Gut microbiota-driven metabolic alterations reveal gut–brain communication in Alzheimer’s disease model mice

ABSTRACT

The gut microbiota (GM) and its metabolites affect the host nervous system and are involved in the pathogeneses of various neurological diseases. However, the specific GM alterations under pathogenetic pressure and their contributions to the “microbiota – metabolite – brain axis” in Alzheimer’s disease (AD) remain unclear. Here, we investigated the GM and the fecal, serum, cortical metabolomes in APP/PS1 and wild-type (WT) mice, revealing distinct hub bacteria in AD mice within scale-free GM networks shared by both groups. Moreover, we identified diverse peripheral – central metabolic landscapes between AD and WT mice that featured bile acids (e.g. deoxycholic and isodeoxycholic acid) and unsaturated fatty acids (e.g. 11Z-eicosenoic and palmitoleic acid). Machine-learning models revealed the relationships between the differential/hub bacteria and these metabolic signatures from the periphery to the brain. Notably, AD-enriched Dubosiella affected AD occurrence via cortical palmitoleic acid and vice versa. Considering the transgenic background of the AD mice, we propose that Dubosiella enrichment impedes AD progression via the synthesis of palmitoleic acid, which has protective properties against inflammation and metabolic disorders. We identified another association involving fecal deoxycholic acid-mediated interactions between the AD hub bacteria Erysipelatoclostridium and AD occurrence, which was corroborated by the correlation between deoxycholate levels and cognitive scores in humans. Overall, this study elucidated the GM network alterations, contributions of the GM to peripheral – central metabolic landscapes, and mediatory roles of metabolites between the GM and AD occurrence, thus revealing the critical roles of bacteria in AD pathogenesis and gut – brain communications under pathogenetic pressure.

KEYWORDS:

• Alzheimer’s disease
• gut microbiota
• metabolomes
• gut–brain communication
• bacterial co-occurrence network
• mediation effect

Acknowledgments

We thank the members of the Chen Laboratory for their helpful discussions and insights.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Y.J.C, Y.H.L, and Y.C conceived of the project, designed the experiments, and wrote the manuscript. Y.J.C. and Y.Y.F performed the experiments. Y.J.C, Y.H.L, S.C., L.C, Y.W.C, and Y.C analyzed the data. All authors reviewed the manuscript.

Data availability statement

The dataset supporting the conclusions of this article is available in the NCBI Sequence Read Archive repository under BioProject accession number PRJNA815698 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA815698?reviewer=4daauib1h4drvj8v5lfbc2rmhh). The code for the complex network-based gut microbiota modeling algorithm is publicly available at GitHub (https://github.com/liyinhu/GM_network_modeling). To acquire any other study information or data for reasonable uses, please kindly message the e-mail for correspondence.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19490976.2024.2302310

Additional information

Funding

This study was supported in part by the National Key R&D Program of China (2021YFE0203000), the National Natural Science Foundation of China (NSFC)/RGC Joint Research Scheme (32061160472), the Guangdong Provincial Fund for Basic and Applied Basic Research (2019B1515130004 and 2019A1515011425), the Guangdong Provincial Key S&T Program (2018B030336001), the Shenzhen Knowledge Innovation Program (JCYJ20220818100800001, JCYJ20200109115631248, and ZDSYS20200828154800001), and the Fundamental research program of Shenzhen Virtual University Park (2021Szvup137).