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ORIGINAL RESEARCH
published: 05 March 2018
doi: 10.3389/fmicb.2018.00317
Edited by:
Maria De Angelis,
Università degli Studi di Bari Aldo
Moro, Italy
Reviewed by:
Marius Vital,
Helmholtz-Zentrum für
Infektionsforschung, Germany
Alinne Castro,
Universidade Católica Dom Bosco,
Brazil
*Correspondence:
Carmen Losasso
closasso@izsvenezie.it
†These authors have contributed
equally to this work.
Specialty section:
This article was submitted to
Food Microbiology,
a section of the journal
Frontiers in Microbiology
Received: 29 November 2017
Accepted: 09 February 2018
Published: 05 March 2018
Citation:
Losasso C, Eckert EM, Mastrorilli E,
Villiger J, Mancin M, Patuzzi I,
Di Cesare A, Cibin V, Barrucci F,
Pernthaler J, Corno G and Ricci A
(2018) Assessing the Influence
of Vegan, Vegetarian and Omnivore
Oriented Westernized Dietary Styles
on Human Gut Microbiota: A Cross
Sectional Study.
Front. Microbiol. 9:317.
doi: 10.3389/fmicb.2018.00317
Assessing the Influence of Vegan,
Vegetarian and Omnivore Oriented
Westernized Dietary Styles on
Human Gut Microbiota: A Cross
Sectional Study
Carmen Losasso1*†, Ester M. Eckert2†, Eleonora Mastrorilli1, Jorg Villiger3,
Marzia Mancin1, Ilaria Patuzzi1,4 , Andrea Di Cesare2,5 , Veronica Cibin1,
Federica Barrucci1, Jakob Pernthaler3, Gianluca Corno2and Antonia Ricci1
1Department of Food Safety, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy, 2Microbial Ecology Group,
Institute of Ecosystem Study, National Research Council, Verbania, Italy, 3Limnological Station, Department of Plant and
Microbial Biology, University of Zurich, Zurich, Switzerland, 4Department of Information Engineering, University of Padova,
Padova, Itay, 5Dipartimento di Scienze della Terra, dell’Ambiente e della Vita, University of Genova, Genova, Italy
Diet and lifestyle have a strong influence on gut microbiota, which in turn has important
implications on a variety of health-related aspects. Despite great advances in the field,
it remains unclear to which extent the composition of the gut microbiota is modulated
by the intake of animal derived products, compared to a vegetable based diet. Here the
specific impact of vegan, vegetarian, and omnivore feeding type on the composition
of gut microbiota of 101 adults was investigated among groups homogeneous for
variables known to have a role in modulating gut microbial composition such as
age, anthropometric variables, ethnicity, and geographic area. The results displayed
a picture where the three different dietetic profiles could be well distinguished on
the basis of participant’s dietetic regimen. Regarding the gut microbiota; vegetarians
had a significantly greater richness compared to omnivorous. Moreover, counts of
Bacteroidetes related operational taxonomic units (OTUs) were greater in vegans
and vegetarians compared to omnivores. Interestingly considering the whole bacterial
community composition the three cohorts were unexpectedly similar, which is probably
due to their common intake in terms of nutrients rather than food, e.g., high fat content
and reduced protein and carbohydrate intake. This finding suggests that fundamental
nutritional choices such as vegan, vegetarian, or omnivore do influence the microbiota
but do not allow to infer conclusions on gut microbial composition, and suggested the
possibility for a preferential impact of other variables, probably related to the general
life style on shaping human gut microbial community in spite of dietary influence.
Consequently, research were individuals are categorized on the basis of their claimed
feeding types is of limited use for scientific studies, since it appears to be oversimplified.
Keywords: gut microbiota, feeding type, nutritional intake
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Losasso et al. Gut Microbiota: A Cross Sectional Study
INTRODUCTION
The human intestine is a complex biological network that
accounts for a great variety of microorganisms. Because this
microbial community is known to have a profound impact on
many aspects of human health including the immune system
(Cerf-Bensussan and Gaboriau-Routhiau, 2010), inflammatory
disease and obesity (Turnbaugh et al., 2006), a number of
studies on the diversity of the bacterial populations of the
human gut have been carried out in recent years (Turnbaugh
et al., 2006;McDonald et al., 2015;Davenport et al., 2017;
Moran-Ramos et al., 2017). Diet and lifestyle strongly profile
the gut microbiota (i.e., the entire gut microbial community)
(De Filippo et al., 2010;De Filippis et al., 2016) and dietary
modifications can provoke changes in the relative abundances of
microbial taxa. Moreover, the human gut microbiota dynamically
interacts with the external environment in a bidirectional manner
as bacteria can move between ecosystems: from animals to
humans, through manure and feces, to water and soil and
return to humans and animals by food and feed (Schjørring
and Krogfelt, 2011). Thus, both, the environmental context
and food sources exert a pivotal influence on the composition
and diversity of the gut microbiota of individuals and whole
populations.
In this context, the vegetarian diets including vegan, have
obtained recognition as healthy and potentially therapeutic
feeding types. If appropriately planned these diets may provide
health benefits for the prevention and treatment of certain
diseases including ischemic heart disease, type 2 diabetes,
hypertension, certain types of cancer, and obesity (Melina et al.,
2016). The possibility that any such health advantage might be
linked to a unique protective gut microbiota profile has been the
object of previous studies (Glick-Bauer and Yeh, 2014).
To describe the differences in the microbiota profile deriving
from different food choices, Wu et al. (2011) found that three
enterotypes could be associated with different dietary profiles:
the genus Prevotella was found to be adapted to a carbohydrate-
dominated metabolism and a vegetarian diet; Bacteroides, in
turn, was linked to diets that were high in protein and animal
derived products (mostly omnivorous) and microbiota rich in
Firmicutes (which includes the enterotype Ruminococcus) was
strongly associated with a fat based westernized diet (De Filippo
et al., 2010) and obesity (Ley et al., 2006), even though it
seems that species of the same taxonomic group may harbor
different metabolic characteristics (De Filippis et al., 2016). On
the other hand, negligible differences were found between the
gut microbiota of two cohorts of vegans and omnivores in the
United States (Wu et al., 2016).
Thus, to date, the available information on the human gut
microbiota does not allow inference of the probability that a
single individual (or a group) will belong to a specific dietary
type, such as vegetarian or omnivorous, from the composition of
their gut microbiota. Moreover, detailed comparative studies of
humans from the same society with different dietary preferences
are still scarce, and that also consider features such as the
nutritional status, the daily food frequencies and the total
composition of the meal and their cumulative impacts.
In order to fill this gap in knowledge, the gut microbiota
composition of three groups following vegan, i.e., free of any
animal derived product, vegetarian or omnivorous diets was
investigated and variables such as anthropometric parameters
and qualitative and detailed quantitative dietary information,
known to exert a considerable impact on modulating gut
microbiota (Derrien and Veiga, 2017) were used to examine
possible differences between the studied groups.
MATERIALS AND METHODS
Participant Recruitment and Metadata
Collection
Between August and December 2013, 101 individuals categorized
as vegans (VG) (N= 26), vegetarians (V) (N= 32), and
omnivores (O) (N= 43), were recruited on a voluntary basis
and informed written consent was signed by each participant
VG and V were enrolled with the collaboration of the Italian
Society of Vegetarian Nutrition1and the Italian League against
Vivisection2, while O participants were recruited with the
collaboration of the Regional Service for Food Hygiene and
Nutrition Promotion. All participants were enrolled using the
following inclusion criteria: being strictly vegan, vegetarian,
or omnivorous for more than 12 months prior to the
study; being adults; currently not taking antibiotics and not
having taken antibiotics in the last 12 months; being non-
smokers and not having smoked in the last 12 months; not
having been hospitalized for at least 24 months prior to
the study; not being prescribed medical drugs; not having
intestinal (Crohn’s disease, chronic ulcerative colitis, bacterial
overgrowth syndrome, constipation, celiac disease, Irritable
Bowel Syndrome) or other pathologies (type I or type II
diabetes, cardiovascular or cerebrovascular diseases, cancer,
neurodegenerative disease, rheumatoid arthritis, allergies); not
being pregnant or lactating.
All participants were checked for their nutritional status
(overweight, normal weight, or underweight), both calculating
the body mass index (BMI) and deriving the total body fat
mass (BFM), total body lean mass (BLM), and total body water
(BWM), by multifrequency bioimpedance analysis (MFBIA)
using a Tanita MC180MA device, then amending possible biases
as suggested by Ellegård et al. (2016). The final sample was
composed of participants fulfilling homogeneity criteria for age,
BMI, BFM, and BLM parameters.
Quantitative and qualitative data on habitual dietary
intake was assayed using a semi-quantitative food frequency
questionnaire (FFQ), spanning 14 days of observation before the
fecal sample was delivered. Moreover, a results reliability test was
applied by means of a 24 h dietary recall (24HR) administered to
each participant, as recommended by National Cancer Institute
(United States)3. Additional questions about consumption of
animal products were asked of each participant in order to
1http://www.scienzavegetariana.it/
2http://www.lav.it
3https://dietassessmentprimer.cancer.gov/approach/table.html
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Losasso et al. Gut Microbiota: A Cross Sectional Study
understand if their dietary habits in the last year diverged from
the self-declared diet type.
FFQ data were then analyzed using WinFoodR
software
(Medimatica Srl, Italy) in order to extract a table of 76 macro-
and micro-nutrients in the dietary compositions among all the
participants. Then, average consumption of all 76 nutrients over
the observed fortnight was considered in all analyses. Secondly,
dietary habit information was summarized by describing the
average percentage of total caloric intake imputed to the three
macronutrient categories of lipids, proteins, and carbohydrates.
Both the complete and reduced nutritional data were used in
subsequent analyses. Informed consent for specimen acquisition
and data processing was acquired.
Fecal Sample Collection and DNA
Extraction
Each donor was provided with a pair of sterile gloves and two
sterile containers (one Coprotainer R
Feces, FL Medical, Italy and
one FecalSwabTM, Copan Diagnostics Inc.), and instructed to
transfer about 50 g of feces into the Coprocontainer and a swab
into the provided Fecal Swab device to deliver for DNA extraction
and to store the sample at 4◦C. DNA was extracted as soon
as the sample was received and stored at −80◦C. In order to
guarantee the best possible representativeness of the whole gut
microbial community, two independent total DNA extractions
were conducted from each participant’s fecal sample by column
based kit QIAamp DNA Stool Mini (Qiagen, United States).
Analysis of 16S rRNA Sequences
Amplicons of V3–V4 regions of the 16S rDNA gene were
sequenced on an Illumina MiSeq platform (LGC Genomics
GmbH, Berlin, Germany4) using the universal bacterial primer
set S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21 (Herlemann
et al., 2011). Data pre-processing included demultiplexing of
all samples using Illumina’s CASAVA data analysis software,
clipping of Illumina adapters, primer detection and clipping and
quality checking of reads during the merging of the paired-
end fragments. Subsequently only sequences with a minimal
mean quality score of 32 over a moving window of 50 bases
were dereplicated resulting in an abundance value for every
sequence. Sequences with an abundance value of less than two
were discarded. Remaining sequences were pairwise aligned
with an altered Smith–Waterman-algorithm (which allows for
reduced homopolymer gap costs and semi-global alignment)
to calculate the genetic distance. An Expectation–Maximization
(EM) algorithm (similar to the PCR error removal in amplicon
noise (Quince et al., 2011) with sigma value of 250 was used to
cluster the sequences into operational taxonomic units (OTUs).
To initialize the EM algorithm, sequences were grouped together
by the following procedure: (i) For each sequence the sum
of abundances of every other dereplicated sequence within
a distance of 3% was calculated. (ii) Sequences were sorted
according to the sum from step one in a descending order. (iii)
All sequences with a distance less than 3% were grouped to
this sequence in an iterative process beginning with the first
4www.lgcgroup.com/genomics
sequence in the sorted list; this procedure was repeated for all
ungrouped sequences until no more sequences were left. (iv)
For each group the initial sequence is the representative of
the grouped sequences, all other sequences are regrouped to
these initial sequences minimizing their distance. For all OTUs
representative sequences the closest relative was calculated by the
minimal pairwise aligned distance to SILVA 115 non-redundant
database resulting in the taxonomy of and the genetic distance
to the closest relative with a minimum of 90% similarity (Quast
et al., 2013).
The final OTU table comprised 2118 OTUs in a total of 202
samples (101 stool samples with 2 technical replicates each).
If consistency within a subject’s DNA extraction replicates (i.e.,
replicas pertaining to the same subject were required to always
be the nearest neighbors in a hierarchical clustering analysis)
could be verified; only the replicate showing the highest library
size was retained for the analysis. Finally, a subset of OTUs were
considered according to several criteria: (i) OTUs assigned to
mitochondria or chloroplasts or a domain other than Bacteria
(i.e., Archaea and Eukarya) were removed; (ii) OTUs assigned to
the Phylum Cyanobacteria were removed due to the interference
of chloroplast-derived 16S rDNA; (iii) two OTUs were removed
after the calculation of the phylogenetic tree (see below) due
conspicuously long branches suggesting chimeric origin; and (iv)
OTUs not having complete taxonomic assignation were assigned
to the lowest taxonomic level available. When not specified
differently, all analyses were conducted on OTU and genera
relative abundances.
Statistical Analysis
Microbiota Profiling and Diversity Analysis
Statistical analysis was carried out using R (version 3.2.1)
(R Core Team, 2015) software packages and in-house scripts.
First, samples were characterized in terms of alpha diversity:
sample richness was explored in terms of observed number of
species and Chao1 index (Chao, 1987); sample evenness was
explored using Simpson and Inverse Simpson indices; overall
sample diversity was explored using the Shannon index using
the aindex function from DiversitySeq package (Finotello et al.,
2016). The overall difference in the alpha diversity of the samples
between V, VG, and O were tested using ANOVA and pairwise
comparisons were computed using Tukey’s Honest Significant
Differences; both tests were corrected for library size interaction.
Then, beta diversity between samples was measured in terms
of unique/shared species between the three groups and using
both weighted and unweighted UniFrac distance (Lozupone and
Knight, 2005;McMurdie and Holmes, 2013). Alpha and beta
diversity analysis was performed at all taxonomic levels (OTU,
Genus, Family, Order, Class, and Phylum); count tables for higher
taxonomic levels were obtained by collapsing OTU abundances
based on taxonomical assignation. The maximum likelihood tree
(RAxML) used for UniFrac distance computation on the OTU
table was calculated using the central/representative sequence
of each OTU using ARB (Ludwig et al., 2004) and the CIPRES
Science Gateway online platform (Stamatakis et al., 2008). All
taxa were considered, even if they were present in one sample
only.
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Principal component analysis (PCA), Principal Coordinate
Analysis (PCoA), and hierarchical clustering based on Bray–
Curtis distance were used to investigate possible sample
clustering by metadata factors. Two supervised learning methods
Random Forest (RF) (Liaw and Wiener, 2002) and Sparse Linear
Discriminant Analysis (sLDA) (Knights et al., 2011;Kuhn and
Clemmensen, 2012) were tested in an effort to build descriptive
models of the data and perform feature ranking on both OTU-
and genera-level data.
Differential analysis of OTU and genera in the three groups
was carried out on both un-normalized and normalized sequence
counts; normalization using TMM (Robinson and Oshlack, 2010)
was performed in place of simple proportions or rarefaction in
order to take into account data heteroscedasticity (McMurdie and
Holmes, 2014). Data normalization was conducted in order to
exclude the potential confounding effect of sequencing depth in
differential abundance analysis. Non-parametric Kruskal–Wallis
testing was carried out, and the false discovery rate (FDR)
(Benjamini and Hochberg, 1995;Storey and Tibshirani, 2003)
values were estimated using the Benjamini–Hochberg method to
control for multiple testing.
Prevalent taxa for each sample were investigated and
associated to each sample’s enterotype (obtained according to the
original manuscript (Arumugam et al., 2011;Wu et al., 2011);
moreover, possible enterotype-diet associations were tested
using Pearson’s Chi-Squared test and quantified by Cramer’s V
measure. Possible correlations between microbiota composition
and anthropometric data were investigated using Spearman’s
correlation coefficient.
Dietary Profiles
Micro- and macro-nutrient data were explored through PCA
and Random Forest to investigate possible group-related
clustering and to perform feature ranking. Differences in
samples’ micro-nutrient abundance between V, VG, and O
were tested using the Kruskal–Wallis non-parametric test.
Possible correlation between micro-nutrient abundance,
anthropometric data, and microbiota composition at genus
level were investigated using Spearman’s correlation coefficient.
Nutritional data were converted to average caloric percentage
of lipids, proteins, and carbohydrates (using the following
conversion: 1 g fat = 9 kcal, 1 g protein = 4 kcal, 1 g
carbohydrates = 4 kcal) and were then compared to standard
guidelines for a healthy diet (Oksanen et al., 2016). They
were also tested for differential abundance between V, VG,
and O using the Kruskal–Wallis test and correlations with
anthropometric data and microbiota alpha diversity were
computed. A Mantel-test was conducted to test for a correlation
between variations in nutrient intake (Euclidean distance of
relative abundance of dietary composition) to the beta-diversity
(Bray–Curtis distance of relative genera abundances profiles of
each participant).
Analysis of Variance Using Beta Diversity Distance
Matrix
Permutational Multivariate Analysis of Variance Using Distance
Matrices (PERMANOVA using the Adonis command in the
Vegan package) (NIH, 2016) with the Bray–Curtis distance
computed on proportional genera profiles was used to investigate
the influence of all analyzed factors in contributing to the
observed beta diversity among the microbiota profiles. All
available continuous and categorical variables were considered:
group, enterotype, richness (expressed as observed number of
species), evenness (expressed as normalized Shannon index,
i.e., Shannon index divided by its maximum value), mean
lipid, protein and carbohydrate consumption, BMI, BFM,
gender, and province of origin of the participant. Firstly, each
variable was tested for significance in explaining the variance
of the measured beta-diversity; then, pairs of variables and
their interaction were tested for significance in partitioning
the measured beta-diversity. All variables and interactions
with a p-value <0.2 (for a conservative approach to feature
selection), were tested in a multivariable complex model.
Finally, only the variables proven to be significant in the
complete model were retained. Indeed, a parsimony approach
was used, trying to select for the simplest model possible
to partition data variability with significant variables. This
approach, therefore, could possibly explain a smaller amount
of the total variability compared to the complete model, but
avoided considering variables that explain a small amount of
variability due to spurious significance. A reduced model was
built in which, by discarding non-significant variables (or their
interaction), more degrees of freedom were available to stabilize
the pseudo-Ftest. Each model was built using 9999 random
permutations.
RESULTS
Sample Description
The three groups (O, VG, and V) of participants of the study were
not significantly different in terms of age, gender, BMI, BLM, or
BFM (Table 1 and Supplementary Figure S1). Anthropometric
data for each sample are detailed in Supplementary Results 1.
TABLE 1 | Characteristics of participants in the study belonging to the cohorts Omnivorous (O), Vegetarian (V), and Vegan (VG): Sample size (N), Females %, average
value ±standard deviation of Age, body mass index (BMI), body lean mass (BLM), body fat mass (BFM).
Diet NFemales % Age BMI BLM % BFM %
O43 73.3 45.0 ±13.9 23.8 ±4.7 76.1 ±9.1 24.9 ±9.1
V32 70 42.3 ±13.2 23.8 ±9.1 76.1 ±8.9 24.9 ±8.9
VG 26 65.3 39.4 ±11.1 23.7 ±3.4 75.4 ±7.2 25.6 ±7.2
Total 101 68.3 42.5 ±13.0 23.8 ±4.4 75.0 ±8.7 24.9 ±8.7
Percentage of male participants can always be computed as 100%−Females %.
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FIGURE 1 | Fist two components of the principal component analysis (PCA) of the nutrient intake profiles of the omnivorous (Omni), vegan and vegetarian (Vegi)
participants in the study. The variance explained by each component is written in brackets.
Dietary Profiles
The dietary profiles of the three groups can be distinguished
starting from their average nutritional intake (Figure 1). Indeed,
57 out of 76 nutrients proved to be differentially distributed
among the three groups (Kruskal–Wallis test with 5% level
of significance). Consequently, supervised learning techniques
discriminated and predicted the three classes (O, VG, and V)
with an acceptable error (Random Forest Out-of-bag estimate of
classification error rate: OOB = 10.43%). Moreover, the available
variables were ranked by their importance in discriminating
between the three groups, using the mean decrease in accuracy
(Supplementary Figure S2): average consumption of animal
proteins and cholesterol, and the two Omega-3 fatty acids, EPA
and DHA, were the top-ranking ones.
Nutritional profiles were further summarized in terms of the
total amount and percentage of total calories (Kcal) imputed to
each of the three macro-nutrient categories of lipids, proteins,
and carbohydrates (Figure 2). Nutrient data for each sample
are detailed in Supplementary Results 1. When compared with
the standard guidelines for healthy average intakes of these
components (30% of total energy intake from fats, 15% from
proteins and 55% from carbohydrates) (NIH, 2016), the groups
proved to be higher than suggested for average calorie intake
from lipids and lower than suggested from proteins (Figure 2).
When the differential abundance between the groups was
examined (Kruskal–Wallis test with 5% level of significance),
lipids showed no significant differential distribution among
the three groups (p-value = 0.13); proteins showed significant
differential distributions between O and plant-based diets [both
VG (p-value <0.0001) and V (p-value = 0.0223)]; carbohydrates
showed a significant differential distribution between Omnivores
and Vegetarians only (p-value = 0.0004).
Sequencing Depth and Reproducibility
A total of 4.8 ×106 reads were produced by the sequencing
runs. The microbial community of the two DNA extractions for
each participant always clustered together (hierarchical clustering
computed on Bray–Curtis distance of microbial profile, data
not shown), indicating that the bacterial community profiling
was consistently reproducible within individual. For this reason,
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FIGURE 2 | Boxplot of average caloric intake for each macrocategory of lipids, carbohydrates, and proteins, divided into the three groups (O = Omnivorous,
V = Vegetarian, and VG = Vegan). The gray dashed lines highlight expected intakes according to the guidelines.
subsequent analyses were conducted using the replicate with the
higher sequencing depth. A total of 1130000 reads were used for
analysis after normalization. The resulting OTU table comprised
1872 OTUs with 253 singletons and 101 samples, with a mean
library size of 11080 ±6704.399, range [1535–29840]. Combined
sequences of the replicates used in the study were deposited in
the European Nucleotide Archive (ENA) with study accession
PRJEB18693.
Microbiota Profiling and Diversity
Analysis
Microbiota profiles were investigated in terms of taxonomic
assignment, most abundant taxa and relative abundance.
A total of 1872 OTUs were found, accounting for 212 genera,
102 families, 61 orders, 39 classes, and 20 phyla. The two
phyla that dominated the relative read abundances were
Bacteroidetes [median = 49.1% of all reads, interquartile range
IQR = (34.96–64.17%)] and Firmicutes [median = 40.61% of all
reads, IQR = (31.43–52.99%)] followed by Proteobacteria and
Verrucomicrobia [with median = 3.02% and IQR = (1.59–5.79%)
and median = 0.11% and IQR = (0.01–0.96%), respectively].
Supplementary Figure S3 shows the stacked bar plots of
proportional abundances of phyla for each recruited sample.
Each taxon was tested for differential abundance (both at
OTU and genus level) between the three sampled groups (O, V,
and VG). When correcting for multiple testing using an overall
FDR rate of 5% to check for false positives, only four OTUs
from three genera were found to be differentially abundant in
the three groups. These OTUs were affiliated with Bacteroides,
Lachnospiraceae, and Ruminococcaceae. However, each of the
highlighted OTU accounted for less than 1% of counts in the
individual samples (data not shown).
On a higher taxonomic level, the number of counts assigned
to Bacteroidetes was significantly different among groups
(p= 0.002) (Kruskal–Wallis test with 5% level of significance,
Figure 3), with higher counts in VG and V compared to O
(VG–O p= 0.013, V–O p= 0.006). Conversely, no significant
difference was observed in the Firmicutes/Bacteroides ratio
distribution among the three groups, or in the Prevotellaceae
abundance distribution (Figure 3). The same data were
used to characterize each enterotype with its prevalent
family (Bacteroidaceae,Prevotellaceae, or Ruminococcaceae,
respectively). The proportion of samples assigned to each of the
three enterotypes was independent from the feeding type group
(Pearson’s chi-squared test with 5% level of significance and
Cramer’s V measure of association; Table 2). Genera proportional
abundance for each sample is detailed in Supplementary
Results 1. Additionally, correlation of anthropometric data, diet
and microbiota were investigated as detailed in Supplementary
Results 2.
Alpha diversity was lower in O compared to V both in terms
of actual OTU richness and estimates by the Chao1 indices
(p-value <0.05; Tables 3A,B). The differences were statistically
significant even when sequencing depth was considered as a
confounding factor. The overall alpha diversity of the samples
was also tested using iSimpson, cSimpson, and Shannon indices,
but they presented no difference between the three groups
(Supplementary Table S1). The beta diversity of the samples was
characterized in terms of shared and unique species between the
three groups (Figure 4). All the OTUs that were found in more
than 90% of the samples within each group, were also shared
between all three groups.
Therefore, a vast proportion of the microbiota in our samples
can be referred to as a shared ‘core’ microbiome. OTUs that
were unique for each group could be mostly attributed to the
individual contribution of one or a few samples. Those included
both OTUs found in one sample only (677 of 1872) as well
as 253 singletons (OTUs with one count only). Several beta
diversity measures were tested, including Bray–Curtis, Canberra
and phylogenetic-based diversity (unweighted and weighted
UniFrac distance), and were used both for the hierarchical
clustering of the samples and for dimensionality reduction
analysis such as PCA/PCoA. Since all the analyses gave similar
results, only the results of the PCA are shown (Figure 5).
None of the conducted analyses resulted in a differential
clustering of the microbial communities by the hosts’ feeding
group (O–V–VG). Similar patterns of data overlap were found
when different labels (gender, enterotype, BMI category) were
investigated instead of the feeding group (data not shown).
Consequently, both supervised learning techniques employed
(RF and sLDA) were unable to infer group based on microbiota
profiling as reported in Supplementary Table S2 (out-of-bag
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FIGURE 3 | Count abundances of the five most abundant families, with different contributions toward each of the three groups (O = Omnivorous, V = Vegetarian,
and VG = Vegan). p-value: ∗<0.05, ∗∗ <0.01, ∗∗ ∗ <0.001.
TABLE 2 | Distribution of enterotypes between the three groups (O, VG, and V),
Person chi square significance and Cramer’s V-value
Enterotype Omnivorous (O) Vegan (VG) Vegetarian (V)
Bacteroidaceae 21 11 11
Prevotellaceae 9 5 5
Ruminococcaceae 13 11 16
P-value Pearson Chi2: 0.5487 Cramer’s V: 0.122375
estimate of error rate for RF: OOB = 50.98 % at OTU
level).
Moreover, Mantel test highlighted that Euclidean distances
of total detailed nutritional intake pattern did not significantly
correlate with the distances of the microbial community
composition (Bray–Curtis on genera proportion) between
participants (r= 0.056, p= 0.19).
Analysis of Variance Using Beta Diversity
Distance Matrix
In order to discover variables that influenced total bacterial
community composition, PERMANOVA analysis on
Bray–Curtis distance was computed on the relative abundances
of genera. After testing each variable individually and its
interaction with all other variables (data not shown), the
variables achieving p-value <0.2 were tested in a complex model
(Supplementary Table S3).
Most of the factors, although achieving significance, explained
less than 2% of total measured variance, while 44.16% of
total variance remained unexplained by the selected variables.
The reduced model, built using significant variables only,
highlighted that sample enterotype, richness (expressed
as observed number of OTUs), evenness and body fatty
mass were the most significant individual variables in
partitioning the measured distance (p<0.05) (Table 4).
Most variance was explained by sample enterotype (36.75%
of total variance) followed by richness and evenness (1.657
and 2.911%, respectively) while body fatty mass was the
least influential of the significant factors (1.14% of total
variance).
The interactions between variables that were significant
in partitioning the measured data variability were:
richness by host group (O, V, and VG) (1.97% of total
variance), highlighting that sample distance varies with
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TABLE 3A | Richness of the microbial community of Omnivorous (O), Vegans (VG), and Vegetarians (V): ANOVA summary results and Tukey’s pairwise comparisons on
sample alpha diversity.
NALPHA: Richness Df Sum of squares F-value p-value
Group O 43 146.7907 2 28039 4.4362 0.01429∗
VG 26 175.4444
V 32 183.4688
Residuals 99 3
Overall 101 165.8824 312866
Group comparisons Difference Lower Upper Adjusted p-value Significance
VG vs. O 28.653747 −4.191709 61.49920 0.1001182 .
V vs. O 36.678052 5.448641 67.90746 0.0170229 ∗
V vs. VG 8.024306 −26.930872 42.97948 0.8486384
Significance levels: p-value: .<0.10; ∗<0.05, ∗∗ <0.01. N = number of observations; df = degree of freedom.
TABLE 3B | Chao1 of the microbial community of Omnivorous (O), Vegans (VG), and Vegetarians (V): ANOVA summary results and Tukey pairwise comparisons on
sample alpha diversity.
NALPHA: Chao Df Sum of squares F-value p-value
Group O 43 207.9632 2 46000 4.0388 0.0206∗
VG 26 246.3368
V 32 254.1096
Residuals 99 8
Overall 101 232.5982 563784
Group comparison Difference Lower Upper Adjusted p-value Significance
VG vs. O 38.373588 −5.717685 82.46486 0.1011775 .
V vs. O 46.146384 4.224466 88.06830 0.0273073 ∗
V vs. VG 7.772796 −39.150538 54.69613 0.9180056
Significance levels: p-value: .<0.10; ∗<0.05, ∗∗ <0.01. N = number of observations; df = degree of freedom.
richness differently among the groups; evenness by
enterotype (3.55% of total variance), highlighting that
sample distance varies with evenness differently among the
enterotypes.
DISCUSSION
The existence of a functional connection between diet and gut
microbiota is well established (De Filippo et al., 2010;De Filippis
et al., 2016). Thus, recent trends of nutritional therapy apply
the beneficial use of diet to improve human health, through gut
microbiota performances (Cho and Blaser, 2012). However, even
though diet composition is known to have a modulating influence
on the gut microbial communities, knowledge on the role exerted
by specific nutrients in driving gut microbial assortment is still
limited.
In the last few years, a plethora of research has focused on
vegan and vegetarian diets as experimental dietary conditions
aimed at mitigating inflammatory diseases and metabolic
syndrome (Peltonen et al., 1997;Kjeldsen-Kragh, 1999;Tonstad
et al., 2013;Glick-Bauer and Yeh, 2014;Jenkins et al.,
2014;Le and Sabaté, 2014;Tuso et al., 2015). However,
standardization for prognosis purposes is hard to address due
FIGURE 4 | Venn diagram of unique and shared operational taxonomic units
(OTUs) between the three groups.
to the variety of foodstuffs that can be included in such
dietary styles. Moreover, few studies rigorously evaluate and
compare omnivorous, vegetarian, and vegan subjects as distinct
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FIGURE 5 | Fist two components of the PCA of the OTU profiles of the omnivorous (Omni), vegan and vegetarian (Vegi) participants in the study. The variance
explained by each component is written in brackets.
experimental groups (Power et al., 2014). Therefore, it is difficult
to discern whether the microbiota-derived features that are
attributed to fiber-rich diets (compared with fat and protein
of animal origin diets), and the derived advantages for health,
could be generalized to the whole vegan/vegetarian dietary
style, independently of food content in terms of nutrients and
associated calories.
Here the specific impact of vegan, vegetarian, and omnivore
food choices on the composition of gut microbiota was
investigated among relatively homogeneous groups for
variables known to have a role in modulating gut microbial
composition such as age, anthropometric variables, ethnicity
and geographic area. Interestingly here we found that there
were broad differences between the cohorts, which were,
however, not reflected when analyzing the total bacterial
composition.
Statistically significantly different alpha diversity indices were
found between the gut microbiota of vegetarians and omnivores,
with vegetarians displaying greater richness. The effects of
the richness of the gut microbiota on the host’s health are
still debated. It is intriguing to speculate that a richer gut
microbiota is advantageous to the host, since greater taxonomic
richness might also mean greater functional diversity, there is,
however still little empirical evidence to support this notion
(Cho and Blaser, 2012;Clemente et al., 2012;Marchesi et al.,
2016).
The Bacteroidetes phylum was prevalent among all the three
investigated groups, and a statistically significant difference
in their relative counts between vegans or vegetarians and
omnivores was observed. In other studies this family has
been found to be adapted to the gut conditions of people
with a typical westernized diet (Power et al., 2014). Here
we show, however, that within an example of a Western
society, the contribution of this genotype differ, and that their
abundance might be specifically related to a low intake of animal
protein.
On the contrary to total richness and Bacteroidetes
abundances, the three dietary types could not be distinguished
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TABLE 4 | Results of the analysis of variance of the Beta-diversity (ADONIS) using parsimoniously reduced statistical model containing only significant variable.
Df Sums of squares Mean of squares F-value R2p-value Significance
Enterotype 2 6.0526 3.02628 33.706 0.36750 0.0001 ∗∗ ∗
Richness 1 0.2729 0.27295 3.040 0.01657 0.0091 ∗ ∗
Normalized shannon 1 0.4794 0.47936 5.339 0.02911 0.0002 ∗ ∗ ∗
BFM 1 0.1878 0.18775 2.091 0.01140 0.0482 ∗
Region 6 0.7059 0.11765 1.310 0.04286 0.1000 .
Group 2 0.2583 0.12915 1.438 0.01568 0.1204
Proteins 1 0.1679 0.16786 1.870 0.01019 0.0687 .
Richness:Group 2 0.3255 0.16274 1.813 0.01976 0.0389 ∗
Enterotype:Normalized shannon 2 0.5847 0.29234 3.256 0.03550 0.0007 ∗ ∗∗
Richness:Normalized shannon 1 0.1077 0.10768 1.191 0.00654 0.2762
Richness:Proteins 1 0.1618 0.16184 1.803 0.00983 0.0772 .
Residuals 81 7.2727 0.08979 0.44159
Total 100 16.4694 1.00000
Significance levels: p-value: .<0.10; ∗<0.05, ∗∗ <0.01. ∗ ∗∗ <0.001. Df = degree of freedom.
when analyzing the whole community composition, i.e., beta-
diversity. The three addressed groups displayed a shared core
microbiota, probably due to their common intake in terms
of nutrients rather than food. One common factor between
the groups was high fat content and reduced protein and
carbohydrate contributions to diet, which might mitigate the
differences between the compositions of microbiota and drive
microbial communities toward a westernized profile. Difference
in composition was rather found in the rare microbiota, which,
however, was not enough to explain differences between the
dietary groups. This was in accordance with previous work
by De Filippis et al., (2016), where no difference in microbial
composition of groups differently adhering to the Mediterranean
Diet was found.
Among the whole dataset, the three described enterotype
classes could be clearly distinguished, although no clear link
between each investigated dietary group and enterotype could be
seen. This might be because of the displayed high inter-individual
variability in microbial composition observed in our sample,
which confirmed a well-known trend in human gut microbiota
studies (De Filippis et al., 2016). Moreover, this enterotype-based
signature explained approximately 37% of the total variance in
beta diversity, with other diversity related parameters (richness
and evenness) accounting for approximately 5% of the total
variance, while other factors and their interactions explained
an additional small amount (10.4%) of total variance. However,
44% of the total variance could not be explained by any of the
investigated variables. Other variables known to contribute in
shaping gut microbiota could come into play in this context,
including participants’ extent of physical activity (Clark and
Mach, 2016), diet history (Jeffery et al., 2012), and their dietary
literacy.
Here we show, that the gut microbiome of vegan, vegetarians,
and omnivores differ in terms of total richness and abundance of
Bacteroidetes. This study, however, also demonstrates that the use
of the labels vegan/vegetarian/omnivorous to infer conclusions
on the detailed gut microbial composition appear to be of limited
discriminatory potential. This might be because these labels
do not sufficiently define the dietary composition in terms of
nutrients that shapes the gut microbiota. Thus, the situation
where the categorization of individuals on the basis of their
claimed feeding types is used for planning studies on the impact
of food consumption on gut microbial composition appear to be
likely oversimplified.
ETHICS STATEMENT
All subjects involved gave their written informed consent for
the inclusion in the study. The protocol was approved by the
Ethics Committee of the Istituto Zooprofilattico Sperimentale
delle Venezie.
AUTHOR CONTRIBUTIONS
CL planned and executed the laboratory work, evaluated the
data, and wrote the paper. CL and VC recruited the participants
and provided the metadata. EE, EM, and MM performed the
bioinformatics and biostatistics and wrote the paper. EE, JV, and
JP provided and pre-processed the sequence data on an in-house
pipeline programmed by JV. ADC performed the laboratory
work and provided input for writing the paper. FB provided
critical input for execution of pre-processing. IP provided critical
input for execution of biostatistics tests. CL, GC, and AR
initiated and planned the study, evaluated the experiments,
and wrote the paper. All authors read and approved the final
manuscript.
FUNDING
This work was supported by the Italian Ministry of Health [Grant
No. 2010-RF-2317095]. English language editorial services
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Losasso et al. Gut Microbiota: A Cross Sectional Study
provided by Dr. Sheryl Avery, Avery Buncic Scientific & English
Editorial Services (ABSeeS).
ACKNOWLEDGMENTS
The authors wish to thank Lisa Barco, Alesandra Longo, and
Sara Petrin from Istituto Zooprofilattico Sperimentale delle
Venezie, for critically reading the manuscript and Angiola
Vanzo from ULSS 6 Vicenza for her help in participant
recruitment.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmicb.
2018.00317/full#supplementary-material
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Conflict of Interest Statement: The authors declare that the research was
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