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

Altered gut microbiome and metabolism in synucleinopathies and iRBD using multimodal differential abundance analyses | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Altered gut microbiome and metabolism in synucleinopathies and iRBD using multimodal differential abundance analyses Juanjuan Du, Pei Huang, Pingchen Zhang, Chao Gao, Jin Liu, Maoxin Huang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5182069/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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. Synucleinopathy Microbiota Short-chain fatty acids biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Synucleinopathies are a group of neurodegenerative disorders characterized by the abnormal accumulation of alpha-synuclein proteins, leading to the formation of Lewy bodies, which are associated with diseases such as Parkinson's disease (PD), Multiple system atrophy(MSA), etc [ 1 ]. PD is the second most common neurodegenerative disease and affects elderly people (> 60 years old) worldwide, characterized by parkinsonism, including bradykinesia, resting tremor and rigidity[ 2 ]. MSA, characterized by parkinsonism, autonomic failure, cerebellar ataxia, and pyramidal symptoms, is one of the main types showing progressive atypical parkinsonism that are frequently misdiagnosed as PD, making the accuracy of diagnosis challenging [ 3 , 4 ]. Idiopathic rapid-eye-movement (REM) sleep behavior disorder (iRBD) is a parasomnia manifested by dream-enacting behaviors due to lack of muscle atonia during rapid eye movement sleep period [ 5 ]. In the past decade, the longitudinal study of subjects with iRBD has shown a high specificity for later development of synucleinopathies including MSA and PD with a high conversion rate [ 6 ]. Thus, reliable and accessible biomarkers for early diagnosis of MSA, PD, and iRBD are urgently needed to identify candidate therapeutic targets. The gut microbiota consists of trillions of microbes that reside in the human gastrointestinal tract and performs many of the functions required for human physiology and survival, which is known as the forgotten organ [ 7 ]. With the gradual deepening of research, the bidirectional communication system between gut microbiota dysbiosis and the brain has been recognized, known as the "microbiota-gut-brain-axis (MGBA)". Many studies have shown that gut dysbiosis is closely related to synucleinopathies such as PD, MSA, and Dementia with Lewy body[ 8 – 10 ]. Alpha-synuclein misfolds in the peripheral enteric nervous system and then transports to the brain through the vagus nerve, while bacterial products and pro-inflammatory cytokines circulate from the intestine to the brain, triggering central alpha-synuclein misfolding and neuroinflammation [ 11 ]. The MPTP and rotenone induced PD models also confirmed that fecal microbiota transplantation inhibited inflammatory responses through the MGBA and exerted neuroprotective effects [ 12 , 13 ]. In recent years, some studies have reported similar gut microbiota alterations in iRBD and PD [ 14 – 16 ], indicating that gut dysbiosis may occur during the prodromal phase of synucleinopathy and these microbiota may be the key components of the transition from iRBD to PD [ 15 ]. While reports of differential microbiota in MSA and PD suggested that unique gut microbiota is related to the development of different synucleinopathies [ 17 ]. Therefore, further identification of the shared microbiota in synucleinopathies and distinct microbiota of different synucleinopathies can provide insights into the transition and differentiation of diseases. Moreover, short-chain fatty acids (SCFAs), which derived from the bacterial fermentation of dietary fibers, exert multiple effects on the gut–brain axis. Our previous research reported similar fecal SCFA alterations in iRBD, MSA, and PD [ 18 ]. SCFA such as butyric acid could exert neuroprotective effects through restoring gut microbiota and inhibiting TLR4 signaling in MPTP-induced PD models [ 19 ]. SCFAs are closely related to the abnormal α-syn aggregation and play a key role in modulating neuroinflammation in PD [ 20 ]. The integration of specific microbiota and SCFAs may have potential diagnostic value for synucleinopathies. At present, many studies have identified specific taxa associated with PD based on 16s rRNA gene amplicon sequencing [ 8 ], but the results of the microbiota were heterogeneous. Traditionally, sequences were clustered into operational taxonomic units (OTUs) with a particular identity threshold (usually 97%) to reduce the interference of sequencing errors [ 21 ]. With the wide application of 16s rRNA sequencing technology, another denoising algorithm such as DADA2 was reported to be more accurate than the OTU clustering method, as it can accurately resolve sequence variants differing by a single nucleotide and present in as few as two reads, identify more real variants, and output fewer spurious sequences [ 22 ]. In order to more accurately determine potential disease-related microbiota, we used both DADA2 denoising algorithm and OTU clustering method to compare gut microbiota between groups through multimodal differential abundance analyses, and then combined with SCFAs to establish more stable disease diagnosis models based on random forest (RF). Our study aimed to (a) identify disease-specific microbiota biomarkers, (b) identify shared gut microbiota alterations in synucleinopathies, which could possibly play as potential microbial biomarkers for the phenoconversion of iRBD to synucleinopathies, and (c) combine SCFAs to establish disease prediction models. 2. Materials and methods 2.1 Subjects A total of 37 iRBD patients, 70 MSA patients, 104 PD patients, and 61 healthy controls (HCs) from the movement disorder clinic and ward at Department of Neurology, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine from September 2016 to August 2023 were included in the study. Participants were randomly divided into a training set (70% of data, 27 iRBD, 49 MSA, 73 PD, 43 HCs) and a testing set (remaining 30% of the data, 10 iRBD, 21 MSA, 31 PD, 18 HCs). The inclusion criteria were as follows: (1) aged 35–85 years. (2) Patients were diagnosed as clinical definite or probable MSA according to MSA diagnostic consensus by senior movement disorder specialists [ 23 ]. PD patients were diagnosed according to MDS criteria with no anti-PD medication intake before the fecal sample collection [ 24 ]. iRBD patients were examined by polysomnography (PSG) and diagnosed by International Classification of Sleep Disorders Criteria-Third Edition [ 25 ]. (3) ability to complete the questionnaires, physical and neurological examination. The exclusion criteria were as follows: (1) vegetarian or malnutrition. (2) use of antibiotics or probiotic supplements within 3 months prior to sample collection. (3) ongoing use of yogurt, statin, corticosteroid, proton pump inhibitor, Metformin, entacapone, anti-neoplastic or immunosuppressant medication. (4) history of secondary neurological or psychiatric diseases such as epilepsy, brain tumor, head trauma, stroke, dementia, cerebral small-vessel disease and so on. (5) chronic gastrointestinal disorder (including inflammatory bowel disease, colitis, colon cancer, gastric or duodenal ulcer) or gastrointestinal surgery. (6) severe cognitive deficit that obstructed the execution of clinical assessment. This study protocol was approved by the Ethics Committee of Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine. Written informed consents were obtained from all participants in the study as well. 2.2 Clinical evaluation Demographic data (age, gender, and BMI (kg/cm 2 )) in all subjects and clinical features including disease duration, constipation condition, motor and non-motor symptoms were recorded in our study. Patients with MSA were assessed using Unified Multiple System Atrophy Rating Scale (UMSARS) and patients with PD were evaluated by International Parkinson and Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Non-motor symptoms were evaluated by Mini-Mental State Examination (MMSE) for cognitive function, 16-item odor identification test from Sniffin' Sticks (SS-16) for olfaction, REM sleep behavior disorder questionnaire-HongKong (RBD-HK) for clinical possible RBD, The Scale for Outcomes in Parkinson’s disease for Autonomic Symptoms (SCOPA-AUT) for autonomic dysfunction, 17-item Hamilton Depression Rating Scale (HAMD-17) for depression, Hamilton Anxiety Scale (HAMA) for anxiety, Wexner constipation score for constipation severity. 2.3 Gut microbiota analysis with 16s rRNA In accordance with our previous study [ 9 ], all subjects were asked to collect a fecal sample in the morning using fecal collection containers. The containers were transferred on ice and stored at -80°C prior to processing. Fecal DNA was extracted from 200mg samples using the QIAamp Fast DNA Stool Mini Kit (Qiagen, Hilden, Germany) after the sample collection. Microbial composition was determined by 16s rRNA gene sequencing of DNA extracted from stool by amplifying the V3–V4 regions. DNA was checked by running the samples on 1.2% agarose gels. Polymerase chain reaction (PCR) amplification of 16s rRNA genes was performed using general bacterial primers (357F and 806R) with two-step amplicon library building on the Novaseq platform. Detailed amplification and sequencing processes were as follows: (1) Genetic quality control. We took 3ul sample DNA for 1.2% agarose gel electrophoresis, all DNA concentrations were greater than 10ng/uL. (2) Primer design. Fusion primers with "5' Miseq linker-barcode-sequencing primer-specific primer-3 '" for bidirectional sequencing was designed. We amplified the V3-V4 functional regions of the bacterial 16S rRNA gene with specific primer sequences (357F 5'-ACTCCTACGGGAGGCAGCAG-3 'and 806R 5'-GGACTACHVGGGTWTCTAAT-3'). (3) PCR amplification and product purification. We performed a total of two PCR reactions (ABI9700 PCR instrument), cycling conditions for both PCR reactions included an initial denaturation at 94 ° C for 2 min, followed by 25 and 8 cycles respectively at 94 ° C for 30 s, 56 ° C for 30 s, and 72 ° C for 30 s, and then a final extension at 72 ° C for 5 min. The PCR products were purified with the Axyprep DNA Gel Recovery Kit (AXYGEN company). (4) Quantification of PCR Products and DNA Sequencing. The purified PCR products were quantified with real-time fluorescence using the FTC-3000 real-time PCR instrument, and sequenced on a high-throughput IllUmina Miseq instrument (2x300 bp). 2.4 SCFA analysis The concentrations of acetic acid, propionic acid, butyric acid, isovaleric acid, isobutyric acid, valeric acid and caproic acid for SCFA analysis were measured by gas chromatography-mass spectrometry (GC-MS) using Tinygene Bio-Tech (shanghai). For each subject, take 400mg of fresh fecal samples into a 2 mL centrifuge tube and add 50 µ L 15% phosphoric acid, 100uL internal standard (4-methylvaleric acid) solution (125 µg/mL), and 400 µ L in a vortex mixer (Lin Bell, QL-866). After mixing for 1minute, the samples were centrifuged at 4°C 12000 rpm for 10 minutes in a frozen centrifuge (Xiangyi, H2050-R). The supernatant was transferred to a new centrifuge tube and tested on the machine. Fecal analyses for individual SCFAs were performed with GC-MS. Chromatography conditions: Thermo Trace 1300 (Thermo Fisher Scientific, USA) gas phase system, chromatographic column using Agilent HP-INNOWAX capillary column (30 m × 0.25 mm ID × 0.25 µ m); split injection, injection volume 1 µ L, the split ratio 10:1. Injection port temperature 250°C; ion source temperature 300 ° C; transmission line temperature 250 ° C. The starting temperature of programmed heating is 90 ° C, raise the temperature to 120°C at 10°C/min; then raise the temperature to 150°Cat 5 ° C/min, finally, heat up to 250°C at 25°C/min for 2 minutes. The carrier gas is helium with a flow rate of 1.0 mL/min. Mass spectrometry conditions: Thermo ISQ 7000 mass spectrometer (Thermo Fisher Scientific, USA), electron bombardment ionization (EI) source, SIM scanning method, electron energy 70 eV. The peak area was used to calculate SCFA concentrations. Raw GC-MS data were processed for peak integration, calibration, and quantitative analysis for SCFAs. 2.5 Statistical analysis The 16s sequences were analyzed by using a combination of software Trimmomatic (version 0.35), Flash (version 1.2.11), UPARSE (version v8.1.1756), mothur (version 1.33.3) and R (version 3.6.3). The raw 16s rRNA gene data were processed by two different algorithms: denoised by using DADA2 and clustered into OTUs at 97% identity. Taxonomy was assigned using Silva 128 as the reference database. We chose the genus level for further analysis. alpha-diversity was measured by observed, chao, ace, shannon, Simpson, and PD whole tree indexes for microbial diversity and richness. Kruskal-Wallis test was used to compare alpha-diversity indexes between groups. beta-diversity represented the difference between samples calculated by unweighted and weighted UniFrac. beta-diversity indexes were visualized with the principal coordinate analysis (PCoA) and compared by analyses of similarities (ANOSIMs). Continuous data were presented as mean and standard deviation (mean ± SD) and compared by Wilcoxon rank-sum or Kruskal-Wallis test. Categorical variables were presented as numbers/percentage and compared by chi-squared (χ 2 ) test. Comparisons of specific microbes between groups were analysed using multiple differential abundance analyses, including analysis of composition of microbiomes (ANCOM), which controls for false discovery rates in compositional data; analysis of composition of microbiomes with bias correction (ANCOM-BC), an extension that provides bias correction for differential abundance estimation; ANOVA-like differential expression 2 (ALDEx2), which uses centered log-ratio transformation and accounts for sampling variation; microbiome multivariable associations with linear models 2 (MaAsLin 2), a multivariate statistical framework for discovering associations between clinical metadata and microbial community abundance; and linear discriminant analysis (LDA) effect size (LEfSe) method ( http://huttenhower.sph.harvard.edu/lefse/ ) with an effect size cut-off of 2.0, which identifies taxa that explain differences between groups with statistical significance and biological relevance. RF models were used to predict disease status based on the microbiota profile (significantly different taxa at genus level). The selection of variables was performed by 10-fold cross-validated least absolute shrinkage and selection operator (LASSO) in R package ‘‘lars”. Receiver operating characteristic (ROC) curve was established to quantify the model’s diagnostic performance for fecal microbiota and SCFAs. The significance levels were set at 0.05 (2-tailed). In our study, we used multimodal differential abundance analyses (ANCOM, ANCOM-BC, ALDEx2, MaAsLin 2 and LEfSe) to identify the differential microbiota of diseases in the training set. Differential microbiota among three or more analysis methods both in DADA2 denoising and OTU clustering were selected for subsequent RF analysis. RF was to build a predictive model based on the differential fecal microbiota profile at genus level. Based on the results of the RF, the differential genera with mean decease accuracy (MDA) > 10 combined with SCFAs were selected for further ROC analysis to establish a disease diagnosis model. The testing set was collected to validate the diagnosis model established based on the training set. The schematic overview of the main procedure is illustrated in Fig. 1 . 3. Results 3.1 Demographic characteristics and microbial diversity among groups A total of 192 participants were enrolled and subsequently divided into four groups consisting of 27 iRBD patients (65.67 ± 7.66 years old, 70.4% male), 49 MSA patients (63.18 ± 6.54 years old, 55.1% male), 73 PD patients (64.14 ± 9.35 years old, 58.9% male), and 43 HCs (62.07 ± 6.96 years old, 39.5% male) in the training set. The demographic characteristics including age, gender, and BMI were listed in Table 1 . No significant differences were found in age, gender, and BMI distribution among the four groups in the training set (Table 1 ). 10 iRBD patients (66.50 ± 6.55 years old, 70.0% male), 21 MSA patients (60.81 ± 6.82 years old, 57.1% male), 31 PD patients (60.55 ± 9.83 years old, 51.6% male), and 18 HCs (61.28 ± 8.19 years old, 61.1% male) in testing set were finally included, with an equal distribution of individuals in the four groups (Additional file 1). Table 1 Characteristics of the participants in the training set. Variables HC(n = 43) iRBD(n = 27) MSA(n = 49) PD(n = 73) age(y) a 62.07±(6.96) 65.67±(7.66) 63.18±(6.54) 64.14±(9.35) 0.279 gender(male/female) b 17/26 19/8 27/22 43/30 0.066 BMI(Kg/cm2) a 23.29±(3.07) 24.11±(2.46) 24.35±(3.04) 23.21±(2.53) 0.100 Smoke(n/y) b 35/8 24/3 44/5 56/17 0.226 Alcohol(n/y) b 37/6 23/4 43/6 55/18 0.262 Tea (n/y) b 33/10 19/8 41/8 54/19 0.526 Coffee(n/y) b 38/5 22/5 45/4 67/6 0.456 Diabetes(n/y) b 38/5 23/4 42/7 59/14 0.729 iRBD/PD/MSA disease duration(y) a / 4.15±(2.82) 3.63±(2.06) 2.43±(2.13) 0.001 Wexner a 1.3±(2.14) 6.93±(4.55) 9.43±(4.95) 3.71±(4.45) < 0.001 SCOPA-AUT a / 7.15±(10.11) 23.16±(9.2) 5.92±(5.75) < 0.001 SS-16 a / 7.44±(3.82) 8.8±(4.34) 7.03±(4.1) 0.068 RBD-HK a / 44.44±(20.08) 28.39±(18.25) 14.25±(15.86) < 0.001 MMSE a 27.6±(1.76) 29±(1.24) 26.02±(3.51) 28.19±(1.97) < 0.001 HAMD-17 a 0.98±(1.85) 3.78±(3.24) 9.63±(4.94) 4.71±(5.59) < 0.001 HAMA a 1.02±(1.85) 4.41±(3.52) 11.1±(5.21) 4.12±(4.73) < 0.001 MDS-UPDRS III / / / 24±(14.3) / UMSARS-II / / 19.33±(7.65) / / BMI: Body Mass Index, HC: healthy control, iRBD: Idiopathic rapid eye movement sleep behavior disorder, PD: Parkinson’s Disease, MSA: multiple system atrophy, SCOPA-AUT: Scale for Outcomes in Parkinson’s disease for Autonomic Symptoms, SS-16: 16-item odor identification test from Sniffin' Sticks, RBD-HK: REM sleep behavior disorder questionnaire-HongKong, HAMD-17: 17-item Hamilton Rating Scale for Depression, HAMA: Hamilton Anxiety Scale, MMSE: Mini-Mental State Examination, MDS-UPDRS III: Movement Disorder Society sponsored version of the Unified Parkinson’s Disease Rating Scale III, UMSARS-II: the Unified Multiple System Atrophy Rating Scale a Data were shown as mean ± SD, compared by ANOVA test. b Data were compared by chi-square/Fisher’s test. 3.2 alpha diversity and beta diversity In DADA 2-denoised samples, the alpha-diversity indexes including observed species (p = 0.008), chao (p = 0.008), ace (p = 0.008), and PD whole tree (p = 0.002) showed remarkable difference among the four groups (Additional file 2). Significant differences were found in beta-diversity based on the unweighted (ANOSIM, R = 0.1332, P = 0.001) and weighted (ANOSIM, R = 0.0318, P = 0.043) UniFrac analysis. In OTU-clustered samples, no significant difference was observed in alpha-diversity between groups. The unweighted (ANOSIM, R = 0.1917, P = 0.001) UniFrac-based beta-diversity revealed a significant difference (Additional file 2). 3.3 Disease-specific microbiota and SCFA biomarkers 3.3.1 Identification of iRBD-specific microbial and SCFA biomarkers A total of 19 genera with increased relative abundance and 7 genera with lower relative abundance were identified in the iRBD group compared with HCs in LEfSe analysis (Fig. 2 A). Based on the multiple differential abundance analyses, 10 genera were significantly higher abundant while 5 genera were significantly lower abundant in iRBD group (Fig. 2 B). We then constructed RF model to predict the importance score of specific genera. the differential fecal microbiota profiles with MDA > 10 including Desulfovibrio , Butyricicoccus , Anaerotruncus , Haemophilus , Phascolarctobacterium , Ruminococcus , and Cloacibacillus in DADA2 denoising and Desulfovibrio , Butyricicoccus , Cloacibacillus , Bilophila , Haemophilus , and Anaerotruncus in OTU clustering were identified (Fig. 2 C). Subsequently, a ROC curve analysis was performed to evaluate the discriminatory power of the fecal microbiota profiles. The LASSO was used before ROC analysis. The differential fecal microbiota profiles selected by LASSO could effectively differentiate iRBD from HCs well by attaining AUCs of 0.864 (95%CI 0.771–0.957) in DADA2 denoising, 0.867 (95%CI 0.776–0.959) in OTU clustering, 0.904 (95%CI 0.825–0.984) in DADA2 and OTU combining, implying that both DADA2 denoising and OTU clustering had good and stable diagnostic value (Fig. 2 D). As for SCFAs, the fecal concentrations of propionic acid, acetic acid, and butyric acid were lower while isovaleric acid and isobutyric acid were higher in iRBD group compared with HCs (p < 0.05) (Fig. 2 E). The combination of microbiota and SCFAs in distinguishing iRBD from HCs yielded higher AUCs of 0.930 (95%CI 0.866–0.995) in DADA2 denoising, 0.935 (95%CI 0.874–0.997) in OTU clustering, and 0.967 (95%CI 0.920–1.000) in DADA2 and OTU combining, which indicated that the predictive value of the combined biomarker assessment was greater than that of the individual biomarkers (Fig. 2 F). Furthermore, the predictive models were revalidated in testing set. The AUC values of microbiota profiles to distinguish iRBD from HCs were 0.672 in DADA2 denoising, 0.800 in OTU clustering, 0.883 in DADA2 and OTU combining, the combination of microbiota and SCFAs produced AUCs of 0.756 in DADA2 denoising, 0.850 in OTU clustering, 0.933 in DADA2 and OTU combining, which indicated that microbiota and SCFAs could also sufficiently distinguish iRBD patients from HCs in testing set. Detailed microbiota data for modeling were listed in the (Additional file 3). 3.3.2 Identification of MSA-specific microbial and SCFA biomarkers A total of 12 genera with increased relative abundance and 13 genera with lower relative abundance were identified in the MSA group compared with HCs in LEfSe analysis (Fig. 3 A). Based on the multiple differential abundance analyses, 7 genera were significantly higher abundant while 7 genera were significantly lower abundant in MSA group (Fig. 3 B). Specifically, the differential fecal microbiota profiles including Butyricicoccus , Haemophilus , Fusicatenibacter , Lactobacillus , Gordonibacter , and Odoribacter in DADA2 denoising and Haemophilus , Fusicatenibacter , Lactobacillus , Butyricicoccus , Odoribacter , and Dialister in OTU clustering had importance scores in the MSA-vs-HCs classifier (Fig. 3 C). The differential fecal microbiota profiles could effectively identify MSA from HCs well by attaining AUCs of 0.887 (95%CI 0.816–0.957) in DADA2 denoising, 0.879 (95%CI 0.807–0.950) in OTU clustering, 0.911 (95%CI 0.849–0.973) in DADA2 and OTU combining, respectively (Fig. 3 D). As for SCFAs, the fecal concentrations of propionic acid, acetic acid, and butyric acid were lower while isovaleric acid was higher in MSA group compared with HCs (p < 0.05) (Fig. 3 E). The combination of microbiota and SCFAs in distinguishing MSA from HCs produced higher AUCs of 0.914 (95%CI 0.851–0.976) in DADA2 denoising, 0.913 (95%CI 0.849–0.976) in OTU clustering, and 0.924 (95%CI 0.865–0.982) in DADA2 and OTU combining, suggesting its potential in diagnostic values (Fig. 3 F). Ultimately, the predictive models were revalidated in testing set. The AUC values of microbiota profiles to distinguish MSA from HCs were 0.775 in DADA2 denoising, 0.706 in OTU clustering, 0.944 in DADA2 and OTU combining, the combination of microbiota and SCFAs produced AUCs of 0.836 in DADA2 denoising, 0.841 in OTU clustering, 0.958 in DADA2 and OTU combining, which indicated that microbiota and SCFAs could also sufficiently distinguish MSA patients from HCs in testing set. Detailed microbiota data for modeling were listed in the (Additional file 4). 3.3.3 Identification of PD-specific microbial and SCFA biomarkers In the comparison between PD and HCs, we identified 14 differential genera in LEfSe analysis (Fig. 4 A). Based on the multiple differential abundance analyses, 4 genera were significantly higher abundant while 4 genera were significantly lower abundant in PD group (Fig. 4 B). Specifically, the differential fecal microbiota profiles with MDA > 10 including Butyricicoccus , Ruminococcus , and Actinomyces both in DADA2 denoising and OTU clustering had importance scores in the PD-vs-HCs classifier (Fig. 4 C). The differential fecal microbiota profiles could distinguish PD from HCs well by attaining AUCs of 0.686 (95%CI 0.588–0.784) in DADA2 denoising, 0.680 (95%CI 0.582–0.779) in OTU clustering, 0.698 (95%CI 0.600–0.796) in DADA2 and OTU combining, respectively (Fig. 4 D). As for SCFAs, the fecal concentrations of acetic acid and butyric acid were lower while isovaleric acid and isobutyric acid were higher in PD group compared with HCs (p < 0.05) (Fig. 4 E). The combination of microbiota and SCFAs in distinguishing PD from HCs resulted in higher AUCs of 0.756 (95%CI 0.662–0.850) in DADA2 denoising, 0.761 (95%CI 0.667–0.855) in OTU clustering, and 0.765 (95%CI 0.673–0.857) in DADA2 and OTU combining (Fig. 4 F). Furthermore, the predictive models were revalidated in testing set. The AUC values of microbiota profiles to distinguish PD from HCs were 0.552 in DADA2 denoising, 0575 in OTU clustering, 0.688 in DADA2 and OTU combining, the combination of microbiota and SCFAs produced AUCs of 0.729 in DADA2 denoising, 0.737 in OTU clustering, 0.769 in DADA2 and OTU combining. Detailed microbiota data for modeling were listed in the (Additional file 5). 3.3.4 Identification of synucleinopathies-specific microbial and SCFA biomarkers iRBD is considered as a prodromal stage of synucleinopathies. We then focused on the overlapping genera of iRBD, MSA, and PD vs. HCs. 2 genera ( Butyricicoccus and Haemophilus ) and 2 SCFAs (acetic acid and butyric acid) were found consistently decreased while isovaleric acid was increased in iRBD, MSA, and PD groups (Fig. 5 A, 5 B). However, only Butyricicoccus remained distinguishable among the overlapping genera of iRBD, MSA, and PD vs. HCs after screening for differential microbiota with MDA > 10 in RF (Fig. 5 C). The diagnostic accuracy for iRBD + MSA + PD vs. HCs based on ROC curve improved from 0.720 (95%CI 0.645–0.795) (DADA2 denoising), 0.697 (95%CI 0.618–0.776) (OTU clustering), and 0.699 (95%CI 0.620–0.777) (DADA2 and OTU combining) with only Butyricicoccus (Fig. 5 D) to 0.774 (95%CI 0.691–0.858) (DADA2 denoising), 0.773 (95%CI 0.689–0.857) (OTU clustering), and 0.772 (95%CI 0.688–0.857) (DADA2 and OTU combining) with the combination of acetic acid, butyric acid, isovaleric acid, and Butyricicoccus (Fig. 5 E). These results suggested that fecal microbiota and SCFAs could be used to distinguish synucleinopathies including iRBD, MSA, and PD from HCs. 3.3.5 Differences of microbiota and SCFAs between MSA and PD Moreover, we further explored differences of microbiota between MSA and PD. A total of 17 genera with increased relative abundance and 7 genera with lower relative abundance were identified in the PD compared with MSA group in LEfSe analysis (Fig. 6 A). Based on the multiple differential abundance analyses, 9 genera were significantly higher abundant while 6 genera were significantly lower abundant in PD group (Fig. 6 B). Specifically, the differential fecal microbiota profiles with MDA > 10 including Dialister , Lactobacillus , Granulicatella , Prevotella , Fusicatenibacter , Anaerostipes , Blautia , Odoribacter , and Haemophilus in DADA2 denoising and Prevotella , Dialister , Lactobacillus , Fusicatenibacter , Granulicatella , and Odoribacter in OTU clustering had importance scores in the MSA-vs-PD classifier (Fig. 6 C). The differential fecal microbiota profile could effectively differentiate MSA from PD well by attaining AUCs of 0.807 (95%CI 0.729–0.884) in DADA2 denoising, 0.795 (95%CI 0.712–0.879) in OTU clustering, 0.831 (95%CI 0.758–0.904) in DADA2 and OTU combining, respectively (Fig. 6 D). As for SCFAs, the fecal concentrations of propionic acid and acetic acid were higher in PD group compared with MSA group (p < 0.05) (Fig. 6 E). The combination of microbiota and SCFAs in distinguishing MSA from PD showed higher AUCs of 0.803 (95%CI 0.725–0.881) in DADA2 denoising, 0.788 (95%CI 0.704–0.871) in OTU clustering, and 0.831 (95%CI 0.759–0.904) in DADA2 and OTU combining, suggesting that microbiota and SCFAs could sufficiently distinguish MSA from PD (Fig. 6 F). Subsequently, the predictive models were also revalidated in testing set. The AUC values of microbiota profiles to distinguish MSA from PD were 0.774 in DADA2 denoising, 0.685 in OTU clustering, 0.932 in DADA2 and OTU combining, the combination of microbiota and SCFAs produced AUCs of 0.869 in DADA2 denoising, 0.819 in OTU clustering, 0.977 in DADA2 and OTU combining. Detailed microbiota data for modeling were listed in the (Additional file 6). 4. Discussion Our study identified microbiota alterations in iRBD, MSA, and PD, and identified Butyricicoccus as a biomarker for synucleinopathy, we also compared the microbiota differences between MSA and PD. Compared with previous articles, we presented a comparison of microbiota under DADA2 denoising algorithm and OTU clustering method through multiple differential analyses, and established disease diagnosis models based on RF. We obtained more stable and accurate microbial biomarkers and provided more reliable support for future prospective studies. This is the first report to investigate the gut microbiota using multimodal analysis methods in synucleinopathies. Our findings suggested prominent microbial alterations in synucleinopathies and its prodromal phase. We found that the abundance of most pro-inflammatory bacteria such as Desulfovibrio , Anaerotruncus , Collinsella , Bilophila , Cloacibacillus , and Actinomyces in iRBD, Butyricimonas , Solobacterium , and Eggerthella in MSA, Collinsella , Actinomyces , Eggerthella , and Solobacterium in PD were elevated. This suggested that the increase of pro-inflammatory bacteria may be closely related to the pathogenesis of synucleinopathy. Several studies reported the abundances of Desulfovibri and Collinsella were elevated in iRBD and PD patients [ 8 , 15 , 26 , 27 ]. Desulfovibrio produce hydrogen sulfide and lipopolysaccharide, and several strains synthesize magnetite, all of which likely induce the oligomerization and aggregation of α-synuclein and influence the development of PD [ 26 , 27 ]. An animal experiment also confirmed that Desulfovibrio could contribute to PD development by inducing alpha-syn aggregation [ 28 ]. Interestingly, Collinsella is a hydrogen-reducing bacteria that could cross-feed with Desulfovibrio [ 29 ], Collinsella enhances gut permeability by decreasing the tight junction protein ZO-1 in a mouse model of rheumatoid arthritis [ 30 ]. Collinsella is associated with a higher level of pro-inflammatory IL-17A, which could exacerbate neuroinflammation and neurodegeneration via microglial activation in PD rodent models [ 30 , 31 ]. The abundance of Collinsella is also elevated in DLB patients, implicating its important role in synucleinopathies [ 10 ]. On the other hand, we observed the decrease of the SCFA-Producing bacteria such as Butyricicoccus , Anaerostipes , Ruminococcus , and Roseburia in iRBD, Butyricicoccus , Anaerostipes , Fusicatenibacter , Lachnospira , and Dialister in MSA, Ruminococcus and Butyricicoccus in PD. This emphasized the potential role of SCFA-Producing bacteria in the pathogenesis of α-synucleinopathy. In our study, Butyricicoccus appears to be crucial in the diagnosis of synucleinopathy, and may be a potential hallmark of phenoconversion of RBD to synucleinopathy. The decrease of Butyricicoccus was also reported in other researches [ 15 , 16 , 32 ], although one study revealed the opposite result in PD [ 33 ]. Butyricicoccus belongs to Firmicutes phylum and actively produced butyric acid. Supplementing with Butyricicoccus pullicaecorum can alleviate colitis in rats by increasing transepithelial resistance and strengthening epithelial barrier function [ 34 ]. Besides Butyricicoccus , Roseburia , Ruminococcus , Lachnospira , Ruminococcus , Dialister , Anaerostipes , and Fusicatenibacter also mainly produce butyric acid, acetic acid, and propionic acid which exert neuroprotective functions in neurological disease such as PD [ 19 , 35 – 38 ]. In our study, the abundance of Haemophilus was also decreased, which was consistent with the results previously reported by our team in iRBD, MSA, and PD [ 9 , 16 , 18 ], as well as the results of other teams in PD [ 8 ]. The mechanism of the decline of Haemophilus in synucleinopathy is unclear and further exploration is needed. As evidenced by the changes of SCFA-Producing bacteria, fecal SFCA levels were also tested. Propionic acid, acetic acid, and butyric acid were decreased in iRBD, MSA, and PD patients, aligning with the observed changes in SCFA-producing bacteria and corroborating our previous findings[ 18 ]. Treatments with butyrate exerted protective effect against PD via preventing the MPTP-induced dopaminergic degeneration and increasing colonic GLP-1 levels [ 35 ]. Propionic acid and butyric acid modulated neurotransmitter synthesis and expression of their receptors, like dopaminergic or GABA receptors [ 37 ]. Moreover, butyrate and high-dose acetate reduced α-syn accumulation in the substantia compactus nigra in MPTP-induced PD models, thus alleviating motor dysfunction in mice [ 38 ]. Besides, the fecal concentration of isovaleric acid was elevated in iRBD, MSA, and PD patients. However, little is known about the functions of isobutyric acid. It was reported that isovaleric acid could strengthen the barrier function in mouse models [ 39 ]. A recent study reported that Bacteroides ovatus colonization in mice increased the abundance of intestinal SCFAs (including acetic acid, propionic acid, and isovaleric acid) and the concentrations of intestinal GABA [ 40 ]. The combination of differential microbiota and SCFAs could improve the accuracy of predictive models in our study, reflecting the importance of microbiota and SCFAs as biomarkers in the diagnosis and differential diagnosis of synucleinopathies. Gut microbiota including pro-inflammatory bacteria and SCFA-Producing bacteria participate in the pathogenesis of synucleinopathy through MGBA. Increase of pro-inflammatory bacteria and loss of protective SCFA-Producing bacteria might damage the integrity of the intestinal mucosal barrier, affect blood-brain barrier and vagus nerve, and participate in neuroinflammation and intestinal inflammation [ 41 ]. Gut microbiota and SCFAs also affects PD through their modulatory interactions with alpha-synuclein, neuroinflammation, and oxidative stress mediated by reactive oxygen and nitrogen species (ROS/RNS) [ 20 ]. Our report revealed that similar gut microbiota alterations were shared in iRBD, MSA, and PD, indicating that microbiota dysbiosis may be an early feature of synucleinopathies. It also suggested that these microbiota exhibiting common functional changes may be crucial to the phenotype transition from iRBD to synucleinopathie. On the other hand, we compared the microbiota differences between MSA and PD, and those different microbiota profiles between MSA and PD may be associated with the development of different synucleinopathies. Our study has several limitations. First, although potential confounders that may influence gut metabolites such as antibiotics, probiotic, and yogurt were excluded in our study, food preferences and dietary patterns should also be considered in the future study. Second, our research population is limited in number, the cross-sectional study design does not allow conclusions about causality and time. Further large longitudinal follow-up studies are warranted to further confirm the role of microbiota alterations as phenoconversion hallmarks. 5. Conclusions In conclusion, our findings suggest that 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. We also compared the microbiota differences between MSA and PD. The combination of microbiota and SCFAs may be potential biomarkers in the diagnosis and differential diagnosis of synucleinopathies. Further large longitudinal follow-up studies are warranted to verify our results. Animal experiments should also help to elucidate the exact mechanisms of the neuroprotective effects of microbiota and SCFAs in synucleinopathies. Declarations Ethics approval and consent to participate This study protocol was approved by the Ethics Committee of Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine. Written informed consents were obtained from all participants in the study as well. Consent for publication Not applicable. Funding The study was supported by the grants from the National Natural Science Foundation of China (82171401, 81971187, 81971183, 82371414, 82201561), Shanghai Municipal Science and Technology Major Project (2018SHZDZX05), and Peak Disciplines (Type IV) of Institutions of Higher Learning in Shanghai. Availability of data and materials The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests The authors have no conflict of interest to report. Authors’ contributions J.D., P.H., and P.Z. contributed to this work and were considered first authors. S.C. and Y.T. formulated the hypothesis, conceived the study design and contributed to the initial and revised draft of the manuscript; J.D., P.H., and P.Z. conceived the study design, assessed the subjects, collected and processed the fecal samples, contributed to data analysis and interpretation and wrote the initial and revised draft of the manuscript. C.G. contributed to collect fecal samples and manuscript preparation. J.L., M.H., H.L., and X.S. contributed to manuscript preparation. All authors approved the final version of this article. Acknowledgements We thank all donors and colleagues of Ruijin Hospital affiliated to Shanghai Jiao Tong University School. of Medicine for their participation and cooperation. References McCann H, Stevens CH, Cartwright H, Halliday GM (2014) α-Synucleinopathy phenotypes. Parkinsonism Relat Disord 20 Suppl 1, S62-67. 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Tan AH, Lim SY, Lang AE (2022) The microbiome-gut-brain axis in Parkinson disease - from basic research to the clinic. Nat Rev Neurol 18, 476–495. Additional Declarations No competing interests reported. Supplementary Files Additionalfile2.tif Additionalfiles.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5182069","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":366786898,"identity":"9ba002f7-f602-4003-bca8-b1291d7c2412","order_by":0,"name":"Juanjuan Du","email":"","orcid":"","institution":"Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine，No. 197 Ruijin 2nd Road, Shanghai, 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15:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5182069/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5182069/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68378282,"identity":"ec1263c1-b060-42a5-af5b-74f6803b7ace","added_by":"auto","created_at":"2024-11-06 15:52:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the design of research and main procedure of analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGut microbiota and fecal SCFAs were measured in two independent data sets using 16S rRNA sequencing and GC-MS. Multimodal differential abundance analyses (ANCOM, ANCOM-BC, ALDEx2, MaAsLin 2 and LEfSe) were used to identify the differential microbiota of diseases in the training set. Differential microbiota among three or more analysis methods both in DADA2 denoising and OTU clustering were selected for subsequent RF analysis. RF was to build a predictive model based on the differential fecal microbiota profile at genus level. Based on the results of the RF, the differential genera with Mean Decease Accuracy (MDA) \u0026gt; 10 combined with SCFAs were selected for further ROC analysis to establish a prediction model. The testing set was collected to validate the diagnosis model established based on the training set.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5182069/v1/851b4e98ecc1729ce35b7aa5.png"},{"id":68377915,"identity":"2c314422-359e-4654-b4ff-d92469df6777","added_by":"auto","created_at":"2024-11-06 15:44:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":254119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecific microbial and SCFA biomarkers for detecting iRBD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Comparisons of gut microbiota between iRBD and HCs according to their LDA values generated from LEfSe analysis. The LDA scores (log10)\u0026gt;2 and p \u0026lt; 0.05 were listed. B. Venn diagram demonstrating the overlapping and differential genera among three or more analysis methods (ANCOM, ANCOM-BC, ALDEx2, MaAsLin 2 and LEfSe). “↑” represented the relative abundance of genus was higher in iRBD compared with HCs. “↓” represented the relative abundance of genus was lower in iRBD compared with HCs. C. The predictive model based on genus-level abundance taxa using a RF model. Differential genera both in DADA2 denoising and OTU clustering were selected for RF. The relative importance of each genus in the predictive model was performed using the MDA. D. ROC curves based on differential genera with MDA \u0026gt; 10 for distinguishing iRBD from HCs. E. Comparisons of the fecal SCFAs levels between iRBD and HCs. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001. F. ROC curves based on the combination of differential genera and SCFAs for distinguishing iRBD from HCs. LDA: linear discriminant analysis, HCs: healthy controls, iRBD: Idiopathic rapid eye movement sleep behavior disorder, PD: Parkinson’s disease, MSA: multiple system atrophy, AUC: area under the curve, ROC: receiving operating characteristic, MDA: mean decease accuracy, SCFAs: short chain fatty acids.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5182069/v1/439750a05a9daf2a96dee694.png"},{"id":68377916,"identity":"8f467eb7-a91f-4b58-bf99-00f5562a22ee","added_by":"auto","created_at":"2024-11-06 15:44:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":223509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecific microbial and SCFA biomarkers for detecting MSA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Comparisons of gut microbiota between MSA and HCs according to their LDA values generated from LEfSe analysis. The LDA scores (log10)\u0026gt;2 and p \u0026lt; 0.05 were listed. B. Venn diagram demonstrating the overlapping and differential genera among three or more analysis methods (ANCOM, ANCOM-BC, ALDEx2, MaAsLin 2 and LEfSe). “↑” represented the relative abundance of genus was higher in MSA compared with HCs. “↓” represented the relative abundance of genus was lower in MSA compared with HCs. C. The predictive model based on genus-level abundance taxa using a RF model. Differential genera both in DADA2 denoising and OTU clustering were selected for RF. The relative importance of each genus in the predictive model was performed using the MDA. D. ROC curves based on differential genera with MDA \u0026gt; 10 for distinguishing MSA from HCs. E. Comparisons of the fecal SCFAs levels between MSA and HCs. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001. F. ROC curves based on the combination of differential genera and SCFAs for distinguishing MSA from HCs. LDA: linear discriminant analysis, HCs: healthy controls, iRBD: Idiopathic rapid eye movement sleep behavior disorder, PD: Parkinson’s disease, MSA: multiple system atrophy, AUC: area under the curve, ROC: receiving operating characteristic, MDA: mean decease accuracy, SCFAs: short chain fatty acids.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5182069/v1/a33473ab8b5694a2bf4a291e.png"},{"id":68377921,"identity":"f2f9dc37-787e-427f-881d-d5edf0b81737","added_by":"auto","created_at":"2024-11-06 15:44:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":194049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecific microbial and SCFA biomarkers for detecting PD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Comparisons of gut microbiota between PD and HCs according to their LDA values generated from LEfSe analysis. The LDA scores (log10)\u0026gt;2 and p \u0026lt; 0.05 were listed. B. Venn diagram demonstrating the overlapping and differential genera among three or more analysis methods (ANCOM, ANCOM-BC, ALDEx2, MaAsLin 2 and LEfSe). “↑” represented the relative abundance of genus was higher in PD compared with HCs. “↓” represented the relative abundance of genus was lower in PD compared with HCs. C. The predictive model based on genus-level abundance taxa using a RF model. Differential genera both in DADA2 denoising and OTU clustering were selected for RF. The relative importance of each genus in the predictive model was performed using the MDA. D. ROC curves based on differential genera with MDA \u0026gt; 10 for distinguishing PD from HCs. E. Comparisons of the fecal SCFAs levels between PD and HCs. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001. F. ROC curves based on the combination of differential genera and SCFAs for distinguishing PD from HCs. LDA: linear discriminant analysis, HCs: healthy controls, iRBD: Idiopathic rapid eye movement sleep behavior disorder, PD: Parkinson’s disease, MSA: multiple system atrophy, AUC: area under the curve, ROC: receiving operating characteristic, MDA: mean decease accuracy, SCFAs: short chain fatty acids.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5182069/v1/38151f89e8ec7a39ac1d9f35.png"},{"id":68378283,"identity":"6c3d903e-2d27-46b7-8063-80dfaac055f0","added_by":"auto","created_at":"2024-11-06 15:52:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":208570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecific microbial and SCFA biomarkers for detecting synucleinopathies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Comparisons of the relative abundance of \u003cem\u003eButyricicoccus\u003c/em\u003e and \u003cem\u003eHaemophilus\u003c/em\u003e between iRBD, MSA, PD, and HCs. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001. B. Comparisons of the fecal SCFAs levels between iRBD, MSA, PD, and HCs. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001. C. Venn diagram representing the overlapping and differential genera among three or more analysis methods (ANCOM, ANCOM-BC, ALDEx2, MaAsLin 2 and LEfSe). “↑” represented the relative abundance of genus was higher in iRBD, MSA, and PD compared with HCs. “↓” represented the relative abundance of genus was lower in iRBD, MSA, and PD compared with HCs. D. ROC curves based on differential genera with MDA \u0026gt; 10 for distinguishing iRBD+MSA+PD from HCs. E. ROC curves based on the combination of differential genera and SCFAs for distinguishing iRBD+MSA+PD from HCs. HCs: healthy controls, iRBD: Idiopathic rapid eye movement sleep behavior disorder, PD: Parkinson’s disease, MSA: multiple system atrophy, AUC: area under the curve, ROC: receiving operating characteristic, MDA: mean decease accuracy, SCFAs: short chain fatty acids.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5182069/v1/536e27d62d39e409039030cd.png"},{"id":68377917,"identity":"80f39fa4-d71e-4e99-b06e-a3260263b3ba","added_by":"auto","created_at":"2024-11-06 15:44:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":237754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences of microbiota and SCFAs between MSA and PD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Comparisons of gut microbiota between MSA and PD according to their LDA values generated from LEfSe analysis. The LDA scores (log10)\u0026gt;2 and p \u0026lt; 0.05 were listed. B. Venn diagram demonstrating the overlapping and differential genera among three or more analysis methods (ANCOM, ANCOM-BC, ALDEx2, MaAsLin 2 and LEfSe). “↑” represented the relative abundance of genus was higher in PD compared with MSA. “↓” represented the relative abundance of genus was lower in PD compared with MSA. C. The predictive model based on genus-level abundance taxa using a RF model. Differential genera both in DADA2 denoising and OTU clustering were selected for RF. The relative importance of each genus in the predictive model was performed using the MDA. D. ROC curves based on differential genera with MDA \u0026gt; 10 for distinguishing MSA from PD. E. Comparisons of the fecal SCFAs levels between MSA and PD. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001. F. ROC curves based on the combination of differential genera and SCFAs for distinguishing MSA from PD. LDA: linear discriminant analysis, HCs: healthy controls, iRBD: Idiopathic rapid eye movement sleep behavior disorder, PD: Parkinson’s disease, MSA: multiple system atrophy, AUC: area under the curve, ROC: receiving operating characteristic, MDA: mean decease accuracy, SCFAs: short chain fatty acids.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5182069/v1/4110d6b869f284dc95ca851d.png"},{"id":72191136,"identity":"cac6f998-942b-4fb7-b97a-9f0c2785e1b0","added_by":"auto","created_at":"2024-12-23 14:17:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1975305,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5182069/v1/67c98c70-6319-4f3d-abec-6da433a1db7d.pdf"},{"id":68377923,"identity":"3ad89c4d-fe61-4bce-b061-91b712cbb1d9","added_by":"auto","created_at":"2024-11-06 15:44:11","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":116165364,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.tif","url":"https://assets-eu.researchsquare.com/files/rs-5182069/v1/67c3593b096e4330e5f73b99.tif"},{"id":68377919,"identity":"d93f70ed-84ec-40b5-8360-6d9ce7ab2712","added_by":"auto","created_at":"2024-11-06 15:44:09","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":27268,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-5182069/v1/9832fa9e1d9c25b8a7d1f1b4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Altered gut microbiome and metabolism in synucleinopathies and iRBD using multimodal differential abundance analyses","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSynucleinopathies are a group of neurodegenerative disorders characterized by the abnormal accumulation of alpha-synuclein proteins, leading to the formation of Lewy bodies, which are associated with diseases such as Parkinson's disease (PD), Multiple system atrophy(MSA), etc [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. PD is the second most common neurodegenerative disease and affects elderly people (\u0026gt;\u0026thinsp;60 years old) worldwide, characterized by parkinsonism, including bradykinesia, resting tremor and rigidity[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MSA, characterized by parkinsonism, autonomic failure, cerebellar ataxia, and pyramidal symptoms, is one of the main types showing progressive atypical parkinsonism that are frequently misdiagnosed as PD, making the accuracy of diagnosis challenging [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Idiopathic rapid-eye-movement (REM) sleep behavior disorder (iRBD) is a parasomnia manifested by dream-enacting behaviors due to lack of muscle atonia during rapid eye movement sleep period [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the past decade, the longitudinal study of subjects with iRBD has shown a high specificity for later development of synucleinopathies including MSA and PD with a high conversion rate [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Thus, reliable and accessible biomarkers for early diagnosis of MSA, PD, and iRBD are urgently needed to identify candidate therapeutic targets.\u003c/p\u003e \u003cp\u003eThe gut microbiota consists of trillions of microbes that reside in the human gastrointestinal tract and performs many of the functions required for human physiology and survival, which is known as the forgotten organ [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. With the gradual deepening of research, the bidirectional communication system between gut microbiota dysbiosis and the brain has been recognized, known as the \"microbiota-gut-brain-axis (MGBA)\". Many studies have shown that gut dysbiosis is closely related to synucleinopathies such as PD, MSA, and Dementia with Lewy body[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Alpha-synuclein misfolds in the peripheral enteric nervous system and then transports to the brain through the vagus nerve, while bacterial products and pro-inflammatory cytokines circulate from the intestine to the brain, triggering central alpha-synuclein misfolding and neuroinflammation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The MPTP and rotenone induced PD models also confirmed that fecal microbiota transplantation inhibited inflammatory responses through the MGBA and exerted neuroprotective effects [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In recent years, some studies have reported similar gut microbiota alterations in iRBD and PD [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], indicating that gut dysbiosis may occur during the prodromal phase of synucleinopathy and these microbiota may be the key components of the transition from iRBD to PD [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While reports of differential microbiota in MSA and PD suggested that unique gut microbiota is related to the development of different synucleinopathies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, further identification of the shared microbiota in synucleinopathies and distinct microbiota of different synucleinopathies can provide insights into the transition and differentiation of diseases.\u003c/p\u003e \u003cp\u003eMoreover, short-chain fatty acids (SCFAs), which derived from the bacterial fermentation of dietary fibers, exert multiple effects on the gut\u0026ndash;brain axis. Our previous research reported similar fecal SCFA alterations in iRBD, MSA, and PD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. SCFA such as butyric acid could exert neuroprotective effects through restoring gut microbiota and inhibiting TLR4 signaling in MPTP-induced PD models [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. SCFAs are closely related to the abnormal α-syn aggregation and play a key role in modulating neuroinflammation in PD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The integration of specific microbiota and SCFAs may have potential diagnostic value for synucleinopathies.\u003c/p\u003e \u003cp\u003eAt present, many studies have identified specific taxa associated with PD based on 16s rRNA gene amplicon sequencing [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], but the results of the microbiota were heterogeneous. Traditionally, sequences were clustered into operational taxonomic units (OTUs) with a particular identity threshold (usually 97%) to reduce the interference of sequencing errors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. With the wide application of 16s rRNA sequencing technology, another denoising algorithm such as DADA2 was reported to be more accurate than the OTU clustering method, as it can accurately resolve sequence variants differing by a single nucleotide and present in as few as two reads, identify more real variants, and output fewer spurious sequences [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In order to more accurately determine potential disease-related microbiota, we used both DADA2 denoising algorithm and OTU clustering method to compare gut microbiota between groups through multimodal differential abundance analyses, and then combined with SCFAs to establish more stable disease diagnosis models based on random forest (RF). Our study aimed to (a) identify disease-specific microbiota biomarkers, (b) identify shared gut microbiota alterations in synucleinopathies, which could possibly play as potential microbial biomarkers for the phenoconversion of iRBD to synucleinopathies, and (c) combine SCFAs to establish disease prediction models.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Subjects\u003c/h2\u003e \u003cp\u003eA total of 37 iRBD patients, 70 MSA patients, 104 PD patients, and 61 healthy controls (HCs) from the movement disorder clinic and ward at Department of Neurology, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine from September 2016 to August 2023 were included in the study. Participants were randomly divided into a training set (70% of data, 27 iRBD, 49 MSA, 73 PD, 43 HCs) and a testing set (remaining 30% of the data, 10 iRBD, 21 MSA, 31 PD, 18 HCs). The inclusion criteria were as follows: (1) aged 35\u0026ndash;85 years. (2) Patients were diagnosed as clinical definite or probable MSA according to MSA diagnostic consensus by senior movement disorder specialists [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. PD patients were diagnosed according to MDS criteria with no anti-PD medication intake before the fecal sample collection [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. iRBD patients were examined by polysomnography (PSG) and diagnosed by International Classification of Sleep Disorders Criteria-Third Edition [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. (3) ability to complete the questionnaires, physical and neurological examination. The exclusion criteria were as follows: (1) vegetarian or malnutrition. (2) use of antibiotics or probiotic supplements within 3 months prior to sample collection. (3) ongoing use of yogurt, statin, corticosteroid, proton pump inhibitor, Metformin, entacapone, anti-neoplastic or immunosuppressant medication. (4) history of secondary neurological or psychiatric diseases such as epilepsy, brain tumor, head trauma, stroke, dementia, cerebral small-vessel disease and so on. (5) chronic gastrointestinal disorder (including inflammatory bowel disease, colitis, colon cancer, gastric or duodenal ulcer) or gastrointestinal surgery. (6) severe cognitive deficit that obstructed the execution of clinical assessment. This study protocol was approved by the Ethics Committee of Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine. Written informed consents were obtained from all participants in the study as well.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical evaluation\u003c/h2\u003e \u003cp\u003eDemographic data (age, gender, and BMI (kg/cm\u003csup\u003e2\u003c/sup\u003e)) in all subjects and clinical features including disease duration, constipation condition, motor and non-motor symptoms were recorded in our study. Patients with MSA were assessed using Unified Multiple System Atrophy Rating Scale (UMSARS) and patients with PD were evaluated by International Parkinson and Movement Disorder Society-Unified Parkinson\u0026rsquo;s Disease Rating Scale (MDS-UPDRS). Non-motor symptoms were evaluated by Mini-Mental State Examination (MMSE) for cognitive function, 16-item odor identification test from Sniffin' Sticks (SS-16) for olfaction, REM sleep behavior disorder questionnaire-HongKong (RBD-HK) for clinical possible RBD, The Scale for Outcomes in Parkinson\u0026rsquo;s disease for Autonomic Symptoms (SCOPA-AUT) for autonomic dysfunction, 17-item Hamilton Depression Rating Scale (HAMD-17) for depression, Hamilton Anxiety Scale (HAMA) for anxiety, Wexner constipation score for constipation severity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Gut microbiota analysis with 16s rRNA\u003c/h2\u003e \u003cp\u003eIn accordance with our previous study [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], all subjects were asked to collect a fecal sample in the morning using fecal collection containers. The containers were transferred on ice and stored at -80\u0026deg;C prior to processing. Fecal DNA was extracted from 200mg samples using the QIAamp Fast DNA Stool Mini Kit (Qiagen, Hilden, Germany) after the sample collection. Microbial composition was determined by 16s rRNA gene sequencing of DNA extracted from stool by amplifying the V3\u0026ndash;V4 regions. DNA was checked by running the samples on 1.2% agarose gels. Polymerase chain reaction (PCR) amplification of 16s rRNA genes was performed using general bacterial primers (357F and 806R) with two-step amplicon library building on the Novaseq platform. Detailed amplification and sequencing processes were as follows: (1) Genetic quality control. We took 3ul sample DNA for 1.2% agarose gel electrophoresis, all DNA concentrations were greater than 10ng/uL. (2) Primer design. Fusion primers with \"5' Miseq linker-barcode-sequencing primer-specific primer-3 '\" for bidirectional sequencing was designed. We amplified the V3-V4 functional regions of the bacterial 16S rRNA gene with specific primer sequences (357F 5'-ACTCCTACGGGAGGCAGCAG-3 'and 806R 5'-GGACTACHVGGGTWTCTAAT-3'). (3) PCR amplification and product purification. We performed a total of two PCR reactions (ABI9700 PCR instrument), cycling conditions for both PCR reactions included an initial denaturation at 94 \u0026deg; C for 2 min, followed by 25 and 8 cycles respectively at 94 \u0026deg; C for 30 s, 56 \u0026deg; C for 30 s, and 72 \u0026deg; C for 30 s, and then a final extension at 72 \u0026deg; C for 5 min. The PCR products were purified with the Axyprep DNA Gel Recovery Kit (AXYGEN company). (4) Quantification of PCR Products and DNA Sequencing. The purified PCR products were quantified with real-time fluorescence using the FTC-3000 real-time PCR instrument, and sequenced on a high-throughput IllUmina Miseq instrument (2x300 bp).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 SCFA analysis\u003c/h2\u003e \u003cp\u003eThe concentrations of acetic acid, propionic acid, butyric acid, isovaleric acid, isobutyric acid, valeric acid and caproic acid for SCFA analysis were measured by gas chromatography-mass spectrometry (GC-MS) using Tinygene Bio-Tech (shanghai). For each subject, take 400mg of fresh fecal samples into a 2 mL centrifuge tube and add 50 \u0026micro; L 15% phosphoric acid, 100uL internal standard (4-methylvaleric acid) solution (125 \u0026micro;g/mL), and 400 \u0026micro; L in a vortex mixer (Lin Bell, QL-866). After mixing for 1minute, the samples were centrifuged at 4\u0026deg;C 12000 rpm for 10 minutes in a frozen centrifuge (Xiangyi, H2050-R). The supernatant was transferred to a new centrifuge tube and tested on the machine. Fecal analyses for individual SCFAs were performed with GC-MS. Chromatography conditions: Thermo Trace 1300 (Thermo Fisher Scientific, USA) gas phase system, chromatographic column using Agilent HP-INNOWAX capillary column (30 m \u0026times; 0.25 mm ID \u0026times; 0.25 \u0026micro; m); split injection, injection volume 1 \u0026micro; L, the split ratio 10:1. Injection port temperature 250\u0026deg;C; ion source temperature 300 \u0026deg; C; transmission line temperature 250 \u0026deg; C. The starting temperature of programmed heating is 90 \u0026deg; C, raise the temperature to 120\u0026deg;C at 10\u0026deg;C/min; then raise the temperature to 150\u0026deg;Cat 5 \u0026deg; C/min, finally, heat up to 250\u0026deg;C at 25\u0026deg;C/min for 2 minutes. The carrier gas is helium with a flow rate of 1.0 mL/min. Mass spectrometry conditions: Thermo ISQ 7000 mass spectrometer (Thermo Fisher Scientific, USA), electron bombardment ionization (EI) source, SIM scanning method, electron energy 70 eV. The peak area was used to calculate SCFA concentrations. Raw GC-MS data were processed for peak integration, calibration, and quantitative analysis for SCFAs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe 16s sequences were analyzed by using a combination of software Trimmomatic (version 0.35), Flash (version 1.2.11), UPARSE (version v8.1.1756), mothur (version 1.33.3) and R (version 3.6.3). The raw 16s rRNA gene data were processed by two different algorithms: denoised by using DADA2 and clustered into OTUs at 97% identity. Taxonomy was assigned using Silva 128 as the reference database. We chose the genus level for further analysis. alpha-diversity was measured by observed, chao, ace, shannon, Simpson, and PD whole tree indexes for microbial diversity and richness. Kruskal-Wallis test was used to compare alpha-diversity indexes between groups. beta-diversity represented the difference between samples calculated by unweighted and weighted UniFrac. beta-diversity indexes were visualized with the principal coordinate analysis (PCoA) and compared by analyses of similarities (ANOSIMs). Continuous data were presented as mean and standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) and compared by Wilcoxon rank-sum or Kruskal-Wallis test. Categorical variables were presented as numbers/percentage and compared by chi-squared (χ\u003csup\u003e2\u003c/sup\u003e ) test. Comparisons of specific microbes between groups were analysed using multiple differential abundance analyses, including analysis of composition of microbiomes (ANCOM), which controls for false discovery rates in compositional data; analysis of composition of microbiomes with bias correction (ANCOM-BC), an extension that provides bias correction for differential abundance estimation; ANOVA-like differential expression 2 (ALDEx2), which uses centered log-ratio transformation and accounts for sampling variation; microbiome multivariable associations with linear models 2 (MaAsLin 2), a multivariate statistical framework for discovering associations between clinical metadata and microbial community abundance; and linear discriminant analysis (LDA) effect size (LEfSe) method (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://huttenhower.sph.harvard.edu/lefse/\u003c/span\u003e\u003cspan address=\"http://huttenhower.sph.harvard.edu/lefse/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with an effect size cut-off of 2.0, which identifies taxa that explain differences between groups with statistical significance and biological relevance. RF models were used to predict disease status based on the microbiota profile (significantly different taxa at genus level). The selection of variables was performed by 10-fold cross-validated least absolute shrinkage and selection operator (LASSO) in R package \u0026lsquo;\u0026lsquo;lars\u0026rdquo;. Receiver operating characteristic (ROC) curve was established to quantify the model\u0026rsquo;s diagnostic performance for fecal microbiota and SCFAs. The significance levels were set at 0.05 (2-tailed).\u003c/p\u003e \u003cp\u003eIn our study, we used multimodal differential abundance analyses (ANCOM, ANCOM-BC, ALDEx2, MaAsLin 2 and LEfSe) to identify the differential microbiota of diseases in the training set. Differential microbiota among three or more analysis methods both in DADA2 denoising and OTU clustering were selected for subsequent RF analysis. RF was to build a predictive model based on the differential fecal microbiota profile at genus level. Based on the results of the RF, the differential genera with mean decease accuracy (MDA)\u0026thinsp;\u0026gt;\u0026thinsp;10 combined with SCFAs were selected for further ROC analysis to establish a disease diagnosis model. The testing set was collected to validate the diagnosis model established based on the training set. The schematic overview of the main procedure is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic characteristics and microbial diversity among groups\u003c/h2\u003e \u003cp\u003eA total of 192 participants were enrolled and subsequently divided into four groups consisting of 27 iRBD patients (65.67\u0026thinsp;\u0026plusmn;\u0026thinsp;7.66 years old, 70.4% male), 49 MSA patients (63.18\u0026thinsp;\u0026plusmn;\u0026thinsp;6.54 years old, 55.1% male), 73 PD patients (64.14\u0026thinsp;\u0026plusmn;\u0026thinsp;9.35 years old, 58.9% male), and 43 HCs (62.07\u0026thinsp;\u0026plusmn;\u0026thinsp;6.96 years old, 39.5% male) in the training set. The demographic characteristics including age, gender, and BMI were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant differences were found in age, gender, and BMI distribution among the four groups in the training set (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 10 iRBD patients (66.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.55 years old, 70.0% male), 21 MSA patients (60.81\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82 years old, 57.1% male), 31 PD patients (60.55\u0026thinsp;\u0026plusmn;\u0026thinsp;9.83 years old, 51.6% male), and 18 HCs (61.28\u0026thinsp;\u0026plusmn;\u0026thinsp;8.19 years old, 61.1% male) in testing set were finally included, with an equal distribution of individuals in the four groups (Additional file 1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the participants in the training set.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC(n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eiRBD(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMSA(n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePD(n\u0026thinsp;=\u0026thinsp;73)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eage(y)\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.07\u0026plusmn;(6.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.67\u0026plusmn;(7.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.18\u0026plusmn;(6.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.14\u0026plusmn;(9.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003egender(male/female)\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17/26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27/22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(Kg/cm2)\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.29\u0026plusmn;(3.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.11\u0026plusmn;(2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.35\u0026plusmn;(3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.21\u0026plusmn;(2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoke(n/y)\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol(n/y)\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTea (n/y)\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoffee(n/y)\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes(n/y)\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59/14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eiRBD/PD/MSA disease duration(y)\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.15\u0026plusmn;(2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.63\u0026plusmn;(2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.43\u0026plusmn;(2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWexner\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u0026plusmn;(2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.93\u0026plusmn;(4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.43\u0026plusmn;(4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.71\u0026plusmn;(4.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSCOPA-AUT\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.15\u0026plusmn;(10.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.16\u0026plusmn;(9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.92\u0026plusmn;(5.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSS-16\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.44\u0026plusmn;(3.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8\u0026plusmn;(4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.03\u0026plusmn;(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRBD-HK\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.44\u0026plusmn;(20.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.39\u0026plusmn;(18.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.25\u0026plusmn;(15.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMSE\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.6\u0026plusmn;(1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u0026plusmn;(1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.02\u0026plusmn;(3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.19\u0026plusmn;(1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHAMD-17\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u0026plusmn;(1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.78\u0026plusmn;(3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.63\u0026plusmn;(4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.71\u0026plusmn;(5.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHAMA\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u0026plusmn;(1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.41\u0026plusmn;(3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.1\u0026plusmn;(5.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.12\u0026plusmn;(4.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMDS-UPDRS III\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u0026plusmn;(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUMSARS-II\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.33\u0026plusmn;(7.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI: Body Mass Index, HC: healthy control, iRBD: Idiopathic rapid eye movement sleep behavior disorder, PD: Parkinson\u0026rsquo;s Disease, MSA: multiple system atrophy, SCOPA-AUT: Scale for Outcomes in Parkinson\u0026rsquo;s disease for Autonomic Symptoms, SS-16: 16-item odor identification test from Sniffin' Sticks, RBD-HK: REM sleep behavior disorder questionnaire-HongKong, HAMD-17: 17-item Hamilton Rating Scale for Depression, HAMA: Hamilton Anxiety Scale, MMSE: Mini-Mental State Examination, MDS-UPDRS III: Movement Disorder Society sponsored version of the Unified Parkinson\u0026rsquo;s Disease Rating Scale III, UMSARS-II: the Unified Multiple System Atrophy Rating Scale\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003eData were shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, compared by ANOVA test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003eData were compared by chi-square/Fisher\u0026rsquo;s test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 alpha diversity and beta diversity\u003c/h2\u003e \u003cp\u003eIn DADA 2-denoised samples, the alpha-diversity indexes including observed species (p\u0026thinsp;=\u0026thinsp;0.008), chao (p\u0026thinsp;=\u0026thinsp;0.008), ace (p\u0026thinsp;=\u0026thinsp;0.008), and PD whole tree (p\u0026thinsp;=\u0026thinsp;0.002) showed remarkable difference among the four groups (Additional file 2). Significant differences were found in beta-diversity based on the unweighted (ANOSIM, R\u0026thinsp;=\u0026thinsp;0.1332, P\u0026thinsp;=\u0026thinsp;0.001) and weighted (ANOSIM, R\u0026thinsp;=\u0026thinsp;0.0318, P\u0026thinsp;=\u0026thinsp;0.043) UniFrac analysis. In OTU-clustered samples, no significant difference was observed in alpha-diversity between groups. The unweighted (ANOSIM, R\u0026thinsp;=\u0026thinsp;0.1917, P\u0026thinsp;=\u0026thinsp;0.001) UniFrac-based beta-diversity revealed a significant difference (Additional file 2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Disease-specific microbiota and SCFA biomarkers\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Identification of iRBD-specific microbial and SCFA biomarkers\u003c/h2\u003e \u003cp\u003e A total of 19 genera with increased relative abundance and 7 genera with lower relative abundance were identified in the iRBD group compared with HCs in LEfSe analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Based on the multiple differential abundance analyses, 10 genera were significantly higher abundant while 5 genera were significantly lower abundant in iRBD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). We then constructed RF model to predict the importance score of specific genera. the differential fecal microbiota profiles with MDA\u0026thinsp;\u0026gt;\u0026thinsp;10 including \u003cem\u003eDesulfovibrio\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eAnaerotruncus\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003ePhascolarctobacterium\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, and \u003cem\u003eCloacibacillus\u003c/em\u003e in DADA2 denoising and \u003cem\u003eDesulfovibrio\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eCloacibacillus\u003c/em\u003e, \u003cem\u003eBilophila\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, and \u003cem\u003eAnaerotruncus\u003c/em\u003e in OTU clustering were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, a ROC curve analysis was performed to evaluate the discriminatory power of the fecal microbiota profiles. The LASSO was used before ROC analysis. The differential fecal microbiota profiles selected by LASSO could effectively differentiate iRBD from HCs well by attaining AUCs of 0.864 (95%CI 0.771\u0026ndash;0.957) in DADA2 denoising, 0.867 (95%CI 0.776\u0026ndash;0.959) in OTU clustering, 0.904 (95%CI 0.825\u0026ndash;0.984) in DADA2 and OTU combining, implying that both DADA2 denoising and OTU clustering had good and stable diagnostic value (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). As for SCFAs, the fecal concentrations of propionic acid, acetic acid, and butyric acid were lower while isovaleric acid and isobutyric acid were higher in iRBD group compared with HCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The combination of microbiota and SCFAs in distinguishing iRBD from HCs yielded higher AUCs of 0.930 (95%CI 0.866\u0026ndash;0.995) in DADA2 denoising, 0.935 (95%CI 0.874\u0026ndash;0.997) in OTU clustering, and 0.967 (95%CI 0.920\u0026ndash;1.000) in DADA2 and OTU combining, which indicated that the predictive value of the combined biomarker assessment was greater than that of the individual biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eFurthermore, the predictive models were revalidated in testing set. The AUC values of microbiota profiles to distinguish iRBD from HCs were 0.672 in DADA2 denoising, 0.800 in OTU clustering, 0.883 in DADA2 and OTU combining, the combination of microbiota and SCFAs produced AUCs of 0.756 in DADA2 denoising, 0.850 in OTU clustering, 0.933 in DADA2 and OTU combining, which indicated that microbiota and SCFAs could also sufficiently distinguish iRBD patients from HCs in testing set. Detailed microbiota data for modeling were listed in the (Additional file 3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Identification of MSA-specific microbial and SCFA biomarkers\u003c/h2\u003e \u003cp\u003e A total of 12 genera with increased relative abundance and 13 genera with lower relative abundance were identified in the MSA group compared with HCs in LEfSe analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Based on the multiple differential abundance analyses, 7 genera were significantly higher abundant while 7 genera were significantly lower abundant in MSA group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Specifically, the differential fecal microbiota profiles including \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003eFusicatenibacter\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eGordonibacter\u003c/em\u003e, and \u003cem\u003eOdoribacter\u003c/em\u003e in DADA2 denoising and \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003eFusicatenibacter\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eOdoribacter\u003c/em\u003e, and \u003cem\u003eDialister\u003c/em\u003e in OTU clustering had importance scores in the MSA-vs-HCs classifier (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe differential fecal microbiota profiles could effectively identify MSA from HCs well by attaining AUCs of 0.887 (95%CI 0.816\u0026ndash;0.957) in DADA2 denoising, 0.879 (95%CI 0.807\u0026ndash;0.950) in OTU clustering, 0.911 (95%CI 0.849\u0026ndash;0.973) in DADA2 and OTU combining, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). As for SCFAs, the fecal concentrations of propionic acid, acetic acid, and butyric acid were lower while isovaleric acid was higher in MSA group compared with HCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The combination of microbiota and SCFAs in distinguishing MSA from HCs produced higher AUCs of 0.914 (95%CI 0.851\u0026ndash;0.976) in DADA2 denoising, 0.913 (95%CI 0.849\u0026ndash;0.976) in OTU clustering, and 0.924 (95%CI 0.865\u0026ndash;0.982) in DADA2 and OTU combining, suggesting its potential in diagnostic values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eUltimately, the predictive models were revalidated in testing set. The AUC values of microbiota profiles to distinguish MSA from HCs were 0.775 in DADA2 denoising, 0.706 in OTU clustering, 0.944 in DADA2 and OTU combining, the combination of microbiota and SCFAs produced AUCs of 0.836 in DADA2 denoising, 0.841 in OTU clustering, 0.958 in DADA2 and OTU combining, which indicated that microbiota and SCFAs could also sufficiently distinguish MSA patients from HCs in testing set. Detailed microbiota data for modeling were listed in the (Additional file 4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Identification of PD-specific microbial and SCFA biomarkers\u003c/h2\u003e \u003cp\u003eIn the comparison between PD and HCs, we identified 14 differential genera in LEfSe analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Based on the multiple differential abundance analyses, 4 genera were significantly higher abundant while 4 genera were significantly lower abundant in PD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Specifically, the differential fecal microbiota profiles with MDA\u0026thinsp;\u0026gt;\u0026thinsp;10 including \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, and \u003cem\u003eActinomyces\u003c/em\u003e both in DADA2 denoising and OTU clustering had importance scores in the PD-vs-HCs classifier (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe differential fecal microbiota profiles could distinguish PD from HCs well by attaining AUCs of 0.686 (95%CI 0.588\u0026ndash;0.784) in DADA2 denoising, 0.680 (95%CI 0.582\u0026ndash;0.779) in OTU clustering, 0.698 (95%CI 0.600\u0026ndash;0.796) in DADA2 and OTU combining, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). As for SCFAs, the fecal concentrations of acetic acid and butyric acid were lower while isovaleric acid and isobutyric acid were higher in PD group compared with HCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The combination of microbiota and SCFAs in distinguishing PD from HCs resulted in higher AUCs of 0.756 (95%CI 0.662\u0026ndash;0.850) in DADA2 denoising, 0.761 (95%CI 0.667\u0026ndash;0.855) in OTU clustering, and 0.765 (95%CI 0.673\u0026ndash;0.857) in DADA2 and OTU combining (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eFurthermore, the predictive models were revalidated in testing set. The AUC values of microbiota profiles to distinguish PD from HCs were 0.552 in DADA2 denoising, 0575 in OTU clustering, 0.688 in DADA2 and OTU combining, the combination of microbiota and SCFAs produced AUCs of 0.729 in DADA2 denoising, 0.737 in OTU clustering, 0.769 in DADA2 and OTU combining. Detailed microbiota data for modeling were listed in the (Additional file 5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4 Identification of synucleinopathies-specific microbial and SCFA biomarkers\u003c/h2\u003e \u003cp\u003eiRBD is considered as a prodromal stage of synucleinopathies. We then focused on the overlapping genera of iRBD, MSA, and PD vs. HCs. 2 genera (\u003cem\u003eButyricicoccus\u003c/em\u003e and \u003cem\u003eHaemophilus\u003c/em\u003e) and 2 SCFAs (acetic acid and butyric acid) were found consistently decreased while isovaleric acid was increased in iRBD, MSA, and PD groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). However, only \u003cem\u003eButyricicoccus\u003c/em\u003e remained distinguishable among the overlapping genera of iRBD, MSA, and PD vs. HCs after screening for differential microbiota with MDA\u0026thinsp;\u0026gt;\u0026thinsp;10 in RF (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The diagnostic accuracy for iRBD\u0026thinsp;+\u0026thinsp;MSA\u0026thinsp;+\u0026thinsp;PD vs. HCs based on ROC curve improved from 0.720 (95%CI 0.645\u0026ndash;0.795) (DADA2 denoising), 0.697 (95%CI 0.618\u0026ndash;0.776) (OTU clustering), and 0.699 (95%CI 0.620\u0026ndash;0.777) (DADA2 and OTU combining) with only \u003cem\u003eButyricicoccus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) to 0.774 (95%CI 0.691\u0026ndash;0.858) (DADA2 denoising), 0.773 (95%CI 0.689\u0026ndash;0.857) (OTU clustering), and 0.772 (95%CI 0.688\u0026ndash;0.857) (DADA2 and OTU combining) with the combination of acetic acid, butyric acid, isovaleric acid, and \u003cem\u003eButyricicoccus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). These results suggested that fecal microbiota and SCFAs could be used to distinguish synucleinopathies including iRBD, MSA, and PD from HCs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.5 Differences of microbiota and SCFAs between MSA and PD\u003c/h2\u003e \u003cp\u003eMoreover, we further explored differences of microbiota between MSA and PD. A total of 17 genera with increased relative abundance and 7 genera with lower relative abundance were identified in the PD compared with MSA group in LEfSe analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Based on the multiple differential abundance analyses, 9 genera were significantly higher abundant while 6 genera were significantly lower abundant in PD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Specifically, the differential fecal microbiota profiles with MDA\u0026thinsp;\u0026gt;\u0026thinsp;10 including \u003cem\u003eDialister\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eGranulicatella\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eFusicatenibacter\u003c/em\u003e, \u003cem\u003eAnaerostipes\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eOdoribacter\u003c/em\u003e, and \u003cem\u003eHaemophilus\u003c/em\u003e in DADA2 denoising and \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eDialister\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eFusicatenibacter\u003c/em\u003e, \u003cem\u003eGranulicatella\u003c/em\u003e, and \u003cem\u003eOdoribacter\u003c/em\u003e in OTU clustering had importance scores in the MSA-vs-PD classifier (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe differential fecal microbiota profile could effectively differentiate MSA from PD well by attaining AUCs of 0.807 (95%CI 0.729\u0026ndash;0.884) in DADA2 denoising, 0.795 (95%CI 0.712\u0026ndash;0.879) in OTU clustering, 0.831 (95%CI 0.758\u0026ndash;0.904) in DADA2 and OTU combining, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). As for SCFAs, the fecal concentrations of propionic acid and acetic acid were higher in PD group compared with MSA group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). The combination of microbiota and SCFAs in distinguishing MSA from PD showed higher AUCs of 0.803 (95%CI 0.725\u0026ndash;0.881) in DADA2 denoising, 0.788 (95%CI 0.704\u0026ndash;0.871) in OTU clustering, and 0.831 (95%CI 0.759\u0026ndash;0.904) in DADA2 and OTU combining, suggesting that microbiota and SCFAs could sufficiently distinguish MSA from PD (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eSubsequently, the predictive models were also revalidated in testing set. The AUC values of microbiota profiles to distinguish MSA from PD were 0.774 in DADA2 denoising, 0.685 in OTU clustering, 0.932 in DADA2 and OTU combining, the combination of microbiota and SCFAs produced AUCs of 0.869 in DADA2 denoising, 0.819 in OTU clustering, 0.977 in DADA2 and OTU combining. Detailed microbiota data for modeling were listed in the (Additional file 6).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study identified microbiota alterations in iRBD, MSA, and PD, and identified \u003cem\u003eButyricicoccus\u003c/em\u003e as a biomarker for synucleinopathy, we also compared the microbiota differences between MSA and PD. Compared with previous articles, we presented a comparison of microbiota under DADA2 denoising algorithm and OTU clustering method through multiple differential analyses, and established disease diagnosis models based on RF. We obtained more stable and accurate microbial biomarkers and provided more reliable support for future prospective studies. This is the first report to investigate the gut microbiota using multimodal analysis methods in synucleinopathies.\u003c/p\u003e \u003cp\u003eOur findings suggested prominent microbial alterations in synucleinopathies and its prodromal phase. We found that the abundance of most pro-inflammatory bacteria such as \u003cem\u003eDesulfovibrio\u003c/em\u003e, \u003cem\u003eAnaerotruncus\u003c/em\u003e, \u003cem\u003eCollinsella\u003c/em\u003e, \u003cem\u003eBilophila\u003c/em\u003e, \u003cem\u003eCloacibacillus\u003c/em\u003e, and \u003cem\u003eActinomyces\u003c/em\u003e in iRBD, \u003cem\u003eButyricimonas\u003c/em\u003e, \u003cem\u003eSolobacterium\u003c/em\u003e, and \u003cem\u003eEggerthella\u003c/em\u003e in MSA, \u003cem\u003eCollinsella\u003c/em\u003e, \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eEggerthella\u003c/em\u003e, and \u003cem\u003eSolobacterium\u003c/em\u003e in PD were elevated. This suggested that the increase of pro-inflammatory bacteria may be closely related to the pathogenesis of synucleinopathy. Several studies reported the abundances of \u003cem\u003eDesulfovibri and Collinsella\u003c/em\u003e were elevated in iRBD and PD patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. \u003cem\u003eDesulfovibrio\u003c/em\u003e produce hydrogen sulfide and lipopolysaccharide, and several strains synthesize magnetite, all of which likely induce the oligomerization and aggregation of α-synuclein and influence the development of PD [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. An animal experiment also confirmed that \u003cem\u003eDesulfovibrio\u003c/em\u003e could contribute to PD development by inducing alpha-syn aggregation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Interestingly, \u003cem\u003eCollinsella\u003c/em\u003e is a hydrogen-reducing bacteria that could cross-feed with \u003cem\u003eDesulfovibrio\u003c/em\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], \u003cem\u003eCollinsella\u003c/em\u003e enhances gut permeability by decreasing the tight junction protein ZO-1 in a mouse model of rheumatoid arthritis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. \u003cem\u003eCollinsella\u003c/em\u003e is associated with a higher level of pro-inflammatory IL-17A, which could exacerbate neuroinflammation and neurodegeneration via microglial activation in PD rodent models [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The abundance of \u003cem\u003eCollinsella\u003c/em\u003e is also elevated in DLB patients, implicating its important role in synucleinopathies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, we observed the decrease of the SCFA-Producing bacteria such as \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eAnaerostipes\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, and \u003cem\u003eRoseburia\u003c/em\u003e in iRBD, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eAnaerostipes\u003c/em\u003e, \u003cem\u003eFusicatenibacter\u003c/em\u003e, \u003cem\u003eLachnospira\u003c/em\u003e, and \u003cem\u003eDialister\u003c/em\u003e in MSA, \u003cem\u003eRuminococcus\u003c/em\u003e and \u003cem\u003eButyricicoccus\u003c/em\u003e in PD. This emphasized the potential role of SCFA-Producing bacteria in the pathogenesis of α-synucleinopathy. In our study, \u003cem\u003eButyricicoccus\u003c/em\u003e appears to be crucial in the diagnosis of synucleinopathy, and may be a potential hallmark of phenoconversion of RBD to synucleinopathy. The decrease of \u003cem\u003eButyricicoccus\u003c/em\u003e was also reported in other researches [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], although one study revealed the opposite result in PD [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. \u003cem\u003eButyricicoccus\u003c/em\u003e belongs to Firmicutes phylum and actively produced butyric acid. Supplementing with \u003cem\u003eButyricicoccus pullicaecorum\u003c/em\u003e can alleviate colitis in rats by increasing transepithelial resistance and strengthening epithelial barrier function [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Besides \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eLachnospira\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eDialister\u003c/em\u003e, \u003cem\u003eAnaerostipes\u003c/em\u003e, and \u003cem\u003eFusicatenibacter\u003c/em\u003e also mainly produce butyric acid, acetic acid, and propionic acid which exert neuroprotective functions in neurological disease such as PD [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In our study, the abundance of \u003cem\u003eHaemophilus\u003c/em\u003e was also decreased, which was consistent with the results previously reported by our team in iRBD, MSA, and PD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], as well as the results of other teams in PD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The mechanism of the decline of \u003cem\u003eHaemophilus\u003c/em\u003e in synucleinopathy is unclear and further exploration is needed.\u003c/p\u003e \u003cp\u003eAs evidenced by the changes of SCFA-Producing bacteria, fecal SFCA levels were also tested. Propionic acid, acetic acid, and butyric acid were decreased in iRBD, MSA, and PD patients, aligning with the observed changes in SCFA-producing bacteria and corroborating our previous findings[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Treatments with butyrate exerted protective effect against PD via preventing the MPTP-induced dopaminergic degeneration and increasing colonic GLP-1 levels [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Propionic acid and butyric acid modulated neurotransmitter synthesis and expression of their receptors, like dopaminergic or GABA receptors [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Moreover, butyrate and high-dose acetate reduced α-syn accumulation in the substantia compactus nigra in MPTP-induced PD models, thus alleviating motor dysfunction in mice [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Besides, the fecal concentration of isovaleric acid was elevated in iRBD, MSA, and PD patients. However, little is known about the functions of isobutyric acid. It was reported that isovaleric acid could strengthen the barrier function in mouse models [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A recent study reported that \u003cem\u003eBacteroides ovatus\u003c/em\u003e colonization in mice increased the abundance of intestinal SCFAs (including acetic acid, propionic acid, and isovaleric acid) and the concentrations of intestinal GABA [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The combination of differential microbiota and SCFAs could improve the accuracy of predictive models in our study, reflecting the importance of microbiota and SCFAs as biomarkers in the diagnosis and differential diagnosis of synucleinopathies.\u003c/p\u003e \u003cp\u003eGut microbiota including pro-inflammatory bacteria and SCFA-Producing bacteria participate in the pathogenesis of synucleinopathy through MGBA. Increase of pro-inflammatory bacteria and loss of protective SCFA-Producing bacteria might damage the integrity of the intestinal mucosal barrier, affect blood-brain barrier and vagus nerve, and participate in neuroinflammation and intestinal inflammation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Gut microbiota and SCFAs also affects PD through their modulatory interactions with alpha-synuclein, neuroinflammation, and oxidative stress mediated by reactive oxygen and nitrogen species (ROS/RNS) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our report revealed that similar gut microbiota alterations were shared in iRBD, MSA, and PD, indicating that microbiota dysbiosis may be an early feature of synucleinopathies. It also suggested that these microbiota exhibiting common functional changes may be crucial to the phenotype transition from iRBD to synucleinopathie. On the other hand, we compared the microbiota differences between MSA and PD, and those different microbiota profiles between MSA and PD may be associated with the development of different synucleinopathies.\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, although potential confounders that may influence gut metabolites such as antibiotics, probiotic, and yogurt were excluded in our study, food preferences and dietary patterns should also be considered in the future study. Second, our research population is limited in number, the cross-sectional study design does not allow conclusions about causality and time. Further large longitudinal follow-up studies are warranted to further confirm the role of microbiota alterations as phenoconversion hallmarks.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, our findings suggest that 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 \u003cem\u003eButyricicoccus\u003c/em\u003e as a biomarker for synucleinopathy, sharing by iRBD, MSA and PD, which may be a potential hallmark of phenoconversion of RBD to synucleinopathy. We also compared the microbiota differences between MSA and PD. The combination of microbiota and SCFAs may be potential biomarkers in the diagnosis and differential diagnosis of synucleinopathies. Further large longitudinal follow-up studies are warranted to verify our results. Animal experiments should also help to elucidate the exact mechanisms of the neuroprotective effects of microbiota and SCFAs in synucleinopathies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was approved by the Ethics Committee of Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine. Written informed consents were obtained from all participants in the study as well.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the grants from the National Natural Science Foundation of China (82171401, 81971187, 81971183, 82371414, 82201561), Shanghai Municipal Science and Technology Major Project (2018SHZDZX05), and Peak Disciplines (Type IV) of Institutions of Higher Learning in Shanghai.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.D., P.H., and P.Z. contributed to this work and were considered first authors. S.C. and Y.T. formulated the hypothesis, conceived the study design and contributed to the initial and revised draft of the manuscript; J.D., P.H., and P.Z. conceived the study design, assessed the subjects, collected and processed the fecal samples, contributed to data analysis and interpretation and wrote the initial and revised draft of the manuscript. C.G. contributed to collect fecal samples and manuscript preparation. J.L., M.H., H.L., and X.S. contributed to manuscript preparation. All authors approved the final version of this article.\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all donors and colleagues of \u0026nbsp;Ruijin Hospital affiliated to Shanghai Jiao Tong University School. of Medicine for their participation and cooperation.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcCann H, Stevens CH, Cartwright H, Halliday GM (2014) α-Synucleinopathy phenotypes. Parkinsonism Relat Disord 20 Suppl 1, S62-67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReich SG, Savitt JM (2019) Parkinson's Disease. Med Clin North Am 103, 337\u0026ndash;350.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTolosa E, Garrido A, Scholz SW, Poewe W (2021) Challenges in the diagnosis of Parkinson's disease. 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Nat Rev Neurol 18, 476\u0026ndash;495.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Synucleinopathy, Microbiota, Short-chain fatty acids, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-5182069/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5182069/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Objectives:\u003c/h2\u003e \u003cp\u003eThe 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\u0026rsquo;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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGut 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGut 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. \u003cem\u003eButyricicoccus\u003c/em\u003e 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.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMicrobiota 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 \u003cem\u003eButyricicoccus\u003c/em\u003e 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.\u003c/p\u003e","manuscriptTitle":"Altered gut microbiome and metabolism in synucleinopathies and iRBD using multimodal differential abundance analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 15:44:04","doi":"10.21203/rs.3.rs-5182069/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d5b2ae87-9fa3-4698-8141-2f94991762d8","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T14:09:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-06 15:44:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5182069","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5182069","identity":"rs-5182069","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}