bile acids; cheminformatics; exposomics; high-resolution mass spectrometry; liquid chromatography; metabolomics; tau Proteins; Amyloid beta-Peptides; Biomarkers; Humans; tau Proteins/cerebrospinal fluid; Amyloid beta-Peptides/cerebrospinal fluid; Pilot Projects; Disease Progression; Alzheimer Disease/cerebrospinal fluid; Alzheimer Disease/diagnosis; Alzheimer Disease/psychology; Neurodegenerative Diseases; Cognitive Dysfunction/cerebrospinal fluid; Cognitive Dysfunction/diagnosis; Cognitive Dysfunction/psychology; Alzheimers disease; Bile acid; Clinical symptoms; Cognitive impairment; Exposomic; High resolution mass spectrometry; Small molecules; Alzheimer Disease; Cognitive Dysfunction; Chemistry (all); Environmental Chemistry
Résumé :
[en] Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disease, which is currently diagnosed via clinical symptoms and nonspecific biomarkers (such as Aβ1-42, t-Tau, and p-Tau) measured in cerebrospinal fluid (CSF), which alone do not provide sufficient insights into disease progression. In this pilot study, these biomarkers were complemented with small-molecule analysis using non-target high-resolution mass spectrometry coupled with liquid chromatography (LC) on the CSF of three groups: AD, mild cognitive impairment (MCI) due to AD, and a non-demented (ND) control group. An open-source cheminformatics pipeline based on MS-DIAL and patRoon was enhanced using CSF- and AD-specific suspect lists to assist in data interpretation. Chemical Similarity Enrichment Analysis revealed a significant increase of hydroxybutyrates in AD, including 3-hydroxybutanoic acid, which was found at higher levels in AD compared to MCI and ND. Furthermore, a highly sensitive target LC-MS method was used to quantify 35 bile acids (BAs) in the CSF, revealing several statistically significant differences including higher dehydrolithocholic acid levels and decreased conjugated BA levels in AD. This work provides several promising small-molecule hypotheses that could be used to help track the progression of AD in CSF samples.
Disciplines :
Sciences du vivant: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
TALAVERA ANDUJAR, Begona ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Environmental Cheminformatics
MARY, Arnaud ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Neuroinflammation Group
VENEGAS MALDONADO, Carmen Jesica ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine > Molecular and Functional Neurobiology > Team Anne GRÜNEWALD
Cheng, Tiejun; National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
Zaslavsky, Leonid; National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
Bolton, Evan E ; National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
HENEKA, Michael ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Fonds National de la Recherche Luxembourg U.S. Department of Health and Human Services
Subventionnement (détails) :
BTA is part of the “Microbiomes in One Health” PhD training program, which is supported by the PRIDE doctoral research funding scheme (PRIDE/11823097) of the Luxembourg National Research Fund (FNR). CV was funded by an FNR CORE Junior Grant (“NeuroFlame”, C20/BM/14548100). The work of EEB, TC, and LZ was supported by the National Center for Biotechnology Information of the National Library of Medicine (NLM), National Institutes of Health. ELS acknowledges funding support from the Luxembourg National Research Fund (FNR) for project A18/BM/12341006, and MTH acknowledges funding support from the FNR within the PEARL program (FNR/16745220).BTA acknowledges support from Gianfranco Frigerio during sample preparation and advice from Corey Griffith and Lorenzo Favilli during data processing/interpretation. Katyeny Manuela Da Silva is acknowledged for her inputs during the manuscript revision. We thank the Metabolomics Platform of the LCSB for their support with the LC-HRMS analysis and other Environmental Cheminformatics and PubChem team members who contributed to this work indirectly via other collaborative and scientific activities. The target LC-MS BA analysis was performed by the Genome BC Proteomics Centre of the University of Victoria (Canada).BTA acknowledges support from Gianfranco Frigerio during sample preparation and advice from Corey Griffith and Lorenzo Favilli during data processing/interpretation. Katyeny Manuela Da Silva is acknowledged for her inputs during the manuscript revision. We thank the Metabolomics Platform of the LCSB for their support with the LC-HRMS analysis and other Environmental Cheminformatics and PubChem team members who contributed to this work indirectly via other collaborative and scientific activities. The target LC–MS BA analysis was performed by the Genome BC Proteomics Centre of the University of Victoria (Canada).
Knopman, D. S.; Amieva, H.; Petersen, R. C.; Chételat, G.; Holtzman, D. M.; Hyman, B. T.; Nixon, R. A.; Jones, D. T. Alzheimer Disease. Nat. Rev. Dis Primers 2021, 7 ( 1), 33, 10.1038/s41572-021-00269-y
Reveglia, P.; Paolillo, C.; Ferretti, G.; De Carlo, A.; Angiolillo, A.; Nasso, R.; Caputo, M.; Matrone, C.; Di Costanzo, A.; Corso, G. Challenges in LC-MS-Based Metabolomics for Alzheimer’s Disease Early Detection: Targeted Approaches versus Untargeted Approaches. Metabolomics 2021, 17 ( 9), 78, 10.1007/s11306-021-01828-w
Vermunt, L.; Sikkes, S. A. M.; van den Hout, A.; Handels, R.; Bos, I.; van der Flier, W. M.; Kern, S.; Ousset, P.-J.; Maruff, P.; Skoog, I.; Verhey, F. R. J.; Freund-Levi, Y.; Tsolaki, M.; Wallin, Å. K.; Olde Rikkert, M.; Soininen, H.; Spiru, L.; Zetterberg, H.; Blennow, K.; Scheltens, P.; Muniz-Terrera, G.; Visser, P. J.; Alzheimer Disease Neuroimaging Initiative; AIBL Research Group; ICTUS/DSA study groups Duration of preclinical, prodromal, and dementia stages of Alzheimer’s disease in relation to age, sex, and APOE genotype. Alzheimers Dement. 2019, 15 ( 7), 888- 898, 10.1016/j.jalz.2019.04.001
Mielke, M. M.; Syrjanen, J. A.; Blennow, K.; Zetterberg, H.; Vemuri, P.; Skoog, I.; Machulda, M. M.; Kremers, W. K.; Knopman, D. S.; Jack, C.; Petersen, R. C.; Kern, S. Plasma and CSF Neurofilament Light. Neurology 2019, 93 ( 3), e252- e260, 10.1212/WNL.0000000000007767
Gaetani, L.; Blennow, K.; Calabresi, P.; Di Filippo, M.; Parnetti, L.; Zetterberg, H. Neurofilament Light Chain as a Biomarker in Neurological Disorders. J. Neurol., Neurosurg. Psychiatry 2019, 90 ( 8), 870- 881, 10.1136/jnnp-2018-320106
Dhiman, K.; Blennow, K.; Zetterberg, H.; Martins, R. N.; Gupta, V. B. Cerebrospinal Fluid Biomarkers for Understanding Multiple Aspects of Alzheimer’s Disease Pathogenesis. Cell. Mol. Life Sci. 2019, 76 ( 10), 1833- 1863, 10.1007/s00018-019-03040-5
Giridharan, V. V.; Barichello De Quevedo, C. E.; Petronilho, F. Microbiota-Gut-Brain Axis in the Alzheimer’s Disease Pathology - an Overview. Neurosci. Res. 2022, 181, 17- 21, 10.1016/j.neures.2022.05.003
Yan, J.; Kuzhiumparambil, U.; Bandodkar, S.; Dale, R. C.; Fu, S. Cerebrospinal Fluid Metabolomics: Detection of Neuroinflammation in Human Central Nervous System Disease. Clin. Transl. Immunol. 2021, 10 ( 8), e1318 10.1002/cti2.1318
Lippa, K. A.; Aristizabal-Henao, J. J.; Beger, R. D.; Bowden, J. A.; Broeckling, C.; Beecher, C.; Clay Davis, W.; Dunn, W. B.; Flores, R.; Goodacre, R.; Gouveia, G. J.; Harms, A. C.; Hartung, T.; Jones, C. M.; Lewis, M. R.; Ntai, I.; Percy, A. J.; Raftery, D.; Schock, T. B.; Sun, J.; Theodoridis, G.; Tayyari, F.; Torta, F.; Ulmer, C. Z.; Wilson, I.; Ubhi, B. K. Reference Materials for MS-Based Untargeted Metabolomics and Lipidomics: A Review by the Metabolomics Quality Assurance and Quality Control Consortium (mQACC). Metabolomics 2022, 18 ( 4), 24, 10.1007/s11306-021-01848-6
Stoop, M. P.; Coulier, L.; Rosenling, T.; Shi, S.; Smolinska, A. M.; Buydens, L.; Ampt, K.; Stingl, C.; Dane, A.; Muilwijk, B.; Luitwieler, R. L.; Sillevis Smitt, P. A.; Hintzen, R. Q.; Bischoff, R.; Wijmenga, S. S.; Hankemeier, T.; van Gool, A. J.; Luider, T. M. Quantitative Proteomics and Metabolomics Analysis of Normal Human Cerebrospinal Fluid Samples*. Mol. Cell. Proteomics 2010, 9 ( 9), 2063- 2075, 10.1074/mcp.M110.000877
van der Velpen, V.; Teav, T.; Gallart-Ayala, H.; Mehl, F.; Konz, I.; Clark, C.; Oikonomidi, A.; Peyratout, G.; Henry, H.; Delorenzi, M.; Ivanisevic, J.; Popp, J. Systemic and Central Nervous System Metabolic Alterations in Alzheimer’s Disease. Alzheimer’s Res. Ther. 2019, 11 ( 1), 93, 10.1186/s13195-019-0551-7
Trushina, E.; Dutta, T.; Persson, X.-M. T.; Mielke, M. M.; Petersen, R. C. Identification of Altered Metabolic Pathways in Plasma and CSF in Mild Cognitive Impairment and Alzheimer’s Disease Using Metabolomics. PLoS One 2013, 8 ( 5), e63644 10.1371/journal.pone.0063644
Nielsen, J. E.; Maltesen, R. G.; Havelund, J. F.; Færgeman, N. J.; Gotfredsen, C. H.; Vestergård, K.; Kristensen, S. R.; Pedersen, S. Characterising Alzheimer’s Disease through Integrative NMR- and LC-MS-Based Metabolomics. Metab. Open 2021, 12, 100125, 10.1016/j.metop.2021.100125
Dong, R.; Denier-Fields, D. N.; Lu, Q.; Suridjan, I.; Kollmorgen, G.; Wild, N.; Betthauser, T. J.; Carlsson, C. M.; Asthana, S.; Johnson, S. C.; Zetterberg, H.; Blennow, K.; Engelman, C. D. Principal Components from Untargeted Cerebrospinal Fluid Metabolomics Associated with Alzheimer’s Disease Biomarkers. Neurobiol. Aging 2022, 117, 12- 23, 10.1016/j.neurobiolaging.2022.04.009
Tynkkynen, J.; Chouraki, V.; van der Lee, S. J.; Hernesniemi, J.; Yang, Q.; Li, S.; Beiser, A.; Larson, M. G.; Sääksjärvi, K.; Shipley, M. J.; Singh-Manoux, A.; Gerszten, R. E.; Wang, T. J.; Havulinna, A. S.; Würtz, P.; Fischer, K.; Demirkan, A.; Ikram, M. A.; Amin, N.; Lehtimäki, T.; Kähönen, M.; Perola, M.; Metspalu, A.; Kangas, A. J.; Soininen, P.; Ala-Korpela, M.; Vasan, R. S.; Kivimäki, M.; van Duijn, C. M.; Seshadri, S.; Salomaa, V. Association of Branched-Chain Amino Acids and Other Circulating Metabolites with Risk of Incident Dementia and Alzheimer’s Disease: A Prospective Study in Eight Cohorts. Alzheimer’s Dementia 2018, 14 ( 6), 723- 733, 10.1016/j.jalz.2018.01.003
Mulak, A. Bile Acids as Key Modulators of the Brain-Gut-Microbiota Axis in Alzheimer’s Disease. J. Alzheimer’s Dis. 2021, 84 ( 2), 461- 477, 10.3233/JAD-210608
Ehtezazi, T.; Rahman, K.; Davies, R.; Leach, A. G. The Pathological Effects of Circulating Hydrophobic Bile Acids in Alzheimer’s Disease. J. Alzheimers Dis. Rep. 2023, 7 ( 1), 173- 211, 10.3233/ADR-220071
Baloni, P.; Funk, C. C.; Yan, J.; Yurkovich, J. T.; Kueider-Paisley, A.; Nho, K.; Heinken, A.; Jia, W.; Mahmoudiandehkordi, S.; Louie, G.; Saykin, A. J.; Arnold, M.; Kastenmüller, G.; Griffiths, W. J.; Thiele, I.; Kaddurah-Daouk, R.; Price, N. D.; Kaddurah-Daouk, R.; Kueider-Paisley, A.; Louie, G.; Doraiswamy, P. M.; Blach, C.; Moseley, A.; Thompson, J. W.; Mahmoudiandehkhordi, S.; Welsh-Balmer, K.; Plassman, B.; Saykin, A.; Nho, K.; Kastenmüller, G.; Arnold, M.; Bhattacharyya, S.; Han, X.; Baillie, R.; Fiehn, O.; Barupal, D.; Meikle, P.; Mazmanian, S.; Kling, M.; Shaw, L.; Trojanowski, J.; Toledo, J.; Van Duijin, C.; Hankemier, T.; Thiele, I.; Heinken, A.; Price, N.; Funk, C.; Baloni, P.; Jia, W.; Wishart, D.; Brinton, R.; Chang, R.; Farrer, L.; Au, R.; Qiu, W.; Würtz, P.; Mangravite, L.; Krumsiek, J.; Newman, J.; Zhang, B.; Moreno, H. Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer’s Disease. Cell Rep. Med. 2020, 1 ( 8), 100138, 10.1016/j.xcrm.2020.100138
Song, Z.; Wang, M.; Zhu, Z.; Tang, G.; Liu, Y.; Chai, Y. Optimization of Pretreatment Methods for Cerebrospinal Fluid Metabolomics Based on Ultrahigh Performance Liquid Chromatography/Mass Spectrometry. J. Pharm. Biomed. Anal. 2021, 197, 113938, 10.1016/j.jpba.2021.113938
Talavera Andújar, B.; Aurich, D.; Aho, V. T. E.; Singh, R. R.; Cheng, T.; Zaslavsky, L.; Bolton, E. E.; Mollenhauer, B.; Wilmes, P.; Schymanski, E. L. Studying the Parkinson’s Disease Metabolome and Exposome in Biological Samples through Different Analytical and Cheminformatics Approaches: A Pilot Study. Anal. Bioanal. Chem. 2022, 414 ( 25), 7399- 7419, 10.1007/s00216-022-04207-z
Zaslavsky, L.; Cheng, T.; Gindulyte, A.; He, S.; Kim, S.; Li, Q.; Thiessen, P.; Yu, B.; Bolton, E. E. Discovering and Summarizing Relationships Between Chemicals, Genes, Proteins, and Diseases in PubChem. Front. Res. Metr. Anal. 2021, 6, 689059, 10.3389/frma.2021.689059
Kim, S.; Cheng, T.; He, S.; Thiessen, P. A.; Li, Q.; Gindulyte, A.; Bolton, E. E. PubChem Protein, Gene, Pathway, and Taxonomy Data Collections: Bridging Biology and Chemistry through Target-Centric Views of PubChem Data. J. Mol. Biol. 2022, 434 ( 11), 167514, 10.1016/j.jmb.2022.167514
Wishart, D. S.; Lewis, M. J.; Morrissey, J. A.; Flegel, M. D.; Jeroncic, K.; Xiong, Y.; Cheng, D.; Eisner, R.; Gautam, B.; Tzur, D.; Sawhney, S.; Bamforth, F.; Greiner, R.; Li, L. The Human Cerebrospinal Fluid Metabolome. J. Chromatogr. B 2008, 871 ( 2), 164- 173, 10.1016/j.jchromb.2008.05.001
WishartLab . CSF Metabolome, 2023 https://csfmetabolome.ca/ (accessed June 06, 2023).
Bolton, E.; Schymanski, E.; Kondic, T.; Thiessen, P.; Zhang, J. PubChemLite for Exposomics (1.12.0) [Data set] Zenodo, 2022. 10.5281/zenodo.6936117.
Schymanski, E. L.; Kondić, T.; Neumann, S.; Thiessen, P. A.; Zhang, J.; Bolton, E. E. Empowering Large Chemical Knowledge Bases for Exposomics: PubChemLite Meets MetFrag. J. Cheminform 2021, 13 ( 1), 19, 10.1186/s13321-021-00489-0
Talavera Andújar, B.; Mary, A.; Venegas, C.; Cheng, T.; Zaslavsky, L.; Bolton, E.; Heneka, M.; Schymanski, E. Alzheimer’s disease CSF cheminformatics pipeline. Environmental Cheminformatics/AD-CSF Repository in GitLab 2023 https://gitlab.lcsb.uni.lu/eci/AD-CSF (accessed June 12, 2023).
Talavera Andújar, B.; Mary, A.; Cheng, T.; Zaslavsky, L.; Bolton, E. E.; Heneka, M. T.; Schymanski, E. L. PubChem Lists to Study the Exposome of Mild Cognitive Impairment and Alzheimer’s Disease on Cerebrospinal Fluid Zenodo, 2023. 10.5281/zenodo.8014420.
Bolton, E., Schymanski, E., Kondic, T., Thiessen, P., Zhang, J. PubChemLite for Exposomics (PubChemLite.0.3.0) [Data set]. Zenodo, 2020 10.5281/zenodo.4183801.
Lai, Z.; Tsugawa, H.; Wohlgemuth, G.; Mehta, S.; Mueller, M.; Zheng, Y.; Ogiwara, A.; Meissen, J.; Showalter, M.; Takeuchi, K.; Kind, T.; Beal, P.; Arita, M.; Fiehn, O. Identifying Metabolites by Integrating Metabolome Databases with Mass Spectrometry Cheminformatics. Nat. Methods 2018, 15 ( 1), 53- 56, 10.1038/nmeth.4512
Helmus, R.; ter Laak, T. L.; van Wezel, A. P.; de Voogt, P.; Schymanski, E. L. patRoon: Open Source Software Platform for Environmental Mass Spectrometry Based Non-Target Screening. J. Cheminf. 2021, 13 ( 1), 1, 10.1186/s13321-020-00477-w
Helmus, R.; van de Velde, B.; Brunner, A. M.; ter Laak, T. L.; van Wezel, A. P.; Schymanski, E. L. patRoon 2.0: Improved Non-Target Analysis Workflows Including Automated Transformation Product Screening. J. Open Source Softw. 2022, 7 ( 71), 4029, 10.21105/joss.04029
Schymanski, E. L.; Jeon, J.; Gulde, R.; Fenner, K.; Ruff, M.; Singer, H. P.; Hollender, J. Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence. Environ. Sci. Technol. 2014, 48 ( 4), 2097- 2098, 10.1021/es5002105
Helmus, R. patRoon Handbook, 2024. https://rickhelmus.github.io/patRoon/handbook_bd/index.html (accessed Jan 23, 2024).
Dodder, N.; Mullen, K. OrgMassSpecR: Organic Mass Spectrometry, 2017. https://CRAN.R-project.org/package=OrgMassSpecR (accessed Aug 01, 2022).
Liao, W.-L.; Heo, G.-Y.; Dodder, N. G.; Pikuleva, I. A.; Turko, I. V. Optimizing the Conditions of a Multiple Reaction Monitoring Assay for Membrane Proteins: Quantification of Cytochrome P450 11A1 and Adrenodoxin Reductase in Bovine Adrenal Cortex and Retina. Anal. Chem. 2010, 82 ( 13), 5760- 5767, 10.1021/ac100811x
Hoh, E.; Dodder, N. G.; Lehotay, S. J.; Pangallo, K. C.; Reddy, C. M.; Maruya, K. A. Nontargeted Comprehensive Two-Dimensional Gas Chromatography/Time-of-Flight Mass Spectrometry Method and Software for Inventorying Persistent and Bioaccumulative Contaminants in Marine Environments. Environ. Sci. Technol. 2012, 46 ( 15), 8001- 8008, 10.1021/es301139q
Chong, J.; Xia, J. Using MetaboAnalyst 4.0 for Metabolomics Data Analysis, Interpretation, and Integration with Other Omics Data. In Computational Methods and Data Analysis for Metabolomics; Li, S., Ed.; Methods in Molecular Biology; Springer US: New York, NY, 2020, pp 337- 360. 10.1007/978-1-0716-0239-3_17.
Xia Lab MetaboAnalyst, 2024 https://www.metaboanalyst.ca/ (accessed Jan 24, 2024).
Wishart, D. S.; Feunang, Y. D.; Marcu, A.; Guo, A. C.; Liang, K.; Vázquez-Fresno, R.; Sajed, T.; Johnson, D.; Li, C.; Karu, N.; Sayeeda, Z.; Lo, E.; Assempour, N.; Berjanskii, M.; Singhal, S.; Arndt, D.; Liang, Y.; Badran, H.; Grant, J.; Serra-Cayuela, A.; Liu, Y.; Mandal, R.; Neveu, V.; Pon, A.; Knox, C.; Wilson, M.; Manach, C.; Scalbert, A. HMDB 4.0: The Human Metabolome Database for 2018. Nucleic Acids Res. 2018, 46 ( D1), D608- D617, 10.1093/nar/gkx1089
WishartLab . Metabolites Ontology Human Metabolome Database. 2023 https://hmdb.ca/metabolites (accessed Dec 05, 2023).
Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B. A.; Thiessen, P. A.; Yu, B.; Zaslavsky, L.; Zhang, J.; Bolton, E. E. PubChem 2023 Update. Nucleic Acids Res. 2023, 51 ( D1), D1373- D1380, 10.1093/nar/gkac956
Han, J.; Liu, Y.; Wang, R.; Yang, J.; Ling, V.; Borchers, C. H. Metabolic Profiling of Bile Acids in Human and Mouse Blood by LC-MS/MS in Combination with Phospholipid-Depletion Solid-Phase Extraction. Anal. Chem. 2015, 87 ( 2), 1127- 1136, 10.1021/ac503816u
Barupal, D. K.; Fiehn, O. Chemical Similarity Enrichment Analysis (ChemRICH) as Alternative to Biochemical Pathway Mapping for Metabolomic Datasets. Sci. Rep. 2017, 7 ( 1), 14567, 10.1038/s41598-017-15231-w
Fiehn Lab ChemRICH, 2023 https://chemrich.fiehnlab.ucdavis.edu/ (accessed Nov 08, 2023).
Obermeier, B.; Verma, A.; Ransohoff, R. M. Chapter 3 - The Blood-Brain Barrier. In Handbook of Clinical Neurology; Pittock, S. J., Vincent, A., Eds.; Autoimmune Neurology; Elsevier, 2016; Vol. 133; pp 39- 59. 10.1016/B978-0-444-63432-0.00003-7.
Ibáñez, C.; Simó, C.; Martín-Álvarez, P. J.; Kivipelto, M.; Winblad, B.; Cedazo-Mínguez, A.; Cifuentes, A. Toward a Predictive Model of Alzheimer’s Disease Progression Using Capillary Electrophoresis-Mass Spectrometry Metabolomics. Anal. Chem. 2012, 84 ( 20), 8532- 8540, 10.1021/ac301243k
Xie, K.; Qin, Q.; Long, Z.; Yang, Y.; Peng, C.; Xi, C.; Li, L.; Wu, Z.; Daria, V.; Zhao, Y.; Wang, F.; Wang, M. High-Throughput Metabolomics for Discovering Potential Biomarkers and Identifying Metabolic Mechanisms in Aging and Alzheimer’s Disease. Front. Cell Dev. Biol. 2021, 9, 602887, 10.3389/fcell.2021.602887
Esteve, C.; Jones, E. A.; Kell, D. B.; Boutin, H.; McDonnell, L. A. Mass spectrometry imaging shows major derangements in neurogranin and in purine metabolism in the triple-knockout 3×Tg Alzheimer mouse model. Biochim. Biophys. Acta, Proteins Proteomics 2017, 1865 ( 7), 747- 754, 10.1016/j.bbapap.2017.04.002
Jensen, N. J.; Wodschow, H. Z.; Nilsson, M.; Rungby, J. Effects of Ketone Bodies on Brain Metabolism and Function in Neurodegenerative Diseases. Int. J. Mol. Sci. 2020, 21 ( 22), 8767, 10.3390/ijms21228767
Reger, M. A.; Henderson, S. T.; Hale, C.; Cholerton, B.; Baker, L. D.; Watson, G. S.; Hyde, K.; Chapman, D.; Craft, S. Effects of β-hydroxybutyrate on cognition in memory-impaired adults. Neurobiol. Aging 2004, 25 ( 3), 311- 314, 10.1016/S0197-4580(03)00087-3
Shippy, D. C.; Wilhelm, C.; Viharkumar, P. A.; Raife, T. J.; Ulland, T. K. β-Hydroxybutyrate inhibits inflammasome activation to attenuate Alzheimer’s disease pathology. J. Neuroinflammation 2020, 17 ( 1), 280, 10.1186/s12974-020-01948-5
Lin, C.-H.; Lin, Y.-N.; Lane, H.-Y.; Chen, C.-J. The Identification of a Potential Plasma Metabolite Marker for Alzheimer’s Disease by LC-MS Untargeted Metabolomics. J. Chromatogr. B 2023, 1222, 123686, 10.1016/j.jchromb.2023.123686
Kaddurah-Daouk, R.; Zhu, H.; Sharma, S.; Bogdanov, M.; Rozen, S. G.; Matson, W.; Oki, N. O.; Motsinger-Reif, A. A.; Churchill, E.; Lei, Z.; Appleby, D.; Kling, M. A.; Trojanowski, J. Q.; Doraiswamy, P. M.; Arnold, S. E.; Pharmacometabolomics Research Network Alterations in Metabolic Pathways and Networks in Alzheimer’s Disease. Transl. Psychiatry 2013, 3 ( 4), e244 10.1038/tp.2013.18
Dou, L.; Sallée, M.; Cerini, C.; Poitevin, S.; Gondouin, B.; Jourde-Chiche, N.; Fallague, K.; Brunet, P.; Calaf, R.; Dussol, B.; Mallet, B.; Dignat-George, F.; Burtey, S. The Cardiovascular Effect of the Uremic Solute Indole-3 Acetic Acid. J. Am. Soc. Nephrol. 2015, 26 ( 4), 876- 887, 10.1681/ASN.2013121283
Beukema, M.; Faas, M. M.; de Vos, P. The Effects of Different Dietary Fiber Pectin Structures on the Gastrointestinal Immune Barrier: Impact via Gut Microbiota and Direct Effects on Immune Cells. Exp. Mol. Med. 2020, 52 ( 9), 1364- 1376, 10.1038/s12276-020-0449-2
Echeverria, V.; Zeitlin, R. Cotinine: A Potential New Therapeutic Agent against Alzheimer’s Disease. CNS Neurosci. Ther. 2012, 18 ( 7), 517- 523, 10.1111/j.1755-5949.2012.00317.x
Patel, S.; Grizzell, J. A.; Holmes, R.; Zeitlin, R.; Solomon, R.; Sutton, T. L.; Rohani, A.; Charry, L. C.; Iarkov, A.; Mori, T.; Echeverria Moran, V. Cotinine Halts the Advance of Alzheimer’s Disease-like Pathology and Associated Depressive-like Behavior in Tg6799 Mice. Front. Aging Neurosci. 2014, 6, 162, 10.3389/fnagi.2014.00162
Mayfield, J. CDK Depict Web Interface, 2023. https://www.simolecule.com/cdkdepict/depict.html (accessed Mar 09, 2023).
Monteiro-Cardoso, V. F.; Corlianò, M.; Singaraja, R. R. Bile Acids: A Communication Channel in the Gut-Brain Axis. NeuroMol. Med. 2021, 23 ( 1), 99- 117, 10.1007/s12017-020-08625-z
Griffiths, W. J.; Abdel-Khalik, J.; Yutuc, E.; Roman, G.; Warner, M.; Gustafsson, J.-Å.; Wang, Y. Concentrations of Bile Acid Precursors in Cerebrospinal Fluid of Alzheimer’s Disease Patients. Free Radical Biol. Med. 2019, 134, 42- 52, 10.1016/j.freeradbiomed.2018.12.020
MahmoudianDehkordi, S.; Arnold, M.; Nho, K.; Ahmad, S.; Jia, W.; Xie, G.; Louie, G.; Kueider-Paisley, A.; Moseley, M. A.; Thompson, J. W.; St John Williams, L.; Tenenbaum, J. D.; Blach, C.; Baillie, R.; Han, X.; Bhattacharyya, S.; Toledo, J. B.; Schafferer, S.; Klein, S.; Koal, T.; Risacher, S. L.; Allan Kling, M.; Motsinger-Reif, A.; Rotroff, D. M.; Jack, J.; Hankemeier, T.; Bennett, D. A.; De Jager, P. L.; Trojanowski, J. Q.; Shaw, L. M.; Weiner, M. W.; Doraiswamy, P. M.; Van Duijn, C. M.; Saykin, A. J.; Kastenmüller, G.; Kaddurah-Daouk, R.; Alzheimer’s Disease Neuroimaging Initiative and the Alzheimer Disease Metabolomics Consortium Altered Bile Acid Profile Associates with Cognitive Impairment in Alzheimer’s Disease─An Emerging Role for Gut Microbiome. Alzheimer’s Dementia 2019, 15 ( 1), 76- 92, 10.1016/j.jalz.2018.07.217
MahmoudianDehkordi, S.; Bhattacharyya, S.; Brydges, C. R.; Jia, W.; Fiehn, O.; Rush, A. J.; Dunlop, B. W.; Kaddurah-Daouk, R. Gut Microbiome-Linked Metabolites in the Pathobiology of Major Depression With or Without Anxiety─A Role for Bile Acids. Front. Neurosci. 2022, 16, 937906, 10.3389/fnins.2022.937906
Mertens, K. L.; Kalsbeek, A.; Soeters, M. R.; Eggink, H. M. Bile Acid Signaling Pathways from the Enterohepatic Circulation to the Central Nervous System. Front. Neurosci. 2017, 11, 617, 10.3389/fnins.2017.00617
Chiang, J. Y. L.; Ferrell, J. M. Bile Acid Metabolism in Liver Pathobiology. Gene Expression 2018, 18 ( 2), 71- 87, 10.3727/105221618X15156018385515
Marksteiner, J.; Blasko, I.; Kemmler, G.; Koal, T.; Humpel, C. Bile Acid Quantification of 20 Plasma Metabolites Identifies Lithocholic Acid as a Putative Biomarker in Alzheimer’s Disease. Metabolomics 2018, 14 ( 1), 1, 10.1007/s11306-017-1297-5
Mapstone, M.; Cheema, A. K.; Fiandaca, M. S.; Zhong, X.; Mhyre, T. R.; MacArthur, L. H.; Hall, W. J.; Fisher, S. G.; Peterson, D. R.; Haley, J. M.; Nazar, M. D.; Rich, S. A.; Berlau, D. J.; Peltz, C. B.; Tan, M. T.; Kawas, C. H.; Federoff, H. J. Plasma Phospholipids Identify Antecedent Memory Impairment in Older Adults. Nat. Med. 2014, 20 ( 4), 415- 418, 10.1038/nm.3466
Connell, E.; Le Gall, G.; Pontifex, M. G.; Sami, S.; Cryan, J. F.; Clarke, G.; Müller, M.; Vauzour, D. Microbial-Derived Metabolites as a Risk Factor of Age-Related Cognitive Decline and Dementia. Mol. Neurodegener. 2022, 17 ( 1), 43, 10.1186/s13024-022-00548-6
Alnouti, Y. Bile Acid Sulfation: A Pathway of Bile Acid Elimination and Detoxification. Toxicol. Sci. 2009, 108 ( 2), 225- 246, 10.1093/toxsci/kfn268
Kastrinou Lampou, V.; Poller, B.; Huth, F.; Fischer, A.; Kullak-Ublick, G. A.; Arand, M.; Schadt, H. S.; Camenisch, G. Novel Insights into Bile Acid Detoxification via CYP, UGT and SULT Enzymes. Toxicol. in Vitro 2023, 87, 105533, 10.1016/j.tiv.2022.105533
Nho, K.; Kueider-Paisley, A.; MahmoudianDehkordi, S.; Arnold, M.; Risacher, S. L.; Louie, G.; Blach, C.; Baillie, R.; Han, X.; Kastenmüller, G.; Jia, W.; Xie, G.; Ahmad, S.; Hankemeier, T.; van Duijn, C. M.; Trojanowski, J. Q.; Shaw, L. M.; Weiner, M. W.; Doraiswamy, P. M.; Saykin, A. J.; Kaddurah-Daouk, R.; Alzheimer’s Disease Neuroimaging Initiative and the Alzheimer Disease Metabolomics Consortium Altered Bile Acid Profile in Mild Cognitive Impairment and Alzheimer’s Disease: Relationship to Neuroimaging and CSF Biomarkers. Alzheimer’s Dementia 2019, 15 ( 2), 232- 244, 10.1016/j.jalz.2018.08.012
Dong, R.; Lu, Q.; Kang, H.; Suridjan, I.; Kollmorgen, G.; Wild, N.; Deming, Y.; Van Hulle, C. A.; Anderson, R. M.; Zetterberg, H.; Blennow, K.; Carlsson, C. M.; Asthana, S.; Johnson, S. C.; Engelman, C. D. CSF Metabolites Associated with Biomarkers of Alzheimer’s Disease Pathology. Front. Aging Neurosci. 2023, 15, 1214932, 10.3389/fnagi.2023.1214932
Jacobs, K. R.; Lim, C. K.; Blennow, K.; Zetterberg, H.; Chatterjee, P.; Martins, R. N.; Brew, B. J.; Guillemin, G. J.; Lovejoy, D. B. Correlation between plasma and CSF concentrations of kynurenine pathway metabolites in Alzheimer’s disease and relationship to amyloid-β and tau. Neurobiol. Aging 2019, 80, 11- 20, 10.1016/j.neurobiolaging.2019.03.015
