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Microbiome Responses to an Uncontrolled Short-Term Diet Intervention in the Frame of the Citizen Science Project

Authors:
  • Institute of Gene Biology
  • Knomics LLC

Abstract and Figures

Personalized nutrition is of increasing interest to individuals actively monitoring their health. The relations between the duration of diet intervention and the effects on gut microbiota have yet to be elucidated. Here we examined the associations of short-term dietary changes, long-term dietary habits and lifestyle with gut microbiota. Stool samples from 248 citizen-science volunteers were collected before and after a self-reported 2-week personalized diet intervention, then analyzed using 16S rRNA sequencing. Considerable correlations between long-term dietary habits and gut community structure were detected. A higher intake of vegetables and fruits was associated with increased levels of butyrate-producing Clostridiales and higher community richness. A paired comparison of the metagenomes before and after the 2-week intervention showed that even a brief, uncontrolled intervention produced profound changes in community structure: resulting in decreased levels of Bacteroidaceae, Porphyromonadaceae and Rikenellaceae families and decreased alpha-diversity coupled with an increase of Methanobrevibacter, Bifidobacterium, Clostridium and butyrate-producing Lachnospiraceae- as well as the prevalence of a permatype (a bootstrapping-based variation of enterotype) associated with a higher diversity of diet. The response of microbiota to the intervention was dependent on the initial microbiota state. These findings pave the way for the development of an individualized diet.
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nutrients
Article
Microbiome Responses to an Uncontrolled
Short-Term Diet Intervention in the Frame of the
Citizen Science Project
Natalia S. Klimenko 1, *, Alexander V. Tyakht 1,2, Anna S. Popenko 1, Anatoly S. Vasiliev 1,
Ilya A. Altukhov 1,3, Dmitry S. Ischenko 1,3, Tatiana I. Shashkova 1,3,4, Daria A. Efimova 1ID ,
Dmitri A. Nikogosov 5, Dmitrii A. Osipenko 5, Sergey V. Musienko 5, Kseniya S. Selezneva 6,
Ancha Baranova 3,5,7,8, Alexander M. Kurilshikov 9, Stepan M. Toshchakov 10, 11,
Aleksei A. Korzhenkov 10 ID , Nazar I. Samarov 10, Margarita A. Shevchenko 10 , Alina V. Tepliuk 10
and Dmitry G. Alexeev 1,2,4
1Knomics LLC, Skolkovo Innovation Center, Bolshoy Bulvar Str., Building 42, Premise 1, Rooms 1293-1296,
Moscow 143026, Russia; a.tyakht@gmail.com (A.V.Ty.); popenko@atlasbiomed.com (A.S.P.);
anatoly.developer@gmail.com (A.S.V.); ilya.altukhov@gmail.com (I.A.A.);
ischenko.dmitry@gmail.com (D.S.I.); shashkova@phystech.edu (T.I.S.); dar9468qwerty@gmail.com (D.A.E.);
dmitry.g.alexeev@gmail.com (D.G.A.)
2Computer Technology Department, ITMO University, Kronverkskiy pr., 49, St. Petersburg 197101, Russia
3Department of Biological and Medical Physics, Moscow Institute of Physics and Technology,
Institutskiy per. 9, Dolgoprudny, Moscow Region 141700, Russia; aancha@gmail.com
4Department of Natural Science, Novosibirsk State University, Pirogova Str., 1, Novosibirsk 630073, Russia
5
Atlas Biomed Group, 92 Albert Embankment, Lambeth, London SE1 7TT, UK; nikogosov@atlas.ru (D.A.N.);
osipenko@atlas.ru (D.A.O.); musienko@atlasbiomed.com (S.V.M.)
6Atlas Medical Center, Kutuzovsky prospekt 34 bld. 14, Moscow 121170, Russia; selezneva@atlas.ru
7Research Centre of Medical Genetics, Moskvorechye Str., 1, Moscow 115478, Russia
8School of Systems Biology, George Mason University, Fairfax, VA 22030, USA
9Department of Genetics, University Medical Center Groningen, University of Groningen,
9712 CP, Groningen, The Netherlands; alexa.kur@gmail.com
10 School of Life Sciences, Immanuel Kant Baltic Federal University, Universitetskaya Str. 2, Room 106,
Kaliningrad 236040, Russia; stepan.toshchakov@gmail.com (S.M.T.); oscypek@yandex.ru (A.A.K.);
nazar.sni@gmail.com (N.I.S.); lionsorciere@gmail.com (M.A.S.); aeternusmare1414@gmail.com (A.V.Te.)
11 Winogradsky Institute of Microbiology, Research Centre of Biotechnology RAS, Leninsky prospect 33-2,
Moscow 119071, Russia
*Correspondence: natasha.klmnk@gmail.com; Tel.: +7-915-088-4603
Received: 11 April 2018; Accepted: 4 May 2018; Published: 8 May 2018


Abstract:
Personalized nutrition is of increasing interest to individuals actively monitoring their
health. The relations between the duration of diet intervention and the effects on gut microbiota
have yet to be elucidated. Here we examined the associations of short-term dietary changes,
long-term dietary habits and lifestyle with gut microbiota. Stool samples from 248 citizen-science
volunteers were collected before and after a self-reported 2-week personalized diet intervention,
then analyzed using 16S rRNA sequencing. Considerable correlations between long-term dietary
habits and gut community structure were detected. A higher intake of vegetables and fruits was
associated with increased levels of butyrate-producing Clostridiales and higher community richness.
A paired comparison of the metagenomes before and after the 2-week intervention showed that even
a brief, uncontrolled intervention produced profound changes in community structure: resulting
in decreased levels of Bacteroidaceae,Porphyromonadaceae and Rikenellaceae families and decreased
alpha-diversity coupled with an increase of Methanobrevibacter,Bifidobacterium,Clostridium and
butyrate-producing Lachnospiraceae- as well as the prevalence of a permatype (a bootstrapping-based
variation of enterotype) associated with a higher diversity of diet. The response of microbiota to
Nutrients 2018,10, 576; doi:10.3390/nu10050576 www.mdpi.com/journal/nutrients
Nutrients 2018,10, 576 2 of 18
the intervention was dependent on the initial microbiota state. These findings pave the way for the
development of an individualized diet.
Keywords:
gut microbiota; personalized diet; microbiome stability; intervention; 16S rRNA
metagenomics; citizen science; responders
1. Introduction
The importance of gut microbiota to human health is hard to overestimate. Comparative surveys
have revealed associations between some non-communicative diseases and the underrepresentation of
certain commensal microbial taxa as well as with the increased prevalence of potential pathobionts [
1
6
].
At the same time, due to the immense variability in microbial composition at the species level, the jury
is still out on what may constitute the elusive “golden standard” of a healthy gut [
7
]. Nevertheless,
microbiota modulations rapidly develop as an avenue of personalized medicine. The international
randomized study Food4Me recently concluded that individualized nutritional recommendations
lead to better health outcomes than a “one-size-fits-all” dietary approach [
8
]. Another large-scale
study showed that microbiota-tailored diets strongly influence a postprandial glycaemic response,
thus allowing for the personalized control of metabolic status [
9
]. However, the durability of the
microbiota-driven response to drastic dietary changes is far from being confirmed [10].
A controlled setting allows the researchers to ensure compliance with dietary requirements [
11
13
].
However, there is always a trade-off between a proper experimental control and the ecological validity
of the findings. Both novelty of environment and an increase in motivation to comply (known as the
Hawthorne effect) are detectable in investigated subjects, and they undermine a real-life generalization
of the findings inferred from laboratory-based research [
14
]. In order to investigate whether gut
microbiota composition changes after a short-term dietary intervention under a free-living setting,
we used metagenomic sequencing. This technique previously allowed researchers to outline the
landscape of a microbiome composition in a series of large-scale international projects [
15
17
] that are
now becoming increasingly available to the public in formats ranging from participation in “citizen
science” initiatives (e.g., American Gut [
18
] or
µ
Biome [
19
]) to the high-frequency sampling of personal
microbiota [
20
,
21
]. Using an Internet-based crowdfunding platform, 248 subjects were recruited from
the urban population (Moscow, Russia). For each individual, stool samples were collected before and
after a 2-week personalized diet intervention and analyzed using 16S rRNA sequencing. At the baseline,
microbiota composition characteristics were significantly linked to food frequency questionnaire items,
body mass index, gender and age. A paired comparison of the metagenomes before and after the
dieting showed that even a brief, uncontrolled intervention may produce considerable changes in the
composition of microbiota.
2. Materials and Methods
2.1. Study Design and Sample Collection
The research was approved by the local ethics committee of the Atlas Medical Center,
LLC. The project was conducted in accordance with the principles expressed in the Declaration
of Helsinki. Volunteers were recruited using an Internet-based crowdfunding platform called
Boomstarter (
https://boomstarter.ru
; a translated information video is available at https://youtu.
be/PI7OzBz7ALo) and signed informed consent forms before entering the study. The exclusion
criteria are listed in Supplementary Methods. Each volunteer filled in a questionnaire about long-term
dietary habits (food frequency questionnaire), lifestyle, medical and anthropometric factors (Table S1,
Supplementary Methods). Food product consumption frequency was assessed as the number of
intakes per month. The volunteers were instructed on how to perform sample collection at home.
Nutrients 2018,10, 576 3 of 18
According to the instructions, fecal samples should be collected and immediately placed into a freezer
individually by each of the volunteers. The frozen samples were transported to the laboratory on
ice, with the transportation time not greater than 2 h. After collecting an initial (baseline) sample,
the volunteers were provided with dietary recommendations essentially targeting a more balanced diet,
in part by increasing fiber content. Dietary recommendations are listed in Table S2. For each participant,
they consisted of the general part (identical for all participants) and individual part (based on the
participant’s answers to questionnaire). The general part included eating more vegetables and fruit,
reducing amount of sugar, salt, saturated fats and “empty calories” as well as distributing food
intake more evenly through the day. The individual part was compiled based on the results of the
questionnaire according to the algorithm described in Table S3. The algorithm aims to increase the
consumption of specific healthy food products underrepresented in the volunteer’s long-term diet
and to decrease the consumption of overrepresented “junk foods”. After two weeks of following the
recommendations in an uncontrolled environment, each volunteer collected the second fecal sample.
The control group—7 subjects who did not change their diet—followed the same sample collection
procedure (like in the test group, the control samples were collected 2 weeks apart).
2.2. Calculation of Sample Size
The sample size analysis was based on the hypothesis that the described diet intervention
significantly affects microbiota community structure and the choice of pairwise PERMANOVA as
a method for testing this hypothesis. In order to identify the minimal number of subjects required to
achieve the statistical power of 80% to detect the pre-specified effect size, we applied a framework for
PERMANOVA power estimation from the micropower R package [
22
]. For this analysis, we estimated
the population distribution of pairwise dissimilarity (mean = 0.52, s.d. = 0.06) using the published
data on the gut microbiota of healthy Russian subjects [
1
] (n= 61 samples) and the expected effect
size (
ω2
= 0.006)—using the data from a study of a short-term high-fiber dietary intervention [
13
]
(n= 10 samples). The resulting required sample size was 70 subjects (see Figure S1). The total number
of the volunteers who took part in the crowdfunding was about three times higher, resulting in power
of >99%.
2.3. Fecal Sample Preparation and Metagenomic Analysis
The extraction and sequencing of DNA is described in the Supplementary Methods. Amplicon
sequencing of the V4 variable region of the microbial 16S rRNA gene was performed using a MiSeq
sequencer (Illumina, San Diego, CA, USA). The reads were analyzed in QIIME v.1.7.0 [
23
] using the
HITdb v. 1.0 database [
24
] for taxonomic assignment (see Supplementary Methods). Prediction of
metabolic potential profiles was performed using Greengenes v. 13.5 database [
25
] and PICRUSt [
26
].
2.4. Data Availability
The raw reads were deposited in the European Nucleotide Archive (project accession ERP018192).
2.5. Statistical Analysis
All statistical analysis was performed in R statistical programming language, version 3.3.0 [
27
].
Data preprocessing steps are described in the Supplementary Methods section.
2.5.1. Identification of the Links between Microbiota Composition and Metadata
Analysis was performed on 207 subjects for whom the questionnaire data was available (see
Supplementary Methods). The associations between each of the factors included in the metadata and
general microbiota composition were estimated using a permutational multivariate analysis of the
variance using distance matrices implemented in the adonis function from the vegan package [
28
]
with generalized UniFrac distance [
29
]. During analysis with the adonis function, the number of
Nutrients 2018,10, 576 4 of 18
permutations was 2000. Categorical factors were tested for homoscedasticity using the PERMDISP2
method implemented in the betadisper function from the vegan package. The associations between
each factor and alpha-diversity were identified using the Spearman correlation. Multiple comparison
correction for the p-values was performed using the Benjamini–Hochberg method (here and elsewhere).
The links between individual microbial taxa and factors were identified using a general linear
model approach implemented in the MaAsLin [
30
] R package. Analysis was performed separately
for each of the factors; the significant factors according to the adonis results were included in each
model for correction. The boosting step was skipped. Low-abundant taxa (present at less than 0.2% of
total microbial abundance in more than 15 samples) were filtered out. The significance criterion for
MaAsLin was: adjusted p< 0.1.
2.5.2. Differential Analysis of Microbial Taxa and Functions
Analysis was performed on 430 samples from 215 subjects. The taxa for which the relative
abundance significantly changed after the intervention were identified using paired analysis in the
metagenomeSeq package [
31
]; validation was performed by applying a compositionality-aware
ALDEx2 [
32
] algorithm (with centered-log ratio transformation, Wilcoxon signed-rank test and
Benjamini–Hochberg p-value adjustment). The effect size of change for each of the identified taxa
was calculated using the LEfSe method [
33
]. The changes were considered significant if the logarithm
(base 10) of the effect size was above 2.0 and the adjusted p-value was below 0.05. Differential analysis
of the predicted metabolic potential of microbiota was performed using the piano R package [
34
]
in a paired fashion with the following parameters: gene set analysis method: “reporter features”,
significance threshold: adjusted p< 0.05.
2.5.3. Identification of Responders among the Volunteers
In order to investigate the individual response to short-term diet intervention, the subjects were
clustered based on the dissimilarity between their paired samples calculated using a generalized
UniFrac metric [
29
]. The clustering procedure was performed with partitioning around the medoids
with estimation of number of clusters algorithm implemented in the fpc R package [
35
] (pamk function)
with a range of cluster numbers from 2 to 10. The optimal number of clusters (2) was selected as the one
that maximizes the average silhouette width of the clusters. Subjects from a cluster with lower value
of mean dissimilarity were named “non-responders” and subjects from a cluster with greater value of
mean dissimilarity were named “responders”. The taxa differentially abundant in the microbiota of
“responders” and “non-responders” were identified using metagenomeSeq. Associations between the
group and questionnaire results as well as personalized recommendations were identified using the
chi-square test (for categorical and logical factors) and the Mann–Whitney test (for quantitative factors).
The random forest classifier was used to predict if a subject belonged to the “responders“ or
“non-responders“ group based on the baseline microbiota composition. R package caret [
36
] was used
for training and testing the classifier and the ROCR [
37
] package was used for obtaining its performance
estimations. Only the significantly different features between responders and non-responders at the
baseline were included in the classifier as predictors. The classifier was created and tested separately
at each taxonomic level. Cross-validation random sampling was performed 10 times for 70% of
samples for the train dataset and 30% for the test dataset to benchmark the classification quality.
For each iteration, a ROC curve was plotted and AUC was calculated. Mean values of all the iterations
were calculated.
2.5.4. Cluster Analysis of Samples and Microbial Taxa
Microbial cooperatives were identified using the SPIEC-EASI package, a compositionally robust
technique to analyze bacterial networks [
38
]. The cooperatives were obtained using the complete set of
metagenomes (in a control analysis, when only the baseline samples were used, similar cooperatives
were produced). Genera having less than 10 reads per sample on average were excluded. In the
Nutrients 2018,10, 576 5 of 18
SPIEC-EASI algorithm, neighbors were selected using the Meinshausen and Bühlmann method, while
the model selection was performed using the StARS algorithm (huge R package [
39
]) (number of
subsamples = 50, number of lambda iterations = 20, minimum lambda ratio = 0.1). Cooperative was
defined as a connected component of the co-occurrence graph with more than two vertices.
Clustering of the metagenomes was performed using the original enterotyping algorithm [
40
] as
well as its novel bootstrapping-based modification, called permatyping (see Supplementary Methods).
For enterotyping and permatyping, 416 samples from 222 subjects were used. These samples excluded
subjects who reported to have lactose or gluten intolerance. Original enterotyping [
40
] was performed
at the genus level using the Jensen–Shannon metric for distance matrix calculation, the PAM clustering
algorithm, and the optimal number of clusters was selected by maximizing the Calinski–Harabasz
index. Associations between the obtained clusters (permatypes) and factors were identified: for
quantitative factors, using the Mann–Whitney test; for categorical and logical factors, using the
chi-square test.
3. Results
3.1. Variation of Gut Microbiota in the Urban Population
Totally 260 subjects initially enrolled to the study, both samples before and after the diet
intervention were provided by 248 subjects, thus demonstrating a relatively high compliance.
After preprocessing 16S rRNA metagenomic reads and removing the subjects with low coverage in one
or both metagenomes, a total of 215 pairs of metagenomes remained. More than 93% of resultant reads
were successfully classified, signifying the validity of the selected technique of composition profiling.
In the examined cohort, microbiota was found to be quite diverse, with individual samples
generally falling into previously reported community structures of the Russian population obtained
using “shotgun” metagenomics [
17
] (Figure S2). A total of 42 families, 122 genera, and 692 species were
detected in at least one sample (the complete table of relative abundance is listed in Table S4). Overall,
the most represented phyla were Firmicutes (83.4
±
9.7%), Bacteroidetes (6.8
±
8.7%), and Actinobacteria
(3.4 ±4.5%).
3.2. Long-Term-Dietary Habits, Antibiotic Intake and Anthropometric Indices Are among the Major Factors
Associated with Microbiota Composition
We analyzed the associations between the gut community structure of the volunteers and various
factors obtained from the questionnaires, including self-reported long-term dietary habits, lifestyle
indicators, anthropometric indices, and medications (see Table S1). Questionnaire results are available
for 207 subjects (see Supplementary Notes). Distribution of the factors from the questionnaire are
listed in Table S5. The cohort included 97 women (24–61 years old) and 110 men (18–64 years old).
The BMI was 24.2
±
4.9 (median
±
s.d.): 15 subjects had low BMI (<18.5), 17—first-degree obesity
(30–34.9), 6—second-degree obesity (35-39.9) and one subject—third-degree obesity (40).
The largest contributions to the overall microbiota composition (see Supplementary Methods)
were detected for intake of antibiotics within the last three months (percentage of the total variance
explained by the factor 1.41%, pseudo-F = 2.93), dairy consumption (1.12%, pseudo-F = 2.47), and
gender (1.11%, pseudo-F = 2.58) (n= 207 subjects, FDR adjusted p< 0.05); see Figure S3.
Analysis of the links between assessed factors and the richness of microbiota (see Supplementary
Methods) showed that, in the subjects who took antibiotics within the last three months, alpha-diversity
was significantly decreased. On the other hand, alpha-diversity was positively linked to the amount of
vegetables in long-term dietary habits (Spearman correlation coefficients of
0.2 and 0.2, respectively,
n= 207 subjects, p< 0.03, FDR adjusted p< 0.05). We also observed a suggestive negative correlation
between alpha-diversity and BMI (n= 207 subjects, alpha-diversity measured via chao1 index, p= 0.02,
FDR adjusted p= 0.09) (Figure S4).
Nutrients 2018,10, 576 6 of 18
An adjustment for the effects of intake of antibiotics, the frequency of dairy consumption and
gender revealed links between the relative abundance of individual taxa and a number of the factors.
Significant associations are listed in Figure 1and Table S6. It is of note that several associations known
from larger studies were replicated, while several new associations were uncovered (Figure 1).
Nutrients 2018, 10, x FOR PEER REVIEW 6 of 18
Significant associations are listed in Figure 1 and Table S6. It is of note that several associations known
from larger studies were replicated, while several new associations were uncovered (Figure 1).
Figure 1. Associations of the microbial taxa with long-term dietary habits and other factors from the
questionnaire (n = 207 subjects). Analysis was performed for the baseline samples at taxonomic levels
from species to phyla. Rows are sorted in alphabetic order. Cell color denotes the value of the linear
model coefficient from the MaAsLin analysis. All significant associations (FDR adjusted p < 0.1) are
marked with one of the symbols (&, #, @): “&”—associations previously reported by Zhernakova et al.,
2016 [41], “#”—reported by Wu et al., 2011 [13], “@”—novel associations.
The factors significantly associated with individual taxa were generally similar to those factors
uncovered by variance analysis to include antibiotic intake, medicinal drug intake, chronic diseases,
gender, body mass index, frequency of consumption of dairy products, fruit, total vegetables and
fruit, and grains and meat (n = 207 subjects, FDR-adjusted p < 0.1).
3.3. Short-Term Dietary Changes Significantly Shift Microbiota Composition
Volunteers have received dietary recommendations according to their long-term dietary habits,
assessed using a questionnaire (see Methods section and Tables S2 and S3). A post-hoc analysis of
individual sets of recommendations revealed a key piece of advice assigned to a majority of the
Figure 1.
Associations of the microbial taxa with long-term dietary habits and other factors from the
questionnaire (n= 207 subjects). Analysis was performed for the baseline samples at taxonomic levels
from species to phyla. Rows are sorted in alphabetic order. Cell color denotes the value of the linear
model coefficient from the MaAsLin analysis. All significant associations (FDR adjusted p< 0.1) are
marked with one of the symbols (&, #, @): “&”—associations previously reported by Zhernakova et al.,
2016 [41], “#”—reported by Wu et al., 2011 [13], “@”—novel associations.
The factors significantly associated with individual taxa were generally similar to those factors
uncovered by variance analysis to include antibiotic intake, medicinal drug intake, chronic diseases,
gender, body mass index, frequency of consumption of dairy products, fruit, total vegetables and fruit,
and grains and meat (n= 207 subjects, FDR-adjusted p< 0.1).
3.3. Short-Term Dietary Changes Significantly Shift Microbiota Composition
Volunteers have received dietary recommendations according to their long-term dietary habits,
assessed using a questionnaire (see Supplementary Methods and Tables S2 and S3). A post-hoc analysis
of individual sets of recommendations revealed a key piece of advice assigned to a majority of the
Nutrients 2018,10, 576 7 of 18
participants. This advice was to increase fiber consumption and avoid habitual consumption of the
“Western diet”. In a sense, this advice was narrowing a variation in the volunteers’ diets (see Figure S5).
The changes in the microbiota composition at the end of the 2-week period when the participants
followed the recommendations were significant (pairwise PERMANOVA, n= 430 paired samples,
p= 0.0005, 4.17% of the total variation explained, pseudo-F = 18.61; see Figure S6; paired sample
identifiers are listed in Table S7). At the individual level, the shifts were quite dramatic (Bray–Curtis
index 0.45
±
0.11 between paired metagenomes), being higher than technical (0.22
±
0.02 between
technical replicates at the level of DNA extraction) and lower than group-wise variation (0.68
±
0.08
between random metagenomes). Notably, for the members of the voluntary control group that did
not change their diet, over the course of two weeks these changes were substantially less pronounced
(Bray–Curtis index 0.26 ±0.08, n= 7 subjects—see Supplementary Methods and Figure S7).
Interestingly, a slight but significant decrease of alpha-diversity in dieting volunteers was
observed—the average fold-change was 1.04 (Shannon index changed from 5.74
±
0.52 to 5.53
±
0.54,
Welch’s test p= 8.2
×
10
5
,n= 430 samples). This effect persisted with the other diversity metrics as
well as the rarefaction depth (see Figure S8).
Diet-associated changes in relative abundance for each of the microbial taxa were analyzed from
the levels of phyla down to species (see Supplementary Methods). The most global changes—at the
levels of phyla and families—are shown in Figure 2, with the complete results presented in Table S8.
There was a decrease in the abundance of many genera from the Bacteroidetes phylum; this was
accompanied by an increase for microbes from Actinobacteria,Firmicutes as well as from Euryarchaeota.
The results were similar when a compositionality-aware approach [
32
] was applied (see Supplementary
Methods, Figures S9 and S10, Table S9). The Bacteroidetes:Firmicutes ratio significantly decreased (from
0.13
±
0.2 to 0.03
±
0.09, Wilcoxon paired test p< 0.0001, n= 430 paired samples, see Figure S11).
Analysis of metabolic potential also showed depletion of many functions inherent to members of
the Bacteroidetes (see Tables S10 and S11), including the biotin and riboflavin biosynthesis pathways
modules (M00125, M00572).
Nutrients 2018, 10, x FOR PEER REVIEW 7 of 18
participants. This advice was to increase fiber consumption and avoid habitual consumption of the
“Western diet”. In a sense, this advice was narrowing a variation in the volunteers’ diets (see Figure S5).
The changes in the microbiota composition at the end of the 2-week period when the participants
followed the recommendations were significant (pairwise PERMANOVA, n = 430 paired samples,
p = 0.0005, 4.17% of the total variation explained, pseudo-F = 18.61; see Figure S6; paired sample
identifiers are listed in Table S7). At the individual level, the shifts were quite dramatic (Bray–Curtis
index 0.45 ± 0.11 between paired metagenomes), being higher than technical (0.22 ± 0.02 between
technical replicates at the level of DNA extraction) and lower than group-wise variation (0.68 ± 0.08
between random metagenomes). Notably, for the members of the voluntary control group that did
not change their diet, over the course of two weeks these changes were substantially less pronounced
(Bray–Curtis index 0.26 ± 0.08, n = 7 subjects—see Supplementary Methods and Figure S7).
Interestingly, a slight but significant decrease of alpha-diversity in dieting volunteers was
observed—the average fold-change was 1.04 (Shannon index changed from 5.74 ± 0.52 to 5.53 ± 0.54,
Welch’s test p = 8.2 × 105, n = 430 samples). This effect persisted with the other diversity metrics as
well as the rarefaction depth (see Figure S8).
Diet-associated changes in relative abundance for each of the microbial taxa were analyzed from
the levels of phyla down to species (see Methods). The most global changes—at the levels of phyla
and families—are shown in Figure 2, with the complete results presented in Table S8. There was a
decrease in the abundance of many genera from the Bacteroidetes phylum; this was accompanied by
an increase for microbes from Actinobacteria, Firmicutes as well as from Euryarchaeota. The results were
similar when a compositionality-aware approach [32] was applied (see Methods, Figures S9 and S10,
Table S9). The Bacteroidetes:Firmicutes ratio significantly decreased (from 0.13 ± 0.2 to 0.03 ± 0.09,
Wilcoxon paired test p < 0.0001, n = 430 paired samples, see Figure S11). Analysis of metabolic
potential also showed depletion of many functions inherent to members of the Bacteroidetes (see
Tables S10 and S11), including the biotin and riboflavin biosynthesis pathways modules (M00125,
M00572).
Figure 2. Major changes in the gut community structure of the volunteers after following the dietary
recommendations. Red branches of the cladogram denote the taxa that were increased in abundance,
while the blue ones—decreased. Significance criterion: p < 0.05 in metagenomeSeq model and log10
of the effect size >2 in LEfSe method (n = 430 paired samples).
Figure 2.
Major changes in the gut community structure of the volunteers after following the dietary
recommendations. Red branches of the cladogram denote the taxa that were increased in abundance,
while the blue ones—decreased. Significance criterion: p< 0.05 in metagenomeSeq model and log10 of
the effect size >2 in LEfSe method (n= 430 paired samples).
Nutrients 2018,10, 576 8 of 18
3.4. The Subjects Vary in How Gut Microbiota Responds to Dietary Intervention
An analysis of pairwise dissimilarity of community structures before and after the intervention
revealed the bimodal nature of the changes observed. Clustering of the subjects by the magnitude
of changes (see Supplementary Methods) yielded 2 clusters: “non-responders” with relatively stable
microbiota (N= 130; distance between paired points 0.19
±
0.03) and “responders” with less stable
microbial communities (N= 85; distance 0.30
±
0.05) (Figure 3A, Table S12). No significant associations
of clusters were detected, neither with any specific recommendation nor with metadata extracted from
the questionnaire (see Supplementary Methods, FDR-adjusted p> 0.1, n= 175 subjects).
Figure 3.
Interindividual variation of gut microbiota response to the diet intervention (n= 215 subjects).
Distribution of taxonomic dissimilarity between the metagenomes before and after the intervention
for each subject (generalized UniFrac metric) is colored in different ways. In panel (
A
), the color
denotes responders (blue) and non-responders (green). In panels (
B
) and (
C
) the color denotes the
average Bacteroidetes:Firmicutes ratio for the samples collected before and after the diet, respectively).
Abbreviations: B:F ratio—Bacteroidetes:Firmicutes ratio, N—non-responders, R—responders.
However, at the baseline, the gut microbiota of the “responders” was distinct from that
of the “non-responders” (see Table S13). “Responders” had lower fractions of Actinobacteria
(Coriobacteriales order), Firmicutes (Bacillales,Erysipelotrichales and Clostridiales order), Proteobacteria
(Enterobacteriales order), and Verrucomicrobia (Verrucomicrobiales order) phyla, while the Bacteroidales
and Sphingomonadales orders were represented at higher levels. In the “responders” cohort, baseline
Bacteroidetes:Firmicutes ratios were significantly higher than in the “non-responders” (Mann-Whitney
test, p= 0.0001, n= 215) (Figure 3B). As expected, the metabolic modules and pathways
enriched in microbiota of “responders” included the modules specific to Gram-negative microbes,
lipopolysaccharide biosynthesis (md:M00060) and NADH:quinone oxidoreductase (md:M00144), while
in “non-responders”, Firmicutes and Actinobacteria-driven enrichment in ABC-transporters (ko02010)
was seen [
42
]. Additionally in “responders”, a “Other glycan degradation pathway” (ko00511) related
to the degradation of the carbohydrate components of the gut mucus was enriched.
Interestingly, when the stool samples of the “responders” were analyzed post-diet, the trends
for a majority of the mentioned orders (Coriobacteriales,Bacillales,Enterobacteriales,Bacteroidales)
were reversed (see Table S14, Figures S12 and S13). Furthermore, “responders” also dropped the
Bacteroidetes:Firmicutes ratio after the diet to significantly lower levels than the “non-responders”
(p= 0.0003, n= 215 subjects) (Figure 3C).
To investigate whether the baseline microbiota state of a subject can be used to predict the
“responder”/“non-responder” status, a random forest classifier was constructed based on microbial
Nutrients 2018,10, 576 9 of 18
markers that significantly differ in abundance between the groups. The classifiers were also constructed
separately for each taxonomic level (Table S15). The performance of classifiers was assessed
using repeated random sub-sampling cross-validation (training set N= 150, test set N= 65) (see
Supplementary Methods). The best average AUC value (0.78) was obtained at the species level
(Figure S14, Table S15).
3.5. Co-Occurring Groups of Microbes Associated with Long- and Short-Term Dietary Factors
For a more comprehensive exploration of the associations between gut microbiota and various
characteristics, the dimensionality of the analysis was reduced by clustering, performed at first for
microbial genera, then for the samples.
At the level of genera, four large “cooperatives”, the groups of co-occurring genera representing
potentially symbiotic subcommunities (see Supplementary Methods), were identified (Figure 4A,
Table S16, Figure S15). These “cooperatives” were denoted as Lachnospiraceae-, Peptostreptococcaceae-,
Ruminococcaceae-, and Bacteroides-dominant according to their respective major driver taxa.
Nutrients 2018, 10, x FOR PEER REVIEW 9 of 18
assessed using repeated random sub-sampling cross-validation (training set N = 150, test set N = 65)
(see Methods). The best average AUC value (0.78) was obtained at the species level (Figure S14,
Table S15).
3.5. Co-Occurring Groups of Microbes Associated with Long- and Short-Term Dietary Factors
For a more comprehensive exploration of the associations between gut microbiota and various
characteristics, the dimensionality of the analysis was reduced by clustering, performed at first for
microbial genera, then for the samples.
At the level of genera, four large “cooperatives”, the groups of co-occurring genera representing
potentially symbiotic subcommunities (see Methods), were identified (Figure 4A, Table S16, Figure S15).
These “cooperatives” were denoted as Lachnospiraceae-, Peptostreptococcaceae-, Ruminococcaceae-, and
Bacteroides-dominant according to their respective major driver taxa.
Figure 4. Cluster analysis for microbial genera and samples. (A) Cooperatives of microbial genera.
Size of the vertices is proportional to the average relative abundance of the genera in all metagenomes.
Postfix “_u” denotes all unclassified genera from the respective family. (B) Links between
cooperatives and permatypes (principal coordinates analysis [PCoA] using generalized UniFrac
metric). (C) Changes in distribution of the participants across permatypes after following the dietary
recommendations.
Figure 4.
Cluster analysis for microbial genera and samples. (
A
) Cooperatives of microbial genera.
Size of the vertices is proportional to the average relative abundance of the genera in all metagenomes.
Postfix “_u” denotes all unclassified genera from the respective family. (
B
) Links between cooperatives
and permatypes (principal coordinates analysis [PCoA] using generalized UniFrac metric). (
C
) Changes
in distribution of the participants across permatypes after following the dietary recommendations.
Identified cooperatives suggest possible functional interactions between gut microbes.
Lachnospiraceae-dominant cooperative is likely to be formed by the cross-feeding of microbial species
specialized in breaking down complex carbohydrates of plant origin, including cellulose and resistant
Nutrients 2018,10, 576 10 of 18
starch (Ruminococcus,Eubacterium) and the bacteria producing the butyrate from secondary glycans
(Anaerostipes,Fusicatenibacter) [
43
,
44
]. Acetogenic bacteria (like Blautia) also benefit from cohabiting
with primary plant degraders, as they consume hydrogen, a product of glycan fermentation [45].
Similar mutualistic patterns may be proposed for the Ruminococcaceae-dominant cooperative,
where the hydrogen generated in the process of carbohydrate fermentation (by Ruminococcus) is
consumed by methanogenic Archaea (Methanobrevibacter) [
43
,
46
]. Methanobrevibacter and Christensenella
were previously observed to be co-occurring [
47
], both are positively associated with low BMI [
47
49
]
and negatively, with an unfavorable lipid profile [50].
In a Bacteroides-dominant cooperative, a number of member genera is associated with diets rich in
animal protein [
51
] and includes Alistipes,Bacteroides,Barnesiella, and Parabacteroides species, some of
which are known to be bile-tolerant [
43
]. An inclusion of Oscillospira in this cooperative may be due to
cross-feeding effects through the use of the fermentation by-products of Bacteroides [52].
The Peptostreptococcaceae-dominant cooperative included several genera from this family:
Intestinibacter,Terrisporobacter, and unclassified ones, as well as Turicibacter. The members of the
cooperative are generally relatively rare in gut community. The high heritability of Turicibacter as well
as its co-occurrence with Peptostreptococcaceae were previously reported [53].
Analysis of associations between the questionnaire items and relative abundances for each
of the four cooperatives showed that the subjects who had recently taken antibiotics had
an increased presence of the Lachnospiraceae-dominant cooperative but a decreased presence of
the Ruminococcaceae-dominant cooperative. Female subjects tended to have increased levels of the
Bacteroides-dominant cooperative as compared to males (MaAsLin method, p< 0.1, n= 207 subjects).
The short-term diet intervention was associated with the decrease of Bacteroides-dominant and the
increase of Lachnospiraceae-dominant cooperatives (Wilcoxon signed-rank test, adjusted p= 2.50
×
10
20
and 3.9 ×105, respectively, n= 430 paired samples).
3.6. Both Short-Term Dietary Intervention and Long-Term Dietary Habits Are Reflected in the Clustering of
Community Structures
For the cluster analysis at sample level, we applied the enterotyping methodology [
40
] and yielded
a total of three enterotypes. It should be noted that a number of studies suggested the entorotyping
results are to be approached with caution [
54
]. For the purpose of this study, we developed and
applied a bootstrapping-based variation of the enterotyping technique, which we called permatyping
(see Supplementary Methods). Accordingly, the obtained clusters of metagenomes were designated
as permatypes (Figure S16, Table S17). Briefly, permatyping shrinks the original enterotypes to
include only the samples that certainly belong to the clusters, while unstable samples are discarded as
unclassified (see Supplementary Methods).
In this study, permatyping produced three clusters including 83, 86, and 81 metagenomes,
respectively, while 166 unstable samples were excluded from further analysis. The distinctive microbial
genera (drivers) of Permatype 1 included Oscillibacter and Prevotella; Permatype 2 included unclassified
genera from Lachnospiraceae,Roseburia, and Bacteroides; and Permatype 3 included Dorea,Blautia and
Staphylococcus (Figure S17). These sets of drivers somewhat resembled the ones described for the
originally discovered enterotypes [
40
] by also including Bacteroides and Prevotella for two different
permatypes. Obviously, their lower ranking was linked to the fact that our sample included few
of the metagenomes enriched in Bacteroides and Prevotella identified in some other studies [
55
].
The relative abundance of the microbial cooperatives of all three permatypes were compared (Figure 4B,
Figure S18). Permatype 1 samples had increased levels of the Ruminococcaceae-dominant cooperative
(Mann–Whitney test, adj. p= 1.2
×
10
4
,n= 250 samples). Permatype 2 was enriched in the
Lachnospiraceae- and Bacteroides-dominant cooperatives (p= 1.5
×
10
22
,n= 250 samples). Permatype
3 was enriched in the Lachnospiraceae-dominant cooperative (p= 6.8
×
10
26
,n= 250 samples).
A comparison of microbial diversity between permatypes showed that the samples of Permatype 1 had
Nutrients 2018,10, 576 11 of 18
significantly higher diversity than each of the two others (Shannon index 6.08
±
0.51 vs. 5.32
±
0.50
and 5.51 ±0.47, respectively, one-sided Welch’s test p< 0.05, n= 250 samples).
In the analysis of variations in long-term dietary habits between the enterotypes and the
permatypes of microbiota, no significant differences were detected for the original enterotypes.
However, for permatypes, some associations were uncovered. Baseline profiles for the three
permatypes included 47, 49, and 18 metagenomes. The dietary habits and demographics of Permatype
1 individuals did not manifest significant differences from that of the entire cohort, while Permatype 2
individuals were, on average, younger (29
±
7 vs. 33
±
9 years) (adj. p< 0.1, n= 114 subjects) and
consumed less vegetables and fruits than the other participants. The members of Permatype 2 also
consumed more meat and beer and less fish and seafood (see Table S18, Figure S19). Overall, Permatype
2 diet tended to be less diverse: the reported variety of consumed foods was lower. Permatype 3
was less distinct in terms of questionnaire data. Its members’ diet was more diverse (adj. p< 0.1,
n= 114 subjects) and they tended to consume more vegetables (adj. p= 0.13, n= 114 subjects).
After a short-term dietary intervention, the distribution of subjects among the permatypes
changed. The dynamics of transitions between the permatypes (160 paired samples from 80 subjects,
Figure 4C) showed that around a third of the subjects initially belonging to Permatypes 1 and 2 moved
to Permatype 3 as a result of dieting. The others preserved their permatype. The subjects initially
belonging to Permatype 3 (n= 12 of 14 subjects) tended to reside in this permatype after the diet,
although at the edge of significance, possibly due to low amounts of paired samples of Permatype 3
(Fisher’s test for permatype 3 vs. other samples, resided in initial permatype, p= 0.055, n= 89 subjects).
4. Discussion
A growing body of evidence accumulated by studies of gut microbiota in world populations
emphasizes that lifestyle and especially diet strongly impact microbiota composition and, thus,
human health. However, it is still unclear how durable are the effects of dietary changes, either
long- or short-term. Effects of self-administered short-term dieting efforts on gut microbiota are
also far from being well understood. To investigate this problem, we used an Internet-based citizen
science-supportive platform to enroll individuals from the Russian urban population into the study
aimed at identifying the links between gut metagenome composition and long-term dietary habits,
assessed using a food frequency and lifestyle questionnaire and short-term dietary changes achieved
during a 2-week intervention.
One of the major observations derived from the analysis of microbiota composition and the
recent medical history of the subjects was an influence exerted on gut community by recent intake
of antibiotics. No detailed breakdown of antimicrobial drugs was available. The diversity of the
antibiotics modes of action might explain the lack of consistency in observations concerning individual
microbial taxa. However, on the level of cooperatives, or symbiotic groups of microbes, a significant
depletion of the Ruminococcaceae-dominant cooperative was detected in the gut of the participants
recently exposed to antibiotics. Interestingly, many drivers of this antibiotic-sensitive cooperative, in
particular Methanobrevibacter and Christensenella, were previously linked to leanness. These bacterial
generally also manifest high heritability [
47
]. Another member of this cooperative, Oscillibacter,
is associated with normal BMI [
56
]. Moreover, many microbes from this cooperative, including
Ruminococcaceae and Oscillibacter, are known to degrade complex dietary fibers, resulting in the
production of butyrate [
57
], an anti-inflammatory compound that also plays essential roles in the
regulation of metabolism, glucose tolerance, and gut motility [
58
]. These observations support our
findings that the abundance of the Ruminococcaceae-dominant cooperative correlates with the long-term
trends in the consumption of fruit and vegetables.
The idea that antibiotics-driven disruption of the ability of microbiota to support host metabolism
contributes to the risk of metabolic diseases, especially in the younger ages, has been discussed
before [
59
61
]. It is tempting to speculate that antibiotics might interfere with the host metabolism,
through the depletion of beneficial microbial taxa with slow recovery rates and high heritability. In turn,
Nutrients 2018,10, 576 12 of 18
the loss of these specialists would render the host less responsive to the microbiota-mediated effects of
high-fiber diet interventions, and, therefore, less likely to return back to the original, “healthier” state.
Out of all long-term diet features, fiber content was the most prominently associated with
microbiota composition. The list of microbes correlated with the consumption of fruit, vegetables,
and grains included taxa actively involved in the degradation of non-digestible polysaccharides, in
particular, species belonging to Oscillibacter [
62
], Eubacterium [
42
], Blautia [
63
] and ones related to
Clostridium clariflavum [
64
]. On the other hand, the consumption of meat products was inversely
correlated with the abundance of unclassified species from Prevotella, a genus linked to diets low in
animal protein and high in fiber [
65
,
66
]. Subjects who consumed more dairy products had higher
levels of Streptococcus in their microbiota, possibly because S. thermophilus, a major component of
starter cultures for fermented milk products, is capable of survival in the human gut [67].
Gender was the only anthropometric factor significantly associated with microbiota composition
(Figure S3). Bacteroides-dominant cooperative was increased in the gut of female participants.
This agrees with the previous observations that harder stools are more common in women, and that
harder stools have higher fraction of Bacteroides than loose samples [68].
In our study, the dietary change recommendations were quite general, with predominant targeting
of the fiber consumption, and an adherence to the recommendations was uncontrolled. Nevertheless,
paired comparison of gut metagenomes before and after the 2-week diet intervention detected
substantial changes in the structure of the gut community. In voluntary dieters, the magnitude
of observed change was about two times higher than that in subjects who did not change their diet,
and several times higher than the technical variation introduced at the stages of DNA extraction,
sample collection, and library preparation (Figure S7).
While some of the identified short-term changes in the microbial landscape resembled the impacts
of long-term high-fiber diet, others were specific and novel. In particular, there was a significant
decrease of the Bacteroides-dominant cooperative as well as of many of its members, including
Bacteroides and Alistipes and in the related Bacteroidaceae,Porphyromonadaceae and Rikenellaceae families.
Many microbes from these taxa are either bile-tolerant or previously positively associated with
long- or short-term diets rich in animal protein and saturated fats [
11
,
69
,
70
]. Apparently, due to
intervention-related increase of the fiber intake and, possibly, to partial replacement of animal products
with the fiber-containing ones, these bacteria yielded to those specializing in a variety of complex
polysaccharides. The microbes that increased after the diet included those associated with a healthy
gut to include Clostridiaceae, particularly, Clostridium genus, previously linked to high-fiber diet [
71
].
Methanobrevibacter and Bifidobacterium were reported to be inversely correlated with BMI [
48
] and
Lachnospiraceae-dominant cooperative was enriched with butyrate producers (Dorea,Ruminococcus
and Eubacterium). Interestingly, we also observed a decrease of Prevotellaceae (on the genus level—of
Prevotella) associated with the long-term consumption of high-fiber diet.
Changes at the microbial taxa level were mirrored by transitions between permatypes (Figure 4C).
After the diet, many subjects originally belonging to Permatypes 1 and 2 moved to Permatype 3,
while a majority of Permatype 3 subjects maintained their permatype. Having a Permatype 3 was
associated with higher consumption of vegetables, higher diversity of diet, and high prevalence of
butyrate-producing Firmicutes. Overall, we conclude that even a brief high-fiber diet intervention
may produce profound effects resembling those typically associated with long-term dietary changes
beneficial to human health.
Interestingly, a slight but significant decrease of gut community diversity after the short-term diet
was observed. This effect of the short-term high-fiber diet appears to be opposite to the correlation
between diversity and long-term vegetable consumption clearly seen in our cohort. Other studies
linked lower alpha-diversity to immune and metabolic disorders as well as to antibiotic intake [
72
].
On the other hand, there is evidence to suggest that two weeks of a high-fiber diet may not be enough
to affect alpha-diversity [
11
,
13
]. A slight drop in alpha-diversity obtained in the current study may
reflect the “shock effects” of a relatively rapid change in the spectrum of incoming nutrients, which may
Nutrients 2018,10, 576 13 of 18
transiently disrupt the ecology of the gut community. Another observed facet of microbiota “stress”
linked to the transitory period is the slightly but significantly increased abundance of Staphylococcus
and Enterobacteriaceae. Apparently, while the beneficial microbes associated with high-fiber already
started to win their niches and extend their presence in two weeks, the disturbance of the ecological
network led to the rise of pathobiont and auxotrophic taxa [30,73].
The extent to which gut microbiota reacts to diet interventions was shown to vary across individuals,
thus confirming previous observations [
74
76
]. In our study, the degree of response was dependent on
initial microbiota composition, but neither on any personalized recommendations nor on any long-term
factor revealed by the questionnaire. In the “responders”, a higher abundance of Bacteroidales and lower
abundance of Coriobacteriales and Clostridiales was noted at the baseline. Interestingly, bacteria that
increased after dieting generally corresponded to the set of taxa underrepresented at the baseline, with
a subsequent decrease of the Bacteroidetes:Firmicutes ratio. Our finding closely resembles observations
reported for a Danish cohort of obese and non-obese subjects exposed to a weight-loss diet [
75
,
76
]:
the microbiota of “responders” was dominated by Bacteroides, while “non-responders” had increased
proportions of Blautia,Alistipes, and Akkermansia.
In “responders”, the microbes overrepresented at the baseline, including Bacteroidaceae, became
underrepresented after the diet (Figures S12 and S13), while underrepresented microbes, including
Coriobacteriaceae, became overrepresented (Figures S12 and S13). This “responders”-specific correctional
“overshoot” led to a pronounced lowering of the Bacteroidetes:Firmicutes ratio (Figure 3B,C) after
the diet. This observation may reflect a momentum-like property of gut community structure
dynamics in the landscape of possible configurations during a high-fiber diet intervention (Figure 5).
The Bacteroidetes-rich microbiota of “responders” appears to reside in a less stable state than the
Firmicutes-rich microbiota of “non-responders”, thus making the “responder” more amenable to
change. On the contrary, the microbiota of “non-responders” changed slightly upon intervention
because of its higher stability. When the microbiota composition of “responders” gains momentum, it
moves towards the “non-responders” and even further to reach a state of lower Bacteroidetes:Firmicutes
ratio, normally not accessible to “non-responders”. Whether this acquired community structure
remained unstable (marked by A in the Figure 5) or stable (marked by B) is still to be determined. It is
also intriguing to examine if other nutritional changes or other types of interventions would result in
alternate dynamics and hence alternate stability landscapes.
Nutrients 2018, 10, x FOR PEER REVIEW 13 of 18
microbiota “stress” linked to the transitory period is the slightly but significantly increased abundance
of Staphylococcus and Enterobacteriaceae. Apparently, while the beneficial microbes associated with
high-fiber already started to win their niches and extend their presence in two weeks, the disturbance
of the ecological network led to the rise of pathobiont and auxotrophic taxa [30,73].
The extent to which gut microbiota reacts to diet interventions was shown to vary across
individuals, thus confirming previous observations [74–76]. In our study, the degree of response was
dependent on initial microbiota composition, but neither on any personalized recommendations nor
on any long-term factor revealed by the questionnaire. In the “responders”, a higher abundance of
Bacteroidales and lower abundance of Coriobacteriales and Clostridiales was noted at the baseline.
Interestingly, bacteria that increased after dieting generally corresponded to the set of taxa
underrepresented at the baseline, with a subsequent decrease of the Bacteroidetes:Firmicutes ratio. Our
finding closely resembles observations reported for a Danish cohort of obese and non-obese subjects
exposed to a weight-loss diet [75,76]: the microbiota of “responders” was dominated by Bacteroides,
while “non-responders” had increased proportions of Blautia, Alistipes, and Akkermansia.
In “responders”, the microbes overrepresented at the baseline, including Bacteroidaceae, became
underrepresented after the diet (Figures S12 and S13), while underrepresented microbes, including
Coriobacteriaceae, became overrepresented (Figures S12 and S13). This “responders”-specific correctional
“overshoot” led to a pronounced lowering of the Bacteroidetes:Firmicutes ratio (Figure 3B,C) after the
diet. This observation may reflect a momentum-like property of gut community structure dynamics
in the landscape of possible configurations during a high-fiber diet intervention (Figure 5). The
Bacteroidetes-rich microbiota of “responders” appears to reside in a less stable state than the
Firmicutes-rich microbiota of “non-responders”, thus making the “responder” more amenable to
change. On the contrary, the microbiota of “non-responders” changed slightly upon intervention
because of its higher stability. When the microbiota composition of “responders” gains momentum,
it moves towards the “non-responders” and even further to reach a state of lower
Bacteroidetes:Firmicutes ratio, normally not accessible to “non-responders”. Whether this acquired
community structure remained unstable (marked by A in the Figure 5) or stable (marked by B) is still
to be determined. It is also intriguing to examine if other nutritional changes or other types of
interventions would result in alternate dynamics and hence alternate stability landscapes.
Figure 5. Gut microbiota momentum after the impact of the short-term diet. In the diagram describing
the suggested effect, circles denote the location of community structures for typical responders (R)
and non-responders (N) before and after the diet in the schematic landscape of possible microbiota
configurations. Arrows represent the change of microbiota under the impact of diet.
Figure 5.
Gut microbiota momentum after the impact of the short-term diet. In the diagram describing
the suggested effect, circles denote the location of community structures for typical responders (R)
and non-responders (N) before and after the diet in the schematic landscape of possible microbiota
configurations. Arrows represent the change of microbiota under the impact of diet.
Nutrients 2018,10, 576 14 of 18
Although in the present study the dietary recommendations were personalized, ultimately at their
core was an advice to consume more high-fiber products. Our observations suggest that a high-fiber
diet is expected to produce more pronounced changes in the microbiota of subjects who initially hosted
a higher fraction of Bacteroides. While this fact could be used to stratify populations before assigning
such an intervention, the current results do not allow us to infer directly neither the changes in various
microbiota types that will occur after the consumption of specific food products nor their implications
for human health. However, our study is one of the first steps towards developing a precision
microbiota-tailored personalized diet. It emphasizes that in microbiota surveys of dietary interventions
it is important to analyze the interindividual response variability—particularly, to facilitate future
meta-analysis. We anticipate further studies on large-scale cohorts from diverse geographic locations
who consume specific dietary interventions (preferably, based on the introduction of a single product
per study) that will identify responders to these pointwise interventions and further utilize these
as a basis to design individual dietary plans. Another important question is related to the concept
of response itself. In our study, we assessed it as an overall extent of change in the gut community
structure. However, in future studies it can be improved by focusing on the increase of species
associated with health, alpha-diversity and/or microbiota resilience—as well as by combining with
the physiological parameters of a subject.
Overall, this study expands the current understanding of the extent of the changes in microbiota
composition caused by short-term dieting. Advancing a microbiota-targeted diet as a novel modality
to be developed in the frame of personalized medicine requires the emergence of early adopters eager
to participate in a new trend at the crossroads of translational medicine and citizen science. In this
cohort, even a brief, uncontrolled high-fiber diet intervention produced considerable beneficial changes
in microbiota. Nevertheless, the observed “shock” effects, although slight, suggest that the duration of
microbiota-targeted interventions should be longer than two weeks.
Supplementary Materials: The following are available online at http://www.mdpi.com/xxx/s1.
Author Contributions:
D.G.A., A.V.Ty., S.V.M. and K.S.S. designed and supervised the study. S.M.T., A.A.K.,
N.I.S., M.A.S. and A.V.Te. performed the experimental work. N.S.K., A.V.Ty., A.S.P., A.S.V., K.S.S., I.A.A.,
D.S.I., T.I.S., D.A.E., D.A.O. and D.A.N. analyzed the sequencing data. A.V.Ty., N.S.K., A.S.P., A.M.K. and A.S.V.
performed statistical analysis. D.A.N., D.A.O., A.V.Ty., A.S.P., N.S.K., A.B. and A.S.V. wrote the manuscript.
D.G.A., A.B., S.V.M. and A.M.K. revised the manuscript. All authors approved and contributed to the preparation
of the manuscript.
Acknowledgments:
Supported by the Russian Ministry of Science and Education under 5–100 Excellence
Programme and the Scientific State Program 6.9899.2017/8.9, Russia. We acknowledge financial support of
this research from participants of the crowdfunding project. We thank Oksana Glushchenko and Konstantin
Yarygin for help with data analysis, Leigh-Ann Stewart for language editing, and Ilia Korvigo for discussion of
the data analysis.
Conflicts of Interest:
The authors declare no conflict of interest. The funding sponsors had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
References
1.
Egshatyan, L.; Kashtanova, D.; Popenko, A.; Tkacheva, O.; Tyakht, A.; Alexeev, D.; Karamnova, N.;
Kostryukova, E.; Babenko, V.; Vakhitova, M.; et al. Gut microbiota and diet in patients with different
glucose tolerance. Endocr. Connect. 2016,5, 1–9. [CrossRef] [PubMed]
2.
Pascal, V.; Pozuelo, M.; Borruel, N.; Casellas, F.; Campos, D.; Santiago, A.; Martinez, X.; Varela, E.;
Sarrabayrouse, G.; Machiels, K.; et al. A microbial signature for Crohn’s disease. Gut
2017
. [CrossRef]
[PubMed]
3.
Jie, Z.; Xia, H.; Zhong, S.L.; Feng, Q.; Li, S.; Liang, S.; Zhong, H.; Liu, Z.; Gao, Y.; Zhao, H.; et al. The gut
microbiome in atherosclerotic cardiovascular disease. Nat. Commun. 2017,8, 845. [CrossRef] [PubMed]
4.
Dubinkina, V.B.; Tyakht, A.V.; Odintsova, V.Y.; Yarygin, K.S.; Kovarsky, B.A.; Pavlenko, A.V.; Ischenko, D.S.;
Popenko, A.S.; Alexeev, D.G.; Taraskina, A.Y.; et al. Links of gut microbiota composition with alcohol
dependence syndrome and alcoholic liver disease. Microbiome 2017,5, 141. [CrossRef] [PubMed]
Nutrients 2018,10, 576 15 of 18
5.
Karlsson, F.H.; Tremaroli, V.; Nookaew, I.; Bergström, G.; Behre, C.J.; Fagerberg, B.; Nielsen, J.; Bäckhed, F.
Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature
2013
,498,
99–103. [CrossRef] [PubMed]
6.
Gevers, D.; Kugathasan, S.; Denson, L.A.; Vázquez-Baeza, Y.; Van Treuren, W.; Ren, B.; Schwager, E.;
Knights, D.; Song, S.J.; Yassour, M.; et al. The treatment-naive microbiome in new-onset Crohn’s disease.
Cell Host Microbe 2014,15, 382–392. [CrossRef] [PubMed]
7.
Tyakht, A.V.; Alexeev, D.G.; Popenko, A.S.; Kostryukova, E.S.; Govorun, V.M. Rural and urban microbiota:
To be or not to be? Gut Microbes 2014,5, 351–356. [CrossRef] [PubMed]
8.
Celis-Morales, C.; Livingstone, K.M.; Marsaux, C.F.; Macready, A.L.; Fallaize, R.; O’donovan, C.B.;
Woolhead, C.; Forster, H.; Walsh, M.C.; Navas-Carretero, S.; et al. Effect of personalized nutrition on
health-related behaviour change: Evidence from the Food4me European randomized controlled trial.
Int. J. Epidemiol. 2016,46, 578–588. [CrossRef] [PubMed]
9.
Zeevi, D.; Korem, T.; Zmora, N.; Israeli, D.; Rothschild, D.; Weinberger, A.; Ben-Yacov, O.; Lador, D.;
Avnit-Sagi, T.; Lotan-Pompan, M.; et al. Personalized nutrition by prediction of glycemic responses. Cell
2015,163, 1079–1094. [CrossRef] [PubMed]
10.
Sonnenburg, J.L.; Bäckhed, F. Diet-microbiota interactions as moderators of human metabolism. Nature
2016
,
535, 56–64. [CrossRef] [PubMed]
11.
David, L.A.; Maurice, C.F.; Carmody, R.N.; Gootenberg, D.B.; Button, J.E.; Wolfe, B.E.; Ling, A.V.; Devlin, A.S.;
Varma, Y.; Fischbach, M.A.; et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature
2014,505, 559–563. [CrossRef] [PubMed]
12.
De Filippis, F.; Pellegrini, N.; Vannini, L.; Jeffery, I.B.; La Storia, A.; Laghi, L.; Serrazanetti, D.I.; Di Cagno, R.;
Ferrocino, I.; Lazzi, C.; et al. High-level adherence to a Mediterranean diet beneficially impacts the gut
microbiota and associated metabolome. Gut 2015. [CrossRef] [PubMed]
13.
Wu, G.D.; Chen, J.; Hoffmann, C.; Bittinger, K.; Chen, Y.Y.; Keilbaugh, S.A.; Bewtra, M.; Knights, D.;
Walters, W.A.; Knight, R.; et al. Linking long-term dietary patterns with gut microbial enterotypes. Science
2011,334, 105–108. [CrossRef] [PubMed]
14.
Cook, R.F.; Billings, D.W.; Hersch, R.K.; Back, A.S.; Hendrickson, A. A field test of a web-based workplace
health promotion program to improve dietary practices, reduce stress, and increase physical activity:
Randomized controlled trial. J. Med. Internet Res. 2007,9. [CrossRef] [PubMed]
15.
Huttenhower, C.; Gevers, D.; Knight, R.; Abubucker, S.; Badger, J.H.; Chinwalla, A.T.; Creasy, H.H.; Earl, A.M.;
FitzGerald, M.G.; Fulton, R.S.; et al. Structure, function and diversity of the healthy human microbiome.
Nature 2012,486, 207–214. [CrossRef] [PubMed]
16.
Qin, J.; Li, R.; Raes, J.; Arumugam, M.; Burgdorf, K.S.; Manichanh, C.; Nielsen, T.; Pons, N.; Levenez, F.;
Yamada, T.; et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature
2010,464, 59–65. [CrossRef] [PubMed]
17.
Tyakht, A.V.; Kostryukova, E.S.; Popenko, A.S.; Belenikin, M.S.; Pavlenko, A.V.; Larin, A.K.; Karpova, I.Y.;
Selezneva, O.V.; Semashko, T.A.; Ospanova, E.A.; et al. Human gut microbiota community structures in
urban and rural populations in Russia. Nat. Commun. 2013,4. [CrossRef] [PubMed]
18.
Debelius, J.W.; Vázquez-Baeza, Y.; McDonald, D.; Xu, Z.; Wolfe, E.; Knight, R. Turning participatory
microbiome research into usable data: Lessons from the American Gut Project. J. Microbiol. Biol. Educ.
2016
,
17, 46–50. [CrossRef] [PubMed]
19.
Almonacid, D.E.; Kraal, L.; Ossandon, F.J.; Budovskaya, Y.V.; Cardenas, J.P.; Bik, E.M.; Goddard, A.D.;
Richman, J.; Apte, Z.S. 16S rRNA gene sequencing and healthy reference ranges for 28 clinically relevant
microbial taxa from the human gut microbiome. PLoS ONE 2017,12, e0176555. [CrossRef] [PubMed]
20.
David, L.A.; Materna, A.C.; Friedman, J.; Campos-Baptista, M.I.; Blackburn, M.C.; Perrotta, A.; Erdman, S.E.;
Alm, E.J. Host lifestyle affects human microbiota on daily timescales. Genome Biol.
2014
,15, R89. [CrossRef]
[PubMed]
21.
Caporaso, J.G.; Lauber, C.L.; Walters, W.A.; Berg-Lyons, D.; Lozupone, C.A.; Turnbaugh, P.J.; Fierer, N.;
Knight, R. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl.
Acad. Sci. USA 2011,108, 4516–4522. [CrossRef] [PubMed]
22.
Kelly, B.J.; Gross, R.; Bittinger, K.; Sherrill-Mix, S.; Lewis, J.D.; Collman, R.G.; Bushman, F.D.; Li, H. Power and
sample-size estimation for microbiome studies using pairwise distances and PERMANOVA. Bioinformatics
2015,31, 2461–2468. [CrossRef] [PubMed]
Nutrients 2018,10, 576 16 of 18
23.
Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Pena, A.G.;
Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data.
Nat. Methods 2010,7, 335–336. [CrossRef] [PubMed]
24.
Ritari, J.; Salojärvi, J.; Lahti, L.; de Vos, W.M. Improved taxonomic assignment of human intestinal 16S rRNA
sequences by a dedicated reference database. BMC Genom. 2015,16, 1056. [CrossRef] [PubMed]
25.
DeSantis, T.Z.; Hugenholtz, P.; Larsen, N.; Rojas, M.; Brodie, E.L.; Keller, K.; Huber, T.; Dalevi, D.; Hu, P.;
Andersen, G.L. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with
ARB. Appl. Environ. Microbiol. 2006,72, 5069–5072. [CrossRef] [PubMed]
26.
Langille, M.G.; Zaneveld, J.; Caporaso, J.G.; McDonald, D.; Knights, D.; Reyes, J.A.; Clemente, J.C.;
Burkepile, D.E.; Thurber, R.L.V.; Knight, R.; et al. Predictive functional profiling of microbial communities
using 16S rRNA marker gene sequences. Nat. Biotechnol. 2013,31, 814–821. [CrossRef] [PubMed]
27.
R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing:
Vienna, Austria, 2014.
28.
Oksanen, J.; Blanchet, F.G.; Friendly, M.; Kindt, R.; Legendre, P.; McGlinn, D.; Minchin, P.R.; O’Hara, R.B.;
Simpson, G.L.; Solymos, P.; et al. Vegan: Community Ecology Package; R Package Version 2.4-3; R Foundation
for Statistical Computing: Vienna, Austria, 2017.
29.
Chen, J.; Bittinger, K.; Charlson, E.S.; Hoffmann, C.; Lewis, J.; Wu, G.D.; Collman, R.G.; Bushman, F.D.; Li, H.
Associating microbiome composition with environmental covariates using generalized UniFrac distances.
Bioinformatics 2012,28, 2106–2113. [CrossRef] [PubMed]
30.
Morgan, X.C.; Tickle, T.L.; Sokol, H.; Gevers, D.; Devaney, K.L.; Ward, D.V.; Reyes, J.A.; Shah, S.A.;
LeLeiko, N.; Snapper, S.B.; et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease
and treatment. Genome Biol. 2012,13, R79. [CrossRef] [PubMed]
31.
Paulson, J.N.; Stine, O.C.; Bravo, H.C.; Pop, M. Differential abundance analysis for microbial marker-gene
surveys. Nat. Methods 2013,10, 1200. [CrossRef] [PubMed]
32.
Fernandes, A.D.; Reid, J.N.; Macklaim, J.M.; McMurrough, T.A.; Edgell, D.R.; Gloor, G.B. Unifying the
analysis of high-throughput sequencing datasets: Characterizing RNA-seq, 16S rRNA gene sequencing and
selective growth experiments by compositional data analysis. Microbiome 2014,2, 15. [CrossRef] [PubMed]
33.
Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic
biomarker discovery and explanation. Genome Biol. 2011,12, R60. [CrossRef] [PubMed]
34.
Väremo, L.; Nielsen, J.; Nookaew, I. Enriching the gene set analysis of genome-wide data by incorporating
directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res.
2013
,
41, 4378–4391. [CrossRef] [PubMed]
35. Hennig, C. FPC: Flexible Procedures for Clustering, Version 2.1-7. 2014. Available online: http://CRAN.R-
project.org/package=fpc (accessed on 1 May 2018).
36.
Kuhn, M. Caret: Classification and Regression Training, Version 6.0-76. 2017. Available online: https:
//CRAN.R-project.org/package=caret (accessed on 1 May 2018).
37.
Sing, T.; Sander, O.; Beerenwinkel, N.; Lengauer, T. ROCR: Visualizing classifier performance in R.
Bioinformatics 2005,21, 3940–3941. [CrossRef] [PubMed]
38.
Kurtz, Z.D.; Müller, C.L.; Miraldi, E.R.; Littman, D.R.; Blaser, M.J.; Bonneau, R.A. Sparse and compositionally
robust inference of microbial ecological networks. PLoS Comput. Biol.
2015
,11, e1004226. [CrossRef]
[PubMed]
39.
Zhao, T.; Li, X.; Liu, H.; Roeder, K.; Lafferty, J.; Wasserman, L. Huge: High-Dimensional Undirected Graph
Estimation; R Package Version 1.2.7; R Foundation for Statistical Computing: Vienna, Austria, 2015.
40.
Arumugam, M.; Raes, J.; Pelletier, E.; Le Paslier, D.; Yamada, T.; Mende, D.R.; Fernandes, G.R.; Tap, J.;
Bruls, T.; Batto, J.M.; et al. Enterotypes of the human gut microbiome. Nature
2011
,473, 174–180. [CrossRef]
[PubMed]
41.
Zhernakova, A.; Kurilshikov, A.; Bonder, M.J.; Tigchelaar, E.F.; Schirmer, M.; Vatanen, T.; Mujagic, Z.;
Vila, A.V.; Falony, G.; Vieira-Silva, S.; et al. Population-based metagenomics analysis reveals markers for gut
microbiome composition and diversity. Science 2016,352, 565–569. [CrossRef] [PubMed]
42.
Koropatkin, N.M.; Cameron, E.A.; Martens, E.C. How glycan metabolism shapes the human gut microbiota.
Nat. Rev. Microbiol. 2012,10, 323–335. [CrossRef] [PubMed]
43.
Louis, P.; Flint, H.J. Diversity, metabolism and microbial ecology of butyrate-producing bacteria from the
human large intestine. FEMS Microbiol. Lett. 2009,294, 1–8. [CrossRef] [PubMed]
Nutrients 2018,10, 576 17 of 18
44.
Flint, H.J.; Scott, K.P.; Duncan, S.H.; Louis, P.; Forano, E. Microbial degradation of complex carbohydrates in
the gut. Gut Microbes 2012,3, 289–306. [CrossRef] [PubMed]
45.
Martínez, I.; Lattimer, J.M.; Hubach, K.L.; Case, J.A.; Yang, J.; Weber, C.G.; Louk, J.A.; Rose, D.J.;
Kyureghian, G.; Peterson, D.A.; et al. Gut microbiome composition is linked to whole grain-induced
immunological improvements. ISME J. 2013,7, 269–280. [CrossRef] [PubMed]
46.
Lozupone, C.A.; Stombaugh, J.I.; Gordon, J.I.; Jansson, J.K.; Knight, R. Diversity, stability and resilience of
the human gut microbiota. Nature 2012,489, 220–230. [CrossRef] [PubMed]
47.
Goodrich, J.K.; Waters, J.L.; Poole, A.C.; Sutter, J.L.; Koren, O.; Blekhman, R.; Beaumont, M.; Van Treuren, W.;
Knight, R.; Bell, J.T.; et al. Human genetics shape the gut microbiome. Cell
2014
,159, 789–799. [CrossRef]
[PubMed]
48.
Million, M.; Maraninchi, M.; Henry, M.; Armougom, F.; Richet, H.; Carrieri, P.; Valero, R.; Raccah, D.;
Vialettes, B.; Raoult, D. Obesity-associated gut microbiota is enriched in Lactobacillus reuteri and depleted
in Bifidobacterium animalis and Methanobrevibacter smithii. Int. J. Obes.
2012
,36, 817–825. [CrossRef]
[PubMed]
49.
Fu, J.; Bonder, M.J.; Cenit, M.C.; Tigchelaar, E.F.; Maatman, A.; Dekens, J.A.; Brandsma, E.; Marczynska, J.;
Imhann, F.; Weersma, R.K.; et al. The gut microbiome contributes to a substantial proportion of the variation
in blood lipidsnovelty and Significance. Circ. Res. 2015,117, 817–824. [CrossRef] [PubMed]
50.
Wang, Z.; Koonen, D.; Hofker, M.; Fu, J. Gut microbiome and lipid metabolism: From associations to
mechanisms. Curr. Opin. Lipidol. 2016,27, 216–224. [CrossRef] [PubMed]
51.
Moreno-Pérez, D.; Bressa, C.; Bailén, M.; Hamed-Bousdar, S.; Naclerio, F.; Carmona, M.; Pérez, M.;
González-Soltero, R.; Montalvo-Lominchar, M.G.; Carabaña, C.; et al. Effect of a protein supplement
on the gut microbiota of endurance athletes: A randomized, controlled, double-blind pilot study. Nutrients
2018,10, 337. [CrossRef] [PubMed]
52.
Konikoff, T.; Gophna, U. Oscillospira: A central, enigmatic component of the human gut microbiota.
Trends Microbiol. 2016,24, 523–524. [CrossRef] [PubMed]
53.
Goodrich, J.K.; Davenport, E.R.; Beaumont, M.; Jackson, M.A.; Knight, R.; Ober, C.; Spector, T.D.; Bell, J.T.;
Clark, A.G.; Ley, R.E. Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe
2016
,19,
731–743. [CrossRef] [PubMed]
54.
Knights, D.; Ward, T.L.; McKinlay, C.E.; Miller, H.; Gonzalez, A.; McDonald, D.; Knight, R. Rethinking
“enterotypes”. Cell Host Microbe 2014,16, 433–437. [CrossRef] [PubMed]
55.
Yatsunenko, T.; Rey, F.E.; Manary, M.J.; Trehan, I.; Dominguez-Bello, M.G.; Contreras, M.; Magris, M.;
Hidalgo, G.; Baldassano, R.N.; Anokhin, A.P.; et al. Human gut microbiome viewed across age and
geography. Nature 2012,486, 222–227. [CrossRef] [PubMed]
56.
Hu, H.J.; Park, S.G.; Jang, H.B.; Choi, M.G.; Park, K.H.; Kang, J.H.; Park, S.I.; Lee, H.J.; Cho, S.H. Obesity
alters the microbial community profile in Korean adolescents. PLoS ONE
2015
,10, e0134333. [CrossRef]
[PubMed]
57.
Rajili´c-Stojanovi´c, M.; de Vos, W.M. The first 1000 cultured species of the human gastrointestinal microbiota.
FEMS Microbiol. Rev. 2014,38, 996–1047. [CrossRef] [PubMed]
58.
Koh, A.; De Vadder, F.; Kovatcheva-Datchary, P.; Bäckhed, F. From dietary fiber to host physiology:
Short-chain fatty acids as key bacterial metabolites. Cell 2016,165, 1332–1345. [CrossRef] [PubMed]
59.
Bailey, L.C.; Forrest, C.B.; Zhang, P.; Richards, T.M.; Livshits, A.; DeRusso, P.A. Association of antibiotics in
infancy with early childhood obesity. JAMA Pediatr. 2014,168, 1063–1069. [CrossRef] [PubMed]
60.
Cox, L.M.; Yamanishi, S.; Sohn, J.; Alekseyenko, A.V.; Leung, J.M.; Cho, I.; Kim, S.G.; Li, H.; Gao, Z.;
Mahana, D.; et al. Altering the intestinal microbiota during a critical developmental window has lasting
metabolic consequences. Cell 2014,158, 705–721. [CrossRef] [PubMed]
61.
Korpela, K.; Salonen, A.; Virta, L.J.; Kekkonen, R.A.; Forslund, K.; Bork, P.; De Vos, W.M. Intestinal
microbiome is related to lifetime antibiotic use in Finnish pre-school children. Nat. Commun.
2016
,7.
[CrossRef] [PubMed]
62.
Walker, A.W.; Ince, J.; Duncan, S.H.; Webster, L.M.; Holtrop, G.; Ze, X.; Brown, D.; Stares, M.D.; Scott, P.;
Bergerat, A.; et al. Dominant and diet-responsive groups of bacteria within the human colonic microbiota.
ISME J. 2011,5, 220–230. [CrossRef] [PubMed]
Nutrients 2018,10, 576 18 of 18
63. Upadhyaya, B.; McCormack, L.; Fardin-Kia, A.R.; Juenemann, R.; Nichenametla, S.; Clapper, J.; Specker, B.;
Dey, M. Impact of dietary resistant starch type 4 on human gut microbiota and immunometabolic functions.
Sci. Rep. 2016,6, 28797. [CrossRef] [PubMed]
64.
Leitch, E.C.M.; Walker, A.W.; Duncan, S.H.; Holtrop, G.; Flint, H.J. Selective colonization of insoluble
substrates by human faecal bacteria. Environ. Microbiol. 2007,9, 667–679. [CrossRef] [PubMed]
65.
De Filippo, C.; Cavalieri, D.; Di Paola, M.; Ramazzotti, M.; Poullet, J.B.; Massart, S.; Collini, S.; Pieraccini, G.;
Lionetti, P. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe
and rural Africa. Proc. Natl. Acad. Sci. USA 2010,107, 14691–14696. [CrossRef] [PubMed]
66.
Kovatcheva-Datchary, P.; Nilsson, A.; Akrami, R.; Lee, Y.S.; De Vadder, F.; Arora, T.; Hallen, A.; Martens, E.;
Björck, I.; Bäckhed, F. Dietary fiber-induced improvement in glucose metabolism is associated with increased
abundance of Prevotella. Cell Metab. 2015,22, 971–982. [CrossRef] [PubMed]
67.
Elli, M.; Callegari, M.L.; Ferrari, S.; Bessi, E.; Cattivelli, D.; Soldi, S.; Morelli, L.; Feuillerat, N.G.; Antoine, J.M.
Survival of yogurt bacteria in the human gut. Appl. Environ. Microbiol.
2006
,72, 5113–5117. [CrossRef]
[PubMed]
68.
Vandeputte, D.; Falony, G.; Vieira-Silva, S.; Tito, R.Y.; Joossens, M.; Raes, J. Stool consistency is strongly
associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut
2015
.
[CrossRef] [PubMed]
69.
Liu, T.; Hougen, H.; Vollmer, A.C.; Hiebert, S.M. Gut bacteria profiles of Mus musculus at the phylum and
family levels are influenced by saturation of dietary fatty acids. Anaerobe
2012
,18, 331–337. [CrossRef]
[PubMed]
70.
Daniel, H.; Gholami, A.M.; Berry, D.; Desmarchelier, C.; Hahne, H.; Loh, G.; Mondot, S.; Lepage, P.;
Rothballer, M.; Walker, A.; et al. High-fat diet alters gut microbiota physiology in mice. ISME J.
2014
,8,
295–308. [CrossRef] [PubMed]
71.
Graf, D.; Di Cagno, R.; Fåk, F.; Flint, H.J.; Nyman, M.; Saarela, M.; Watzl, B. Contribution of diet to the
composition of the human gut microbiota. Microb. Ecol. Health Dis. 2015,26. [CrossRef] [PubMed]
72.
Mosca, A.; Leclerc, M.; Hugot, J.P. Gut microbiota diversity and human diseases: Should we reintroduce key
predators in our ecosystem? Front. Microbiol. 2016,7, 455. [CrossRef] [PubMed]
73.
Claassen-Weitz, S.; Shittu, A.O.; Ngwarai, M.R.; Thabane, L.; Nicol, M.P.; Kaba, M. Fecal carriage of
Staphylococcus aureus in the hospital and community setting: A systematic review. Front. Microbiol.
2016
,7,
449. [CrossRef] [PubMed]
74.
Griffin, N.W.; Ahern, P.P.; Cheng, J.; Heath, A.C.; Ilkayeva, O.; Newgard, C.B.; Fontana, L.; Gordon, J.I.
Prior dietary practices and connections to a human gut microbial metacommunity alter responses to diet
interventions. Cell Host Microbe 2017,21, 84–96. [CrossRef] [PubMed]
75.
Le Chatelier, E.; Nielsen, T.; Qin, J.; Prifti, E.; Hildebrand, F.; Falony, G.; Almeida, M.; Arumugam, M.;
Batto, J.M.; Kennedy, S.; et al. Richness of human gut microbiome correlates with metabolic markers. Nature
2013,500, 541–546. [CrossRef] [PubMed]
76.
Cotillard, A.; Kennedy, S.P.; Kong, L.C.; Prifti, E.; Pons, N.; Le Chatelier, E.; Almeida, M.; Quinquis, B.;
Levenez, F.; Galleron, N.; et al. Dietary intervention impact on gut microbial gene richness. Nature
2013
,500,
585–588. [CrossRef] [PubMed]
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... Polyphenols are enriched in fruits, vegetables, and seeds and could increase Bifidobacterium and Lactobacillus, thus increasing the SCFA production and benefiting human health [27]. However, the above evidence is mainly derived from crosssectional studies using FFQ to capture habitual dietary intake or short-term interventions [9,24,28,29]. The responses of gut microbiota to dietary changes has not been well understood, and many findings are contradictory [28]. ...
... These two methods offer opportunities to integrate long-term and short-term dietary intake for a wide range of diet-microbiome association studies. Second, studies supported that long-term dietary habits exhibited a larger influence on gut community composition [29], while short-term dietary changes showed slight but significant temporary effects [29,31]. ...
... These two methods offer opportunities to integrate long-term and short-term dietary intake for a wide range of diet-microbiome association studies. Second, studies supported that long-term dietary habits exhibited a larger influence on gut community composition [29], while short-term dietary changes showed slight but significant temporary effects [29,31]. ...
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Background The interplay among the plant-based dietary pattern, gut microbiota, and cardiometabolic health is still unclear, and evidence from large prospective cohorts is rare. We aimed to examine the association of long-term and short-term plant-based dietary patterns with gut microbiota and to assess the prospective association of the identified microbial features with cardiometabolic biomarkers. Methods Using a population-based prospective cohort study: the China Health and Nutrition Survey, we included 3096 participants from 15 provinces/megacities across China. We created an overall plant-based diet index (PDI), a healthful plant-based diet index (hPDI), and an unhealthful plant-based diet index (uPDI). The average PDIs were calculated using repeat food frequency questionnaires collected in 2011 and 2015 to represent a long-term dietary pattern. Short-term dietary pattern was estimated using 3-day 24-h dietary recalls collected in 2015. Fecal samples were collected in 2015 and measured using 16S rRNA sequencing. We investigated the association of long-term and short-term plant-based dietary patterns with gut microbial diversity, taxonomies, and functional pathways using linear mixed models. Furthermore, we assessed the prospective associations between the identified gut microbiome signatures and cardiometabolic biomarkers (measured in 2018) using linear regression. Results We found a significant association of short-term hPDI with microbial alpha-diversity. Both long-term and short-term plant-based diet indices were correlated with microbial overall structure, whereas long-term estimates explained more variance. Long-term and short-term PDIs were differently associated with microbial taxonomic composition, yet only microbes related to long-term estimates showed association with future cardiometabolic biomarkers. Higher long-term PDI was associated with the lower relative abundance of Peptostreptococcus, while this microbe was positively correlated with the high-sensitivity C-reactive protein and inversely associated with high-density lipoprotein cholesterol. Conclusions We found shared and distinct gut microbial signatures of long-term and short-term plant-based dietary patterns. The identified microbial genera may provide insights into the protective role of long-term plant-based dietary pattern for cardiometabolic health, and replication in large independent cohorts is needed.
... We compared the Yakut microbiome with published data on metropolitan Russians [37] and Inuits [31] using the "external comparison report" feature of Knomics-Biota via closed-reference picking with the GreenGenes database v13.5 [35] and QIIME 1.9 [38]. ...
... To identify the unique features of the Yakut gut microbiome, we compared the healthy Yakut microbiome profiles with those of residents of Moscow. The Moscow residents were participants of the crowd-funded OhMyGut project linking microbiome characteristics with diet and lifestyle [37] (n = 101 subjects; only the baseline microbiome profiles before the dietary intervention were considered). Alpha-diversity was lower in the urban residents (p = 0.0). ...
... In zoos, scat samples can be easily collected by animal care staff; however, collecting an adequate number of samples across multiple locations for wild populations can be difficult. Some studies have successfully collected scat or other material (such as swabs or ticks) to analyse microbiomes from large and geographically dispersed datasets through citizen science initiatives (Hulcr et al., 2012;Klimenko et al., 2018;McDonald et al., 2018;Chauhan et al., 2020). As echidnas cover a vast geographic range, citizen scientists have been enlisted to collect echidna scats through the project EchidnaCSI (Echidna Conservation Science Initiative; Stenhouse et al., 2021;Perry et al., 2022), leading to the largest echidna material collection to date. ...
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... All procedures involving European Journal of Nutrition research with the study participants were approved by the Local Ethics Committee at Poznań University of Medical Sciences (permit number 486/2016). Since the primary outcome of this study was to detect the overall differences in gut microbiota composition between people with different dietary patterns, we calculated the sample size using the R samplesize package, assuming a 0.2 Shannon index difference between two groups and a standard deviation of mean value of 0.5 [27]. The resulting required sample size was one hundred participants in each group. ...
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Purpose Some dietary habits cluster together, and for this reason it is advised to study the impact of entire dietary patterns on human health, rather than that of individual dietary habits. The main objective of this study was to evaluate differences in gut microbiota composition and their predicted functional properties between people with a healthy (HDP) and western (WDP) dietary pattern. Methods A cross-sectional, observational study was carried out on 200 participants enrolled 2017–2018 in Poznań, Poland, equally distributed into HDP and WDP groups. Diet was estimated using 3-day food records and information on stool transit times was collected. Fecal microbiota composition was assessed by 16S rRNA gene sequencing and its functional properties were predicted by the PICRUSt2 workflow. Results The α-diversity did not differ between people with WDP and HDP, but β-diversity was associated with dietary pattern. People with HDP had higher relative abundances (RA) of Firmicutes and Faecalibacterium and lower RA of Bacteroidota and Escherichia–Shigella than participants with WDP. Only a small proportion of the variance in microbiota composition (1.8%) and its functional properties (2.9%) could be explained by dietary intake (legumes, simple sugars and their sources, like fruit, soft drinks) and stool transit characteristics. Conclusion Gut microbiota composition and predicted metabolic potential is shaped by overall diet quality as well as the frequency of defecation; however, the cumulative effect of these explain only a relatively low proportion of variance.
... These relatively rapid changes to the gut microbiota could be a 'shock reaction' to an invasion of incoming nutrients, possibly causing a transient disruption of microbial composition (Klimenko et al., 2018). Howbeit, coping with this kind of stress or collapse is part of the inherent plastic nature of the normal microbiota. ...
Thesis
The human intestinal microbiota is composed of several types of microorganisms, including bacteria, archaea, fungi, unicellular eukaryotes and viruses. Among them, bacteria are the most diverse and abundant with a gene catalog 150 times larger than the genes present in the human genome, which represents a tremendous metabolic potential. These bacteria actively participate in the maintenance of intestinal homeostasis. Dysbiosis of the gut microbiota could be observed at course of many human pathologies, particularly inflammatory diseases intestinal chronic diseases (IBD), such as Crohn's disease (CD) or Ulcerative colitis (UC). These dysbiosis could contribute to the onset and progression of diseases. For example, gut microbiota transplantation experiments in murine model have allowed to show that a dysbiotic microbiota is sufficient to induce chronic inflammation in the colon and thus lead to the development of a metabolic syndrome or colitis. Different intervention strategies, including fecal transplantation, administration of probiotics or even special nutritional diets have been developed to act on the microbial communities of the digestive tract and to restore homeostasis of host tissues. The success of some interventions like Fecal transplantation represent a proof of concept in humans that acting on the composition of the intestinal microbiota is a strong lever to resolve certain physio pathological situations associated with gut microbiota dysbiosis. Diet is another important method for modulating the gut microbiota since it is the most important factor influencing its composition. In fact, the nutrients ingested can act directly on the composition of the microbiota by serving as substrates for microorganisms and indirectly by modulating intestinal homeostasis and components of the immune system associated, themselves contributing to regulate the composition microbiota. It is expected that ingestion of these beneficial microorga nisms can stimulate the immune system, promote intestinal homeostasis and to some extent contribute to the balance of the microbiota intestinal. The use of probiotic microorganisms is found to be very effective in some studies to treat different physiopathological situations (colitis, metabolic syndrome) in laboratory model organisms (rats, mice), however the use of these same probiotics in humans have given relatively disappointing clinical results, with poorly reproducible results across cohorts of patients. Except for the treatment of antibiotic-associated diarrhea. These discrepancies in results between pre-clinical models and clinical trials encourage better characterization of the molecular mechanisms used by probiotics to exert their beneficial effects and especially better understand the relationship of these probiotic microorganisms with the resident microbiota and diet.Among the different rising intervention strategies practiced nowadays in the purpose to shape the microbiota, a growing interest is given to other dietary interventions, like caloric restriction (CR) which has demonstrated several beneficial effects on various physiological systems, including the gastro-intestinal system, by modulating the innate and adaptative immune responses. In fact, emerging evidence suggests that the immune system function might be heavily influenced by the sensing of nutrient, reinforcing the idea that diet can deeply influence the inflammatory responses.
... The reduction in Christensenellaceae abundance was observed in prediabetes individuals (He et al., 2018), while the normal abundance of Christensenellaceae was associated with healthy glucose metabolism (Lippert et al., 2017). Furthermore, Christensenellaceae was enriched following healthy lifestyle behavior, including regular consumption of fruits and vegetables (Bowyer et al., 2018;Klimenko et al., 2018). This change could also be observed when feeding rodents with dietary fiber (Ferrario et al., 2017). ...
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... It is well known that metabolic disorders are often correlated with dietary patterns (39). Additionally, f_Christensenellaceae has been indicated to be responsive to diet; studies have associated f_Christensenellaceae with the healthy dietary habits of decreased refined sugar and improved consumption of fruits and vegetables (40)(41)(42). Interestingly, our results also showed that there was a significant coexistence relationship between the g_Christensenellaceae_R-7_group and the g_Eubacterium_coprostanoligenes_group. ...
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... Figure 6b shows that, for most methods for composition estimation, alpha diversity differed significantly by location, using both Shannon's index and Simpson's index. Interestingly enough, the empirical Bayesian estimates from mbDenoise-zinb and PPCA-NB showed the opposite result that alpha diversity of Bhopal (carbohydrate-rich diet) samples was higher than that of Kerala (protein-rich diet) samples, which seems more reasonable and is consistent with previous observations [34,35]. ...
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The analysis of microbiome data has several technical challenges. In particular, count matrices contain a large proportion of zeros, some of which are biological, whereas others are technical. Furthermore, the measurements suffer from unequal sequencing depth, overdispersion, and data redundancy. These nuisance factors introduce substantial noise. We propose an accurate and robust method, mbDenoise, for denoising microbiome data. Assuming a zero-inflated probabilistic PCA (ZIPPCA) model, mbDenoise uses variational approximation to learn the latent structure and recovers the true abundance levels using the posterior, borrowing information across samples and taxa. mbDenoise outperforms state-of-the-art methods to extract the signal for downstream analyses.
... A preliminary taxonomic profiling of the patients' gut metagenomes was performed using unique clade-specific gene markers (see section "Materials and Methods"). It revealed pronounced dysbiosis, particularly, with the levels of Proteobacteria 1-2 orders higher than observed from NGS microbiome surveys for the general Russian population (Tyakht et al., 2013;Klimenko et al., 2018;Volokh et al., 2019). The disruption of gut community structures has been confirmed via a complementary analysis using taxonspecific qPCR (Additional File 2: Supplementary Table 2). ...
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Background Optimal nutritional choices are linked with better health, but many current interventions to improve diet have limited effect. We tested the hypothesis that providing personalized nutrition (PN) advice based on information on individual diet and lifestyle, phenotype and/or genotype would promote larger, more appropriate, and sustained changes in dietary behaviour. Methods : Adults from seven European countries were recruited to an internet-delivered intervention (Food4Me) and randomized to: (i) conventional dietary advice (control) or to PN advice based on: (ii) individual baseline diet; (iii) individual baseline diet plus phenotype (anthropometry and blood biomarkers); or (iv) individual baseline diet plus phenotype plus genotype (five diet-responsive genetic variants). Outcomes were dietary intake, anthropometry and blood biomarkers measured at baseline and after 3 and 6 months’ intervention. Results At baseline, mean age of participants was 39.8 years (range 18–79), 59% of participants were female and mean body mass index (BMI) was 25.5 kg/m2. From the enrolled participants, 1269 completed the study. Following a 6-month intervention, participants randomized to PN consumed less red meat [-5.48 g, (95% confidence interval:-10.8,-0.09), P = 0.046], salt [-0.65 g, (−1.1,-0.25), P = 0.002] and saturated fat [-1.14 % of energy, (−1.6,-0.67), P < 0.0001], increased folate [29.6 µg, (0.21,59.0), P = 0.048] intake and had higher Healthy Eating Index scores [1.27, (0.30, 2.25), P = 0.010) than those randomized to the control arm. There was no evidence that including phenotypic and phenotypic plus genotypic information enhanced the effectiveness of the PN advice. Conclusions Among European adults, PN advice via internet-delivered intervention produced larger and more appropriate changes in dietary behaviour than a conventional approach.
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