain
| | - Coral Barbas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Spain
| | - Gabriel A. Martos-Moreno
- Department of Pediatrics and Pediatric Endocrinology, Hospital Infantil Universitario Niño Jesús, Instituto de Investigación Sanitaria La Princesa, Universidad Autónoma de Madrid, Madrid, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Francisco J. Rupérez
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Spain
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11
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Boris V, Vanessa V. Molecular systems biology approaches to investigate mechanisms of gut-brain communication in neurological diseases. Eur J Neurol 2023; 30:3622-3632. [PMID: 37038632 DOI: 10.1111/ene.15819] [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: 01/05/2023] [Revised: 04/03/2023] [Accepted: 04/05/2023] [Indexed: 04/12/2023]
Abstract
BACKGROUND Whilst the incidence of neurological diseases is increasing worldwide, treatment remains mostly limited to symptom management. The gut-brain axis, which encompasses the communication routes between microbiota, gut and brain, has emerged as a crucial area of investigation for identifying new preventive and therapeutic targets in neurological disease. METHODS Due to the inter-organ, systemic nature of the gut-brain axis, together with the multitude of biomolecules and microbial species involved, molecular systems biology approaches are required to accurately investigate the mechanisms of gut-brain communication. High-throughput omics profiling, together with computational methodologies such as dimensionality reduction or clustering, machine learning, network inference and genome-scale metabolic models, allows novel biomarkers to be discovered and elucidates mechanistic insights. RESULTS In this review, the general concepts of experimental and computational methodologies for gut-brain axis research are introduced and their applications are discussed, mainly in human cohorts. Important aspects are further highlighted concerning rational study design, sampling procedures and data modalities relevant for gut-brain communication, strengths and limitations of methodological approaches and some future perspectives. CONCLUSION Multi-omics analyses, together with advanced data mining, are essential to functionally characterize the gut-brain axis and put forward novel preventive or therapeutic strategies in neurological disease.
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Affiliation(s)
- Vandemoortele Boris
- Laboratory for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Vermeirssen Vanessa
- Laboratory for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
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12
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Kang EY, Kim DY, Kim HK, Shin WS, Park YS, Kim TH, Kim W, Cao L, Lee SG, Gang G, Shin M, Kim JM, Go GW. Modified Korean MIND Diet: A Nutritional Intervention for Improved Cognitive Function in Elderly Women through Mitochondrial Respiration, Inflammation Suppression, and Amino Acid Metabolism Regulation. Mol Nutr Food Res 2023; 67:e2300329. [PMID: 37650267 DOI: 10.1002/mnfr.202300329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 07/19/2023] [Indexed: 09/01/2023]
Abstract
SCOPE Mild cognitive impairment is associated with a high prevalence of dementia. The study examines the benefits of a modified Korean MIND (K-MIND) diet and explores biomarkers using multi-omics analysis. METHODS AND RESULTS The K-MIND diet, tailored to the elderly Korean population, includes perilla oil, milk, or fermented milk, and avoids alcohol consumption. As a result, the K-MIND diet significantly improves subjects "orientation to place" in the Korean version of the Mini-Mental State Examination, 2nd edition test. According to multi-omics analysis, the K-MIND diet upregulates genes associated with mitochondrial respiration, including ubiquinone oxidoreductase, cytochrome C oxidase, and ATP synthase, and immune system processes, and downregulates genes related to nuclear factor kappa B activity and inflammatory responses. In addition, K-MIND affects the metabolic pathways of glycine, serine, threonine, tryptophan, and sphingolipids, which are closely linked to cognitive function through synthesis of neurotransmitters and structures of brain cell membranes. CONCLUSION The findings imply that the K-MIND diet improves cognitive function by upregulating key genes involved in oxidative phosphorylation and downregulating pro-inflammatory cytokines.
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Affiliation(s)
- Eun Young Kang
- Department of Food and Nutrition, Hanyang University, Seoul, 04763, Republic of Korea
| | - Do-Young Kim
- Department of Animal Science and Technology, Chung-Ang University, Gyeonggi-do, 17546, Republic of Korea
| | - Hyun Kyung Kim
- Department of Food and Nutrition, Hanyang University, Seoul, 04763, Republic of Korea
| | - Weon-Sun Shin
- Department of Food and Nutrition, Hanyang University, Seoul, 04763, Republic of Korea
| | - Young-Sook Park
- Department of Food and Nutrition, Hanyang University, Seoul, 04763, Republic of Korea
| | - Tae Hoon Kim
- Department of Food and Nutrition, Hanyang University, Seoul, 04763, Republic of Korea
| | - Wooki Kim
- Department of Food Science and Biotechnology, Kyung Hee University, Gyeonggi-do, 17104, Republic of Korea
| | - Lei Cao
- Department of Food Science and Biotechnology, Gachon University, Seongnam, 13120, Republic of Korea
| | - Sang-Gil Lee
- Department of Food Science and Nutrition, Pukyong National University, Busan, 48513, Republic of Korea
- Department of Smart Green Technology Engineering, Pukyong National University, Busan, 48513, Republic of Korea
| | - Gyoungok Gang
- Department of Food Science and Nutrition, Pukyong National University, Busan, 48513, Republic of Korea
| | - Minhye Shin
- Department of Microbiology, Inha University, Incheon, 22212, Republic of Korea
| | - Jun-Mo Kim
- Department of Animal Science and Technology, Chung-Ang University, Gyeonggi-do, 17546, Republic of Korea
| | - Gwang-Woong Go
- Department of Food and Nutrition, Hanyang University, Seoul, 04763, Republic of Korea
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13
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Ambeskovic M, Hopkins G, Hoover T, Joseph JT, Montina T, Metz GAS. Metabolomic Signatures of Alzheimer's Disease Indicate Brain Region-Specific Neurodegenerative Progression. Int J Mol Sci 2023; 24:14769. [PMID: 37834217 PMCID: PMC10573054 DOI: 10.3390/ijms241914769] [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: 09/01/2023] [Revised: 09/25/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
Pathological mechanisms contributing to Alzheimer's disease (AD) are still elusive. Here, we identified the metabolic signatures of AD in human post-mortem brains. Using 1H NMR spectroscopy and an untargeted metabolomics approach, we identified (1) metabolomic profiles of AD and age-matched healthy subjects in post-mortem brain tissue, and (2) region-common and region-unique metabolome alterations and biochemical pathways across eight brain regions revealed that BA9 was the most affected. Phenylalanine and phosphorylcholine were mainly downregulated, suggesting altered neurotransmitter synthesis. N-acetylaspartate and GABA were upregulated in most regions, suggesting higher inhibitory activity in neural circuits. Other region-common metabolic pathways indicated impaired mitochondrial function and energy metabolism, while region-unique pathways indicated oxidative stress and altered immune responses. Importantly, AD caused metabolic changes in brain regions with less well-documented pathological alterations that suggest degenerative progression. The findings provide a new understanding of the biochemical mechanisms of AD and guide biomarker discovery for personalized risk prediction and diagnosis.
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Affiliation(s)
- Mirela Ambeskovic
- Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (M.A.); (G.H.); (T.H.)
| | - Giselle Hopkins
- Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (M.A.); (G.H.); (T.H.)
| | - Tanzi Hoover
- Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (M.A.); (G.H.); (T.H.)
| | - Jeffrey T. Joseph
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada;
| | - Tony Montina
- Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
- Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
| | - Gerlinde A. S. Metz
- Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (M.A.); (G.H.); (T.H.)
- Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
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Luo K, Chen J, Li H, Wu D, Du Y, Zhao S, Liu T, Li L, Dai Z, Li Y, Zhao Y, Tang L, Fu X. Design, synthesis and biological evaluation of new multi-target scutellarein hybrids for treatment of Alzheimer's disease. Bioorg Chem 2023; 138:106596. [PMID: 37186997 DOI: 10.1016/j.bioorg.2023.106596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/25/2023] [Accepted: 05/04/2023] [Indexed: 05/17/2023]
Abstract
Scutellarein hybrids were designed, synthesized and evaluated as multifunctional therapeutic agents for the treatment of Alzheimer's disease (AD). Compounds 11a-i, containing a 2-hydroxymethyl-3,5,6-trimethylpyrazine fragment at the 7-position of scutellarein, were found to have balanced and effective multi-target potencies against AD. Among them, compound 11e exhibited the most potent inhibition of electric eel and human acetylcholinesterase enzymes with IC50 values of 6.72 ± 0.09 and 8.91 ± 0.08 μM, respectively. In addition, compound 11e displayed not only excellent inhibition of self- and Cu2+-induced Aβ1-42 aggregation (91.85% and 85.62%, respectively) but also induced disassembly of self- and Cu2+-induced Aβ fibrils (84.54% and 83.49% disaggregation, respectively). Moreover, 11e significantly reduced tau protein hyperphosphorylation induced by Aβ25-35, and also exhibited good inhibition of platelet aggregation. A neuroprotective assay demonstrated that pre-treatment of PC12 cells with 11e significantly decreased lactate dehydrogenase levels, increased cell viability, enhanced expression of relevant apoptotic proteins (Bcl-2, Bax and caspase-3) and inhibited RSL3-induced PC12 cell ferroptosis. Furthermore, hCMEC/D3 and hPepT1-MDCK cell line permeability assays indicated that 11e would have optimal blood-brain barrier and intestinal absorption characteristics. In addition, in vivo studies revealed that compound 11e significantly attenuated learning and memory impairment in an AD mice model. Toxicity experiments with the compound did not reveal any safety concerns. Notably, 11e significantly reduced β-amyloid precursor protein (APP) and β-site APP cleaving enzyme-1 (BACE-1) protein expression in brain tissue of scopolamine-treated mice. Taken together, these outstanding properties qualified compound 11e as a promising multi-target candidate for AD therapy, worthy of further studies.
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Affiliation(s)
- Keke Luo
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Jiao Chen
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Hui Li
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Dirong Wu
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Yuanjiang Du
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Shanshan Zhao
- State Key Laboratory of Functions and Applications of Medicinal Plants & College of Pharmacy, Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, Guizhou Medical University, Guiyang 550025, China
| | - Ting Liu
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; Guizhou Provincial Key Laboratory of Pharmaceutics, Guizhou Medical University, Guiyang 550004, China
| | - Li Li
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China
| | - Zeqin Dai
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China
| | - Yongjun Li
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; Guizhou Provincial Key Laboratory of Pharmaceutics, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Yonglong Zhao
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Lei Tang
- State Key Laboratory of Functions and Applications of Medicinal Plants & College of Pharmacy, Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, Guizhou Medical University, Guiyang 550025, China.
| | - Xiaozhong Fu
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China.
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15
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Berezhnoy G, Laske C, Trautwein C. Metabolomic profiling of CSF and blood serum elucidates general and sex-specific patterns for mild cognitive impairment and Alzheimer's disease patients. Front Aging Neurosci 2023; 15:1219718. [PMID: 37693649 PMCID: PMC10483152 DOI: 10.3389/fnagi.2023.1219718] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/26/2023] [Indexed: 09/12/2023] Open
Abstract
Background Beta-amyloid (Abeta) and tau protein in cerebrospinal fluid (CSF) are established diagnostic biomarkers for Alzheimer's disease (AD). However, these biomarkers may not the only ones existing parameters that reflect Alzheimer's disease neuropathological change. The use of quantitative metabolomics approach could provide novel insights into dementia progression and identify key metabolic alterations in CSF and serum. Methods In the present study, we quantified a set of 45 metabolites in CSF (71 patients) and 27 in serum (76 patients) in patients with mild cognitive impairment (MCI), AD, and controls using nuclear magnetic resonance (NMR)-based metabolomics. Results We found significantly reduced CSF (1.32-fold, p = 0.0195) and serum (1.47-fold, p = 0.0484) levels of the ketone body acetoacetate in AD and MCI patients. Additionally, we found decreased levels (1.20-fold, p = 0.0438) of the branched-chain amino acid (BCAA) valine in the CSF of AD patients with increased valine degradation pathway metabolites (such as 3-hydroxyisobutyrate and α-ketoisovalerate). Moreover, we discovered that CSF 2-hydroxybutyrate is dramatically reduced in the MCI patient group (1.23-fold, p = 0.039). On the other hand, vitamin C (ascorbate) was significantly raised in CSF of these patients (p = 0.008). We also identified altered CSF protein content, 1,5-anhydrosorbitol and fructose as further metabolic shifts distinguishing AD from MCI. Significantly decreased serum levels of the amino acid ornithine were seen in the AD dementia group when compared to healthy controls (1.36-fold, p = 0.011). When investigating the effect of sex, we found for AD males the sign of decreased 2-hydroxybutyrate and acetoacetate in CSF while for AD females increased serum creatinine was identified. Conclusion Quantitative NMR metabolomics of CSF and serum was able to efficiently identify metabolic changes associated with dementia groups of MCI and AD patients. Further, we showed strong correlations between these changes and well-established metabolomic and clinical indicators like Abeta.
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Affiliation(s)
- Georgy Berezhnoy
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Tübingen, Germany
| | - Christoph Laske
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University Hospital Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Christoph Trautwein
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Tübingen, Germany
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16
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Borkowski K, Seyfried NT, Arnold M, Lah JJ, Levey AI, Hales CM, Dammer EB, Blach C, Louie G, Kaddurah-Daouk R, Newman JW. Integration of plasma and CSF metabolomics with CSF proteomic reveals novel associations between lipid mediators and central nervous system vascular and energy metabolism. Sci Rep 2023; 13:13752. [PMID: 37612324 PMCID: PMC10447532 DOI: 10.1038/s41598-023-39737-8] [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: 03/03/2023] [Accepted: 07/30/2023] [Indexed: 08/25/2023] Open
Abstract
Integration of the omics data, including metabolomics and proteomics, provides a unique opportunity to search for new associations within metabolic disorders, including Alzheimer's disease. Using metabolomics, we have previously profiled oxylipins, endocannabinoids, bile acids, and steroids in 293 CSF and 202 matched plasma samples from AD cases and healthy controls and identified both central and peripheral markers of AD pathology within inflammation-regulating cytochrome p450/soluble epoxide hydrolase pathway. Additionally, using proteomics, we have identified five cerebrospinal fluid protein panels, involved in the regulation of energy metabolism, vasculature, myelin/oligodendrocyte, glia/inflammation, and synapses/neurons, affected in AD, and reflective of AD-related changes in the brain. In the current manuscript, using metabolomics-proteomics data integration, we describe new associations between peripheral and central lipid mediators, with the above-described CSF protein panels. Particularly strong associations were observed between cytochrome p450/soluble epoxide hydrolase metabolites, bile acids, and proteins involved in glycolysis, blood coagulation, and vascular inflammation and the regulators of extracellular matrix. Those metabolic associations were not observed at the gene-co-expression level in the central nervous system. In summary, this manuscript provides new information regarding Alzheimer's disease, linking both central and peripheral metabolism, and illustrates the necessity for the "omics" data integration to uncover associations beyond gene co-expression.
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Affiliation(s)
- Kamil Borkowski
- West Coast Metabolomics Center, Genome Center, University of California Davis, Davis, CA, 95616, USA.
| | - Nicholas T Seyfried
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27708, USA
| | - James J Lah
- Department of Neurology, Emory University, Atlanta, GA, 30329, USA
| | - Allan I Levey
- Department of Neurology, Emory University, Atlanta, GA, 30329, USA
| | - Chadwick M Hales
- Department of Neurology, Emory University, Atlanta, GA, 30329, USA
| | - Eric B Dammer
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Colette Blach
- Duke Molecular Physiology Institute, Duke University, Durham, NC, 27708, USA
| | - Gregory Louie
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27708, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27708, USA.
- Duke Institute for Brain Sciences, Duke University, Durham, NC, 27708, USA.
- Department of Medicine, Duke University, Durham, NC, 27708, USA.
| | - John W Newman
- West Coast Metabolomics Center, Genome Center, University of California Davis, Davis, CA, 95616, USA
- Western Human Nutrition Research Center, United States Department of Agriculture-Agriculture Research Service, Davis, CA, 95616, USA
- Department of Nutrition, University of California-Davis, Davis, CA, 95616, USA
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17
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Chen C, Wang J, Pan D, Wang X, Xu Y, Yan J, Wang L, Yang X, Yang M, Liu G. Applications of multi-omics analysis in human diseases. MedComm (Beijing) 2023; 4:e315. [PMID: 37533767 PMCID: PMC10390758 DOI: 10.1002/mco2.315] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 08/04/2023] Open
Abstract
Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.
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Affiliation(s)
- Chongyang Chen
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
| | - Jing Wang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Donghui Pan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xinyu Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Yuping Xu
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Junjie Yan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Lizhen Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xifei Yang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Min Yang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Gong‐Ping Liu
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
- Department of PathophysiologySchool of Basic MedicineKey Laboratory of Ministry of Education of China and Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
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18
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Marshall CR, Farrow MA, Djambazova KV, Spraggins JM. Untangling Alzheimer's disease with spatial multi-omics: a brief review. Front Aging Neurosci 2023; 15:1150512. [PMID: 37533766 PMCID: PMC10390637 DOI: 10.3389/fnagi.2023.1150512] [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: 01/24/2023] [Accepted: 06/13/2023] [Indexed: 08/04/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of neurological dementia, specified by extracellular β-amyloid plaque deposition, neurofibrillary tangles, and cognitive impairment. AD-associated pathologies like cerebral amyloid angiopathy (CAA) are also affiliated with cognitive impairment and have overlapping molecular drivers, including amyloid buildup. Discerning the complexity of these neurological disorders remains a significant challenge, and the spatiomolecular relationships between pathogenic features of AD and AD-associated pathologies remain poorly understood. This review highlights recent developments in spatial omics, including profiling and molecular imaging methods, and how they are applied to AD. These emerging technologies aim to characterize the relationship between how specific cell types and tissue features are organized in combination with mapping molecular distributions to provide a systems biology view of the tissue microenvironment around these neuropathologies. As spatial omics methods achieve greater resolution and improved molecular coverage, they are enabling deeper characterization of the molecular drivers of AD, leading to new possibilities for the prediction, diagnosis, and mitigation of this debilitating disease.
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Affiliation(s)
- Cody R. Marshall
- Chemical and Physical Biology Program, Vanderbilt University School of Medicine, Nashville, TN, United States
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Melissa A. Farrow
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Katerina V. Djambazova
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Jeffrey M. Spraggins
- Chemical and Physical Biology Program, Vanderbilt University School of Medicine, Nashville, TN, United States
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
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19
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Chen S, Xing X, Hou X, Zhuang Q, Tan N, Cui Y, Wang J, Zhang M, Hu S, Xiao Y. The molecular pathogenesis of achalasia: a paired lower esophageal sphincter muscle and serum 4D label-free proteomic study. Gastroenterol Rep (Oxf) 2023; 11:goad031. [PMID: 37324545 PMCID: PMC10260389 DOI: 10.1093/gastro/goad031] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 04/11/2023] [Accepted: 05/03/2023] [Indexed: 06/17/2023] Open
Abstract
Background Achalasia is a primary esophageal motility disorder with potential molecular pathogenesis remaining uncertain. This study aimed to identify the differentially expressed proteins and potential pathways among achalasia subtypes and controls to further reveal the molecular pathogenesis of achalasia. Methods Paired lower esophageal sphincter (LES) muscle and serum samples from 24 achalasia patients were collected. We also collected 10 normal serum samples from healthy controls and 10 normal LES muscle samples from esophageal cancer patients. The 4D label-free proteomic analysis was performed to identify the potential proteins and pathways involved in achalasia. Results Analysis of Similarities showed distinct proteomic patterns of serum and muscle samples between achalasia patients and controls (both P < 0.05). Functional enrichment analysis suggested that these differentially expressed proteins were immunity-, infection-, inflammation-, and neurodegeneration-associated. The mfuzz analysis in LES specimens showed that proteins involved in the extracellular matrix-receptor interaction increased sequentially between the control group, type III, type II, and type I achalasia. Only 26 proteins altered in the same directions in serum and muscle samples. Conclusions This first 4D label-free proteomic study of achalasia indicated that there were specific protein alterations in both the serum and muscle of achalasia, involving immunity, inflammation, infection, and neurodegeneration pathways. Distinct protein clusters between types I, II, and III revealed the potential molecular pathways associated with different disease stages. Analysis of proteins changed in both muscle and serum samples highlighted the importance of further studies on LES muscle and revealed potential autoantibodies.
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Affiliation(s)
| | | | - Xun Hou
- Gastrointestinal Surgery Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Qianjun Zhuang
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Niandi Tan
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Yi Cui
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Jinhui Wang
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Mengyu Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Shixian Hu
- Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Yinglian Xiao
- Corresponding author. Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou, Guangdong 510080, P. R. China. Tel: +86-13560172116;
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20
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Yen NTH, Anh NK, Jayanti RP, Phat NK, Vu DH, Ghim JL, Ahn S, Shin JG, Oh JY, Phuoc Long N, Kim DH. Multimodal plasma metabolomics and lipidomics in elucidating metabolic perturbations in tuberculosis patients with concurrent type 2 diabetes. Biochimie 2023:S0300-9084(23)00086-X. [PMID: 37062470 DOI: 10.1016/j.biochi.2023.04.009] [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: 03/14/2023] [Revised: 04/13/2023] [Accepted: 04/13/2023] [Indexed: 04/18/2023]
Abstract
Type 2 diabetes mellitus (DM) poses a major burden for the treatment and control of tuberculosis (TB). Characterization of the underlying metabolic perturbations in DM patients with TB infection would yield insights into the pathophysiology of TB-DM, thus potentially leading to improvements in TB treatment. In this study, a multimodal metabolomics and lipidomics workflow was applied to investigate plasma metabolic profiles of patients with TB and TB-DM. Significantly different biological processes and biomarkers in TB-DM vs. TB were identified using a data-driven, knowledge-based framework. Changes in metabolic and signaling pathways related to carbohydrate and amino acid metabolism were mainly captured by amide HILIC column metabolomics analysis, while perturbations in lipid metabolism were identified by the C18 metabolomics and lipidomics analysis. Compared to TB, TB-DM exhibited elevated levels of bile acids and molecules related to carbohydrate metabolism, as well as the depletion of glutamine, retinol, lysophosphatidylcholine, and phosphatidylcholine. Moreover, arachidonic acid metabolism was determined as a potential important factor in the interaction between TB and DM pathophysiology. In a correlation network of the significantly altered molecules, among the central nodes, chenodeoxycholic acid was robustly associated with TB and DM. Fatty acid (22:4) was a component of all significant modules. In conclusion, the integration of multimodal metabolomics and lipidomics provides a thorough picture of the metabolic changes associated with TB-DM. The results obtained from this comprehensive profiling of TB patients with DM advance the current understanding of DM comorbidity in TB infection and contribute to the development of more effective treatment.
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Affiliation(s)
- Nguyen Thi Hai Yen
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Ky Anh
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Rannissa Puspita Jayanti
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Ky Phat
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Dinh Hoa Vu
- The National Centre of Drug Information and Adverse Drug Reaction Monitoring, Hanoi University of Pharmacy, Hanoi, Viet Nam
| | - Jong-Lyul Ghim
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan, Republic of Korea
| | - Sangzin Ahn
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Jae-Gook Shin
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan, Republic of Korea
| | - Jee Youn Oh
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Nguyen Phuoc Long
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea.
| | - Dong Hyun Kim
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.
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21
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Amin AM, Mostafa H, Khojah HMJ. Insulin resistance in Alzheimer's disease: The genetics and metabolomics links. Clin Chim Acta 2023; 539:215-236. [PMID: 36566957 DOI: 10.1016/j.cca.2022.12.016] [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: 10/30/2022] [Revised: 12/16/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease with significant socioeconomic burden worldwide. Although genetics and environmental factors play a role, AD is highly associated with insulin resistance (IR) disorders such as metabolic syndrome (MS), obesity, and type two diabetes mellitus (T2DM). These findings highlight a shared pathogenesis. The use of metabolomics as a downstream systems' biology (omics) approach can help to identify these shared metabolic traits and assist in the early identification of at-risk groups and potentially guide therapy. Targeting the shared AD-IR metabolic trait with lifestyle interventions and pharmacological treatments may offer promising AD therapeutic approach. In this narrative review, we reviewed the literature on the AD-IR pathogenic link, the shared genetics and metabolomics biomarkers between AD and IR disorders, as well as the lifestyle interventions and pharmacological treatments which target this pathogenic link.
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Affiliation(s)
- Arwa M Amin
- Department of Clinical and Hospital Pharmacy, College of Pharmacy, Taibah University, Madinah, Saudi Arabia.
| | - Hamza Mostafa
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, Food Innovation Network (XIA), Nutrition and Food Safety Research Institute (INSA), Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona (UB), 08028 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid 28029, Spain
| | - Hani M J Khojah
- Department of Clinical and Hospital Pharmacy, College of Pharmacy, Taibah University, Madinah, Saudi Arabia
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22
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Strefeler A, Jan M, Quadroni M, Teav T, Rosenberg N, Chatton JY, Guex N, Gallart-Ayala H, Ivanisevic J. Molecular insights into sex-specific metabolic alterations in Alzheimer's mouse brain using multi-omics approach. Alzheimers Res Ther 2023; 15:8. [PMID: 36624525 PMCID: PMC9827669 DOI: 10.1186/s13195-023-01162-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 01/02/2023] [Indexed: 01/11/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is characterized by altered cellular metabolism in the brain. Several of these alterations have been found to be exacerbated in females, known to be disproportionately affected by AD. We aimed to unravel metabolic alterations in AD at the metabolic pathway level and evaluate whether they are sex-specific through integrative metabolomic, lipidomic, and proteomic analysis of mouse brain tissue. METHODS We analyzed male and female triple-transgenic mouse whole brain tissue by untargeted mass spectrometry-based methods to obtain a molecular signature consisting of polar metabolite, complex lipid, and protein data. These data were analyzed using multi-omics factor analysis. Pathway-level alterations were identified through joint pathway enrichment analysis or by separately evaluating lipid ontology and known proteins related to lipid metabolism. RESULTS Our analysis revealed significant AD-associated and in part sex-specific alterations across the molecular signature. Sex-dependent alterations were identified in GABA synthesis, arginine biosynthesis, and in alanine, aspartate, and glutamate metabolism. AD-associated alterations involving lipids were also found in the fatty acid elongation pathway and lysophospholipid metabolism, with a significant sex-specific effect for the latter. CONCLUSIONS Through multi-omics analysis, we report AD-associated and sex-specific metabolic alterations in the AD brain involving lysophospholipid and amino acid metabolism. These findings contribute to the characterization of the AD phenotype at the molecular level while considering the effect of sex, an overlooked yet determinant metabolic variable.
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Affiliation(s)
- Abigail Strefeler
- grid.9851.50000 0001 2165 4204Metabolomics Unit, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
| | - Maxime Jan
- grid.9851.50000 0001 2165 4204Bioinformatics Competence Center, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
| | - Manfredo Quadroni
- grid.9851.50000 0001 2165 4204Protein Analysis Facility, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
| | - Tony Teav
- grid.9851.50000 0001 2165 4204Metabolomics Unit, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
| | - Nadia Rosenberg
- grid.9851.50000 0001 2165 4204Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Jean-Yves Chatton
- grid.9851.50000 0001 2165 4204Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Nicolas Guex
- grid.9851.50000 0001 2165 4204Bioinformatics Competence Center, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
| | - Hector Gallart-Ayala
- grid.9851.50000 0001 2165 4204Metabolomics Unit, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
| | - Julijana Ivanisevic
- grid.9851.50000 0001 2165 4204Metabolomics Unit, Faculty of Biology and Medicine, University de Lausanne, Lausanne, Switzerland
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23
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Clark C, Rabl M, Dayon L, Popp J. The promise of multi-omics approaches to discover biological alterations with clinical relevance in Alzheimer's disease. Front Aging Neurosci 2022; 14:1065904. [PMID: 36570537 PMCID: PMC9768448 DOI: 10.3389/fnagi.2022.1065904] [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: 10/10/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022] Open
Abstract
Beyond the core features of Alzheimer's disease (AD) pathology, i.e. amyloid pathology, tau-related neurodegeneration and microglia response, multiple other molecular alterations and pathway dysregulations have been observed in AD. Their inter-individual variations, complex interactions and relevance for clinical manifestation and disease progression remain poorly understood, however. Heterogeneity at both pathophysiological and clinical levels complicates diagnosis, prognosis, treatment and drug design and testing. High-throughput "omics" comprise unbiased and untargeted data-driven methods which allow the exploration of a wide spectrum of disease-related changes at different endophenotype levels without focussing a priori on specific molecular pathways or molecules. Crucially, new methodological and statistical advances now allow for the integrative analysis of data resulting from multiple and different omics methods. These multi-omics approaches offer the unique advantage of providing a more comprehensive characterisation of the AD endophenotype and to capture molecular signatures and interactions spanning various biological levels. These new insights can then help decipher disease mechanisms more deeply. In this review, we describe the different multi-omics tools and approaches currently available and how they have been applied in AD research so far. We discuss how multi-omics can be used to explore molecular alterations related to core features of the AD pathologies and how they interact with comorbid pathological alterations. We further discuss whether the identified pathophysiological changes are relevant for the clinical manifestation of AD, in terms of both cognitive impairment and neuropsychiatric symptoms, and for clinical disease progression over time. Finally, we address the opportunities for multi-omics approaches to help discover novel biomarkers for diagnosis and monitoring of relevant pathophysiological processes, along with personalised intervention strategies in AD.
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Affiliation(s)
- Christopher Clark
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zürich, Switzerland,Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland,*Correspondence: Christopher Clark,
| | - Miriam Rabl
- Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland,University of Lausanne, Lausanne, Switzerland
| | - Loïc Dayon
- Nestlé Institute of Food Safety and Analytical Sciences, Nestlé Research, Lausanne, Switzerland,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Julius Popp
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zürich, Switzerland,Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland,Old Age Psychiatry, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
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24
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Aerqin Q, Wang ZT, Wu KM, He XY, Dong Q, Yu JT. Omics-based biomarkers discovery for Alzheimer's disease. Cell Mol Life Sci 2022; 79:585. [PMID: 36348101 DOI: 10.1007/s00018-022-04614-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 10/22/2022] [Accepted: 10/26/2022] [Indexed: 11/09/2022]
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorders presenting with the pathological hallmarks of amyloid plaques and tau tangles. Over the past few years, great efforts have been made to explore reliable biomarkers of AD. High-throughput omics are a technology driven by multiple levels of unbiased data to detect the complex etiology of AD, and it provides us with new opportunities to better understand the pathophysiology of AD and thereby identify potential biomarkers. Through revealing the interaction networks between different molecular levels, the ultimate goal of multi-omics is to improve the diagnosis and treatment of AD. In this review, based on the current AD pathology and the current status of AD diagnostic biomarkers, we summarize how genomics, transcriptomics, proteomics and metabolomics are all conducing to the discovery of reliable AD biomarkers that could be developed and used in clinical AD management.
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Affiliation(s)
- Qiaolifan Aerqin
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China
| | - Zuo-Teng Wang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Kai-Min Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China
| | - Xiao-Yu He
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
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25
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Desai RJ, Mahesri M, Lee SB, Varma VR, Loeffler T, Schilcher I, Gerhard T, Segal JB, Ritchey ME, Horton DB, Kim SC, Schneeweiss S, Thambisetty M. No association between initiation of phosphodiesterase-5 inhibitors and risk of incident Alzheimer's disease and related dementia: results from the Drug Repurposing for Effective Alzheimer's Medicines study. Brain Commun 2022; 4:fcac247. [PMID: 36330433 PMCID: PMC9598543 DOI: 10.1093/braincomms/fcac247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 07/11/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022] Open
Abstract
We evaluated the hypothesis that phosphodiesterase-5 inhibitors, including sildenafil and tadalafil, may be associated with reduced incidence of Alzheimer's disease and related dementia using a patient-level cohort study of Medicare claims and cell culture-based phenotypic assays. We compared incidence of Alzheimer's disease and related dementia after phosphodiesterase-5 inhibitor initiation versus endothelin receptor antagonist initiation among patients with pulmonary hypertension after controlling for 76 confounding variables through propensity score matching. Across four separate analytic approaches designed to address specific types of biases including informative censoring, reverse causality, and outcome misclassification, we observed no evidence for a reduced risk of Alzheimer's disease and related dementia with phosphodiesterase-5 inhibitors;hazard ratio (95% confidence interval): 0.99 (0.69-1.43), 1.00 (0.71-1.42), 0.67 (0.43-1.06), and 1.15 (0.57-2.34). We also did not observe evidence that sildenafil ameliorated molecular abnormalities relevant to Alzheimer's disease in most cell culture-based phenotypic assays. These results do not provide support to the hypothesis that phosphodiesterase-5 inhibitors are promising repurposing candidates for Alzheimer's disease and related dementia.
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Affiliation(s)
- Rishi J Desai
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, MA 02115, USA
| | - Mufaddal Mahesri
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, MA 02115, USA
| | - Su Been Lee
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, MA 02115, USA
| | - Vijay R Varma
- Clinical & Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
| | - Tina Loeffler
- QPS Austria GmbH, Parkring 12, 8074 Grambach, Austria
| | | | - Tobias Gerhard
- Rutgers Center for Pharmacoepidemiology and Treatment Science, New Brunswick, NJ 08901, USA
- Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Jodi B Segal
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Mary E Ritchey
- Rutgers Center for Pharmacoepidemiology and Treatment Science, New Brunswick, NJ 08901, USA
| | - Daniel B Horton
- Rutgers Center for Pharmacoepidemiology and Treatment Science, New Brunswick, NJ 08901, USA
- Rutgers Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08901, USA
| | - Seoyoung C Kim
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, MA 02115, USA
| | - Madhav Thambisetty
- Clinical & Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
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26
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Zhao N, Quicksall Z, Asmann YW, Ren Y. Network approaches for omics studies of neurodegenerative diseases. Front Genet 2022; 13:984338. [PMID: 36186441 PMCID: PMC9523597 DOI: 10.3389/fgene.2022.984338] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
The recent methodological advances in multi-omics approaches, including genomic, transcriptomic, metabolomic, lipidomic, and proteomic, have revolutionized the research field by generating “big data” which greatly enhanced our understanding of the molecular complexity of the brain and disease states. Network approaches have been routinely applied to single-omics data to provide critical insight into disease biology. Furthermore, multi-omics integration has emerged as both a vital need and a new direction to connect the different layers of information underlying disease mechanisms. In this review article, we summarize popular network analytic approaches for single-omics data and multi-omics integration and discuss how these approaches have been utilized in studying neurodegenerative diseases.
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Affiliation(s)
- Na Zhao
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, United States
| | - Zachary Quicksall
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States
| | - Yan W. Asmann
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States
| | - Yingxue Ren
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States
- *Correspondence: Yingxue Ren,
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27
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Mumtaz I, Ayaz MO, Khan MS, Manzoor U, Ganayee MA, Bhat AQ, Dar GH, Alghamdi BS, Hashem AM, Dar MJ, Ashraf GM, Maqbool T. Clinical relevance of biomarkers, new therapeutic approaches, and role of post-translational modifications in the pathogenesis of Alzheimer's disease. Front Aging Neurosci 2022; 14:977411. [PMID: 36158539 PMCID: PMC9490081 DOI: 10.3389/fnagi.2022.977411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/18/2022] [Indexed: 12/14/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that causes progressive loss of cognitive functions like thinking, memory, reasoning, behavioral abilities, and social skills thus affecting the ability of a person to perform normal daily functions independently. There is no definitive cure for this disease, and treatment options available for the management of the disease are not very effective as well. Based on histopathology, AD is characterized by the accumulation of insoluble deposits of amyloid beta (Aβ) plaques and neurofibrillary tangles (NFTs). Although several molecular events contribute to the formation of these insoluble deposits, the aberrant post-translational modifications (PTMs) of AD-related proteins (like APP, Aβ, tau, and BACE1) are also known to be involved in the onset and progression of this disease. However, early diagnosis of the disease as well as the development of effective therapeutic approaches is impeded by lack of proper clinical biomarkers. In this review, we summarized the current status and clinical relevance of biomarkers from cerebrospinal fluid (CSF), blood and extracellular vesicles involved in onset and progression of AD. Moreover, we highlight the effects of several PTMs on the AD-related proteins, and provide an insight how these modifications impact the structure and function of proteins leading to AD pathology. Finally, for disease-modifying therapeutics, novel approaches, and targets are discussed for the successful treatment and management of AD.
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Affiliation(s)
- Ibtisam Mumtaz
- Laboratory of Nanotherapeutics and Regenerative Medicine, Department of Nanotechnology, University of Kashmir, Srinagar, India
| | - Mir Owais Ayaz
- Laboratory of Cell and Molecular Biology, Department of Cancer Pharmacology, CSIR-Indian Institute of Integrative Medicine, Jammu, India
- Centre for Scientific and Innovative Research, Ghaziabad, Utter Pradesh, India
| | - Mohamad Sultan Khan
- Neurobiology and Molecular Chronobiology Laboratory, Department of Animal Biology, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Umar Manzoor
- Laboratory of Immune and Inflammatory Disease, Jeju Research Institute of Pharmaceutical Sciences, Jeju National University, Jeju, South Korea
| | - Mohd Azhardin Ganayee
- Laboratory of Nanotherapeutics and Regenerative Medicine, Department of Nanotechnology, University of Kashmir, Srinagar, India
- Department of Chemistry, Indian Institute of Technology Madras, Chennai, India
| | - Aadil Qadir Bhat
- Laboratory of Cell and Molecular Biology, Department of Cancer Pharmacology, CSIR-Indian Institute of Integrative Medicine, Jammu, India
- Centre for Scientific and Innovative Research, Ghaziabad, Utter Pradesh, India
| | - Ghulam Hassan Dar
- Sri Pratap College, Cluster University Srinagar, Jammu and Kashmir, India
| | - Badrah S. Alghamdi
- Department of Physiology, Neuroscience Unit, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Pre-clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Anwar M. Hashem
- Department of Medical Microbiology and Parasitology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Vaccines and Immunotherapy Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mohd Jamal Dar
- Laboratory of Cell and Molecular Biology, Department of Cancer Pharmacology, CSIR-Indian Institute of Integrative Medicine, Jammu, India
- Centre for Scientific and Innovative Research, Ghaziabad, Utter Pradesh, India
| | - Gulam Md. Ashraf
- Pre-clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Tariq Maqbool
- Laboratory of Nanotherapeutics and Regenerative Medicine, Department of Nanotechnology, University of Kashmir, Srinagar, India
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Xu K, Li H, Zhang B, Le M, Huang Q, Fu R, Croppi G, Qian G, Zhang J, Zhang G, Lu Y. Integrated transcriptomics and metabolomics analysis of the hippocampus reveals altered neuroinflammation, downregulated metabolism and synapse in sepsis-associated encephalopathy. Front Pharmacol 2022; 13:1004745. [PMID: 36147346 PMCID: PMC9486403 DOI: 10.3389/fphar.2022.1004745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 08/12/2022] [Indexed: 11/13/2022] Open
Abstract
Sepsis-associated encephalopathy (SAE) is an intricated complication of sepsis that brings abnormal emotional and memory dysfunction and increases patients’ mortality. Patients’ alterations and abnormal function seen in SAE occur in the hippocampus, the primary brain region responsible for memory and emotional control, but the underlying pathophysiological mechanisms remain unclear. In the current study, we employed an integrative analysis combining the RNA-seq-based transcriptomics and liquid chromatography/mass spectrometry (LC-MS)-based metabolomics to comprehensively obtain the enriched genes and metabolites and their core network pathways in the endotoxin (LPS)-injected SAE mice model. As a result, SAE mice exhibited behavioral changes, and their hippocampus showed upregulated inflammatory cytokines and morphological alterations. The omics analysis identified 81 differentially expressed metabolites (variable importance in projection [VIP] > 1 and p < 0.05) and 1747 differentially expressed genes (Foldchange >2 and p < 0.05) were detected in SAE-grouped hippocampus. Moreover, 31 compounds and 100 potential target genes were employed for the Kyoto Encyclopedia of Genes and Genomes (KEGG) Markup Language (KGML) network analysis to explore the core signaling pathways for the progression of SAE. The integrative pathway analysis showed that various dysregulated metabolism pathways, including lipids metabolism, amino acids, glucose and nucleotides, inflammation-related pathways, and deregulated synapses, were tightly associated with hippocampus dysfunction at early SAE. These findings provide a landscape for understanding the pathophysiological mechanisms of the hippocampus in the progression of SAE and pave the way to identify therapeutic targets in future studies.
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Affiliation(s)
- Kejia Xu
- Department of Anesthesiology and Hongqiao International Institute of Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hui Li
- Department of Anesthesiology and Hongqiao International Institute of Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bing Zhang
- Department of Anesthesiology and Hongqiao International Institute of Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Meini Le
- Department of Anesthesiology and Hongqiao International Institute of Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiong Huang
- Department of Neurology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rao Fu
- Department of Neurology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | - Gang Qian
- Department of Anesthesiology and Hongqiao International Institute of Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junjie Zhang
- Department of Anesthesiology and Hongqiao International Institute of Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guangming Zhang
- Department of Anesthesiology and Hongqiao International Institute of Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
- *Correspondence: Guangming Zhang, ; Yinzhong Lu,
| | - Yinzhong Lu
- Department of Anesthesiology and Hongqiao International Institute of Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Guangming Zhang, ; Yinzhong Lu,
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Park J, Barahona‐Torres N, Jang S, Mok KY, Kim HJ, Han S, Cho K, Zhou X, Fu AKY, Ip NY, Seo J, Choi M, Jeong H, Hwang D, Lee DY, Byun MS, Yi D, Han JW, Mook‐Jung I, Hardy J. Multi-Omics-Based Autophagy-Related Untypical Subtypes in Patients with Cerebral Amyloid Pathology. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2201212. [PMID: 35694866 PMCID: PMC9376815 DOI: 10.1002/advs.202201212] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/26/2022] [Indexed: 05/05/2023]
Abstract
Recent multi-omics analyses paved the way for a comprehensive understanding of pathological processes. However, only few studies have explored Alzheimer's disease (AD) despite the possibility of biological subtypes within these patients. For this study, unsupervised classification of four datasets (genetics, miRNA transcriptomics, proteomics, and blood-based biomarkers) using Multi-Omics Factor Analysis+ (MOFA+), along with systems-biological approaches following various downstream analyses are performed. New subgroups within 170 patients with cerebral amyloid pathology (Aβ+) are revealed and the features of them are identified based on the top-rated targets constructing multi-omics factors of both whole (M-TPAD) and immune-focused models (M-IPAD). The authors explored the characteristics of subtypes and possible key-drivers for AD pathogenesis. Further in-depth studies showed that these subtypes are associated with longitudinal brain changes and autophagy pathways are main contributors. The significance of autophagy or clustering tendency is validated in peripheral blood mononuclear cells (PBMCs; n = 120 including 30 Aβ- and 90 Aβ+), induced pluripotent stem cell-derived human brain organoids/microglia (n = 12 including 5 Aβ-, 5 Aβ+, and CRISPR-Cas9 apolipoprotein isogenic lines), and human brain transcriptome (n = 78). Collectively, this study provides a strategy for precision medicine therapy and drug development for AD using integrative multi-omics analysis and network modelling.
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Affiliation(s)
- Jong‐Chan Park
- Department of Neurodegenerative DiseaseUCL Queen Square Institute of NeurologyUniversity College LondonLondonWC1N 3BGUK
- Department of Biochemistry and Biomedical SciencesCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- Neuroscience Research InstituteMedical Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- SNU Korea Dementia Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
| | - Natalia Barahona‐Torres
- Department of Neurodegenerative DiseaseUCL Queen Square Institute of NeurologyUniversity College LondonLondonWC1N 3BGUK
| | - So‐Yeong Jang
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and TechnologyDaejeon34141Republic of Korea
| | - Kin Y. Mok
- Department of Neurodegenerative DiseaseUCL Queen Square Institute of NeurologyUniversity College LondonLondonWC1N 3BGUK
| | - Haeng Jun Kim
- Department of Biochemistry and Biomedical SciencesCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- SNU Korea Dementia Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
| | - Sun‐Ho Han
- Department of Biochemistry and Biomedical SciencesCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- Neuroscience Research InstituteMedical Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- SNU Korea Dementia Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
| | - Kwang‐Hyun Cho
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and TechnologyDaejeon34141Republic of Korea
| | - Xiaopu Zhou
- Division of Life ScienceState Key Laboratory of Molecular NeuroscienceMolecular Neuroscience CenterThe Hong Kong University of Science and TechnologyClear Water Bay, KowloonHong Kong999077China
- Hong Kong Center for Neurodegenerative DiseasesHong Kong Science ParkHong Kong999077China
- Guangdong Provincial Key Laboratory of Brain ScienceDisease and Drug DevelopmentHKUST Shenzhen Research InstituteShenzhen‐Hong Kong Institute of Brain ScienceShenzhenGuangdong518057China
| | - Amy K. Y. Fu
- Division of Life ScienceState Key Laboratory of Molecular NeuroscienceMolecular Neuroscience CenterThe Hong Kong University of Science and TechnologyClear Water Bay, KowloonHong Kong999077China
- Hong Kong Center for Neurodegenerative DiseasesHong Kong Science ParkHong Kong999077China
- Guangdong Provincial Key Laboratory of Brain ScienceDisease and Drug DevelopmentHKUST Shenzhen Research InstituteShenzhen‐Hong Kong Institute of Brain ScienceShenzhenGuangdong518057China
| | - Nancy Y. Ip
- Division of Life ScienceState Key Laboratory of Molecular NeuroscienceMolecular Neuroscience CenterThe Hong Kong University of Science and TechnologyClear Water Bay, KowloonHong Kong999077China
- Hong Kong Center for Neurodegenerative DiseasesHong Kong Science ParkHong Kong999077China
- Guangdong Provincial Key Laboratory of Brain ScienceDisease and Drug DevelopmentHKUST Shenzhen Research InstituteShenzhen‐Hong Kong Institute of Brain ScienceShenzhenGuangdong518057China
| | - Jieun Seo
- Department of Laboratory MedicineSeverance HospitalYonsei University College of MedicineSeoul03722Republic of Korea
| | - Murim Choi
- Department of Biochemistry and Biomedical SciencesCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
| | - Hyobin Jeong
- European Molecular Biology LaboratoryGenome Biology UnitHeidelberg69117Germany
| | - Daehee Hwang
- Department of Biological SciencesSeoul National UniversitySeoul08826Republic of Korea
| | - Dong Young Lee
- Institute of Human Behavioral MedicineMedical Research CenterSeoul National UniversitySeoul03080Republic of Korea
- Department of PsychiatryCollege of medicineSeoul National UniversitySeoul03080Republic of Korea
- Department of NeuropsychiatrySeoul National University HospitalSeoul03080Republic of Korea
| | - Min Soo Byun
- Department of PsychiatryPusan National University Yangsan HospitalYangsan50612Republic of Korea
| | - Dahyun Yi
- Biomedical Research InstituteSeoul National University HospitalSeoul03082Republic of Korea
| | - Jong Won Han
- Department of Biochemistry and Biomedical SciencesCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
| | - Inhee Mook‐Jung
- Department of Biochemistry and Biomedical SciencesCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- Neuroscience Research InstituteMedical Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
- SNU Korea Dementia Research CenterCollege of MedicineSeoul National UniversitySeoul03080Republic of Korea
| | - John Hardy
- Department of Neurodegenerative DiseaseUCL Queen Square Institute of NeurologyUniversity College LondonLondonWC1N 3BGUK
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30
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Dar MA, Arafah A, Bhat KA, Khan A, Khan MS, Ali A, Ahmad SM, Rashid SM, Rehman MU. Multiomics technologies: role in disease biomarker discoveries and therapeutics. Brief Funct Genomics 2022; 22:76-96. [PMID: 35809340 DOI: 10.1093/bfgp/elac017] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/21/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes. SUMMARY POINTS
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Ma Y, Lu C, Ji B, Qin J, Cai C, Yang Y, Zhao Y, Liang G, Guo X, Cao G, Li B, Gao P. Integrated Omics Analysis Reveals Alterations in the Intestinal Microbiota and Metabolites of Piglets After Starvation. Front Microbiol 2022; 13:881099. [PMID: 35783381 PMCID: PMC9240708 DOI: 10.3389/fmicb.2022.881099] [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: 02/22/2022] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
Obesity is a serious public health problem. Short-term starvation is an effective way to lose weight but can also cause harm to the body. However, a systematic assessment of the relationship between the intestinal microbiota and metabolites after complete fasting is lacking. Pigs are the best animal models for exploring the mechanisms of human nutrition digestion and absorption, metabolism, and disease treatment. In this study, 16S rRNA sequencing and liquid chromatography-mass spectrometry were used to analyze the changes in the intestinal microbiota and metabolite profiles in piglets under starvation stress. The results show that the microbial composition was changed significantly in the starvation groups compared with the control group (P < 0.05), suggesting that shifts in the microbial composition were induced by starvation stress. Furthermore, differences in the correlation of the intestinal microbiota and metabolites were observed in the different experimental groups. Starvation may disrupt the homeostasis of the intestinal microbiota and metabolite profile and affect the health of piglets. However, piglets can regulate metabolite production to compensate for the effects of short-term starvation. Our results provide a background to explore the mechanism of diet and short-term hunger for intestinal homeostasis.
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Hawksworth J, Fernández E, Gevaert K. A new generation of AD biomarkers: 2019 to 2021. Ageing Res Rev 2022; 79:101654. [PMID: 35636691 DOI: 10.1016/j.arr.2022.101654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 05/17/2022] [Accepted: 05/25/2022] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) is the most common form of dementia and cases are rising worldwide. The effort to fight this disease is hampered by a lack of disease-modifying treatments and the absence of an early, accurate diagnostic tool. Neuropathology begins years or decades before symptoms occur and, upon onset of symptoms, diagnosis can take a year or more. Such delays postpone treatment and make research into the early stages of the disease difficult. Ideally, clinicians require a minimally invasive test that can detect AD in its early stages, before cognitive symptoms occur. Advances in proteomic technologies have facilitated the study of promising biomarkers of AD. Over the last two years (2019-2021) studies have identified and validated many species which can be measured in cerebrospinal fluid (CSF), plasma, or in both fluids, and which have a high predictive value for AD. We herein discuss proteins which have been highlighted as promising biomarkers of AD in the last two years, and consider implications for future research within the research framework of the amyloid (A), tau (T), neurodegeneration (N) scoring system. We review recently identified species of amyloid and tau which may improve diagnosis when used in combination with current measures such as amyloid-beta-42 (Aβ42), total tau (t-tau) and phosphorylated tau (p-tau). In addition, several proteins have been identified as likely proxies for neurodegeneration, including neurofilament light (NfL), synaptosomal-associated protein 25 (SNAP-25) and neurogranin (NRGN). Finally, proteins originating from diverse processes such as neuroinflammation, lipid transport and mitochondrial dysfunction could aid in both AD diagnosis and patient stratification.
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Dayon L, Cominetti O, Affolter M. Proteomics of Human Biological Fluids for Biomarker Discoveries: Technical Advances and Recent Applications. Expert Rev Proteomics 2022; 19:131-151. [PMID: 35466824 DOI: 10.1080/14789450.2022.2070477] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate collection invasiveness and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids. AREAS COVERED We review proteomic technologies for the identification of biomarkers. Those are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that had until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from last five years in the Scopus database) using MS-based proteomics. EXPERT OPINION While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached a certain maturity. They are better suited to discover innovative and robust biofluid biomarkers.
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Affiliation(s)
- Loïc Dayon
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland.,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Ornella Cominetti
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland
| | - Michael Affolter
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland
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Wu D, Chen J, Luo K, Li H, Liu T, Li L, Dai Z, Li Y, Zhao Y, Fu X. Design, synthesis and evaluation of novel scutellarin and scutellarein-N,N-bis-substituted carbamate-l-amino acid derivatives as potential multifunctional therapeutics for Alzheimer's disease. Bioorg Chem 2022; 122:105760. [PMID: 35349945 DOI: 10.1016/j.bioorg.2022.105760] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/27/2022] [Accepted: 03/21/2022] [Indexed: 12/15/2022]
Abstract
In this study, we designed, synthesized and evaluated a series of scutellarin and scutellarein-N,N-bis-substituted carbamate-l-amino acid derivatives as multifunctional therapeutic agents for the treatment of Alzheimer's disease (AD). Compounds containing scutellarein as the parent nucleus (6a-l) had good inhibitory activity against acetyl cholinesterase (AChE), with compound 6 h exhibiting the most potent inhibition of electric eel AChE and human AChE enzymes with IC50 values of 6.01 ± 1.66 and 7.91 ± 0.49 μM, respectively. In addition, compound 6 h displayed not only excellent inhibition of self- and Cu2+-induced Aβ1-42 aggregation (89.17% and 86.19% inhibition) but also induced disassembly of self- and Cu2+-induced Aβ fibrils (84.25% and 78.73% disaggregation). Moreover, a neuroprotective assay demonstrated that pre-treatment of PC12 cells with 6 h significantly decreased lactate dehydrogenase levels, increased cell viability, enhanced expression of relevant apoptotic proteins (Bcl-2, Bax, and caspase-3) and inhibited RSL3 induced PC12 cell ferroptosis. Furthermore, hCMEC/D3 and hPepT1-MDCK cell line permeability assays indicated that 6 h would have optimal blood-brain barrier and intestinal absorption characteristics. The in vivo experimental data suggested that 6 h ameliorated learning and memory impairment in mice by decreasing AChE activity, increasing ACh levels and alleviating pathological damage of hippocampal tissue cells. These multifunctional properties highlight compound 6 h as a promising candidate for development as a multifunctional drug against AD.
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Affiliation(s)
- Dirong Wu
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Jiao Chen
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Keke Luo
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Hui Li
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Ting Liu
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; Guizhou Provincial Key Laboratory of Pharmaceutics, Guizhou Medical University, Guiyang 550004, China
| | - Li Li
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China
| | - Zeqin Dai
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China
| | - Yongjun Li
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; Guizhou Provincial Key Laboratory of Pharmaceutics, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Yonglong Zhao
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China
| | - Xiaozhong Fu
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550004, Guizhou, China; School of Pharmacy, Guizhou Medical University, Guiyang 550004, China; National Engineering Research Center of Miao's Medicines & Engineering Research Center for the Development and Application of Ethnic Medicine and TCM, Ministry of Education, Guiyang 550004, Guizhou, China.
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Lindner K, Beckenbauer K, van Ek LC, Titeca K, de Leeuw SM, Awwad K, Hanke F, Korepanova AV, Rybin V, van der Kam EL, Mohler EG, Tackenberg C, Lakics V, Gavin AC. Isoform- and cell-state-specific lipidation of ApoE in astrocytes. Cell Rep 2022; 38:110435. [PMID: 35235798 DOI: 10.1016/j.celrep.2022.110435] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 12/16/2021] [Accepted: 02/03/2022] [Indexed: 01/21/2023] Open
Abstract
Apolipoprotein E transports lipids and couples metabolism between astrocytes and neurons. The E4 variant (APOE4) affects these functions and represents a genetic predisposition for Alzheimer's disease, but the molecular mechanisms remain elusive. We show that ApoE produces different types of lipoproteins via distinct lipidation pathways. ApoE forms high-density lipoprotein (HDL)-like, cholesterol-rich particles via the ATP-binding cassette transporter 1 (ABCA1), a mechanism largely unaffected by ApoE polymorphism. Alternatively, ectopic accumulation of fat in astrocytes, a stress-associated condition, redirects ApoE toward the assembly and secretion of triacylglycerol-rich lipoproteins, a process boosted by the APOE4 variant. We demonstrate in vitro that ApoE can detect triacylglycerol in membranes and spontaneously assemble lipoprotein particles (10-20 nm) rich in unsaturated triacylglycerol, and that APOE4 has remarkable properties behaving as a strong triacylglycerol binder. We propose that fatty APOE4 astrocytes have reduced ability to clear toxic fatty acids from the extracellular milieu, because APOE4 reroutes them back to secretion.
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Affiliation(s)
- Karina Lindner
- Department of Cell Physiology and Metabolism, Faculty of Medicine, University of Geneva, Rue Michel-Servet 1, 1211 Geneva, Switzerland
| | - Katharina Beckenbauer
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany; AbbVie Deutschland GmbH & Co. KG Neuroscience Discovery, Knollstrasse, 67061 Ludwigshafen, Germany
| | - Larissa C van Ek
- Department of Cell Physiology and Metabolism, Faculty of Medicine, University of Geneva, Rue Michel-Servet 1, 1211 Geneva, Switzerland
| | - Kevin Titeca
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Sherida M de Leeuw
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, 8952 Schlieren, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Khader Awwad
- AbbVie Deutschland GmbH & Co. KG Drug Metabolism and Pharmacokinetics, Knollstrasse, 67061 Ludwigshafen, Germany
| | - Franziska Hanke
- AbbVie Deutschland GmbH & Co. KG Drug Metabolism and Pharmacokinetics, Knollstrasse, 67061 Ludwigshafen, Germany
| | | | - Vladimir Rybin
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | | | - Eric G Mohler
- AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, USA
| | - Christian Tackenberg
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, 8952 Schlieren, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Viktor Lakics
- AbbVie Deutschland GmbH & Co. KG Neuroscience Discovery, Knollstrasse, 67061 Ludwigshafen, Germany
| | - Anne-Claude Gavin
- Department of Cell Physiology and Metabolism, Faculty of Medicine, University of Geneva, Rue Michel-Servet 1, 1211 Geneva, Switzerland; European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany.
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36
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Gallucci M, Cenesi L, White C, Antuono P, Quaglio G, Bonanni L. Lights and Shadows of Cerebrospinal Fluid Biomarkers in the Current Alzheimer's Disease Framework. J Alzheimers Dis 2022; 86:1061-1072. [PMID: 35180122 PMCID: PMC9108561 DOI: 10.3233/jad-215432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The most significant biomarkers that are included in the Alzheimer's disease (AD) research framework are amyloid-β plaques deposition, p-tau, t-tau, and neurodegeneration.Although cerebrospinal fluid (CSF) biomarkers are included in the most recent AD research criteria, their use is increasing in the routine clinical practice and is applied also to the preclinical phases of AD, including mild cognitive impairment. The role of these biomarkers is still unclear concerning the preclinical stage of AD diagnosis, the CSF methodology, and the costs-benefits of the biomarkers' tests. The controversies regarding the use of biomarkers in the clinical practice are related to the concepts of analytical validity, clinical validity, and clinical utility and to the question of whether they are able to diagnose AD without the support of AD clinical phenotypes. OBJECTIVE The objective of the present work is to expose the strengths and weaknesses of the use of CSF biomarkers in the diagnosis of AD in a clinical context. METHODS We used PubMed as main source for articles published and the final reference list was generated on the basis of relevance to the topics covered in this work. RESULTS The use of CSF biomarkers for AD diagnosis is certainly important but its indication in routine clinical practice, especially for prodromal conditions, needs to be regulated and also contextualized considering the variety of possible clinical AD phenotypes. CONCLUSION We suggest that the diagnosis of AD should be understood both as clinical and pathological.
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Affiliation(s)
- Maurizio Gallucci
- Cognitive Impairment Center, Local Health Authority n. 2 Marca Trevigiana, Treviso, Italy.,Associazione Alzheimer Treviso Onlus, Treviso, Italy
| | - Leandro Cenesi
- Cognitive Impairment Center, Local Health Authority n. 2 Marca Trevigiana, Treviso, Italy
| | - Céline White
- Cognitive Impairment Center, Local Health Authority n. 2 Marca Trevigiana, Treviso, Italy
| | - Piero Antuono
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Gianluca Quaglio
- Scientific Foresight Unit (STOA), European Parliamentary Research Service, European Parliament, Brussels, Belgium
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
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Schumacher-Schuh A, Bieger A, Borelli WV, Portley MK, Awad PS, Bandres-Ciga S. Advances in Proteomic and Metabolomic Profiling of Neurodegenerative Diseases. Front Neurol 2022; 12:792227. [PMID: 35173667 PMCID: PMC8841717 DOI: 10.3389/fneur.2021.792227] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 12/20/2021] [Indexed: 12/12/2022] Open
Abstract
Proteomics and metabolomics are two emerging fields that hold promise to shine light on the molecular mechanisms causing neurodegenerative diseases. Research in this area may reveal and quantify specific metabolites and proteins that can be targeted by therapeutic interventions intended at halting or reversing the neurodegenerative process. This review aims at providing a general overview on the current status of proteomic and metabolomic profiling in neurodegenerative diseases. We focus on the most common neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. We discuss the relevance of state-of-the-art metabolomics and proteomics approaches and their potential for biomarker discovery. We critically review advancements made so far, highlighting how metabolomics and proteomics may have a significant impact in future therapeutic and biomarker development. Finally, we further outline technologies used so far as well as challenges and limitations, placing the current information in a future-facing context.
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Affiliation(s)
- Artur Schumacher-Schuh
- Departamento de Farmacologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Serviço de Neurologia, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Andrei Bieger
- Department of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Wyllians V. Borelli
- Serviço de Neurologia, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Makayla K. Portley
- Neurodegenerative Disorders Clinic, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Paula Saffie Awad
- Movement Disorders Clinic, Centro de Trastornos de Movimiento (CETRAM), Santiago, Chile
| | - Sara Bandres-Ciga
- Neurodegenerative Disorders Clinic, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
- Laboratory of Neurogenetics, Molecular Genetics Section, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
- *Correspondence: Sara Bandres-Ciga
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38
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Jansson D, Wang M, Thomas RG, Erickson MA, Peskind ER, Li G, Iliff J. Markers of Cerebrovascular Injury, Inflammation, and Plasma Lipids Are Associated with Alzheimer's Disease Cerebrospinal Fluid Biomarkers in Cognitively Normal Persons. J Alzheimers Dis 2022; 86:813-826. [PMID: 35124650 PMCID: PMC10010435 DOI: 10.3233/jad-215400] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is a multifactorial process that takes years to manifest clinically. We propose that brain-derived indicators of cerebrovascular dysfunction and inflammation would inform on AD-related pathological processes early in, and perhaps prior to neurodegenerative disease development. OBJECTIVE Define the relationship between cerebrospinal fluid (CSF) markers of cerebrovascular dysfunction and neuroinflammation with AD CSF biomarkers in cognitively normal individuals. METHODS Analytes were measured from CSF and plasma collected at baseline from two randomized control trials. We performed Pearson correlation analysis (adjusting for age, sex, APOE haplotype, and education) between markers of central nervous system (CNS) barrier disruption, cerebrovascular dysfunction, CSF inflammatory cytokines and chemokines, and plasma lipid levels. We then developed a statistical prediction model using machine learning to test the ability of measured CSF analytes and blood lipid profiles to predict CSF AD biomarkers (total tau, phospho-tau (181), Aβ42) in this clinical population. RESULTS Our analysis revealed a significant association between markers of CNS barrier dysfunction and markers of cerebrovascular dysfunction, acute inflammatory responses, and CSF inflammatory cytokines. There was a significant association of blood lipid profiles with cerebrovascular injury markers, and CSF inflammatory cytokine levels. Using machine learning, we show that combinations of blood lipid profiles, CSF markers of CNS barrier disruption, cerebrovascular dysfunction and CSF inflammatory cytokines predict CSF total tau, p-tau, and, to a lesser extent, Aβ42 in cognitively normal subjects. CONCLUSION This suggests that these parallel pathological processes may contribute to the development of AD-related neuropathology in the absence of clinical manifestations.
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Affiliation(s)
- Deidre Jansson
- VA Northwest Mental Illness Research, Education, and Clinical Center (MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA.,Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, USA
| | - Marie Wang
- VA Northwest Mental Illness Research, Education, and Clinical Center (MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA.,Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, USA
| | - Ronald G Thomas
- Department of Family Medicine and Public Health, University of California, San Diego, San Diego, CA, USA
| | - Michelle A Erickson
- Geriatrics Research Education and Clinical Center (GRECC), VA Puget Sound Healthcare System, Seattle, WA, USA.,Division of Gerontology and Geriatric Medicine, Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Elaine R Peskind
- VA Northwest Mental Illness Research, Education, and Clinical Center (MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA.,Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, USA
| | - Ge Li
- VA Northwest Mental Illness Research, Education, and Clinical Center (MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA.,Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, USA.,Geriatrics Research Education and Clinical Center (GRECC), VA Puget Sound Healthcare System, Seattle, WA, USA
| | - Jeffrey Iliff
- VA Northwest Mental Illness Research, Education, and Clinical Center (MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA.,Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, USA.,Department of Neurology, University of Washington School of Medicine, Seattle, WA, USA
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Li Z, Jiang X, Wang Y, Kim Y. Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data. Emerg Top Life Sci 2021; 5:765-777. [PMID: 34881778 PMCID: PMC8786302 DOI: 10.1042/etls20210249] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/05/2021] [Accepted: 11/17/2021] [Indexed: 01/26/2023]
Abstract
Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.
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Affiliation(s)
- Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
| | - Xiaoqian Jiang
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, U.S.A
| | - Yizhuo Wang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
| | - Yejin Kim
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, U.S.A
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40
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Samieri C, Yassine HN, Melo van Lent D, Lefèvre-Arbogast S, van de Rest O, Bowman GL, Scarmeas N. Personalized nutrition for dementia prevention. Alzheimers Dement 2021; 18:1424-1437. [PMID: 34757699 DOI: 10.1002/alz.12486] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 08/26/2021] [Accepted: 08/30/2021] [Indexed: 12/17/2022]
Abstract
The role of nutrition has been investigated for decades under the assumption of one-size-fits-all. Yet there is heterogeneity in metabolic and neurobiological responses to diet. Thus a more personalized approach may better fit biological reality and have increased efficacy to prevent dementia. Personalized nutrition builds on the food exposome, defined as the history of diet-related exposures over the lifetime, and on its interactions with the genome and other biological characteristics (eg, metabolism, the microbiome) to shape health. We review current advances of personalized nutrition in dementia research. We discuss key questions, success milestones, and future roadmap from observational epidemiology to clinical studies through basic science. A personalized nutrition approach based on the best prescription for the most appropriate target population in the most relevant time-window has the potential to strengthen dementia-prevention efforts.
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Affiliation(s)
- Cécilia Samieri
- Univ. Bordeaux, ISPED, Inserm, Bordeaux Population Health Research Center, Bordeaux, France
| | - Hussein N Yassine
- Department of Medicine, Keck School of Medicine USC, Los Angeles, California, USA.,Department of Neurology, Keck School of Medicine USC, Los Angeles, California, USA
| | - Debora Melo van Lent
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA.,Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
| | | | - Ondine van de Rest
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, the Netherlands
| | - Gene L Bowman
- Department of Neurology and Layton Aging and Alzheimer's Disease Center, Oregon Health and Science University, Portland, Oregon, USA.,Helfgott Research Institute, National University of Natural Medicine, Portland, Oregon, USA
| | - Nikolaos Scarmeas
- 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece.,Taub Institute for Research in Alzheimer's Disease and the Aging Brain, The Gertrude H. Sergievsky Center, Department of Neurology, Columbia University, New York, New York, USA
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41
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Tan MS, Cheah PL, Chin AV, Looi LM, Chang SW. A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach. Comput Biol Med 2021; 139:104947. [PMID: 34678481 DOI: 10.1016/j.compbiomed.2021.104947] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/12/2021] [Accepted: 10/12/2021] [Indexed: 12/26/2022]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.
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Affiliation(s)
- Mei Sze Tan
- Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Phaik-Leng Cheah
- Department of Pathology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Ai-Vyrn Chin
- Division of Geriatric Medicine, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Lai-Meng Looi
- Department of Pathology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Siow-Wee Chang
- Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia.
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Picard M, Scott-Boyer MP, Bodein A, Périn O, Droit A. Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J 2021; 19:3735-3746. [PMID: 34285775 PMCID: PMC8258788 DOI: 10.1016/j.csbj.2021.06.030] [Citation(s) in RCA: 124] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/17/2021] [Accepted: 06/21/2021] [Indexed: 12/25/2022] Open
Abstract
Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems. Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.
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Affiliation(s)
- Milan Picard
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- Corresponding author.
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43
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La Cognata V, Morello G, Cavallaro S. Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases. Int J Mol Sci 2021; 22:ijms22094820. [PMID: 34062930 PMCID: PMC8125201 DOI: 10.3390/ijms22094820] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/23/2021] [Accepted: 04/29/2021] [Indexed: 12/17/2022] Open
Abstract
Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease management and the development of effective treatments. To this end, omics technologies-such as genomics, transcriptomics, proteomics and metabolomics-are contributing to offer a more comprehensive view of molecular pathways underlying the development of NDs, helping to differentiate subtypes of patients based on their specific molecular signatures. In this article, we discuss how omics technologies and their integration have provided new insights into the molecular heterogeneity underlying the most prevalent NDs, aiding to define early diagnosis and progression markers as well as therapeutic targets that can translate into stratified treatment approaches, bringing us closer to the goal of personalized medicine in neurology.
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