Parkinson’s Disease and PD Medications Have Distinct
Signatures of the Gut Microbiome
Erin M. Hill-Burns, PhD
1
, Justine W. Debelius, PhD
2
, James T. Morton, BS
3
, William T.
Wissemann, BA
1
, Matthew R. Lewis, MS
1
, Zachary D. Wallen, MS
1
, Shyamal D. Peddada,
PhD
4
, Stewart A. Factor, DO
5
, Eric Molho, MD
6
, Cyrus P. Zabetian, MD, MS
7
, Rob Knight,
PhD
2,3,8
, and Haydeh Payami, PhD
1,9,*
1
Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, 35233,
USA
2
Department of Pediatrics, University of California San Diego, La Jolla, California, 92093, USA
3
Department of Computer Science and Engineering, University of California San Diego, La Jolla,
California, 92093, USA
4
Biostatistics and Computational Biology Branch, National Institute of Environmental Health
Sciences (NIH/NIEHS), RTP, North Carolina, 27709, USA
5
Department of Neurology, Emory University School of Medicine, Atlanta, GA, 30322, USA
6
Department of Neurology, Albany Medical College, Albany, NY, 12208, USA
*
Corresponding author: Haydeh Payami, MCLM 460, 1720 2
nd
Ave South, Birmingham, AL, 35294, USA
haydehpayami@uabmc.edu, 205-934-0371.
Financial disclosure/conflict of interest: None
Data accession
Sequence and metadata are in EBI as accession number ERP016332.
Full financial disclosure for the previous 12 months:
EMH – NIH grants NS036960 and NS067469 (to HP)
JWD – Robert Wood Johnson Foundation (to RK)
JTM – NSF GRFP DGE-1144086
WTW – NIH grants NS036960 and NS067469 (to HP)
MRL – NIH grant NS067469 (to HP)
ZDW – UAB graduate student funds and by NIH grant NS067469 (to HP).
SDP – NIEHS grant ZIA ES103066-05
SAF – Honoraria: Neurocrine, Lundbeck, Auspex/Teva, Avanir, Cynapsus, Adamas, UCB.
Grants: Ipsen, Allergan, Medtronics, Auspex, US World Meds, Pharm-Olam, Cynapsus Therapeutics, Solstice, CHDI Foundation,
Michael J. Fox Foundation, NIH
Royalties: Demos, Blackwell Futura for textbooks, Uptodate
EM – Research support from: Merz Pharmaceuticals, CHDI, Kyowa Hakko Kirin Pharma, US World Meds, Auspex Pharmaceuticals,
Acadia Pharmaceuticals, Pfizer, Civitas
Speakers Honoraria: US World Meds
Consulting Fees: US World Meds, Neurocrine Biosciences
CPZ – American Parkinson Disease Association, Department of Veterans Affairs, NIH, Dolsen Foundation
RK – Univ. of Wisconsin, Madison Moore Foundation, Binational Science Foundation, UC San Francisco National Multiple Sclerosis
Society, Templeton, Harvard-Broad, NIH P01DK078669, Ludwig Maximilian Universitat Munchen, Alfred P. Sloan Foundation, Oak
Crest Institute of Science, Department of Justice, Laureate Institute for Brain Research, Kenneth Rainin Foundation, Robert Wood
Johnson Foundation, University of Colorado-Boulder /DOJ, University of Colorado-Boulder/ONR, USAMRAA, NSF, NIH
U01AI24316 and P30MH062512.
HP – NIH grants NS036960 and NS067469, The John T and Juanelle D Strain Endowed Chair, Department of Neurology and School
of Medicine of University of Alabama at Birmingham.
HHS Public Access
Author manuscript
Mov Disord
. Author manuscript; available in PMC 2018 May 01.
Published in final edited form as:
Mov Disord
. 2017 May ; 32(5): 739–749. doi:10.1002/mds.26942.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
7
VA Puget Sound Health Care System and Department of Neurology, University of Washington,
Seattle, WA, 98108, USA
8
Center for Microbiome Innovation, University of California San Diego, La Jolla, California, 92093,
USA
9
Center for Genomic Medicine, HudsonAlpha Institute for Biotechnology, Huntsville, Alabama,
35806, USA
Abstract
Background—There is mounting evidence for a connection between the gut and Parkinson’s
disease (PD). Dysbiosis of gut microbiota could explain several features of PD.
Objective—To determine if PD involves dysbiosis of gut microbiome, disentangle effects of
confounders, and identify candidate taxa and functional pathways to guide research.
Methods—197 PD cases and 130 controls were studied. Microbial composition was determined
by 16S rRNA gene sequencing of DNA extracted from stool. Metadata were collected on 39
potential confounders including medications, diet, gastrointestinal symptoms, and demographics.
Statistical analyses were conducted while controlling for potential confounders and correcting for
multiple testing. We tested differences in the overall microbial composition, taxa abundance, and
functional pathways.
Results—Independent microbial signatures were detected for PD (P=4E-5), subjects’ region of
residence within the United States (P=3E-3), age (P=0.03), sex (P=1E-3) and dietary fruits/
vegetables (P=0.01). Among patients, independent signals were detected for catechol-O-
methyltransferase-inhibitors (P=4E-4), anticholinergics (P=5E-3), and possibly carbidopa/
levodopa (P=0.05). We found significantly altered abundance of
Bifidobacteriaceae,
Christensenellaceae
,
[Tissierellaceae], Lachnospiraceae
,
Lactobacillaceae
,
Pasteurellaceae
and
Verrucomicrobiaceae
families. Functional predictions revealed changes in numerous pathways
including metabolism of plant-derived compounds and xenobiotics degradation.
Conclusion—PD is accompanied by dysbiosis of gut microbiome. Results coalesce divergent
findings of prior studies, reveal altered abundance of several taxa, nominate functional pathways,
and demonstrate independent effects of PD medications on the microbiome. The findings provide
new leads and testable hypotheses on the pathophysiology and treatment of PD.
Keywords
Parkinson’s disease; Medications; Confounding; Gut microbiome; Functional Pathways
Evidence linking PD to the gut precedes our recent appreciation of the microbiome.
Gastrointestinal (GI) symptoms, including constipation, often precede the motor signs of
PD.
1
Lewy bodies and α-synuclein, which are the neuropathological hallmarks of PD, may
appear in the gut before they appear in the brain.
2
Colonic inflammation has also been
documented in PD.
3
These observations have led to the hypothesis that PD starts in the gut
and spreads to the brain. Increased intestinal permeability in conjunction with presence of α-
synuclein in the gut at early stages of disease
4
suggests that a leaky gut membrane may
contribute to the spread of the disease. Decreased incidence of PD among individuals who
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underwent vagotomy
5
adds to the evidence that PD might start in the gut and spread to the
brain via the enteric nervous system.
The human gut hosts tens of trillions of microorganisms including more than 1000 species of
bacteria.
6, 7
The collective genomes of the microorganisms in the gut (the microbiome) is
over 100 times larger than the number of genes in the human genome. A well-balanced gut
microbiota is critical for maintaining general health. Alterations in the composition of gut
microbiota have been linked to a range of disorders including inflammatory, metabolic,
neurologic, and oncologic (reviewed in
8
). Research on human disease and the gut
microbiota is a relatively new field, and so far, most studies have treated the disease as a
single predictor, disregarding the wide range of variables that could also affect the
microbiome and obscure the disease signature. The need to disentangle the gut microbiota
signature of disease from that of medication and other confounders is becoming increasingly
evident.
9
Studies linking the gut microbiome to PD include one conducted in mice, which showed
colonization with microbiota from PD patients enhanced neuro-inflammation and motor
symptoms in animals overexpressing α-synuclein,
10
and four conducted in humans which
reached divergent conclusions.
11–14
A direct comparison of the results is difficult because
they had relatively small sample sizes (68 to 144 cases and controls combined), and differed
in subject inclusion/exclusion criteria, sequencing techniques, statistical methods, and the
treatment of confounders. Here we report a case-control study which included 327 subjects
and a systematic analysis of 39 variables as potential confounders. We applied different
techniques when available to assure results were robust to methodological differences, and
examined the gut microbiome at global, taxonomic, and functional levels. The results help
coalesce a seemingly inconsistent literature.
Patients and Methods
Subject recruitment and data collection
Institutional Review Boards and Human Subject Committees at participating institutions
approved the study. Written informed consent was obtained. 212 PD cases and 136 control
subjects were enrolled from among the participants of the NeuroGenetics Research
Consortium (NGRC) in Seattle, WA; Atlanta, GA; and Albany, NY. The methods, and the
clinical and genetic characteristics of NGRC dataset have been described in detail.
15
Briefly,
PD subjects were diagnosed by a movement disorder specialist according to the modified
UK Brain Bank criteria.
16
Controls were self-reported as being free of neurodegenerative
disease. None of the patients and controls was genetically related to any other patient or
control. Fifty-four case-control pairs were spouses; 143 cases and 76 controls were not
connected.
Medication data were extracted from the medical records by the treating neurologists and
included only the medications that the patient was prescribed for the treatment of PD at the
time of this study. Spousal relationships were collected at each study site. Hoehn & Yahr
(H&Y) and Movement Disorder Society (MDS) UPDRS III scores were assessed on the
“on” state, as were in prior studies, and were used only to replicate prior reports. Disease
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duration was the difference between age-at-study and age-at-onset. All other metadata were
collected using questionnaires that were completed by the subject on the day of stool sample
collection.
Stool samples were collected at home using DNA/RNA-free sterile swabs (BD BBL
CultureSwab Sterile, Media-free Swabs kit from Fisher Scientific) and shipped immediately
via standard US postal service at ambient temperature.
Two subjects were excluded for having unreliable metadata and 19 were excluded based on
sequencing metrics (see below). The final sample size for analysis was 197 PD cases and
130 controls (Table S1).
16S rRNA amplicon analysis
DNA extraction from stool and 16S rRNA amplicon sequencing were performed according
to the Earth Microbiome Project Protocols, as previously described.
17, 18
Sequencing was
done using an Illumina MiSeq (La Jolla, CA). All samples were sequenced at once and at
one laboratory.
Operational taxonomic units (OTUs) were picked using a closed reference in Quantitative
Insights Into Microbial Ecology (QIIME) 1.9.1
19
using SortMeRNA 2.0
20
against the
August 2013 release of the Greengenes 16S rRNA gene sequence database
21
at 97%
similarity. To ensure consistency, we also used
de-novo
OTU calling with HITdb as
reference
22
and the RDP classifier,
23
which yielded similar results as Greengenes. A total of
4567 OTUs were called. Rarefaction at 5,000 sequences/sample resulted in the exclusion of
19 samples.
Confounders
Thirty-nine variables were interrogated as potential confounders (Table S1). PD
medications, disease duration, spousal relationship and geographic site were automatically
tagged as potential confounders. The remaining variables were tested to determine if they
differed between cases and controls, using Fisher’s exact test for dichotomous variables and
Mann-Whitney U test for quantitative variables. Since the purpose of this test was to protect
against potential confounding, we used a cautious uncorrected P<0.1 to tag the variables as a
potential confounder. In all, 20 of the 39 variables were chosen as potential confounders
(Table S1). The variables were tested for collinearity with PD using variance inflation factor
(VIF) in the R package HH. Twelve of the 20 variables had no evidence for collinearity with
PD (VIF<2) and were treated as covariates. The remaining 8 variables (6 PD medications,
disease duration and Caesarean section (C-section)) were seen exclusively in patients and
were treated individually, as described below.
Analysis of overall composition of gut microbiome
We calculated the dissimilarities (distance) between the microbiomes of the 197 PD and 130
control samples. To ensure that the choice of the metric did not affect the results, we
calculated the distances using three metrics: Unweighted UniFrac,
24
Weighted UniFrac,
24
and Canberra distance.
25, 26
The rarefied OTU table was used for all three metrics. UniFrac
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distances were calculated in QIIME 1.9.1 and Canberra distances in the R package
vegan_2.4-0. The differences between cases and controls were then tested, for each metric in
turn, using Permutational Multivariate Analysis of Variance (PERMANOVA).
27
Significance was determined using the adonis2 function in vegan in R with 99,999
permutations, and if significance reached its maximum possible at P=1E-5, permutations
were increased to 9,999,999 for added precision down to P=1E-7.
To test for confounding, we conducted adjusted PERMANOVA with PD and 12 covariates in
the model and tested the marginal effects:
where age (years), transit-time (days), and BMI were continuous variables, and the other
variables were categorical (see Table S1). To test confounding by C-section, we excluded the
subjects born by C-section and repeated PERMANOVA of PD vs. controls. To test effect of
PD-medications on the microbiome, PEMANOVA was used in patients only (PD-
medications were not collinear (VIF<2)):
PD-medications that were significant were re-tested while adjusting for covariates:
Fraction of the total variance explained by each variable was calculated in the
PERMANOVA model.
Testing differences in the abundance of taxa in PD vs. controls
Differences were tested at OTU, genus and family level. Taxa present in <10% of samples
were removed, resulting in 709 OTUs, 103 genera and 55 families. We tested the abundance
of each taxon in cases vs. controls, using the Analysis of Composition of Microbiomes
(ANCOM)
28
and Kruskal-Wallis rank sum test.
29
Kruskal-Wallis tests the null hypothesis
that the taxon abundance in a random specimen taken from two or more ecosystems are
equal in distribution, whereas ANCOM tests the null hypothesis that the taxon abundance
(per unit volume) in two or more ecosystems are equal on average. Thus, ANCOM makes
comparisons at the ecosystem level whereas the Kruskal-Wallis test makes comparisons at
the specimen level. If results differed, we cautiously proceeded with the subset of findings
that were significant by both methods. ANCOM was conducted using default parameters in
the python implementation of ANCOM in scikit-bio 0.4.2. Kruskal-Wallis test was run using
kruskal.test in R. Both analyses incorporate false-discovery rate (FDR) correction for
multiple testing (FDR<0.05).
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