Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease.

Alzheimer’s disease Co-metabolism Constraint-based modelling Formate Host-microbiome Metabolic modelling Metabolomics Microbiome Pathways

Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
13 03 2024
Historique:
received: 29 08 2023
accepted: 29 02 2024
medline: 15 3 2024
pubmed: 14 3 2024
entrez: 14 3 2024
Statut: epublish

Résumé

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

Identifiants

pubmed: 38480804
doi: 10.1038/s41598-024-55960-3
pii: 10.1038/s41598-024-55960-3
pmc: PMC10937638
doi:

Substances chimiques

Formates 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

6095

Subventions

Organisme : NIGMS NIH HHS
ID : T32 GM081061
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG069901
Pays : United States
Organisme : NIA NIH HHS
ID : U19 AG063744
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG054047
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG021155
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG027161
Pays : United States
Organisme : NINDS NIH HHS
ID : F99 NS130922
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG059093
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG061359
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG070973
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG037639
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG046171
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG057452
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG062715
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG058942
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG051550
Pays : United States

Informations de copyright

© 2024. The Author(s).

Références

Ricci, M., Cimini, A., Chiaravalloti, A., Filippi, L. & Schillaci, O. Positron emission tomography (PET) and neuroimaging in the personalized approach to neurodegenerative causes of dementia. Int. J. Mol. Sci. https://doi.org/10.3390/ijms21207481 (2020).
doi: 10.3390/ijms21207481 pubmed: 33419357 pmcid: 7795508
Knopman, D. S. et al. Alzheimer disease. Nat. Rev. Dis. Primers 7, 33. https://doi.org/10.1038/s41572-021-00269-y (2021).
doi: 10.1038/s41572-021-00269-y pubmed: 33986301 pmcid: 8574196
Castro, D. M., Dillon, C., Machnicki, G. & Allegri, R. F. The economic cost of Alzheimer’s disease: Family or public health burden?. Dement. Neuropsychol. 4, 262–267. https://doi.org/10.1590/S1980-57642010DN40400003 (2010).
doi: 10.1590/S1980-57642010DN40400003 pubmed: 29213697 pmcid: 5619058
Toledo, J. B. et al. Metabolic network failures in Alzheimer’s disease: A biochemical road map. Alzheimers Dement. 13, 965–984. https://doi.org/10.1016/j.jalz.2017.01.020 (2017).
doi: 10.1016/j.jalz.2017.01.020 pubmed: 28341160 pmcid: 5866045
Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379. https://doi.org/10.1056/NEJMra1600266 (2016).
doi: 10.1056/NEJMra1600266 pubmed: 27974040
Alexander, M. & Turnbaugh, P. J. deconstructing mechanisms of diet-microbiome-immune interactions. Immunity 53, 264–276. https://doi.org/10.1016/j.immuni.2020.07.015 (2020).
doi: 10.1016/j.immuni.2020.07.015 pubmed: 32814025 pmcid: 7441819
Cryan, J. F. et al. The microbiota-gut-brain axis. Physiol. Rev. 99, 1877–2013. https://doi.org/10.1152/physrev.00018.2018 (2019).
doi: 10.1152/physrev.00018.2018 pubmed: 31460832
Gheorghe, C. E. et al. Focus on the essentials: Tryptophan metabolism and the microbiome-gut-brain axis. Curr. Opin. Pharmacol. 48, 137–145. https://doi.org/10.1016/j.coph.2019.08.004 (2019).
doi: 10.1016/j.coph.2019.08.004 pubmed: 31610413
Bonfili, L. et al. Microbiota modulation as preventative and therapeutic approach in Alzheimer’s disease. FEBS J. https://doi.org/10.1111/febs.15571 (2020).
doi: 10.1111/febs.15571 pubmed: 32969566
Goyal, D., Ali, S. A. & Singh, R. K. Emerging role of gut microbiota in modulation of neuroinflammation and neurodegeneration with emphasis on Alzheimer’s disease. Prog. Neuropsychopharmacol. Biol. Psychiatry 106, 110112. https://doi.org/10.1016/j.pnpbp.2020.110112 (2021).
doi: 10.1016/j.pnpbp.2020.110112 pubmed: 32949638
MahmoudianDehkordi, S. et al. Altered bile acid profile associates with cognitive impairment in Alzheimer’s disease-An emerging role for gut microbiome. Alzheimers Dement. 15, 76–92. https://doi.org/10.1016/j.jalz.2018.07.217 (2019).
doi: 10.1016/j.jalz.2018.07.217 pubmed: 30337151
Nho, K. et al. Altered bile acid profile in mild cognitive impairment and Alzheimer’s disease: Relationship to neuroimaging and CSF biomarkers. Alzheimers Dement. 15, 232–244. https://doi.org/10.1016/j.jalz.2018.08.012 (2019).
doi: 10.1016/j.jalz.2018.08.012 pubmed: 30337152
Pietzke, M., Meiser, J. & Vazquez, A. Formate metabolism in health and disease. Mol. Metab. 33, 23–37. https://doi.org/10.1016/j.molmet.2019.05.012 (2020).
doi: 10.1016/j.molmet.2019.05.012 pubmed: 31402327
Wang, Y. et al. Systematic evaluation of urinary formic acid as a new potential biomarker for Alzheimer’s disease. Front. Aging Neurosci. https://doi.org/10.3389/fnagi.2022.1046066 (2022).
doi: 10.3389/fnagi.2022.1046066 pubmed: 36875261 pmcid: 9816409
Palsson, B. Ø. Systems Biology: Constraint-based Reconstruction and Analysis (Cambridge University Press, 2015).
doi: 10.1017/CBO9781139854610
Thiele, I. & Palsson, B. Ø. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protocols 5, 93–121 (2010).
doi: 10.1038/nprot.2009.203 pubmed: 20057383
Orth, J. D., Thiele, I. & Palsson, B. O. What is flux balance analysis?. Nat. Biotechnol. 28, 245–248 (2010).
doi: 10.1038/nbt.1614 pubmed: 20212490 pmcid: 3108565
Magnusdottir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81–89. https://doi.org/10.1038/nbt.3703 (2017).
doi: 10.1038/nbt.3703 pubmed: 27893703
Heinken, A. et al. Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01628-0 (2023).
doi: 10.1038/s41587-022-01628-0 pubmed: 36658342 pmcid: 10497413
Heinken, A. et al. APOLLO: A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites. bioRxiv https://doi.org/10.1101/2023.10.02.560573 (2023).
doi: 10.1101/2023.10.02.560573 pubmed: 37873072 pmcid: 10592896
Baldini, F. et al. The microbiome modeling toolbox: From microbial interactions to personalized microbial communities. Bioinformatics https://doi.org/10.1093/bioinformatics/bty941 (2018).
doi: 10.1093/bioinformatics/bty941 pmcid: 6596895
Heinken, A. & Thiele, I. Microbiome Modelling Toolbox 2.0: Efficient, tractable modelling of microbiome communities. Bioinformatics 38, 2367–2368. https://doi.org/10.1093/bioinformatics/btac082 (2022).
doi: 10.1093/bioinformatics/btac082 pubmed: 35157025 pmcid: 9004645
Hertel, J., Heinken, A., Martinelli, F. & Thiele, I. Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of Fusobacterium spp. on community butyrate production. Gut Microbes 13, 1–23. https://doi.org/10.1080/19490976.2021.1915673 (2021).
doi: 10.1080/19490976.2021.1915673 pubmed: 34057024
Thiele, I. et al. Personalized whole-body models integrate metabolism, physiology, and the gut microbiome. Mol. Syst. Biol. 16, e8982. https://doi.org/10.15252/msb.20198982 (2020).
doi: 10.15252/msb.20198982 pubmed: 32463598 pmcid: 7285886
Basile, A. et al. Longitudinal flux balance analyses of a patient with episodic colonic inflammation reveals microbiome metabolic dynamics. Gut Microbes 15, 2226921. https://doi.org/10.1080/19490976.2023.2226921 (2023).
doi: 10.1080/19490976.2023.2226921 pubmed: 37438876 pmcid: 10339767
Thiele, I. & Fleming, R. M. T. Whole-body metabolic modelling predicts isoleucine dependency of SARS-CoV-2 replication. Comput. Struct. Biotechnol. J. 20, 4098–4109. https://doi.org/10.1016/j.csbj.2022.07.019 (2022).
doi: 10.1016/j.csbj.2022.07.019 pubmed: 35874091 pmcid: 9296228
Jessen, F. et al. Design and first baseline data of the DZNE multicenter observational study on predementia Alzheimer’s disease (DELCODE). Alzheimers Res. Ther. 10, 15. https://doi.org/10.1186/s13195-017-0314-2 (2018).
doi: 10.1186/s13195-017-0314-2 pubmed: 29415768 pmcid: 5802096
Vogt, N. M. et al. Gut microbiome alterations in Alzheimer’s disease. Sci. Rep. 7, 13537. https://doi.org/10.1038/s41598-017-13601-y (2017).
doi: 10.1038/s41598-017-13601-y pubmed: 29051531 pmcid: 5648830
Saunders, A. M. et al. Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer’s disease. Neurology 43, 1467–1472. https://doi.org/10.1212/wnl.43.8.1467 (1993).
doi: 10.1212/wnl.43.8.1467 pubmed: 8350998
Naj, A. C. et al. Effects of multiple genetic loci on age at onset in late-onset Alzheimer disease: A genome-wide association study. JAMA Neurol. 71, 1394–1404. https://doi.org/10.1001/jamaneurol.2014.1491 (2014).
doi: 10.1001/jamaneurol.2014.1491 pubmed: 25199842 pmcid: 4314944
Zhu, Q. et al. Phylogeny-aware analysis of metagenome community ecology based on matched reference genomes while bypassing taxonomy. mSystems 7, e00167-e122. https://doi.org/10.1128/msystems.00167-22 (2022).
doi: 10.1128/msystems.00167-22 pubmed: 35369727 pmcid: 9040630
Zhu, Q. et al. Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains Bacteria and Archaea. Nat. Commun. 10, 5477. https://doi.org/10.1038/s41467-019-13443-4 (2019).
doi: 10.1038/s41467-019-13443-4 pubmed: 31792218 pmcid: 6889312
Gonzalez, A. et al. Qiita: Rapid, web-enabled microbiome meta-analysis. Nat. Methods 15, 796–798. https://doi.org/10.1038/s41592-018-0141-9 (2018).
doi: 10.1038/s41592-018-0141-9 pubmed: 30275573 pmcid: 6235622
Brosnan, M. E. & Brosnan, J. T. Formate: The neglected member of one-carbon metabolism. Annu. Rev. Nutr. 36, 369–388. https://doi.org/10.1146/annurev-nutr-071715-050738 (2016).
doi: 10.1146/annurev-nutr-071715-050738 pubmed: 27431368
Wörheide, M. A., Krumsiek, J., Kastenmüller, G. & Arnold, M. Multi-omics integration in biomedical research: A metabolomics-centric review. Anal. Chim. Acta 1141, 144–162. https://doi.org/10.1016/j.aca.2020.10.038 (2021).
doi: 10.1016/j.aca.2020.10.038 pubmed: 33248648
Watanabe, Y. et al. Alterations in glycerolipid and fatty acid metabolic pathways in Alzheimer’s disease identified by urinary metabolic profiling: A pilot study. Front. Neurol. https://doi.org/10.3389/fneur.2021.719159 (2021).
doi: 10.3389/fneur.2021.719159 pubmed: 34777195 pmcid: 8578168
Yilmaz, A. et al. Targeted metabolic profiling of urine highlights a potential biomarker panel for the diagnosis of Alzheimer’s disease and mild cognitive impairment: A pilot study. Metabolites https://doi.org/10.3390/metabo10090357 (2020).
doi: 10.3390/metabo10090357 pubmed: 32878308 pmcid: 7569858
Boeniger, M. F. Formate in urine as a biological indicator of formaldehyde exposure: A review. Am. Ind. Hyg. Assoc. J. 48, 900–908. https://doi.org/10.1080/15298668791385787 (1987).
doi: 10.1080/15298668791385787 pubmed: 3321963
Hajjar, I., Liu, C., Jones, D. P. & Uppal, K. Untargeted metabolomics reveal dysregulations in sugar, methionine, and tyrosine pathways in the prodromal state of AD. Alzheimer’s Dement. 12, e12064. https://doi.org/10.1002/dad2.12064 (2020).
doi: 10.1002/dad2.12064
Kaddurah-Daouk, R. et al. Alterations in metabolic pathways and networks in Alzheimer’s disease. Transl. Psychiatry 3, e244. https://doi.org/10.1038/tp.2013.18 (2013).
doi: 10.1038/tp.2013.18 pubmed: 23571809 pmcid: 3641405
Maitre, M., Klein, C., Patte-Mensah, C. & Mensah-Nyagan, A. G. Tryptophan metabolites modify brain Aβ peptide degradation: A role in Alzheimer’s disease?. Progr. Neurobiol. 190, 101800. https://doi.org/10.1016/j.pneurobio.2020.101800 (2020).
doi: 10.1016/j.pneurobio.2020.101800
Clarke, J. R., Ribeiro, F. C., Frozza, R. L., De Felice, F. G. & Lourenco, M. V. Metabolic dysfunction in Alzheimer’s disease: From basic neurobiology to clinical approaches. J. Alzheimer’s Dis. 64, S405-s426. https://doi.org/10.3233/jad-179911 (2018).
doi: 10.3233/jad-179911
Griffin, J. W. & Bradshaw, P. C. Amino acid catabolism in alzheimer’s disease brain: Friend or foe?. Oxid. Med. Cell. Longev. 2017, 5472792. https://doi.org/10.1155/2017/5472792 (2017).
doi: 10.1155/2017/5472792 pubmed: 28261376 pmcid: 5316456
Schwarcz, R. & Stone, T. W. The kynurenine pathway and the brain: Challenges, controversies and promises. Neuropharmacology 112, 237–247. https://doi.org/10.1016/j.neuropharm.2016.08.003 (2017).
doi: 10.1016/j.neuropharm.2016.08.003 pubmed: 27511838
Porter, R. J. et al. Cognitive deficit induced by acute tryptophan depletion in patients with Alzheimer’s disease. Am. J. Psychiatry 157, 638–640. https://doi.org/10.1176/appi.ajp.157.4.638 (2000).
doi: 10.1176/appi.ajp.157.4.638 pubmed: 10739429
Whiley, L. et al. Metabolic phenotyping reveals a reduction in the bioavailability of serotonin and kynurenine pathway metabolites in both the urine and serum of individuals living with Alzheimer’s disease. Alzheimers Res. Ther. 13, 20. https://doi.org/10.1186/s13195-020-00741-z (2021).
doi: 10.1186/s13195-020-00741-z pubmed: 33422142 pmcid: 7797094
van der Velpen, V. et al. Systemic and central nervous system metabolic alterations in Alzheimer’s disease. Alzheimer’s Res. Ther. 11, 93. https://doi.org/10.1186/s13195-019-0551-7 (2019).
doi: 10.1186/s13195-019-0551-7
Tait-Mulder, J., Hodge, K., Sumpton, D., Zanivan, S. & Vazquez, A. The conversion of formate into purines stimulates mTORC1 leading to CAD-dependent activation of pyrimidine synthesis. Cancer Metab. 8, 20. https://doi.org/10.1186/s40170-020-00228-3 (2020).
doi: 10.1186/s40170-020-00228-3 pubmed: 32974014 pmcid: 7507243
Zhang, X. et al. The association between folate and Alzheimer’s disease: A systematic review and meta-analysis. Front. Neurosci. https://doi.org/10.3389/fnins.2021.661198 (2021).
doi: 10.3389/fnins.2021.661198 pubmed: 35462734 pmcid: 8733384
Bergau, N., Maul, S., Rujescu, D., Simm, A. & Navarrete Santos, A. Reduction of glycolysis intermediate concentrations in the cerebrospinal fluid of alzheimer’s disease patients. Front. Neurosci. 13, 871. https://doi.org/10.3389/fnins.2019.00871 (2019).
doi: 10.3389/fnins.2019.00871 pubmed: 31496932 pmcid: 6713159
Gu, C., Kim, G. B., Kim, W. J., Kim, H. U. & Lee, S. Y. Current status and applications of genome-scale metabolic models. Genome Biol. 20, 121. https://doi.org/10.1186/s13059-019-1730-3 (2019).
doi: 10.1186/s13059-019-1730-3 pubmed: 31196170 pmcid: 6567666
Brunk, E. et al. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat. Biotechnol. 36, 272–281. https://doi.org/10.1038/nbt.4072 (2018).
doi: 10.1038/nbt.4072 pubmed: 29457794 pmcid: 5840010
Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419–425. https://doi.org/10.1038/nbt.2488 (2013).
doi: 10.1038/nbt.2488 pubmed: 23455439
Heinken, A., Magnúsdóttir, S., Fleming, R. M. T. & Thiele, I. DEMETER: Efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations. Bioinformatics 37, 3974–3975. https://doi.org/10.1093/bioinformatics/btab622 (2021).
doi: 10.1093/bioinformatics/btab622 pubmed: 34473240 pmcid: 8570805
Reimer, L. C. et al. BacDive in 2019: Bacterial phenotypic data for High-throughput biodiversity analysis. Nucleic Acids Res. 47, D631–D636. https://doi.org/10.1093/nar/gky879 (2018).
doi: 10.1093/nar/gky879 pmcid: 6323973
Jessen, F. et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimers Dement. 10, 844–852. https://doi.org/10.1016/j.jalz.2014.01.001 (2014).
doi: 10.1016/j.jalz.2014.01.001 pubmed: 24798886 pmcid: 4317324
Albert, M. S. et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 270–279. https://doi.org/10.1016/j.jalz.2011.03.008 (2011).
doi: 10.1016/j.jalz.2011.03.008 pubmed: 21514249 pmcid: 3312027
McKhann, G. M. et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 263–269. https://doi.org/10.1016/j.jalz.2011.03.005 (2011).
doi: 10.1016/j.jalz.2011.03.005 pubmed: 21514250 pmcid: 3312024
Pietzner, M. et al. Urine metabolomics by (1)H-NMR spectroscopy indicates associations between serum 3,5–T2 concentrations and intermediary metabolism in euthyroid humans. Eur. Thyroid J. 4, 92–100. https://doi.org/10.1159/000381308 (2015).
doi: 10.1159/000381308 pubmed: 26601079 pmcid: 4640298
Pietzner, M. et al. Hepatic steatosis is associated with adverse molecular signatures in subjects without diabetes. J. Clin. Endocrinol. Metab. 103, 3856–3868. https://doi.org/10.1210/jc.2018-00999 (2018).
doi: 10.1210/jc.2018-00999 pubmed: 30060179
Hertel, J. et al. Dilution correction for dynamically influenced urinary analyte data. Anal. Chim. Acta 1032, 18–31. https://doi.org/10.1016/j.aca.2018.07.068 (2018).
doi: 10.1016/j.aca.2018.07.068 pubmed: 30143216
Besser, L. et al. Version 3 of the National Alzheimer’s Coordinating Center’s uniform data set. Alzheimer Dis. Assoc. Disord. 32, 351–358. https://doi.org/10.1097/wad.0000000000000279 (2018).
doi: 10.1097/wad.0000000000000279 pubmed: 30376508 pmcid: 6249084
Johnson, S. C. et al. The Wisconsin Registry for Alzheimer’s Prevention: A review of findings and current directions. Alzheimer’s Dement. 10, 130–142. https://doi.org/10.1016/j.dadm.2017.11.007 (2018).
doi: 10.1016/j.dadm.2017.11.007
Chen, S., Zhou, Y., Chen, Y. & Gu, J. FASTP: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890. https://doi.org/10.1093/bioinformatics/bty560 (2018).
doi: 10.1093/bioinformatics/bty560 pubmed: 30423086 pmcid: 6129281
Li, H. Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100. https://doi.org/10.1093/bioinformatics/bty191 (2018).
doi: 10.1093/bioinformatics/bty191 pubmed: 29750242 pmcid: 6137996
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359. https://doi.org/10.1038/nmeth.1923 (2012).
doi: 10.1038/nmeth.1923 pubmed: 22388286 pmcid: 3322381
Heinken, A., Sahoo, S., Fleming, R. M. & Thiele, I. Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut. Gut Microbes 4, 28–40. https://doi.org/10.4161/gmic.22370 (2013).
doi: 10.4161/gmic.22370 pubmed: 23022739 pmcid: 3555882
Thiele, I., Fleming, R. M., Bordbar, A., Schellenberger, J. & Palsson, B. Functional characterization of alternate optimal solutions of Escherichia coli’s transcriptional and translational machinery. Biophys. J. 98, 2072–2081. https://doi.org/10.1016/j.bpj.2010.01.060 (2010).
doi: 10.1016/j.bpj.2010.01.060 pubmed: 20483314 pmcid: 2872367
Noronha, A. et al. The Virtual Metabolic Human database: Integrating human and gut microbiome metabolism with nutrition and disease. Nucleic Acids Res. 47, D614–D624. https://doi.org/10.1093/nar/gky992 (2019).
doi: 10.1093/nar/gky992 pubmed: 30371894
Heirendt, L. et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox vol 3.0. Nat. Protocols 14, 639–702. https://doi.org/10.1038/s41596-018-0098-2 (2019).
doi: 10.1038/s41596-018-0098-2 pubmed: 30787451
Gudmundsson, S. & Thiele, I. Computationally efficient flux variability analysis. BMC Bioinform. 11, 489 (2010).
doi: 10.1186/1471-2105-11-489
Wan, Y. W. et al. Meta-analysis of the alzheimer’s disease human brain transcriptome and functional dissection in mouse models. Cell Rep. 32, 107908. https://doi.org/10.1016/j.celrep.2020.107908 (2020).
doi: 10.1016/j.celrep.2020.107908 pubmed: 32668255 pmcid: 7428328

Auteurs

Filippo Martinelli (F)

School of Medicine, University of Galway, Galway, Ireland.
The Ryan Institute, University of Galway, Galway, Ireland.

Almut Heinken (A)

School of Medicine, University of Galway, Galway, Ireland.
The Ryan Institute, University of Galway, Galway, Ireland.
Inserm UMRS 1256 NGERE, University of Lorraine, Nancy, France.

Ann-Kristin Henning (AK)

Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany.

Maria A Ulmer (MA)

Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.

Tim Hensen (T)

School of Medicine, University of Galway, Galway, Ireland.
The Ryan Institute, University of Galway, Galway, Ireland.

Antonio González (A)

Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.

Matthias Arnold (M)

Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
Department of Psychiatry and Behavioural Sciences, Duke University, Durham, NC, USA.

Sanjay Asthana (S)

Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA.

Kathrin Budde (K)

Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany.

Corinne D Engelman (CD)

Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.

Mehrbod Estaki (M)

Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.

Hans-Jörgen Grabe (HJ)

German Center of Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Germany.

Margo B Heston (MB)

Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA.

Sterling Johnson (S)

Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA.

Gabi Kastenmüller (G)

Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.

Cameron Martino (C)

Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.

Daniel McDonald (D)

Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.

Federico E Rey (FE)

Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA.

Ingo Kilimann (I)

German Center of Neurodegenerative Diseases (DZNE), Rostock, Germany.
Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany.

Olive Peters (O)

German Center of Neurodegenerative Diseases (DZNE), Berlin, Germany.
Department of Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany.

Xiao Wang (X)

Department of Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany.

Eike Jakob Spruth (EJ)

German Center of Neurodegenerative Diseases (DZNE), Berlin, Germany.
Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany.

Anja Schneider (A)

German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany.
Department of Neurology, University of Bonn, Bonn, Germany.

Klaus Fliessbach (K)

German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany.
Department of Neurology, University of Bonn, Bonn, Germany.

Jens Wiltfang (J)

German Center of Neurodegenerative Diseases (DZNE), Goettingen, Germany.
Department of Psychiatry and Psychotherapy, University of Goettingen, Goettingen, Germany.
Neurosciences and Signaling Group, Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal.

Niels Hansen (N)

Department of Psychiatry and Psychotherapy, University of Goettingen, Goettingen, Germany.

Wenzel Glanz (W)

German Center of Neurodegenerative Diseases (DZNE), Magdeburg, Germany.

Katharina Buerger (K)

German Center of Neurodegenerative Diseases (DZNE), Munich, Germany.
Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.

Daniel Janowitz (D)

Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.

Christoph Laske (C)

German Center of Neurodegenerative Diseases (DZNE), Tübingen, Germany.
Section for Dementia Research, Hertie Institute for Clinical Brain Research, Tübingen, Germany.
Section for Dementia Research, Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.

Matthias H Munk (MH)

German Center of Neurodegenerative Diseases (DZNE), Tübingen, Germany.
Section for Dementia Research, Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.

Annika Spottke (A)

German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany.
Department of Neurology, University of Bonn, Bonn, Germany.

Nina Roy (N)

German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany.

Matthias Nauck (M)

Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany.
DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany.

Stefan Teipel (S)

German Center of Neurodegenerative Diseases (DZNE), Rostock, Germany.
Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany.

Rob Knight (R)

Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
Shu Chien-Gene Lay Department of Engineering, University of California San Diego, La Jolla, CA, USA.
Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.

Rima F Kaddurah-Daouk (RF)

Department of Medicine, Duke University Medical Center, Durham, USA.

Barbara B Bendlin (BB)

Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA.

Johannes Hertel (J)

School of Medicine, University of Galway, Galway, Ireland. Johannes.Hertel@med.uni-greifswald.de.
DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany. Johannes.Hertel@med.uni-greifswald.de.

Ines Thiele (I)

School of Medicine, University of Galway, Galway, Ireland. ines.thiele@universityofgalway.ie.
The Ryan Institute, University of Galway, Galway, Ireland. ines.thiele@universityofgalway.ie.
School of Microbiology, University of Galway, Galway, Ireland. ines.thiele@universityofgalway.ie.
APC Microbiome Ireland, Cork, Ireland. ines.thiele@universityofgalway.ie.

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