Metagenomics Indicates an Interplay of the Microbiome and Functional Pathways in Parkinson's Disease

Metagenomics Indicates an Interplay of the Microbiome and Functional Pathways in Parkinson's Disease | 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 Article Metagenomics Indicates an Interplay of the Microbiome and Functional Pathways in Parkinson's Disease Sarah Jaehwa Park, Barış Erhan Özdinç, Kathryn Grace Coker, Dana M. Walsh, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7209227/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jan, 2026 Read the published version in npj Parkinson's Disease → Version 1 posted 9 You are reading this latest preprint version Abstract Previous studies suggest there are distinct gut microbial and functional variations in patients with Parkinson’s disease (PwPD) that may reveal potential microbiome signatures or biomarkers to aid in early detection of the disease. In this case-control study, we used whole genome sequencing to compare the stool samples of 55 PwPD to 42 age-matched healthy controls (HC) from a public database (BioProject Accession PRJEB39223). For bacteria, we observed a greater relative abundance in Firmicutes and Actinobacteria among PwPD, while that of Bacteroidetes was lower. For phages, PwPD had a greater relative abundance of Siphoviridae, Tectiviridae, and Podoviridae , while Microviridae was lower. Moreover, we identified 10 functional pathways that significantly varied between PwPD and HC (all p < 0.0001). In conclusion, significant differences were observed in gut bacteria, phages, and functional pathways between PwPD and HC that both support and conflict with previous case-control studies and warrant further validation. Biological sciences/Microbiology Biological sciences/Molecular biology Parkinson’s disease gut-brain axis microbiome metagenome whole genome sequencing gut microbiota gastrointestinal dysfunction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 INTRODUCTION Parkinson’s disease (PD) is a progressive neurodegenerative disease, pathologically characterized by the loss of nigrostriatal dopaminergic innervation and the aggregation of misfolded α-synuclein (α-syn) ( 1 ). Gastrointestinal (GI) dysfunction is among the earliest and most common non-motor manifestations of PD ( 2 )and often precedes or parallels the onset of motor deficits by which PD is clinically diagnosed ( 2 – 4 ). Moreover, GI conditions including chronic constipation ( 5 , 6 ), irritable bowel syndrome (IBS) ( 7 , 8 ), inflammatory bowel disease (IBD) ( 9 ), and colonic diverticular disease ( 10 ) have been associated with increased risk of PD diagnosis, which suggest a potential link between the gut and PD pathophysiology. Gut dysbiosis is known to lead to inflammation, increased intestinal permeability, and aberrant immune responses, all of which are thought to contribute to the initiation of α-syn misfolding and the following cascade of neurodegeneration in PD ( 11 – 13 ). It has been postulated that the gut microbiota and its metabolites may mediate PD pathophysiology via the microbiota-gut-brain axis ( 11 , 14 ). Mice studies have demonstrated that microbial changes in the gut occurred prior to motor dysfunction in PD( 12 , 15 ) and that bacterial colonization and specific microbial metabolites were required to induce α-synucleinopathy, neuroinflammation, and motor deficits ( 12 , 15 ). Unsurprisingly, there has been growing interest in investigating the gut microbiome for potential PD microbiome signatures or biomarkers. Whole genome sequencing (WGS) enables a comprehensive analysis of genetic content including bacteria, viruses, fungi, protists, and functional pathways. To the best of our knowledge, observational studies using WGS to compare the gut microbiome between patients with PD (PwPD) and healthy controls (HC) have been conducted in Germany ( 16 ), China ( 17 – 20 ), South Korea ( 21 ), Taiwan ( 22 , 23 ), Japan ( 24 ), Italy ( 25 ), London ( 26 ), Canada ( 27 , 28 ), and the US ( 29 , 30 ). Among these studies, several have reported an increased Firmicutes to Bacteroidetes (F/B) ratio among PwPD ( 16 , 17 , 20 , 29 , 30 ), as well as significant functional differences in short-chain fatty acid (SCFA) metabolism and pathways involved in lipopolysaccharide, β-glucuronate, and tryptophan degradation ( 16 – 18 ). However, further research is needed to validate these findings. We therefore sought to build upon this body of research by comparing the gut microbiota of PwPD compared to HC in North America using WGS. RESULTS Baseline characteristics for PwPD and HC were provided in Table 1. In the PD group, the mean age (± standard deviation; SD) was 66.0 years (± 7.7), with gender evenly distributed. Participants were largely based in the US (95.0%), representing 17 unique states, and identified as white (90.9%), having an annual salary exceeding $150,000 USD (50.9%), and having earned a graduate or professional degree (50.9%). In terms of clinical characteristics, participants primarily had idiopathic PD (98.2%) and reported a mean (± SD) of 7.0 years since diagnosis (± 11.5), with over half in Hoehn and Yahr stage 1 (56.4%). The HC group had a mean age (± SD) of 54.8 years (± 5.2); 76.2% were female; and all were based in Massachusetts, USA. Other sociodemographic information was not available for the HC group. Table 1 Sociodemographic and clinical characteristics of patients with Parkinson’s disease who provided stool samples for whole genome sequencing compared to healthy controls.1 Parkinson Group ( n = 55) Control Group ( n = 42) Age, years – mean ± SD 66 ± 7.7 54.8 (± 5.2) Gender – no. (%) Male 27 (45.7%) 10 (23.8%) Female 28 (54.2%) 32 (76.2%) Geolocation – no. (%) United States 52 (95.0%) 42 (100.0%) Canada 3 (5.0%) 0 (0.0%) Race – no. (%) N/A White 50 (90.9%) Non-white 5 (9.1%) Annual household income, USD – no. (%) N/A < $60,000 8 (14.5%) $60,000 to < $80,000 4 (7.2%) $80,000 to < $100,000 7 (12.7%) $100,000 to $150,000 6 (10.9%) ≥ $150,000 30 (50.9%) Education level – no. (%) N/A Less than college degree 13 (23.6%) College degree 14 (25.5%) Graduate/professional degree 28 (50.9%) Type of Parkinsonism – no. (%) N/A Idiopathic Parkinson’s disease 54 (98.2%) Other Parkinsonism 1 (1.8%) Years since Parkinson diagnosis – mean ± SD 7.0 ± 11.5 N/A Estimated Hoehn & Yahr stage – no. (%) N/A 1 (unilateral involvement only, minimal disability) 31 (56.4%) 2 (both sides affected, balance is stable) 13 (23.6%) 3 (mild to moderate disability, balance affected) 11 (20.0%) 1 Data for healthy controls were obtained from a public database (BioProject Accession PRJEB39223). There was limited sociodemographic information available for the control group. Clinical characteristics related to Parkinsonism were not applicable. Bacteria We first evaluated differences in gut bacteria between PwPD and HC at the phylum level and observed the relative abundance of Firmicutes was greater in PwPD while that of Bacteroidetes is lower (Fig. 1). Actinobacteria abundance appeared to be double in PwPD as compared to HC. PwPD exhibited significantly greater α-diversity as indicated by both Shannon's (Wilcoxon rank sum p = 0.004; Fig. 2) and Simpson's diversity indices ( p = 0.0002; Fig. 3). The Simpson’s diversity index, which gives more weight to common species, suggested that the gut microbiome in PwPD may have a higher number of dominant species with similar evenness in abundance compared to the gut microbiome in HC. Bray-Curtis β-diversity dissimilarity suggested that the groups were significantly dissimilar in bacterial composition (PERMANOVA p = 0.001; Fig. 4). Although PD and HC samples did not cluster separately in the heat map (Fig. 5), there was some grouping of healthy samples among the PD samples, suggesting potential family-level distinctions between PwPD and HC. Phages and viruses Identification of the phage communities, or phageome, within PwPD and HC showed differences in relative abundance at the family level. Specifically, PwPD had less Microviridae and unclassified viruses, but more Siphoviridae, Tectiviridae and Podoviridae than HC (Fig. 6). After calculating α-diversity using Simpson’s, Shannon’s, and Chao1 indices, only the Chao1 index showed that PD patients had less phage richness than HC (Wilcoxon rank sum p = 0.000; Fig. 7), which suggested a difference in the number of taxa rather than their relative abundances. Bray-Curtis β-diversity suggested significant dissimilarity between the phageome of the two groups (PERMANOVA p = 0.002). The principal coordinates analysis (PcoA) plot (Fig. 8) suggested that there may be several different sample clusters based on the phage community composition. To determine what phages may be driving this clustering, we plotted the relative abundance of the samples in the heat map in Fig. 9. Here, it was clear that groups of samples containing mostly PD samples were dominated by Siphoviridae, Microviridae , and Myoviridae . Clusters dominated by HC had high abundance of either an unclassified phage virus family or moderate abundance of both Siphoviridae and Myoviridae . After plotting the abundance of the entire viral communities (Fig. 10), we observed some HC samples with relatively high abundance of the Adenoviridae and Partitiviridae families but little detection of virus families among PwPD. Protists and fungi When we compared protists and eukaryotic fungi between PwPD and HC (Figs. 11 and 12), we observed few dominant organisms shared among participants resulting in a lack of power to detect statistical significance. However, the Blastocystis genus was detected more frequently among PwPD. Functional pathways Lastly, we evaluated functional pathways between PwPD and HC. Chao1 functional α-diversity was significantly higher in PwPD ( p < 0.001; Fig. 13), which suggested a greater number of different MetaCyc functions detected. Bray-Curtis functional β-diversity of PwPD and HC was significantly different ( p = 0.001; Fig. 14), which suggested compositional differences between the groups in functional pathways and their relative abundance. In Fig. 15, we identified MetaCyc pathway terms that significantly differed in mean abundance between the PwPD and HC, including colanic acid building blocks biosynthesis, transfer RNA (tRNA) processing, PEPCK-type C4 photosynthetic carbon assimilation cycle, guanosine nucleotide degradation III, glutaryl CoA degradation, pentose phosphate pathway, fatty acid and β-oxidation I, inosine-5-phosphate biosynthesis III, anhydromuropeptides recycling, and the trichloroacetic acid (TCA) cycle VI (all p < 0.001). Other MetaCyc pathways with non-significant differences are shown in Supplementary Table 1 . DISCUSSION In this case-control study, we observed significant differences in the diversity and composition of specific bacteria, viruses and phages, and functional pathways between a self-selected North American cohort of PwPD and age-matched HC. For bacteria, there was a greater relative abundance in Firmicutes and Actinobacteria among PwPD, while that of Bacteroidetes was lower. While there was little detection of DNA viruses among PwPD compared to HC, PwPD had a greater relative abundance of phages including Siphoviridae, Tectiviridae, and Podoviridae , while Microviridae was lower. Further, we identified 10 unique functional pathways that significantly differed between the two groups. Firmicutes and Bacteroidetes are the two dominant phyla in the human gut microbiome ( 31 , 32 ), and changes in their composition have been observed in various conditions. Specifically, an increased F/B ratio has been associated with obesity ( 31 , 33 – 35 ), while a decreased F/B ratio has been associated with weight loss ( 31 ) and IBD ( 35 – 37 ). Theoretically, one would expect to see the latter in PwPD as weight loss and IBD are both commonly seen PD and may mediate potential associations between F/B ratio and PD ( 9 , 38 ). However, we observed an increased F/B ratio among PwPD in the present analysis, which was consistent with several previous case-control studies using WGS ( 16 , 17 , 20 , 29 , 30 ). We also observed a greater abundance of Actinobacteria among PwPD. Previous studies have reported both a lower ( 29 , 30 ) and greater abundance ( 39 ) of bacteria expressing SFCA-degradation pathways. Specific microbes in the Firmicutes and Actinobacteria phyla produce butyrate, an important SCFA that plays an important role in maintaining gut barrier integrity and reducing mucosal inflammation ( 40 ). It is possible that the amplification of Firmicutes and Actinobacteria may have been due to a lower abundance of other SCFA-producing bacteria ( 30 , 41 ). Namely, we observed a lower abundance of the Bacteroidetes phylum, which produces acetate, the most abundant SCFA in the gut. Although we did not assess SCFAs in the present analysis, several studies have reported lower concentrations of fecal SCFAs in PwPD compared to HC ( 17 , 42 – 45 ). Moreover, Chen et al ( 17 ) reported that reductions in fecal SCFAs were associated with worse cognitive and motor outcomes. Further research integrating fecal and plasma SCFA measurement is needed to explore potential mechanisms of these changes in bacterial composition and their associations with PD outcomes. Specific phages are thought to play an important role in the maintenance of a healthy gut microbiome. In the present study, we observed a decreased abundance of Microviridae and increased abundance of Siphoviridae , Tectiviridae , and Podoviridae , which are consistent with changes observed in IBD ( 46 ). Tetz et al ( 47 ) reported similar findings for Microviridae and Podoviridae and hypothesized that there may be an inverse relationship between bacteriophages and bacterial hosts in PwPD as this population may have a greater abundance of lytic phages. However, this was not evaluated in the present study. Previous studies both supported ( 16 ) and conflicted ( 17 , 47 ) the differences observed in phage α- and β-diversities between PwPD and HC. Furthermore, our finding of a lower relative abundance of DNA viruses was consistent with another case-control study by Bedarf et al ( 16 ), which involved early levodopa-naïve PwPD. On the other hand, Qian et al ( 18 ) identified viral enrichment in the PD cohort, however, does not identify any specific viruses linked to PD in the literature. Viruses such as influenza virus, Coxsackie virus, Japanese encephalitis virus, and the human immunodeficiency virus have been associated with secondary PD ( 48 , 49 ). Recently, a study from Taiwan ( 50 ) also showed that hepatitis C virus infection is associated with the risk of developing PD. Qian et al ( 18 ) indicated viral databases employed as a potential source of variation, but extraction methods between the studies may also differ; however, the extraction method used by Bedarf et al ( 16 ) was not referenced. Given that over half of our study population were also estimated to be in Hoehn & Yahr stage 1, this suggests there may be potential reductions in viral richness among PwPD that occur early in the disease. This difference could also be based on ethnicity given that our finding was in line with Bedarf et al ( 16 ) was conducted in Germany, while Qian et al ( 18 ) was conducted in China, suggesting Caucasian descendance may be linked to lower viral diversity in PD. Further research is needed to understand whether there are differences in viral diversity based on PD stage, ethnicity, or other unknown factors. Lastly, we observed PwPD had a greater abundance of bacteria capable of expressing 10 MetaCyc pathways. Current literature and WGS limitations constrain inferences about the pathways identified; however, mechanistic evidence on byproducts of these pathways may suggest linkages to PD pathogenesis. For instance, tRNA processing is disrupted under stress conditions, leading to the accumulation of tRNA-derived fragments (tRFs) ( 51 , 52 ), which have been suggested as a potential biomarker and therapeutic target for PD and other neurodegenerative disorders ( 53 – 55 ). Accumulation of tRF with unique tRF signatures have been observed in serum, cerebrospinal fluid, and the prefrontal cortex that distinguished PwPD from HC with high sensitivity and specificity ( 51 , 52 ). Further research is necessary to determine whether tRFs are reliable biomarkers and to establish optimal methods for measuring tRF accumulation. Another significant pathway identified was the guanosine nucleotide degradation III pathway. Metcalfe-Roach et al ( 28 ) also identified a significant depletion of purine nucleotide metabolism in PD; salvage indicates recycling of free purine ribonucelosides, deoxyribonucleosides and nucleobases into nucleotides, thus we observed enriched guanosine degradation may be the nucleoside of the purine guanine. Altogether, our findings are in line with Metacalfe-Roach et al ( 28 ), suggesting a degradatory guanine metabolism. A byproduct of this pathway is uric acid ( 56 ), of which lower levels have been associated with increased PD risk, progression, and symptom severity ( 57 – 61 ). The present study found a greater abundance of bacteria expressing this pathway among PwPD, which could suggest either a down-regulation of the pathway leading to reduced uric acid levels, or a compensatory increase in bacteria due to low uric acid levels in these individuals. However, without serum uric acid measurements, these conclusions remain speculative. Further research is needed to elucidate whether these functional pathways may induce differences in metabolite concentration with linkages to PD symptoms. This case-control study contributes to the growing body of metagenomic research in PD, which may lead to future identification of potential microbiome signatures or biomarkers that could enhance the prediction, diagnosis, and treatment of PD. WGS enables acquisition of genetic information across the complete spectrum of microorganisms including bacteria, viruses, phages, protists, fungi, and functional pathways using an assembly-free, k -mer-based algorithm that allows for superior identification of the microorganism ( 62 ). However, the results of this study must be interpreted in the context of several limitations. First, we used historical controls to characterize our HC group, which may lead to technical variation between the PD and HC groups as the data were prepared using different kits by different people, in different labs, with different protocols, and using different sequencing instruments. Although the controls were age-matched, we did not control for potential confounding by other sociodemographic, clinical, or lifestyle factors, which likely influenced dietary habits and, consequently, the gut microbiota of our participants. Thus, these results warrant further validation and should be considered hypothesis-generating. Additionally, potential changes in the concentrations of byproducts or their associations with functional pathways could not be determined due to the absence of metabolite data; gene copy number variations also prevented a clear correlation between bacterial abundance and pathway gene expression. Lastly, the PD group was predominantly white, affluent, well-educated, and based in North America, which limits the generalizability of the findings to the broader PD population. Future research should involve longitudinal analyses with more diverse groups to explore how specific metabolites, such as tRFs, uric acid, and SCFAs may be related to PD risk and progression over time. CONCLUSIONS We observed significant bacterial, viral, and functional differences between among a self-selected group of patients with Parkinson’s disease and healthy historical controls that both supported and conflicted previous studies using whole genome sequencing. Additional research is needed to determine the reproducibility of these results in larger, more diverse populations, and in clarifying whether these microbial and functional changes are causal factors or consequences of Parkinson’s disease. METHODS We conducted a case-control study using WGS to evaluate the microbial and functional differences between PwPD and HC. In August 2019, 60 PwPD from across the US and Canada attended Parkinson Summer School, an annual intensive five-day retreat in Washington state designed to promote wellness and improve outcomes among PwPD. Prior to attending the retreat, stool sample collection kits were mailed to participants’ homes. Of the 60 individuals registered for the retreat, 57 participants collected their own stool samples and mailed the sample directly to the lab. In the lab, the samples were stored at -80 o C until extraction, no more than one month after receipt. Two samples were discarded because one was from outside of North America and the other arrived more than six months after the others, resulting in a total of 55 PD cases included for analysis. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Bastyr University (IRB #21-1698; approved 1 December 2021). Informed consent was obtained for all participants. PD cases were then matched to HC from a public database (BioProject Accession PRJEB39223; n = 42) ( 63 ). The HC samples were extracted from stool using the Qiagen Dneasy 96 PowerSoil Pro Kit. The DNA libraries were prepared using NEBNext Ultra II Kit and were sequenced using an Illumina NovaSeq 6000 platform. HC samples were matched to the PD samples by country, age, and read depth through subsampling of an average of 11.1 million reads to match the average of the PD group. Whole genome sequencing The microbial DNA content in the PD samples were extracted using the Qiagen Powersoil Pro Kit. DNA libraries were prepared using the Nextera XT DNA Library Preparation Kit (Illumina) and IDT Unique Dual Indexes with total DNA input of 1ng. Genomic DNA was fragmented using a proportional amount of Illumina Nextera XT fragmentation enzyme. Unique dual indexes were added to each sample followed by 12 cycles of PCR to construct libraries. DNA libraries were purified using Ampure magnetic beads (Beckman Coulter) and eluted in QIAGEN EB buffer. DNA libraries were quantified using Qubit 4 fluorometer and Qubit™ dsDNA HS Assay Kit. The sequencing libraries were prepared from the extracted DNA using the Nextera XT Kit (Illumina). The libraries were sequenced on the Illumina NextSeq 2000 platform by CosmosID. Paired-end sequencing at a length of 150bp was used. After sequencing, the data were analyzed using the CosmosID-HUB, which utilized a high-performance data-mining k -mer algorithm that rapidly disambiguates millions of short sequence reads into the discrete genomes engendering the particular sequences. The pipeline had two separable comparators. The first consisted of a pre-computation phase for reference databases, and the second was a per-sample computation. The input to the pre-computation phase were databases of reference genomes, virulence markers and antimicrobial resistance markers that are continuously curated by CosmosID scientists. The output of the pre-computational phase was a phylogeny tree of microbes, together with sets of variable length k -mer fingerprints (biomarkers) uniquely associated with distinct branches and leaves of the tree. The second per-sample computational phase searched the hundreds of millions of short sequence reads, or alternatively contigs from draft de novo assemblies, against the fingerprint sets. This query enabled the sensitive yet highly precise detection and taxonomic classification of microbial NGS reads. The resulting statistics were analyzed to return the fine-grain taxonomic and relative abundance estimates for the microbial NGS datasets. To exclude false positive identifications, the results were filtered using a filtering threshold derived based on internal statistical scores that were determined by analyzing a large number of diverse metagenomes. The same approach was applied to enable the sensitive and accurate detection of genetic markers for virulence and for resistance to antibiotics. Initial quality control, adapter trimming and preprocessing of metagenomic sequencing reads were done using Bbduk. The quality-controlled reads were then subjected to a translated search against a comprehensive and non-redundant protein sequence database, UniRef_90. The UniRef90 database, provided by UniProt ( 64 ), represents a clustering of all non-redundant protein sequences in UniProt, such that each sequence in a cluster aligns with 90% identity and 80% coverage of the longest sequence in the cluster. The mapping of metagenomic reads to gene sequences were weighted by mapping quality, coverage and gene sequence length to estimate community-wide weighted gene family abundances ( 64 ). Gene families were then annotated to MetaCyc ( 65 ) reactions (metabolic enzymes) to reconstruct and quantify MetaCyc metabolic pathways in the community ( 64 ). Furthermore, the UniRef_90 gene families were also regrouped to GO terms ( 66 ) in order to get an overview of GO functions in the community. Lastly, to facilitate comparisons across multiple samples with different sequencing depths, the abundance values were normalized using total-sum scaling normalization to produce “copies per million” (analogous to TPMs in RNA-Seq) units. Data analyses Relative abundance stacked bar figures, heat maps, and α-diversity boxplots, and β-diversity PCoA were generated from CosmosID-HUB using phylum, genus, species, and strain-level filtered matrices (for bacteria) from the HUB pipeline. Chao1, Simpson, and Shannon α-diversity metrics were calculated to evaluate species evenness and richness. Shannon’s index accounted for both abundance and evenness of the species present, providing a measure that increases with the number of species and their relative abundances ( 67 ). Simpson’s index measured the probability that two individuals randomly selected from a sample belonged to the same species ( 67 ), giving more weight to the common species in the community. Chao1 index was used to estimate species richness in the virome, accounting for the number of rare species that were likely to be present in the community but not observed ( 67 ). Boxplots were generated for each group, and significance was assessed via Wilcoxon Rank-Sum tests. We then calculated Bray-Curtis dissimilarity to assess β-diversity as a measure of the differences in microbial community composition between the two groups, which also helped to understand the extent of diversity shared between different environments or subjects ( 67 ). We evaluated β-diversity using Bray-Curtis Dissimilarity. This metric calculates the dissimilarity between two samples based on the abundance of species ( 68 ). β-diversity was visualized via PCoA and heat maps, and significance was assessed using a PERMANOVA test. P values ≤ 0.05 were considered to indicate statistical significance. Declarations ACKNOWLEDGEMENTS The authors would like to thank the patients who participated in the Parkinson Summer School at Bastyr University for providing us with their stool samples. The authors would also like to thank our research assistants, Ali DeMatteo and Fjorda Jusufi, for their help with the study. Author Contributions: LKM, BEO, and DMW contributed to the conception of the study and design of the study. BEO, DMW, and KM performed the statistical analysis, data curation, and data visualization. LKM provided supervision and DJF provided management of the project. LKM, SEE, JF were involved in data collection. SJP and BEO wrote the first draft of the manuscript. All authors: provided critical edits to the manuscript and read and approved the submitted version. Funding: None declared. Conflict of Interest: Authors BEO, DMW, KM were employed by CosmosID Inc at the time of the study. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Data Availability: The de-identified datasets are available upon request to the corresponding author. Supplementary Material: Supplementary Table 1 is provided in a separate document. References Braak H, Braak E. Pathoanatomy of Parkinson’s disease. J Neurol. 2000 Apr;247 Suppl 2:II3-10. Cersosimo MG, Raina GB, Pecci C, Pellene A, Calandra CR, Gutiérrez C, et al. Gastrointestinal manifestations in Parkinson’s disease: prevalence and occurrence before motor symptoms. J Neurol. 2013 May;260(5):1332–8. Skjærbæk C, Knudsen K, Horsager J, Borghammer P. Gastrointestinal Dysfunction in Parkinson’s Disease. J Clin Med. 2021 Jan 31;10(3). Zeng J, Wang X, Pan F, Mao Z. The relationship between Parkinson’s disease and gastrointestinal diseases. Front Aging Neurosci. 2022;14:955919. Adams-Carr KL, Bestwick JP, Shribman S, Lees A, Schrag A, Noyce AJ. Constipation preceding Parkinson’s disease: a systematic review and meta-analysis. J Neurol Neurosurg Psychiatry. 2016 Jul;87(7):710–6. Lin CH, Lin JW, Liu YC, Chang CH, Wu RM. Risk of Parkinson’s disease following severe constipation: a nationwide population-based cohort study. Parkinsonism Relat Disord. 2014 Dec;20(12):1371–5. Lu S, Jiang HY, Shi YD. Association between irritable bowel syndrome and Parkinson’s disease: A systematic review and meta-analysis. Acta Neurol Scand. 2022 Apr;145(4):442–8. Zhang X, Svn Z, Liv M, Yang Y, Zeng R, Huang Q, et al. Association Between Irritable Bowel Syndrome and Risk of Parkinson’s Disease: A Systematic Review and Meta-Analysis. Front Neurol. 2021;12:720958. Zhu F, Li C, Gong J, Zhu W, Gu L, Li N. The risk of Parkinson’s disease in inflammatory bowel disease: A systematic review and meta-analysis. Dig Liver Dis. 2019 Jan;51(1):38–42. Macerollo A, Lu MK, Huang HC, Chen HJ, Lin CC, Kao CH, et al. Colonic diverticular disease: A new risk factor for Parkinson’s disease? Parkinsonism Relat Disord. 2017 Sep;42:61–5. Mulak A, Bonaz B. Brain-gut-microbiota axis in Parkinson’s disease. World J Gastroenterol. 2015 Oct 7;21(37):10609–20. Sampson TR, Debelius JW, Thron T, Janssen S, Shastri GG, Ilhan ZE, et al. Gut Microbiota Regulate Motor Deficits and Neuroinflammation in a Model of Parkinson’s Disease. Cell. 2016 Dec 1;167(6):1469-1480.e12. Sun MF, Shen YQ. Dysbiosis of gut microbiota and microbial metabolites in Parkinson’s Disease. Ageing Res Rev. 2018 Aug;45:53–61. Liddle RA. Parkinson’s disease from the gut. Brain Res. 2018 Aug 15;1693(Pt B):201–6. Bhattarai Y, Si J, Pu M, Ross OA, McLean PJ, Till L, et al. Role of gut microbiota in regulating gastrointestinal dysfunction and motor symptoms in a mouse model of Parkinson’s disease. Gut Microbes. 2021 Jan 1;13(1):1866974. Bedarf JR, Hildebrand F, Coelho LP, Sunagawa S, Bahram M, Goeser F, et al. Functional implications of microbial and viral gut metagenome changes in early stage L-DOPA-naïve Parkinson’s disease patients. Genome Med. 2017 Apr 28;9(1):39. Chen SJ, Chen CC, Liao HY, Lin YT, Wu YW, Liou JM, et al. Association of Fecal and Plasma Levels of Short-Chain Fatty Acids With Gut Microbiota and Clinical Severity in Patients With Parkinson Disease. Neurology. 2022 Feb 22;98(8):e848–58. Qian Y, Yang X, Xu S, Huang P, Li B, Du J, et al. Gut metagenomics-derived genes as potential biomarkers of Parkinson’s disease. Brain. 2020 Aug 1;143(8):2474–89. Nie S, Jing Z, Wang J, Deng Y, Zhang Y, Ye Z, et al. The link between increased Desulfovibrio and disease severity in Parkinson’s disease. Appl Microbiol Biotechnol. 2023 May;107(9):3033–45. Qian Y, Yang X, Xu S, Wu C, Song Y, Qin N, et al. Alteration of the fecal microbiota in Chinese patients with Parkinson’s disease. Brain Behav Immun. 2018 May;70:194–202. Jo S, Kang W, Hwang YS, Lee SH, Park KW, Kim MS, et al. Oral and gut dysbiosis leads to functional alterations in Parkinson’s disease. NPJ Parkinsons Dis. 2022 Jul 7;8(1):87. Yan Z, Yang F, Cao J, Ding W, Yan S, Shi W, et al. Alterations of gut microbiota and metabolome with Parkinson’s disease. Microb Pathog. 2021 Nov;160:105187. Chen SJ, Wu YJ, Chen CC, Wu YW, Liou JM, Wu MS, et al. Plasma metabolites of aromatic amino acids associate with clinical severity and gut microbiota of Parkinson’s disease. NPJ Parkinsons Dis. 2023 Dec 14;9(1):165. Nishiwaki H, Ueyama J, Ito M, Hamaguchi T, Takimoto K, Maeda T, et al. Meta-analysis of shotgun sequencing of gut microbiota in Parkinson’s disease. NPJ Parkinsons Dis. 2024 May 21;10(1):106. Bolliri C, Fontana A, Cereda E, Barichella M, Cilia R, Ferri V, et al. Gut Microbiota in Monozygotic Twins Discordant for Parkinson’s Disease. Ann Neurol. 2022 Oct;92(4):631–6. De Pablo-Fernandez E, Gebeyehu GG, Flain L, Slater R, Frau A, Ijaz UZ, et al. The faecal metabolome and mycobiome in Parkinson’s disease. Parkinsonism Relat Disord. 2022 Feb;95:65–9. Cirstea MS, Sundvick K, Golz E, Yu AC, Boutin RCT, Kliger D, et al. The Gut Mycobiome in Parkinson’s Disease. J Parkinsons Dis. 2021;11(1):153–8. Metcalfe-Roach A, Cirstea MS, Yu AC, Ramay HR, Coker O, Boroomand S, et al. Metagenomic Analysis Reveals Large-Scale Disruptions of the Gut Microbiome in Parkinson’s Disease. Mov Disord. 2024 Oct;39(10):1740–51. Wallen ZD, Demirkan A, Twa G, Cohen G, Dean MN, Standaert DG, et al. Metagenomics of Parkinson’s disease implicates the gut microbiome in multiple disease mechanisms. Nat Commun. 2022 Nov 15;13(1):6958. Boktor JC, Sharon G, Verhagen Metman LA, Hall DA, Engen PA, Zreloff Z, et al. Integrated Multi-Cohort Analysis of the Parkinson’s Disease Gut Metagenome. Mov Disord. 2023 Mar;38(3):399–409. Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes associated with obesity. Nature. 2006 Dec 21;444(7122):1022–3. Mariat D, Firmesse O, Levenez F, Guimarăes V, Sokol H, Doré J, et al. The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC Microbiol. 2009 Jun 9;9:123. Koliada A, Syzenko G, Moseiko V, Budovska L, Puchkov K, Perederiy V, et al. Association between body mass index and Firmicutes/Bacteroidetes ratio in an adult Ukrainian population. BMC Microbiol. 2017 May 22;17(1):120. Magne F, Gotteland M, Gauthier L, Zazueta A, Pesoa S, Navarrete P, et al. The Firmicutes/Bacteroidetes Ratio: A Relevant Marker of Gut Dysbiosis in Obese Patients? Nutrients. 2020 May 19;12(5). Stojanov S, Berlec A, Štrukelj B. The Influence of Probiotics on the Firmicutes/Bacteroidetes Ratio in the Treatment of Obesity and Inflammatory Bowel disease. Microorganisms. 2020 Nov 1;8(11). Kostic AD, Xavier RJ, Gevers D. The microbiome in inflammatory bowel disease: current status and the future ahead. Gastroenterology. 2014 May;146(6):1489–99. Stappenbeck TS, Rioux JD, Mizoguchi A, Saitoh T, Huett A, Darfeuille-Michaud A, et al. Crohn disease: a current perspective on genetics, autophagy and immunity. Autophagy. 2011 Apr;7(4):355–74. Chen Z, Li G, Liu J. Autonomic dysfunction in Parkinson’s disease: Implications for pathophysiology, diagnosis, and treatment. Neurobiol Dis. 2020 Feb;134:104700. Mao L, Zhang Y, Tian J, Sang M, Zhang G, Zhou Y, et al. Cross-Sectional Study on the Gut Microbiome of Parkinson’s Disease Patients in Central China. Front Microbiol. 2021;12:728479. Binda C, Lopetuso LR, Rizzatti G, Gibiino G, Cennamo V, Gasbarrini A. Actinobacteria: A relevant minority for the maintenance of gut homeostasis. Dig Liver Dis. 2018 May;50(5):421–8. Bui TPN, Shetty SA, Lagkouvardos I, Ritari J, Chamlagain B, Douillard FP, et al. Comparative genomics and physiology of the butyrate-producing bacterium Intestinimonas butyriciproducens. Environ Microbiol Rep. 2016 Dec;8(6):1024–37. Unger MM, Spiegel J, Dillmann KU, Grundmann D, Philippeit H, Bürmann J, et al. Short chain fatty acids and gut microbiota differ between patients with Parkinson’s disease and age-matched controls. Parkinsonism Relat Disord. 2016 Nov;32:66–72. Mulak A. A controversy on the role of short-chain fatty acids in the pathogenesis of Parkinson’s disease. Mov Disord. 2018 Mar;33(3):398–401. Tan AH, Chong CW, Lim SY, Yap IKS, Teh CSJ, Loke MF, et al. Gut Microbial Ecosystem in Parkinson Disease: New Clinicobiological Insights from Multi-Omics. Ann Neurol. 2021 Mar;89(3):546–59. Aho VTE, Houser MC, Pereira PAB, Chang J, Rudi K, Paulin L, et al. Relationships of gut microbiota, short-chain fatty acids, inflammation, and the gut barrier in Parkinson’s disease. Mol Neurodegener. 2021 Feb 8;16(1):6. Manrique P, Bolduc B, Walk ST, van der Oost J, de Vos WM, Young MJ. Healthy human gut phageome. Proc Natl Acad Sci U S A. 2016 Sep 13;113(37):10400–5. Tetz G, Brown SM, Hao Y, Tetz V. Parkinson’s disease and bacteriophages as its overlooked contributors. Sci Rep. 2018 Jul 17;8(1):10812. Leta V, Urso D, Batzu L, Lau YH, Mathew D, Boura I, et al. Viruses, parkinsonism and Parkinson’s disease: the past, present and future. J Neural Transm (Vienna). 2022 Sep;129(9):1119–32. Jang H, Boltz DA, Webster RG, Smeyne RJ. Viral parkinsonism. Biochim Biophys Acta. 2009 Jul;1792(7):714–21. Tsai HH, Liou HH, Muo CH, Lee CZ, Yen RF, Kao CH. Hepatitis C virus infection as a risk factor for Parkinson disease: A nationwide cohort study. Neurology. 2016 Mar 1;86(9):840–6. Prehn JHM, Jirström E. Angiogenin and tRNA fragments in Parkinson’s disease and neurodegeneration. Acta Pharmacol Sin. 2020 Apr;41(4):442–6. Yu X, Xie Y, Zhang S, Song X, Xiao B, Yan Z. tRNA-derived fragments: Mechanisms underlying their regulation of gene expression and potential applications as therapeutic targets in cancers and virus infections. Theranostics. 2021;11(1):461–9. Magee R, Londin E, Rigoutsos I. TRNA-derived fragments as sex-dependent circulating candidate biomarkers for Parkinson’s disease. Parkinsonism Relat Disord. 2019 Aug;65:203–9. Tian H, Hu Z, Wang C. The Therapeutic Potential of tRNA-derived Small RNAs in Neurodegenerative Disorders. Aging Dis. 2022 Apr;13(2):389–401. Kuo MC, Liu SCH, Hsu YF, Wu RM. The role of noncoding RNAs in Parkinson’s disease: biomarkers and associations with pathogenic pathways. J Biomed Sci. 2021 Nov 18;28(1):78. Paganoni S, Schwarzschild MA. Urate as a Marker of Risk and Progression of Neurodegenerative Disease. Neurotherapeutics. 2017 Jan;14(1):148–53. Aerqin Q, Jia SS, Shen XN, Li Q, Chen KL, Ou YN, et al. Serum Uric Acid Levels in Neurodegenerative Disorders: A Cross-Sectional Study. J Alzheimers Dis. 2022;90(2):761–73. Seifar F, Dinasarapu AR, Jinnah HA. Uric Acid in Parkinson’s Disease: What Is the Connection? Mov Disord. 2022 Nov;37(11):2173–83. Dănău A, Dumitrescu L, Lefter A, Popescu BO. Serum Uric Acid Levels in Parkinson’s Disease: A Cross-Sectional Electronic Medical Record Database Study from a Tertiary Referral Centre in Romania. Medicina (Kaunas). 2022 Feb 6;58(2). Shi X, Zheng J, Ma J, Wang Z, Sun W, Li M, et al. Low serum uric acid levels are associated with the nonmotor symptoms and brain gray matter volume in Parkinson’s disease. Neurol Sci. 2022 Mar;43(3):1747–54. Ari BC, Tur EK, Domac FM, Kenangil GO. Uric acid: The role in the pathophysiology and the prediction in the diagnosis of Parkinson’s disease: A Turkish-based study. Ideggyogy Sz. 2022 Jan 30;75(1–02):51–9. Miller JR, Koren S, Sutton G. Assembly algorithms for next-generation sequencing data. Genomics. 2010 Jun;95(6):315–27. Asnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G, Drew DA, et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat Med. 2021 Feb;27(2):321–32. Franzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M, Weingart G, et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods. 2018 Nov;15(11):962–8. Caspi R, Billington R, Ferrer L, Foerster H, Fulcher CA, Keseler IM, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2016 Jan 4;44(D1):D471-80. Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S, et al. AmiGO: online access to ontology and annotation data. Bioinformatics. 2009 Jan 15;25(2):288–9. Finotello F, Mastrorilli E, Di Camillo B. Measuring the diversity of the human microbiota with targeted next-generation sequencing. Brief Bioinform. 2018 Jul 20;19(4):679–92. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011 Mar 15;108 Suppl 1(Suppl 1):4516–22. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 30 Jan, 2026 Read the published version in npj Parkinson's Disease → Version 1 posted Editorial decision: Revision requested 18 Aug, 2025 Reviews received at journal 15 Aug, 2025 Reviews received at journal 13 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers invited by journal 30 Jul, 2025 Editor assigned by journal 30 Jul, 2025 Submission checks completed at journal 30 Jul, 2025 First submitted to journal 24 Jul, 2025 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-7209227","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":494714648,"identity":"4b746545-237e-406c-adf7-a7e5943b26ef","order_by":0,"name":"Sarah Jaehwa Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACxgYGNiiTjfExXJiHSC3MxkBSgqAWkFIYzSZNlBbm9sPHHvPUMOTxzz6WVl1QU1dncPsA44O3bXgc1pOWbsxzjKFY4lzasdszjh2WMDiXwGw4F5+Whhwz6Rw2hsSGM+xtt3nYDkgYnGFgk+bFp6X//TfpnH8MifOBWop5/tWBtLD/xqtlRg6bdG4bQ+KGM2zHmHnbmMG2MOPX8sxM+m+fRLHhGbZk6Zl9hyVnnmFslpxzDrcWw/7kZ5IzvtnkyZ1hM/xc8K2On+8M88EPb8rwaGkAUxIJcBGFA4wNuNUDgTyURmiRx69hFIyCUTAKRiAAAFFiTddVJcWiAAAAAElFTkSuQmCC","orcid":"","institution":"Bastyr University Research Institute, Bastyr University","correspondingAuthor":true,"prefix":"","firstName":"Sarah","middleName":"Jaehwa","lastName":"Park","suffix":""},{"id":494714653,"identity":"c4e41235-47e4-4004-8e4e-bbcfa07edd5d","order_by":1,"name":"Barış Erhan Özdinç","email":"","orcid":"","institution":"CosmosID Inc","correspondingAuthor":false,"prefix":"","firstName":"Barış","middleName":"Erhan","lastName":"Özdinç","suffix":""},{"id":494714656,"identity":"d8bac747-a59a-4b1f-85ad-6defc5a23390","order_by":2,"name":"Kathryn Grace Coker","email":"","orcid":"","institution":"Bastyr University Research Institute, Bastyr University","correspondingAuthor":false,"prefix":"","firstName":"Kathryn","middleName":"Grace","lastName":"Coker","suffix":""},{"id":494714662,"identity":"cdc1fd98-4441-4236-88a3-86bed7d93fda","order_by":3,"name":"Dana M. Walsh","email":"","orcid":"","institution":"CosmosID Inc","correspondingAuthor":false,"prefix":"","firstName":"Dana","middleName":"M.","lastName":"Walsh","suffix":""},{"id":494714667,"identity":"0772abfd-7357-4c34-8f03-b488084c5c72","order_by":4,"name":"Devon J. Fox","email":"","orcid":"","institution":"Parkinson Center for Pragmatic Research","correspondingAuthor":false,"prefix":"","firstName":"Devon","middleName":"J.","lastName":"Fox","suffix":""},{"id":494714674,"identity":"dbcb10e6-51a0-4358-866e-1380d5e5224b","order_by":5,"name":"Samantha Evans","email":"","orcid":"","institution":"Bastyr University Research Institute, Bastyr University","correspondingAuthor":false,"prefix":"","firstName":"Samantha","middleName":"","lastName":"Evans","suffix":""},{"id":494714676,"identity":"acb1a147-1673-477d-9021-846d37d964db","order_by":6,"name":"Joshua Farahnik","email":"","orcid":"","institution":"Bastyr University Research Institute, Bastyr University","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Farahnik","suffix":""},{"id":494714679,"identity":"68c86e1a-0dae-4096-9c4b-39b2b2e6c903","order_by":7,"name":"Kelly Moffat","email":"","orcid":"","institution":"CosmosID Inc","correspondingAuthor":false,"prefix":"","firstName":"Kelly","middleName":"","lastName":"Moffat","suffix":""},{"id":494714680,"identity":"33e5b5e7-ffe1-410a-8b9f-45371fc89cb8","order_by":8,"name":"Margaret Boomgaarden","email":"","orcid":"","institution":"Bastyr University Research Institute, Bastyr University","correspondingAuthor":false,"prefix":"","firstName":"Margaret","middleName":"","lastName":"Boomgaarden","suffix":""},{"id":494714682,"identity":"f71f9802-b292-481b-b2c1-a9400aed2648","order_by":9,"name":"Laurie K. Mischley","email":"","orcid":"","institution":"Bastyr University Research Institute, Bastyr University","correspondingAuthor":false,"prefix":"","firstName":"Laurie","middleName":"K.","lastName":"Mischley","suffix":""}],"badges":[],"createdAt":"2025-07-25 01:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7209227/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7209227/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41531-026-01271-5","type":"published","date":"2026-01-30T15:59:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88311740,"identity":"ba57a6b8-d265-432a-b26f-ce58656a6364","added_by":"auto","created_at":"2025-08-05 07:00:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147583,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of \u003cem\u003eFirmicutes \u003c/em\u003eand \u003cem\u003eActinobacteria\u003c/em\u003e were greater in Parkinson cases compared to healthy controls while that of \u003cem\u003eBacteroidetes\u003c/em\u003e was less.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/823882978f7e03ee3d537cbb.png"},{"id":88309917,"identity":"20192df3-109f-450e-945f-51e5822a5c5f","added_by":"auto","created_at":"2025-08-05 06:44:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100565,"visible":true,"origin":"","legend":"\u003cp\u003eParkinson cases had significantly greater Shannon’s index α-diversity compared to healthy controls (Wilcoxon rank sum \u003cem\u003ep\u003c/em\u003e=0.004).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/b57f572d66de6ae398d20e2d.png"},{"id":88309914,"identity":"54fece70-bc3a-4c77-9b38-54fa31940646","added_by":"auto","created_at":"2025-08-05 06:44:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":108675,"visible":true,"origin":"","legend":"\u003cp\u003eParkinson cases had significantly greater Simpson’s index α-diversity compared to healthy controls (Wilcoxon rank sum \u003cem\u003ep\u003c/em\u003e=0.0002).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/b0c3e21d09ff354d79ca1838.png"},{"id":88311741,"identity":"807d8fa7-91e6-4060-86ae-11834f8b05bd","added_by":"auto","created_at":"2025-08-05 07:00:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":331957,"visible":true,"origin":"","legend":"\u003cp\u003eBray-Curtis β-diversity was significantly different between Parkinson cases and healthy controls (PERMANOVA \u003cem\u003ep\u003c/em\u003e=0.001).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/fe605e5525192c7d000fbd8d.png"},{"id":88310364,"identity":"c2bf3188-4472-4021-9e86-8767eea7814c","added_by":"auto","created_at":"2025-08-05 06:52:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":124394,"visible":true,"origin":"","legend":"\u003cp\u003ePhylum level heatmap with clustering by Euclidean distance showed some grouping of healthy controls but no clear separation between the groups.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/abbea4a55fb3b26c45555399.png"},{"id":88309923,"identity":"efc70804-3f89-492a-b104-448484c894bc","added_by":"auto","created_at":"2025-08-05 06:44:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":86489,"visible":true,"origin":"","legend":"\u003cp\u003eThe phage community composition showed differences in relative abundance at the family level for the Parkinson cases compared to that of healthy controls.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/7934da61e7ac04571c172a44.png"},{"id":88310366,"identity":"97c516e0-f26c-4119-8b19-b8139bd9d3b2","added_by":"auto","created_at":"2025-08-05 06:52:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":63806,"visible":true,"origin":"","legend":"\u003cp\u003eCHAO1 α-diversity was significantly decreased in the phageome of Parkinson cases compared to healthy controls (Wilcoxon rank sum \u003cem\u003ep\u003c/em\u003e=0.000).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/065c18a4d13ce4985992f693.png"},{"id":88310368,"identity":"5476df71-3ced-4587-8435-866929b20840","added_by":"auto","created_at":"2025-08-05 06:52:33","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":340028,"visible":true,"origin":"","legend":"\u003cp\u003eBray-Curtis β-diversity was significantly different within the phageome of Parkinson cases compared to healthy controls (PERMANOVA \u003cem\u003ep\u003c/em\u003e=0.002).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/c29549267c795c4bf4daadb9.png"},{"id":88311743,"identity":"0db20107-e03b-4222-b235-f0aeef9c3642","added_by":"auto","created_at":"2025-08-05 07:00:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":86431,"visible":true,"origin":"","legend":"\u003cp\u003eThe phageome of Parkinson cases showed high abundance of the phage families \u003cem\u003eSiphoviridae\u003c/em\u003eand \u003cem\u003eMicroviridae\u003c/em\u003e compared to healthy controls.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/f78154085b746ef35f3b0688.png"},{"id":88309935,"identity":"87a9c87f-9654-4158-a6b3-276566f69387","added_by":"auto","created_at":"2025-08-05 06:44:33","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":78283,"visible":true,"origin":"","legend":"\u003cp\u003eParkinson cases showed few detected viral families as compared to healthy controls.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/c208d9b9249a6c8682f48cdb.png"},{"id":88310371,"identity":"b9d9a80f-cd95-4cd7-ac74-429a0461678d","added_by":"auto","created_at":"2025-08-05 06:52:33","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":82401,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies level relative abundance of protists per group showed detection of more \u003cem\u003eBlastocystis \u003c/em\u003especies among Parkinson cases compared to healthy controls.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/91fb9b13ca11addb821535c9.png"},{"id":88310372,"identity":"fa6b34c9-a21f-4a45-a538-8dd619b9edb7","added_by":"auto","created_at":"2025-08-05 06:52:33","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":98477,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies level relative abundance of fungi per group showed overall detection of few fungi in both Parkinson cases and healthy controls.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/2cf8b6945ef9a6e07d39f60b.png"},{"id":88311742,"identity":"2d3ca011-2538-42d5-96d6-d2c2713eca09","added_by":"auto","created_at":"2025-08-05 07:00:32","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":76346,"visible":true,"origin":"","legend":"\u003cp\u003eCHAO1 α-diversity was significantly greater in Parkinson cases among MetaCyc functional pathways compared to healthy controls (Wilcoxon rank sum \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/556cd7bbe97ec098f64f1ec2.png"},{"id":88309943,"identity":"480a6f4c-2ca9-49a4-b6c1-7c7a881aa659","added_by":"auto","created_at":"2025-08-05 06:44:33","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":544164,"visible":true,"origin":"","legend":"\u003cp\u003eBray-Curtis functional pathway β-diversity was significantly different between Parkinson cases and healthy controls (PERMANOVA \u003cem\u003ep\u003c/em\u003e=0.001).\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/1abc3160c1f21add0aa166ac.png"},{"id":88310373,"identity":"8c3c174a-c3ff-44b3-bd8a-e293ae61fda1","added_by":"auto","created_at":"2025-08-05 06:52:33","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":615616,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional pathways with significant differences between Parkinson cases and healthy controls in abundance of MetaCyc pathways (all \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001).\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/83c9ada575b7e44fa1ef5e97.png"},{"id":101690568,"identity":"6f9ce3b2-704a-406e-9973-5a407bcf6b5e","added_by":"auto","created_at":"2026-02-02 16:05:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3834398,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/2078cd3c-f54f-497c-9cd9-7754752753ef.pdf"},{"id":88310361,"identity":"43ac0ef7-4539-4011-b3ef-639c5836435b","added_by":"auto","created_at":"2025-08-05 06:52:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":51446,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7209227/v1/43a5a3b982bc8a43df4e6b0e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metagenomics Indicates an Interplay of the Microbiome and Functional Pathways in Parkinson's Disease","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is a progressive neurodegenerative disease, pathologically characterized by the loss of nigrostriatal dopaminergic innervation and the aggregation of misfolded α-synuclein (α-syn) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Gastrointestinal (GI) dysfunction is among the earliest and most common non-motor manifestations of PD (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)and often precedes or parallels the onset of motor deficits by which PD is clinically diagnosed (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Moreover, GI conditions including chronic constipation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), irritable bowel syndrome (IBS) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), inflammatory bowel disease (IBD) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), and colonic diverticular disease (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) have been associated with increased risk of PD diagnosis, which suggest a potential link between the gut and PD pathophysiology.\u003c/p\u003e\u003cp\u003eGut dysbiosis is known to lead to inflammation, increased intestinal permeability, and aberrant immune responses, all of which are thought to contribute to the initiation of α-syn misfolding and the following cascade of neurodegeneration in PD (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). It has been postulated that the gut microbiota and its metabolites may mediate PD pathophysiology via the microbiota-gut-brain axis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Mice studies have demonstrated that microbial changes in the gut occurred prior to motor dysfunction in PD(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and that bacterial colonization and specific microbial metabolites were required to induce α-synucleinopathy, neuroinflammation, and motor deficits (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Unsurprisingly, there has been growing interest in investigating the gut microbiome for potential PD microbiome signatures or biomarkers.\u003c/p\u003e\u003cp\u003eWhole genome sequencing (WGS) enables a comprehensive analysis of genetic content including bacteria, viruses, fungi, protists, and functional pathways. To the best of our knowledge, observational studies using WGS to compare the gut microbiome between patients with PD (PwPD) and healthy controls (HC) have been conducted in Germany (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), China (\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), South Korea (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), Taiwan (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), Japan (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), Italy (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), London (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), Canada (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), and the US (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Among these studies, several have reported an increased \u003cem\u003eFirmicutes\u003c/em\u003e to \u003cem\u003eBacteroidetes\u003c/em\u003e (F/B) ratio among PwPD (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), as well as significant functional differences in short-chain fatty acid (SCFA) metabolism and pathways involved in lipopolysaccharide, β-glucuronate, and tryptophan degradation (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, further research is needed to validate these findings. We therefore sought to build upon this body of research by comparing the gut microbiota of PwPD compared to HC in North America using WGS.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eBaseline characteristics for PwPD and HC were provided in Table\u0026nbsp;1. In the PD group, the mean age (± standard deviation; SD) was 66.0 years (± 7.7), with gender evenly distributed. Participants were largely based in the US (95.0%), representing 17 unique states, and identified as white (90.9%), having an annual salary exceeding $150,000 USD (50.9%), and having earned a graduate or professional degree (50.9%). In terms of clinical characteristics, participants primarily had idiopathic PD (98.2%) and reported a mean (± SD) of 7.0 years since diagnosis (± 11.5), with over half in Hoehn and Yahr stage 1 (56.4%). The HC group had a mean age (± SD) of 54.8 years (± 5.2); 76.2% were female; and all were based in Massachusetts, USA. Other sociodemographic information was not available for the HC group.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSociodemographic and clinical characteristics of patients with Parkinson’s disease who provided stool samples for whole genome sequencing compared to healthy controls.1\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParkinson Group (\u003cem\u003en\u003c/em\u003e = 55)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl Group (\u003cem\u003en\u003c/em\u003e = 42)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years – mean ± SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 ± 7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.8 (± 5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender – no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (54.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (76.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeolocation – no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (95.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026nbsp;(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace – no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (90.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-white\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual household income, USD – no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; $60,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$60,000 to \u0026lt; $80,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$80,000 to \u0026lt; $100,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$100,000 to $150,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≥ $150,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (50.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level – no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than college degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate/professional degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (50.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eType of Parkinsonism – no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIdiopathic Parkinson’s disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (98.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Parkinsonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears since Parkinson diagnosis – mean ± SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0 ± 11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstimated Hoehn \u0026amp; Yahr stage – no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (unilateral involvement only, minimal disability)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (56.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (both sides affected, balance is stable)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (mild to moderate disability, balance affected)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Data for healthy controls were obtained from a public database (BioProject Accession PRJEB39223). There was limited sociodemographic information available for the control group. Clinical characteristics related to Parkinsonism were not applicable.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eBacteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe first evaluated differences in gut bacteria between PwPD and HC at the phylum level and observed the relative abundance of \u003cem\u003eFirmicutes\u003c/em\u003e was greater in PwPD while that of \u003cem\u003eBacteroidetes\u003c/em\u003e is lower (Fig.\u0026nbsp;1). \u003cem\u003eActinobacteria\u003c/em\u003e abundance appeared to be double in PwPD as compared to HC. PwPD exhibited significantly greater α-diversity as indicated by both Shannon's (Wilcoxon rank sum \u003cem\u003ep\u003c/em\u003e = 0.004; Fig.\u0026nbsp;2) and Simpson's diversity indices (\u003cem\u003ep\u003c/em\u003e = 0.0002; Fig.\u0026nbsp;3). The Simpson’s diversity index, which gives more weight to common species, suggested that the gut microbiome in PwPD may have a higher number of dominant species with similar evenness in abundance compared to the gut microbiome in HC. Bray-Curtis β-diversity dissimilarity suggested that the groups were significantly dissimilar in bacterial composition (PERMANOVA \u003cem\u003ep\u003c/em\u003e = 0.001; Fig.\u0026nbsp;4). Although PD and HC samples did not cluster separately in the heat map (Fig.\u0026nbsp;5), there was some grouping of healthy samples among the PD samples, suggesting potential family-level distinctions between PwPD and HC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhages and viruses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentification of the phage communities, or phageome, within PwPD and HC showed differences in relative abundance at the family level. Specifically, PwPD had less \u003cem\u003eMicroviridae\u003c/em\u003e and unclassified viruses, but more \u003cem\u003eSiphoviridae, Tectiviridae\u003c/em\u003e and \u003cem\u003ePodoviridae\u003c/em\u003e than HC (Fig.\u0026nbsp;6). After calculating α-diversity using Simpson’s, Shannon’s, and Chao1 indices, only the Chao1 index showed that PD patients had less phage richness than HC (Wilcoxon rank sum \u003cem\u003ep\u003c/em\u003e = 0.000; Fig.\u0026nbsp;7), which suggested a difference in the number of taxa rather than their relative abundances. Bray-Curtis β-diversity suggested significant dissimilarity between the phageome of the two groups (PERMANOVA \u003cem\u003ep\u003c/em\u003e = 0.002). The principal coordinates analysis (PcoA) plot (Fig.\u0026nbsp;8) suggested that there may be several different sample clusters based on the phage community composition. To determine what phages may be driving this clustering, we plotted the relative abundance of the samples in the heat map in Fig.\u0026nbsp;9. Here, it was clear that groups of samples containing mostly PD samples were dominated by \u003cem\u003eSiphoviridae, Microviridae\u003c/em\u003e, and \u003cem\u003eMyoviridae\u003c/em\u003e. Clusters dominated by HC had high abundance of either an unclassified phage virus family or moderate abundance of both \u003cem\u003eSiphoviridae\u003c/em\u003e and \u003cem\u003eMyoviridae\u003c/em\u003e. After plotting the abundance of the entire viral communities (Fig.\u0026nbsp;10), we observed some HC samples with relatively high abundance of the \u003cem\u003eAdenoviridae\u003c/em\u003e and \u003cem\u003ePartitiviridae\u003c/em\u003e families but little detection of virus families among PwPD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtists and fungi\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen we compared protists and eukaryotic fungi between PwPD and HC (Figs.\u0026nbsp;11 and 12), we observed few dominant organisms shared among participants resulting in a lack of power to detect statistical significance. However, the \u003cem\u003eBlastocystis\u003c/em\u003e genus was detected more frequently among PwPD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLastly, we evaluated functional pathways between PwPD and HC. Chao1 functional α-diversity was significantly higher in PwPD (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; Fig.\u0026nbsp;13), which suggested a greater number of different MetaCyc functions detected. Bray-Curtis functional β-diversity of PwPD and HC was significantly different (\u003cem\u003ep\u003c/em\u003e = 0.001; Fig.\u0026nbsp;14), which suggested compositional differences between the groups in functional pathways and their relative abundance. In Fig.\u0026nbsp;15, we identified MetaCyc pathway terms that significantly differed in mean abundance between the PwPD and HC, including colanic acid building blocks biosynthesis, transfer RNA (tRNA) processing, PEPCK-type C4 photosynthetic carbon assimilation cycle, guanosine nucleotide degradation III, glutaryl CoA degradation, pentose phosphate pathway, fatty acid and β-oxidation I, inosine-5-phosphate biosynthesis III, anhydromuropeptides recycling, and the trichloroacetic acid (TCA) cycle VI (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Other MetaCyc pathways with non-significant differences are shown in \u003cstrong\u003eSupplementary Table\u0026nbsp;1\u003c/strong\u003e.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this case-control study, we observed significant differences in the diversity and composition of specific bacteria, viruses and phages, and functional pathways between a self-selected North American cohort of PwPD and age-matched HC. For bacteria, there was a greater relative abundance in \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e among PwPD, while that of \u003cem\u003eBacteroidetes\u003c/em\u003e was lower. While there was little detection of DNA viruses among PwPD compared to HC, PwPD had a greater relative abundance of phages including \u003cem\u003eSiphoviridae, Tectiviridae, and Podoviridae\u003c/em\u003e, while \u003cem\u003eMicroviridae\u003c/em\u003e was lower. Further, we identified 10 unique functional pathways that significantly differed between the two groups.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e are the two dominant phyla in the human gut microbiome (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), and changes in their composition have been observed in various conditions. Specifically, an increased F/B ratio has been associated with obesity (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), while a decreased F/B ratio has been associated with weight loss (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and IBD (\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Theoretically, one would expect to see the latter in PwPD as weight loss and IBD are both commonly seen PD and may mediate potential associations between F/B ratio and PD (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). However, we observed an increased F/B ratio among PwPD in the present analysis, which was consistent with several previous case-control studies using WGS (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe also observed a greater abundance of \u003cem\u003eActinobacteria\u003c/em\u003e among PwPD. Previous studies have reported both a lower (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and greater abundance (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) of bacteria expressing SFCA-degradation pathways. Specific microbes in the \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e phyla produce butyrate, an important SCFA that plays an important role in maintaining gut barrier integrity and reducing mucosal inflammation (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). It is possible that the amplification of \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e may have been due to a lower abundance of other SCFA-producing bacteria (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Namely, we observed a lower abundance of the \u003cem\u003eBacteroidetes\u003c/em\u003e phylum, which produces acetate, the most abundant SCFA in the gut. Although we did not assess SCFAs in the present analysis, several studies have reported lower concentrations of fecal SCFAs in PwPD compared to HC (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Moreover, Chen et al (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) reported that reductions in fecal SCFAs were associated with worse cognitive and motor outcomes. Further research integrating fecal and plasma SCFA measurement is needed to explore potential mechanisms of these changes in bacterial composition and their associations with PD outcomes.\u003c/p\u003e\u003cp\u003eSpecific phages are thought to play an important role in the maintenance of a healthy gut microbiome. In the present study, we observed a decreased abundance of \u003cem\u003eMicroviridae\u003c/em\u003e and increased abundance of \u003cem\u003eSiphoviridae\u003c/em\u003e, \u003cem\u003eTectiviridae\u003c/em\u003e, and \u003cem\u003ePodoviridae\u003c/em\u003e, which are consistent with changes observed in IBD (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Tetz et al (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) reported similar findings for \u003cem\u003eMicroviridae\u003c/em\u003e and \u003cem\u003ePodoviridae\u003c/em\u003e and hypothesized that there may be an inverse relationship between bacteriophages and bacterial hosts in PwPD as this population may have a greater abundance of lytic phages. However, this was not evaluated in the present study. Previous studies both supported (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and conflicted (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) the differences observed in phage α- and β-diversities between PwPD and HC.\u003c/p\u003e\u003cp\u003eFurthermore, our finding of a lower relative abundance of DNA viruses was consistent with another case-control study by Bedarf et al (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), which involved early levodopa-na\u0026iuml;ve PwPD. On the other hand, Qian et al (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) identified viral enrichment in the PD cohort, however, does not identify any specific viruses linked to PD in the literature. Viruses such as influenza virus, Coxsackie virus, Japanese encephalitis virus, and the human immunodeficiency virus have been associated with secondary PD (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Recently, a study from Taiwan (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) also showed that hepatitis C virus infection is associated with the risk of developing PD. Qian et al (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) indicated viral databases employed as a potential source of variation, but extraction methods between the studies may also differ; however, the extraction method used by Bedarf et al (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) was not referenced. Given that over half of our study population were also estimated to be in Hoehn \u0026amp; Yahr stage 1, this suggests there may be potential reductions in viral richness among PwPD that occur early in the disease. This difference could also be based on ethnicity given that our finding was in line with Bedarf et al (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) was conducted in Germany, while Qian et al (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) was conducted in China, suggesting Caucasian descendance may be linked to lower viral diversity in PD. Further research is needed to understand whether there are differences in viral diversity based on PD stage, ethnicity, or other unknown factors.\u003c/p\u003e\u003cp\u003eLastly, we observed PwPD had a greater abundance of bacteria capable of expressing 10 MetaCyc pathways. Current literature and WGS limitations constrain inferences about the pathways identified; however, mechanistic evidence on byproducts of these pathways may suggest linkages to PD pathogenesis. For instance, tRNA processing is disrupted under stress conditions, leading to the accumulation of tRNA-derived fragments (tRFs) (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), which have been suggested as a potential biomarker and therapeutic target for PD and other neurodegenerative disorders (\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Accumulation of tRF with unique tRF signatures have been observed in serum, cerebrospinal fluid, and the prefrontal cortex that distinguished PwPD from HC with high sensitivity and specificity (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Further research is necessary to determine whether tRFs are reliable biomarkers and to establish optimal methods for measuring tRF accumulation.\u003c/p\u003e\u003cp\u003eAnother significant pathway identified was the guanosine nucleotide degradation III pathway. Metcalfe-Roach et al (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) also identified a significant depletion of purine nucleotide metabolism in PD; salvage indicates recycling of free purine ribonucelosides, deoxyribonucleosides and nucleobases into nucleotides, thus we observed enriched guanosine degradation may be the nucleoside of the purine guanine. Altogether, our findings are in line with Metacalfe-Roach et al (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), suggesting a degradatory guanine metabolism. A byproduct of this pathway is uric acid (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), of which lower levels have been associated with increased PD risk, progression, and symptom severity (\u003cspan additionalcitationids=\"CR58 CR59 CR60\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). The present study found a greater abundance of bacteria expressing this pathway among PwPD, which could suggest either a down-regulation of the pathway leading to reduced uric acid levels, or a compensatory increase in bacteria due to low uric acid levels in these individuals. However, without serum uric acid measurements, these conclusions remain speculative. Further research is needed to elucidate whether these functional pathways may induce differences in metabolite concentration with linkages to PD symptoms.\u003c/p\u003e\u003cp\u003eThis case-control study contributes to the growing body of metagenomic research in PD, which may lead to future identification of potential microbiome signatures or biomarkers that could enhance the prediction, diagnosis, and treatment of PD. WGS enables acquisition of genetic information across the complete spectrum of microorganisms including bacteria, viruses, phages, protists, fungi, and functional pathways using an assembly-free, \u003cem\u003ek\u003c/em\u003e-mer-based algorithm that allows for superior identification of the microorganism (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, the results of this study must be interpreted in the context of several limitations. First, we used historical controls to characterize our HC group, which may lead to technical variation between the PD and HC groups as the data were prepared using different kits by different people, in different labs, with different protocols, and using different sequencing instruments. Although the controls were age-matched, we did not control for potential confounding by other sociodemographic, clinical, or lifestyle factors, which likely influenced dietary habits and, consequently, the gut microbiota of our participants. Thus, these results warrant further validation and should be considered hypothesis-generating. Additionally, potential changes in the concentrations of byproducts or their associations with functional pathways could not be determined due to the absence of metabolite data; gene copy number variations also prevented a clear correlation between bacterial abundance and pathway gene expression. Lastly, the PD group was predominantly white, affluent, well-educated, and based in North America, which limits the generalizability of the findings to the broader PD population. Future research should involve longitudinal analyses with more diverse groups to explore how specific metabolites, such as tRFs, uric acid, and SCFAs may be related to PD risk and progression over time.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eWe observed significant bacterial, viral, and functional differences between among a self-selected group of patients with Parkinson’s disease and healthy historical controls that both supported and conflicted previous studies using whole genome sequencing. Additional research is needed to determine the reproducibility of these results in larger, more diverse populations, and in clarifying whether these microbial and functional changes are causal factors or consequences of Parkinson’s disease.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eWe conducted a case-control study using WGS to evaluate the microbial and functional differences between PwPD and HC. In August 2019, 60 PwPD from across the US and Canada attended Parkinson Summer School, an annual intensive five-day retreat in Washington state designed to promote wellness and improve outcomes among PwPD. Prior to attending the retreat, stool sample collection kits were mailed to participants\u0026rsquo; homes. Of the 60 individuals registered for the retreat, 57 participants collected their own stool samples and mailed the sample directly to the lab. In the lab, the samples were stored at -80\u003csup\u003eo\u003c/sup\u003e C until extraction, no more than one month after receipt. Two samples were discarded because one was from outside of North America and the other arrived more than six months after the others, resulting in a total of 55 PD cases included for analysis. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Bastyr University (IRB #21-1698; approved 1 December 2021). Informed consent was obtained for all participants.\u003c/p\u003e\n\u003cp\u003ePD cases were then matched to HC from a public database (BioProject Accession PRJEB39223; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;42) (\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e). The HC samples were extracted from stool using the Qiagen Dneasy 96 PowerSoil Pro Kit. The DNA libraries were prepared using NEBNext Ultra II Kit and were sequenced using an Illumina NovaSeq 6000 platform. HC samples were matched to the PD samples by country, age, and read depth through subsampling of an average of 11.1\u0026nbsp;million reads to match the average of the PD group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhole genome sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe microbial DNA content in the PD samples were extracted using the Qiagen Powersoil Pro Kit. DNA libraries were prepared using the Nextera XT DNA Library Preparation Kit (Illumina) and IDT Unique Dual Indexes with total DNA input of 1ng. Genomic DNA was fragmented using a proportional amount of Illumina Nextera XT fragmentation enzyme. Unique dual indexes were added to each sample followed by 12 cycles of PCR to construct libraries. DNA libraries were purified using Ampure magnetic beads (Beckman Coulter) and eluted in QIAGEN EB buffer. DNA libraries were quantified using Qubit 4 fluorometer and Qubit\u0026trade; dsDNA HS Assay Kit. The sequencing libraries were prepared from the extracted DNA using the Nextera XT Kit (Illumina). The libraries were sequenced on the Illumina NextSeq 2000 platform by CosmosID. Paired-end sequencing at a length of 150bp was used.\u003c/p\u003e\n\u003cp\u003eAfter sequencing, the data were analyzed using the CosmosID-HUB, which utilized a high-performance data-mining \u003cem\u003ek\u003c/em\u003e-mer algorithm that rapidly disambiguates millions of short sequence reads into the discrete genomes engendering the particular sequences. The pipeline had two separable comparators. The first consisted of a pre-computation phase for reference databases, and the second was a per-sample computation. The input to the pre-computation phase were databases of reference genomes, virulence markers and antimicrobial resistance markers that are continuously curated by CosmosID scientists. The output of the pre-computational phase was a phylogeny tree of microbes, together with sets of variable length \u003cem\u003ek\u003c/em\u003e-mer fingerprints (biomarkers) uniquely associated with distinct branches and leaves of the tree.\u003c/p\u003e\n\u003cp\u003eThe second per-sample computational phase searched the hundreds of millions of short sequence reads, or alternatively contigs from draft de novo assemblies, against the fingerprint sets. This query enabled the sensitive yet highly precise detection and taxonomic classification of microbial NGS reads. The resulting statistics were analyzed to return the fine-grain taxonomic and relative abundance estimates for the microbial NGS datasets. To exclude false positive identifications, the results were filtered using a filtering threshold derived based on internal statistical scores that were determined by analyzing a large number of diverse metagenomes. The same approach was applied to enable the sensitive and accurate detection of genetic markers for virulence and for resistance to antibiotics.\u003c/p\u003e\n\u003cp\u003eInitial quality control, adapter trimming and preprocessing of metagenomic sequencing reads were done using Bbduk. The quality-controlled reads were then subjected to a translated search against a comprehensive and non-redundant protein sequence database, UniRef_90. The UniRef90 database, provided by UniProt (\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e), represents a clustering of all non-redundant protein sequences in UniProt, such that each sequence in a cluster aligns with 90% identity and 80% coverage of the longest sequence in the cluster. The mapping of metagenomic reads to gene sequences were weighted by mapping quality, coverage and gene sequence length to estimate community-wide weighted gene family abundances (\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e). Gene families were then annotated to MetaCyc (\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e) reactions (metabolic enzymes) to reconstruct and quantify MetaCyc metabolic pathways in the community (\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e). Furthermore, the UniRef_90 gene families were also regrouped to GO terms (\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e) in order to get an overview of GO functions in the community. Lastly, to facilitate comparisons across multiple samples with different sequencing depths, the abundance values were normalized using total-sum scaling normalization to produce \u0026ldquo;copies per million\u0026rdquo; (analogous to TPMs in RNA-Seq) units.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelative abundance stacked bar figures, heat maps, and \u0026alpha;-diversity boxplots, and \u0026beta;-diversity PCoA were generated from CosmosID-HUB using phylum, genus, species, and strain-level filtered matrices (for bacteria) from the HUB pipeline. Chao1, Simpson, and Shannon \u0026alpha;-diversity metrics were calculated to evaluate species evenness and richness. Shannon\u0026rsquo;s index accounted for both abundance and evenness of the species present, providing a measure that increases with the number of species and their relative abundances (\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e). Simpson\u0026rsquo;s index measured the probability that two individuals randomly selected from a sample belonged to the same species (\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e), giving more weight to the common species in the community. Chao1 index was used to estimate species richness in the virome, accounting for the number of rare species that were likely to be present in the community but not observed (\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e). Boxplots were generated for each group, and significance was assessed via Wilcoxon Rank-Sum tests.\u003c/p\u003e\n\u003cp\u003eWe then calculated Bray-Curtis dissimilarity to assess \u0026beta;-diversity as a measure of the differences in microbial community composition between the two groups, which also helped to understand the extent of diversity shared between different environments or subjects (\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e). We evaluated \u0026beta;-diversity using Bray-Curtis Dissimilarity. This metric calculates the dissimilarity between two samples based on the abundance of species (\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e). \u0026beta;-diversity was visualized via PCoA and heat maps, and significance was assessed using a PERMANOVA test. \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered to indicate statistical significance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the patients who participated in the Parkinson Summer School at Bastyr University for providing us with their stool samples. The authors would also like to thank our research assistants, Ali DeMatteo and Fjorda Jusufi, for their help with the study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eLKM, BEO, and DMW contributed to the conception of the study and design of the study. BEO, DMW, and KM performed the statistical analysis, data curation, and data visualization. LKM provided supervision and DJF provided management of the project. LKM, SEE, JF were involved in data collection. SJP and BEO wrote the first draft of the manuscript. All authors: provided critical edits to the manuscript and read and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;None declared.\u003c/p\u003e\n\u003cp\u003eConflict of Interest:\u0026nbsp;Authors BEO, DMW, KM were employed by CosmosID Inc at the time of the study. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eData Availability:\u0026nbsp;The de-identified datasets are available upon request to the corresponding author.\u003c/p\u003e\n\u003cp\u003eSupplementary Material:\u0026nbsp;Supplementary Table 1 is provided in a separate document.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBraak H, Braak E. Pathoanatomy of Parkinson\u0026rsquo;s disease. J Neurol. 2000 Apr;247 Suppl 2:II3-10. \u003c/li\u003e\n\u003cli\u003eCersosimo MG, Raina GB, Pecci C, Pellene A, Calandra CR, Guti\u0026eacute;rrez C, et al. Gastrointestinal manifestations in Parkinson\u0026rsquo;s disease: prevalence and occurrence before motor symptoms. J Neurol. 2013 May;260(5):1332\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eSkj\u0026aelig;rb\u0026aelig;k C, Knudsen K, Horsager J, Borghammer P. Gastrointestinal Dysfunction in Parkinson\u0026rsquo;s Disease. J Clin Med. 2021 Jan 31;10(3). \u003c/li\u003e\n\u003cli\u003eZeng J, Wang X, Pan F, Mao Z. The relationship between Parkinson\u0026rsquo;s disease and gastrointestinal diseases. Front Aging Neurosci. 2022;14:955919. \u003c/li\u003e\n\u003cli\u003eAdams-Carr KL, Bestwick JP, Shribman S, Lees A, Schrag A, Noyce AJ. Constipation preceding Parkinson\u0026rsquo;s disease: a systematic review and meta-analysis. J Neurol Neurosurg Psychiatry. 2016 Jul;87(7):710\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eLin CH, Lin JW, Liu YC, Chang CH, Wu RM. Risk of Parkinson\u0026rsquo;s disease following severe constipation: a nationwide population-based cohort study. Parkinsonism Relat Disord. 2014 Dec;20(12):1371\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eLu S, Jiang HY, Shi YD. Association between irritable bowel syndrome and Parkinson\u0026rsquo;s disease: A systematic review and meta-analysis. Acta Neurol Scand. 2022 Apr;145(4):442\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eZhang X, Svn Z, Liv M, Yang Y, Zeng R, Huang Q, et al. Association Between Irritable Bowel Syndrome and Risk of Parkinson\u0026rsquo;s Disease: A Systematic Review and Meta-Analysis. Front Neurol. 2021;12:720958. \u003c/li\u003e\n\u003cli\u003eZhu F, Li C, Gong J, Zhu W, Gu L, Li N. The risk of Parkinson\u0026rsquo;s disease in inflammatory bowel disease: A systematic review and meta-analysis. Dig Liver Dis. 2019 Jan;51(1):38\u0026ndash;42. \u003c/li\u003e\n\u003cli\u003eMacerollo A, Lu MK, Huang HC, Chen HJ, Lin CC, Kao CH, et al. Colonic diverticular disease: A new risk factor for Parkinson\u0026rsquo;s disease? Parkinsonism Relat Disord. 2017 Sep;42:61\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eMulak A, Bonaz B. Brain-gut-microbiota axis in Parkinson\u0026rsquo;s disease. World J Gastroenterol. 2015 Oct 7;21(37):10609\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eSampson TR, Debelius JW, Thron T, Janssen S, Shastri GG, Ilhan ZE, et al. Gut Microbiota Regulate Motor Deficits and Neuroinflammation in a Model of Parkinson\u0026rsquo;s Disease. Cell. 2016 Dec 1;167(6):1469-1480.e12. \u003c/li\u003e\n\u003cli\u003eSun MF, Shen YQ. Dysbiosis of gut microbiota and microbial metabolites in Parkinson\u0026rsquo;s Disease. Ageing Res Rev. 2018 Aug;45:53\u0026ndash;61. \u003c/li\u003e\n\u003cli\u003eLiddle RA. Parkinson\u0026rsquo;s disease from the gut. Brain Res. 2018 Aug 15;1693(Pt B):201\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eBhattarai Y, Si J, Pu M, Ross OA, McLean PJ, Till L, et al. Role of gut microbiota in regulating gastrointestinal dysfunction and motor symptoms in a mouse model of Parkinson\u0026rsquo;s disease. Gut Microbes. 2021 Jan 1;13(1):1866974. \u003c/li\u003e\n\u003cli\u003eBedarf JR, Hildebrand F, Coelho LP, Sunagawa S, Bahram M, Goeser F, et al. Functional implications of microbial and viral gut metagenome changes in early stage L-DOPA-na\u0026iuml;ve Parkinson\u0026rsquo;s disease patients. Genome Med. 2017 Apr 28;9(1):39. \u003c/li\u003e\n\u003cli\u003eChen SJ, Chen CC, Liao HY, Lin YT, Wu YW, Liou JM, et al. Association of Fecal and Plasma Levels of Short-Chain Fatty Acids With Gut Microbiota and Clinical Severity in Patients With Parkinson Disease. Neurology. 2022 Feb 22;98(8):e848\u0026ndash;58. \u003c/li\u003e\n\u003cli\u003eQian Y, Yang X, Xu S, Huang P, Li B, Du J, et al. Gut metagenomics-derived genes as potential biomarkers of Parkinson\u0026rsquo;s disease. Brain. 2020 Aug 1;143(8):2474\u0026ndash;89. \u003c/li\u003e\n\u003cli\u003eNie S, Jing Z, Wang J, Deng Y, Zhang Y, Ye Z, et al. The link between increased Desulfovibrio and disease severity in Parkinson\u0026rsquo;s disease. Appl Microbiol Biotechnol. 2023 May;107(9):3033\u0026ndash;45. \u003c/li\u003e\n\u003cli\u003eQian Y, Yang X, Xu S, Wu C, Song Y, Qin N, et al. Alteration of the fecal microbiota in Chinese patients with Parkinson\u0026rsquo;s disease. Brain Behav Immun. 2018 May;70:194\u0026ndash;202. \u003c/li\u003e\n\u003cli\u003eJo S, Kang W, Hwang YS, Lee SH, Park KW, Kim MS, et al. Oral and gut dysbiosis leads to functional alterations in Parkinson\u0026rsquo;s disease. NPJ Parkinsons Dis. 2022 Jul 7;8(1):87. \u003c/li\u003e\n\u003cli\u003eYan Z, Yang F, Cao J, Ding W, Yan S, Shi W, et al. Alterations of gut microbiota and metabolome with Parkinson\u0026rsquo;s disease. Microb Pathog. 2021 Nov;160:105187. \u003c/li\u003e\n\u003cli\u003eChen SJ, Wu YJ, Chen CC, Wu YW, Liou JM, Wu MS, et al. Plasma metabolites of aromatic amino acids associate with clinical severity and gut microbiota of Parkinson\u0026rsquo;s disease. NPJ Parkinsons Dis. 2023 Dec 14;9(1):165. \u003c/li\u003e\n\u003cli\u003eNishiwaki H, Ueyama J, Ito M, Hamaguchi T, Takimoto K, Maeda T, et al. Meta-analysis of shotgun sequencing of gut microbiota in Parkinson\u0026rsquo;s disease. NPJ Parkinsons Dis. 2024 May 21;10(1):106. \u003c/li\u003e\n\u003cli\u003eBolliri C, Fontana A, Cereda E, Barichella M, Cilia R, Ferri V, et al. Gut Microbiota in Monozygotic Twins Discordant for Parkinson\u0026rsquo;s Disease. Ann Neurol. 2022 Oct;92(4):631\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eDe Pablo-Fernandez E, Gebeyehu GG, Flain L, Slater R, Frau A, Ijaz UZ, et al. The faecal metabolome and mycobiome in Parkinson\u0026rsquo;s disease. Parkinsonism Relat Disord. 2022 Feb;95:65\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eCirstea MS, Sundvick K, Golz E, Yu AC, Boutin RCT, Kliger D, et al. The Gut Mycobiome in Parkinson\u0026rsquo;s Disease. J Parkinsons Dis. 2021;11(1):153\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eMetcalfe-Roach A, Cirstea MS, Yu AC, Ramay HR, Coker O, Boroomand S, et al. Metagenomic Analysis Reveals Large-Scale Disruptions of the Gut Microbiome in Parkinson\u0026rsquo;s Disease. Mov Disord. 2024 Oct;39(10):1740\u0026ndash;51. \u003c/li\u003e\n\u003cli\u003eWallen ZD, Demirkan A, Twa G, Cohen G, Dean MN, Standaert DG, et al. Metagenomics of Parkinson\u0026rsquo;s disease implicates the gut microbiome in multiple disease mechanisms. Nat Commun. 2022 Nov 15;13(1):6958. \u003c/li\u003e\n\u003cli\u003eBoktor JC, Sharon G, Verhagen Metman LA, Hall DA, Engen PA, Zreloff Z, et al. Integrated Multi-Cohort Analysis of the Parkinson\u0026rsquo;s Disease Gut Metagenome. Mov Disord. 2023 Mar;38(3):399\u0026ndash;409. \u003c/li\u003e\n\u003cli\u003eLey RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes associated with obesity. Nature. 2006 Dec 21;444(7122):1022\u0026ndash;3. \u003c/li\u003e\n\u003cli\u003eMariat D, Firmesse O, Levenez F, Guimarăes V, Sokol H, Dor\u0026eacute; J, et al. The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC Microbiol. 2009 Jun 9;9:123. \u003c/li\u003e\n\u003cli\u003eKoliada A, Syzenko G, Moseiko V, Budovska L, Puchkov K, Perederiy V, et al. Association between body mass index and Firmicutes/Bacteroidetes ratio in an adult Ukrainian population. BMC Microbiol. 2017 May 22;17(1):120. \u003c/li\u003e\n\u003cli\u003eMagne F, Gotteland M, Gauthier L, Zazueta A, Pesoa S, Navarrete P, et al. The Firmicutes/Bacteroidetes Ratio: A Relevant Marker of Gut Dysbiosis in Obese Patients? Nutrients. 2020 May 19;12(5). \u003c/li\u003e\n\u003cli\u003eStojanov S, Berlec A, \u0026Scaron;trukelj B. The Influence of Probiotics on the Firmicutes/Bacteroidetes Ratio in the Treatment of Obesity and Inflammatory Bowel disease. Microorganisms. 2020 Nov 1;8(11). \u003c/li\u003e\n\u003cli\u003eKostic AD, Xavier RJ, Gevers D. The microbiome in inflammatory bowel disease: current status and the future ahead. Gastroenterology. 2014 May;146(6):1489\u0026ndash;99. \u003c/li\u003e\n\u003cli\u003eStappenbeck TS, Rioux JD, Mizoguchi A, Saitoh T, Huett A, Darfeuille-Michaud A, et al. Crohn disease: a current perspective on genetics, autophagy and immunity. Autophagy. 2011 Apr;7(4):355\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eChen Z, Li G, Liu J. Autonomic dysfunction in Parkinson\u0026rsquo;s disease: Implications for pathophysiology, diagnosis, and treatment. Neurobiol Dis. 2020 Feb;134:104700. \u003c/li\u003e\n\u003cli\u003eMao L, Zhang Y, Tian J, Sang M, Zhang G, Zhou Y, et al. Cross-Sectional Study on the Gut Microbiome of Parkinson\u0026rsquo;s Disease Patients in Central China. Front Microbiol. 2021;12:728479. \u003c/li\u003e\n\u003cli\u003eBinda C, Lopetuso LR, Rizzatti G, Gibiino G, Cennamo V, Gasbarrini A. Actinobacteria: A relevant minority for the maintenance of gut homeostasis. Dig Liver Dis. 2018 May;50(5):421\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eBui TPN, Shetty SA, Lagkouvardos I, Ritari J, Chamlagain B, Douillard FP, et al. Comparative genomics and physiology of the butyrate-producing bacterium Intestinimonas butyriciproducens. Environ Microbiol Rep. 2016 Dec;8(6):1024\u0026ndash;37. \u003c/li\u003e\n\u003cli\u003eUnger MM, Spiegel J, Dillmann KU, Grundmann D, Philippeit H, B\u0026uuml;rmann J, et al. Short chain fatty acids and gut microbiota differ between patients with Parkinson\u0026rsquo;s disease and age-matched controls. Parkinsonism Relat Disord. 2016 Nov;32:66\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eMulak A. A controversy on the role of short-chain fatty acids in the pathogenesis of Parkinson\u0026rsquo;s disease. Mov Disord. 2018 Mar;33(3):398\u0026ndash;401. \u003c/li\u003e\n\u003cli\u003eTan AH, Chong CW, Lim SY, Yap IKS, Teh CSJ, Loke MF, et al. Gut Microbial Ecosystem in Parkinson Disease: New Clinicobiological Insights from Multi-Omics. Ann Neurol. 2021 Mar;89(3):546\u0026ndash;59. \u003c/li\u003e\n\u003cli\u003eAho VTE, Houser MC, Pereira PAB, Chang J, Rudi K, Paulin L, et al. Relationships of gut microbiota, short-chain fatty acids, inflammation, and the gut barrier in Parkinson\u0026rsquo;s disease. Mol Neurodegener. 2021 Feb 8;16(1):6. \u003c/li\u003e\n\u003cli\u003eManrique P, Bolduc B, Walk ST, van der Oost J, de Vos WM, Young MJ. Healthy human gut phageome. Proc Natl Acad Sci U S A. 2016 Sep 13;113(37):10400\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eTetz G, Brown SM, Hao Y, Tetz V. Parkinson\u0026rsquo;s disease and bacteriophages as its overlooked contributors. Sci Rep. 2018 Jul 17;8(1):10812. \u003c/li\u003e\n\u003cli\u003eLeta V, Urso D, Batzu L, Lau YH, Mathew D, Boura I, et al. Viruses, parkinsonism and Parkinson\u0026rsquo;s disease: the past, present and future. J Neural Transm (Vienna). 2022 Sep;129(9):1119\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eJang H, Boltz DA, Webster RG, Smeyne RJ. Viral parkinsonism. Biochim Biophys Acta. 2009 Jul;1792(7):714\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eTsai HH, Liou HH, Muo CH, Lee CZ, Yen RF, Kao CH. Hepatitis C virus infection as a risk factor for Parkinson disease: A nationwide cohort study. Neurology. 2016 Mar 1;86(9):840\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003ePrehn JHM, Jirstr\u0026ouml;m E. Angiogenin and tRNA fragments in Parkinson\u0026rsquo;s disease and neurodegeneration. Acta Pharmacol Sin. 2020 Apr;41(4):442\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eYu X, Xie Y, Zhang S, Song X, Xiao B, Yan Z. tRNA-derived fragments: Mechanisms underlying their regulation of gene expression and potential applications as therapeutic targets in cancers and virus infections. Theranostics. 2021;11(1):461\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eMagee R, Londin E, Rigoutsos I. TRNA-derived fragments as sex-dependent circulating candidate biomarkers for Parkinson\u0026rsquo;s disease. Parkinsonism Relat Disord. 2019 Aug;65:203\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eTian H, Hu Z, Wang C. The Therapeutic Potential of tRNA-derived Small RNAs in Neurodegenerative Disorders. Aging Dis. 2022 Apr;13(2):389\u0026ndash;401. \u003c/li\u003e\n\u003cli\u003eKuo MC, Liu SCH, Hsu YF, Wu RM. The role of noncoding RNAs in Parkinson\u0026rsquo;s disease: biomarkers and associations with pathogenic pathways. J Biomed Sci. 2021 Nov 18;28(1):78. \u003c/li\u003e\n\u003cli\u003ePaganoni S, Schwarzschild MA. Urate as a Marker of Risk and Progression of Neurodegenerative Disease. Neurotherapeutics. 2017 Jan;14(1):148\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eAerqin Q, Jia SS, Shen XN, Li Q, Chen KL, Ou YN, et al. Serum Uric Acid Levels in Neurodegenerative Disorders: A Cross-Sectional Study. J Alzheimers Dis. 2022;90(2):761\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eSeifar F, Dinasarapu AR, Jinnah HA. Uric Acid in Parkinson\u0026rsquo;s Disease: What Is the Connection? Mov Disord. 2022 Nov;37(11):2173\u0026ndash;83. \u003c/li\u003e\n\u003cli\u003eDănău A, Dumitrescu L, Lefter A, Popescu BO. Serum Uric Acid Levels in Parkinson\u0026rsquo;s Disease: A Cross-Sectional Electronic Medical Record Database Study from a Tertiary Referral Centre in Romania. Medicina (Kaunas). 2022 Feb 6;58(2). \u003c/li\u003e\n\u003cli\u003eShi X, Zheng J, Ma J, Wang Z, Sun W, Li M, et al. Low serum uric acid levels are associated with the nonmotor symptoms and brain gray matter volume in Parkinson\u0026rsquo;s disease. Neurol Sci. 2022 Mar;43(3):1747\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eAri BC, Tur EK, Domac FM, Kenangil GO. Uric acid: The role in the pathophysiology and the prediction in the diagnosis of Parkinson\u0026rsquo;s disease: A Turkish-based study. Ideggyogy Sz. 2022 Jan 30;75(1\u0026ndash;02):51\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eMiller JR, Koren S, Sutton G. Assembly algorithms for next-generation sequencing data. Genomics. 2010 Jun;95(6):315\u0026ndash;27. \u003c/li\u003e\n\u003cli\u003eAsnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G, Drew DA, et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat Med. 2021 Feb;27(2):321\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eFranzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M, Weingart G, et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods. 2018 Nov;15(11):962\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eCaspi R, Billington R, Ferrer L, Foerster H, Fulcher CA, Keseler IM, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2016 Jan 4;44(D1):D471-80. \u003c/li\u003e\n\u003cli\u003eCarbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S, et al. AmiGO: online access to ontology and annotation data. Bioinformatics. 2009 Jan 15;25(2):288\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eFinotello F, Mastrorilli E, Di Camillo B. Measuring the diversity of the human microbiota with targeted next-generation sequencing. Brief Bioinform. 2018 Jul 20;19(4):679\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eCaporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011 Mar 15;108 Suppl 1(Suppl 1):4516\u0026ndash;22. \u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s disease, gut-brain axis, microbiome, metagenome, whole genome sequencing, gut microbiota, gastrointestinal dysfunction","lastPublishedDoi":"10.21203/rs.3.rs-7209227/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7209227/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious studies suggest there are distinct gut microbial and functional variations in patients with Parkinson\u0026rsquo;s disease (PwPD) that may reveal potential microbiome signatures or biomarkers to aid in early detection of the disease. In this case-control study, we used whole genome sequencing to compare the stool samples of 55 PwPD to 42 age-matched healthy controls (HC) from a public database (BioProject Accession PRJEB39223). For bacteria, we observed a greater relative abundance in \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e among PwPD, while that of \u003cem\u003eBacteroidetes\u003c/em\u003e was lower. For phages, PwPD had a greater relative abundance of \u003cem\u003eSiphoviridae, Tectiviridae, and Podoviridae\u003c/em\u003e, while \u003cem\u003eMicroviridae\u003c/em\u003e was lower. Moreover, we identified 10 functional pathways that significantly varied between PwPD and HC (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In conclusion, significant differences were observed in gut bacteria, phages, and functional pathways between PwPD and HC that both support and conflict with previous case-control studies and warrant further validation.\u003c/p\u003e","manuscriptTitle":"Metagenomics Indicates an Interplay of the Microbiome and Functional Pathways in Parkinson's Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-05 06:44:27","doi":"10.21203/rs.3.rs-7209227/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-18T19:58:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-16T01:44:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-13T17:25:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232452436895292840260633786241602012359","date":"2025-08-07T00:18:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184962577938427320914242356943601083586","date":"2025-08-04T10:53:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T16:00:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T15:50:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-30T13:09:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Parkinson's Disease","date":"2025-07-25T00:56:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e732c1bc-db63-4f60-81b7-a0021d90c138","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52543050,"name":"Biological sciences/Microbiology"},{"id":52543051,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2026-02-02T16:02:06+00:00","versionOfRecord":{"articleIdentity":"rs-7209227","link":"https://doi.org/10.1038/s41531-026-01271-5","journal":{"identity":"npj-parkinsons-disease","isVorOnly":false,"title":"npj Parkinson's Disease"},"publishedOn":"2026-01-30 15:59:14","publishedOnDateReadable":"January 30th, 2026"},"versionCreatedAt":"2025-08-05 06:44:27","video":"","vorDoi":"10.1038/s41531-026-01271-5","vorDoiUrl":"https://doi.org/10.1038/s41531-026-01271-5","workflowStages":[]},"version":"v1","identity":"rs-7209227","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7209227","identity":"rs-7209227","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}