Gut Microbiota and Metabolome Alterations Associated with Parkinson's Disease

Abstract

Parkinson's disease is a neurodegenerative disorder characterized by the accumulation of intracellular aggregates of misfolded alpha-synuclein along the cerebral axis. Several studies report the association between intestinal dysbiosis and Parkinson's disease, although a cause-effect relationship remains to be established. Herein, the gut microbiota composition of 64 Italian patients with Parkinson's disease and 51 controls was determined using a next-generation sequencing approach. A real metagenomics shape based on gas chromatography-mass spectrometry was also investigated. The most significant changes within the Parkinson's disease group highlighted a reduction in bacterial taxa, which are linked to anti-inflammatory/neuroprotective effects, particularly in the Lachnospiraceae family and key members, such as Butyrivibrio, Pseudobutyrivibrio, Coprococcus, and Blautia The direct evaluation of fecal metabolites revealed changes in several classes of metabolites. Changes were seen in lipids (linoleic acid, oleic acid, succinic acid, and sebacic acid), vitamins (pantothenic acid and nicotinic acid), amino acids (isoleucine, leucine, phenylalanine, glutamic acid, and pyroglutamic acid) and other organic compounds (cadaverine, ethanolamine, and hydroxy propionic acid). Most modified metabolites strongly correlated with the abundance of members belonging to the Lachnospiraceae family, suggesting that these gut bacteria correlate with altered metabolism rates in Parkinson's disease.IMPORTANCE To our knowledge, this is one of the few studies thus far that correlates the composition of the gut microbiota with the direct analysis of fecal metabolites in patients with Parkinson's disease. Overall, our data highlight microbiota modifications correlated with numerous fecal metabolites. This suggests that Parkinson's disease is associated with gut dysregulation that involves a synergistic relationship between gut microbes and several bacterial metabolites favoring altered homeostasis. Interestingly, a reduction of short-chain fatty acid (SCFA)-producing bacteria influenced the shape of the metabolomics profile, affecting several metabolites with potential protective effects in the Parkinson group. On the other hand, the extensive impact that intestinal dysbiosis has at the level of numerous metabolic pathways could encourage the identification of specific biomarkers for the diagnosis and treatment of Parkinson's disease, also in light of the effect that specific drugs have on the composition of the intestinal microbiota.

Keywords: 16S RNA; PD; gut microbiota; metabolome.

FIG 1

Beta-diversity analysis was presented as a two-dimensional (2D) plot based on principal coordinate analysis (PCoA). The statistical significance was assessed using permutational multivariate analysis of variance (PERMANOVA). (a) Bray Curtis (P = 0.001; F = 4.217; R2 = 0.036); (b) UniFrac weighted (P = 0.001; F = 9.628; R2 = 0.079); (c) UniFrac unweighted (P = 0.001; F = 3.255; R2  =  0.028). CTRL, control.

FIG 2

LEfSE analysis. The plot was generated using a Galaxy computational tool. (a) The bar plots represent the significantly differential taxa between PD patients (green) and controls (ctrl) (red), based on effect size (LDA score [log 10] > 2). Enriched taxa in PD patients (positive LDA score) and enriched taxa in controls (negative LDA score). Differences among classes were obtained by the Kruskal-Wallis test (α = 0.05). (b) Cladogram showed the differences in enriched taxa in PD (green) versus enriched taxa in controls (red). Differences among classes were obtained by the Kruskal-Wallis test (α = 0.05).

FIG 3

Heat maps of microbiota composition in PD cases and controls performed using the statistical analysis of metagenomic profiles (STAMP) software. The results were tested for statistical significance by the Mann-Whitney U test (Statistical Package for the Social Sciences Version [SPSS] 25.0) (60).

FIG 4

Metabolomics multivariate statistical analysis (MVA). (a) OPLS-DA score plots of PD patients versus control subjects. (b) Validation parameters. R2X and R2Y indicated the cumulative explained fraction of the variation of the X block and Y block for the extracted components. Q2 values indicated cumulative predicted fraction of the variation of the Y block for the extracted components. R2 and Q2 intercept values are indicative of a valid model. (c) The permutation test was evaluated on the corresponding partial least-square discriminant analysis (PLS-DA) model.

FIG 5

Statistically significant metabolites in fecal samples of PD patients versus control subjects comparison. Discriminant metabolites obtained with the MVA underwent a Mann-Whitney U test with Holm-Bonferroni correction to determine which metabolites were statistically significantly different. The resulted metabolites obtained are shown and expressed on the y axes of the graphs as ranks (data transformation in which numerical or ordinal values are replaced by their rank when the data are sorted). The levels of significance are indicated by asterisks as follows: *, P < 0.05; **, P < 0.01.

FIG 6

The heat maps represent Spearman correlation of the relative abundance of differential bacteria, selected by the linear discriminant analysis effect size (LEfSe) method followed by FDR correction test, and the concentrations of metabolites, selected by the MVA, underwent a Mann-Whitney U test with Holm-Bonferroni correction. (a) Heat map control (n = 51); (b) heat map PD (n = 64). The r values are represented by gradient colors, where red and green cells indicate positive and negative correlations, respectively. The asterisks indicate levels of significance as follows: *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.