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ARTICLE
Received 19 Aug 2014 | Accepted 4 Feb 2015 | Published 25 Mar 2015
Subsistence strategies in traditional societies
distinguish gut microbiomes
Alexandra J. Obregon-Tito
1,2,3,
*, Raul Y. Tito
1,2,
*, Jessica Metcalf
4
, Krithivasan Sankaranarayanan
1
,
Jose C. Clemente
5
, Luke K. Ursell
4
, Zhenjiang Zech Xu
4
, Will Van Treuren
4
, Rob Knight
6
, Patrick M. Gaffney
7
,
Paul Spicer
1
, Paul Lawson
1
, Luis Marin-Reyes
8
, Omar Trujillo-Villarroel
8
, Morris Foster
9
, Emilio Guija-Poma
2
,
Luzmila Troncoso-Corzo
2
, Christina Warinner
1
, Andrew T. Ozga
1
& Cecil M. Lewis
1
Recent studies suggest that gut microbiomes of urban-industrialized societies are different
from those of traditional peoples. Here we examine the relationship between lifeways and gut
microbiota through taxonomic and functional potential characterization of faecal samples
from hunter-gatherer and traditional agriculturalist communities in Peru and an
urban-industrialized community from the US. We find that in addition to taxonomic and
metabolic differences between urban and traditional lifestyles, hunter-gatherers form a
distinct sub-group among traditional peoples. As observed in previous studies, we find that
Treponema are characteristic of traditional gut microbiomes. Moreover, through genome
reconstruction (2.2–2.5 MB, coverage depth 26–513) and functional potential character-
ization, we discover these Treponema are diverse, fall outside of pathogenic clades and are
similar to Treponema succinifaciens, a known carbohydrate metabolizer in swine. Gut
Treponema are found in non-human primates and all traditional peoples studied to date,
suggesting they are symbionts lost in urban-industrialized societies.
DOI: 10.1038/ncomms7505
OPEN
1
Department of Anthropology, University of Oklahoma, Dale Hall Tower, 521 Norman, Oklahoma 73019, USA.
2
Universidad Cientı
´
fica del Sur, Lima 18, Peru
´
.
3
City of Hope, NCI-designated Comprehensive Cancer Center, Duarte, California 91010, USA.
4
Department of Chemistry and Biochemistry, University of
Colorado, Boulder, Colorado 80309, USA.
5
Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.
6
Departments of Pediatrics and
Computer Science & Engineering University of California San Diego, La Jolla, CA 92093, USA.
7
Oklahoma Medical Research Foundation, Oklahoma City,
Oklahoma 73104, USA.
8
Instituto Nacional de Salud, Lima 11, Peru
´
.
9
Old Dominion University, Norfolk, Virginia 23529, USA. * These authors contributed
equally to this work. Correspondence and requests for materials should be addressed to C.M.L. (email: cmlewis@ou.edu).
NATURE COMMUNICATIONS | 6:6505 | DOI: 10.1038/ncomms7505 | www.nature.com/naturecommunications 1
& 2015 Macmillan Publishers Limited. All rights reserved.
U
nderstanding the human microbiome has the potential to
transform health and medicine. Yet, despite large-scale
sequencing efforts, the full extent of human gut microbial
diversity remains underexplored. Extant people living traditional
lifestyles are especially under-studied, limited to one population
of hunter-gatherers from Tanzania
1
, and three rural
agriculturalist communities in Burkina Faso
2
, Malawi and
Venezuela
3
. Studies of peoples maintaining traditional
subsistence practices are critical for understanding the ancestral
state of the human microbiome and providing a foundation for
understanding how the human microbiome responds to
urbanism and Westernization, especially regarding diseases of
civilization, such as obesity and chronic inflammatory disorders.
To date, only two studies have focused on the gut microbiomes of
communities exclusively eating local, non-industrially produced
foods: a study by De Filippo et al.
2
that focused on children up to
6 years old from Burkina Faso, whose diet was primarily
composed of locally grown cereals, legumes and vegetables
2
,
and a study by Schnorr et al.
1
that explored the gut microbiome
of African hunter-gatherers from Tanzania. A study on rural
agriculturalist communities from Venezuela and Malawi
3
included adults with more diverse diets including industrial
goods such as soda in Malawi, and milk products, canned
products and soda in Venezuela.
Because of their unique cultural, behavioural and ecological
environment, we hypothesize that remote hunter-gatherer
communities harbour novel microbiome profiles that depart
from those previously described in urban and semi-urban
settings, and that may be tailored to the specific dietary sources
within each population. To test this hypothesis, here we use a
combination of high throughput 16S ribosomal RNA (rRNA)
gene amplicon sequencing and shotgun metagenomic sequencing
to characterize the gut microbiota of peoples from three different
lifeways: traditional hunter-gatherers, traditional agriculturalists
and urban-industrialized peoples. In addition to previously
published data, we provide novel data from: (1) the Matses, a
remote hunter-gatherer population from the Peruvian Amazon;
(2) Tunapuco, a traditional agricultural community from the
Andean highlands; and (3) residents of Norman, Oklahoma, a
typical US university community that serves as a comparative
population following an urban-industrialized lifestyle.
Results
Diet and engagement . While both rural communities live within
the national borders of Peru, the lifeways of the Matses and
residents of Tunapuco are startlingly different. The Matses live at
an elevation of 150 m above sea level in a pocket of natural
hyperdiversity that extends across the Brazilian border, and, until
recently, the Matses have been geographically, historically and
socially, isolated
4
. The Matses are traditional hunter-gatherers
whose subsistence focuses primarily on gathered tubers (Manihot
spp.) and invasive plantains (Musa spp.) (Supplementary
Table 1). Fish is their primary protein source, complemented
by sporadic consumption of game meat (monkey, sloth, capybara,
alligator and so on.). Consumption of dairy or processed food is
very rare, and only as a result of sporadic visitors. In contrast,
Tunapuco is situated in the central Andes, at an elevation
between 2,500 and 3,100 m above sea level. The diet of this rural
agriculturalist community is based on local agricultural produce
and homegrown small animals. Their main sources of nutrition
include stem tubers such as potatoes (Solanum tuberosum spp.)
and root tubers like oca ( Oxalis tuberosa) and mashua
(Tropaeolum tuberosum), which they eat at every meal. Tocosh,
a typical dish of the central Andes made out of potatoes that have
been fermented in wet soil, is eaten at least once a week by
families in Tunapuco (Supplementary Table 2). Residents of
Tunapuco eat fruits that they buy from lowland rural
communities from the same region. Guinea pig, pork, lamb and
infrequent cow cheese are the main animal protein sources in
their diet. Intake of dairy products and processed foods is limited,
and rice and bread are the main products they buy to supplement
their diet. Residents of Norman self-report diets typical of urban-
industrial communities, with regular consumption of processed
foods including canned fruits and vegetables, bread and
prepackaged meals. In addition, residents of Norman also
reported regular dairy consumption in the form of milk, cheese
and other dairy products.
This study was conducted under the supervision of the
University of Oklahoma and the Ethics Committee of
the Peruvian National Institute of Health, in collaboration with
the Matses and Tunapuco communities (Supplementary Fig. 1).
Our model of research with indigenous populations consists of
longitudinal engagement; Community Based Participatory
Research was designed
5
to ethically engage vulnerable
indigenous communities in microbiome research (Methods).
Our participants range from 1–52 years of age for the Matses, 3–
63 years of age for Tunapuco and 7–50 years of age for the
Norman population. Body mass index, age and sex of our
participants are summarized in Supplementary Table 3.
Rural communities have higher richness. Previous reports
have indicated that Western populations have lower microbial
richness than non-Western populations
3
. Our analyses of
microbial richness yielded similar results. We used targeted
amplification and sequencing of the V4 region of the 16S rRNA
gene (Methods), followed by clustering of sequences into
Operational Taxonomic Units (OTUs). We find that the Matses
and Tunapuco populations have higher richness than the
Norman population. The trend is observed with both
phylogenetic (Faith’s phylogenetic diversity (PD)) and non-
phylogenetic (observed species) richness metrics (Fig. 1a).
Further, these differences in richness between traditional and
industrialized societies are robust to OTU assignment strategy
(Methods) and rarefaction, being detected with as few as 5,000
reads per sample (Supplementary Fig. 2). No significant
differences in richness are observed between the two traditional
populations. The magnitude of difference observed between
phylogenetic and non-phylogenetic richness indices indicates that
the gut microbiomes of traditional societies are composed of
larger number of phylogenetically diverse taxa, while the gut
microbiomes of industrialized societies are composed of fewer
closely related taxa (Fig. 1a).
Next, we compared microbial community structure
(beta diversity) among the three populations using Principal
Coordinates Analysis (PCoA) transformation of weighted
UniFrac
6
distances (Fig. 1b). The traditional and industrialized
populations show separation in PCoA space, and among the
traditional populations the Matses form a separate cluster
(PERMANOVA, Po0.001 and Po0.001 respectively). Further,
the Tunapuco population is characterized by high interpersonal
variation, evident in both PC axes 1 and 2. Supervised learning
using Random Forests
7
, a machine learning method utilizing
microbial community signatures, accurately assigned samples to
their source population based on taxonomic profiles at the OTU
level (100% accuracy, all populations).
Taxonomic characterization. To test whether subsistence tradi-
tions harbour distinct microbial communities, we compared
relative abundance of taxa between each of our populations. The
three populations show differences in taxonomic distribution at
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms7505
2 NATURE COMMUNICATIONS | 6:6505 | DOI: 10.1038/ncomms7505 | www.nature.com/naturecommunications
& 2015 Macmillan Publishers Limited. All rights reserved.
the phylum level (Fig. 2a), with 8 out of 20 phyla having a sig-
nificant difference in abundance in at least 1 population (False
Discovery Rate (FDR)-corrected Kruskal–Wallis test: Po0.0006)
(Supplementary Table 4). Three of the eight phyla show a tra-
ditional/urban-industrial distribution, with the traditional popu-
lations (Matses and Tunapuco) enriched for Proteobacteria and
Spirochaetes and the urban-industrial population (Norman)
enriched for Actinobacteria (Supplementary Fig. 3). In
addition, the Matses differ from the Tunapuco and Norman
populations in being enriched for Cyanobacteria, Tenericutes
and Euryarchaeota (Supplementary Fig. 3). Finally, the
Tunapuco population is enriched for Bacteroidetes, while the
Norman and Matses populations are enriched for Firmicutes
(Supplementary Fig. 3).
To further characterize taxonomic differences, we performed
Kruskal–Wallis tests on genus-level taxa and identified 33 genera
showing significant differences in abundance between the three
populations (FDR-corrected Kruskal–Wallis test: Po0.05; Fig. 2b,
Supplementary Table 5). The traditional/urban-industrial trends
observed among Actinobacteria, Proteobacteria and Spirochaetes
are driven by the genera Bifidobacterium, Succinivibrio and
Treponema, respectively (Supplementary Fig. 3). While a high
relative abundance of Bacteroidetes distinguishes Tunapuco from
the Matses and Norman populations, at the genus level this is
further resolved into a traditional/urban-industrial trend
driven by higher levels of Prevotella among traditional gut
microbiomes and Bacteroides among urban-industrial gut micro-
biomes. This pattern is similar to previous reports for non-
Western populations
1,3
(Supplementary Fig. 3). With respect to
Firmicutes, we observe a complex pattern driven by the
enrichment of different genera among the three populations.
Specifically, the Norman population is enriched for
Ruminococcus, Blautia, Dorea and an unknown genus in the
family Lachnospiraceae (Supplementary Fig. 3). The Matses are
enriched for Clostridium, Catenibacterium, Eubacterium,
Lachnospira and an unknown genus in the class Clostridiales
(Supplementary Fig. 3). The Tunapuco population, while overall
having lower levels of Firmicutes, is specifically enriched for the
genus Dialister (Supplementary Fig. 3). Overall, these taxa
distribution patterns are concordant with trends reported from
previous studies on hunter-gatherer and rural agriculturalist
communities
1,3
.
To evaluate population discrimination, we performed super-
vised clustering using Random Forests on taxa tables summarized
at higher taxonomic levels (genus to phylum). The Norman
population consistently had a 100% classification accuracy at all
taxonomic levels. In contrast, the Matses and Tunapuco
populations had a 93% and 100% classification accuracy,
respectively, at the genus level, reducing to 77% and 91% at the
phylum level (Supplementary Table 6). Misclassification occurred
exclusively between the rural populations, with samples being
cross-assigned between the Matses and Tunapuco, indicating
shared community signatures at higher taxonomic levels between
these two populations.
Finally, we compared genus-level taxa abundance profiles
between our populations, and those from two previous studies of
remote agrarian and hunter-gatherer human gut microbiomes
1,3
.
PCoA of a Bray–Curtis distance matrix generated from genus-
level taxa tables shows a clear separation between traditional and
urban-industrial microbiomes (Fig. 3a), consistent across the
three different studies. In addition, the hunter-gatherer
populations (Matses and Hadza) form a distinct sub-cluster
nested within the other traditional populations (Tunapuco,
Venezuela and Malawi). To further explore this trend, we
performed Bayesian source tracking
8
on the Matses, Tunapuco
and Norman samples using the previously published data sets as
source populations (traditional hunter-gatherer: Hadza; rural
agriculturalist: Venezuela, Malawi; and urban-industrial: USA,
Italy; Fig. 3b). Consistent with previous analyses, the urban
sources formed the primary contribution (B84% average) to the
Norman samples, while the combined rural and hunter-gatherer
sources accounted for B95–98% for the Tunapuco and Matses
samples. Specifically, the Matses samples had a higher
contribution (B58%) from the Hadza hunter-gatherer source,
while the Tunapuco samples had a higher contribution (B66%)
from the rural Venezuela and Malawi source. Within populations,
individuals show variation (Fig. 3c), but overall between B64 and
85% of individuals have profiles consistent with their subsistence
strategy. Thus, while the studies were conducted with differences
in sample handling (freezing and desiccation), extraction
methods (MoBio PowerSoil and phenol-chloroform) and choice
of PCR primers, they nevertheless show a pattern in which two
hunter-gatherer populations from two separate continents (Africa
and South America) have a greater affinity to each other than to
other traditional or urban populations. This is similarly true for
the rural agriculturalists in Africa and South America and the
urban industrial populations in Europe and North America.
–0.4 0.40.0
–0.2
0.2
0.0
PC axis 1, ~46%
PC axis 2, ~12%
0.3
0.1
–0.1
–0.2 0.2–0.6
0
200
400
600
0
20
40
60
Observed species
Phylogenetic diversity
Norman
Matses
Tunapuco
Tunapuco
Matses
Norman
Tunapuco
Matses
Norman
Figure 1 | Alpha- and beta-diversity comparisons of the gut microbiomes
of the Matses, Tunapuco and Norman populations. Analyses were
performed on 16S rRNA V4 region data, with a rarefaction depth of 10,000
reads per sample. (a) Alpha diversity comparisons based on phylogenetic
and non-phylogenetic richness (Faith’s PD, observed species). The urban
population has significantly lower microbial richness compared with the two
rural populations. This observation is robust and observable even with
o5,000 reads per sample (Supplementary Fig. 2). Whiskers in the boxplot
represent the range of minimum and maximum alpha diversity values
within a population, excluding outliers (b) Principal coordinates analysis of
weighted UniFrac distances. Proportion of variance explained by each
principal coordinate axis is denoted in the corresponding axis label. The
rural and urban populations show clear separation.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms7505 ARTICLE
NATURE COMMUNICATIONS | 6:6505 | DOI: 10.1038/ncomms7505 | www.nature.com/naturecommunications 3
& 2015 Macmillan Publishers Limited. All rights reserved.
Functional characterization. We performed shotgun metagen-
ome sequencing (Illumina, see Methods) to investigate whether
the Matses, Tunapuco and Norman gut microbiomes harbour
differences in functional capacity. To improve annotation quality,
the short reads obtained from metagenome sequencing were
assembled de novo using Ray-Meta
9
to generate longer contigs
(Methods). Functional capacity was then inferred from
annotation of Open Reading Frames (ORFs) predicted from
these contigs. We used an annotation pipeline incorporating
microbial genomes (draft and complete) obtained from the HMP
DACC
10
, IMG (v3.5; ref. 11), and NCBI GenBank databases
12
as
references.
Supervised clustering using KEGG Orthology (KO)
13,14
profiles distinguished the traditional and urban-industrial
populations with 100% accuracy (Supplementary Table 7).
Within the traditional populations, the Matses samples had a
100% classification accuracy, while 1 Tunapuco sample (out of
12) was misassigned to the Matses. Beta-diversity plots generated
from Bray–Curtis distance matrices (PC transformed) of KO
tables showed a clear separation between the traditional and
urban-industrial populations (Fig. 4a). Procrustes analyses
comparing spatial fit between PC plots generated from UniFrac
(taxonomic) and Bray–Curtis (functional) distances showed
concordance, indicating consistency between taxonomic and
functional profiles (Fig. 4b).
To identify KOs showing differential abundance between the
three populations, we performed Kruskal–Wallis tests on KO
tables. Overall, we identified 112 KOs showing a significant
difference in abundance in at least 1 population (Supplementary
Table 8). Of these, 78 KOs (69.6%) show enrichment among the
traditional populations; these KOs are predominantly associated
with metabolism and genetic information processing. Among the
remaining KOs, 20 (17.8%) show enrichment specific to the
urban-industrial population and 14 (12.5%) show similar
distributions between the urban-industrial and at least one of
the two traditional populations. The KOs uniquely enriched in
the urban-industrial populations are predominantly associated
with membrane transport functions. In addition, 37 of the 78
KOs enriched in the traditional populations are found at higher
abundance among the residents of Tunapuco compared with the
Matses.
To further characterize some of these functional differences, we
performed statistical analyses on orthologue tables annotated
using Enzyme Commission (EC) codes
15
. Overall, we identified
91 ECs showing significant differences between the populations
(Fig. 5, Supplementary Table 9). Of these, 79 ECs (86.8%) are
0%
Norman Matses Tunapuco
Unknown tenericutes
Unknown clostridiales
Unknown cyanobacteria
Prevotella (paraprevotellaceae)
Unknown paraprevotellaceae
Unknown bacteroidales
Unknown coriobacteriaceae
–1 0 1 2
Norman Matses Tunapuco
Row Z-score
100%
80%
60%
40%
20%
Others
Verrucomicrobia
Cyanobacteria
Euryarchaeota
Tenericutes
Spirochaetes
Actinobacteria
Proteobacteria
Firmicutes
Bacteroidetes
Methanobrevibacter
Bifidobacterium
Collinsella
Bacteroides
Prevotella
Unknown mogibacteriaceae
Unknown christensenellaceae
Clostridium
Unknown lachnospiraceae
Ruminococcus (lachnospiraceae)
Blautia
Coprococcus
Dorea
Lachnospira
Ruminococcus
Dialister
Megasphaera
Unknown erysipelotrichaceae
Eubacterium
Bulleidia
Catenibacterium
Unknown erysipelotrichaceae
Succinivibrio
Unknown enterobacteriaceae
Treponema
Unknown S24.7
–2
Figure 2 | Taxonomic profile of the gut microbiomes of the Matses, Tunapuco and Norman populations. Analyses were performed on 16S rRNA V4
region data, rarefied to a depth of 10,000 reads per sample. (a) Relative taxa abundance plots for individuals from the three populations, summarized
at the phylum level. Individuals are represented along the horizontal axis, and relative taxa frequency is denoted by the vertical axis. (b) Heatmap showing
33 genera with significant differences in abundance between populations (Kruskal–Wallis, FDR-corrected Po0.05). Individual boxplots for phyla and
genera are shown in Supplementary Fig. 3. Heatmap is colour-coded based on row z-scores.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms7505
4 NATURE COMMUNICATIONS | 6:6505 | DOI: 10.1038/ncomms7505 | www.nature.com/naturecommunications
& 2015 Macmillan Publishers Limited. All rights reserved.
enriched among the traditional populations, including several
associated with the Tricarboxylic acid cycle (for example,
succinate dehydrogenase and malate dehydrogenase) and amino
acid metabolism (for example, amino acid transfer RNA ligases).
These pathways are related to enhanced capacity for energy
production and dietary amino acid uptake. Similar to our
observations with the KOs, a subset of 39 ECs show higher
abundance within Tunapuco compared with the Matses. Further,
a second group comprised of 34 ECs is enriched within a subset
of individuals from both the Matses and Tunapuco. The
remaining 12 ECs (13.2%) were enriched in the Norman
population and included 3 ECs related to Vitamin B1 and B12
biosynthesis.
Age stratification and Bifidobacterium. A previous study on US,
Malawi and Venezuelan populations
3
found that age resulted
in a significant gradient of bacterial abundances, with newborns
initially showing high variation but little differentiation among
populations, and eventually resembling the adults of their
respective communities by 3 years of age
3,16
. Further, this trend
was shown to correlate with the abundance of Bifidobacterium,a
genus thought to be associated with dietary dairy consumption.
As the number of children of age o3 years in our study is limited
to four individuals from the Matses, we instead performed
correlation analyses between age and PC axes generated from a
weighted UniFrac distance matrix. A negative correlation was
observed between the first PC axis and age for the
Matses population (r ¼0.59, Po0.002). While the relative
abundance of Bifidobacterium in children shows no direct
correlation with age, 10 out of 13 individuals (total n ¼ 25)
showing presence of the genus were o7 years old. In contrast, all
individuals sampled from our Norman population showed
presence of Bifidobacterium, with no correlation between age
and levels of Bifidobacterium . This is consistent with regular dairy
consumption self-reported by the Norman individuals.
Treponema and rural populations. Although Spirochaetes have
been previously reported from the gut microbiome of non-human
primates
17–19
and ancient human populations
20
, they have only
been observed in high abundance among extant human
populations with non-Western lifestyles, such as a traditional
community in Burkina Faso
1
and a hunter-gatherer community
in Tanzania
1
. As such, they may represent a part of the human
ancestral gut microbiome that has been lost through the adoption
of industrial agriculture and/or other lifestyle changes
(Supplementary Table 10). Similar to previous studies on
traditional populations, we find that both the Matses and
Tunapuco are enriched for Spirochaetes, specifically of the
genus Treponema. Phylogenetic analysis of these Spirochaetes
indicates the presence of at least five Treponema OTUs
(Supplementary Table 11, Supplementary Fig. 4) found in
traditional populations today. Of these, two OTUs (Greengenes
13.5 OTU ids: 300310, 338950) occur at high frequencies and are
shared between the Matses and Tunapuco, and a third OTU
(Greengenes 13.5 OTU id: 4307383) is present at high frequencies
in the Tunapuco population but is rare among the Matses.
The phylogenetic similarity of these OTUs with Treponema
Matses
PC axis 1, ~19%
PC axis 2,~10%
0.4
a
c
b
Tunapuco
Norman
0
1.0
Matses Tunapuco Norman
Urban
Hunter-gatherer
Rural agriculturalist
Unknown
Source proportion
0.2
0.0
–0.2
–0.2
–0.4
–0.4
0.0
0.2
0.4
0.8
0.6
0.4
0.2
Figure 3 | Comparison of the gut microbiomes of Matses, Tunapuco and
Norman populations to published data from hunter-gatherer, rural
agriculturalist and urban-industrial communities. Analyses were
performed on genus-level taxa tables rarefied to 4,000 reads per sample.
(a) Principal coordinate analysis of Bray–Curtis distances generated from
taxa tables summarized at the genus level. Proportion of variance explained
by each principal coordinate axis is denoted in the corresponding axis label.
Populations are colour coded by subsistence strategy. Data sets are
represented by triangles (this study), circles (Yatsunenko et al.
3
), and
squares (Schnorr et al.
1
). Ellipses correspond to 95% confidence boundaries
for each of the three subsistence categories. (b) Results from Bayesian
source-tracking analysis. Source contributions are averaged across samples
within the population. (c) Results from Bayesian source tracking for
individual samples.
PC axis 1, ~33%
PC axis 2,~17%
Norman
Matses
Tunapuco
Shotgun
16S
PC axis 1, ~25%
PC axis 2, ~22%
–0.15
–0.15
0.15
0.15
0.05
0.05
0.1
0.05
0.0
0.05
–0.1
0.2
0.1
0.0
–0.1
–0.2
–0.3
–0.2
–0.1
0.0
0.1
0.2
–0.1
0.0
0.1
Figure 4 | Comparison of taxonomic and functional diversity of gut microbiomes between populations. Proportion of variance explained by each
principal coordinate axis is denoted in the corresponding axis label (a) Principal Coordinates Analysis of Bray–Curtis distances generated from KEGG
Orthologue tables rarefied to 200,000 counts per sample. (b) Procrustes analysis between the taxonomic and the functional data sets on paired samples
from the Matses, Tunapuco and Norman populations.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms7505 ARTICLE
NATURE COMMUNICATIONS | 6:6505 | DOI: 10.1038/ncomms7505 | www.nature.com/naturecommunications 5
& 2015 Macmillan Publishers Limited. All rights reserved.
succinifaciens, a non-pathogenic carbohydrate metabolizer and a
member of the swine gut microbiome
21
, offers support to the
hypothesis that these organisms might be selected for under high
fibre diets.
To further characterize the phylogenetic and functional
relationships of the Matses gut Treponema to other currently
available reference strains from this genus, we retrieved contigs
matching Treponema from metagenomes assembled de novo
(Methods) from four Matses samples. These samples were
selected based on high frequencies of Treponema observed in
their taxonomic profiles. Samples from Tunapuco were not
included in this analysis as they had lower sequencing coverage
and often contained multiple Treponema strains leading to poor
assemblies. Phylogenetic analysis using 16S rRNA gene sequences
retrieved from these contigs confirmed the presence of two
distinct strains of Treponema within these samples, one with
B99% sequence similarity to T. succinifaciens (found in all four
samples, referred to as Strain 1) and the other with B90%
sequence similarity to T. succinifaciens (found in two samples,
referred to as Strain 2) (Fig. 6a). A second phylogenetic tree
constructed using concatenated amino acid sequences from 35
single copy marker loci
22
(predominantly composed of ribosomal
small and large subunit proteins) showed similar topology,
confirming the presence of two distinct strains of Treponema
within our samples (Fig. 6b). Overall, we retrieved between 2.19
and 2.46 Mb of genome sequence data for the Treponema strains
through a combination of methods, including sequence identity
to the reference T. succinifaciens, GC% and coverage statistics
(Methods). We annotated these partial assemblies using the
‘prokka’ pipeline
23
, followed by evaluation of metabolic potential
using MAPLE
24
. We then performed hierarchical clustering using
metabolic Module Completion Ratio (MCR) data obtained from
−2 −1 0 1 2
Row Z-score
1.6.-.-
UDP-glucose dehydrogenase
Didehydrogluconate reductase
Glutamate synthase
IMP dehydrogenase
1.17.7.1
Succinate dehydrogenase
1.6.5.-
Malate dehydrogenase
Portein disulfide reductase
Adenosylcobyric acid synthase
Fatty acid CoA ligase
6.3.2.9
NAD+ synthase
Fatty acid CoA ligase
Glutamate tRNA ligase
Asparagine tRNA ligase
Cysteine tRNA ligase
Phenylalanine tRNA ligase
Aspartate tRNA ligase
6.3.4.2
Leucine tRNA ligase
Valine tRNA ligase
Alanine tRNA ligase
Arginine tRNA ligase
Phosphoribosylformyl-
-glycinamidine synthase
Thiamine diphosphokinase
Cobyrinic acid adenosyltransferase
Histidine kinase
2.1.1.14
Adenine phosphoribosyltransferase
2-dehydro-3-deoxygluconokinase
2.1.2.1
2.7.7.6
Polyribonucleotide nucleotidyltransferase
2.1.1.31
DNA methyltransferase
2.1.2.3
Glucose-1-P thymidylyltransferase
2.7.1.6
Xylulose-5-phosphate synthase
Uridine kinase
2.-.-.-
2.5.1.75
Lipid-A-disaccharide synthase
2.4.1.25
Polynucleotide adenylyltransferase
2.4.1.11
2.7.9.2
2.6.1.1
Fructose-6-P phosphotransferase
Lactoylglutathione lyase
Sirohydrochlorin cobaltochelatase
4.1.2.4
Arginine decarboxylase
dTDP-glucose dehydratase
Selenocysteine lyase
4.2.1.3
Adenylosuccinate lyase
PEP carboxykinase
Citrate lyase
Phosphoglucomutase
Arabinose-phosphate isomerase
Phosphomannomutase
5.99.1.-
5.-.-.-
UDP-acetylglucosamine
Epimerase
Tyrosine phosphatase
Alpha-L-rhamnosidase
Acetylhexosaminidase
K+ ATPase
Mg++ ATPase
Tripeptide aminopeptidase
Carbamoylputrescine amidase
Endopeptidase
3.4.24.-
3.4.-.-
3.6.1.-
DNA helicase
3.2.1.1
3.6.3.-
Xaa-Pro aminopeptidase
Glucosamine deaminase
RNA helicase
3.4.21.-
Dipeptidyl peptidase
Bleomycin hydrolase
dCMP deaminase
DNase Type II
DNase Type I
DNase Type III
Norman
Matses
Tunapuco
Figure 5 | Heatmap of ECs showing significant differences between the gut microbiomes of Matses, Tunapuco and Norman populations. Enzymes are
grouped based on EC class. Comparisons between populations were performed using Kruskal–Wallis tests (FDR-corrected Po0.05). Heatmap is colour
coded based on row z-scores.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms7505
6 NATURE COMMUNICATIONS | 6:6505 | DOI: 10.1038/ncomms7505 | www.nature.com/naturecommunications
& 2015 Macmillan Publishers Limited. All rights reserved.
the MAPLE
24
pipeline (Fig. 7). Based on predicted metabolic
potential, the reconstructed Treponema strains cluster most
closely with T. succinifaciens and are nested with other gut-
associated treponemes reported from termites (T. azotonutricium
and T. primitia)
25
and a digital dermatitis associated Treponema
reported from cattle (T. brennaborense)
26
. In addition, these
strains functionally cluster with gut-associated members of the
Brachyspira clade of Spirochaetes, along with several gut-
associated bacteria from other phyla, including Ruminococcus,
Eubacterium and Butyrivibrio. In contrast several pathogenic
Spirochaetes including T. pallidum (syphilis), Borrellia
burgdorferi (Lyme disease) and T. denticola (periodontal
disease), form a functionally separate clade outside of the gut-
associated bacteria. Overall, these results give further support for
a potential metabolic role for the Treponema strains observed in
the gut microbiomes of traditional human populations.
Discussion
Characterizing microbial communities and their functions in
populations living relatively ancestral lifestyles is essential for
understanding the coevolution of humans as a species with their
microbiomes. Our results strongly support the need for human
microbiome research on a larger sampling of human lifeways and
traditions. Such work with vulnerable populations is challenging,
especially with respect to building trust and establishing reason-
able informed consent, but remains possible, even with very
remote and traditional peoples. Without these insights, the
benefit of research may be more applicable to the Westernized,
affluent, urban populations, further exacerbating health
disparities for the underrepresented. Here we present a
microbiome profile that may be more consistent with the
ancestral state of human biology. Such information provides a
potential foundation for understanding microbiome-associated
‘diseases of civilization’.
Methods
Community engagement. Collaborative research with remote human commu-
nities requires careful planning and extensive outreach. As with many other
indigenous populations, the Matses and Tunapuco have experienced and resent the
idea of safari or helicopter research, a common model of research on indigenous
populations. In addition, foreign companies’ recent attempts to extract oil from the
Matses’ natural reservation have fuelled the Matses’ distrust tow ards the outside
world. To maximize protection of the communities, we consulted with colleagues at
the Center for Intercultural Health of the Peruvian Institute of Health from the
early stages of the study design.
Recognizing communities’ vulnerabilities and concerns, in addition to the
official efforts aiming to protect them, we initiated our work by engaging political
and traditional authorities in the review of our protocol. Political authorities
included regional and national authorities. The traditional authority we first
approached was the Peruvian leader of the ethnic group. All concerns from these
authorities were addressed in the protocol before submission to the ethics
committee of the Peruvian National Institute of Health, which approved the
protocol.
The protocol for the Matses community includes oversight by additional local
authorities. On protocol approval, and with the authorization of the leader of the
Matses ethnic group, we presented our project to the local authority of the District
Yaquerana, who oversees all activities in the Matses reservation, and later to the
leader of the Comunidad Nativa Matses Anexo San Mateo, who introduced us to
T. caldaria DSM 7334
0.98
1
0.98
1
0.82
1
1
1
1
1
1
1
1
0.95
0.05
0.99
0.99
1
0.88
1
1
0.99
0.99
0.99
1
0.99
0.98
0.94
0.90
0.83
0.99
1
T. azotonutricium ZAS-9
T. primitia ZAS-2
Borrelia burgdorferi B31
T. pallidum str. fribourg-blanc
T. pallidum subsp. pertenue str. samoaD
T. paraluiscuniculi cuniculi A
T. denticola ATCC 35405
T. pedis str. T A4
T. brennaborense DSM 12168
T. succinifaciens DSM 2489
SM42
SM03
SM23
SM23
SM28
SM42
Matses Treponema strain 2
Matses Treponema
strain 1
Borrelia burgdorferi B31
0.2
T. azotonutricium ZAS-9
T. primitia ZAS-2
T. pallidum str. fribourg-blanc
T. pallidum subsp. pertenue str. samoa
D
T. paraluiscuniculi cuniculi A
T. denticola ATCC 35405
T. pedis str. T A4
T. brennaborense DSM 12168
SM23
SM42
SM42
SM03
SM23
SM28
T. succinifaciens DSM 2489
Matses Treponema strain 2
Matses Treponema
strain 1
Figure 6 | Phylogenetic trees showing relationship of Matses Treponema strains to reference Treponema species. (a) Maximum likelihood tree
constructed using 16S rRNA sequences from de novo assemblies of shotgun data. (b) Maximum likelihood tree constructed using concatenated amino acid
sequences from 35 single copy marker loci, retrieved from de novo assemblies of shotgun data. Both trees show similar topology, with the Matses
Treponema strains grouping with Treponema succinifaciens, a known carbohydrate metabolizer in the swine gut microbiome.
Escherichia coli
Treponema brennaborense
SM42_strain1*
SM28_strain1*
Treponema succinifaciens
SM03_strain1*
SM23_strain1*
SM42_strain2*
SM23_strain2*
Treponema azotonutricium
Treponema primitia
Spirochaeta smaragdinae
Brachyspira hyodysenteriae
Brachyspira murdochii
Brachyspira pilosicoli 95/1000
Eubacterium eligens
Eubacterium rectale ATCC 33656
Bifidobacterium longum NCC2705
Bifidobacterium adolescentis
Butyrivibrio proteoclasticus
Ruminococcus albus
Methanobrevibacter smithii ATCC 35061
Methanobrevibacter ruminantium
Fibrobacter succinogenes
Prevotella ruminicola
Bacteroides fragilis YCH46
Akkermansia muciniphila
Leptospira borgpetersenii JB197
Leptospira interrogans serovar Lai 56601
Veillonella parvula
Helicobacter pylori HPAG1
Campylobacter jejuni RM1221
Enterococcus faecalis V583
Treponema pedis
Treponema denticola
Treponema pallidum
Borrelia burgdorferi B31
Borrelia turicatae
Borrelia hermsii
024
Figure 7 | Hierarchical clustering of Matses Treponema and reference
bacterial strains based on KEGG functional potential data. Open reading
frames (ORFs) predicted from reconstructed Matses Treponema genomes
were annotated using the MAPLE server
24
and compared with reference
bacterial genomes (including Spirochaetes). The Matses Treponema strains
share functional similarities with Treponema succinifaciens, a known
carbohydrate metabolizer and apathogenic member of the swine gut
microbiome. *denotes the Matses Treponema strains.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms7505 ARTICLE
NATURE COMMUNICATIONS | 6:6505 | DOI: 10.1038/ncomms7505 | www.nature.com/naturecommunications 7
& 2015 Macmillan Publishers Limited. All rights reserved.
the community members. Such structures are unavailable for Tunapuco. For both
communities, we implemented a public meeting for community consu ltation and
obtained community consent. In addition to community consent, all volunteer
participants were individually consented when they arrived to deposit their
samples.
In an effort to maximize benefits and prevent potentially coercive incentives for
the community, we avoided individual presents or compensation. Instead, we
offered on-site parasite screening, making a microscope available for the
community to observe the analysis we performed. This experience also served to
anchor the discussion about microorganisms, emphasizing the informed part of the
consent process. A Matses interpreter, who was fluent in Spanish, mediated
communication with the Matses community.
For both the Matses and Tunapuco communities, once preliminary results
became available we returned to the community to disseminate our findings. We
obtained authorization from the community for data publication and to use the
community’s name in association with our findings.
Sample collection and processing
. Faecal samples from participants were col-
lected in polypropylene containers. Samples from the Matses (n ¼ 25) and Tuna-
puco populations (n ¼ 31) were stored in ice for up to 4 days until arriving at Lima,
and they were kept frozen until DNA was extracted at the laboratory in Oklahoma.
In addition, faecal samples were collected from 23 individuals from Norman,
Oklahoma to serve as a comparative population with an urban-industrial lifestyle.
These samples were kept on ice during transport to the laboratory and frozen
within 24 h of collection.
DNA extraction from the Matses and Tunapuco faecal samples was performed
using the PowerSoil DNA Isolation Kit (MoBio) following manufacturer’s
instructions, with the addition of two heating steps: 10 min at 60 °C before
vortexing the samples with the PowerBeads and later 10 min at 90 °C. For the
Norman faecal samples, DNA extraction was performed using the
PowerMicrobiome RNA Isolation Kit (MoBio) with the exclusion of the DNase I
step. Both extraction methods included an initial bead-beating step.
To characterize the taxonomic profile of the gut microbiome, we amplified the
V4 hypervariable region of the bacterial 16S rRNA gene using the universal
bacterial/archaeal primers F515 (5
0
-CACGGTCGKCGGCGCCATT-3
0
) and R806
(5
0
-GGACTACHVGGGTWTCTAAT-3
0
)
27
. These same primers were used to
generate 16S rRNA data in a previous study of agrarian and urban gut
microbiomes
3
. A 12 bp GOLAY error-correcting barcode was added to the reverse
primer to enable sample multiplexing. Reactions were performed in triplicate using
the AccuPrime Taq DNA Polymerase High Fidelity system. Read statistics from the
16S V4 sequencing runs are summarized in Supplementary Table 3. To
characterize gut microbiome functional potential, we performed shotgun
metagenomic sequencing of faecal samples. Libraries were prepared using the
Nextera DNA sample preparation kit for NGS libraries (Illumina platform).
16S sequencing data processing
. The 16S rRNA sequencing data from the
Illumina runs were filtered and trimmed using the programme ‘sickle’ (https://
github.com/najoshi/sickle) to remove bases with a quality score o30, followed by
discarding sequences with ambiguous bases (‘N’) and a length o90 bp. These
trimmed reads were demultiplexed, chimera filtered (‘usearch’)
28
, and assigned to
OTUs using packages implemented in QIIME
29
. We initially performed closed-
reference OTU assignment using ‘uclust’
28
with a 97% sequence similarity
threshold against the Greengenes 13.5 database
30
as a reference. Overall, 495% of
the total sequences were assigned to OTUs using this approach, with the urban
population from Norman having B97
±
2% and rural Matses and Tunapuco
populations having B96
±
2% and B95
±
3%, respectively, assigned to OTUs. In
addition, to document the impact of potentially novel OTUs on microbial richness,
the remaining unassigned sequences were clustered de novo using a 97% sequence
similarity threshold, and the resulting OTU table merged with the one generated
using the closed-reference approach. Comparative 16S rRNA data sets were
obtained from previously published studies
1,3
, and are composed of hunter-
gatherers (Hadza, n ¼ 27), rural agriculturalists (Venezuela, n ¼ 60; Malawi,
n ¼ 20) and urban populations (USA, n ¼ 65; Italy, n ¼ 16). The data set composed
of Venezuela, Malawi and USA individuals
3
had been sequenced on an Illumina
platform and were processed using the same quality filtering and OTU assignment
criteria as employed by this study. The data set composed of the Hadza and Italian
individuals
1
had been sequenced on a Roche 454 platform, and were processed
using QIIME’s de novo clustering strategy using a 97% sequence similarity
threshold to maximize read assignment to OTUs. All comparisons between
sequences generated in this study and the two previously published data sets are
limited to genus-level taxa tables.
Shotgun read processing
. The data sets generated from shotgun metagenome
sequencing were quality filtered and trimmed to remove bases with a quality score
o30, followed by discarding sequences with ambiguous bases (‘N’) and a length
o25 bp. De novo metagenome assembly was performed on these trimmed
sequences using Ray Meta
9
, with a k-mer length of 21. Metagenome assembly was
performed on the OU Supercomputing Center for Education and Research
(OSCER) platform. ORF prediction was performed on assembled contigs using
‘FragGeneScan’
31
. Predicted ORFs were assigned annotations through comparisons
with 382 gut microbial genomes from the Human Microbiome Project (HMP
DACC)
10,32
. Unmapped ORFs were then compared sequentially to JGI’s Integrated
Microbial Genomes data set
11
(IMG, v 3.50, 12 October 2012), followed by
sequenced microbial genomes from NCBI
12
. Annotations were performed using
the ‘ublast’ module implemented in ‘usearch’
28
, with a sequence identity threshold
of 60%, query coverage fraction of 50% and e-value of 1e–5. Assembly and
annotation statistics are summarized in Supplementary Table 12. Depth o f
coverage for contigs was calculated through mapping of raw reads to assembled
contigs using Bowtie2 (ref. 33), followed by processing using ‘samtools’
34
and
custom R scripts. Median depth of coverage over the entire contig was then
assigned as its abundance. Biological Observation Matrix (BIOM) files
35
were
created incorporating ORF abundance, and annotation using the KO
13
information
and EC
15
codes. These BIOM files were subsequently used for comparisons of
functional potential between the three populations.
Data analyses
. Alpha diversity analyses were performed using observed species
and PD indices, as implemented in QIIME
29
. Beta-diversity analyses were
performed using weighted UniFrac
6
(16S rRNA), and Bray–Curtis (Genus tables,
shotgun KO) distance metrics, as implemented in QIIME. Statistical analyses
including PCoA, PERMANOVA tests, supervised machine learning (Random
Forest)
7,36
and Bayesian source-tracking
8
were performed in QIIME
29
.
Comparison of taxonomic and functional counts data between the three
populations were performed using Kruskal–Wallis tests with multiple testing
correction (FDR) implemented in R. Boxplots, heatmaps and two-dimensional
PCoA plots were generated using R
37
. PERMANOVA were performed using 1,000
permutations to estimate P values for differences among categori es (that is,
country). Machine learning analyses utilized Random Forest classifiers with 10-fold
cross-validation and 1,000 trees.
Treponema genome reconstruction
. Contigs assembled from shotgun metage-
nomic reads obtained from four Matses individuals (SM03, SM23, SM28 and
SM42) were screened for 16S rRNA gene sequences and 35 single copy marker loci
sequences
22
using a combination of NCBI-BLAST
38,39
and Hidden Markov Models
(HMM)
40
profile searches. Contigs with best matches within the Treponema genus
were filtered. Two strains of Treponema were identified in our samples. Strain 1,
found in all four samples, had a 499% sequence identity to T. succinifaciens at the
16S rRNA locus (nucleotide) and 35 single copy marker loci (average, amino acid).
The second strain (Strain 2), found in samples SM23 and SM42 had B90–91%
sequence identity (nucleotide) at the 16S rRNA locus and B88% sequence identity
(average, amino acid) at the single copy marker loci to T. succinifaciens. Several of
the single copy marker loci co-assembled on contigs. Depth of coverage was
consistent for marker loci on different contigs. Further, in samples with both
strains (SM23 and SM42) the strains were observed to have different depths of
coverage, consistently observed across their respective contigs. Additional contigs
were assigned to the two strains using a combination of NCBI-BLAST, depth of
coverage and GC%. Assembly evaluation was performed using the ‘reapr’
pipeline
41
. Assembly statistics are presented in Supplementary Table 13. Functional
analysis and annotation were performed on filtered contigs using the ‘prokka’
pipeline
23
. Predicted ORFs were submitted through the MAPLE server
24
,to
evaluate functional potential. T h e functional potential (MCR, KEGG pathways) of
the Matses Treponema strains were compared using hierarchical clustering with a
collection of reference genomes, including other Spirochaetes and several gut-
associated bacteria across other phyla.
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Acknowledgements
We acknowledge the collaboration of the Comunidad Nativa Matses Anexo San Mateo
and Caserı
´
o de Tunapuco who opened their communities to our research enterprise. We
acknowledge the contribution of Susan Polo and Maria Elena Medina during fieldwork
and Alison Mann during data analysis. Research reported in this publication was pri-
marily supported by the National Institutes of Health under award numbers R01
HG005172 and R01 GM089886. Additional support included grants from the National
Institutes of Health (U54GM104938) and the National Science Foundation (#0845314).
A.J.O.-T. was partially supported by the National Institutes of Health grant R25
CA085771 during the writing phase of this project.
Author contributions
C.M.L. led the project and C.M.L., A.J.O.-T. and R.Y.T., conceived the initial project
design, with inputs from J.M., K.S. and R.K. during the later phases. C.M.L., A.J.O.-T.,
R.Y.T., L.M.-R., O.T.-V., A.T.O., E.G.-P., L.T.-C. designed the field study, human subjects
protocols and consent, and collected the samples. C.M.L., A.J.O.-T., R.Y.T., J.M., K.S.,
J.C.C., L.K.U., Z.Z.X., W.V.T., R.K., P.M.G., C.W. and A.T.O. performed the experimen ts
and analyzed the data. C.M.L. provided financial support for the initial project design,
with additional materials and bioinformatic support provided by R.K., P.M.G., M.F., P.S.
and P.L. at later phases. C.M.L., A.J.O.-T. and R.Y.T. wrote the initial manuscript with
significant contributions from J.M., K.S. and R.K., and critical input from all other
authors. The funders had no role in this study design, data collection and analysis,
decision to publish or preparation of the manuscript.
Additional information
Accession codes: 16S rRNA gene sequences from the study have been deposited in the
QIIME database under study id 1448 (Illumina HiSeq V4). Shotgun sequence data sets
have been deposited in the NCBI SRA database under the BioProject id PRJNA268964.
Supplementary Information accompanies this paper at http://www.nature.com/
naturecommunications
Competing financial interests: There are no competing financial interests.
Reprints and permission information is available online at http://npg.nature.com/
reprintsandpermissions/
How to cite this article: Obregon-Tito, A. J. et al. Subsistence strategies in traditional
societies distinguish gut microbiomes. Nat. Commun. 6:6505 doi: 10.1038/ncomms7505
(2015).
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