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Liang A, Zhao W, Lv T, Zhu Z, Haotian R, Zhang J, Xie B, Yi Y, Hao Z, Sun L, Luo A. Advances in novel biosensors in biomedical applications. Talanta 2024; 280:126709. [PMID: 39151317 DOI: 10.1016/j.talanta.2024.126709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 07/09/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024]
Abstract
Biosensors, devices capable of detecting biomolecules or bioactive substances, have recently become one of the important tools in the fields of bioanalysis and medical diagnostics. A biosensor is an analytical system composed of biosensitive elements and signal-processing elements used to detect various biological and chemical substances. Biomimetic elements are key to biosensor technology and are the components in a sensor that are responsible for identifying the target analyte. The construction methods and working principles of biosensors based on synthetic biomimetic elements, such as DNAzyme, molecular imprinted polymers and aptamers, and their updated applications in biomedical analysis are summarised. Finally, the technical bottlenecks and future development prospects for biomedical analysis are summarised and discussed.
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Affiliation(s)
- Axin Liang
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Weidong Zhao
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Tianjian Lv
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Ziyu Zhu
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Ruilin Haotian
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Jiangjiang Zhang
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Bingteng Xie
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Yue Yi
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Zikai Hao
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Liquan Sun
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Aiqin Luo
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China.
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Zhuang H, Cao X, Tang X, Zou Y, Yang H, Liang Z, Yan X, Chen X, Feng X, Shen L. Investigating metabolic dysregulation in serum of triple transgenic Alzheimer's disease male mice: implications for pathogenesis and potential biomarkers. Amino Acids 2024; 56:10. [PMID: 38315232 PMCID: PMC10844422 DOI: 10.1007/s00726-023-03375-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/11/2023] [Indexed: 02/07/2024]
Abstract
Alzheimer's disease (AD) is a multifactorial neurodegenerative disease that lacks convenient and accessible peripheral blood diagnostic markers and effective drugs. Metabolic dysfunction is one of AD risk factors, which leaded to alterations of various metabolites in the body. Pathological changes of the brain can be reflected in blood metabolites that are expected to explain the disease mechanisms or be candidate biomarkers. The aim of this study was to investigate the changes of targeted metabolites within peripheral blood of AD mouse model, with the purpose of exploring the disease mechanism and potential biomarkers. Targeted metabolomics was used to quantify 256 metabolites in serum of triple transgenic AD (3 × Tg-AD) male mice. Compared with controls, 49 differential metabolites represented dysregulation in purine, pyrimidine, tryptophan, cysteine and methionine and glycerophospholipid metabolism. Among them, adenosine, serotonin, N-acetyl-5-hydroxytryptamine, and acetylcholine play a key role in regulating neural transmitter network. The alteration of S-adenosine-L-homocysteine, S-adenosine-L-methionine, and trimethylamine-N-oxide in AD mice serum can served as indicator of AD risk. The results revealed the changes of metabolites in serum, suggesting that metabolic dysregulation in periphery in AD mice may be related to the disturbances in neuroinhibition, the serotonergic system, sleep function, the cholinergic system, and the gut microbiota. This study provides novel insights into the dysregulation of several key metabolites and metabolic pathways in AD, presenting potential avenues for future research and the development of peripheral biomarkers.
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Affiliation(s)
- Hongbin Zhuang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Xueshan Cao
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Xiaoxiao Tang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Yongdong Zou
- Center for Instrumental Analysis, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Hongbo Yang
- Center for Instrumental Analysis, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Zhiyuan Liang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Xi Yan
- The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, 550025, People's Republic of China
| | - Xiaolu Chen
- The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, 550025, People's Republic of China
| | - Xingui Feng
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Liming Shen
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China.
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, People's Republic of China.
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Bhalala OG, Watson R, Yassi N. Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer's Disease. Int J Mol Sci 2024; 25:1231. [PMID: 38279230 PMCID: PMC10816901 DOI: 10.3390/ijms25021231] [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: 12/07/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 01/28/2024] Open
Abstract
Late-onset Alzheimer's disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer's disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer's disease at various timepoints within the disease course, including at the preclinical stage. In this review, we discuss recent advances in such blood biomarkers for determining disease risk. We highlight how leveraging polygenic risk scores, based on genome-wide association studies, can help stratify individuals along their risk profile. We summarize studies analyzing protein biomarkers, as well as report on recent proteomic- and metabolomic-based prediction models. Finally, we discuss how a combination of multi-omic blood biomarkers can potentially be used in memory clinics for diagnosis and to assess the dynamic risk an individual has for developing Alzheimer's disease dementia.
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Affiliation(s)
- Oneil G. Bhalala
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
| | - Rosie Watson
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
| | - Nawaf Yassi
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
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Nie Y, Chu C, Qin Q, Shen H, Wen L, Tang Y, Qu M. Lipid metabolism and oxidative stress in patients with Alzheimer's disease and amnestic mild cognitive impairment. Brain Pathol 2024; 34:e13202. [PMID: 37619589 PMCID: PMC10711261 DOI: 10.1111/bpa.13202] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 07/26/2023] [Indexed: 08/26/2023] Open
Abstract
Lipid metabolism and oxidative stress are key mechanisms in Alzheimer's disease (AD). The link between plasma lipid metabolites and oxidative stress in AD patients is poorly understood. This study was to identify markers that distinguish AD and amnestic mild cognitive impairment (aMCI) from NC, and to reveal potential links between lipid metabolites and oxidative stress. We performed non-targeted lipid metabolism analysis of plasma from patients with AD, aMCI, and NC using LC-MS/MS. The plasma malondialdehyde (MDA), glutathione peroxidase (GSH-Px), and superoxide dismutase (SOD) levels were assessed. We found significant differences in lipid metabolism between patients with AD and aMCI compared to those in NC. AD severity is associated with lipid metabolites, especially TG (18:0_16:0_18:0) + NH4, TG (18:0_16:0_16:0) + NH4, LPC(16:1e)-CH3, and PE (20:0_20:4)-H. SPH (d16:0) + H, SPH (d18:1) + H, and SPH (d18:0) + H were high-performance markers to distinguish AD and aMCI from NC. The AUC of three SPHs combined to predict AD was 0.990, with specificity and sensitivity as 0.949 and 1, respectively; the AUC of three SPHs combined to predict aMCI was 0.934, with specificity and sensitivity as 0.900, 0.981, respectively. Plasma MDA concentrations were higher in the AD group than in the NC group (p = 0.003), whereas plasma SOD levels were lower in the AD (p < 0.001) and aMCI (p = 0.045) groups than in NC, and GSH-Px activity were higher in the AD group than in the aMCI group (p = 0.007). In addition, lipid metabolites and oxidative stress are widely associated. In conclusion, this study distinguished serum lipid metabolism in AD, aMCI, and NC subjects, highlighting that the three SPHs can distinguish AD and aMCI from NC. Additionally, AD patients showed elevated oxidative stress, and there are complex interactions between lipid metabolites and oxidative stress.
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Affiliation(s)
- Yuting Nie
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Changbiao Chu
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Qi Qin
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Huixin Shen
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Lulu Wen
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Yi Tang
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Miao Qu
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
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5
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Bittremieux W, Avalon NE, Thomas SP, Kakhkhorov SA, Aksenov AA, Gomes PWP, Aceves CM, Caraballo-Rodríguez AM, Gauglitz JM, Gerwick WH, Huan T, Jarmusch AK, Kaddurah-Daouk RF, Kang KB, Kim HW, Kondić T, Mannochio-Russo H, Meehan MJ, Melnik AV, Nothias LF, O'Donovan C, Panitchpakdi M, Petras D, Schmid R, Schymanski EL, van der Hooft JJJ, Weldon KC, Yang H, Xing S, Zemlin J, Wang M, Dorrestein PC. Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics. Nat Commun 2023; 14:8488. [PMID: 38123557 PMCID: PMC10733301 DOI: 10.1038/s41467-023-44035-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023] Open
Abstract
Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.
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Affiliation(s)
- Wout Bittremieux
- Department of Computer Science, University of Antwerp, 2020, Antwerpen, Belgium.
| | - Nicole E Avalon
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, 92093, USA
| | - Sydney P Thomas
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Sarvar A Kakhkhorov
- Laboratory of Physical and Chemical Methods of Research, Center for Advanced Technologies, Tashkent, 100174, Uzbekistan
- Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958, Frederiksberg C, Denmark
| | - Alexander A Aksenov
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Chemistry, University of Connecticut, Storrs, CT, 06269, USA
- Arome Science inc., Farmington, CT, 06032, USA
| | - Paulo Wender P Gomes
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Christine M Aceves
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Andrés Mauricio Caraballo-Rodríguez
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Julia M Gauglitz
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - William H Gerwick
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Tao Huan
- Department of Chemistry, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada
| | - Alan K Jarmusch
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Immunity, Inflammation, and Disease Laboratory, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Durham, NC, 27709, USA
| | - Rima F Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, 27701, USA
- Department of Medicine, Duke University, Durham, NC, 27710, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, 27710, USA
| | - Kyo Bin Kang
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Sookmyung Women's University, Seoul, 04310, Korea
| | - Hyun Woo Kim
- College of Pharmacy and Integrated Research Institute for Drug Development, Dongguk University, Goyang, 10326, Korea
| | - Todor Kondić
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg
| | - Helena Mannochio-Russo
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Biochemistry and Organic Chemistry, Institute of Chemistry, São Paulo State University, Araraquara, 14800-901, Brazil
| | - Michael J Meehan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Alexey V Melnik
- Department of Chemistry, University of Connecticut, Storrs, CT, 06269, USA
- Arome Science inc., Farmington, CT, 06032, USA
| | - Louis-Felix Nothias
- Université Côte d'Azur, CNRS, ICN, Nice, France
- Interdisciplinary Institute for Artificial Intelligence (3iA) Côte d'Azur, Nice, France
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Morgan Panitchpakdi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Daniel Petras
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tuebingen, 72076, Tuebingen, Germany
- Department of Biochemistry, University of California Riverside, Riverside, CA, 92507, USA
| | - Robin Schmid
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg
| | - Justin J J van der Hooft
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Bioinformatics Group, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands
| | - Kelly C Weldon
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Heejung Yang
- Laboratory of Natural Products Chemistry, College of Pharmacy, Kangwon National University, Chuncheon, 24341, Korea
| | - Shipei Xing
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Chemistry, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada
| | - Jasmine Zemlin
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Mingxun Wang
- Department of Computer Science and Engineering, University of California Riverside, Riverside, CA, 92507, USA
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA.
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA.
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Hou Y, Mishra R, Zhao Y, Ducrée J, Harrison JD. An Automated Centrifugal Microfluidic Platform for Efficient Multistep Blood Sample Preparation and Clean-Up towards Small Ion-Molecule Analysis. MICROMACHINES 2023; 14:2257. [PMID: 38138426 PMCID: PMC10745919 DOI: 10.3390/mi14122257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/14/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023]
Abstract
Sample preparation for mass spectroscopy typically involves several liquid and solid phase clean-ups, extractions, and other unit operations, which are labour-intensive and error-prone. We demonstrate a centrifugal microfluidic platform that automates the whole blood sample's preparation and clean-up by combining traditional liquid-phase and multiple solid-phase extractions for applications in mass spectroscopy (MS)-based small molecule detection. Liquid phase extraction was performed using methanol to precipitate proteins in plasma separated from a blood sample under centrifugal force. The preloaded solid phase composed of C18 beads then removed lipids with a combination of silica particles, which further cleaned up any remaining proteins. We further integrated the application of this sample prep disc with matrix-assisted laser desorption/ionization (MALDI) MS by using glancing angle deposition films, which further cleaned up the processed sample by segregating the electrolyte background from the sample salts. Additionally, hydrophilic interaction liquid chromatography (HILIC) MS was employed for detecting targeted free amino acids. Therefore, several representative ionic metabolites, including several amino acids and organic acids from blood samples, were analysed by both MALDI-MS and HILIC-MS to demonstrate the performance of this sample preparation disc. The fully automated blood sample preparation procedure only took 35 mins, with a throughput of three parallel units.
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Affiliation(s)
- Yuting Hou
- Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2, Canada; (Y.Z.); (J.D.H.)
| | - Rohit Mishra
- FPC@DCU—Fraunhofer Project Centre for Embedded Bioanalytical Systems, Dublin City University, D09 V209 Dublin, Ireland
- School of Physical Sciences, Dublin City University, D09 V209 Dublin, Ireland;
| | - Yufeng Zhao
- Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2, Canada; (Y.Z.); (J.D.H.)
- Centre for Research and Applications in Fluidic Technologies, National Research Council Canada, Toronto, ON M5S 3G8, Canada
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
| | - Jens Ducrée
- School of Physical Sciences, Dublin City University, D09 V209 Dublin, Ireland;
| | - Jed D. Harrison
- Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2, Canada; (Y.Z.); (J.D.H.)
- FPC@DCU—Fraunhofer Project Centre for Embedded Bioanalytical Systems, Dublin City University, D09 V209 Dublin, Ireland
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7
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Jiao B, Zhang S, Bei Y, Bu G, Yuan L, Zhu Y, Yang Q, Xu T, Zhou L, Liu Q, Ouyang Z, Yang X, Feng Y, Tang B, Chen H, Shen L. A detection model for cognitive dysfunction based on volatile organic compounds from a large Chinese community cohort. Alzheimers Dement 2023; 19:4852-4862. [PMID: 37032600 DOI: 10.1002/alz.13053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 04/11/2023]
Abstract
INTRODUCTION We explored whether volatile organic compound (VOC) detection can serve as a screening tool to distinguish cognitive dysfunction (CD) from cognitively normal (CN) individuals. METHODS The cognitive function of 1467 participants was assessed and their VOCs were detected. Six machine learning algorithms were conducted and the performance was determined. The plasma neurofilament light chain (NfL) was measured. RESULTS Distinguished VOC patterns existed between CD and CN groups. The CD detection model showed good accuracy with an area under the receiver-operating characteristic curve (AUC) of 0.876. In addition, we found that 10 VOC ions showed significant differences between CD and CN individuals (p < 0.05); three VOCs were significantly related to plasma NfL (p < 0.005). Moreover, a combination of VOCs with NfL showed the best discriminating power (AUC = 0.877). DISCUSSION Detection of VOCs from exhaled breath samples has the potential to provide a novel solution for the dilemma of CD screening.
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Affiliation(s)
- Bin Jiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Sizhe Zhang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yuzhang Bei
- Department of Neurology, Liuyang Jili Hospital, Changsha, China
| | - Guiwen Bu
- Department of Neurology, Liuyang Jili Hospital, Changsha, China
| | - Li Yuan
- Department of Neurology, Liuyang Jili Hospital, Changsha, China
| | - Yuan Zhu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Qijie Yang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Tianyan Xu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Lu Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Qianqian Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ziyu Ouyang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xuan Yang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yong Feng
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, China
| | - Beisha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Haibin Chen
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, China
| | - Lu Shen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, China
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Chang CW, Hsu JY, Hsiao PZ, Chen YC, Liao PC. Identifying Hair Biomarker Candidates for Alzheimer's Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:550-561. [PMID: 36973238 DOI: 10.1021/jasms.2c00294] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
High-resolution mass spectrometry (HRMS)-based untargeted metabolomics strategies have emerged as an effective tool for discovering biomarkers of Alzheimer's disease (AD). There are various HRMS-based untargeted metabolomics strategies for biomarker discovery, including the data-dependent acquisition (DDA) method, the combination of full scan and target MS/MS, and the all ion fragmentation (AIF) method. Hair has emerged as a potential biospecimen for biomarker discovery in clinical research since it might reflect the circulating metabolic profiles over several months, while the analytical performances of the different data acquisition methods for hair biomarker discovery have been rarely investigated. Here, the analytical performances of three data acquisition methods in HRMS-based untargeted metabolomics for hair biomarker discovery were evaluated. The human hair samples from AD patients (N = 23) and cognitively normal individuals (N = 23) were used as an example. The most significant number of discriminatory features was acquired using the full scan (407), which is approximately 10-fold higher than that using the DDA strategy (41) and 11% higher than that using the AIF strategy (366). Only 66% of discriminatory chemicals discovered in the DDA strategy were discriminatory features in the full scan dataset. Moreover, compared to the deconvoluted MS/MS spectra with coeluted and background ions from the AIF method, the MS/MS spectrum obtained from the targeted MS/MS approach is cleaner and purer. Therefore, an untargeted metabolomics strategy combining the full scan with the targeted MS/MS method could obtain most discriminatory features along with a high quality MS/MS spectrum for discovering the AD biomarkers.
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Affiliation(s)
- Chih-Wei Chang
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan 704, Taiwan
| | - Jen-Yi Hsu
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan 704, Taiwan
| | - Ping-Zu Hsiao
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan 704, Taiwan
| | - Yuan-Chih Chen
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan 704, Taiwan
| | - Pao-Chi Liao
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan 704, Taiwan
- Department of Food Safety/Hygiene and Risk Management, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan 704, Taiwan
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9
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Qian XH, Liu XL, Zhang B, Lin Y, Xu JH, Ding GY, Tang HD. Investigating the causal association between branched-chain amino acids and Alzheimer's disease: A bidirectional Mendelian randomized study. Front Nutr 2023; 10:1103303. [PMID: 37063328 PMCID: PMC10102518 DOI: 10.3389/fnut.2023.1103303] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 03/06/2023] [Indexed: 04/03/2023] Open
Abstract
BackgroundThere are many metabolic pathway abnormalities in Alzheimer's disease (AD). Several studies have linked branched-chain amino acid (BCAA) metabolism disorders with AD but have not obtained consistent results. The purpose of this study is to explore the causal association between BCAA concentration and the risk of AD.MethodsA bidirectional Mendelian randomized (MR) study was applied to explore the causal effect between BCAA level and the risk of AD. Genetic instrumental variables from the genome-wide association study (GWAS) of serum BCAA levels [total BCAAs (115,047 participants), valine (115,048 participants), leucine (115,074 participants), and isoleucine (115,075 participants)] from the UK Biobank and AD (21,982 AD cases and 41,944 controls) from the International Genomics of Alzheimer's Project were applied to explore the causal effect through the inverse variance-weighted (IVW) method, MR-Egger, and weighted median, accompanied by multiple pluripotency and heterogeneity tests.ResultsThe forward MR analysis showed that there was no causal effect of total BCAAs (OR: 1.067, 95% CI: 0.838–1.358; p = 0.838), valine (OR: 1.106, 95% CI: 0.917–1.333; p = 0.292), leucine (OR: 1.096, 95% CI: 0.861–1.396; p = 0.659), and isoleucine (OR: 1.457, 95% CI: 1.024–2.742; p = 0.037) levels on the risk of AD. The reverse analysis showed that AD was related to reduced levels of total BCAAs (OR: 0.979, 95% CI: 0.989–0.990; p < 0.001), valine (OR: 0.977, 95% CI: 0.963–0.991; p = 0.001), leucine (OR: 0.983, 95% CI: 0.973–0.994; p = 0.002), and isoleucine (OR: 0.982, 95% CI: 0.971–0.992; p = 0.001).ConclusionWe provide robust evidence that AD was associated with a decreased level of BCAAs, which can serve as a marker for early diagnosis of AD.
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Affiliation(s)
- Xiao-hang Qian
- Department of Geriatrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medical Center on Aging of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao-li Liu
- Department of Neurology, Shanghai University of Medicine and Health Sciences Affiliated Sixth People's Hospital South Campus, Shanghai, China
| | - Bin Zhang
- Department of Neurology, Shanghai University of Medicine and Health Sciences Affiliated Sixth People's Hospital South Campus, Shanghai, China
| | - Yuan Lin
- Department of Gastroenterology, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Jian-hua Xu
- Department of Neurology, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Gang-yu Ding
- Department of Neurology, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China
- *Correspondence: Gang-yu Ding
| | - Hui-dong Tang
- Department of Geriatrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medical Center on Aging of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Hui-dong Tang
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10
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Su YH, Chang CW, Hsu JY, Li SW, Sung PS, Wang RH, Wu CH, Liao PC. Discovering Hair Biomarkers of Alzheimer's Disease Using High Resolution Mass Spectrometry-Based Untargeted Metabolomics. Molecules 2023; 28:molecules28052166. [PMID: 36903413 PMCID: PMC10004788 DOI: 10.3390/molecules28052166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/20/2023] [Accepted: 02/23/2023] [Indexed: 03/02/2023] Open
Abstract
Hair may be a potential biospecimen to discover biomarkers for Alzheimer's disease (AD) since it reflects the integral metabolic profiles of body burden over several months. Here, we described the AD biomarker discovery in the hair using a high-resolution mass spectrometry (HRMS)-based untargeted metabolomics approach. A total of 24 patients with AD and 24 age- and sex-matched cognitively healthy controls were recruited. The hair samples were collected 0.1-cm away from the scalp and further cut into 3-cm segments. Hair metabolites were extracted by ultrasonication with methanol/phosphate-buffered saline 50/50 (v/v) for 4 h. A total of 25 discriminatory chemicals in hair between the patients with AD and controls were discovered and identified. The AUC value achieved 0.85 (95% CI: 0.72~0.97) in patients with very mild AD compared to healthy controls using a composite panel of the 9 biomarker candidates, indicating high potential for the initiation or promotion phase of AD dementia in the early stage. A metabolic panel combined with the nine metabolites may be used as biomarkers for the early detection of AD. The hair metabolome can be used to reveal metabolic perturbations for biomarker discovery. Investigating perturbations of the metabolites will offer insight into the pathogenesis of AD.
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Affiliation(s)
- Yu-Hsiang Su
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City 60002, Taiwan
| | - Chih-Wei Chang
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Jen-Yi Hsu
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Shih-Wen Li
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Pi-Shan Sung
- Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Ru-Hsueh Wang
- Department of Family Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Chih-Hsing Wu
- Department of Family Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
| | - Pao-Chi Liao
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Food Safety/Hygiene and Risk Management, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
- Correspondence:
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11
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Zhang F, Rakhimbekova A, Lashley T, Madl T. Brain regions show different metabolic and protein arginine methylation phenotypes in frontotemporal dementias and Alzheimer's disease. Prog Neurobiol 2023; 221:102400. [PMID: 36581185 DOI: 10.1016/j.pneurobio.2022.102400] [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: 08/06/2022] [Revised: 11/05/2022] [Accepted: 12/19/2022] [Indexed: 12/27/2022]
Abstract
Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disease with multiple histopathological subtypes. FTD patients share similar symptoms with Alzheimer's disease (AD). Hence, FTD patients are commonly misdiagnosed as AD, despite the consensus clinical diagnostic criteria. It is therefore of great clinical need to identify a biomarker that can distinguish FTD from AD and control individuals, and potentially further differentiate between FTD pathological subtypes. We conducted a metabolomic analysis on post-mortem human brain tissue from three regions: cerebellum, frontal cortex and occipital cortex from control, FTLD-TDP type A, type A-C9, type C and AD. Our results indicate that the brain subdivisions responsible for different functions show different metabolic patterns. We further explored the region-specific metabolic characteristics of different FTD subtypes and AD patients. Different FTD subtypes and AD share similar metabolic phenotypes in the cerebellum, but AD exhibited distinct metabolic patterns in the frontal and occipital regions compared to FTD. The identified brain region-specific metabolite biomarkers could provide a tool for distinguishing different FTD subtypes and AD and provide the first insights into the metabolic changes of FTLD-TDP type A, type A-C9, type C and AD in different regions of the brain. The importance of protein arginine methylation in neurodegenerative disease has come to light, so we investigated whether the arginine methylation level contributes to disease pathogenesis. Our findings provide new insights into the relationship between arginine methylation and metabolic changes in FTD subtypes and AD that could be further explored, to study the molecular mechanism of pathogenesis.
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Affiliation(s)
- Fangrong Zhang
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, China; Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Research Unit Integrative Structural Biology, Medical University of Graz, 8010 Graz, Austria.
| | - Anastasia Rakhimbekova
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Research Unit Integrative Structural Biology, Medical University of Graz, 8010 Graz, Austria.
| | - Tammaryn Lashley
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK; Queen Square Brain Bank for Neurological Diseases, UCL Queen Square Institute of Neurology, London, UK.
| | - Tobias Madl
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Research Unit Integrative Structural Biology, Medical University of Graz, 8010 Graz, Austria; BioTechMed-Graz, 8010 Graz, Austria.
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12
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Blusztajn JK, Slack BE. Accelerated Breakdown of Phosphatidylcholine and Phosphatidylethanolamine Is a Predominant Brain Metabolic Defect in Alzheimer's Disease. J Alzheimers Dis 2023; 93:1285-1289. [PMID: 37182883 PMCID: PMC10885637 DOI: 10.3233/jad-230061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Numerous studies have demonstrated defects in multiple metabolic pathways in Alzheimer's disease (AD), detected in autopsy brains and in the cerebrospinal fluid in vivo. However, until the advent of techniques capable of measuring thousands of metabolites in a single sample, it has not been possible to rank the relative magnitude of these abnormalities. A recent study provides evidence that the abnormal turnover of the brain's most abundant phospholipids: phosphatidylcholine and phosphatidylethanolamine, constitutes a major metabolic pathology in AD. We place this observation in a historical context and discuss the implications of a central role for phospholipid metabolism in AD pathogenesis.
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Affiliation(s)
- Jan Krzysztof Blusztajn
- Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Barbara E Slack
- Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
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13
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Zacharias HU, Kaleta C, Cossais F, Schaeffer E, Berndt H, Best L, Dost T, Glüsing S, Groussin M, Poyet M, Heinzel S, Bang C, Siebert L, Demetrowitsch T, Leypoldt F, Adelung R, Bartsch T, Bosy-Westphal A, Schwarz K, Berg D. Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets. Metabolites 2022; 12:metabo12121222. [PMID: 36557259 PMCID: PMC9786685 DOI: 10.3390/metabo12121222] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.
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Affiliation(s)
- Helena U. Zacharias
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 30625 Hannover, Germany
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Correspondence: (H.U.Z.); (C.K.)
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Correspondence: (H.U.Z.); (C.K.)
| | | | - Eva Schaeffer
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Henry Berndt
- Research Group Comparative Immunobiology, Zoological Institute, Kiel University, 24118 Kiel, Germany
| | - Lena Best
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
| | - Thomas Dost
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
| | - Svea Glüsing
- Institute of Human Nutrition and Food Science, Food Technology, Kiel University, 24118 Kiel, Germany
| | - Mathieu Groussin
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Mathilde Poyet
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sebastian Heinzel
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Medical Informatics and Statistics, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Corinna Bang
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Leonard Siebert
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Functional Nanomaterials, Department of Materials Science, Kiel University, 24143 Kiel, Germany
| | - Tobias Demetrowitsch
- Institute of Human Nutrition and Food Science, Food Technology, Kiel University, 24118 Kiel, Germany
- Kiel Network of Analytical Spectroscopy and Mass Spectrometry, Kiel University, 24118 Kiel, Germany
| | - Frank Leypoldt
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Neuroimmunology, Institute of Clinical Chemistry, University Medical Center Schleswig-Holstein, 24105 Kiel, Germany
| | - Rainer Adelung
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Functional Nanomaterials, Department of Materials Science, Kiel University, 24143 Kiel, Germany
| | - Thorsten Bartsch
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Anja Bosy-Westphal
- Institute of Human Nutrition and Food Science, Kiel University, 24107 Kiel, Germany
| | - Karin Schwarz
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Institute of Human Nutrition and Food Science, Food Technology, Kiel University, 24118 Kiel, Germany
- Kiel Network of Analytical Spectroscopy and Mass Spectrometry, Kiel University, 24118 Kiel, Germany
| | - Daniela Berg
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
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14
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Yamada Y, Kobayashi M, Shinkawa K, Nemoto M, Ota M, Nemoto K, Arai T. Characteristics of Drawing Process Differentiate Alzheimer’s Disease and Dementia with Lewy Bodies. J Alzheimers Dis 2022; 90:693-704. [DOI: 10.3233/jad-220546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Early differential diagnosis of Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) is important for treatment and disease management, but it remains challenging. Although computer-based drawing analysis may help differentiate AD and DLB, it has not been extensively studied. Objective: We aimed to identify the differences in features characterizing the drawing process between AD, DLB, and cognitively normal (CN) individuals, and to evaluate the validity of using these features to identify and differentiate AD and DLB. Methods: We collected drawing data with a digitizing tablet and pen from 123 community-dwelling older adults in three clinical diagnostic groups of mild cognitive impairment or dementia due to AD (n = 47) or Lewy body disease (LBD; n = 27), and CN (n = 49), matched for their age, sex, and years of education. We then investigated drawing features in terms of the drawing speed, pressure, and pauses. Results: Reduced speed and reduced smoothness in speed and pressure were observed particularly in the LBD group, while increased pauses and total durations were observed in both the AD and LBD groups. Machine-learning models using these features achieved an area under the receiver operating characteristic curve (AUC) of 0.80 for AD versus CN, 0.88 for LBD versus CN, and 0.77 for AD versus LBD. Conclusion: Our results indicate how different types of drawing features were particularly discriminative between the diagnostic groups, and how the combination of these features can facilitate the identification and differentiation of AD and DLB.
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Affiliation(s)
| | | | | | - Miyuki Nemoto
- Department of Psychiatry, Division of Clinical Medicine, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Miho Ota
- Department of Psychiatry, Division of Clinical Medicine, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Kiyotaka Nemoto
- Department of Psychiatry, Division of Clinical Medicine, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Tetsuaki Arai
- Department of Psychiatry, Division of Clinical Medicine, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
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15
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Kadyrov M, Whiley L, Brown B, Erickson KI, Holmes E. Associations of the Lipidome with Ageing, Cognitive Decline and Exercise Behaviours. Metabolites 2022; 12:metabo12090822. [PMID: 36144226 PMCID: PMC9505967 DOI: 10.3390/metabo12090822] [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: 08/01/2022] [Revised: 08/22/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
One of the most recognisable features of ageing is a decline in brain health and cognitive dysfunction, which is associated with perturbations to regular lipid homeostasis. Although ageing is the largest risk factor for several neurodegenerative diseases such as dementia, a loss in cognitive function is commonly observed in adults over the age of 65. Despite the prevalence of normal age-related cognitive decline, there is a lack of effective methods to improve the health of the ageing brain. In light of this, exercise has shown promise for positively influencing neurocognitive health and associated lipid profiles. This review summarises age-related changes in several lipid classes that are found in the brain, including fatty acyls, glycerolipids, phospholipids, sphingolipids and sterols, and explores the consequences of age-associated pathological cognitive decline on these lipid classes. Evidence of the positive effects of exercise on the affected lipid profiles are also discussed to highlight the potential for exercise to be used therapeutically to mitigate age-related changes to lipid metabolism and prevent cognitive decline in later life.
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Affiliation(s)
- Maria Kadyrov
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Discipline of Exercise Science, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
- Correspondence: (M.K.); (B.B.); (E.H.)
| | - Luke Whiley
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, WA 6009, Australia
| | - Belinda Brown
- Discipline of Exercise Science, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
- School of Medical Sciences, Sarich Neuroscience Research Institute, Edith Cowan University, Nedlands, WA 6009, Australia
- Correspondence: (M.K.); (B.B.); (E.H.)
| | - Kirk I. Erickson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA
- AdventHealth Research Institute, Neuroscience Institute, Orlando, FL 32804, USA
- PROFITH “PROmoting FITness and Health Through Physical Activity” Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, 18071 Granada, Spain
| | - Elaine Holmes
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Division of Integrative Systems and Digestive Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
- Correspondence: (M.K.); (B.B.); (E.H.)
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