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S O U R C E : nature MEDICINE
The gut microbiome is emerging as a key regulator of several metabolic, immune and neuroendocrine pathways1,2. Gut microbiome deregulation has been implicated in major conditions such as obesity, type 2 diabetes, cardiovascular disease, non-alcoholic fatty acid liver disease and cancer3,4,5,6, but its precise role in aging remains to be elucidated. Here, we find that two different mouse models of progeria are characterized by intestinal dysbiosis with alterations that include an increase in the abundance of Proteobacteria and Cyanobacteria, and a decrease in the abundance of Verrucomicrobia. Consistent with these findings, we found that human progeria patients also display intestinal dysbiosis and that long-lived humans (that is, centenarians) exhibit a substantial increase in Verrucomicrobia and a reduction in Proteobacteria. Fecal microbiota transplantation from wild-type mice enhanced healthspan and lifespan in both progeroid mouse models, and transplantation with the verrucomicrobia Akkermansia muciniphila was sufficient to exert beneficial effects. Moreover, metabolomic analysis of ileal content points to the restoration of secondary bile acids as a possible mechanism for the beneficial effects of reestablishing a healthy microbiome. Our results demonstrate that correction of the accelerated aging-associated intestinal dysbiosis is beneficial, suggesting the existence of a link between aging and the gut microbiota that provides a rationale for microbiome-based interventions against age-related diseases.
Traditionally seen as detrimental, the pathophysiological impli-cations of the microbiota have expanded considerably in recent years. It is now known that the microbiota has essential metabolic and immunological functions that are conserved from worms7 to humans1,2. In mammals, the gut microbiota is involved in food pro-cessing, activation of satiety pathways, protection against pathogens and production of metabolites including vitamins, short-chain fatty acids and secondary bile acids8–10. The gut microbiota also signals to distant organs, contributing to the maintenance of host physi-ology11. Intestinal microbiota alterations are associated with major conditions like obesity, type 2 diabetes, cardiovascular disease, non-alcoholic fatty acid liver disease, cancer and the response to antineo-plastic therapy3–6.
Although some works have explored the microbiome profile of long-lived humans12,13, no alterations have been described in accel-erated aging syndromes. In this work, we study the gut microbiome of two mouse models of Hutchinson–Gilford progeria syndrome (HGPS), patients with HGPS14 and Nestor–Guillermo progeria syn-drome (NGPS)15, as well as human centenarians and their controls. We found intestinal dysbiosis in both mouse models and progeria patients. In turn, the microbiota of centenarians is characterized by the presence of both pathological and health-associated bacte-rial genera. We show that fecal microbiota transplantation (FMT) from wild-type (WT) donors to progeroid recipients attenuates the accelerated-aging phenotype and increases survival, whereas FMT from progeroid donors to WT recipients induces metabolic altera-tions. Analysis of centenarians and progeria mouse models points to a beneficial role for the genus Akkermansia, as oral gavage of Akkermansia muciniphila extends the lifespan of progeroid mice.
To explore the relevance of the microbiome in progeria, we first studied the gut metagenome profile of the LmnaG609G/G609G mouse model of HGPS16, by comparing WT and LmnaG609G/G609G mice at three different ages: 1 month (WT 1mo and LmnaG609G/G609G 1mo), 4 months (when LmnaG609G/G609G mice exhibit a progeroid phenotype; WT 4mo and LmnaG609G/G609G 4mo) and 22 months (for WT mice only; WT 22mo; Extended Data Fig. 1a). To assess how progeria affects the gut microbial community structure, we studied the alpha- and beta-diversity associated with each genotype and compared the micro-bial diversity within and between communities. Alpha-diversity was analyzed by calculating the Chao1 (a proxy for community richness) and Shannon’s index (a proxy for diversity, taking into account both richness and evenness). We did not observe differences in bacterial diversity or richness within any of the mouse groups (Extended Data LmnaG609G/G609G 4mo mice in a principal coordinates analysis (PCoA) using the Bray–Curtis dissimilarity (qualitative measure) (Fig. 1a) and Jaccard distances (quantitative measure) (Extended Data Fig. 1d).
Biodiversity in wild-type (WT) and LmnaG609G/G609G mice at different ages a, Scheme representing the microbiome profiling experiment in LmnaG609G/G609G mice. b,c, Comparison of alpha-diversity of gut microbiota among all groups of mice using Shannon’s index (diversity) (b) and the Chao1 index (richness) ©. Analyses were performed using Kruskal–Wallis test (Supplementary Table 1). In the box plots, upper and lower hinges correspond to the first and third quartiles, the center line represents the median, whiskers indicate the highest and lowest values that are within 1.5 × IQR and data beyond the end of the whiskers are outliers and plotted as points. d, Principal coordinates analysis (PCoA) of beta-diversity using the Jaccard distance metric among samples of the five groups analyzed (P < 0.001; PERMANOVA) (Supplementary Table 1). e,f, UPGMA trees representing the beta-diversity analysis of all groups analyzed using the Bray–Curtis dissimilarity (e) and the Jaccard distance metrics (f). For a–e, WT 1mo, n = 6; G609G 1mo, n = 5; WT 4mo, n = 8; G609G 4mo, n = 9; WT 22mo, n = 8.
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F O R T H E R E S T O F T H E S T U D Y , P L E A S E V I S I T T H E S O U R C E .
Edited by Engadin, 31 August 2019 - 07:25 PM.
