Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Mar 13;14(1):6095.
doi: 10.1038/s41598-024-55960-3.

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

Filippo Martinelli  1   2 , Almut Heinken  1   2   3 , Ann-Kristin Henning  4 , Maria A Ulmer  5 , Tim Hensen  1   2 , Antonio González  6 , Matthias Arnold  5   7 , Sanjay Asthana  8 , Kathrin Budde  4 , Corinne D Engelman  9 , Mehrbod Estaki  6 , Hans-Jörgen Grabe  10 , Margo B Heston  8 , Sterling Johnson  8 , Gabi Kastenmüller  5 , Cameron Martino  6 , Daniel McDonald  6 , Federico E Rey  11 , Ingo Kilimann  12   13 , Olive Peters  14   15 , Xiao Wang  15 , Eike Jakob Spruth  14   16 , Anja Schneider  17   18 , Klaus Fliessbach  17   18 , Jens Wiltfang  19   20   21 , Niels Hansen  20 , Wenzel Glanz  22 , Katharina Buerger  23   24 , Daniel Janowitz  24 , Christoph Laske  25   26   27 , Matthias H Munk  25   27 , Annika Spottke  17   18 , Nina Roy  17 , Matthias Nauck  4   28 , Stefan Teipel  12   13 , Rob Knight  6   29   30   31   32 , Rima F Kaddurah-Daouk  33 , Barbara B Bendlin  8 , Johannes Hertel #  34   35 , Ines Thiele #  36   37   38   39
Affiliations

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

Filippo Martinelli et al. Sci Rep. .

Erratum in

  • Author Correction: Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease.
    Martinelli F, Heinken A, Henning AK, Ulmer MA, Hensen T, González A, Arnold M, Asthana S, Budde K, Engelman CD, Estaki M, Grabe HJ, Heston MB, Johnson S, Kastenmüller G, Martino C, McDonald D, Rey FE, Kilimann I, Peters O, Wang X, Spruth EJ, Schneider A, Fliessbach K, Wiltfang J, Hansen N, WenzelGlanz, Buerger K, Janowitz D, Laske C, Munk MH, Spottke A, Roy N, Nauck M, Teipel S, Knight R, Kaddurah-Daouk RF, Bendlin BB, Hertel J, Thiele I. Martinelli F, et al. Sci Rep. 2024 Nov 12;14(1):27692. doi: 10.1038/s41598-024-78228-2. Sci Rep. 2024. PMID: 39532987 Free PMC article. No abstract available.

Abstract

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.

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

PubMed Disclaimer

Conflict of interest statement

G.K. and M.A. are co-inventors of several patent applications on the use of metabolomics in Alzheimer’s disease and own equity and IP in Chymia LLC and IP in PsyProtix unrelated to this work. R.K.D. is an inventor of a series of patents on the use of metabolomics for the diagnosis and treatment of CNS diseases and holds equity in Metabolon Inc., Chymia LLC and PsyProtix. S.T. has served on national and international advisory boards of Roche, Eisai, Grifols, and Biogen, and is a member of the independent data safety and monitoring board of the study ENVISION (Biogen). H.J.G. has received travel grants and speaker’s honoraria from Fresenius Medical Care, Neuraxpharm, Servier, and Janssen Cilag as well as research funding from Fresenius Medical Care. R.K. is a scientific advisory board member, and consultant for BiomeSense, Inc., has equity and receives income. He is a scientific advisory board member and has equity in GenCirq. He is a consultant and scientific advisory board member for DayTwo, and receives income. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a cofounder of Micronoma, and has equity and is a scientific advisory board member. D.M. is a consultant for, and has stock in, BiomeSense, Inc. The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies.

Figures

Figure 1
Figure 1
Workflow summary HC Healthy Controls, SCD Subjective Cognitive Decline, MCI Mild Cognitive Impairment, AD Alzheimer’s disease.
Figure 2
Figure 2
1D-NMR metabolomic data on urine samples from the DELCODE study cohort support altered metabolism in AD, including formate metabolism. (A) Descriptive statistics for the analysed DELCODE samples. *p-value derived from one-factorial ANOVA, #p-value derived from Fisher’s exact test. SCD subjective cognitive decline, MCI mild cognitive impairment, AD Alzheimer’s disease, BMI body mass index. (B, C) Box plots for formate and fumarate (both creatinine normalised) over the four study groups. p-Values were derived from multivariable regressions adjusting for age, sex, and BMI using heteroscedastic robust standard errors. SCD subjective cognitive decline. MCI mild cognitive impairment. AD Alzheimer’s disease.
Figure 3
Figure 3
Descriptive statistics for the analysed samples and their corresponding Qiita-derived microbiome models, and diversity analysis on microbial relative abundance. (A) Samples and model characteristics of the Wisconsin cohort studies. AD Alzheimer’s disease, SD Standard deviation, ap-value from Welch t-tests, bp-value from Fisher’s exact test. Full results can be found in Table S03. (B) Boxplot of gOTU log-ratio analysis on microbes, whose models’ relative abundances were found to be altered between healthy and dementia-AD participants, p-value from Welch t-tests. The full results of the relative abundance analysis can be found in Table S04.
Figure 4
Figure 4
Evaluation of host-microbiome involvement in urine secretion of microbe-derived metabolites. (A, B) Breakdown of maximum sex-specific urine formate secretion highlighting the presence of microbiome-host co-metabolism. (C) Average metabolite microbiome secretion when the host-microbiome WBMs were interrogated for the maximum urine secretion, and urine secretions when the specific metabolites were unconstrained in the diet for the germ-free models.
Figure 5
Figure 5
Cellular metabolism involved in the production of formate including reactions found responsible for host-microbiota co-metabolism. Dotted lines represent diet constituent involvement in the overall formate production, circled reactions when deleted together largely reduced the formate urinary production. Metabolites names (abbreviations are given in VMH IDs, www.vmh.life,): 10fth 10-formyl-THF; 3 pg 3-phosphoglyceric acid; chol choline; cys_L L-cysteine; dmgly dimethylglycine; fald formaldehyde; for formate; glc_D D-glucose; gly glycine; glyb betaine; glyc glycerol; glyc3p glycerol-3phosphate; gthrd GSH; his_L L-histidine; lac_D D-lactate; lkynr L-kynurenine; meoh methanol; methf 5,10-methenyl-THF; mlthf 5,10-methylene-THF; mma methylamine; orn ornithine; sarcs sarcosine; ser_L L-serine; thf tetrahydrofolate; trp_L L-tryptophane; tyr_L L-tyrosine. Reactions’ names can be found in Table S07. Complete KO analysis can be found in Table S08. Differential gene expression (DEG) and differential protein abundance (DEP) analysis of association to AD-related phenotypes can be found in Table S09.
See this image and copyright information in PMC

Update of

References

    1. 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.10.3390/ijms21207481 (2020). - PMC - PubMed
    1. Knopman, D. S. et al. Alzheimer disease. Nat. Rev. Dis. Primers7, 33. 10.1038/s41572-021-00269-y (2021). - PMC - PubMed
    1. 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. 10.1590/S1980-57642010DN40400003 (2010). - PMC - PubMed
    1. Toledo, J. B. et al. Metabolic network failures in Alzheimer’s disease: A biochemical road map. Alzheimers Dement.13, 965–984. 10.1016/j.jalz.2017.01.020 (2017). - PMC - PubMed
    1. Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med.375, 2369–2379. 10.1056/NEJMra1600266 (2016). - PubMed