Effect of Parkinson’s disease and related medications on the composition of the fecal bacterial microbiota

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders. PD patients suffer from gastrointestinal dysfunctions and alterations of the autonomous nervous system,

especially its part in the gut wall, i.e., the enteric nervous system (ENS). Such alterations and functional gastrointestinal deficits often occur years before the classical clinical

symptoms of PD appear. Until now, only little is known about PD-associated changes in gut microbiota composition and their potential implication in PD development. In order to increase

knowledge in this field, fecal samples of 34 PD patients and 25 healthy, age-matched control persons were investigated. Here, the V4 and V5 hypervariable region of bacterial 16S rRNA genes

was PCR-amplified and sequenced using an Ion Torrent PGM platform. Within the PD group, we observed a relative decrease in bacterial taxa which are linked to health-promoting,

anti-inflammatory, neuroprotective or other beneficial effects on the epithelial barrier, such as Faecalibacterium and Fusicatenibacter. Both taxa were lowered in PD patients with elevated

levels of the fecal inflammation marker calprotectin. In addition, we observed an increase in shares of the Clostridiales family XI and their affiliated members in these samples. Finally, we

found that the relative abundances of the bacterial genera Peptoniphilus, Finegoldia, Faecalibacterium Fusicatenibacter, Anaerococcus, Bifidobacterium, Enterococcus, and Ruminococcus were

significantly influenced by medication with L-dopa and entacapone, respectively. Our data confirm previously reported effects of COMT inhibitors on the fecal microbiota of PD patients and

suggest a possible effect of L-dopa medication on the relative abundance of several bacterial genera.

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders. So far, there is no causal treatment for PD that is able to halt the neurodegenerative process.1 Besides motor

and cognitive symptoms, most PD patients suffer from gastrointestinal (GI) symptoms, such as constipation or prolonged intestinal transit time.2,3,4,5 These symptoms can occur several years

ahead of classical motor symptoms, which gives rise to the hypothesis that the enteric nervous system (ENS) becomes compromised before the central nervous system.6,7,8 These observations

support the hypothesis that PD may begin in the GI tract.9

The ENS is a complex of several networks of neurons, glial cells, and interconnecting fibers within the mammal GI tract. The ENS communicates with the brain bidirectionally via the vagus

nerve as the hard-wired section of the so called brain-gut axis.10 Experimental data suggest that neurons and fibers from the ENS and vagus nerve provide a neuronal chain, that allow

pathological peptides to travel between the gut and the brain in a prion-like way and modulate the course of neurological diseases.11,12,13

Recent studies indicate, that the pathological process of PD alongside the gut-brain-axis might be modulated or even initiated by the gut microbiota.14,15 Indicators are fecal markers of gut

inflammation and permeability, which are increased in PD.16 Furthermore, bacterial metabolites, which may have an influence on the ENS, differ between PD patients and healthy controls.16,17

Clearly, the cause-effect relationship between the intestinal microbiota composition and its metabolic capacity on the one hand and PD pathogenesis on the other hand are still obscure.

Constipation and reduced gut motility in PD may be important triggers to alter the microbiota composition.18,19

In addition, previous studies also suggested that some PD medication may alter the microbiota composition.20,21,22 L-dopa (L-3,4-Dihydroxyphenylalanine, levodopa) application targets the

striatal dopamine deficiency in PD patients and may stimulate the dopamine transporter on the terminal nerve.23,24 Long term therapy with L-dopa is known to induce side effects like

increased inflammation and oxidative stress.23 Catechol-O-methyltransferases (COMT) are able to methylate L-dopa rendering it ineffective.25,26 COMT are able to methylate a wide range of

catechols and thereby eliminate biologically active or toxic molecules27 including L-dopa.25,26,28 Entacapone is a COMT inhibitor28 preventing the degradation and increasing the plasma

availability of L-dopa.28,29 To the best of our knowledge, it is still unknown in which mechanistic way these drugs may alter gut microbiota composition and/or functionality.

Increasing evidence indicates a difference in fecal microbiota composition between PD and healthy controls, irrespective of the applied method.17,22,30,31,32 The methods used so far were

quantitative real-time PCR (qPCR) to detect highly abundant bacterial taxa,17 pyrosequencing of the V4 variable region of the 16S rRNA gene14 and Illumina MiSeq sequencing of the V1 and V2

region32 or V4 region.22 Other studies addressed the microbial and viral gut metagenome in the early stage of PD33 in L-dopa naïve patients with shotgun sequencing methods.34 Correlations

between a medication with entacapone and the (relative) abundances of distinct bacterial taxa were previously reported,17,22,30 suggesting PD medication as an important influencing factor

for the PD microbiota. However, to the best of our knowledge no study reported medication with L-dopa as an influencing factor so far.

In this study, we used next generation sequencing to screen for difference in fecal microbiota composition between PD patients and matched controls. We analyzed the same set of samples

previously detailed by Unger et al.,17 albeit with Ion Torrent-based next generation sequencing of the V4 and V5 region of the bacterial 16S rRNA gene instead of qPCR. While qPCR is suitable

for the quantitative determination of distinct bacterial taxa, it is also limited to known sequence types. We believe that monitoring differences in fecal microbiota composition in PD

patients compared to suitable controls is a first step to elucidate whether the gut microbiota might play any functional role in PD pathogenesis. Clearly, such differences might also depend

on factors such as the type of PD medication. In the long run, microorganisms which differ significantly between healthy persons and PD patients (independent on factors such as medication)

might play an indicative role in early PD diagnosis. This may increase our knowledge on the aetiopathogenesis of PD.

Four combined sequencing datasets yielded 11,752,187 partial bacterial 16S rRNA gene sequences with a mean of 199,190 sequences per sample (min: 70,225; max: 394,784 sequences). Following

exclusion of sequences that were present in less than 10% of all samples, 12,935 OTUs affiliated with 192 genera, 55 families, 28 orders, 17 classes, and 8 phyla were identified.

Searching for interdependencies among the sample attributes, CramérV contingency coefficients did not show strong association (cV > 0.7)35 and no significant Χ² p-values were detected

between most of the sample attributes. Only, L-dopa medication showed a significance in the Χ²-test (p = 0.048) and a medium strong association with the Hoehn–Yahr stage (cV = 0.53).

According to the Spearman-Ρ, the Hoehn–Yahr stage was significantly positively correlated with disease duration (p = 0.0016; Ρ = 0.5183). The numbers of samples per investigated patient or

control subgroup are summarized in Table 1. Important metadata are detailed in the Supplementary Table 1.

Alpha diversity indices (Fig. 1a) revealed a significant decrease in bacterial diversity in PD patients compared to the control (Ctrl) regarding observed species (pObserved = 0.032) and

estimated species (pChao1 = 0.032). Shannon and Simpson metrics for alpha diversity did not show significant differences between PD and control (pShannon = 0.197, pSimpson = 0.197).

Alpha and beta diversity plots to visualize the difference in microbiota structure between PD and control group. Shown are alpha diversity measures with the most common indices (a) and PCoA

plots showing the beta diversity with unweighted (b) and weighted (c) UniFrac measures. Blue: PD samples, orange: controls. Box plots (a) show median, as well as lower and upper quartiles.

Each dot represents an individual sample. Whiskers represent minimum and maximum spread. PCoA plots show dimensions with the highest differences, and normal confidence ellipsoids for the

sample sets.

Non-parametric multivariate analysis of variance (ADONIS), calculated for the beta diversity of PD microbiota and the control microbiota (Fig. 1b + c), revealed that neither the weighted

(pweighted-UniFrac = 0.249) nor the unweighted (punweighted UniFrac = 0.226) UniFrac measure were significantly different between the PD and the control group.

One bacterial family and three genera were found to be significantly different in relative abundance between PD and controls (p