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The influence of a short-term gluten-free diet on the human gut microbiome


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BackgroundA gluten-free diet (GFD) is the most commonly adopted special diet worldwide. It is an effective treatment for coeliac disease and is also often followed by individuals to alleviate gastrointestinal complaints. It is known there is an important link between diet and the gut microbiome, but it is largely unknown how a switch to a GFD affects the human gut microbiome. Methods We studied changes in the gut microbiomes of 21 healthy volunteers who followed a GFD for four weeks. We collected nine stool samples from each participant: one at baseline, four during the GFD period, and four when they returned to their habitual diet (HD), making a total of 189 samples. We determined microbiome profiles using 16S rRNA sequencing and then processed the samples for taxonomic and imputed functional composition. Additionally, in all 189 samples, six gut health-related biomarkers were measured. ResultsInter-individual variation in the gut microbiota remained stable during this short-term GFD intervention. A number of taxon-specific differences were seen during the GFD: the most striking shift was seen for the family Veillonellaceae (class Clostridia), which was significantly reduced during the intervention (p = 2.81 × 10−05). Seven other taxa also showed significant changes; the majority of them are known to play a role in starch metabolism. We saw stronger differences in pathway activities: 21 predicted pathway activity scores showed significant association to the change in diet. We observed strong relations between the predicted activity of pathways and biomarker measurements. ConclusionsA GFD changes the gut microbiome composition and alters the activity of microbial pathways.
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R E S E A R C H Open Access
The influence of a short-term gluten-free
diet on the human gut microbiome
Marc Jan Bonder
, Ettje F. Tigchelaar
, Xianghang Cai
, Gosia Trynka
, Maria C. Cenit
, Barbara Hrdlickova
Huanzi Zhong
, Tommi Vatanen
, Dirk Gevers
, Cisca Wijmenga
, Yang Wang
and Alexandra Zhernakova
Background: A gluten-free diet (GFD) is the most commonly adopted special diet worldwide. It is an effective treatment
for coeliac disease and is also often followed by individuals to alleviate gastrointestinal complaints. It is known there is an
important link between diet and the gut microbiome, but it is largely unknown how a switch to a GFD affects the human
gut microbiome.
Methods: We studied changes in the gut microbiomes of 21 healthy volunteers who followed a GFD for four weeks. We
collected nine stool samples from each participant: one at baseline, four during the GFD period, and four when they
returned to their habitual diet (HD), making a total of 189 samples. We determined microbiome profiles using 16S rRNA
sequencing and then processed the samples for taxonomic and imputed functional composition. Additionally, in all 189
samples, six gut health-related biomarkers were measured.
Results: Inter-individual variation in the gut microbiota remained stable during this short-term GFD intervention.
A number of taxon-specific differences were seen during the GFD: the most striking shift was seen for the family
Veillonellaceae (class Clostridia), which was significantly reduced during the intervention (p=2.81×10
). Seven
other taxa also showed significant changes; the majority of them are known to play a role in starch metabolism.
We saw stronger differences in pathway activities: 21 predicted pathway activity scores showed significant
association to the change in diet. We observed strong relations between the predicted activity of pathways and
biomarker measurements.
Conclusions: A GFD changes the gut microbiome composition and alters the activity of microbial pathways.
Keywords: Microbiome, Gluten-free diet, Biomarker, Observation study
Gluten is a major dietary component of wheat, barley, and
rye. In genetically susceptible individuals, the consumption
of gluten triggers the development of coeliac disease an
autoimmune disorder commonly seen in populations of
European ancestry (with a frequency of approximately 1 %)
[1]. In the absence of any medication, the only treatment is
a life-long gluten-free diet (GFD), which is effective and
well tolerated by the majority of patients. Non-coeliac glu-
ten sensitivity, another common disorder linked to the
consumption of gluten-containing food and resulting in a
range of symptoms of intestinal discomfort (such as diar-
rhea and abdominal pain), has also been shown to improve
on a GFD [2, 3]. More recently, a GFD is being considered
as a way to ameliorate symptoms in patients with irritable
bowel syndrome (IBS) [4].
However, beyond these medical indications, more and
more individuals are starting on a GFD to improve their
health and/or to control weight. The dietspopularityhas
risen rapidly in the last few years, making it one of the most
popular diets worldwide, along with a low-carbohydrate
diet and a fat-free diet. The numbers of those adopting the
diet for non-medical reasons now surpass the numbers
of those who are addressing a permanent gluten-related
disorder [3].
Several studies have reported the effect of a GFD on
the composition of the gut microbiome in coeliac disease
* Correspondence:
Equal contributors
Department of Genetics, University of Groningen, University Medical Centre
Groningen, Groningen, The Netherlands
Top Institute Food and Nutrition, Wageningen, The Netherlands
Full list of author information is available at the end of the article
© 2016 Bonder et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (, which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
( applies to the data made available in this article, unless otherwise stated.
Bonder et al. Genome Medicine (2016) 8:45
DOI 10.1186/s13073-016-0295-y
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
patients [57]. In these studies, the microbiome compos-
ition in coeliac patients on a GFD was compared with un-
treated patients and healthy individuals. The most
consistent observation across these studies is the differ-
ence in the abundance and diversity of Lactobacillus and
Bifidobacterium in the treated and untreated coeliac dis-
ease patients. It should be noted that these studies were
relatively small (seven to 30 participants in each group).
Specifically, De Palma et al. [8] assessed the effect of a
one-month GFD on ten healthy individuals, but the study
was limited to the use of non-sequence based methods,
including FISH and qPCR. Their study described how Bifi-
dobacterium,Clostridium lituseburense,Faecalibacterium
prausnitzii,Lactobacillus,andBifidobacterium longum
were decreased during GFD, whereas Escherichia coli,
Enterobacteriaceae,andBifidobacterium angulatum were
increased. To the best of our knowledge, there has been
no comprehensive analysis of the effect of a GFD on the
entire gut microbiome composition using a next-generation
sequencing approach.
The effect of other diet interventions on the micro-
biome composition was recently studied using the 16S
rRNA sequencing method [9]. In particular, it was shown
that a short-term animal-based diet led to an increased
abundance of bile-tolerant microorganisms (Alistipes,
Bilophila,andBacteroides) and a decreased abundance of
Firmicutes, which metabolize dietary plant polysaccha-
rides (Roseburia,Eubacterium rectale,andRuminococcus
bromii) [9].
In this work we assessed the effect of a GFD on gut
microbiota using the next-generation 16S rRNA sequen-
cing method. The analysis was performed in 189 samples,
representing up to nine time points for 21 individuals. We
investigated the diet-related changes both on the level of
taxonomic units as well as on the predicted bacterial path-
ways. Next to this, we assessed a set of selected bio-
markers to assess the gut health in relation to changes in
bacterial composition and their association to a GFD. Our
study offers insights into the interaction between the gut
microbiota and a GFD.
Study design
We enrolled 21 participants (nine men and twelve
women), without any known food intolerance and without
known gastrointestinal disorders, in our GFD study for
13 weeks (Fig. 1). After baseline measurements (T = 0), all
the participants started a GFD for four weeks (T = 14),
followed by a wash-outperiod of five weeks. Subse-
quently, data were collected when they returned to their
habitual diets (HD, gluten-containing) for a period of four
weeks (T = 58) (Fig. 1). Fecal samples were collected at all
time points. Blood was collected at baseline, at T = 2 and
The participants were aged between 16 and 61 years
(mean age, 36.3 years). Mean BMI was 24.0 and 28.6 %
(n= 6) of participants were smokers. The majority of
participants were European (n= 19), two participants
were South American, and one was Asian. Except for
one, none of the participants had taken an antibiotic
treatment for the year prior to the study start. In both diet
periods (GFD, HD), participants kept a detailed three-day
food record. All 21 participants completed the GFD
period; for 17 participants all data points were available.
An overview of the participantscharacteristics can be
found in Additional file 1: Figure S1.
Written consent was obtained from all participants and
the study followed the sampling protocol of the LifeLines-
DEEP study [10], which was approved by the ethics
committee of the University Medical Centre Groningen,
document no. METC UMCG LLDEEP: M12.113965.
Gluten-free diet and dietary intake assessment
Methods to assess GFD adherence and dietary intake
have been described previously by Baranska et al. [11] In
short, before the start of the study, the participants were
given information on gluten-containing food products
by a dietician and they were instructed how to keep a
three-day food record. The food records were checked
for completeness and the macronutrient intake was cal-
culated. Days on which a participant had a daily energy
intake below 500 kcal or above 5000 kcal were excluded
from our analysis (n= 2). Of 21 participants, 15 (71 %)
completed the dietary assessments; three were excluded
from food intake analysis because of incomplete food re-
cords. We used the paired t-test to compare group
means between GFD and HD.
Blood sample collection
Participantsblood samples were collected after an over-
night fast by a trained physician assistant. We collected
two EDTA tubes of whole blood at baseline (T0) and dur-
ing the GFD period at time points T2 and T4; during the
HD period one EDTA tube was collected at time points
T6 and T8. Plasma was extracted from the whole blood
within 8 h of collection and stored at 80 °C for later
Fig. 1 Timeline of GFD study, including number of participants and
collected samples
Bonder et al. Genome Medicine (2016) 8:45 Page 2 of 11
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Microbiome analysis
Fecal sample collection
Fecal samples were collected at home and immediately
stored at 20 °C. At the end of the 13-week study
period, all samples were stored at 80 °C. Aliquots were
made and DNA was isolated with the QIAamp DNA
Stool Mini Kit. Isolated DNA was sequenced at the
Beijing Genomics Institute (BGI).
We used 454 pyrosequencing to determine the bacterial
composition of the fecal samples. Hyper-variable region
V3 to V4 was selected using forward primer F515
(GTGCCAGCMGCCGCGG) and reverse primer: E.
coli 907-924(CCGTCAATTCMTTTRAGT) to exam-
ine the bacterial composition.
We used QIIME [12], v1.7.0, to process the raw data
files from the sequencer. The raw data files, sff files, were
processed with the defaults of QIIME v1.7.0, however we
did not trim the primers. Six out of 161 samples had fewer
than 3000 reads and were excluded from the analysis. The
average number of reads was 5862, with a maximum of
12,000 reads.
OTU picking
The operational taxonomic unit (OTU) formation was
performed using the QIIME reference optimal picking,
which uses UCLUST [13], version 1.2.22q, to perform the
clustering. As a reference database, we used a primer-
specific version of the full GreenGenes 13.5 database [14].
Using TaxMan [15], we created the primer-specific ref-
erence database, containing only reference entries that
matched our selected primers. During this process we
restricted the mismatches of the probes to the references
to a maximum of 25 %. The 16S regions that were cap-
tured by our primers, including the primer sequences,
were extracted from the full 16S sequences. For each of
the reference clusters, we determined the overlapping
part of the taxonomy of each of the reference reads in
the clusters and used this overlapping part as the taxo-
nomic label for the cluster. This is similar to the pro-
cesses described in other studies [9, 1518].
OTUs had to be supported by at least 100 reads and
had to be identified in two samples; less abundant OTUs
were excluded from the analysis.
Estimation of gene abundance and pathway activity
After filtering the OTUs, we used PICRUSt [19] to esti-
mate the gene abundance and the PICRUSt output was
then used in HUMAnN [20] to calculate the bacterial
pathway activity. First, the reference database was clus-
tered based on 97 % similarity to the reference sequence
to better reflect the normal GreenGenes 97 % database
required for PICRUSt. Three out of 1166 OTUs did not
contain a representative sequence in the GreenGenes
97 % set and were therefore excluded from the analysis.
Since merging the reference database at 97 % similarity
level led to merging of previously different clusters, for
the pathway analysis we chose to permute the cluster
representative names in the OTU-table 25 times; this
was to be sure that our OTU picking strategy would not
cause any problems in estimating the genes present in
each micro-organism. Next, we ran PICRUSt on the 25
permuted tables and calculated the average gene abun-
dance per sample. The average correlations between the
permutations within a sample was higher than 0.97
(Pearson r). Hence, we averaged the PICRUSt output,
which was then used to calculate the pathway activity in
Changes in the gut microbiome or in gene abundance due
to diet
To identify differentially abundant taxa, microbial bio-
markers, and differences in pathway activity between the
GFD and HD periods, we used QIIME and MaAsLin
[21]. QIIME was used for the alpha-diversity analysis,
principal coordinate analysis (PCoA) over unifrac dis-
tances, and visualization. In the MaAsLin analysis we
corrected for ethnicity (defined as continent of birth)
and gender. MaAsLin was used to search for differen-
tially abundant taxonomic units to discriminate between
the GFD and HD time points. Additionally, we tested for
during transition from HD to GFD (T0T4). MaAsLin
uses a boosted, additive, general linear model to discrim-
inate between groups of data.
In the MaAsLin analysis we did not test individual
OTUs, but focused on the most detailed taxonomic label
each OTU represented. Using the QIIMETOMAASLIN
[22] tool, we aggregated the OTUs if the taxonomic label
was identical and, if multiple OTUs represented a higher
order taxa, we added this higher order taxa to the ana-
lysis. In this process, we went from 1166 OTUs to 114
separate taxonomic units that were included in our ana-
lysis. Using the same tool, QIIMETOMAASLIN, we nor-
malized the microbial abundance using acrsin square
root transformation. This transformation leads to the
percentages being normally distributed.
In all our analyses we used the Q-value calculated using
the R [23] Q-value package [24] to correct for multiple
testing. The Q-value is the minimal false discovery rate at
which a test may be called significant. We used a Q-value
of 0.05 as a cutoff in our analyses.
Six biomarkers related to gut health were measured in
the Dr. Stein & Colleaguesmedical laboratory
(Maastricht, the Netherlands). These biomarkers in-
cluded: fecal calprotectin and a set of plasma cytokines as
Bonder et al. Genome Medicine (2016) 8:45 Page 3 of 11
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markers for the immune system activation [2527];
fecal human-β-defensin-2 as a marker for defense
against invading microbes [28, 29]; fecal chromogranin
A as a marker for neuro-endocrine system activation
[3032]; fecal short-chain fatty acids (SCFA) secretion
as a marker for colonic metabolism [33]; and plasma
citrulline as a measure for enterocyte mass [34, 35].
The plasma citrulline level and the panel of cytokines
(IL-1β, IL-6, IL-8, IL-10, IL-12, and TNFα) were mea-
sured by high-performance liquid chromatography
(HPLC) and electro-chemiluminescence immunoassay
(ECLIA), respectively. In feces, we measured calpro-
tectin and human-β-defensin-2 levels by enzyme-
linked immunosorbent assay (ELISA), chromogranin
A level by radioimmunoassay (RIA), and the short-
chain fatty acids acetate, propionate, butyrate, valerate,
and caproate by gas chromatographymass spectrom-
etry (GC-MS). All biomarker analyses were performed
non-parametrically, with tie handling, because of the
high number of samples with biomarker levels below
the detection limit. We used the Wilcoxon test to
compare the average biomarker levels between the diet
periods and the Spearman correlation to search for re-
lations between the microbiome or gene activity data
and the biomarker levels.
Food intake
We first investigated if a GFD had a significant effect on
the daily intake of macronutrients by analyzing the GFD
and HD food records from participants (Additional file 2:
Table S1). Mean (SD) daily intakes of energy, protein, fat,
and carbohydrate during GFD and HD are shown in
Table 1. We observed slightly higher carbohydrate intake
and a slightly lower fat intake on GFD; however, none of
the differences in energy or macronutrient intake were
significantly different. We therefore concluded that dietary
macronutrient composition was not significantly changed
by following a GFD.
Microbial differences due to diet
In total we used 155 fecal samples, originating from 21
individuals, for the microbiota analysis and we observed
114 different taxonomic units. We first checked if GFD
influenced the number and proportion of bacteria in in-
dividual participants, for which we investigated differ-
ences in alpha diversity between the GFD and HD time
points using several alpha diversity measures (Observed
species, Shannon, Chao1, and Simpson indexes). We
found no differences in the alpha diversity in any of
these tests. Therefore, we concluded that a change in
diet did not influence the bacterial diversity within a
Next, we tested if there was any difference in the bacter-
ial diversity related to variation in diet between participants
(beta-diversity) by comparing the unweighted unifrac dis-
tance in sample groups. We observed a strong difference
when comparing different time points from a single indi-
vidual to all other individuals, regardless of diet type,
Wilcoxon pvalue <2.2 × 10
. When we compared the
diet-induced differences within the same individual, we
saw a small but significant change, Wilcoxon pvalue =
0.024, although the same diet time points were slightly
more alike (Additional file 3: Figure S2).
In the PCoA analysis over the unweighted unifrac dis-
tance (Fig. 2a), we also saw that the main driver of the
diversity is the inter-individual difference, with partici-
pants clustering together, both during and after the
dietary intervention. In the first ten principal coordi-
nates, which explain more than half of the total vari-
ation, we observed changes between the time points for
individual participants, although there was no single
component, or combination of components, capturing
in the first ten components.
We therefore concluded that a GFD has a significant
effect on the diversity between the groups, but that the
inter-individual effect on the variation of the micro-
biome is stronger than the effect of diet.
We further investigated changes in beta-diversity in
relation to the time points (Fig. 2b). When we plotted
PCo1 versus the time points, we observed a separation
into two groups. Since PCo1 describes the difference
in alpha-diversity between samples, we concluded that
this separation is based on richness. The richness
separates all but one participant into either a clear
high-richness or low-richness group (Fig. 2b). There is
a significant difference in richness between the two
groups, Wilcoxon pvalue = 0.0016, excluding the one
participant who seems to be an intermediate. How-
did not see any significant difference in stability, i.e. in
variation in richness, between the low- and high-
richness groups.
Table 1 Mean and standard deviation (SD) of energy, protein,
carbohydrates, and fat intake during the gluten-free diet (GFD)
and habitual diet (HD). g = grams, en% = energy %
GFD (n= 12) HD (n= 12)
Nutrient Mean SD Mean SD pvalue
Energy (kcal) 1709.5 344.0 1811.5 433.9 0.243
Protein (g) 73.1 18.4 78.1 18.2 0.401
Protein (en%) 17.1 17.2
Carbohydrates (g) 211.1 50.3 199.9 63.2 0.275
Carbohydrates (en%) 49.4 44.1
Fat (g) 63.7 18.1 72.5 24.3 0.109
Fat (en%) 33.6 36.0
Bonder et al. Genome Medicine (2016) 8:45 Page 4 of 11
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Differentially abundant taxa
When comparing the HD and GFD time points, corrected
for age and ethnicity in MaAsLin, we observed eight sig-
nificant microbial changes (Fig. 3 and Table 2). The stron-
gest association was found to the family Veillonellaceae,of
which the abundance in the gut dropped significantly on a
GFD (p=2.81×10
, q = 0.003) (Fig. 3b and Additional
file 4: Figure S3). Other species that decreased on a GFD
included Ruminococcus bromii (p= 0.0003, q = 0.01) and
Roseburia faecis (p= 0.002, q = 0.03). While families Victi-
vallaceae (p= 0.0002, q = 0.01), Clostridiaceae (p= 0.0006,
q = 0.015), and Coriobacteriaceae (p= 0.003, q = 0.035),
order ML615J-28 (p= 0.001, q = 0.027), and genus Slackia
(p= 0.002, q = 0.01) increased in abundance on a GFD.
Next, we tested for trends during the diet change;
however, we did not observe a time-dependent change
in the microbiome composition. Since we observed two
different groups based on richness in the PCoA analysis,
we tested for different reactions to the change in diet in
the high-richness- and low-richness groups. However,
no significant associations were found in this analysis.
Since six out of the 28 participants smoked, we tested for
overlap between smoke-associated bacteria and diet-related
bacteria. We did not find any overlap; Additional file 5:
Imputation of bacterial function
Next to the taxonomic associations, we also aimed to
study differences in pathway composition in relation to
GFD. We applied PICRUSt and HUMAnN for pathway
annotation, as described in Methods. In total, 161 path-
ways and 100 modules were predicted, all of the pathways
and modules were found in at least 1 % of the samples.
We used MaAsLin to identify differences in the path-
way composition and conducted the same tests GFD
versus HD and the time-series test as for the microbial
Fig. 2 PCoA plot showing the differences in the samples. aSamples plotted on PCoA 1 and 2, percentage of explained variation is given in the
legends. Each color represents an individual, the larger and less opaque spheres are gluten-free diet time points, and the smaller spheres inthesamecolor
are habitual diet time points. bThe differences in the first component over the time points. There are two groups based on richness, i.e. high versus low,
one individual had samples in both groups. The sample belonging to both richness groups has a bolder color
Fig. 3 aCladogram showing the differentially abundant taxa. This plot shows the different levels of taxonomy. Gray indicates bacteria higher in
the habitual diet and red indicates those higher in the gluten-free diet. The different circles represent the different taxonomic levels. (From inside
to outside: Kingdom, Phylum, Class, Order, Family, Genus, and Species). bComparison of the abundance of Veillonellaceae* in the gluten-free diet
vs. habitual diet. In the plot, the aggregate overall weeksincluding correction is shown. * Veillonellaceae is placed in the order Clostridiales in
GreenGenes 13.5. However, according to the NCBI classification, it belongs to order Negativicutes
Bonder et al. Genome Medicine (2016) 8:45 Page 5 of 11
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composition. The data were again corrected for age and
ethnicity. We observed that 19 KEGG pathways and two
KEGG modules (Table 3) were different in abundance
between GFD and HD. We did not observe associations
related to the transition from GFD to HD (T0T4). Four
out of five top associations, all with a Q-value <0.0003,
are related to metabolism changes: tryptophan metabol-
ism, butyrate metabolism (Fig. 4a), fatty acid metabol-
ism, and seleno-compound metabolism.
Biomarkers in relation to diet changes
Biomarkers related to GFD versus HD
We measured four biomarkers in feces: calprotectin,
human-β-defensin-2, chromogranin A, and a set of five
short-chain fatty acids (acetate, propionate, butyrate, val-
erate, and caproate). In addition, we measured citrulline
levels and a panel of cytokines (IL-1β, IL-6, IL-8, IL-10,
IL-12, and TNFα) in blood. The Wilcoxon test was used
to test biomarker level differences between the average
Table 2 GFD-induced changes in taxonomic composition
Taxonomic unit Coefficient N.not.0/N pvalue Q-value
0.0424 155/155 2.81 × 10
p_Lentisphaerae|c_Lentisphaeria|o_Victivallales|f_Victivallaceae 0.0093 89/155 2.30 × 10
p_Firmicutes|c_Clostridia|o_Clostridiales|f_Ruminococcaceae|g_Ruminococcus|s_bromii 0.0151 99/155 2.94 × 10
p_Firmicutes|c_Clostridia|o_Clostridiales|f_Clostridiaceae 0.0121 150/155 5.69 × 10
p_Tenericutes|c_RF3|o_ML615J-28 0.0095 82/155 1.30 × 10
p_Firmicutes|c_Clostridia|o_Clostridiales|f_Lachnospiraceae|g_Roseburia|s_faecis 0.0065 100/155 1.88 × 10
p_Actinobacteria|c_Coriobacteriia|o_Coriobacteriales|f_Coriobacteriaceae|g_Slackia 0.0044 43/155 2.14 × 10
p_Actinobacteria|c_Coriobacteriia|o_Coriobacteriales|f_Coriobacteriaceae 0.0137 155/155 2.67 × 10
A positive coefficient means more of the microbe was present during the habitual diet, while a negative coefficient means less of the microbe was present during the
habitual diet. All associations were to the kingdom bacteria, for readability the kingdom label is not presented.
Veillonellaceae is placed in the order Clostridiales in
GreenGenes 13.5. However, according to the NCBI classification, it belongs to order Negativicutes
Table 3 GFD-induced changes in pathway and module activity
Feature Coefficient N.not.0/N pvalue Q-value
KO00380: Tryptophan metabolism 0.0011 155/155 2.45 × 10
KO00650: Butyrate metabolism 0.0014 155/155 2.72 × 10
KO00071: Fatty acid metabolism 0.0011 155/155 4.74 × 10
KO00450: Selenocompound metabolism 0.0009 155/155 9.23 × 10
KO00630: Glyoxylate and dicarboxylate metabolism 0.0010 155/155 2.53 × 10
KO00520 Amino sugar and nucleotide sugar metabolism 0.0009 155/155 2.83 × 10
M00064: ADP-L-glycero-D-manno-heptose biosynthesis 0.0066 155/155 4.12 × 10
KO00643: Styrene degradation 0.0013 155/155 4.29 × 10
M00077: Chondroitin sulphate degradation Chondroitin sulphate degradation 0.0037 76/155 5.81 × 10
KO00760: Nicotinate and nicotinamide metabolism 0.0008 155/155 6.79 × 10
KO00620: Pyruvate metabolism 0.0012 155/155 0.002 0.023
KO00253: Tetracycline biosynthesis 0.0027 155/155 0.002 0.024
KO00471: D-Glutamine and D-glutamate metabolism 0.0012 155/155 0.002 0.024
KO04122: Sulphur relay system 0.0020 155/155 0.002 0.024
KO00633: Nitrotoluene degradation 0.0022 155/155 0.002 0.024
KO00072: Synthesis and degradation of ketone bodies 0.0020 155/155 0.003 0.028
KO00310: Lysine degradation 0.0007 155/155 0.003 0.031
KO00624: Polycyclic aromatic hydrocarbon degradation 0.0006 155/155 0.005 0.043
KO00561: Glycerolipid metabolism 0.0012 155/155 0.005 0.043
KO00680: Methane metabolism 0.0006 155/155 0.006 0.047
KO00550: Peptidoglycan biosynthesis 0.0011 155/155 0.007 0.047
A positive coefficient means more activity of the pathway/module during the habitual diet, while a negative coefficient means less activity of the pathway/module during
the habitual diet
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values and the GFD and HD period values. We saw no
significant change in biomarker levels in relation to GFD
(Table 4A and B).
Correlations between fecal biomarkers and microbiome
We correlated the fecal biomarker levels to the micro-
biome composition as well as to the microbiome predicted
pathways and modules. After multiple testing correction,
we observed many statistically significant correlations
between the levels of biomarkers and microbiome/
pathway abundances; the absolute correlation, Spearman
Rho, was between 0.14 and 0.59. An expected observation
was the correlation of the butyrate pathway activity to the
butyrate biomarker, as we had previously observed a
significant correlation between the predicted butyrate
pathway activity and diet change (Table 3). When correlat-
ing the actual butyrate measurements with the predicted
activity of the butyrate metabolism, we observed a low but
significant correlation of 0.269 (p= 0.0009, q = 0.0012,
Additional file 6: Figure S4). However, there was no
Table 4 Median and 25 %/75 % quantiles of the measured biomarkers
Habitual diet Gluten-free diet Wilcoxon test pvalue
A) Plasma
Citrullin (mol/L) 45.60 (38.1551.50) 48.00 (36.3556.85) 0.9328
IL 1 Beta (g/L) 1.60 (0.682.10) 1.23 (0.791.68) 0.8870
IL 6 (g/L) BDL (BDL1.60) BDL (BDL0.38) 0.1240
IL 8 (g/L) 6.04 (2.8912.61) 5.41 (3.3411.19) 0.9030
IL 10 (g/L) 0.83 (0.741.01) 0.83 (0.740.97) 0.9322
IL 12P70 (g/L) 1.53 (0.951.78) 1.53 (0.952.11) 0.2131
TNF Alpha (g/L) 0.56 (BDL4.33) BDL (BDL5.13) 0.9761
B) Feces
Chromogranin A (nmol/g) 10.85 (7.6923.09) 11.44 (7.3727.18) 0.8128
Beta Defensin 2 (ng/g) 24.90 (18.7835.03) 26.10 (20.0346.90) 0.5256
Calprotectin (g/g) 21.55 (BDL42.88) 13.05 (BDL31.28) 0.0528
Acetate (mol/g) 24.37 (17.3534.34) 23.61 (18.5835.12) 0.8651
Propionate (mol/g) 7.55 (4.2410.98) 6.84 (4.679.07) 0.6986
Butyrate (mol/g) 6.86 (3.5310.63) 6.48 (4.2710.40) 0.8882
Valerat (mol/g) 1.09 (0.741.76) 1.24 (0.791.70) 0.6824
Caproat (mol/g) 0.28 (0.050.85) 0.21 (0.040.66) 0.2488
None of the differences were statistically significant. BDL = below detection limit
Fig. 4 Box plot of predicted activity of butyrate metabolism per diet period (a) and the butyrate levels (mol/g) per diet period (b). There was a
significant increase in activity in butyrate metabolism (q = 0.001877), but no change in butyrate level was observed
Bonder et al. Genome Medicine (2016) 8:45 Page 7 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
significant difference in butyrate levels in the two diet
periods (Fig. 4b and Table 4). Another interesting correl-
ation was found between the predicted pyruvate metab-
olism pathway and the levels of propionate (mol/g),
since propionate can be oxidized to pyruvate [37], for
which we observed a correlation of 0.54 (p=9.4
, Additional file 7: Figure S5). A
complete list of the significant correlations between
the fecal biomarkers and the microbiome composi-
tions, the predicted KEGG pathway activity scores, and
predicted activity of KEGG modules can be found in
Additional file 8: Tables S3, Additional file 9: Table S4,
and Additional file 10: Table S5.
We investigated the role of a four-week GFD on micro-
biome composition in healthy individuals and identified
moderate but significant changes in their microbiome
compositions and even stronger effects on the imputed
activity levels of bacterial pathways.
On a taxonomic level we identified eight bacteria that
change significantly in abundance on GFD: Veillonella-
ceae,Ruminococcus bromii, and Roseburia faecis de-
creased on GFD, and Victivallaceae,Clostridiaceae,
ML615J-28,Slackia, and Coriobacteriaceae increased on
GFD. The strongest effect was seen in the decrease of
Veillonellaceae during GFD, Gram-negative bacteria
known for lactate fermentation. This is the first time
that the Veillonellaceae family has been associated to a
dietary intervention, but it was recently shown to be de-
creased in autistic patients [38]. Remarkably, the patients
in that study were more often on a GFD (9/10) than the
control group (5/10). Our findings suggest that GFD, ra-
ther than autism, can be the cause of a lower abundance
of Veillonellaceae in these patients, thus highlighting the
importance of including dietary information in analyses
of microbiota in relation to diseases. Veillonellaceae is
considered to be a pro-inflammatory family of bacteria; an
increase in Veillonellaceae abundance was consistently re-
ported in IBD, IBS, and cirrhosis patients [3941]. It is
conceivable that a decrease in Veillonellaceae abundance
might be one of the mediators of the GFDsbeneficialef-
fect observed in patients with IBS and gluten-related
Several of the associated bacteria have been previously
linked to diet changes and starch metabolism. In par-
ticular, Ruminococcus bromii is important for the deg-
radation of resistant starch in the human colon [42] and
is increased when on a resistant starch diet [43]. It is
also known that degradation of cellulose by Ruminococ-
cus results in the production of SCFA and hydrogen gas
[44]; a decrease in abundance of Ruminococcus and its
fermentation products might explain the beneficial effect
of a GFD that is experienced by some IBS patients as
previously reported by Aziz et al. [45]. Both Ruminococ-
cus bromii and Roseburia faecis were recently reported
to be influenced by switching from a vegetarian to a
meat-containing diet [9]. It is likely that changes in these
bacteria observed in relation to GFD are the conse-
quences of the different starch composition of a GFD
versus HD. Moreover, stool consistency could influence
the results of microbiome composition [46]; unfortu-
nately, data on stool composition were not collected in
our study.
The five bacteria for which we found an increased abun-
dance on GFD are less well characterized although the
Slackia genus, its family Coriobacteriaceae, and the family
Clostridiaceae have been previously linked to gastrointes-
tinal diseases in humans inflammatory bowel disease,
celiac disease, and colorectal cancer [4749]. The Victival-
laceae family and ML615J-28 order have not been previ-
ously associated to diet change or phenotypic change in
human. However, in general, it could be hypothesized that
these bacteria benefit from a change in available substrates
as a result from the change in diet, which could in turn re-
sult in altered metabolite production and related gastro-
intestinal complaints.
In this study we found a stronger effect of diet on the
imputed KEGG pathways than on the taxonomic level. So,
although the changes in the overall microbiome were
moderate, there were more profound effects on the path-
way activities of the microbiome.
The strength of our study lies in our analysis of the
microbiome at multiple time points for the same indi-
viduals. We identified that the inter-individual variability
is the strongest determinant of sample variability, sug-
gesting that in healthy individuals the gut microbiome is
stable, even with short-term changes in the habitual diet.
We did not observe differences in the downstream effect
of GFD in relation to high or low richness, which con-
tradicts previous observations [50]. The study by David
et al. [9] identified a profound effect of short-term diet
change from a vegetarian to an animal-based diet and
vice versa. This profound short-term diet effect was not
observed in our study when changing from a gluten-
containing to a gluten-free diet. Induced by the diet
change, David et al. [9] found significant differences in
macronutrient intake between meat-based and plant-
based diet, whereas macronutrient intake in this study
was not changed during the diets. These results suggest
that changing the main energy source (meat vs. plant)
has a more profound effect on the microbiome than
changing the carbohydrate source (gluten). Although
De Palma et al. [8] did observe a reduction in polysac-
charide intake for GFD in healthy individuals, we were
unable to reproduce their finding because we could not
distinguish between different classes of carbohydrates
in our dataset as the food composition data on GFD
Bonder et al. Genome Medicine (2016) 8:45 Page 8 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
foods lacked this information. Further, it is possible that
changes in nutritional intake other than those driven by
gluten exclusion might influence microbiome changes.
For our selection of blood and stool biomarkers, we ob-
served no significant associations with the diet change. All
the selected biomarkers are markers of inflammation or
metabolic changes and remained in the normal range in
all our participants, with a high proportion of the values
of blood inflammatory markers being below the detection
limit. Overall, we conclude that a GFD and its down-
stream effects on the microbiome do not cause major
inflammatory or metabolic changes in gut function in
healthy participants. However, the lower abundance of
Veillonellaceae, the pro-inflammatory bacterium linked to
Crohns disease and other gut disease phenotypes, sug-
gests a reduction in gut inflammatory state. This change
in bacterial composition might be linked with a beneficial
effect of GFD for patients with gut disorders such as
gluten-related disorders and/or IBS.
We have identified eight taxa and 21 bacterial pathways
associated with a change from a habitual diet to a GFD
in healthy individuals. We conclude that the effect of
gluten intake on the microbiota is less pronounced than
that seen for a shift from a meat-based diet to a vegetar-
ian diet (or vice versa). However, a GFD diet clearly in-
fluences the abundance of several species, in particular
those involved specifically in carbohydrate and starch
metabolism. Our study illustrates that variations in diet
could confound the results of microbiome analysis in re-
lation to disease phenotypes, so dietary variations should
be carefully considered and reported in such studies.
The short-term GFD did not influence the levels of in-
flammatory gut biomarkers in healthy individuals. Fur-
ther research is needed to assess the impact of a GFD
on inflammatory and metabolic changes in gut function
in individuals with gastrointestinal conditions such as
IBS and gluten-related disorders.
Ethics approval and consent to participate
This GFD study followed the sampling protocol of the
LifeLines-DEEP study, which was approved by the ethics
committee of the University Medical Centre Groningen
and conform the Declaration of Helsinki, document no.
METC UMCG LLDEEP: M12.113965. All participants
signed their informed consent prior to study enrolment.
Availability of data and materials
The supporting data are available to researchers in the
European Nucleotide Archive, under study accession
number PRJEB13219 (
Additional files
Additional file 1: Figure S1. Baseline characteristics of the GFD study
group. (TIF 1068 kb)
Additional file 2: Table S1. Results macronutrient intake per participant.
(XLSX 13 kb)
Additional file 3: Figure S2. Unweighted unifrac distances when
comparing inter-individual vs intra individual distances. In group 1 the
intra-individual differences are shown regardless of diet. Group 2 shows
the intra-sample differences are shown within the same diet. Group 3
shows the intra-individual differences are shown between the two diet
groups. In group 4 the inter-individual differences are shown regardless
of diet. Group 5 shows the inter-sample differences are shown within
the same diet. Group 6 shows the inter-individual differences are shown
between the two diet groups. The main difference is the intra- vs. inter-
individual difference. Also the same diet points in the samples are slightly
closer to each other. However, we do not see such a phenomenon for group
Additional file 4: Figure S3. Abundance of Veillonellaceae family in the
GFD participants. In all but four participants we see a clear trend of higher
levels of Veillonellaceae on the habitual diet. The rightmost samples do not
show this phenomenon. (TIF 4036 kb)
Additional file 5: Table S2. Relation of smoking and microbiome
composition. (XLSX 9 kb)
Additional file 6: Figure S4. Measured butyrate levels vs. the predicted
activity of butyrate metabolism. (TIF 6 kb)
Additional file 7: Figure S5. Measured propionate levels vs. the predicted
activity of pyruvate metabolism. (TIF 646 kb)
Additional file 8: Table S3. Correlation of bacteria and levels of fecal
biomarkers. (PDF 139 kb)
Additional file 9: Table S4. Correlation of predicted HUMAnN pathway
activity and levels of fecal biomarkers. (PDF 228 kb)
Additional file 10: Table S5. Correlation of predicted HUMAnN
module activity and levels of fecal biomarkers. (PDF 143 kb)
BGI: Beijing Genomics Institute; ECLIA: electro-chemiluminescence immunoassay;
EDTA: ethylenediaminetetraacetic acid; ELISA: enzyme-linked immunosorbent
essay; FISH: fluorescence in situ hybridization; GC-MS: gas chromatographymass
spectrometry; GFD: gluten-free diet; HD: habitual diet; HPLC: high performance
liquid chromatography; IBS: irritable bowel syndrome; KEGG: Kyoto encyclopedia
of genes and genomes; OTU: operational taxonomic unit; PCoA: principal
coordinate analysis; qPCR: quantitative real-time polymerase chain reaction;
RIA: radioimmunoassay; SCFA: short chain fatty acids; SD: standard deviation.
Competing interests
The authors declare that they have no competing interests.
GT, AZ, and CW designed the study. GT, ET, BH, and MC were involved in
sample collection and DNA isolation. XC, HZ, and YW performed the data
generation. MJB, TV, DG, SZ, MC, and ET were involved in data processing,
analysis, and interpretation. MJB, SZ, and ET drafted the work. All authors have
critically revised this article and approved the final version to be published.
We thank all the participants for their collaboration as well as Jackie Senior
and Kate Mc Intyre for editing the manuscript. We thank Jackie Dekens, Zlatan
Mujagic and Daisy Jonkers for support with the biomarker analyses and Steins
lab for measuring the biomarkers. We thank Hermie Harmsen for the helpful
discussions during the project.
This study was funded by a European Research Council advanced grant (FP/
20072013/ERC grant 2012322698) to CW, a grant from the Top Institute Food
and Nutrition Wageningen (GH001) to CW, and a Rosalind Franklin Fellowship
Bonder et al. Genome Medicine (2016) 8:45 Page 9 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
from the University of Groningen to AZ. GT is supported by the Wellcome Trust
Sanger Institute, Cambridge, UK (WT098051).
Author details
Department of Genetics, University of Groningen, University Medical Centre
Groningen, Groningen, The Netherlands.
Top Institute Food and Nutrition,
Wageningen, The Netherlands.
BGI, Shenzhen 518083, China.
Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Broad Institute of
MIT and Harvard, Cambridge, MA 02142, USA.
Department of Computer
Science, Aalto University School of Science, Espoo 02150, Finland.
Received: 29 October 2015 Accepted: 5 April 2016
1. Sollid LM. Coeliac disease: dissecting a complex inflammatory disorder. Nat
Rev Immunol. 2002;2:64755.
2. Sapone A, Bai JC, Ciacci C, Dolinsek J, Green PH, Hadjivassiliou M, et al.
Spectrum of gluten-related disorders: consensus on new nomenclature and
classification. BMC Med. 2012;10:13.
3. Catassi C, Bai JC, Bonaz B, Bouma G, Calabrò A, Carroccio A, et al. Non-celiac
gluten sensitivity: the new frontier of gluten related disorders. Nutrients.
4. Vazquez-Roque MI, Camilleri M, Smyrk T, Murray JA, Marietta E, ONeill J, et al.
A controlled trial of gluten-free diet in patients with irritable bowel syndrome-
diarrhea: effects on bowel frequency and intestinal function. Gastroenterology.
5. Collado MC, Donat E, Ribes-Koninckx C, Calabuig M, Sanz Y. Specific duodenal
and faecal bacterial groups associated with paediatric coeliac disease. J Clin
Pathol. 2009;62:2649.
6. Di Cagno R, Rizzello CG, Gagliardi F, Ricciuti P, Ndagijimana M, Francavilla R,
et al. Different fecal microbiotas and volatile organic compounds in treated
and untreated children with celiac disease. Appl Environ Microbiol. 2009;75:
7. Nistal E, Caminero A, Vivas S, de Morales JMR, de Miera LES, Rodríguez-
Aparicio LB, et al. Differences in faecal bacteria populations and faecal
bacteria metabolism in healthy adults and celiac disease patients. Biochimie.
8. De Palma G, Nadal I, Collado MC, Sanz Y. Effects of a gluten-free diet on gut
microbiota and immune function in healthy adult human subjects. Br J
Nutr. 2009;102:115460.
9. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE,
et al. Diet rapidly and reproducibly alters the human gut microbiome.
Nature. 2014;505:55963.
10. Tigchelaar EF, Zhernakova A, Dekens JAM, Hermes G, Baranska A, Mujagic Z,
et al. An Introduction to LifeLines DEEP: Study Design and Baseline
Characteristics. Cold Spring Harbor Labs J. 2014.
11. Baranska A, Tigchelaar E, Smolinska A, Dallinga JW, Moonen EJC, Dekens
JAM, et al. Profile of volatile organic compounds in exhaled breath changes
as a result of gluten-free diet. J Breath Res. 2013;7:037104.
12. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello
EK. QIIME allows analysis of high-throughput community sequencing data.
Nat Methods. 2010;7:3356.
13. Edgar RC. Search and clustering orders of magnitude faster than BLAST.
Bioinformatics. 2010;26:24601.
14. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al.
Greengenes, a chimera-checked 16S rRNA gene database and workbench
compatible with ARB. Appl Environ Microbiol. 2006;72:506972.
15. Brandt BW, Bonder MJ, Huse SM, Zaura E. TaxMan: a server to trim rRNA
reference databases and inspect taxonomic coverage. Nucleic Acids Res.
2012;40(Web Server issue)):W827.
16. Bonder MJ, Abeln S, Zaura E, Brandt BW. Comparing clustering and pre-
processing in taxonomy analysis. Bioinformatics. 2012;28:28917.
17. May A, Abeln S, Crielaard W, Heringa J, Brandt BW. Unraveling the outcome
of 16S rDNA-based taxonomy analysis through mock data and simulations.
Bioinformatics. 2014;30:15308.
18. Ding T, Schloss PD. Dynamics and associations of microbial community
types across the human body. Nature. 2014;509:35760.
19. Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes J, et al.
Predictive functional profiling of microbial communities using 16S rRNA
marker gene sequences. Nat Biotechnol. 2013;31:81421.
20. Abubucker S, Segata N, Goll J, Schubert AM, Izard J, Cantarel BL, et al.
Metabolic reconstruction for metagenomic data and its application to the
human microbiome. PLoS Comput Biol. 2012;8:e1002358.
21. Tickle T, Waldron L, Yiren Lu, Huttenhower C. Multivariate association of microbial
communities with rich metadata in high-dimensional studies. (In progress)
22. Tickle T. QiimeToMaAsLin. 2013.
23. R Development Core Team RFFSC. R: A Language and Environment for
Statistical Computing. R Foundation for Statistical Computing. Vienna: R
Development; 2008.
24. Dabney A, Storey JD, qvalue Tutorial PRS, Install R, Packages IB, Packages UB,
Vignettes P, Cloud AMI, Team C. Q-value estimation for false discovery rate
control. Medicine. 2004:539548
25. Tibble JA, Sigthorsson G, Foster R, Scott D, Fagerhol MK, Roseth A, et al.
High prevalence of NSAID enteropathy as shown by a simple faecal test.
Gut. 1999;45:3626.
26. Joshi S, Lewis SJ, Creanor S, Ayling RM. Age-related faecal calprotectin,
lactoferrin and tumour M2-PK concentrations in healthy volunteers. Ann
Clin Biochem. 2010;47(Pt 3):25963.
27. Maheshwari A, Lu W, Lacson A, Barleycorn AA, Nolan S, Christensen RD,
et al. Effects of interleukin-8 on the developing human intestine. Cytokine.
28. Harder J, Bartels J, Christophers E, Schröder JM. A peptide antibiotic from
human skin. Nature. 1997;387:861.
29. Langhorst J, Junge A, Rueffer A, Wehkamp J, Foell D, Michalsen A, et al.
Elevated human beta-defensin-2 levels indicate an activation of the innate
immune system in patients with irritable bowel syndrome. Am J
Gastroenterol. 2009;104:40410.
30. El-Salhy M, Lomholt-Beck B, Hausken T. Chromogranin A as a possible tool in the
diagnosis of irritable bowel syndrome. Scand J Gastroenterol. 2010;45:14359.
31. SidhuR,DrewK,McAlindonME,LoboAJ,SandersDS.Elevatedserum
chromogranin A in irritable bowel syndrome (IBS) and inflammatory
bowel disease (IBD): a shared model for pathogenesis? Inflamm Bowel
Dis. 2010;16:361.
32. Ohman L, Stridsberg M, Isaksson S, Jerlstad P, Simrén M. Altered levels of fecal
chromogranins and secretogranins in IBS: relevance for pathophysiology and
symptoms? Am J Gastroenterol. 2012;107:4407.
33. Hamer HM, Jonkers D, Venema K, Vanhoutvin S, Troost FJ, Brummer R-J.
Review article: the role of butyrate on colonic function. Aliment Pharmacol
Ther. 2008;27:10419.
34. Windmueller HG, Spaeth AE. Source and fate of circulating citrulline. Am J
Physiol. 1981;241:E47380.
35. Crenn P, Messing B, Cynober L. Citrulline as a biomarker of intestinal failure
due to enterocyte mass reduction. Clin Nutr. 2008;27:32839.
36. Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, et al.
Richness of human gut microbiome correlates with metabolic markers.
Nature. 2013;500:5416.
37. Brock M, Maerker C, Schütz A, Völker U, Buckel W. Oxidation of propionate
to pyruvate in Escherichia coli: Involvement of methylcitrate dehydratase
and aconitase. Eur J Biochem. 2002;269:618494.
38. Kang D-W, Park JG, Ilhan ZE, Wallstrom G, LaBaer J, Adams JB, et al. Reduced
incidence of prevotella and other fermenters in intestinal microflora of autistic
children. PLoS One. 2013;8:e68322.
39. Gevers D, Kugathasan S, Denson LA, Vázquez-Baeza Y, Van Treuren W, Ren
B, et al. The treatment-naive microbiome in new-onset Crohns disease. Cell
Host Microbe. 2014;15:38292.
40. Haberman Y, Tickle TL, Dexheimer PJ, Kim M, Tang D, Karns R, et al. Pediatric
Crohn disease patients exhibit specific ileal transcriptome and microbiome
signature. J Clin Invest. 2014;124:361733.
41. Shukla R, Ghoshal U, Dhole TN, Ghoshal UC. Fecal microbiota in patients
with irritable bowel syndrome compared with healthy controls using
real-time polymerase chain reaction: an evidence of dysbiosis. Dig Dis Sci.
42. Ze X, Duncan SH, Louis P, Flint HJ. Ruminococcus bromii is a keystone species for
the degradation of resistant starch in the human colon. ISME J. 2012;6:153543.
43. Walker AW, Ince J, Duncan SH, Webster LM, Holtrop G, Ze X, et al. Dominant
and diet-responsive groups of bacteria within the human colonic microbiota.
ISME J. 2011;5:22030.
44. Rajilić-StojanovićM. Function of the microbiota. Best Pract Res Clin
Gastroenterol. 2013;27:516.
45. Aziz I, Trott N, Briggs R, North JR, Hadjivassiliou M, Sanders DS. Efficacy of
a gluten-free diet in subjects with irritable bowel syndrome-diarrhea
Bonder et al. Genome Medicine (2016) 8:45 Page 10 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
unaware of their HLA-DQ2/8 Genotype. Clin Gastroenterol Hepatol.
2015. doi:10.1016/j.cgh.2015.12.031 [Epub ahead of print].
46. Tigchelaar EF, Bonder MJ, Jankipersadsing SA, Fu J, Wijmenga C, Zhernakova
A. Gut microbiota composition associated with stool consistency. Gut. 2016;
47. Maukonen J, Kolho K-L, Paasela M, Honkanen J, Klemetti P, Vaarala O, et al.
Altered fecal microbiota in paediatric inflammatory bowel disease. J Crohns
Colitis. 2015;9:108895.
48. Chen W, Liu F, Ling Z, Tong X, Xiang C. Human intestinal lumen and
mucosa-associated microbiota in patients with colorectal cancer. PLoS
One. 2012;7:e39743.
49. Olivares M, Neef A, Castillejo G, De Palma G, Varea V, Capilla A, et al. The
HLA-DQ2 genotype selects for early intestinal microbiota composition in
infants at high risk of developing coeliac disease. Gut. 2015;64:40617.
50. Fang S, Evans RM. Microbiology: Wealth management in the gut. Nature.
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... Positive association of genus Slackia (member of family Coriobacteriaceae) with adaptive IF13, which includes production of the T-cell cytokines IL-10, IFN-γ, and IL-17 has not been reported previously. However, the family Coriobacteriaceae and genus Slackia have been associated with increased gut permeability and inflammation (48)(49)(50)(51)(52)(53) which could cause increased activation and development of effector/memory T-cells producing these effector cytokines. Secondary analysis of individual immune variables which identified a negative association between Slackia and the percentage of intermediate monocytes is also a novel finding and should be assessed by experimental studies. ...
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Interactions among intestinal bacteria and the immune system contribute to the maintenance of a functional intestinal barrier in healthy individuals, and possibly to systemic immune activity. We hypothesized that intestinal bacteria would be associated with systemic biomarkers of innate and adaptive immune responses in healthy adults. 79 immune function markers were subjected to factor analysis resulting in 17 Immune Factors (IFs), each composed of 2–10 immune variables. Bacterial taxa from stool samples were identified at the family and genus levels by 16S rRNA amplicon sequence analysis and their read counts and relative abundances were utilized in a multiple linear regression model to identify microbial taxa associated with the IFs. A total of 10 significant associations were identified between bacterial taxa and IFs. The family Rikenellaceae showed a positive association with innate IF5 (including 5 chemokines, 2 cytokines, 2 adhesion molecules, and the macrophage metabolite neopterin) and a negative association with adaptive IF4 (including T-cells with activation marker HLA-DR). Additionally, Pseudomonadaceae and its genus Pseudomonas showed a negative relationship with innate IF5, and adaptive IF13 (including T-cell cytokines IL-10, IL-17, and IFN-γ) was negatively associated with Butyrivibrio and positively associated with Slackia . These associations suggest ongoing interactions between gut bacteria and the systemic immune system in healthy adults. The association of these taxa with the IFs may result from specific microbial-immune system interactions that play a role in maintenance of a healthy barrier integrity in our cohort of healthy adults. IMPORTANCE Chronic inflammation may develop over time in healthy adults as a result of a variety of factors, such as poor diet directly affecting the composition of the intestinal microbiome, or by causing obesity, which may also affect the intestinal microbiome. These effects may trigger the activation of an immune response that could eventually lead to an inflammation-related disease, such as colon cancer. Before disease develops it may be possible to identify subclinical inflammation or immune activation attributable to specific intestinal bacteria normally found in the gut that could result in future adverse health impacts. In the present study, we examined a group of healthy men and women across a wide age range with and without obesity to determine which bacteria were associated with particular types of immune activation to identify potential preclinical markers of inflammatory disease risk. Several associations were found that may help develop dietary interventions to lower disease risk.
... The Mediterranean diet is considered beneficial to health [106]. According to research on micronutrients, gluten-free bread can help alleviate microbiota dysbiosis, but further investigation is needed to gain a deeper understanding of these hypotheses [107]. ...
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It is acknowledged that humans have a diverse and abundant microbial community known as the human microbiome. Nevertheless, our comprehension of the numerous functions these microorganisms have in human health is still in its early stages. Microorganisms belonging to the human microbiome typically coexist with their host, but in certain situations, they can lead to diseases. They are found in several areas of the human body in healthy individuals. The microbiome is highly diverse, and its composition varies depending on the body site. It primarily comprises bacteria that are crucial for upholding a state of well-being and equilibrium. The microbiome’s influence on atopic dermatitis development was, therefore, analyzed. The importance of maintaining a balanced and functional commensal microbiota, as well as the use of prebiotics and probiotics in the prevention and treatment of atopic dermatitis were also explored. The skin microbiome’s association with atopic dermatitis will allow for a better understanding of pathogenesis and also exploring new therapeutic approaches, making the skin microbiome an increasingly relevant therapeutic target.
... Furthermore, it is reasonable to suppose that GFD may also improve gut microbiota composition and the dysbiotic state, which, in turn, may sustain the vicious circle of gut epithelium damage, chronic inflammation, and, in people with a genetic predisposing background, the trigger of autoimmunity (34)(35)(36). ...
... Like above, though, the environment-based microbiota (family members' non-gluten-free habits, shared environment, genetics) may have led to decreased microbiota shifts in CD children. While a slew of studies found evidence for microbial shifts following gluten-free diet both in the context of CD (34,36) and in its absence (37), there is also some evidence that CD diet control does not completely restore the microbiota to that of healthy controls, though in some contexts, some shifts away from CD-associated dysbiosis have been recorded (38). A study comparing CD patients with strict gluten-free diet adherence (and TG control) to those with weak adherence (no TG control) did not find differences between the groups' microbiota profiles either (39). ...
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Accumulating evidence supports the role of microbiota in autoimmune processes, but research regarding the role of the gut microbiota in celiac disease (CD) is still emerging, and a consistent CD-associated dysbiosis pattern has not yet been defined. Here, we characterized the microbiota of children newly diagnosed with CD, with their unaffected family members as a healthy control group to reduce confounding factors including genetic background, hygiene, dietary habits, and environment, and followed children with CD over 1 year of dietary intervention (exclusion of gluten) to understand if the microbiota is associated with CD and its mediation. We did not find differences in the microbiota of siblings with and without CD, despite a wealth of evidence in the literature supporting CD-specific microbiota. CD is common among first-degree relatives, so this could suggest that unaffected family members in this study may be living in a pre-CD state, currently below clinical detection. Interestingly, despite the effectiveness of diet in CD control, we did not observe diet-mediated microbiota changes, except for short-term increase in Akkermansia muciniphila . This lack of effect could suggest a very strong CD microbial signature even when controlled or could be a technical shortcoming. Expanded future studies with both related and unrelated controls and diet interventions in both the CD and control arms can provide further context to our findings. IMPORTANCE The microbiota is the community of microbes that live in and on us. These microbes are essential to our health and everyday function. Disruption of the community is associated with diseases ranging from metabolic syndrome to autoimmune diseases to mental disorders. In the case of celiac disease (CD), research remains inconclusive regarding implications of the microbiota in etiology. Here, we compared microbiota of children with CD to those of their unaffected family members and found very few differences in microbiota profiles. We next examined how gluten elimination in CD patients affects the microbiota. Surprisingly, despite diet adherence, microbiota shifts were minimal, with only a short-term increase in Akkermansia muciniphila . Previous studies suggest that family members of CD patients may be living in a pre-CD state, which could explain their microbial similarity. A larger study with unrelated controls and increased microbiota monitoring during diet intervention should give our findings more perspective.
... Furthermore, it is reasonable to suppose that GFD may also improve gut microbiota composition and the dysbiotic state, which, in turn, may sustain the vicious circle of gut epithelium damage, chronic inflammation, and, in people with a genetic predisposing background, the trigger of autoimmunity (34)(35)(36). ...
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Background Hashimoto’s thyroiditis (HT) is the most common autoimmune disease. HT may be associated with nonthyroidal autoimmune diseases, including celiac disease (CD) or other gluten-related conditions (GRC). In the last years, interest about gluten-free diet (GFD) has increased for its supposed extraintestinal anti-inflammatory effect; thus, many patients with HT initiate GFD on their own. Objectives The aim of this meta-analysis is to examine all available data in literature about the effect of a GFD on TgAb, TPOAb, TSH, FT4, and FT3 levels in patients with HT and no symptoms or histology of CD. Methods The study was conducted according to MOOSE (Meta-analysis Of Observational Studies in Epidemiology). The search was performed on databases PubMed and Scopus. The last search was performed on 7 February 2023. Quality assessment was performed. Meta-analyses were performed using the random-effect model. Hedges’ g was used to measure the effect size (ES). Statistical analyses were performed using StataSE 17. Results The online search retrieved 409 articles, and 4 studies with a total of 87 patients were finally included for quantitative analysis. The risk of bias was generally low. The mean period of GFD was almost 6 months. The meta-analyses showed reduction in antibody levels with ES: −0.39 for TgAb (95% CI: −0.81 to +0.02; p = 0.06; I ² = 46.98%) and −0.40 for TPOAb (95% CI: −0.82 to +0.03; p = 0.07; I ² = 47.58%). TSH showed a reduction with ES: −0.35 (95% CI: −0.64 to −0.05; p = 0.02; I ² = 0%) and FT4 showed an increase with ES: +0.35% (95% CI: 0.06 to 0.64; p = 0.02; I ² = 0%). FT3 did not display variations (ES: 0.05; 95% CI: −0.38 to +0.48; p = 0.82; I ² = 51%). The heterogeneity of TgAb, TPOAb, and FT3 data was solved performing sub-analyses between patients with or without GRC (TgAb p = 0.02; TPOAb p = 0.02; FT3 p = 0.04) and only for FT3, performing a sub-analysis between patients taking and not taking LT4 ( p = 0.03). Conclusion This is the first meta-analysis investigating the effect of GFD on HT. Our results seem to indicate a positive effect of the gluten deprivation on thyroid function and its inflammation, particularly in patients with HT and GRC. However, current lines of evidence are not yet sufficient to recommend this dietary approach to all patients with a diagnosis of HT.
... Studies examining a gluten-free diet in both diseased and healthy humans have shown alterations in the gut microbiota composition [24][25][26], mainly with reduced levels of Bifidobacterium [25,26]. However, these results are believed to relate to the simultaneous reduction of dietary fiber and not the intake of gluten per se [26,27]. Interestingly, a study on healthy subjects consuming a diet rich in gluten found no effects on the gut microbiota composition [26]. ...
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Background: A mechanistic understanding of the effects of dietary treatment in irritable bowel syndrome (IBS) is lacking. Our aim was therefore to investigate how fermentable oligo- di-, monosaccharides, and polyols (FODMAPs) and gluten affected gut microbiota and circulating metabolite profiles, as well as to investigate potential links between gut microbiota, metabolites, and IBS symptoms. Methods: We used data from a double-blind, randomized, crossover study with week-long provocations of FODMAPs, gluten, and placebo in participants with IBS. To study the effects of the provocations on fecal microbiota, fecal and plasma short-chain fatty acids, the untargeted plasma metabolome, and IBS symptoms, we used Random Forest, linear mixed model and Spearman correlation analysis. Results: FODMAPs increased fecal saccharolytic bacteria, plasma phenolic-derived metabolites, 3-indolepropionate, and decreased isobutyrate and bile acids. Gluten decreased fecal isovalerate and altered carnitine derivatives, CoA, and fatty acids in plasma. For FODMAPs, modest correlations were observed between microbiota and phenolic-derived metabolites and 3-indolepropionate, previously associated with improved metabolic health, and reduced inflammation. Correlations between molecular data and IBS symptoms were weak. Conclusions: FODMAPs, but not gluten, altered microbiota composition and correlated with phenolic-derived metabolites and 3-indolepropionate, with only weak associations with IBS symptoms. Thus, the minor effect of FODMAPs on IBS symptoms must be weighed against the effect on microbiota and metabolites related to positive health factors.
Objective This study aimed to investigate the impact of esketamine on the intestinal flora and microenvironment in mice using mRNA transcriptome sequencing and 16S rRNA sequencing. Methods Ten female mice were randomly assigned to two groups. One group received daily intramuscular injections of sterile water, while the other group received esketamine. After 24 days, the mice were sacrificed, and their intestinal tissues and contents were collected for 16S rRNA sequencing and mRNA transcriptome sequencing. The intergroup differences in the mouse intestinal flora were analyzed. Differentially expressed genes were utilized to construct ceRNA networks and transcription factor regulatory networks to assess the effects of esketamine on the intestinal flora and intestinal tissue genes. Results Esketamine significantly altered the abundance of intestinal microbiota, including Adlercreutzia equolifaciens and Akkermansia muciniphila. Differential expression analysis revealed 301 significantly upregulated genes and 106 significantly downregulated genes. The ceRNA regulatory network consisted of 6 lncRNAs, 44 miRNAs, and 113 mRNAs, while the regulatory factor network included 13 transcription factors and 53 target genes. Gene Ontology enrichment analysis indicated that the differentially expressed genes were primarily associated with immunity, including B-cell activation and humoral immune response mediation. The biological processes in the ceRNA regulatory network primarily involved transport, such as organic anion transport and monocarboxylic acid transport. The functional annotation of target genes in the TF network was mainly related to epithelial cells, including epithelial cell proliferation and regulation. Conclusion Esketamine induces changes in gut microbiota and the intestinal microenvironment, impacting the immune environment and transport modes.
Purpose Previous studies have found that a gluten-free diet (GFD) may have improve obesity-related factors. For this reason, we conducted a systematic review and meta-analysis to investigate the effect of a GFD on anthropometric indicators. Methods We performed a systematic search in databases from inception until July 12, 2022. We included all relevant articles that evaluate efficacy of a GFD on anthropometric indicators in patients with and without celiac disease (CD). Random-effects models were applied to combine the data. The main outcomes were then analyzed using weight mean differences (WMDs) and 95% CIs. Findings A total of 27 articles met the eligible criteria and were included. Pooled results from the random-effects model indicated that the GFD has no significant effect on any of the factors of anthropometry, including weight (WMD, 1.20 kg; 95% CI, −1.16 to 3.55 kg; P = 0.319), body mass index (WMD, 0.70 kg/m2; 95% CI, −0.45 to 1.84 kg/m2; P = 0.233), waist circumference (WMD, 0.92 cm; 95% CI, −1.34 to 3.17 cm; P = 0.497), and body fat (WMD, 1.02%; 95% CI, −0.38% to 2.42%; P = 0.153). The subgroup results indicated that after implementation of a GFD significant increased weight and body fat occurred in patients with compared with without CD. In addition, the effect of this diet on the increase of BMI and body fat in the intervention of more than 48 weeks was significantly higher. Implications The results of the present study indicate that a GFD can have a significant and beneficial effect on weight and body fat in patients with CD.
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Individual variability in drug response (IVDR) can be a major cause of adverse drug reactions (ADRs) and prolonged therapy, resulting in a substantial health and economic burden. Despite extensive research in pharmacogenomics regarding the impact of individual genetic background on pharmacokinetics (PK) and pharmacodynamics (PD), genetic diversity explains only a limited proportion of IVDR. The role of gut microbiota, also known as the second genome, and its metabolites in modulating therapeutic outcomes in human diseases have been highlighted by recent studies. Consequently, the burgeoning field of pharmacomicrobiomics aims to explore the correlation between microbiota variation and IVDR or ADRs. This review presents an up-to-date overview of the intricate interactions between gut microbiota and classical therapeutic agents for human systemic diseases, including cancer, cardiovascular diseases (CVDs), endocrine diseases, and others. We summarise how microbiota, directly and indirectly, modify the absorption, distribution, metabolism, and excretion (ADME) of drugs. Conversely, drugs can also modulate the composition and function of gut microbiota, leading to changes in microbial metabolism and immune response. We also discuss the practical challenges, strategies, and opportunities in this field, emphasizing the critical need to develop an innovative approach to multi-omics, integrate various data types, including human and microbiota genomic data, as well as translate lab data into clinical practice. To sum up, pharmacomicrobiomics represents a promising avenue to address IVDR and improve patient outcomes, and further research in this field is imperative to unlock its full potential for precision medicine.
The aim of this study was to investigate changes in gut microbiome both during and after the consumption of malted rice amazake (MR-Amazake), a fermented food from Japan, in home healthcare patients with disabilities including patients with severe motor and intellectual disabilities (SMID). We monitored 12 patients who consumed MR-Amazake for six weeks, investigating them before and after the intervention as well as six weeks after the end of intake to compare their physical condition, diet, the type of their medication, Constipation Assessment Scale (CAS), and an analysis of their comprehensive fecal microbiome using 16S rRNA sequencing. Their constipation symptom significantly alleviated and principal coordinates analysis revealed that 30% of patients showed significant changes in gut microbiome after MR-Amazake ingestion. Furthermore, Bifidobacterium was strongly associated with these changes. These changes were observed only during MR-Amazake intake; the original gut microbiome was restored when MR-Amazake intake was discontinued. These results suggest that six weeks is a reasonable period of time for MR-Amazake to change human gut microbiome and that continuous consumption of MR-Amazake is required to sustain such changes.
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There is a critical need for population-based prospective cohort studies because they follow individuals before the onset of disease, allowing for studies that can identify biomarkers and disease-modifying effects and thereby contributing to systems epidemiology. This paper describes the design and baseline characteristics of an intensively examined subpopulation of the LifeLines cohort in the Netherlands. For this unique sub-cohort, LifeLines DEEP, additional blood (n=1387), exhaled air (n=1425), fecal samples (n=1248) and gastrointestinal health questionnaires (n=1176) were collected for analysis of the genome, epigenome, transcriptome, microbiome, metabolome and other biological levels. Here, we provide an overview of the different data layers in LifeLines DEEP and present baseline characteristics of the study population including food intake and quality of life. We also describe how the LifeLines DEEP cohort allows for the detailed investigation of genetic, genomic and metabolic variation on a wealth of phenotypic outcomes. Finally, we examine the determinants of gastrointestinal health, an area of particular interest to us that can be addressed by LifeLines DEEP.
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Background and aims: Several factors support the view of IBD origin in the host responsiveness to intestinal bacteria although no single bacterial species has been shown as a causative agent in the pathogenesis. Our aim was to analyze the fecal microbiota of pediatric IBD patients at different stages of the disease. In addition, the characteristics of immune response to the bacterial isolates showing very low abundance in IBD were studied. Methods: Fecal samples (1-3 samples/child) were collected from 10 pediatric patients with CD, 12 with UC and from 8 healthy children for polyphasic microbiological analysis (culture, real-time PCR and denaturing gradient gel electrophoresis). In addition, in vitro cytokine responses of PBMCs to the bacterial isolates, which showed very low abundance in IBD, were studied. Results: Although predominant bacterial diversity was higher in IBD, the numbers of Lachnospiraceae and Coriobacteriaceae bacteria were lower in IBD patients as compared to control children (p<0.05). In addition, Ruminococcaceae population diversity was lower in IBD (p<0.05) and correlated negatively with fecal calprotection levels. Both abundance and diversity of bifidobacterial populations were lower in children with IBD (p<0.05), and particularly low numbers of certain bifidobacterial isolates were detected. In CD, we found enhanced up-regulation of IL-6 transcripts and impaired RORC response to bifidobacteria, whereas decreased IFN-γ response was observed in both CD and UC. Conclusion: We demonstrate altered fecal microbiota in pediatric IBD, particularly low numbers and diversity of bifidobacterial populations. Interestingly, immunological response to bifidobacteria differed between pediatric CD patients and control children.
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Interactions between the host and gut microbial community likely contribute to Crohn disease (CD) pathogenesis; however, direct evidence for these interactions at the onset of disease is lacking. Here, we characterized the global pattern of ileal gene expression and the ileal microbial community in 359 treatment-naive pediatric patients with CD, patients with ulcerative colitis (UC), and control individuals. We identified core gene expression profiles and microbial communities in the affected CD ilea that are preserved in the unaffected ilea of patients with colon-only CD but not present in those with UC or control individuals; therefore, this signature is specific to CD and independent of clinical inflammation. An abnormal increase of antimicrobial dual oxidase (DUOX2) expression was detected in association with an expansion of Proteobacteria in both UC and CD, while expression of lipoprotein APOA1 gene was downregulated and associated with CD-specific alterations in Firmicutes. The increased DUOX2 and decreased APOA1 gene expression signature favored oxidative stress and Th1 polarization and was maximally altered in patients with more severe mucosal injury. A regression model that included APOA1 gene expression and microbial abundance more accurately predicted month 6 steroid-free remission than a model using clinical factors alone. These CD-specific host and microbe profiles identify the ileum as the primary inductive site for all forms of CD and may direct prognostic and therapeutic approaches.
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Objective Intestinal dysbiosis has been associated with coeliac disease (CD), but whether the alterations are cause or consequence of the disease is unknown. This study investigated whether the human leukocyte antigen (HLA)-DQ2 genotype is an independent factor influencing the early gut microbiota composition of healthy infants at family risk of CD. Design As part of a larger prospective study, a subset (n=22) of exclusively breastfed and vaginally delivered infants with either high genetic risk (HLA-DQ2 carriers) or low genetic risk (non-HLA-DQ2/8 carriers) of developing CD were selected from a cohort of healthy infants with at least one first-degree relative with CD. Infant faecal microbiota was analysed by 16S rRNA gene pyrosequencing and real time quantitative PCR. Results Infants with a high genetic risk had significantly higher proportions of Firmicutes and Proteobacteria and lower proportions of Actinobacteria compared with low-risk infants. At genus level, high-risk infants had significantly less Bifidobacterium and unclassified Bifidobacteriaceae proportions and more Corynebacterium, Gemella, Clostridium sensu stricto, unclassified Clostridiaceae, unclassified Enterobacteriaceae and Raoultella proportions. Quantitative real time PCR also revealed lower numbers of Bifidobacterium species in infants with high genetic risk than in those with low genetic risk. In high-risk infants negative correlations were identified between Bifidobacterium species and several genera of Proteobacteria (Escherichia/Shigella) and Firmicutes (Clostridium). Conclusions The genotype of infants at family risk of developing CD, carrying the HLA-DQ2 haplotypes, influences the early gut microbiota composition. This finding suggests that a specific disease-biased host genotype may also select for the first gut colonisers and could contribute to determining disease risk.
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A primary goal of the Human Microbiome Project (HMP) was to provide a reference collection of 16S ribosomal RNA gene sequences collected from sites across the human body that would allow microbiologists to better associate changes in the microbiome with changes in health. The HMP Consortium has reported the structure and function of the human microbiome in 300 healthy adults at 18 body sites from a single time point. Using additional data collected over the course of 12-18 months, we used Dirichlet multinomial mixture models to partition the data into community types for each body site and made three important observations. First, there were strong associations between whether individuals had been breastfed as an infant, their gender, and their level of education with their community types at several body sites. Second, although the specific taxonomic compositions of the oral and gut microbiomes were different, the community types observed at these sites were predictive of each other. Finally, over the course of the sampling period, the community types from sites within the oral cavity were the least stable, whereas those in the vagina and gut were the most stable. Our results demonstrate that even with the considerable intra- and interpersonal variation in the human microbiome, this variation can be partitioned into community types that are predictive of each other and are probably the result of life-history characteristics. Understanding the diversity of community types and the mechanisms that result in an individual having a particular type or changing types, will allow us to use their community types to assess disease risk and to personalize therapies.
Background & aims: A gluten-containing diet alters bowel barrier function in patients with irritable bowel syndrome with diarrhea (IBS-D), particularly those who are positive for HLA allele DQ2/8. We studied the effects of a gluten-free diet (GFD) in patients with IBS-D who have not previously considered the effects of gluten in their diet and were unaware of their HLA-DQ2/8 genotype. Methods: We performed a prospective study of 41 patients with IBS-D (20 HLA-DQ2/8-positive and 21 HLA-DQ2/8-negative) at the Royal Hallamshire Hospital in Sheffield, United Kingdom, from September 2012 through July 2015. All subjects were placed on a 6-week GFD following evaluation by a dietician. Subjects completed validated questionnaires at baseline and Week 6 of the GFD. The primary endpoint was mean change in IBS Symptom Severity Score; a 50-point reduction was considered to indicate a clinical response. Secondary endpoints were changes in hospital anxiety and depression score, fatigue impact score, and Short Form-36 results. Clinical responders who chose to continue a GFD after the study period were evaluated on average 18 months later to assess diet durability, symptom scores, and anthropometric and biochemical status. Results: A 6-week GFD reduced IBS Symptom Severity Score by ≥50 points in 29 patients overall (71%). The mean total IBS Symptom Severity Score decreased from 286 before the diet to 131 points after 6 weeks on the diet (P < .001); the reduction was similar in each HLA-DQ group. However, HLA-DQ2/8-negative subjects had a greater reduction in abdominal distention (P = .04). Both groups had marked mean improvements in hospital anxiety and depression scores, fatigue impact score, and Short Form-36 results, although HLA-DQ2/8-positive subjects had a greater reduction in depression score and increase in vitality score than HLA-DQ2/8-negative subjects (P = .02 and P = .03, respectively). Twenty-one of the 29 subjects with a clinical response (72%) planned to continue the GFD long term; 18 months after the study they were still on a GFD, with maintained symptom reductions, and demonstrated similar anthropometric and biochemical features compared with baseline. Conclusions: A dietitian-led GFD provided sustained benefit to patients with IBS-D. The symptoms that improved differed in magnitude according to HLA-DQ status. Clinical no: NCT02528929.
Vandeputte et al 1 recently reported a strong effect of stool consistency—as measured by the Bristol Stool Scale (BSS)—on the composition of the gut microbiota in 53 healthy females. This work potentially has a large impact on future microbiome studies as it suggests that such studies may need to be corrected for BSS scores. However, the generalisability of their study is not immediately evident as it did not include a replication cohort and was limited to females aged 20–55 years. We analysed gut microbiota in relation to BSS in LifeLines-DEEP, a large population-based cohort.2 From 1126 LifeLines-DEEP participants, with both males (n=454) and females (n=672) aged 18–81 years (table 1), the BSS score was recorded for seven consecutive days and a fresh-frozen stool sample was collected in the same week. We calculated the average stool type of 7-day records for each participant. Stool DNA was isolated using AllPrep DNA/RNA Mini Kit (Qiagen; cat. #80204), and subsequently we performed 16s rRNA gene sequencing using forward primer …
The pathway of the oxidation of propionate to pyruvate in Escherichia coli involves five enzymes, only two of which, methylcitrate synthase and 2-methylisocitrate lyase, have been thoroughly characterized. Here we report that the isomerization of (2S,3S)-methylcitrate to (2R,3S)-2-methylisocitrate requires a novel enzyme, methylcitrate dehydratase (PrpD), and the well-known enzyme, aconitase (AcnB), of the tricarboxylic acid cycle. AcnB was purified as 2-methylaconitate hydratase from E. coli cells grown on propionate and identified by its N-terminus. The enzyme has an apparent Km of 210 µm for (2R,3S)-2-methylisocitrate but shows no activity with (2S,3S)-methylcitrate. On the other hand, PrpD is specific for (2S,3S)-methylcitrate (Km = 440 µm) and catalyses in addition only the hydration of cis-aconitate at a rate that is five times lower. The product of the dehydration of enzymatically synthesized (2S,3S)-methylcitrate was designated cis-2-methylaconitate because of its ability to form a cyclic anhydride at low pH. Hence, PrpD catalyses an unusual syn elimination, whereas the addition of water to cis-2-methylaconitate occurs in the usual anti manner. The different stereochemistries of the elimination and addition of water may be the reason for the requirement for the novel methylcitrate dehydratase (PrpD), the sequence of which seems not to be related to any other enzyme of known function. Northern-blot experiments showed expression of acnB under all conditions tested, whereas the RNA of enzymes of the prp operon (PrpE, a propionyl-CoA synthetase, and PrpD) was exclusively present during growth on propionate. 2D gel electrophoresis showed the production of all proteins encoded by the prp operon during growth on propionate as sole carbon and energy source, except PrpE, which seems to be replaced by acetyl-CoA synthetase. This is in good agreement with investigations on Salmonella enterica LT2, in which disruption of the prpE gene showed no visible phenotype.
Dysbiosis may play a role in irritable bowel syndrome (IBS), hitherto an enigmatic disorder. We evaluated selected fecal microbes in IBS patients and healthy controls (HC). Fecal 16S rRNA copy number of selected bacteria was studied using qPCR in 47 patients with IBS (Rome III) and 30 HC. Of 47 patients, 20 had constipation (IBS-C), 20 diarrhea (IBS-D), and seven unclassified IBS (IBS-U). Relative difference in 16S rRNA copy number of Bifidobacterium (P = 0.042) was lower, while those of Ruminococcus productus-Clostridium coccoides (P = 0.016), Veillonella (P = 0.008), Bacteroides thetaiotamicron (P < 0.001), Pseudomonas aeruginosa (P < 0.001), and Gram-negative bacteria (GNB, P = 0.001) were higher among IBS patients than HC. Number of Lactobacillus (P = 0.002) was lower, while that of Bacteroides thetaiotamicron (P < 0.001) and segmented filamentous bacteria (SFB, P < 0.001) was higher among IBS-D than IBS-C. Numbers of Bacteroides thetaiotamicron (P < 0.001), P. aeruginosa (P < 0.001), and GNB (P < 0.01) were higher among IBS-C and IBS-D than HC. Quantity of SFB was higher among IBS-D (P = 0.011) and lower among IBS-C (P = 0.002) than HC. Number of Veillonella species was higher among IBS-C than HC (P = 0.002). P. aeruginosa was frequently detected among IBS than HC (46/47 [97.9 %] vs. 10/30 [33.3 %], P < 0.001). Abdominal distension (n = 34/47) was associated with higher number of Bacteroides thetaiotamicron, Clostridium coccoides, P. aeruginosa, SFB, and GNB; bloating (n = 22/47) was associated with Clostridium coccoides and GNB. Microbial flora was different among IBS than HC on principal component analysis. Fecal microbiota was different among IBS than HC, and different sub-types were associated with different microbiota. P. aeruginosa was more frequent and higher in number among IBS patients.