The intestinal microbiota influence neurodevelopment, modulate behavior, and contribute to neurological disorders. However, a functional link between gut bacteria and neurodegenerative diseases remains unexplored. Synucleinopathies are characterized by aggregation of the protein α-synuclein (αSyn), often resulting in motor dysfunction as exemplified by Parkinson’s disease (PD). Using mice that overexpress αSyn, we report herein that gut microbiota are required for motor deficits, microglia activation, and αSyn pathology. Antibiotic treatment ameliorates, while microbial re-colonization promotes, pathophysiology in adult animals, suggesting that postnatal signaling between the gut and the brain modulates disease. Indeed, oral administration of specific microbial metabolites to germ-free mice promotes neuroinflammation and motor symptoms. Remarkably, colonization of αSyn-overexpressing mice with microbiota from PD-affected patients enhances physical impairments compared to microbiota transplants from healthy human donors. These findings reveal that gut bacteria regulate movement disorders in mice and suggest that alterations in the human microbiome represent a risk factor for PD.

Based on the common occurrence of GI symptoms in PD, dysbiosis among PD patients, and evidence that the microbiota impacts CNS function, we tested the hypothesis that gut bacteria regulate the hallmark motor deficits and pathophysiology of synucleinopathies. Herein, we report that the microbiota is necessary to promote αSyn pathology, neuroinflammation, and characteristic motor features in a validated mouse model. We identify specific microbial metabolites that are sufficient to promote disease symptoms. Remarkably, fecal microbes from PD patients impair motor function significantly more than microbiota from healthy controls when transplanted into mice. Together, these results suggest that gut microbes may play a critical and functional role in the pathogenesis of synucleinopathies such as PD.

Gut bacteria control the differentiation and function of immune cells in the intestine, periphery, and brain (). Intriguingly, subjects with PD exhibit intestinal inflammation (), and GI abnormalities such as constipation often precede motor defects by many years (). Braak’s hypothesis posits that aberrant αSyn accumulation initiates in the gut and propagates via the vagus nerve to the brain in a prion-like fashion (). This notion is supported by pathophysiologic evidence: αSyn inclusions appear early in the enteric nervous system (ENS) and the glossopharyngeal and vagal nerves (), and vagotomized individuals are at reduced risk for PD (). Further, injection of αSyn fibrils into the gut tissue of healthy rodents is sufficient to induce pathology within the vagus nerve and brainstem (). However, the notion that αSyn aggregation initiates in the ENS and spreads to the CNS via retrograde transmission remains controversial (), and experimental support for a gut microbial connection to PD is lacking.

Direct evidence of Parkinson pathology spread from the gastrointestinal tract to the brain in rats.

Is alpha-synuclein in the colon a biomarker for premotor Parkinson’s disease? Evidence from 3 cases.

Idiopathic Parkinson’s disease: possible routes by which vulnerable neuronal types may be subject to neuroinvasion by an unknown pathogen.

Idiopathic Parkinson’s disease: possible routes by which vulnerable neuronal types may be subject to neuroinvasion by an unknown pathogen.

The human body is permanently colonized by microbes on virtually all environmentally exposed surfaces, the majority of which reside within the GI tract (). Increasingly, research is beginning to uncover the profound impacts that the microbiota can have on neurodevelopment and the CNS (). Germ-free (GF) mice and antibiotic-treated specific-pathogen-free (SPF) mice are altered in hippocampal neurogenesis, resulting in impaired spatial and object recognition (). The microbiota regulate expression of the 5-hydroxytryptamine receptor (5-HT), brain-derived neurotropic factor (BDNF), and NMDA receptor subunit 2 (NR2A) (). GF mice have altered cortical myelination and impaired blood-brain barrier function (). Additionally, the microbiota promotes enteric and circulating serotonin production in mice () and affects anxiety, hyperactivity, and cognition (). To augment mouse models, dysbiosis (alterations to the microbial composition) of the human microbiome has been reported in subjects diagnosed with several neurological diseases (). For example, fecal and mucosa-associated gut microbes are different between individuals with PD and healthy controls (). Yet, how dysbiosis arises and whether this feature contributes to PD pathogenesis remains unknown.

Signals from the gut microbiota to distant organs in physiology and disease.

Although neurological diseases have been historically studied within the CNS, peripheral influences have been implicated in the onset and/or progression of diseases that impact the brain (). Indeed, emerging data suggest bidirectional communication between the gut and the brain in anxiety, depression, nociception, and autism spectrum disorder (ASD), among others (). Gastrointestinal (GI) physiology and motility are influenced by signals arising both locally within the gut and from the CNS. Neurotransmitters, immune signaling, hormones, and neuropeptides produced within the gut may, in turn, impact the brain (). Research into how the gut-brain axis influences neurological conditions may reveal insights into disease etiology.

Signals from the gut microbiota to distant organs in physiology and disease.

The impact of gut microbiota on brain and behaviour: implications for psychiatry.

Neurological dysfunction is the basis of numerous human diseases. Behavioral, psychiatric, and neurodegenerative disorders often display hallmark neuropathologies within the central nervous system (CNS). One neuropathology, amyloidosis, results from aberrant aggregation of specific neuronal proteins that disrupt many cellular functions. Affected tissues often contain insoluble aggregates of proteins that display altered conformations, a feature believed to contribute to an estimated 50 distinct human diseases (). Neurodegenerative amyloid disorders, including Alzheimer’s, Huntington’s, and Parkinson’s diseases (PD), are each associated with a distinct amyloid protein (). PD is the second most common neurodegenerative disease in the United States, affecting an estimated 1 million people and 1% of the US population over 60 years of age (). Worldwide, about 3 million patients and caregivers suffer from the often-debilitating symptoms of PD, which involve motor deficits including tremors, muscle rigidity, bradykinesia, and impaired gait. It is a multifactorial disorder that has a strong environmental component, as less than 10% of cases are hereditary (). Aggregation of α-synuclein (αSyn) is thought to be pathogenic in a family of diseases termed synucleinopathies, which includes PD, multiple system atrophy, and Lewy body disease (). αSyn aggregation is a stepwise process, leading to oligomeric species and intransient fibrils that accumulate within neurons. Dopaminergic neurons of the substantia nigra pars compacta (SNpc) appear particularly vulnerable to effects of αSyn aggregates. Dopamine modulators are a first-line therapeutic in PD; however, treatments can carry serious side effects and often lose effectiveness (). Discovery of safe and effective therapeutics are needed to address the increasing burden of PD in an ever-aging population, a paradoxical consequence of mankind’s achievements in increased lifespan.

International Parkinson’s Disease Genomics Consortium (IPDGC) Parkinson’s Study Group (PSG) Parkinson’s Research: The Organized GENetics Initiative (PROGENI) 23andMe GenePD NeuroGenetics Research Consortium (NGRC) Hussman Institute of Human Genomics (HIHG) Ashkenazi Jewish Dataset Investigator Cohorts for Health and Aging Research in Genetic Epidemiology (CHARGE) North American Brain Expression Consortium (NABEC) United Kingdom Brain Expression Consortium (UKBEC) Greek Parkinson’s Disease Consortium Alzheimer Genetic Analysis Group Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease.

International Parkinson’s Disease Genomics Consortium (IPDGC) Parkinson’s Study Group (PSG) Parkinson’s Research: The Organized GENetics Initiative (PROGENI) 23andMe GenePD NeuroGenetics Research Consortium (NGRC) Hussman Institute of Human Genomics (HIHG) Ashkenazi Jewish Dataset Investigator Cohorts for Health and Aging Research in Genetic Epidemiology (CHARGE) North American Brain Expression Consortium (NABEC) United Kingdom Brain Expression Consortium (UKBEC) Greek Parkinson’s Disease Consortium Alzheimer Genetic Analysis Group Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease.

To assess microbiota function, groups of humanized animals from each of the donor pairs were tested for motor function. Consistent among four of the six pairs (pairs #1, 3, 4, and 5), microbiota derived from individuals with PD promote increased αSyn-mediated motor dysfunction ( Figures 7 A–7F). Beam traversal, pole descent, and nasal adhesive removal are significantly impaired in ASO animals colonized with PD microbiota compared to genotype-matched recipient mice harboring gut bacteria from healthy controls. Hindlimb reflex scores, on the other hand, are generally not different between individual donors. Interestingly, microbiota from one pair of samples did not induce significant genotype effects in the beam traversal and pole descent tasks (pair #2, Figure 7 B), reflecting potential heterogeneity in the population that needs to be addressed through well-powered cohort studies. We observed no notable effects in motor function by WT recipient animals colonized with microbiota from either donor group ( Figures 7 A–7F). This finding in a preclinical mouse model supports the notion the PD microbiota contributes to disease symptoms in genetically susceptible hosts. Notably, recipient animals display little alteration to weight and GI function as measured by fecal output ( Figures S7 A–S7F). Compilation of performance data from all groups reveals that microbiota from PD patients induce increased motor impairment in ASO animals compared to microbes from healthy controls in three of four tests used in this study ( Figure 7 G). In fact, depicting all motor function by PCoA displays striking global differences between animals colonized with microbiota from PD donors, compared to those colonized with gut bacteria derived from healthy individuals ( Figure S7 G). The observation that gut bacteria from PD patients compared to healthy controls enhance motor deficits in a mouse model provides evidence for a functional contribution by the microbiota to synuclienopathies.

Animals were tested at 12-13 weeks of age. N = 3-6, error bars represent the mean and standard error from 3 trials per animal. # 0.05 < p < 0.1; ∗ p ≤ 0.05; ∗∗ p ≤ 0.01. HC = germ-free mice colonized with fecal microbes from healthy controls, PD = germ-free mice colonized with fecal microbes from Parkinson’s disease patients, WT = wild-type, ASO = Thy1-α-synuclein genotype.

(A–F) Body weight and fecal output following 15 min in novel environment for each PD and HC humanized pair. (A) Pair #1, (B) Pair #2, (C) Pair #3, (D) Pair #4, (E) Pair #5, (F) Pair #6.

Animals were tested at 12–13 weeks of age. n = 3–6, error bars represent the mean and standard error from 3 trials per animal.0.05 < p < 0.1;p ≤ 0.05;p ≤ 0.01;p ≤ 0.001;p ≤ 0.0001. Abbreviations: HC, germ-free mice colonized with fecal microbes from healthy controls; PD, germ-free mice colonized with fecal microbes from Parkinson’s disease patients; WT, wild-type; ASO, Thy1-α-synuclein genotype. See also Figure S7 and Table S1

(G) Compilation of all independent cohorts in each motor task: beam traversal, pole descent, adhesive removal, and hindlimb clasping reflex score, grouped by health status of fecal donor.

(A–F) Time to cross a beam, time to descend the pole, time to remove nasal adhesive, and hindlimb clasping reflex scores of mice humanized with microbiota from either PD patients or matched healthy controls.

We identified a number of genera that are altered in animals colonized with microbiota derived from PD donors, compared to healthy controls ( Figure 6 E), as well as altered KEGG pathways between these groups as indicated by Bray-Curtis distances ( Figures S6 A–S6C). OTUs increased in abundance in mice with PD microbiomes include Proteus sp., Bilophila sp., and Roseburia sp., with a concomitant loss of members of families Lachnospiraceae, Rikenellaceae, and Peptostreptococcaceae, as well as Butyricicoccus sp. ( Figure 6 E). Interestingly, some taxa are altered only in ASO animals (e.g., Proteus sp., Bilophila sp., and Lachnospiraceae), while others display significant changes independent of mouse genotype (e.g., Rosburia sp., Rikenellaceae, and Enterococcus sp.) ( Figure 6 E). Intriguingly, the abundance of three SCFA-producing KEGG families (K00929, butyrate kinase, and K01034 and K01035, acetate CoA/acetoacetate CoA transferase alpha and beta) are increased in mice that received fecal microbes derived from PD donors ( Figure S6 D). Further, we observe that animals receiving PD donor-derived microbiota display a significantly altered SCFA profile, with a lower concentration of acetate and higher relative abundances of propionate and butyrate, compared to animals colonized with microbes from healthy controls ( Figure S6 E). Together, these data indicate that differences in fecal microbial communities between PD patients and controls can be maintained after transfer into mice. Further, αSyn overexpression engenders distinct alterations to the gut microbiome profile after transplantation.

N = 3-6, over 3 time points post-colonization for KEGG analysis, N = 21-24 for SCFA abundances. Error bars represent the mean and standard error. ∗ p ≤ 0.05; ∗∗ p ≤ 0.01; ∗∗∗ p ≤ 0.001.

(E) Fecal concentrations and relative abundances of acetate, propionate, and butyrate from humanized animals, normalized to soluble chemical oxygen demans. Compiled from 6 independent donor pairs, HC = healthy controls; PD = Parkinson’s disease.

(B and C) Bray-Curtis mean distance comparisons between (B) identical versus different donors, or (C) wild-type (WT) versus Thy1- α-synuclein (ASO) genotypes.

Fecal microbiota from PD patients or controls were transplanted into individual groups of GF recipient animals via oral gavage. Fecal pellets were collected from “humanized” mice, bacterial DNA was extracted, and 16S rRNA sequencing was performed. Sequences were annotated into operational taxonomic units (OTUs), using closed reference picking against the Greengenes database and metagenome function was predicted by PICRUSt. Recipient animal groups were most similar to their respective human donor’s profile in unweighted UniFrac (), based on PCoA ( Figures 6 A and 6B ). Strikingly, the disease status of the donor had a strong effect on the microbial communities within recipient mice. Humanized mouse groups from PD donors are significantly more similar to each other than to communities transplanted from healthy donors, with this trend persisting when stratified by genetic background ( Figures 6 C and 6D). Furthermore, there are significant differences between the healthy and PD donors in the ASO background compared to WT recipients, suggesting genotype effects on microbial community configuration ( Figures 6 C and 6D).

n = 3–6, over 3 time points post-colonization. Error bars represent the mean and standard error.p ≤ 0.001, 999 permutations. Abbreviations: HC, germ-free mice colonized with fecal microbes from healthy controls; PD, germ-free mice colonized with fecal microbes from Parkinson’s disease patients; WT, wild-type; ASO, Thy1-α-synuclein genotype. See also Figure S6

(E) Taxa-level analysis of individual genera altered between PD and healthy donors as a function of recipient mouse genotype. Left column indicates percentage with significant differences observed; right column indicates fold change between PD and healthy donors. Light colors indicate non-statistically significant differences.

(C) Unweighted and weighted UniFrac analysis of microbial communities in recipient animals based on mouse genotype.

(A) Unweighted UniFrac Principle Coordinate Analysis of microbial communities of human donors (large circles) and recipient mice (small circles). Each donor and recipient sample are matched by color.

Given recent evidence that PD patients display altered microbiomes (), we sought to determine whether human gut microbes affect disease outcomes when transferred into GF mice. We collected fecal samples from six human subjects diagnosed with PD as well as six matched healthy controls ( Table S1 ). To limit confounding effects, only new-onset, treatment-naive PD patients with healthy intestinal histology were chosen, among other relevant inclusion and exclusion criteria (see STAR Methods and Table S1 ).

To explore a link between microbial metabolites and motor symptoms in the Thy1-αSyn model, GF animals were treated with the SCFA mixture beginning at 5–6 weeks of age, and motor function was assessed at 12–13 weeks of age. SCFA-ASO mice display significantly impaired performance in several motor tasks compared to untreated GF-ASO animals ( Figures 5 C–5F), including impairment in beam traversal, pole descent, and hindlimb reflex (compare GF-ASO to SCFA-ASO mice). All effects by SCFAs are genotype specific to the Thy1-αSyn mice. GI deficits are also observed in the SCFA-treated transgenic animals ( Figures 5 G and 5H). Oral treatment of GF animals with heat-killed bacteria does not induce motor deficits ( Figures S4 J–S4M), suggesting that bacteria need to be metabolically active. Additionally, oral treatment of SCFA-fed animals with the anti-inflammatory compound minocycline is sufficient to reduce TNF-α production, reduce αSyn aggregation, and improve motor function, without altering transgene expression ( Figures S5 A–S5H). We propose that the microbiota actively produce metabolites, such as SCFAs, that are required for microglia activation and αSyn aggregation, contributing to motor dysfunction in a preclinical model of PD.

Animals were tested at 12-13 weeks of age. N = 6-12, error bars represent the mean and standard error from 3 trials per animal. Data are compiled from 2 independent cohorts. # 0.05 < p < 0.1; ∗ p ≤ 0.05; ∗∗ p ≤ 0.01; ∗∗∗ p ≤ 0.001; ∗∗∗∗ p ≤ 0.0001. SPF = specific pathogen free; GF = germ-free; WT = wild-type, ASO = Thy1-α-synuclein genotype.

(C) Densitometry quantification of dot blots from the CP and inferior midbrain (Mid).

Corresponding to microglia morphology, we reveal αSyn aggregates in mice administered SCFAs compared to untreated and Abx-treated mice, and similar to Ex-GF animals ( Figures S3 F–S3I). Strikingly, we observe that postnatal signaling by microbes induces increased αSyn aggregation in the CP and SN ( Figures S3 F and S3G), with no observable difference in the FC and CB ( Figures S3 H and S3I), confirmed by quantification and western blot ( Figures S3 J–S3O). SCFAs either singly or in a mixture, over a range of concentrations, do not expedite the aggregation of human αSyn in vitro ( Figures S4 A–S4G), nor do they alter the overall structure of αSyn amyloid fibrils ( Figures S4 H and S4I). Though additional studies are needed, it appears that SCFAs accelerate in vivo αSyn aggregation, albeit independently of direct molecular interactions.

Animals were tested at 12-13 weeks of age. N = 6-12, error bars represent the mean and standard error from 3 trials per animal. Data are compiled from 2 independent cohorts and plotted with controls from Figure 4 for clarity.p ≤ 0.05;p ≤ 0.01;p ≤ 0.001;p ≤ 0.0001. SPF = specific pathogen free; GF = germ-free; HK = heat-killed bacteria-treated; WT = wild-type, ASO = Thy1-α-synuclein genotype.

(H and I) Representative atomic force microscopy of the final product from the above αSyn aggregation assays in the (H) absence of SCFA or (I) presence of SCFA Mix 1.

(F and G) Time to half-max fluorescence intensity for (F) individual SCFA treatments or (G) SCFA mixtures. N = 3, bars represent the mean and standard error.

(D and E) αSyn aggregation kinetics, as measured by ThT fluorescence in the presence of independent mixtures of SCFAs. (D) SCFA Mix 1- 29.6 μM acetate, 11 μM propionate, and 18.5 μM butyrate; SCFA Mix 2- 88.8 μM acetate, 33 μM propionate, and 55.5 μM butyrate. (E) SCFA Mix 3- 0.4 mM acetate, 0.15 mM propionate, and 0.24 mM butyrate; SCFA Mix 4-0.8 mM acetate, 0.3 mM propionate, and 0.47 mM butyrate; SCFA Mix 5- 2.0 mM acetate, 0.74 mM propionate, and 1.18 mM butyrate.

(A–C) αSyn aggregation kinetics, as measured by ThT fluorescence in the presence of the indicated concentrations of (A) sodium acetate, (B) sodium propionate, or (C) sodium butyrate.

Recently, it was revealed that gut bacteria modulate microglia activation during viral infection through production of microbial metabolites, namely short-chain fatty acids (SCFAs) (). Indeed, we observe lower fecal SCFA concentrations in GF and Abx-treated animals, compared to SPF mice ( Figure S3 A;). To address whether SCFAs impact neuroimmune responses in a mouse model of PD, we treated GF-ASO and GF-WT animals with a mixture of the SCFAs acetate, propionate, and butyrate (while the animals remained microbiologically sterile) and significantly restored fecal SCFA concentrations ( Figure S3 A). Within affected brain regions (i.e., CP and SN), microglia in SCFA-administered animals display morphology indicative of increased activation compared to untreated mice, and similar to cells from Ex-GF and SPF mice ( Figures 5 A, 5B, S3 B, and S3C; see also Figures 3 and 4 ). Microglia from GF-ASO mice fed SCFAs (SCFA-ASO) are significantly larger in diameter than those of GF-WT animals treated with SCFAs (SCFA-WT), with a concomitant decrease in the length and total number of branches. Abx-treated animals, however, display microglia morphology similar to GF animals ( Figures 5 B, S3 B, and S3C; see also Figures 3 and 4 ). Changes in microglia diameter are also observed in the FC, but not the CB, demonstrating region-specific responses ( Figures S3 D and S3E).

Animals were tested at 12–13 weeks of age. n = 6–12, error bars represent the mean and standard error from 3 trials per animal, and compiled from 2 independent cohorts or 20–60 microglia per region analyzed. Data are plotted with controls from Figure 4 for clarity.p ≤ 0.05;p ≤ 0.01;p ≤ 0.001;p ≤ 0.0001. Abbreviations: SPF, specific-pathogen-free; GF, germ-free; SCFA, short-chain fatty acid-treated; WT, wild-type; ASO, Thy1-α-synuclein genotype. See also Figures S3 S4 , and S5

(G) Time course of fecal output in a novel environment over 15 min.

(A) Representative 3D reconstructions of Iba1-stained microglia residing in the caudoputamen (CP) of wild-type or ASO SCFA-treated animals.

Animals were tested at 12-13 weeks of age. N = 3-6, with 20-60 microglia per region analyzed. Error bars represent the mean and standard error. ∗ p ≤ 0.05; ∗∗ p ≤ 0.01; ∗∗∗ p ≤ 0.001; ∗∗∗∗ p ≤ 0.0001. SPF = specific pathogen free, GF = germ-free, Abx = antibiotic-treated animals, Ex-GF = recolonized germ-free animals, SCFA = short-chain fatty acid-treated animals, WT = wild-type, ASO = Thy1-α-synuclein genotype.

(O) Western blot for αSyn from Triton soluble and insoluble fractions of CP homogenates derived from Abx- and SCFA-ASO animals.

(J) Dot blot images of CP, inferior midbrain (Mid), FC, and CB homogenate from Abx-ASO, ex-GF-ASO, and SCFA-ASO animals immunostained with aggregation-specific αSyn antibody.

(F–I) Representative images of the (F) CP, (G) SN, (H) FC, and (I) CB from Abx-ASO, ex-GF-ASO, or SCFA-ASO animals stained with aggregation-specific αSyn antibody (red), Phospho-Ser129-αSyn antibody (green), and Neurotrace/Nissl (blue).

(D and E) Diameter of microglia resident in the (D) frontal cortex (FC) and (E) cerebellum (CB).

SCFA Alterations and Increased αSyn Pathology in Abx, Ex-GF, and SCFA Mice, Related to Figures 4 and 5

The microbiota influence neurological outcomes during gestation, as well as via active gut-to-brain signaling in adulthood. In order to differentiate between these mechanisms, we treated SPF animals with an antibiotic cocktail to postnatally deplete the microbiota ( Figure 4 A). Conversely, we colonized groups of 5- to 6-week-old GF mice with a complex microbiota from SPF-WT animals ( Figure 4 A). Remarkably, antibiotic-treated (Abx) animals display little αSyn-dependent motor dysfunction, closely resembling mice born under GF conditions ( Figures 4 B–4E). Postnatal colonization of previously GF animals (Ex-GF) recapitulates the genotype effect observed in SPF mice, with mice that overexpress αSyn displaying significant motor dysfunction ( Figures 4 B–4E). GI function, as measured by fecal output, is also significantly improved in Abx-treated animals, while Ex-GF mice exhibit an αSyn-dependent decrease in total fecal output ( Figures 4 F and 4G). Furthermore, in the transgenic ASO line, microglia from Ex-GF animals have increased cell body diameters comparable to those in SPF mice ( Figures 4 H and 4I). Abx-ASO animals, however, harbor microglia with diameters similar to GF animals ( Figures 4 H and 4I). While not excluding a role for the microbiota during prenatal neurodevelopment, modulation of microglia activation during adulthood contributes to αSyn-mediated motor dysfunction and neuroinflammation, suggesting active gut-brain signaling by the microbiota.

Animals were tested at 12–13 weeks of age. n = 6–12; error bars represent the mean and standard error from 3 trials per animal, and compiled from 2 independent cohorts or 20–60 microglia per region analyzed.0.05 < p < 0.1;p ≤ 0.05;p ≤ 0.01;p ≤ 0.001;p ≤ 0.0001. Abbreviations: SPF, specific-pathogen-free; GF, germ-free; Abx, antibiotic-treated; Ex-GF, recolonized germ-free animals; WT, wild-type; ASO, Thy1-α-synuclein genotype. See also Figure S3

(H) Representative 3D reconstructions of Iba1-stained microglia residing in the caudoputamen (CP) of Abx-ASO or Ex-GF-ASO animals.

(F) Time course of fecal output in a novel environment over 15 min.

Extending these observations to a disease model, microglia from SPF-ASO mice display significant increases in cell body diameter, along with fewer processes of shorter length compared to GF-ASO mice ( Figures 3 A–3C). Tissue homogenates from the CP and inferior midbrain of SPF-ASO mice contain a marked increase in the pro-inflammatory cytokines tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) compared to GF-ASO mice ( Figures 3 D and 3E). Both cytokines are elevated in the brains of PD patients (). Gene expression analysis of RNA from enriched CD11bcells (primarily microglia) reveals increased Tnfa and Il6 expression in SPF-ASO animals, which is nearly absent in GF animals ( Figure 3 F). Neuroprotective Bdnf and the cell cycle marker Ddit4 levels are upregulated in GF animals ( Figure S2 I), as observed in previous studies (). Neuroinflammatory responses are region specific with increased in microglia diameter and TNF-α production in the FC but not the CB ( Figures 3 G and 3H). Overall, these findings support the hypothesis that gut microbes promote αSyn-dependent activation of microglia within specific brain regions involved in disease.

Tumor necrosis factor-alpha (TNF-alpha) increases both in the brain and in the cerebrospinal fluid from parkinsonian patients.

Interleukin-1 beta, interleukin-6, epidermal growth factor and transforming growth factor-alpha are elevated in the brain from parkinsonian patients.

The microbiota modulates immune development in the CNS (), and αSyn aggregates activate immune cells, including brain-resident microglia (). Microglia undergo significant morphological changes upon activation, transitioning from thin cell bodies with numerous branched extensions to round, amoeboid cells with fewer branches (). In situ 3D reconstructions of individual microglia cells from confocal fluorescence microscopy reveals that wild-type GF animals harbor microglia that are distinct from SPF animals. Within the CP and SN, microglia in GF-WT mice display increased numbers and total lengths of microglia branches compared to SPF-WT animals ( Figures 3 A–3C). These morphological features are indicative of an arrest in microglia maturation and/or a reduced activation state in GF animals, corroborating a recent report that gut bacteria affect immune cells in the brain ().

Tissues collected from mice at 12–13 weeks of age. n = 3–4 (with 20–60 cells per region per animal analyzed); error bars represent the mean and standard error.p ≤ 0.05;p ≤ 0.01;p ≤ 0.001;p ≤ 0.0001. Abbreviations: SPF, specific-pathogen-free; GF, germ-free; WT, wild-type; ASO, Thy1-α-synuclein genotype. See also Figure S2

(H) ELISA analysis for TNF-α present in homogenates from the FC or CB.

(E) ELISA analysis for TNF-α and IL-6 present in homogenates from the inferior midbrain (Mid).

(D) ELISA analysis for TNF-α and IL-6 present in homogenates from the CP.

(A) Representative 3D reconstructions of Iba1-stained microglia residing in the caudoputamen (CP) of SPF-WT, SPF-ASO, GF-WT, and GF-ASO animals.

Neuroimmunological processes in Parkinson’s disease and their relation to α-synuclein: microglia as the referee between neuronal processes and peripheral immunity.

Neuron-released oligomeric α-synuclein is an endogenous agonist of TLR2 for paracrine activation of microglia.

Motor deficits in PD coincide with the aggregation of αSyn. Utilizing an antibody that recognizes only conformation-specific αSyn aggregates and fibrils, we performed immunofluorescence microscopy to visualize αSyn inclusions in the brains of mice. Under SPF conditions, we observe notable aggregation of αSyn in the caudoputamen (CP) and substantia nigra (SN) of ASO animals ( Figures 2 A and 2B ), brain regions of the nigrostriatal pathway affected in both mouse models and human PD (). Surprisingly, GF-ASO mice display appreciably fewer αSyn aggregates ( Figures 2 A and 2B). To quantify αSyn aggregation, we performed western blots of brain extracts ( Figure 2 C). We reveal significantly less insoluble αSyn in brains of GF-ASO animals ( Figures 2 C–2E). To further confirm these findings, we performed dot blot analysis for aggregated αSyn in the CP and inferior midbrain, where the SN is located, and observe similarly decreased αSyn aggregation in GF-ASO animals ( Figures S2 A–S2C). Interestingly, we observe regional specificity of αSyn aggregation: in the frontal cortex (FC), GF-ASO animals harbor fewer αSyn aggregation than SPF animals, while in the cerebellum (CB), we observe nearly equal quantities of αSyn in SPF and GF mice ( Figures S2 D–S2H). To ensure that these findings do not reflect differences in transgene expression, we report similar levels of αSyn transcript and protein in the inferior midbrain and the CP between SPF- and GF-ASO animals ( Figures 2 F and 2G). These data suggest that the microbiota regulates pathways that promote αSyn aggregation and/or prevent the clearance of insoluble protein aggregates.

Animals were tested at 12-13 weeks of age. N = 3-4, error bars represent the mean and standard error. ∗ p ≤ 0.05. SPF = specific pathogen free, GF = germ-free, WT = wild-type, ASO = Thy1-α-synuclein genotype.

(G and H) Densitometry quantification of dot blots from the (G) FC and (H) CB.

(F) Dot blot images of homogenates from the FC or CB from SPF-ASO and GF-ASO animals, immunostained with aggregation-specific αSyn antibody.

(E) Representative images of the cerebellum (CB) from SPF-ASO or GF-ASO animals, stained as above.

(D) Representative images of the frontal cortex (FC) from SPF-ASO or GF-ASO animals stained with aggregation-specific αSyn antibody (red), Phospho-Ser129-αSyn antibody (green), and Neurotrace/Nissl (blue).

(B and C) Densitometry quantification of dot blots of the (B) CP or (C) inferior midbrain.

Tissues collected from mice at 12–13 weeks of age. n = 3–4, error bars represent the mean and standard error.p ≤ 0.05;p ≤ 0.01;p ≤ 0.001. Abbreviations: SPF, specific-pathogen-free; GF, germ-free; WT, wild-type; ASO, Thy1-α-synuclein genotype. See also Figure S2

(G) ELISA analysis of total αSyn present in homogenates from the CP or inferior midbrain (Mid).

(D and E) Densitometry quantification of anti-αSyn western blots for (D) all αSyn and (E) ratio of insoluble to soluble αSyn staining.

(B) Representative images of the substantia nigra (SN) from SPF-ASO or GF-ASO animals, stained as above.

(A) Representative images of the caudoputamen (CP) from SPF-ASO or GF-ASO animals stained with aggregation-specific αSyn antibody (red), Phospho-Ser129-αSyn antibody (green), and Neurotrace/Nissl (blue).

As in PD, motor dysfunction in this mouse model co-occurs with decreased GI function and constipation (). In SPF-ASO animals, we observe a marked decrease in the total output of fecal pellets, at both 12–13 weeks and 24–25 weeks of age, while fecal output is unaltered in GF-ASO animals ( Figures 1 E, 1F, S1 H, and S1I). Further, fecal pellets produced by SPF-ASO mice contain reduced water content compared to GF-ASO mice ( Figure S1 J), together revealing reduced GI defects in GF animals. Indeed, compilation of all motor phenotypes into a principal-component analysis (PCoA) displays a striking segregation by the SPF-ASO group, while GF-ASO animals cluster more similarly to WT mice ( Figure S1 K). Together, these data demonstrate that the presence of gut microbes promote the hallmark motor and intestinal dysfunction in a preclinical model of PD.

The Thy1-αSyn (alpha-synuclein-overexpressing [ASO]) mouse displays progressive deficits in fine and gross motor function, as well as gut motility defects (). Recent evidence has linked unregulated αSyn expression in humans to a higher risk of PD (), providing an epidemiological foundation for the Thy1-αSyn mouse model. Defects in coordinated motor tasks become evident at 12 weeks of age (). Motor function was measured via four tests: beam traversal, pole descent, nasal adhesive removal, and hindlimb clasping reflexes, as previously validated in this model (). 12- to 13-week-old ASO animals harboring a complex microbiota (SPF-ASO) require significantly more time to cross a challenging beam compared to wild-type littermates (SPF-WT) and also exhibit increased time to descend a pole, two measures of gross motor function ( Figures 1 A and 1B ). Removal of an adhesive from the nasal bridge, a test of fine motor control, is impaired in SPF-ASO mice compared to SPF-WT mice ( Figure 1 C). Finally, the hindlimb clasping reflex, a measure of striatal dysfunction (), is defective in SPF-ASO mice ( Figure 1 D). To assess the contribution of gut bacteria, we re-derived ASO mice (GF-ASO) and wild-type mice (GF-WT) under germ-free conditions. Remarkably, 12- to 13-week-old GF-ASO animals exhibit reduced deficits in beam traversal, pole descent, adhesive removal, and hindlimb clasping ( Figures 1 A–1D). In fact, the execution of motor function tasks by GF-ASO mice resembles performance levels of WT animals in many cases. GF-ASO mice do not exhibit differences in weight compared to SPF-ASO animals ( Figure S1 A), while both SPF-ASO and GF-ASO animals display defects in the inverted grid assay, a measure of limb strength ( Figure S1 B)—thus, outcomes in motor tests are not due to weight or physical strength. At a later age (24–25 weeks old), SPF-ASO animals exhibit a progressive decline in motor function ( Figures S1 C–S1G), which is significantly delayed in GF-ASO animals ( Figures S1 C–S1G). We do not observe consistent differences in motor tasks between GF-WT and SPF-WT animals, providing evidence for gene-microbiome interactions.

N = 4-6, error bars represent the mean and standard error. From 3 trials per animal for motor tests. Data are representative of 2 experiments. # 0.1 > p > 0.05; ∗ p ≤ 0.05; ∗∗ p ≤ 0.01; ∗∗∗ p ≤ 0.001; ∗∗∗∗ p ≤ 0.0001. SPF = specific-pathogen free, GF = germ-free, WT = wild-type, ASO = Thy1-α-synuclein genotype.

(H) Time course of fecal output in a novel environment over 15 min by 24-25 week old animals.

Body Weight of SPF and GF Animals, and Analysis of Aged Mice, Related to Figure 1

Animals were tested at 12–13 weeks of age. n = 4–6, error bars represent the mean and standard error from three trials per animal. Data are representative of two experiments.p ≤ 0.05;p ≤ 0.01;p ≤ 0.001;p ≤ 0.0001. Abbreviations: SPF, specific-pathogen-free; GF, germ-free; WT, wild-type; ASO, Thy1-α-synuclein genotype. See also Figure S1

(E) Time course of fecal output in a novel environment over 15 min.

Discussion

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Bronstein J.M. Of pesticides and men: a California story of genes and environment in Parkinson’s disease. Parkinson’s disease represents a growing health concern for an ever-aging population. While genetic risks have been identified, environmental factors and gene-environment interactions probably account for most PD cases (). We provide evidence that the gut microbiota are required for postnatal events that promote hallmark motor deficits in an animal model. Under GF conditions, or when bacteria are depleted with antibiotics, transgenic animals overexpressing human αSyn display reduced microglia activation, αSyn inclusions, and motor deficits compared to animals with a complex microbiota. Treatment with microbially produced SCFAs restores all major features of disease in GF mice, identifying potential molecular mediators involved in gut-brain signaling. Exacerbated motor symptoms in humanized mice transplanted with a PD microbiota compared to healthy controls suggest that αSyn overexpression (genetics) and dysbiosis (environment) combine to influence disease outcomes in mice. Extrapolation of these preclinical findings to humans may embolden the concept that gene-microbiome interactions represent a previously unrecognized etiology for PD.

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Stanton C. Bacterial neuroactive compounds produced by psychobiotics. While the microbiota promote microglia maturation, there are likely other disease-modifying processes that remain undiscovered. These include effects by the microbiota on autophagy (), a cellular recycling process that is genetically linked to PD risk and when impaired may lead to reduced clearance of αSyn aggregates (). Additionally, intestinal bacteria have been shown to modulate proteasome function (), which may also aid in the clearance of αSyn inclusions. The protective effects of autophagy and the proteasome are not specific to synuclienopathies, and the ability of the microbiota to modulate these critical cellular functions suggests that other amyloid disorders, such as Alzheimer’s and Huntington’s diseases, may be impacted by gut bacteria. In fact, recent studies have implicated the gut microbiota in promoting amyloid beta pathology in a model of Alzheimer’s disease (). Though we have explored postnatal effects of the microbiota in a model of neurodegenerative disease, our findings do not address the likely important role of microbial signals during prenatal neurodevelopment. Whether gut microbes alter the development of the dopaminergic system, perhaps by modulating neurogenesis or neural differentiation in utero or early life, remains unexplored. Furthermore, gut microbes can produce dopamine and its precursors from dietary substrates, with almost half of the body’s dopamine generated in the GI tract (). Deciphering microbiota effects on microglia activation, cellular protein clearance pathways, neurotransmitter production, and/or other mechanisms may offer an integrated approach to understand the pathogenesis of a complex and enigmatic disorder such as PD.

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Moir R.D. Amyloid-β peptide protects against microbial infection in mouse and worm models of Alzheimer’s disease. What causes dysbiosis in PD? Physiological functions in affected individuals, such as altered intestinal absorption, reduced gastric motility, or dietary habits, represent factors that may change the microbiome. Epidemiological evidence has linked specific pesticide exposure to the incidence of PD (), with some pesticides known to impact microbiome configuration (). Given the structure of αSyn and its ability to associate with membranes (), it is tempting to speculate that extracellular αSyn may act as an antimicrobial, similar to recent observations with amyloid beta (), and shape the PD microbiome. Whether microbial community alterations are caused by extrinsic or intrinsic factors, the PD microbiota may be missing or reduced in protective microbes, harbor increased pathogenic resident microbes, or both. In turn, dysbiosis will result in differential production of microbial molecules in the gut. Metabolites produced by a deranged microbiota may enter the circulation (or even the brain) and impact neurological function. Identification of bacterial taxa or microbial metabolites that are altered in PD may serve as disease biomarkers or even drug targets, and interventions that correct dysbiosis may provide safe and effective treatments to slow or halt the progression of often debilitating motor symptoms.

Our findings establish that the microbiota are required for the hallmark motor and GI dysfunction in a mouse model of PD, via postnatal gut-brain signaling by microbial molecules that impact neuroinflammation and αSyn aggregation. Coupled with emerging research that has linked gut bacteria to disorders such as anxiety, depression, and autism, we propose the provocative hypothesis that certain neurologic conditions that have classically been studied as disorders of the brain may also have etiologies in the gut.