Altered gut microbiome and metabolism in synucleinopathies and iRBD using multimodal differential abundance analyses

Research overview

The text below is the source article’s original abstract under a verified reuse license. cc by

Abstract Background and Objectives: The microbiota-gut-brain axis has been suggested to play an important role in synucleinopathy. Microbiota dysbiosis may occur in synucleinopathies including multiple system atrophy (MSA) and Parkinson’s disease (PD), however, the results of the microbiota were heterogeneous. Here we performed a cross-sectional study to profile gut microbiota across Idiopathic rapid-eye-movement sleep behavior disorder (iRBD), MSA, PD, and healthy controls (HCs) using multimodal differential abundance analyses based on DADA2 denoising algorithm and operational taxonomic unit (OTU) clustering method. Methods Gut microbiota and fecal Short-chain fatty acids (SCFAs) levels were measured in 37 iRBD, 70 MSA, 104 PD, and 61 HCs matched by age, gender and BMI, using 16S rRNA sequencing and gas chromatography-mass spectrometry respectively. Additionally, the samples were divided into training set and testing set to ensure robustness in our findings. Results Gut microbiota compositions were significantly altered in iRBD, MSA, and PD. The increase in the abundance of pro-inflammatory bacteria and decrease in the abundance of SCFA-Producing bacteria were observed in iRBD, MSA, and PD. Butyricicoccus remained distinctive among the overlapping gut microbiota genera of iRBD, MSA, and PD compared to HCs as revealed by random forest analysis. The fecal SCFAs levels (acetic acid, butyric acid, and isovaleric acid) were also altered in iRBD, MSA, and PD. The combination of differential microbiota and SCFAs could improve the accuracy of predictive models in the diagnosis and differential diagnosis of synucleinopathies. Conclusions Microbiota dysbiosis was observed in iRBD, sharing overlapping gut microbiota changes with synucleinopathies, indicating microbiota dysbiosis might be an early change in the disease process of synucleinopathies. Consequent functional alterations, such as SCFA changes, may provide microbiological explanations for pathogenesis of synucleinopathy. We identified Butyricicoccus as a biomarker for synucleinopathy, sharing by iRBD, MSA and PD, which may be a potential hallmark of phenoconversion of RBD to synucleinopathy. The combination of microbiota and SCFAs may be potential biomarkers in the diagnosis and differential diagnosis of synucleinopathies.

Related topics