Effect of Piribedil on the Gut Microbiota of Patients with Parkinson's Disease

Summary

This study used 16S rRNA sequencing to compare gut microbiota among healthy controls, untreated PD patients, and piribedil-treated PD patients, and identified differences in microbial composition associated with piribedil treatment.

Abstract

This study aimed to determine whether piribedil therapy alters the gut microbiota of patients with Parkinson’s disease (PD), providing a theoretical reference for understanding gut microbiota alterations induced by piribedil and their potential clinical implications. Fecal samples were analyzed using 16S ribosomal RNA (16S rRNA) gene sequencing. Comparisons were made between patients with PD who took piribedil (piribedil group), patients with PD who did not take piribedil (PD group), and healthy controls (blank group). Compared with the blank group, the relative abundance of Staphylococcus, Rhodococcus, and other genera was higher in the PD group, whereas the relative abundance of Achromobacter and Delftia was lower. Furthermore, the relative abundance of Achromobacter, Delftia, and Stenotrophomonas was higher in the piribedil group compared to the PD group, whereas the relative abundance of Staphylococcus, Rhodococcus, and other species was lower. Compared to healthy individuals, the gut microbiota of patients with PD exhibited significant changes. The gut microbiota of patients taking piribedil significantly differs from that of untreated patients. These results suggest an association between piribedil and altered gut microbiota in patients with PD.

Introduction

The incidence of Parkinson’s disease (PD) is gradually increasing; however, its etiology and pathogenesis have not yet been fully clarified. More studies have indicated that the gut microbiota is closely related to the onset and development of PD. He et al.¹documented that the gut microbiota composition in patients with PD varies from that of healthy people, and that an increase in intestinal pathogens may be associated with the onset of PD. Scheperans et al.²confirmed that the relative abundance of Prevotella and Bacteroides in the gut of patients with PD is reduced, and that these bacterial genera are strongly associated with intestinal barrier function.

Several studies have investigated the connection between the gut microbiota and PD. Research suggests that dysbiosis may play a crucial role in the pathophysiology of PD, influencing motor and non-motor symptoms through multiple mechanisms, including inflammation and metabolic disorders³. In addition, drug treatments for PD have been shown to alter the composition of the gut microbiome^4,5, underscoring the complex interplay between drugs and the gut microbiome.

As a non-selective dopamine receptor agonist, piribedil is instrumental in managing PD; it can improve motor symptoms in patients by stimulating dopamine receptors⁶. However, its clinical use is often limited by adverse reactions, mainly gastrointestinal symptoms including nausea, vomiting, and abdominal discomfort⁷. The underlying mechanism of these side effects remains unclear, and whether they are related to changes in gut microbiota induced by piribedil is still unknown.

The link between the gut microbiota and adverse drug reactions has garnered increasing interest in recent years. Hill-Burns et al.⁸analyzed the gut microbiota in patients with PD and found that gut microbiota diversity was associated with response to drug therapy. Additionally, dopamine agonists may impact intestinal motility and microbial composition⁹. The mechanisms behind these changes are thought to involve alterations in gut transport timing, which in turn affect microbial diversity and abundance. Moreover, the gut microbiota can influence the drug absorption and metabolism¹⁰, thereby affecting the efficacy and side effects of these medications. However, there is still a lack of systematic research on whether and how piribedil, a commonly used agent for Parkinson’s disease, modulates gut microbiota structure, as well as the association between such microbial shifts and its gastrointestinal adverse reactions.

In the present study, we compared the gut microbiota structure among healthy controls, untreated PD patients, and PD patients treated with piribedil. We hypothesized that piribedil treatment is associated with distinct alterations in gut microbiota composition in patients with PD and that these microbial changes may be linked to piribedil-associated gastrointestinal side effects. We aimed to identify specific microbial alterations associated with piribedil and to explore their potential association with gastrointestinal side effects, thereby providing a theoretical basis for understanding the mechanism of piribedil-related adverse reactions.

The gut microbiota is an important direction for research on the pathogenesis of PD¹¹. By observing the effects of piribedil on the gut microbiota in patients with PD, this study helps to clarify the clinical significance of drug-related gut microbiota changes and provides a reference for optimizing the clinical use and safety management of piribedil.

Protocol

This study was reviewed by the Ethics Committee of the First Affiliated Hospital of Anhui University of Chinese Medicine (Ethics review number: 2025AH-101, Ethical approval date: 2025-07-02).

Research subjects

Patients with PD and healthy controls at the First Affiliated Hospital of Anhui University of Chinese Medicine were enrolled. Fresh fecal samples were collected for follow-up analysis. Fresh fecal samples were collected, aliquoted into sterile EP tubes, immediately snap-frozen in liquid nitrogen, and then stored at −80 °C for long-term preservation. The inclusion criteria of patients with PD were as follows: (1) clinically diagnosed PD; (2) age >70 years old; (3) for the Piribedil group: initiated piribedil monotherapy after enrollment and had no prior exposure to this drug; for the PD group: not undergoing treatment with anti-Parkinson's drugs; (4) no other serious systemic diseases, such as serious heart disease, liver disease, kidney disease, or cancer. Prior to enrollment, written informed consent was obtained from each participant. The exclusion criteria were as follows: (1) severe complications of PD, such as severe motor dysfunction; (2) recently received antibiotic treatment; (3) presence of other health conditions that may affect the study results, such as inflammatory bowel disease, irritable bowel syndrome, etc.; (4) allergic to piribedil. The healthy control subjects (Blank group) were age-matched (age >70 years old) individuals with no history of neurological or major gastrointestinal diseases.

All participants were required to maintain their regular dietary habits before enrollment. None had received probiotics, prebiotics, dietary fiber supplements, or other nutritional supplements. In addition, all participants had not received any antibiotic treatment for at least three months prior to sample collection. No standardized dietary intervention was performed during the study, but all participants were instructed to maintain a stable lifestyle and diet to minimize potential confounding effects on the gut microbiota.

Research Grouping

This study cohort was divided into three groups: PD group (n = 10): fecal samples collected from Parkinson's disease patients who were not taking anti-Parkinson's medication. Piribedil group (n=10): fecal samples collected from Parkinson's disease patients after 21 consecutive days of treatment with piribedil (50 mg, twice daily). Blank control group (n=5): fecal samples collected from healthy individuals.

16S ribosomal RNA sequencing

Sequencing was performed on a sequencing platform, as reported in other studies^12,13, Briefly, total microbial DNA was isolated from fecal specimens using the sodium dodecyl sulfate (SDS) method^14,15,16. The integrity of extracted DNA was verified by 1% agarose gel electrophoresis, and DNA concentration and purity were determined with a spectrophotometer. The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified from genomic DNA using degenerate primers 341F (5’-CCTAYGGGRBGCASCAG-3’) and 806R (5’-GGACTACNNGGGTATCTAAT-3’) with Polymerase Chain Reaction (PCR) thermocycler. The amplification of the bacterial 16S rRNA gene was carried out under the following thermal conditions: an initial pre-denaturation step at 94 °C for 2 min, followed by 28 cycles consisting of 94 °C denaturation for 30 s, 55 °C annealing for 30 s, and 72 °C elongation for 30 s. A final extension was conducted at 72 °C for 10 min, and the reaction was then maintained at 4 °C. The PCR mixtures contain 2× PCR master mix 25 μL, forward primer (10 μM) 2 μL, reverse primer (10 μM) 2 μL, template DNA 10 ng, and finally ddH₂O up to 50 μL. PCR reactions were performed in triplicate. The PCR mixtures contain 2× PCR master mix 25 μL, forward primer (10 μM) 2 μL, reverse primer (10 μM) 2 μL, template DNA 10 ng, and finally ddH₂O up to 50 μL. PCR reactions were performed in triplicate. The PCR product was extracted from 2% agarose gel and purified using a gel extraction kit. Universal Plus DNA Library Prep Kit for MGI V2 was used to build the library: (1) Splinter link; (2) Use magnetic bead screening to remove the joint self-contiguous segments; (3) Enrichment of library templates by PCR amplification; (4) Magnetic beads to recover PCR products; (5) Cyclization of PCR products. After library validation, sequencing was performed.

Quality checkpoints: Checkpoint 1 (Sample collection): Samples were snap-frozen immediately after collection and stored at −80°C. Any sample with visible signs of thawing or contamination was discarded. Checkpoint 2 (DNA extraction): Acceptable purity: A260/A280 = 1.8–2.0, A260/A230 ≥1.8; integrity verified by a single high-molecular-weight band on 1% agarose gel. Checkpoint 3 (PCR amplification): A single band at ~460bp on 2% agarose gel indicates successful amplification (primers 341F/806R, V3-V4 region); if absent or non-specific, adjust annealing temperature or template concentration. Recovery concentration ≥0.5 ng/μl was required for library preparation. Checkpoint 4 (Library preparation and sequencing): Performed by the sequencing facility following their standard validated protocols; no additional in-house checkpoint (e.g., Bioanalyzer sizing) was applied.

Safety and waste disposal: Gloves and lab coats were worn during all sample handling. Work surfaces were cleaned with 70% ethanol. All contaminated consumables (tips, tubes, gels) were autoclaved (121°C, 20 min) before disposal as biohazardous waste.

Bioinformatics

Raw sequencing reads of the 16S rRNA gene were first demultiplexed, subjected to quality filtering using fastp (version 0.20.0), and assembled via FLASH (version 1.2.11) under standardized screening criteria. Briefly: (i) Raw 300 bp sequences were trimmed when the average quality score dropped below 20 within a 50 bp sliding window. Reads with truncated lengths shorter than 50 bp or containing ambiguous bases were removed; (ii) Sequence assembly was performed only for overlaps exceeding 10 bp, with the maximum mismatch ratio of the overlapping region set at 0.2. Unpaired reads that failed to be assembled were eliminated; (iii) Individual samples were partitioned based on barcode and primer sequences for subsequent orientation adjustment. Barcode matching was performed with exact alignment, while primer matching allowed a maximum of 2 nucleotide mismatches. 16S rRNA reads were analyzed using the Quantitative Insights into Microbial Ecology (QIIME) pipeline (version 1.8.0)¹⁷. Tag sequences were appended to paired-end reads using FLASH (version 1.2.11)¹⁸. Trimming was performed using UPARSE (version 7.0.1090) with a minimum length of 200 bp and an average quality of 25, followed by clustering at a 97% cutoff to generate operational taxonomic units (OTUs)¹⁹. Representative OTU reads were taxonomically classified via RDP Classifier 2.2, with a confidence limit ≥0.6. Tag and read abundances were determined by comparison using USEARCH global²⁰. The identification of chimeric sequences was based on comparisons with the Gold database using UCHIME (v4.2.40)²¹. Principal coordinate analysis (PCoA) was subsequently conducted on the sparse dataset based on Bray–Curtis dissimilarity, with sequence similarity threshold set at 97% for clustering. Representative OTUs and corresponding taxonomic profiles were further analyzed using R software (version 3.1.1). The Linear discriminant analysis effect size (LEfSe) algorithm, based on linear discriminant analysis (LDA) effect size, was applied to screen biomarkers with significant differential abundance at the OTU level. This method emphasizes both statistical significance and biological relevance by first identifying features with consistent differences among groups (using the Kruskal-Wallis test for multi-group comparison) and then estimating the effect size of each differentially abundant feature using LDA. An LDA score >2.0 is defined as a significantly increased abundance²².

Statistical Methods

Statistical analyses were conducted using SPSS. Normally distributed data were analyzed using the one-way analysis of variance (ANOVA). Data that did not follow the normal distribution were analyzed using the Wilcoxon rank-sum test. All quantitative data were expressed as mean ± standard error (SE) for parameters conforming to a normal distribution, while non-normally distributed data were presented as median with interquartile range. A p-value < 0.05 was considered statistically significant.

Results

Microbial diversity differences associated with piribedil treatment

DNA extraction quality control results (concentration range, purity) are provided in Supplementary Table 1. PCR amplification quality control (band size, recovery concentrations) is shown in Supplementary Table 2. Sequencing quality control metrics (read counts, depth, coverage) are detailed in Supplementary Table 3. The ACE, Chao1, InvSimpson, Shannon, and Simpson indices wer...

Discussion

Parkinson’s disease ranks as the second most common neurodegenerative disorder globally, only preceded by Alzheimer’s disease²³. Since 1970, piribedil, as a dopamine receptor agonist, has been widely used in the treatment of PD²⁴. However, in clinical practice, piribedil may cause some adverse reactions, such as nausea, vomiting, and gastrointestinal discomfort. Growing research demonstrates that the development of Parkinson’s disease, as well as therapeut...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
2× reaction buffer (Taq MasterMix)	Kangweii	CW0690
Agarose	(general)	not applicable	molecular biology grade
AxyPrep DNA Gel Extraction Kit	Axygen Biosciences	(not provided)
Benchtop high-speed freezing centrifuge	Hunan Kosei	GTR116C
DNA extraction reagents (SDS, phenol, chloroform, ethanol)	(general)	not applicable
DNBSEQ-G99 PE300 sequencing platform	MGI Tech Co., Ltd	DNBSEQ-G99
Gel electrophoresis apparatus	Beijing Liuyi	DYY-8C
Gel imaging system	Shanghai Jiapeng	JP-2880
LEfSe software	(open source)	online version
NanoDrop spectrophotometer	Hangzhou Haipe	Aurora-900
PCR machine	Hangzhou Langji	A200
Piribedil	Servier	6094078
Primers: 341F and 806R (synthesized)	(custom synthesis)	not applicable
QIIME software	(open source)	http://qiime.org	version 1.8.0
Quantus Fluorometer	Promega	E6150
Qubit 4.0 Fluorometer	Invitrogen	Q33226
SDS (sodium dodecyl sulfate)	(general)	not applicable
UPARSE software	(open source)	http://drive5.com/uparse	version 7.0.1090
VAHTS Universal Plus DNA Library Prep Kit for MGI V2	Vazyme	NDM627-01