Metabolic Reprogramming in Gut Microbiota Exposed to Polystyrene Microplastics

Abstract

Background: Microplastics (MPs) are small plastic fragments with diameters less than 5 mm in size and are prevalent in everyday essentials and consumables. Large global plastic production has now led to a flooding of MPs in our natural environment. Due to their detrimental impacts on the planet's ecosystems and potentially our health, MPs have emerged as a significant public health concern. In this pilot study, we hypothesize that MPs exposure will negatively affect gut microbiota composition and function, in which metabolic reprogramming plays an important role. Methods: Using in vitro experiments, three bacterial strains (Escherichia coli MG1655, Nissle 1917, and Lactobacillus rhamnosus) were selected to investigate the impacts of MPs exposure. The bacterial strains were individually cultured in an anaerobic chamber and exposed to 1 µm polystyrene MPs at various concentrations (0, 10, 20, 50, 100, and 500 µg/mL) in the culture medium. Results: MPs exposure reduced the growth of all three bacterial strains in a dose-dependent manner. Liquid chromatography mass spectrometry (LC-MS)-based untargeted metabolomics revealed significant differences in multiple metabolic pathways, such as sulfur metabolism and amino sugar and nucleotide sugar metabolism. In addition, we extracted gut microbiota from C57BL/6 mice, and 16S rRNA sequencing results showed a significant upregulation of Lactobacillales and a significant reduction in Erysipelotrichales due to MPs exposure. Furthermore, targeted and untargeted metabolomics corroborated the in vitro results and revealed alterations in microbial tryptophan metabolism and energy producing pathways, such as glycolysis/gluconeogenesis and the pentose phosphate pathway. Conclusions: These findings provide evidence that MPs exposure causes comprehensive changes to healthy gut microbiota, which may also provide insights into the mechanistic effects of MPs exposure in humans.

Keywords: 16S rRNA; gut microbiota; mass spectrometry; metabolomics; microplastics.

Figure 1

The overall scheme of the research processes.

Figure 2

MPs induce decrease of bacterial viabilities. (A,D) E. coli, (B,E) Nissle 1917, and (C,F) L. rhamnosus growth in response to MPs treatment (0, 10, 20, 50, 100, 500 µg/mL) for 24 h. Cell density was measured from optical density (600 nm). Data are presented as mean ± SD (n = 6). * p < 0.05, ** p < 0.01 and **** p < 0.0001 versus 0 µg/mL.

Figure 3

Fluorescence staining of (A,B) E. coli, (C,D) Nissle 1917, and (E,F) L. rhamnosus which were incubated with broth with or without MPs (100 µg/mL) for 24 h, respectively. Scale bars, 20 µm. Live bacteria with intact cell membranes emit green fluorescence, whereas dead bacteria with damaged membranes give red fluorescence.

Figure 4

PCA score plots of the untargeted metabolomics data from three different gut bacteria. (A) E. coli, (B) Nissle1917, and (C) L. rhamnosus. Green dots: control group; blue dots: 50 µg/mL (E. coli) or 20 µg/mL (Nissle1917 and L. rhamnosus) MPs exposure group; red dots: 100 µg/mL MPs exposure group.

Figure 5

Metabolic pathway and enzyme enrichment analyses of three different gut bacteria after 100 µg/mL MPs exposure. (A,B) E. coli, (C,D) Nissle 1917, (E,F) L. rhamnosus. The metabolic pathways are represented as circles according to their scores of enrichment (vertical axis, shade of red) and topology (pathway impact, horizontal axis, circle diameter) analysis using MetaboAnalyst 6.0. Estimated enriched enzymes are depicted by p-values and enrichment ratios.

Figure 6

The effects of MPs exposure to the gut microbiome extracted from C57BL/6 mouse fecal samples. (A) The relative frequency of gut microbes at the genus level from the control and MPs groups, (B) ten most abundant taxa identified by QIIME2 (red line indicates analyzed taxa), (C) operational taxonomical units of Lactobacillales and Erysipelotrichales in Control vs. MPs groups, * p < 0.05, and (D) functional analysis results shown as metabolic pathways vs. log-transformed fold changes.

Figure 7

(A) PCA score plot of the untargeted metabolomics data from the mouse fecal microbiota samples with and without MPs exposure (100 μg/mL MPs for 24 h), (B) metabolic pathway analysis, and (C) enrichment analyses of intracellular enzymes using MetaboAnalyst 6.0. The metabolic pathways are represented as circles according to their scores of enrichment (vertical axis, shade of red) and topology (pathway impact, horizontal axis, circle diameter). Enzyme enrichment is plotted as enrichment ratio, and more significant p-values are denoted by darker shade of red.

Figure 8

The impacts of MPs exposure on microbial tryptophan metabolism in gut microbiota extracted from C57BL/6 mouse fecal samples. (A) mouse fecal microbiota, and (B) medium samples. Orange: significantly increased (p < 0.05); green: significantly decreased (p < 0.05).