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Changes in Gut and Plasma Microbiome following Exercise Challenge in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

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Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disease characterized by intense and debilitating fatigue not due to physical activity that has persisted for at least 6 months, post-exertional malaise, unrefreshing sleep, and accompanied by a number of secondary symptoms, including sore throat, memory and concentration impairment, headache, and muscle/joint pain. In patients with post-exertional malaise, significant worsening of symptoms occurs following physical exertion and exercise challenge serves as a useful method for identifying biomarkers for exertion intolerance. Evidence suggests that intestinal dysbiosis and systemic responses to gut microorganisms may play a role in the symptomology of ME/CFS. As such, we hypothesized that post-exertion worsening of ME/CFS symptoms could be due to increased bacterial translocation from the intestine into the systemic circulation. To test this hypothesis, we collected symptom reports and blood and stool samples from ten clinically characterized ME/CFS patients and ten matched healthy controls before and 15 minutes, 48 hours, and 72 hours after a maximal exercise challenge. Microbiomes of blood and stool samples were examined. Stool sample microbiomes differed between ME/CFS patients and healthy controls in the abundance of several major bacterial phyla. Following maximal exercise challenge, there was an increase in relative abundance of 6 of the 9 major bacterial phyla/genera in ME/CFS patients from baseline to 72 hours post-exercise compared to only 2 of the 9 phyla/genera in controls (p = 0.005). There was also a significant difference in clearance of specific bacterial phyla from blood following exercise with high levels of bacterial sequences maintained at 72 hours post-exercise in ME/CFS patients versus clearance in the controls. These results provide evidence for a systemic effect of an altered gut microbiome in ME/CFS patients compared to controls. Upon exercise challenge, there were significant changes in the abundance of major bacterial phyla in the gut in ME/CFS patients not observed in healthy controls. In addition, compared to controls clearance of bacteria from the blood was delayed in ME/CFS patients following exercise. These findings suggest a role for an altered gut microbiome and increased bacterial translocation following exercise in ME/CFS patients that may account for the profound post-exertional malaise experienced by ME/CFS patients.
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RESEARCH ARTICLE
Changes in Gut and Plasma Microbiome
following Exercise Challenge in Myalgic
Encephalomyelitis/Chronic Fatigue Syndrome
(ME/CFS)
Sanjay K. Shukla
1
*, Dane Cook
2,3
, Jacob Meyer
2
, Suzanne D. Vernon
4
, Thao Le
1
,
Derek Clevidence
3¤a
, Charles E. Robertson
5
, Steven J. Schrodi
1
, Steven Yale
6¤b
, Daniel
N. Frank
5
1Marshfield Clinic Research Foundation, Marshfield, WI, United States of America, 2William S. Middleton
Memorial Veterans Hospital, Madison, WI, United States of America, 3University of Wisconsin, Madison, WI,
United States of America, 4Bateman Horne Center of Excellence, Salt Lake City, UT, United States of
America, 5University of Colorado Denver Anschutz Medical Campus, Aurora, CO, United States of America,
6Marshfield Clinic, Marshfield, WI, United States of America
¤a Current address: Meriter Medical Group, Monoa, WI, United States of America
¤b Current address: North Florida Regional Medical Center, Gainseville, FL, United States of America
*shukla.sanjay@mcrf.mfldclin.edu
Abstract
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disease characterized
by intense and debilitating fatigue not due to physical activity that has persisted for at least 6
months, post-exertional malaise, unrefreshing sleep, and accompanied by a number of sec-
ondary symptoms, including sore throat, memory and concentration impairment, headache,
and muscle/joint pain. In patients with post-exertional malaise, significant worsening of
symptoms occurs following physical exertion and exercise challenge serves as a useful
method for identifying biomarkers for exertion intolerance. Evidence suggests that intestinal
dysbiosis and systemic responses to gut microorganisms may play a role in the symptomol-
ogy of ME/CFS. As such, we hypothesized that post-exertion worsening of ME/CFS symp-
toms could be due to increased bacterial translocation from the intestine into the systemic
circulation. To test this hypothesis, we collected symptom reports and blood and stool sam-
ples from ten clinically characterized ME/CFS patients and ten matched healthy controls
before and 15 minutes, 48 hours, and 72 hours after a maximal exercise challenge. Micro-
biomes of blood and stool samples were examined. Stool sample microbiomes differed
between ME/CFS patients and healthy controls in the abundance of several major bacterial
phyla. Following maximal exercise challenge, there was an increase in relative abundance
of 6 of the 9 major bacterial phyla/genera in ME/CFS patients from baseline to 72 hours
post-exercise compared to only 2 of the 9 phyla/genera in controls (p = 0.005). There was
also a significant difference in clearance of specific bacterial phyla from blood following
exercise with high levels of bacterial sequences maintained at 72 hours post-exercise in
ME/CFS patients versus clearance in the controls. These results provide evidence for a sys-
temic effect of an altered gut microbiome in ME/CFS patients compared to controls. Upon
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 1/15
OPEN ACCESS
Citation: Shukla SK, Cook D, Meyer J, Vernon SD,
Le T, Clevidence D, et al. (2015) Changes in Gut and
Plasma Microbiome following Exercise Challengein
Myalgic Encephalomyelitis/Chronic Fatigue
Syndrome (ME/CFS). PLoS ONE 10(12): e0145453.
doi:10.1371/journal.pone.0145453
Editor: Francesco Cappello, University of Palermo,
ITALY
Received: July 13, 2015
Accepted: December 3, 2015
Published: December 18, 2015
Copyright: © 2015 Shukla et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: All sequences and
quality scores were deposited into the NCBI Short
Read Archive under project accession number
PRJNA302040. Additional data are available from the
PI Sanjay K. Shukla, Marshfield Clinic Research
Foundation, upon request for researchers who meet
the criteria for access to confidential data: [CONTACT
INFORMATION: Sanjay K. Shukla, PhD, Research
Scientist, Marshfield Clinic Research Foundation].
Funding: This study was funded from a grant from
The CFIDS Association of America to SKS and
donors to the Marshfield Clinic Research Foundation.
exercise challenge, there were significant changes in the abundance of major bacterial
phyla in the gut in ME/CFS patients not observed in healthy controls. In addition, compared
to controls clearance of bacteria from the blood was delayed in ME/CFS patients following
exercise. These findings suggest a role for an altered gut microbiome and increased bacte-
rial translocation following exercise in ME/CFS patients that may account for the profound
post-exertional malaise experienced by ME/CFS patients.
Introduction
The Centers for Disease Control estimates that between 836,000 and 2.5 million people in the
U.S. suffer from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) [1], resulting
in substantial disability [2] and significant socio-economic impact [3,4]. ME/CFS is a complex
disorder that has been associated with neuro-inflammatory and oxidative processes [5]. One
current model of disease suggests that a trigger event (e.g. infection) results in a chronic inflam-
matory state characterized by increased proinflammatory cytokine production, increased reac-
tive oxygen and nitrogen species, altered intracellular signaling, increased intestinal
permeability and systemic activation of innate immune receptors, altered glutaminergic and
dopaminergic neurotransmission, mitochondrial dysfunction, and aberrant autoimmune
responses [611]. These pathogenic processes appear to be self-sustaining and self-amplifying
and account for the characteristic symptoms of ME/CFS and other systemic disorders charac-
terized by central and peripheral fatigue [5,9]. Despite recent advances in understanding the
biochemical underpinnings of disease, diagnosis continues to rely on clinical presentation [12].
Guidelines established in 1994 by Fukuda et al as part of the International Chronic Fatigue
Syndrome Study Group define CFS as clinically evaluated, unexplained, persistent fatigue that
is not alleviated by rest that occurs in conjunction with four or more characteristic symptoms,
including impaired memory or concentration, sore throat, tender lymph nodes, muscle or joint
pain in the absence of redness or swelling, headaches, unrefreshing sleep, and post-exertional
malaise (PEM) lasting more than 24 hours, persisting or recurring over 6 or more consecutive
months of illness [13]. However, not all patients experience the same symptoms indicating the
existence of ME/CFS subgroups. For example, subgroups based on the presence or absence of
gastrointestinal symptoms [14] and post-exertional malaise [15] has been described.
In particular, post-exertional malaise has emerged as a distinguishing feature of ME/CFS. It
is described as significant worsening of several symptoms following physical and mental exer-
tion. Post-exertional malaise has been associated with abnormal neurovascular regulation [16]
and altered immune and metabolic response to aerobic exercise [1719]. Variability in symp-
tom constellation and severity over time make ME/CFS patients a heterogeneous population
and several studies have failed to detect differences between patients and controls at baseline
[17,18,20]. As such, exercise challenge in those suffering from PEM may serve as a useful tool
for exacerbating symptoms in a controlled setting to attempt to calibrate symptom constella-
tion and severity across the patient group and to uncover potential differences between patients
and controls that might not be apparent at rest. Characterization of the gut microbiome in
patients with ME/CFS has demonstrated significant alterations compared to healthy controls
[21,22]. Additionally, systemic antibody responses to enteric microorganisms have been shown
to occur in patients with ME/CFS, suggesting that increased intestinal permeability and bacte-
rial translocation across the intestinal barrier may result in further inflammation and contrib-
ute to ME/CFS symptoms [9,23,24]. IgA antibody responses to enteric bacteria in ME/CFS
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 2/15
The funding agency has no role in the study design or
in the preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
patients have to date been associated with higher serum IL-1, TNFα, and neopterin levels, auto-
immune responses to serotonin, and increased symptoms of irritable bowel syndrome [25,26].
We hypothesized that the ecology of the gut microbiota of ME/CFS patients would differ from
that of matched healthy controls and that these differences would be associated with increased
bacterial translocation from the gut to the circulatory system following exercise challenge with
corresponding worsening of symptoms (i.e, pain, fatigue, and mood). The results presented
here add further to the previous findings suggesting that ME/CFS patients have an altered gut
microbiome and further suggest that increased bacterial translocation following exercise pro-
vides a potential explanation for the profound post-exertional malaise experienced by some
ME/CFS patients.
Methods
Study Subjects
We enrolled 10 ME/CFS patients and 10 healthy controls from the Madison and Marshfield,
WI areas from 122 potential subjects responding to study advertisements in local clinics. We
were able to reach 104 subjects by phone for screening, 45 of whom did not qualify. By phone,
13 potential patients and 46 potential controls were identified. We enrolled 10 ME/CFS
patients and 10 healthy controls from this group for the microbiome study. All patients met
the ME/CFS case definition criteria established by Fukuda et al in 1994 [13]. The control group
was comprised of healthy people who had no persistent complaints of fatigue. Patients and
controls were matched based on age, gender, BMI, and self-reported general activity patterns.
Subject age ranged between 20 and 60 years. Each subject gave written consent in an IRB
approved consent form to participate in the study. This study was approved by Marshfield
Clinic and University of WisconsinMadison Institutional Review Boards.
Inclusion and Exclusion Criteria
Standard medical history was reviewed and a physical exam was conducted for each subject to
rule out any major illnesses other than ME/CFS. Routine blood and urine chemistry tests were
also used to screen for exclusionary medical conditions or other conditions that may explain
the patientssymptoms. Exclusionary conditions included untreated hypothyroidism, sleep dis-
orders, side effects of medication, relapsing of past medical issues (e.g. Lyme disease, hepatitis
B or C), major psychiatric issues, including major depressive disorder with psychotic or melan-
cholic features, alcohol or other substance abuse within two years of the onset of ME/CFS, and
severe obesity as defined by a body mass index (BMI) of greater than 40 kg/m
2
. Potential par-
ticipants were also excluded if they had: 1) current use of immunomodulatory medications,
stool softeners, laxatives, anti-diarrheal agents, antibiotics, or probiotics, 2) current use of opi-
oids, 3) a history of cardiovascular disease or uncontrolled hypertension, or 4) current fatigue
sufficient to interfere with or preclude exercise testing. Patients were asked to confirm the
absence of exclusionary medications on the day of testing and to list other current medication
use. Although we did not explicitly exclude for low-dose antidepressant use, gastrointestinal
disease, or smoking, which could influence gut flora, none of the participants were currently
taking this class of medication, reported any gastrointestinal disorder, or were smokers. Fur-
ther, none of the participants were diagnosed with depression.
After completion of informed consent, study subjects underwent four non-consecutive days
of testing at the Exercise Psychology laboratory at the University of WisconsinMadison. Day
1 included a clinical interview and screening blood draw. Day 2 occurred approximately one
week later and involved pre-exercise symptom assessment, stool sample collection, maximal
exercise test, post-exercise symptom assessment, and blood draws. Expired gases, heart rate,
Microbiome after Exercise Challenge in ME/CFS
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and ratings of perceived exertion and leg muscle pain were collected during exercise and recov-
ery. Participants returned to the laboratory at 48 and 72 hours after exercise to complete self-
report symptom questionnaires and provide follow-up stool and blood samples.
Exercise Test
Participants performed a maximal exercise test on an electronically braked cycle ergometer
(Sensormedics, Loma Linda, CA) following the protocol previously described by Cook et al
[27]. Seat height was adjusted prior to exercise to the desired fit of the participant. Resistance
was software-controlled (Sensormedics, Loma Linda, CA) via an interface cable from a com-
puter to the ergometer. Participants were seated on the stationary bicycle for 1 minute of quiet
rest to acquaint themselves to breathing through the mouthpiece and the overall setup. Exercise
began with a 3-minute warm-up at 25 Watts (W). Participants were instructed to maintain a
pedal rate between 60 and 70 revolutions per minute (RPM) throughout the test. After the
warm-up period the work rate was increased by 5 W every 20 seconds (15 W/min) until voli-
tional exhaustion. The exercise test ended when either, 1) the participant could no longer keep
up the pedal rate of 6070 RPMs, 2) the participant stopped pedaling, or 3) some other factor
caused us to stop the test (e.g. testing became unsafe for the participant, equipment malfunc-
tion). Peak effort was determined based on meeting at least two of the following criteria: 1)
respiratory exchange ratio 1.1, 2) achievement of 85% of age-predicted maximum heart rate,
3) rating of perceived exertion (RPE) 17, and 4) a change in VO
2
of <200 ml with an
increase in work. Following peak effort, the workload was reduced to 20 W and the participant
underwent a three-minute active recovery phase maintaining the pedal rate of 6070 RPM.
After the three-minute recovery period, the participants were provided water ad libitum. Par-
ticipants were guided to a reclined chair for continued recovery. Blood was drawn at 15 min-
utes post peak effort and immediately followed by questionnaire completion.
Symptom Assessment
On day 1, participants completed the Centers for Disease Control Symptom Inventory [28]to
document the presence, frequency, and severity of the symptoms most commonly reported by
ME/CFS patients. On day 1 and all subsequent days, participants completed the 1) McGill Pain
Questionnaireshort-form (MPQ) [29], 2) Profile of Mood States (POMS) [30], 3) Fatigue
Visual Analog Scale (FVAS) [31], and the 4) Multidimensional Fatigue Inventory (MFI) [32]
both before and immediately, 48-hrs, and 72-hrs post exercise.
Sample Collection
A total of 80 blood samples (four per subject) were collected in PAXgene blood DNA collection
tubes (Qiagen) and 60 stool samples (three per subject) were collected separately in single use,
disposable, Protocult collection hats followed by self-transference to a 100 ml sterile capped
container (abcinc.org). Blood samples were collected at baseline (pre-exercise) and 15 minutes,
48 hours, and 72 hours post-exercise. Stool samples were collected at baseline (pre-exercise)
and 48 and 72 hours post-exercise. After collection, stool samples were immediately stored in a
freezer at -20°C or on dry ice before being transferred to an additional freezer for long-term
storage at -80°C. Frozen samples were sent for laboratory processing on dry ice.
Molecular Methods
Total genomic DNA was extracted from each clinical sample using the QIAamp DNA Blood
Mini Kit, Qiagen, Valencia, CA for blood samples and QIAamp DNA Stool Mini Kit (Qiagen)
Microbiome after Exercise Challenge in ME/CFS
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for stool samples. All DNA samples were quantified using the Qubit DSDNA HS assay kit (Life
Technologies, Grand Island, NY) and stored at -80°C until use. All barcoded primers were
designed using Roches User manual for the 454 sequencing platform and synthesized by Inte-
grated DNA Technologies (Ames, IA). DNA Samples were amplified using a mixture of bar-
coded 27F forward primers in a 4:1:1:1 ratio (27f-YM:27f-Bif:27f-Bor:27f-Chl) and reverse
primer 534R [33]. The 40 μl PCR mixture consisted of 20 pmol forward and reverse primers,
40 μg bovine serum albumin, 20 μl Hot Start mixture, and 2 μl template DNA. The PCR was
done in a PE9700 thermocycler (Applied Biosystems, Waltham, MA) with 25 cycles for the
stool samples and 40 cycles for the blood samples. Cycling conditions were as follows: initial
denaturation at 95°C for 15 min followed by the number of cycles described above (25 or 40) of
denaturation at 94°C for 30 seconds, annealing at 60°C for 30 seconds, and elongation at 72°C
for 40 seconds, with a final elongation time of 10 minutes. Each amplicon was normalized to
20 ng/μl using the SequalPrep Normalization Kit (Life Technologies, Grand Island, NY).
Amplicons were sequenced using the 454 GS FLX+ system using Titanium chemistry (Roche,
Branford, CT).
DNA Sequence Analysis
Polishing and bioinformatic analysis of 16S sequences was performed as previously described
[3437]. In brief, all pyrosequences were screened for nucleotide quality and sequences with
bases at 5and 3ends with mean Q <20 over a 10 nucleotide window, ambiguous bases with
any N residues, or shorter than minimum length (<300 nucleotides) were discarded. Chimera
screening was performed using the ChimeraSlayer tool, which requires that sequences be previ-
ously aligned with NAST-iEr (Hass BJ 2011). Putative chimeras and other sequences that could
not be aligned by NAST-iEr were removed from subsequent analyses. Genus-level taxonomic
calls were produced by the RDP Classifier [38], which performs naïve Bayesian taxonomic clas-
sification versus a training set. Operational taxonomic units (OTUs) were produced by cluster-
ing sequences with identical taxonomic assignments. Relative abundances of OTUs were
calculated for each subject by dividing the sequence counts observed for each OTU by the total
number of high-quality bacterial sequences generated for the subject. Goods coverage index
was calculated for each sample using the Explicet software package [39].
Statistical Analysis
Independent samples t-tests were used to determine group differences at baseline and during
exercise. A two group (ME/CFS and control) X 3 time (immediately post, 48-hrs post, and
72-hrs post) doubly-multivariate repeated measures MANOVA was used to analyze symptom
changes (i.e. post-exertional malaise (PEM)) from baseline. The overall MANOVA compared
the groups on a linear combination of symptom variables. This analysis was used to determine
the presence of PEM in our ME/CFS participants and is similar to the approach taken previ-
ously by our lab [18]. In brief, we chose variables a priori to be entered into the model. Variable
selection was based on three conditions: 1) the variables were not highly related, 2) they dis-
criminated between patients and controls, and 3) they were responsive to exercise. These
choices were made to avoid issues of multicollinearity and artificial inflation of variance
explained by the model. With this model we are able to determine whether symptoms are
changing over time (i.e. time main effect), whether they differ between ME/CFS and control
patients (i.e. group main effect), and whether the changes in symptoms over time differ
between the groups (i.e. group X time interaction). In addition to this omnibus test for PEM,
we also calculated effect sizes (d) [40] to illustrate the magnitude of each symptom change
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post-exercise. These analyses were conducted using SPSS for Windows (Version 22.0, Chicago,
IL).
We hypothesized that changes in relative abundances of bacterial OTUs in blood samples
from baseline to post-exercise challenge would differ between ME/CFS patients and healthy
controls. To test this hypothesis for each taxonomic group, we calculated the difference
between the pre-exercise OTU relative abundances and the average of the 48 hour and 72 hour
post-exercise counts for each individual. The change in sequence read counts was compared
between patients at ten taxonomic groups using four different statistical tests: (i) a T-test using
10,000 permutations to obtain P-values to evaluate the significance of the mean differences in
count change between patients and controls; (ii) to evaluate the difference in variances (of
change in sequence reads) between patients and controls, an F-test was performed, also with
10,000 permutations for the P-values and to compare distribution differences between patients
and controls, both (iii) a nonparametric Kolmogorov-Smirnov test was performed (no permu-
tations) and a (iv) Mann-Whitney U test with 10,000 permutations. These analyses were per-
formed using the XLISP-STAT programming language for the permutation routines and
Mathematica for the calculation of the Kolmogorov-Smirnov test.
Results
Phenotypic Characteristics
The phenotypic characteristics and fatigue measurements of patients and controls at baseline
are presented in Table 1. Age, weight, and height were similar for patients and healthy controls
and 80% of subjects were female in both groups. The patient group had greater fatigue, less
vigor, and scored more poorly on mood symptoms than controls at baseline (p<0.05). Particu-
larly germane to this study, only 3 of the 10 patients and 2 of the 10 controls reported gastroin-
testinal symptoms and all participants reported mild severity and symptoms present a little of
the time.All subjects completed a maximal exercise test as described above with results pre-
sented in Table 2. Results of maximal exercise testing were similar between the two groups,
except that average heart rate was significantly lower in the ME/CFS case group than in the
healthy control group.
Post-exertion Malaise
Self-reported symptoms and effect sizes for selected symptom changes pre- to post-exercise for
ME/CFS patients and controls are presented in Table 3. For a complete detailed analysis of all
symptoms pre- and post-exercise challenge please see Meyer et al [18]. For the doubly-multi-
variate repeated measures MANOVA, we compared symptom changes for fatigue (Fatigue
VAS), pain (MPQ total) and confusion (POMS Confusion subscale) at three points post-exer-
cise (immediate, 48-hrs, and 72-hrs). Results indicated a significant main effect of Time
[WilksΛ= 0.408; F(6,68) = 6.416; p = 0.000] and a significant Group x Time interaction
[WilksΛ= 0.611; F(6,68) = 3.163; p = 0.009], showing that symptoms were changing from
pre- to post-exercise and that these changes were different between ME/CFS and control
patients. Effect size calculations showed that ME/CFS patients had large changes in their symp-
toms of pain, fatigue, and confusion at various times post-exercise compared to controls.
Microbiome Characteristics
A total of 406,880 high quality sequencing reads were generated with a median of 2,160 reads
per specimen. The sequencing coverage at the genus level as estimated by Goods coverage
mean index was 95% for sequence libraries from all individual blood and stool samples,
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 6/15
indicating that each sequence dataset adequately captured the underlying biodiversity in the
sample from which it was generated. The blood samples, as expected, yielded a lower number
of bacterial sequences (n = 111,492; 1,394 per sample) than the stool samples (n = 295,388;
4,923 reads per sample).
Table 2. Maximal Exercise Test Results for ME/CFS Patients (n = 10) and Health Controls (n = 10).
Results at Peak Exercise ME/CFS Patients, mean (±SD) Controls, mean (±SD)
VO
2peak
(ml/kg/min) 28.6 (±9.0) 28.2 (±9.6)
Respiratory Exchange Ratio 1.19 (±0.10) 1.20 (±0.11)
Rating of Perceived Exertion 18.2 (±2.0) 16.4 (±2.7)
Leg Muscle Pain 5.6 (±3.3) 4.2 (±1.6)
Heart Rate (beats per minute) 159.0 (±16.5)
a
178.8 (±6.8)
Time to Fatigue (min) 11.7 (±2.8) 13.1 (±3.4)
ME/CFS, myalgic encephalomyelitis/chronic fatigue syndrome; SD, standard deviation
a
Signicantly different from control (p<0.05)
doi:10.1371/journal.pone.0145453.t002
Table 1. Baseline Characteristics of ME/CFS Patients (n = 10) and Healthy Controls (n = 10).
Characteristics ME/CFS Patients, mean (±SD) Controls, mean (±SD)
Age (yrs) 48.6 (±10.5) 46.5 (±13.0)
Height (in) 66.6 (±3.7) 64.4 (±4.1)
Weight (lbs) 150.7 (±30.3) 144.3 (±18.3)
BMI (kg/m
2
)23.9 (±4.3) 24.6 (±3.3)
Multidimensional Fatigue Inventory (MFI) Scores
General 14.6 (±1.1)
a
10.3 (±1.9)
Physical 15.4 (±4.0)
a
6.8 (±2.7)
Reduced Activity 14.0 (±5.3)
a
7.6 (±3.4)
Reduced Motivation 9.4 (±2.3)
a
6.3 (±2.9)
Mental 14.5 (±3.4)
a
7.6 (±3.4)
Prole of Mood States (POMS) Scores
Tension 7.5 (±4.6) 4.4 (±3.5)
Depression 7.5 (±10.5) 5.2 (±9.1)
Anger 4.8 (±7.2) 3.4 (±4.7)
Vigor 8.6 (±3.4)
a
18.9 (±4.9)
Fatigue 14.5 (±4.8)
a
4.8 (±6.0)
Confusion 9.1 (±3.6)
a
4.7 (±4.6)
Total Mood Disturbance 134.8 (±25.6)
a
103.6 (±28.7)
McGill Pain Questionnaire 6.8 (±5.8) 2.5 (±5.2)
CDC Symptom Inventory: Abdominal Symptoms
Diarrhea 0.6 (±1.0) 0.3 (±0.7)
Stomach/Abdominal Pain 1.1 (±2.1) 0.0
CDC Symptom Inventory: Neurocognitive Symptoms
Memory Problems 4.0 (±3.0) 0.2 (±0.7)
Concentration Problems 4.7 (±2.8) 0.5 (±1.6)
ME/CFS, myalgic encephalomyelitis/chronic fatigue syndrome; SD, standard deviation.
a
Signicantly different from control (p<0.05)
doi:10.1371/journal.pone.0145453.t001
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Average relative abundances of the bacterial taxa observed in all blood and stool samples
collected from patients and controls are shown in Table 4 with pooling for all time points. In
blood samples, there was a lower relative abundance of Bacteroidetes and higher relative abun-
dance of Firmicutes observed in ME/CFS patients than in healthy controls. In contrast, in the
stool samples there was a higher relative abundance of Bacteroidetes and lower abundance of
Firmicutes observed in ME/CFS patients compared to healthy controls. The relative abundance
of Actinobacteria in the gut was significantly lower in ME/CFS patients than healthy controls
by a Kolmogorov-Smirnov test (p = 0.007). However, differences were not found to be statisti-
cally significant for other bacterial taxa in the blood or stool samples using a non-parametric
test to account for both prevalence and abundance [41].
Microbiome Response to Maximal Exercise
Changes in average relative abundances of bacterial taxa in stool samples were observed follow-
ing maximal exercise testing and these changes were different in ME/CFS patients and healthy
controls. The average relative abundance of 7 out of 9 major taxa increased in the stool in
patients from baseline to 72 hours post-exercise compared to an increase in only 2 of the 9
major phyla/genera in healthy controls (p = 0.005) (Fig 1). In contrast to the ME/CFS patients,
the relative abundance of most major phyla decreased at 72 h in the stool samples from healthy
Table 3. Symptom Responses to Maximal Exercise for ME/CFS Patients (n = 10) and Healthy Controls (n = 10).
Group Means (±SD) Effect sizes (d) for change
scores
Measure Group Pre-Ex Post-Ex 48-hr 72-hr Post-Ex 48-hr 72-hr
Fatigue VAS ME/CFS 50.4 (23.3) 67.1 (15.0) 59.5 (23.7) 57.3 (20.0) 0.25 0.91 0.96
Control 12.3 (12.7) 25.0 (14.5) 7.9 (8.5) 4.1 (6.3) - - -
MPQ Total ME/CFS 6.9 (5.8) 10.2 (6.9) 7.8 (6.1) 5.4 (4.5) 1.23 0.44 -0.31
Control 0.8 (1.1) 1.4 (1.1) 0.6 (0.8) 0.2 (0.4) - - -
POMS Confusion ME/CFS 9.1 (3.6) 12.6 (3.9) 11.3 (4.0) 11.1 (4.9) 1.26 0.85 1.16
Control 4.1 (3.3) 3.3 (3.1) 3.7 (2.7) 2.4 (2.0) - - -
ME/CFS, myalgic encephalomyelitis/chronic fatigue syndrome; SD, standard deviation; VAS, visual analog scale; MPQ, McGill Pain Questionnaire;
POMS, Prole of Mood States; Ex, exercise.
doi:10.1371/journal.pone.0145453.t003
Table 4. Relative Abundance of Bacterial Phyla in Blood and Stool Samples from ME/CFS Patients and Health Controls.
Blood Samples
a
Stool Samples
b
Phyla ME/CFS Patients Controls ME/CFS Patients Controls
Actinobacteria 10.52% 10.80% 0.58%
c
1.06%
Bacteroidetes 17.84% 22.38% 27.71% 22.43%
Firmicutes 9.52% 7.89% 58.40% 65.29%
Other 7.97% 8.55% 9.71% 9.11%
Proteobacteria 54.14% 50.38% 3.59% 2.12%
ME/CFS, myalgic encephalomyelitis/chronic fatigue syndrome.
a
Mean relative abundance for 10 case and 10 control samples collected at baseline and 15 min, 48 hours, and 72 hours post-exercise challenge.
b
Mean relative abundance for 10 case and 10 control samples collected at baseline and 48 and 72 hours post-exercise challenge.
c
Signicantly different from control (p<0.05)
doi:10.1371/journal.pone.0145453.t004
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 8/15
controls suggesting that the bacterial load in ME/CFS patients is preferentially enhanced dur-
ing post-exertional malaise.
Given the high relative abundance of Firmicutes in stool samples, we further assessed the
potential for translocation of organisms from this phylum into the bloodstream after exercise
challenge. Differential changes in the relative abundance of Firmicutes/Bacilli organisms were
observed in blood and stool samples over time (Fig 2). Of note is the significant increase in the
relative abundance of Bacilli in blood samples collected from ME/CFS patients at the 48 hour
time point. We also observed rapid changes in the relative abundances of the Clostridium XIVa
and IV clusters, belonging to the phylum Firmicutes, in blood samples collected 15 min after
maximal exercise from ME/CFS patients, but not healthy controls (Fig 3). We speculate that
these bacteria may have translocated into the blood stream from the gut after the maximal
exercise challenge. This phenomenon appears to be specific to particular taxa and more promi-
nent in patients than controls.
Discussion
Neuroinflammation and oxidative dysregulation have been shown to occur in patients with
ME/CFS [5]. Although, differential intestinal microbiome characteristics have been described
Fig 1. Relative abundance of the major bacterial taxa in stool samples collected at baseline (0 hr) and 72 hr after exercise challenge in ME/CFS
patients and healthy controls.
doi:10.1371/journal.pone.0145453.g001
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 9/15
for ME/CFS patients and healthy controls [21] and systemic antibody responses to enteric bac-
teria have been associated with increased inflammation, worsening fatigue, and gastrointestinal
symptoms [25,26], the potential for transient changes in bacterial colonization in the gut and/
or bloodstream to modulate symptomology has not been evaluated in ME/CFS patients. The
evidence of altered intestinal microbiota and bacterial translocation into the bloodstream fol-
lowing exercise challenge in patients with ME/CFS is consistent with previous findings and
provides novel evidence of systemic bacterial signal and exercise induced bacterial transloca-
tionone potential explanation for the worsening of symptoms seen in patients when they
attempt to become more physically active.
Fig 2. Changes in the relative abundance of Firmicutes/Bacilli in blood and stool samples before (0 hr) and after maximal exercise.
doi:10.1371/journal.pone.0145453.g002
Fig 3. Relative abundance of (A) Firmicutes/Clostridia/.../LachnoXIVa and (B) Firmicutes/Clostridia/.../LachnoIV in blood samples collected at
baseline and 15 minutes after exercise challenge in ME/CFS patients and healthy controls.
doi:10.1371/journal.pone.0145453.g003
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 10 / 15
Over the last several years our understanding of how alterations in the human microbiome
influence health and disease has increased considerably [42,43]. Murine models suggest that
the gut microbiota could contribute to leanness, obesity, stress, and emotional behavior
through endocrine and neuroendocrine pathways [44] and considerable evidence suggests sim-
ilar effects in humans via effects on host immunity and metabolism [45,46]. The differences in
the relative abundance of bacteria making up the intestinal microbiome noted in the present
study are consistent with those previously reported in the literature [21]. Although no typical
enterotype has been defined to date, reports of dysbiosis in patients with ME/CFS in Norway
and Belgium are consistent with the changes in relative abundance of Firmicutes and Bacteroi-
detes observed here [21].
Perhaps more important than confirmation of intestinal dysbiosis in the context of ME/CFS
is the evidence of temporal changes in microbiome composition following maximal exercise
challenge and the novel finding of bacterial signal in the bloodstream of both ME/CFS patients
and healthy controls occurring concomitantly with symptom exacerbation. This finding is con-
sistent with the systemic response to enteric microorganisms reported by Maes et al in a num-
ber of reports and demonstration of correlation between this systemic response and
pathological ME/CFS processes and symptoms, including higher serum IL-1, TNFα, and neop-
terin levels, autoimmune responses to serotonin, and increased symptoms of irritable bowel
syndrome [9,2326]. In the present study, changes in microbiome and bacterial measures were
accompanied by large changes in fatigue, pain, and confusion in ME/CFS patients. Such exer-
cise-induced changes in the microbiome are consistent with the changes described above and
additional recent reports of altered immune response to exercise in patients with ME/CFS,
while also providing a plausible mechanistic explanation for such alterations [19].
Although intestinal microbiome composition is known to change over time with develop-
ment and aging as well as in response to dietary changes, temporal examination of the human
intestinal microbiome generally spans weeks, months, or even years to assess evidence of an
interventions effect. The notion that exercise might influence gut microbiota composition has
been described in both animal and human models [4751]. The evidence presented here sug-
gests that not only does physical activity influence gut microbiota composition, but that the
temporal effects of such physical activity may manifest differently in healthy and diseased indi-
viduals. These changes are one potential explanation for why acute exercise may make some
individuals with ME/CFS sicker.
Blood is generally considered sterile, although evidence of transient, asymptomatic bacter-
emia has been reported following dental extraction [52] and intestinal insult [53]. In the con-
text of ME/CFS, systemic responses to gut microorganisms suggest that bacterial translocation
across the intestinal barrier may also occur as part of this disease [9,2326]. The notion that
exercise may also result in translocation of bacteria across the intestinal barrier is particularly
interesting, especially in the case of ME/CFS where post-exertional malaise can be a key charac-
teristic of the disease. Following maximal exercise testing, we detected bacterial signal in blood
samples from both ME/CFS patients and healthy controls. Consistent with differences in the
intestinal microbiomes between the two groups, we noted an increased relative abundance of
Firmicutes, particularly those from Clostridium clusters XIVa and IV, in blood samples from
ME/CFS patients at 15 minutes post-exercise challenge. In vitro functional studies will be able
to address this observation better. However, we speculate that some members of Firmicutes
and Bacilli because of their stronger cell walls and inherent ability to survive in harsher envi-
ronmental conditions may have contributed to it surviving longer in bloodstream. Further
investigation of the potential for transient translocation of intestinal microorganisms into the
bloodstream following exercise and how the dysbiosis characteristic of certain disease states,
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 11 / 15
such as ME/CFS, might influence this translocation may provide considerable insight into how
the microbiome influences disease symptoms.
Evidence for altered intestinal permeability in patients with ME/CFS has been mounting [9]
and preliminary studies suggest that treatments designed to modulate the gut microbiota or
enhance intestinal barrier function may be able to improve ME/CFS symptoms [24,5456].
Our ability to detect changes in intestinal microbiome composition over time and to observe
what appears to be transient bacterial translocation from the gut into the bloodstream follow-
ing exercise challenge may provide a protocol for testing future treatments designed to alter
these outcomes and to determine whether this is the mechanism of action for such treatments.
Treatment paradigms that have been tested with some success for other chronic, inflammatory,
non-communicable diseases thought to be related to intestinal dysbiosis include probiotics,
prebiotics, dietary fiber, and fecal microbiotia transplantation [46,57]. Similar trials in ME/CFS
patients may benefit from temporal monitoring for bacterial signal in both the gastrointestinal
tract and bloodstream.
Proper diagnosis of ME/CFS is a very involved and thorough process. One of the strengths
of this study was that clinicians with expertise in diagnosing ME/CFS were engaged in identi-
fying both patients and controls from a cohort of more than 100 possible patients. Identified
patients were included only after examining results of screening blood tests to rule out any
major comorbid conditions that could explain the ME/CFS symptoms and controls were
carefully selected for matching based on age, gender, BMI, and self-reported general activity
patterns. However, given that this was a small pilot study with only 10 patients and 10 con-
trols it was not possible to control for all potential confounders, including the broad age
range of participants, historical use of medications and supplements (e.g. pain killers, antioxi-
dants), and additional medical diagnoses (e.g. depression, gastrointestinal symptoms, aller-
gies). While the careful selection process allowed for high quality case and control
populations, the study sample size was small and as such, many observations failed to rise to
the level of statistical significance. Additionally, the small sample size precluded us from
directly examining the associations between symptoms and changes to the gut and plasma
microbiome. Given these limitations, findings must be interpreted with caution. An increase
in sample size would help us to better assess clinically relevant observations. However, even
this relatively small study points to important temporal differences in intestinal microbiome
composition and transient bacteremia that will inform future larger studies designed to
understand how these differences relate to ME/CFS etiology and/or symptomology. Another
study limitation was in the depth of microbiome sequencing. Our approach did satisfy the
Goods coverage index of >0.95, but additional deep sequencing of the samples already col-
lected would likely increase the statistical power to detect significant changes in the rarer bac-
terial taxa.
We are still a long way from fully understanding how the intestinal microbiota impacts eti-
ology and symptomology in ME/CFS, but the evidence presented here and elsewhere suggests
that changes in gut microbiome are associated with this disease. Here, we present additional
evidence to support the idea that temporal changes in microbial composition in the gut and
translocation of gut microbes into the bloodstream may influence the symptoms of ME/CFS.
Future studies of ME/CFS etiology and treatment approaches should incorporate temporal
microbial analyses to further elucidate this interesting finding.
Acknowledgments
We are grateful to participants of the study for their time and commitment. We also thank
Rachel Stankowski for her excellent help in preparation and editing of this manuscript.
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 12 / 15
Author Contributions
Conceived and designed the experiments: SKS D. Cook DNF. Performed the experiments: JM
TL CER D. Cook D. Clevidence SY. Analyzed the data: TL CER DNF SJS D. Cook JM SY SDV
SKS. Contributed reagents/materials/analysis tools: DNF. Wrote the paper: SKS SJS D. Cook
DNF SDV SY.
References
1. Centers for Disease Control and Prevention. Chronic Fatigue Syndrome (CFS): Whos at Risk? Avail-
able: http://www.cdc.gov/cfs/causes/risk-groups.html.
2. Solomon L, Nisenbaum R, Reyes M, Papanicolaou DA, Reeves WC. Functional status of persons with
chronic fatigue syndrome in the Wichita, Kansas, population. Health Qual Life Outcomes. 2003; 1: 48.
PMID: 14577835
3. Jason LA, Benton MC, Valentine L, Johnson A, Torres-Harding S. The economic impact of ME/CFS:
individual and societal costs. Dyn Med. 2008; 7: 6. doi: 10.1186/1476-5918-7-6 PMID: 18397528
4. Reynolds KJ, Vernon SD, Bouchery E, Reeves WC. The economic impact of chronic fatigue syndrome.
Cost Eff Resour Alloc. 2004; 2: 4. PMID: 15210053
5. Morris G, Berk M, Galecki P, Walder K, Maes M. The neuro-immune pathophysiology of central and
peripheral fatigue in systemic immune-inflammatory and neuro-immune diseases. Mol Neurobiol. 2015
Jan 20. [Epub ahead of print]
6. Maes M. Inflammatory and oxidative and nitrosative stress pathways underpinning chronic fatigue,
somatization and psychosomatic symptoms. Curr Opin Psychiatry. 2009; 22: 7583. PMID: 19127706
7. Maes M, Twisk FN. Chronic fatigue syndrome: Harvey and Wessleys (bio)psychosocial model versus
a bio(psychosocial) model based on inflammatory and oxidative and nitrosative stress pathways. BMC
Med. 2010; 8: 35. doi: 10.1186/1741-7015-8-35 PMID: 20550693
8. Morris G, Maes M. A neuro-immune model of myalgic encephalomyelitis/chronic fatigue syndrome.
Metab Brain Dis. 2013; 28: 523540. doi: 10.1007/s11011-012-9324-8 PMID: 22718491
9. Morris G, Maes M. Oxidative and nitrosative stress and immune-inflammatory pathways in patients with
myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS). Curr Neuropharmacol 2014; 12:
168185. doi: 10.2174/1570159X11666131120224653 PMID: 24669210
10. Morris G, Berk M, Galecki P, Maes M. The emerging role of autoimmunity in myalgic encephalomyelitis/
chronic fatigue syndrome (ME/cfs). Mol Neurobiol. 2014; 49: 741756. doi: 10.1007/s12035-013-8553-
0PMID: 24068616
11. Morris G, Maes M. Mitochondrial dysfunctions in myalgic encephalomyelitis/chronic fatigue syndrome
explained by activated immuno-inflammatory, oxidative and nitrosative stress pathways. Metab Brain
Dis. 2014; 29: 1936. doi: 10.1007/s11011-013-9435-x PMID: 24557875
12. Morris G, Maes M. Case definitions and diagnostic criteria for myalgic encephalomyelitis and chronic
fatigue syndrome: from clinical-consensus to evidence-based case definitions. Neuro Endocrinol Lett.
2013; 34: 185199. PMID: 23685416
13. Fukuda K, Straus SE, Hickie I, Sharpe MC, Dobbins JG, Komaroff A, et al. The chronic fatigue syn-
drome: a comprehensive approach to its definition and study. Ann Intern Med. 1994; 121: 953959.
PMID: 7978722
14. Maes M, Leunis JC, Geffard M, Berk M. Evidence for the existence of myalgic encephalomyelitis/
chronic fatigue syndrome (ME/CFS) with and without abdominal discomfort (irritable bowel) syndrome.
Neuro Endocrinol Lett. 2014; 35: 445453. PMID: 25433843
15. Maes M, Twisk FN, Johnson C. Myalgic encephalomyelitis (ME), chronic fatigue syndrome (CFS), and
chronic fatigue (CF) are distinguished accurately: results of supervised learning techniques applied on
clinical and inflammatory data. Psychiatry Res. 2012; 200: 754760. doi: 10.1016/j.psychres.2012.03.
031 PMID: 22521895
16. Cook DB, Nagelkirk PR, Peckerman A, Poluri A, Lamanca JJ, Natelson BH. Perceived exertion in
fatiguing illness: civilians with chronic fatigue syndrome. Med Sci Sports Exerc. 2003; 35: 563568.
PMID: 12673137
17. Light AR, White AT, Hughen RW, Light KC. Moderate exercise increases expression for sensory,
adrenergic, and immune genes in chronic fatigue syndrome patients but not in normal subjects. J Pain.
2009; 10: 10991112. doi: 10.1016/j.jpain.2009.06.003 PMID: 19647494
18. Meyer J, Light A, Shukla SK, Clevidence D, Yale S, Stegner AJ, et al. Post-exertion malaise in chronic
fatigue syndrome: symptoms and gene expression. Fatigue: Biomedicine, Health, & Behavior. 2013; 1:
190209.
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 13 / 15
19. Nijs J, Nees A, Paul L, De Kooning M, Ickmans K, Meeus M, et al. Altered immune response to exercise
in patients with chronic fatigue syndrome/myalgic encephalomyelitis: a systematic literature review.
Exerc Immunol Rev. 2014; 20: 94116. PMID: 24974723
20. Natelson BH, Haghighi MH, Ponzio NM. Evidence for the presence of immune dysfunction in chronic
fatigue syndrome. Clin Diagn Lab Immunol. 2002; 9: 747752. PMID: 12093668
21. Frémont M, Coomans D, Massart S, De Meirleir K. High-throughput 16S rRNA gene sequencing
reveals alterations of intestinal microbiota in myalgic encephalomyelitis/chronic fatigue syndrome
patients. Anaerobe. 2013; 22: 5056. doi: 10.1016/j.anaerobe.2013.06.002 PMID: 23791918
22. Lakhan SE, Kirchgessner A. Gut inflammation in chronic fatigue syndrome. Nutr Metab (Lond). 2010; 7:
79.
23. Maes M, Leunis JC. Normalization of leaky gut in chronic fatigue syndrome (CFS) is accompanied by a
clinical improvement: effects of age, duration of illness and the translocation of LPS from gram-negative
bacteria. Neuro Endocrinol Lett. 2008; 29: 902910. PMID: 19112401
24. Maes M, Mihaylova I, Leunis JC. Increased serum IgA and IgM against LPS of enterobacteria in chronic
fatigue syndrome (CFS): indication for the involvement of gram-negative enterobacteria in the etiology
of CFS and for the presence of an increased gut-intestinal permeability. J Affect Disord. 2007; 99: 237
240. PMID: 17007934
25. Maes M, Twisk FN, Kubera M, Ringel K, Leunis JC, Geffard M. Increased IgA responses to the LPS of
commensal bacteria is associated with inflammation and activation of cell-mediated immunity in chronic
fatigue syndrome. J Affect Disord. 2012; 136: 909917. doi: 10.1016/j.jad.2011.09.010 PMID:
21967891
26. Maes M, Ringel K, Kubera M, Anderson G, Morris G, Galecki P, Geffard M. In myalgic encephalomyeli-
tis/chronic fatigue syndrome, increased autoimmune activity against 5-HT is associated with immune-
inflammatory pathways and bacterial translocation. J Affect Disord. 2013; 150: 223230. doi: 10.1016/j.
jad.2013.03.029 PMID: 23664637
27. Cook DB, Stegner AJ, Nagelkirk PR, Meyer JD, Togo F, Natelson BH. Responses to exercise differ for
chronic fatigue syndrome patients with fibromyalgia. Med Sci Sports Exerc. 2012; 44: 11861193. doi:
10.1249/MSS.0b013e3182417b9a PMID: 22157881
28. Wagner D, Nisenbaum R, Heim C, Jones JF, Unger ER, Reeves WC. Psychometric properties of the
CDC Symptom Inventory for assessment of chronic fatigue syndrome. Popul Health Metr. 2005; 3: 8.
PMID: 16042777
29. Melzack R. The short-form McGill pain questionnaire. Pain. 1987; 30: 191197. PMID: 3670870
30. McNair DM, Lorr M, Droppleman LF. Profile of mood states: manual. San Diego, California: Educa-
tional and Industrial Testing Services; 1992.
31. OConnor PJ. Mental energy: assessing the mood dimension. Nutr Rev. 2006; 64: S7S9. PMID:
16910215
32. Smets EM, Garssen B, Bonke B, Haes JC. The multidimensional fatigue inventory (MFI) psychometric
qualities of an instrument to assess fatigue. J Psychosom Res. 1995; 39: 315325. PMID: 7636775
33. Frank JA, Reich CI, Sharma S, Weisbaum JS, Wilson BA, Olsen GJ. Critical evaluation of two primers
commonly used for amplification of bacterial 16S rRNA genes. Appl Environ Microbiol. 2008; 74: 2461
2470. doi: 10.1128/AEM.02272-07 PMID: 18296538
34. Frank DN, Manigart O, Leroy V, Meda N, Valéa D, Zhang W, et al. Altered vaginal microbiota are asso-
ciated with perinatal mother-to-child transmission of HIV in African women from Burkina Faso. J Acquir
Immune Defic Syndr. 2012; 60: 299306. doi: 10.1097/QAI.0b013e31824e4bdb PMID: 22343176
35. Krebs NF, Sherlock LG, Westcott J, Culbertson D, Hambidge KM, Feazel LM, et al. Effects of different
complementary feeding regimens on iron status and enteric microbiota in breastfed infants. J Pediatr.
2013; 163: 416423. doi: 10.1016/j.jpeds.2013.01.024 PMID: 23452586
36. Li E, Hamm CM, Gultai AS, Sartor RB, Chen H, Wu X, et al. Inflammatory bowel diseases phenotype,
C. difficile and NOD2 genotype are associated with shifts in human ileum associated microbial compo-
sition. PLoS One. 2012; 7: e26284. doi: 10.1371/journal.pone.0026284 PMID: 22719818
37. Ramakrishnan VR, Hauser LJ, Feazel LM, Ir D, Robertson CE, Frank DN. Sinus microbiota varies
among chronic rhinosinusitis phenotypes and predicts surgical outcome. J Allergy Clin Immunol. 2015
Mar 25. [Epub ahead of print]
38. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA
sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007; 73: 52615267. PMID:
17586664
39. Robertson CE, Harris JK, Wagner BD, Granger D, Browne K, Tatem B, Feazel LM, Park K, Pace NR,
Frank DN. Explicet: graphical user interface software for metadata-driven management, analysis and
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 14 / 15
visualization of microbiome data. Bioinformatics. 2013; 29: 31003101. doi: 10.1093/bioinformatics/
btt526 PMID: 24021386
40. Cohen J. Statistical power analysis for the behavioral sciences. 1st ed. New York and London: Aca-
demic Press; 1969.
41. Wagner BD, Robertson CE, Harris JK. Application of two-part statistics for comparison of sequence
variant counts. PLoS One. 2011; 6: e20296. doi: 10.1371/journal.pone.0020296 PMID: 21629788
42. Frank DN, Zhu W, Sartor RB, Li E. Investigating the biological and clinical significance of human dys-
bioses. Trends Microbiol. 2011; 19: 427434. doi: 10.1016/j.tim.2011.06.005 PMID: 21775143
43. Shukla SK, Murali N, Brilliant M. Personalized medicine going precise: from genomics to microbiomics.
Trends Mol Med. 2015 Jun 27. [Epub ahead of print]
44. Mayer EA, Tillisch K, Gupta A. Gut/brain axis and the microbiota. J Clin Invest. 2015; 125: 926938.
doi: 10.1172/JCI76304 PMID: 25689247
45. Maranduba CM, De Castro SB, de Souza GT, Rossato C, de Guia FC, Valente MA, et al. Intestinal
microbiota as modulators of the immune system and neuroimmune system: impact on host health and
homeostasis. J Immunol Res. 2015; 2015: 931574. doi: 10.1155/2015/931574 PMID: 25759850
46. West CE, Renz H, Jenmalm MC, Kozyrskyi AL, Allen KJ, Vuillermin P, et al. The gut microbiotaand
inflammatory noncommunicable disease: associations and potentials for gut microbiota therapies. J
Allergy Clin Immunol. 2015; 135: 313. doi: 10.1016/j.jaci.2014.11.012 PMID: 25567038
47. OSullivan O, Cronin O, Clarke SF, Murphy EF, Molloy MG, Shanahan F, et al. Exercise and the micro-
biota. Gut Microbes. 2015; 6: 131136. doi: 10.1080/19490976.2015.1011875 PMID: 25800089
48. Evans CC, LePard KJ, Kwak JW, Stancukas MC, Laskowski S, Dougherty J, et al. Exercise prevents
weight gain and alters the gut microbiota in a mouse model of high fat diet-induced obesity. PLoS One.
2014; 9: e92193. doi: 10.1371/journal.pone.0092193 PMID: 24670791
49. Mika A, Van Treuren W, González A, Herrera JJ, Knight R, Fleshner M. Exercise is more effective at
altering gut microbial composition and producing stable changes in lean mass in juvenile versus adult
male F344 rates. PLoS One. 2015; 10: e0125889. doi: 10.1371/journal.pone.0125889 PMID:
26016739
50. Lambert JE, Myslicki JP, Bomhof MR, Belke DD, Shearer J, Reimer RA. Exercise training modifies gut
microbiota in normal and diabetic mice. Appl Physiol Nutr Metab. 2015; 40: 749752. doi: 10.1139/
apnm-2014-0452 PMID: 25962839
51. Clarke SF, Murphy EF, OSullivan O, Lucey AJ, Humphreys M, Hogan A, et al. Exercise and associated
dietary extremes impact on gut microbial diversity. Gut. 2014; 63: 19131920. doi: 10.1136/gutjnl-
2013-306541 PMID: 25021423
52. Benítez-Páez A, Álvarez M, Belda-Ferre P, Rubido S, Mira A, Tomás I. Detection of transient bacterae-
mia following dental extractions by 16S rDNA by pyrosequencing: a pilot study. PLoS One. 2013; 8:
e57782. doi: 10.1371/journal.pone.0057782 PMID: 23469240
53. Lee TH, Hsueh PR, Yeh WC, Wang HP, Wang TH, Lin JT. Low frequency of bacteremia after endo-
scopic mucosal resection. Gastrointest Endosc. 2000; 52: 223225. PMID: 10922095
54. Maes M, Coucke F, Leunis JC. Normalization of the increased translocation of endotoxin from gram
negative enterobacteria (leaky gut) is accompanied by a remission of chronic fatigue syndrome. Neuro
Endocrinol Lett. 2007; 28: 739744. PMID: 18063928
55. Proal AD, Albert PJ, Marshall TG, Blaney GP, Lindseth IA. Immunostimulation in the treatment for
chronic fatigue syndrome/myalgic encephalomyelitis. Immunol Res. 2013; 56: 398412. doi: 10.1007/
s12026-013-8413-z PMID: 23576059
56. Rao AV, Bested AC, Beauline TM, Katzman MA, Iorio C, Berardi JM, et al. A randomized, double-blind,
placebo-controlled pilot study of a probiotic in emotional symptoms of chronic fatigue syndrome. Gut
Pathog. 2009; 1: 6. doi: 10.1186/1757-4749-1-6 PMID: 19338686
57. Ianiro G, BibbòS, Gasbarrini A, Cammarota G. Therapeutic modulation of gut microbiota: current clini-
cal applications and future perspectives. Curr Drug Targets. 2014; 15: 762770. PMID: 24909808
Microbiome after Exercise Challenge in ME/CFS
PLOS ONE | DOI:10.1371/journal.pone.0145453 December 18, 2015 15 / 15
... Fig. 1 illustrates the PRISMA flow diagram of the search process. A total of 12,503 articles were detected across the four databases, and after removing the duplicates and noneligible articles, 91 studies were included in this systematic review: 50 observational studies (all cross-sectional), 37-86 9 studies reported the acute effects of PA (e.g., following a marathon, rowing, etc.) on microbiome, 25,[87][88][89][90][91][92][93][94] and 32 studies reported the chronic effects of PA on microbiome (17 non-RCT, 13 RCT and 2 randomized controlled cross-over trials). 24, Of the 50 cross-sectional studies, 8 were eligible (based on availability of microbiome diversity data and healthy participants) for the first meta-analysis comparing groups of high and low PA levels in non-athletes, 41,42,44,46,53,73,76,84 and 11 were included in the second meta-analysis comparing athletes vs. non-athletes. ...
... Three studies detected significant differences in alpha-diversity, 90,91,93 while 8 studies described significant changes in the relative abundance of certain bacteria after acute PA interventions. 25,87,88,[90][91][92][93][94] Seventeen studies detected significant differences in alpha-and/or betadiversity, 24,95,97,98,101,103,106,109,110,112,115,118,119,121,122,124,125 and 24 studies described significant changes in the relative abundance of certain bacteria after chronic PA interventions. 24,95,97,[99][100][101][102][103][105][106][107]109,110,[113][114][115][116][118][119][120][121][122][123]125 The sample sizes ranged from 1 91 to 2183. ...
... 39,41,71,79 Twenty-two cross-sectional studies recruited athletes from different sports such as rugby, athletics or football, among others. [56][57][58][59][60][61][62][63][64][65][66][67][68][69][80][81][82][83][84][85][86]112 Six studies analyzed the effects of a marathon, footrace or rowing race on microbiomes, 25,88,90,91,93,94 three reported the effect of a single bout of PA (i.e., no sport competition) on microbiomes 87,89,92 and 32 conducted a long-term PA intervention ranged from two weeks 99 to thirty-four weeks, 122 mostly consisting of aerobic training 24,95,98,99,105,106,111,113,114,116,[118][119][120]122,125 or a combination of aerobic and resistance training. 96,97,[100][101][102][103][104][107][108][109][110]112,115,117,121,123,124 Most of the studies analyzed the gut microbiome, with the exception of ten which collected saliva, oral, oropharyngeal, muscle, blood or vaginal samples. ...
... Although these observations may suggest the role of gut dysbiosis in symptom development [53], a specific microbial signature has not yet been found [2]. Investigations of the microbiome dysbiosis have been intensively studied in ME/CFS, with results indicating a potential link between changes in gut composition, including deficient butyrate-producing capacity and bacterial network disturbances, increased immune activation [54][55][56] and symptoms of fatigue symptoms [2,57]. The decreased availability of butyrate, along with other key mediators of the gut-brain axis produced during microbial fermentation, including serotonin and γ-aminobutyric acid (GABA) via the influence of intestinal permeability, may have considerable impact on the neuroimmunoendocrine system [58]. ...
... The decreased availability of butyrate, along with other key mediators of the gut-brain axis produced during microbial fermentation, including serotonin and γ-aminobutyric acid (GABA) via the influence of intestinal permeability, may have considerable impact on the neuroimmunoendocrine system [58]. In addition, studies from physically fit populations have indicated a strong correlation between increased gut microbial diversity, high fecal butyrate levels and extent of physical fitness, characterized by higher VO 2 peaks and, consequently, reduced LPS levels and lower inflammation [59], thereby suggesting that the decreased abundance of anti-inflammatory bacteria, such as Firmicutes and Actinobacteria [54] along with butyrate-producer bacteria, may result in PEM followed by inactivity and increased LPS-induced inflammation in ME/CFS. In addition, ME/CFS patients, compared with healthy controls, seem to also have higher levels of pro-inflammatory Gram-negative bacteria species, such as Alistipes, Bacteroidetes and Enterobacteriaceae [54,56,60], which may worsen the disease course, leading to an increased severity of fatigue, pain and confusion [54]. ...
... In addition, studies from physically fit populations have indicated a strong correlation between increased gut microbial diversity, high fecal butyrate levels and extent of physical fitness, characterized by higher VO 2 peaks and, consequently, reduced LPS levels and lower inflammation [59], thereby suggesting that the decreased abundance of anti-inflammatory bacteria, such as Firmicutes and Actinobacteria [54] along with butyrate-producer bacteria, may result in PEM followed by inactivity and increased LPS-induced inflammation in ME/CFS. In addition, ME/CFS patients, compared with healthy controls, seem to also have higher levels of pro-inflammatory Gram-negative bacteria species, such as Alistipes, Bacteroidetes and Enterobacteriaceae [54,56,60], which may worsen the disease course, leading to an increased severity of fatigue, pain and confusion [54]. In addition, ME/CFS has been associated with the reduction of certain bacteria taxa of the Firmicutes phylum, including Faecalibacterium, Roseburia and Clostridium genera, of which, a decrease in the butyrate-producer Faecalibacterium was considered a potential biomarker with diagnostic value in ME/CFS [56]. ...
Article
Full-text available
Myalgic encephalomyelitis, also known as chronic fatigue syndrome (ME/CFS), and long COVID are complex, multisystemic and long-term disabling conditions characterized by debilitating post-exertional malaise and other core symptoms related to immune dysregulation resultant from post-viral infection, including mitochondrial dysfunction, chronic neuroinflammation and gut dysbiosis. The reported associations between altered microbiota composition and cardinal symptoms of ME/CFS and long COVID suggest that the use of microbial preparations, such as probiotics, by restoring the homeostasis of the brain–immune–gut axis, may help in the management of symptoms in both conditions. Therefore, this review aims to investigate the implications of alerted gut microbiome and assess the evidence supporting use of microbial-based preparations, including probiotics, synbiotics, postbiotics alone and/or in combination with other nutraceuticals in the management of fatigue, inflammation and neuropsychiatric and gastrointestinal symptoms among patients with ME/CFS and long COVID.
... Although these observations may suggest the role of gut dysbiosis in the symptom development [53] a specific microbial signature has not been found yet [2]. Investigations of the microbiome dysbiosis was intensively studied in ME/CFS, with results indicating for a potential link between changes gut composition, including deficient butyrate-producing capacity and bacterial network disturbances, increased immune activation [54][55][56], and symptoms of fatigue symptoms [2,57]. That decreased availability of butyrate, along with other key mediators of the gut-brain axis produced during microbial fermentation, including serotonin and γ-aminobutyric acid (GABA) via influencing on intestinal permeability may have considerable impact on the neuro-immunoendocrine system [58]. ...
... That decreased availability of butyrate, along with other key mediators of the gut-brain axis produced during microbial fermentation, including serotonin and γ-aminobutyric acid (GABA) via influencing on intestinal permeability may have considerable impact on the neuro-immunoendocrine system [58]. In addition, studies from physically fit populations indicated for a strong correlation between increased gut microbial diversity, high fecal butyrate levels and extent of physical fitness characterized by the higher VO2 peaks and consequently reduced LPS levels and lower inflammation [59], thereby suggesting that the decreased abundance of anti-inflammatory bacteria, such as Firmicutes and Actinobacteria [54] along with butyrate-producers may result in PEM followed by inactivity and increased LPS-induced inflammation in ME/CFS. In addition, ME/CFS patients, compared to healthy controls, seem to have also higher levels of pro-inflammatory gram-negative bacteria species, such as Alistipes, Bacteroidetes and Enterobacteriaceae [54,56,60] which may worsen disease course, leading to increased severity of feeling of fatigue, pain, and confusion [54]. ...
... In addition, studies from physically fit populations indicated for a strong correlation between increased gut microbial diversity, high fecal butyrate levels and extent of physical fitness characterized by the higher VO2 peaks and consequently reduced LPS levels and lower inflammation [59], thereby suggesting that the decreased abundance of anti-inflammatory bacteria, such as Firmicutes and Actinobacteria [54] along with butyrate-producers may result in PEM followed by inactivity and increased LPS-induced inflammation in ME/CFS. In addition, ME/CFS patients, compared to healthy controls, seem to have also higher levels of pro-inflammatory gram-negative bacteria species, such as Alistipes, Bacteroidetes and Enterobacteriaceae [54,56,60] which may worsen disease course, leading to increased severity of feeling of fatigue, pain, and confusion [54]. In addition, ME/CFS was associated with the reduction of certain bacteria taxa of the Firmicutes phylum, including Faecalibacterium, Roseburia and Clostridium genera, of which a decrease of butyrate-producer Faecalibacterium was considered as potential biomarker with diagnostic value in ME/CFS [56]. ...
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Full-text available
Myalgic Encephalomyelitis, also known as Chronic Fatigue Syndrome (ME/CFS) and Long COVID are characterized by debilitating post-exertional malaise and other core symptoms related to immune dysregulation resultant from post-viral infection, including mitochondrial dysfunction, chronic neuroinflammation and gut dysbiosis. The reported associations between altered microbiota composition and cardinal symptoms of ME/CFS and Long COVID, suggesting that use of microbial preparations, such as probiotics, by restoring the homeostasis of the brain-immune-gut axis may help in the management of symptoms in both conditions. Therefore, this review aims to investigate the implications of alerted gut microbiome and assess the evidence supporting use of microbial-based preparations, including probiotics, synbiotics, postbiotics alone and/or in combination with other nutraceuticals in the management of fatigue, inflammation, as well as neuropsychiatric and gastrointestinal symptoms among patients with ME/CFS and Long COVID.
... Interestingly, a large proportion of ME/CFS patients (35 to 90%) have gastrointestinal complaints, which are often associated with the comorbidities IBS and IBD (34). Various studies have demonstrated intestinal dysbiosis in ME/CFS (Table 1) (13,33,34,(36)(37)(38)(39)(40)(41)(42)(43)(44), although uniform microbiome signatures are non-existent. This is mainly due to the high intra-and interindividual variability of the microbiome. ...
... This raises the question of whether a leaky gut is also important in ME/CFS. Various findings suggest this (42,(51)(52)(53). In a controlled study, Shukla and colleagues were able to detect a pathological bacterial translocation of 6 of 9 main bacterial genera after 72 hours after a defined stress test compared to 2 of 9 in controls (42). ...
... Various findings suggest this (42,(51)(52)(53). In a controlled study, Shukla and colleagues were able to detect a pathological bacterial translocation of 6 of 9 main bacterial genera after 72 hours after a defined stress test compared to 2 of 9 in controls (42). Moreover, there are increased specific IgA and IgA levels against LPS, a bacterial marker for gram-negative bacterial translocation, in patients with ME/CFS. ...
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Like other infections, a SARS-CoV-2 infection can also trigger Post-Acute Infection Syndromes (PAIS), which often progress into myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). ME/CFS, characterized by post-exercise malaise (PEM), is a severe multisystemic disease for which specific diagnostic markers or therapeutic concepts have not been established. Despite numerous indications of post-infectious neurological, immunological, endocrinal, and metabolic deviations, the exact causes and pathophysiology remain unclear. To date, there is a paucity of data, that changes in the composition and function of the gastrointestinal microbiota have emerged as a potential influencing variable associated with immunological and inflammatory pathways, shifts in ME/CFS. It is postulated that this dysbiosis may lead to intestinal barrier dysfunction, translocation of microbial components with increased oxidative stress, and the development or progression of ME/CFS. In this review, we detailed discuss the findings regarding alterations in the gastrointestinal microbiota and its microbial mediators in ME/CFS. When viewed critically, there is currently no evidence indicating causality between changes in the microbiota and the development of ME/CFS. Most studies describe associations within poorly defined patient populations, often combining various clinical presentations, such as irritable bowel syndrome and fatigue associated with ME/CFS. Nevertheless, drawing on analogies with other gastrointestinal diseases, there is potential to develop strategies aimed at modulating the gut microbiota and/or its metabolites as potential treatments for ME/CFS and other PAIS. These strategies should be further investigated in clinical trials.
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Background/Objectives: The human gut microbiome is a complex ecosystem of microorganisms that can influence our health and exercise habits. On the other hand, physical exercise can also impact our microbiome, affecting our health. Our narrative review examines the bidirectional relationship between physical activity and the gut microbiome, as well as the potential for targeted probiotic regimens to enhance sports performance. Methods: We conducted a comprehensive literature review to select articles published up till January 2024 on the topics of physical exercise, sports, probiotics, and gut microbiota from major scientific databases, incorporating over 100 studies. Results: We found that the impact of physical activity on the gut microbiome varies with the type and intensity of exercise. Moderate exercise promotes a healthy immune system, while high-intensity exercise for a long duration can cause a leaky gut and consequent systemic inflammation, which may disrupt the microbial balance. Combining aerobic and resistance training significantly affects bacterial diversity, linked to a lower prevalence of chronic metabolic disorders. Furthermore, exercise enhances gut microbiome diversity, increases SCFA production, improves nutrient utilization, and modulates neural and hormonal pathways, improving gut barrier integrity. Our findings also showed probiotic supplementation is associated with decreased inflammation, enhanced sports performance, and fewer gastrointestinal disturbances, suggesting that the relationship between the gut microbiome and physical activity is mutually influential. Conclusions: The bidirectional relationship between physical activity and the gut microbiome is exemplified by how exercise can promote beneficial bacteria while a healthy gut microbiome can potentially enhance exercise ability through various mechanisms. These findings underscore the importance of adding potential tailored exercise regimens and probiotic supplementation that consider individual microbiome profiles into exercise programs.
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Background/Objectives: The human gut microbiome is a complex ecosystem of microorganisms that can influence our health and exercise habits. On the other hand, physical exercise can also impact our microbiome, affecting our health. Our narrative review examines the bidirectional relationship between physical activity and the gut microbiome, as well as the potential for targeted probiotic regimens to enhance sports performance. Methods: We conducted a comprehensive literature review to select articles published up till January 2024 on the topics of physical exercise, sports, probiotics, and gut microbiota from major scientific databases, incorporating over 100 studies. Results: We found that the impact of physical activity on the gut microbiome varies with the type and intensity of exercise. Moderate exercise promotes a healthy immune system, while high-intensity exercise for a long duration can cause a leaky gut and consequent systemic inflammation, which may disrupt the microbial balance. Combining aerobic and resistance training significantly affects bacterial diversity, linked to a lower prevalence of chronic metabolic disorders. Furthermore, exercise enhances gut microbiome diversity, increases SCFA production, improves nutrient utilization, and modulates neural and hormonal pathways, improving gut barrier integrity. Our findings also showed probiotic supplementation is associated with decreased inflammation, enhanced sports performance, and fewer gastrointestinal disturbances, suggesting that the relationship between the gut microbiome and physical activity is mutually influential. Conclusions: The bidirectional relationship between physical activity and the gut microbiome is exemplified by how exercise can promote beneficial bacteria while a healthy gut microbiome can potentially enhance exercise ability through various mechanisms. These findings underscore the importance of adding potential tailored exercise regimens and probiotic supplementation that consider individual microbiome profiles into exercise programs.
Chapter
For neurotypical adults, a single bout of low-to-moderate intensity physical activity usually transiently improves feelings of energy. Similar bouts of exercise have the opposite effect of increased feelings of fatigue when performed by samples with chronic multisymptom illnesses (CMIs) such as Long-COVID, Gulf War Illness (GWI), or Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). The short-term adoption of regular moderate intensity physical activity (typical experiments are 1 to 6 months) among neurotypical adults results in small-to-moderate improvements in self-reported feelings of fatigue, energy, and vitality. Small improvements in these feelings, or no change at all, occur for CMIs, but limited data precludes strong conclusions. The mechanisms of exercise effects on fatigue, whether acute or chronic, are poorly understood but likely involve multiple neural circuits and associated transmitters. For CMIs, the mechanisms of acute worsening of fatigue with exercise may be driven by the yet unknown pathophysiological mechanisms of the disease (perhaps involving brain, immune and autonomic system dysfunction, and their interactions). Likewise, fatigue improvements may depend on whether chronic physical activity is a disease-modifying treatment.
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