Sex-specific associations between AD genotype and the microbiome of human amyloid beta knock-in (hAβ-KI) mice.
3xTg‐AD
Alzheimer's disease
Turicibacter
cage effects
hAβ‐KI
late‐onset Alzheimer's disease
metabolomics
metagenomics
microbiome
mouse models for Alzheimer's disease
Journal
Alzheimer's & dementia : the journal of the Alzheimer's Association
ISSN: 1552-5279
Titre abrégé: Alzheimers Dement
Pays: United States
ID NLM: 101231978
Informations de publication
Date de publication:
04 Apr 2024
04 Apr 2024
Historique:
revised:
20
02
2024
received:
24
10
2023
accepted:
23
02
2024
medline:
4
4
2024
pubmed:
4
4
2024
entrez:
4
4
2024
Statut:
aheadofprint
Résumé
Emerging evidence links changes in the gut microbiome to late-onset Alzheimer's disease (LOAD), necessitating examination of AD mouse models with consideration of the microbiome. We used shotgun metagenomics and untargeted metabolomics to study the human amyloid beta knock-in (hAβ-KI) murine model for LOAD compared to both wild-type (WT) mice and a model for early-onset AD (3xTg-AD). Eighteen-month female (but not male) hAβ-KI microbiomes were distinct from WT microbiomes, with AD genotype accounting for 18% of the variance by permutational multivariate analysis of variance (PERMANOVA). Metabolomic diversity differences were observed in females, however no individual metabolites were differentially abundant. hAβ-KI mice microbiomes were distinguishable from 3xTg-AD animals (81% accuracy by random forest modeling), with separation primarily driven by Romboutsia ilealis and Turicibacter species. Microbiomes were highly cage specific, with cage assignment accounting for more than 40% of the PERMANOVA variance between the groups. These findings highlight a sex-dependent variation in the microbiomes of hAβ-KI mice and underscore the importance of considering the microbiome when designing studies that use murine models for AD. Microbial diversity and the abundance of several species differed in human amyloid beta knock-in (hAβ-KI) females but not males. Correlations to Alzheimer's disease (AD) genotype were stronger for the microbiome than the metabolome. Microbiomes from hAβ-KI mice were distinct from 3xTg-AD mice. Cage effects accounted for most of the variance in the microbiome and metabolome.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIA NIH HHS
ID : U54 AG054349
Pays : United States
Organisme : NIA NIH HHS
ID : T32 AG00096-38
Pays : United States
Informations de copyright
© 2024 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
Références
Ursell LK, Metcalf JL, Parfrey LW, Knight R. Defining the human microbiome. Nutr Rev. 2012;70(1):S38‐S44. doi:10.1111/j.1753‐4887.2012.00493.x
Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. Diversity, stability and resilience of the human gut microbiota. Nature. 2012;489(7415):220‐230. doi:10.1038/nature11550
Limbana T, Khan F, Eskander N. Gut microbiome and depression: how microbes affect the way we think. Cureus. 2020;12(8):e9966. doi:10.7759/cureus.9966
Carabotti M, Scirocco A, Maselli MA, Severi C. The gut‐brain axis: interactions between enteric microbiota, central and enteric nervous systems. Ann Gastroenterol. 2015;28(2):203‐209.
Vogt NM, Kerby RL, Dill‐McFarland KA, et al. Gut microbiome alterations in Alzheimer's disease. Sci Rep. 2017;7(1):13537. doi:10.1038/s41598‐017‐13601‐y
Angelucci F, Cechova K, Amlerova J, Hort J. Antibiotics, gut microbiota, and Alzheimer's disease. J Neuroinflammation. 2019;16(1):108. doi:10.1186/s12974‐019‐1494‐4
Nassar ST, Tasha T, Desai A, et al. Fecal microbiota transplantation role in the treatment of Alzheimer's disease: a systematic review. Cureus. 2022;14(10):e29968. doi:10.7759/cureus.29968
Sochocka M, Donskow‐Łysoniewska K, Diniz BS, Kurpas D, Brzozowska E, Leszek J. The gut microbiome alterations and inflammation‐driven pathogenesis of Alzheimer's disease‐a critical review. Mol Neurobiol. 2019;56(3):1841‐1851. doi:10.1007/s12035‐018‐1188‐4
Dunham SJB, McNair KA, Adams ED, et al. Longitudinal analysis of the microbiome and metabolome in the 5xfAD mouse model of Alzheimer's disease. mBio. 2022;13(6):e0179422. doi:10.1128/mbio.01794‐22
Petrisko TJ, Gargus M, Chu SH, Selvan P, Whiteson KL, Tenner AJ. Influence of complement protein C1q or complement receptor C5aR1 on gut microbiota composition in wildtype and Alzheimer's mouse models. J Neuroinflammation. 2023;20(1):211. doi:10.1186/s12974‐023‐02885‐9
Thurman CE, Klores MM, Wolfe AE, et al. Effect of housing condition and diet on the gut microbiota of weanling immunocompromised mice. Comp Med. 2021;71(6):485‐491. 10.30802/AALAS‐CM‐21‐000015
Ericsson AC, Gagliardi J, Bouhan D, Spollen WG, Givan SA, Franklin CL. The influence of caging, bedding, and diet on the composition of the microbiota in different regions of the mouse gut. Sci Rep. 2018;8(1):4065. doi:10.1038/s41598‐018‐21986‐7
Russell A, Copio JN, Shi Y, Kang S, Franklin CL, Ericsson AC. Reduced housing density improves statistical power of murine gut microbiota studies. Cell Rep. 2022;39(6):110783. doi:10.1016/j.celrep.2022.110783
Singh G, Brass A, Cruickshank SM, Knight CG. Cage and maternal effects on the bacterial communities of the murine gut. Sci Rep. 2021;11(1):9841. doi:10.1038/s41598‐021‐89185‐5
Peng C, Xu X, Li Y, et al. Sex‐specific association between the gut microbiome and high‐fat diet‐induced metabolic disorders in mice. Biol Sex Differ. 2020;11(1):5. doi:10.1186/s13293‐020‐0281‐3
Wang X, Sun G, Feng T, et al. Sodium oligomannate therapeutically remodels gut microbiota and suppresses gut bacterial amino acids‐shaped neuroinflammation to inhibit Alzheimer's disease progression. Cell Res. 2019;29(10):787‐803. doi:10.1038/s41422‐019‐0216‐x
Lipinski JH, Zhou X, Gurczynski SJ, et al. Cage environment regulates gut microbiota independent of toll‐like receptors. Infect Immun. 2021;89(9):e0018721. doi:10.1128/IAI.00187‐21
Borsom EM, Conn K, Keefe CR, et al. Predicting neurodegenerative disease using prepathology gut microbiota composition: a longitudinal study in mice modeling Alzheimer's disease pathologies. Microbiol Spectr. 2023;11(2):e0345822. doi:10.1128/spectrum.03458‐22
Baglietto‐Vargas D, Forner S, Cai L, et al. Generation of a humanized Aβ expressing mouse demonstrating aspects of Alzheimer's disease‐like pathology. Nat Commun. 2021;12(1):2421. doi:10.1038/s41467‐021‐22624‐z
Weihe C, Avelar‐Barragan J, Next generation shotgun library preparation for Illumina sequencing – low volume. protocols.io. 2021 Jul. doi: 10.17504/protocols.io.bvv8n69w
Ding J, Ji J, Rabow Z, et al. A metabolome atlas of the aging mouse brain. Nat Commun. 2021;12:6021. doi:10.1038/s41467‐021‐26310‐y
Fan S, Kind T, Cajka T, et al. Systematic error removal using random forest for normalizing large‐scale untargeted lipidomics data. Anal Chem. 2019;91(5):3590‐3596. doi:10.1021/acs.analchem.8b05592
Djoumbou Feunang Y, Eisner R, Knox C, et al. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform. 2016;8:61. doi:10.1186/s13321‐016‐0174‐y
Wohlgemuth G, Haldiya PK, Willighagen E, Kind T, Fiehn O. The chemical translation service–a web‐based tool to improve standardization of metabolomic reports. Bioinformatics. 2010;26(20):2647‐2648. doi:10.1093/bioinformatics/btq476
Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20(1):257. doi:10.1186/s13059‐019‐1891‐0
Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Computer Sci. 2017;3:e104. doi:10.7717/peerj‐cs.104
Zhang Z, Ersoz E, Lai C‐Q, et al. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2014. http://www.R‐project.org/
Wickham H. ggplot2: elegant graphics for data analysis. Springer‐Verlag; 2009.
Kolde R. pheatmap: pretty heatmaps. 2019. https://cran.r‐project.org/web/packages/pheatmap/index.html
Oksanen J, Blanchet FG, Friendly M, et al. vegan: community ecology package. 2017. Accessed on 1 April 2021. https://github.com/vegandevs/vegan
Archer E, rfPermute: estimate permutation p‐values for random forest importance metrics. 2020. R package version 2.1.81. https://www.CRANR‐projectorg/package=rfPermute
Kuznetsova A, Brockhoff PB, Christensen RHB. {lmerTest} package: tests in linear mixed effects models. J Statistical Software. 2017;82:1‐26. doi:10.18637/jss.v082.i13
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2013. ISBN 3‐900051‐07‐0, URL http://www.R‐project.org/</bib
Cox LM, Schafer MJ, Sohn J, et al. Calorie restriction slows age‐related microbiota changes in an Alzheimer's disease model in female mice. Sci Rep. 2019;9(1):17904. doi:10.1038/s41598‐019‐54187‐x
Bosch M, Dodiya H, Michalkiewicz J, et al. Sodium oligomannate alters gut microbiota, reduces cerebral amyloidosis, and reactive microglia in a dose‐ and sex‐specific manner. Research Square. 2023. doi:10.21203/rs.3.rs‐3394003/v1
Fisher DW, Bennett DA, Dong H. Sexual dimorphism in predisposition to Alzheimer's disease. Neurobiol Aging. 2018;70:308‐324. doi:10.1016/j.neurobiolaging.2018.04.004
Mapstone M, Cheema AK, Fiandaca MS, et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med. 2014;20(4):415‐418. doi:10.1038/nm.3466
Fiandaca MS, Zhong X, Cheema AK, et al. Plasma 24‐metabolite panel predicts preclinical transition to clinical stages of Alzheimer's disease. Front Neurol. 2015;6:237. doi:10.3389/fneur.2015.00237
Ferreiro AL, Choi J, Ryou J, et al. Gut microbiome composition may be an indicator of preclinical Alzheimer's disease. Sci Transl Med. 2023;15(700):eabo2984. doi:10.1126/scitranslmed.abo2984
Zhan Y, Al‐Nusaif M, Ding C, Zhao L, Dong C. The potential of the gut microbiome for identifying Alzheimer's disease diagnostic biomarkers and future therapies. Front Neurosci. 2023;17:1130730. doi:10.3389/fnins.2023.1130730
Brown K, Thomson CA, Wacker S, et al. Microbiota alters the metabolome in an age‐ and sex‐ dependent manner in mice. Nat Commun. 2023;14(1):1348. doi:10.1038/s41467‐023‐37055‐1
Falony G, Joossens M, Vieira‐Silva S, et al. Population‐level analysis of gut microbiome variation. Science. 2016;352(6285):560‐564. doi:10.1126/science.aad3503
Fung TC, Vuong HE, Luna CDG, et al. Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut. Nat Microbiol. 2019;4(12):2064‐2073. doi:10.1038/s41564‐019‐0540‐4
Aaldijk E, Vermeiren Y. The role of serotonin within the microbiota‐gut‐brain axis in the development of Alzheimer's disease: a narrative review. Ageing Res Rev. 2022;75:101556. doi:10.1016/j.arr.2021.101556
Patiño‐Navarrete R, Moya A, Latorre A, Peretó J. Comparative genomics of Blattabacterium cuenoti: the frozen legacy of an ancient endosymbiont genome. Genome Biol Evol. 2013;5(2):351‐361. doi:10.1093/gbe/evt011
Moran NA. Microbe Profile: buchnera aphidicola: ancient aphid accomplice and endosymbiont exemplar. Microbiology (Reading). 2021;167(12):001127. doi:10.1099/mic.0.001127
Olsson LM, Boulund F, Nilsson S, et al. Dynamics of the normal gut microbiota: a longitudinal one‐year population study in Sweden. Cell Host Microbe. 2022;30(5):726‐739. doi:10.1016/j.chom.2022.03.002
Bonilla Salinas M, Fardeau ML, Thomas P, Cayol JL, Patel BKC, Ollivier B. Mahella australiensis gen. nov., sp. nov., a moderately thermophilic anaerobic bacterium isolated from an Australian oil well. Int J Syst Evol Microbiol. 2004;54:2169‐2173. doi:10.1099/ijs.0.02926‐0
da Silva RR, Dorrestein PC, Quinn RA. Illuminating the dark matter in metabolomics. Proc Natl Acad Sci U S A. 2015;112(41):12549‐12550. doi:10.1073/pnas.1516878112
Zünd M, Dunham SJB, Rothman JA, Whiteson KL. What lies beneath? Taking the plunge into the murky waters of phage biology. mSystems. 2023;8(1):e0080722. doi:10.1128/msystems.00807‐22
Song SJ, Lauber C, Costello EK, et al. Cohabiting family members share microbiota with one another and with their dogs. Elife. 2013;2:e00458. doi:10.7554/eLife.00458
Valles‐Colomer M, Blanco‐Míguez A, Manghi P, et al. The person‐to‐person transmission landscape of the gut and oral microbiomes. Nature. 2023;614(7946):125‐135. doi:10.1038/s41586‐022‐05620‐1
Rothman JA, Riis JL, Hamilton KR, Blair C, Granger DA, Whiteson KL. Oral microbial communities in children, caregivers, and associations with salivary biomeasures and environmental tobacco smoke exposure. mSystems. 2023;8(4):e0003623. doi:10.1128/msystems.00036‐23
Deloris Alexander A, Orcutt RP, Henry JC, Baker J Jr, Bissahoyo AC, Threadgill DW. Quantitative PCR assays for mouse enteric flora reveal strain‐dependent differences in composition that are influenced by the microenvironment. Mamm Genome. 2006;17:1093‐1104. doi:10.1007/s00335‐006‐0063‐1
Hildebrand F, Nguyen TL, Brinkman B, et al. Inflammation‐associated enterotypes, host geno‐ type, cage and inter‐individual effects drive gut microbiota variation in common laboratory mice. Genome Biol. 2013;14:R4. doi:10.1186/gb‐2013‐14‐1‐r4
Kim D, Hofstaedter CE, Zhao C, et al. Optimizing methods and dodging pitfalls in microbiome research. Microbiome. 2017;5:52. doi:10.1186/s40168‐017‐0267‐5
Ericsson AC, Franklin CL. The gut microbiome of laboratory mice: considerations and best practices for translational research. Mamm Genome. 2021;32(4):239‐250. doi:10.1007/s00335‐021‐09863‐7
Rosshart SP, Herz J, Vassallo BG, et al. Laboratory mice born to wild mice have natural microbiota and model human immune responses. Science. 2019;365(6452):eaaw4361. doi:10.1126/science.aaw4361