Dynamic network modeling of gut microbiota during Alzheimer’s disease progression in mice

ABSTRACT

The intimate association between the gut microbiota (GM) and the central nervous system points to potential intervention strategies for neurological diseases. Nevertheless, there is currently no theoretical framework for selecting the window period and target bacteria for GM interventions owing to the complexity of the gut microecosystem. In this study, we constructed a complex network-based modeling approach to evaluate the topological features of the GM and infer the window period and bacterial candidates for GM interventions. We used Alzheimer’s disease (AD) as an example and traced the GM dynamic changes in AD and wild-type mice at one, two, three, six, and nine months of age. The results revealed alterations of the topological features of the GM from a scale-free network into a random network during AD progression, indicating severe GM disequilibrium at the late stage of AD. Through stability and vulnerability assessments of the GM networks, we identified the third month after birth as the optimal window period for GM interventions in AD mice. Further computational simulations and robustness evaluations determined that the hub bacteria were potential candidates for GM interventions. Moreover, our GM functional analysis suggested that Lachnospiraceae UCG-001 – the hub and enriched bacterium in AD mice – was the keystone bacterium for GM interventions owing to its contributions to quinolinic acid synthesis. In conclusion, this study established a complex network-based modeling approach as a practical strategy for disease interventions from the perspective of the gut microecosystem.

KEYWORDS:

• Alzheimer’s disease
• complex networks
• gut microbiota
• computational simulation
• window period
• hub bacteria

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

Data availability statement

The complex network-based GM modeling algorithm codes and computational simulation process are publicly available in the GitHub repository (https://github.com/liyinhu/GM_network_modeling). The 16S rRNA sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) repository under BioProject accession number PRJNA543965 (https://www.ncbi.nlm.nih.gov/bioproject/543965). All other data are available from the authors upon reasonable request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19490976.2023.2172672

Additional information

Funding

This study was supported in part by the National Key R&D Program of China (2018YFE0203600 and 2021YFE0203000); the National Natural Science Foundation of China (NSFC)/RGC Joint Research Scheme (32061160472); the National Natural Science Foundation of China (32100778); the Guangdong Provincial Fund for Basic and Applied Basic Research (2019B1515130004); the Guangdong Provincial Key S&T Program (2018B030336001); the Shenzhen Knowledge Innovation Program (JCYJ20200109115631248, JCYJ20220818100800001, and ZDSYS20200828154800001).