Polystyrene Accelerates Aging Related-Gut Microbiome Dysbiosis and -Metabolites in Old-Aged Mouse

Abstract

Microplastics, particularly polystyrene (PS), are ubiquitous environmental contaminants and concerns about their potential detrimental effects on human health are increasing. Emerging evidence suggests that microplastics may disrupt the gut microbiota, a critical ecosystem involved in regulating host metabolism, immunity, and aging processes. However, the specific effects of PS on the gut microbiota composition and its potential role in modulating aging are yet to be fully elucidated. In this study, we aimed to investigate the effects of PS exposure on gut microbiota dysbiosis and its potential role in the acceleration of aging. Gut microbiota composition was assessed using 16S rDNA sequencing, while fecal metabolites were analyzed using gas chromatography-mass spectrometry. Exposure to PS resulted in a significant reduction in the abundance of beneficial microbiota, including Blautia. In contrast, there was an increase in the relative abundance of potentially harmful taxa, such as Lachnospiraceae UCG-001, and Candidatus Arthromitus. Metabolomic analysis revealed elevated levels of several metabolites associated with stress responses and altered host metabolism, including alanine, serine, tryptophan, 5-aminovaleric acid, thymine, threonine, methionine, and benzoic acid. These findings demonstrate that PS exposure in aged mice exacerbated gut microbiome dysbiosis and altered key metabolic markers associated with aging, suggesting an increased vulnerability to age-related diseases as a consequence of microplastic exposure.

Keywords: 16S rDNA sequencing; Polystyrene; aging; gut microbiota; metabolite.

Fig. 1. Measurement of physiologic index on YN, ON and PS group.

(A) Schematic of experiment design. (B) Body weight calculated at 5 weeks; Data were compared using one-way ANOVA with a Benjamini and Hochberg multiple comparison test for individual time points. (C) Comparison of the average grip strength of YN, ON and PS group. (D-G) Histological weight. (H-K) Measurement of serum biochemical indices; AST, ALT, creatinine and glucose levels. Data are expressed as mean ± SD. **** p < 0.0001, * p < 0.05, N.S : not significant.

Fig. 2. Effects of PS intake on the composition of gut microbiota.

Analysis of alpha diversity in control group with young and old mouse compared with treated PS groups (A). Principal coordinate of (B) Bray curtis and Jaccard distance.

Fig. 3. Relative abundances of gut microbiota and LDA in YN, ON and PS groups.

Bar graphs of the LDA score based on LefSe analysis (A). Bacterial composition at the phylum (B), and genus (D) levels. Relative abundance of microbiota at the phylum (C) and genus level showing significant differences among the three groups (YN, ON and PS) (E). Data are expressed as mean ± SD. * p < 0.05.

Fig. 4. Fecal metabolites were altered by control (YN, ON) and PS treatment group.

Principal-component analysis (PCA) plot (A) and partial least squares discriminant analysis (PLS-DA) score plot (B) of fecal metabolites in three groups. Relative abundance of metabolites showing significant differences among the three groups (YN, ON and PS) (C). Data are expressed as mean ± SD. Selected metabolites identified with a VIP score of 1.0 and significantly different metabolites are marked with * p < 0.05.

Fig. 5. Fecal microbiome and metabolite were altered by PS treatment group of administration date.

Principal-component analysis (PCA) plot of Bray curtis of fecal microbiome (A) Principal-component analysis (PCA) plot (B) of fecal metabolites in three groups. (C) Microbial taxa and (D) metabolite that exhibited an increasing or decreasing trend following PS administration. Data are expressed as mean ± SD. Selected metabolites identified with a VIP score of 1.0 and significantly different metabolites are marked with * p < 0.05.

Fig. 6. Summary of pathway analysis by MetaboAnalyst 6.0.

The top pathways are ranked based on gamma-adjusted p-values for permutation per pathway (y-axis) and the total number of occurrences per pathway (x-axis). The color gradient transitions from white to yellow, orange, and red as both x and y values increase. (A) Metabolic pathways that show significant changes among the three groups (YN, ON, PS). (B) Metabolic pathways significantly altered during the PS administration period.