Figures
Abstract
Host fitness is impacted by trillions of bacteria in the gastrointestinal tract that facilitate development and are inextricably tied to life history. During development, microbial colonization primes the gut metabolism and physiology, thereby setting the stage for adult nutrition and health. However, the ecological rules governing microbial succession are poorly understood. In this study, we examined the relationship between host lineage, captive diet, and life stage and gut microbiota characteristics in three primate species (infraorder, Lemuriformes). Fecal samples were collected from captive lemur mothers and their infants, from birth to weaning. Microbial DNA was extracted and the v4 region of 16S rDNA was sequenced on the Illumina platform using protocols from the Earth Microbiome Project. Here, we show that colonization proceeds along different successional trajectories in developing infants from species with differing dietary regimes and ecological profiles: frugivorous (fruit-eating) Varecia variegata, generalist Lemur catta, and folivorous (leaf-eating) Propithecus coquereli. Our analyses reveal community membership and succession patterns consistent with previous studies of human infants, suggesting that lemurs may serve as a useful model of microbial ecology in the primate gut. Each lemur species exhibits distinct species-specific bacterial diversity signatures correlating to life stages and life history traits, implying that gut microbial community assembly primes developing infants at species-specific rates for their respective adult feeding strategies.
Citation: McKenney EA, Rodrigo A, Yoder AD (2015) Patterns of Gut Bacterial Colonization in Three Primate Species. PLoS ONE 10(5): e0124618. https://doi.org/10.1371/journal.pone.0124618
Academic Editor: Markus M. Heimesaat, Charité, Campus Benjamin Franklin, GERMANY
Received: October 31, 2014; Accepted: March 3, 2015; Published: May 13, 2015
Copyright: © 2015 McKenney et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: A fastq file containing all joined reads that were included in the analysis was deposited in the NCBI Sequence Read Archive under Project ID PRJNA270617.
Funding: Duke Biology Department Grant-in-Aid; Duke Lemur Center Director's Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Research on the human gut microbiome (GM) has burgeoned in the past decade [1–9]. These studies have revealed that the GM has profound impacts on phenotype, ranging from cognition [10] to locomotion [11]. Given that humans represent but a single species, however, these studies are unable to establish the general evolutionary and ecological "rules" by which the GM is developed and maintained. Despite intensifying scientific interest and enhanced technical feasibility, it remains difficult to tease apart the relative significance of host characteristics in shaping the GM and to determine how and to what extent microbial niches and succession are determined by phylogeny or ecology. Although it has been shown that dietary intake can radically alter the composition and efficacy of the GM [12], the mechanisms by which these shifts are produced remain uncharacterized. Here, we study the composition and community development of GMs across multiple species within a phylogenetically related but ecologically diverse group of mammals. Whereas other recent research on primate microbiomes has relied on limited cross-sectional sampling [3,6–8,12–17] we present an extensive longitudinal study of the GM diversity of mother-infant pairs of three different lemur species from birth to weaning.
Our focal host species are within the primate clade Lemuriformes, which (together with the lorisiform primates) is sister to the haplorrhine primates (monkeys, apes and humans). In our study system, species are fed captive dietary regimes formulated to approximate the nutritional composition of natural diets consumed by conspecifics in the wild. The macronutrient composition within each species’ captive diet remains constant, while food items may be substituted to accommodate individual food preference and seasonal availability. Lemurs have evolved diverse GIT morphologies to adapt to species-specific feeding ecologies, making them an ideal group for teasing apart the potentially divergent effects of phylogenetic history and captive diet on GM community structure and for examining the associated community signatures for potential effects on host health and nutritional uptake.
Gut microbial ecology
The mammalian gastrointestinal tract (GIT) is characterized by extremely low diversity at birth, but by adulthood, it teems with trillions of microbes that perform a variety of functions including fiber digestion and defense against pathogens [8,12]. Colonization of the GIT is initiated during birth and thus affected by the mode of delivery [18]. Progressive succession within the human infant GIT corresponds with key stages of development such as nursing, weaning, and intestinal development and maturation [1,3,6–8,12–17], in addition to life events such as antibiotic or novel dietary regimes [7,8]. Given the crucial role the GM plays in infant development, its permanent impact on the neonatal immune system [19] and brain development [10], and the observed persistence of gut bacterial strains across years of sampling [4], colonization events have the power to impact the host for its entire lifecycle. It is therefore likely that GMs are inextricably tied to life history (growth and reproduction rates, reproductive duration; [20]).
Gut microbes themselves are governed to varying extents by several host characteristics. First, genetically derived differences in immuno-“tolerance,” affect the host’s ability to recognize microbes as either helpful or harmful to the host [21]. Second, the host’s nutritional intake contributes to available microbial niche space [10,15,19,22,23]. Third, gut morphology provides a more or less complex topography for the microbes to colonize. After maternal inoculations initiate the infant GM during birth, diversity and membership increase with age and the introduction of solid foods [8,15]. Opportunistic pioneer species alter the GIT environment and create additional niche space in the form of fermentation byproducts [24]. These metabolites can be used by other microbes and thus help to drive succession.
After the assembly process is complete, the stable GM climax community prevents further colonization (i.e. by pathogenic microbes), possibly due to the functional redundancy among bacterial species [25]. Once established, the healthy adult human climax GM remains fairly stable except to reflect changing environmental conditions or health [26]. Of particular interest, Faith et al. [4] detected fluctuations in human and other mammals’ GM membership and metagenomic profiles in response to diet. Researchers have also observed significant shifts in response to global relocation, illness, and antibiotics [26,27].
Our study system
We examine GM assembly in Varecia variegata, Lemur catta, and Propithecus coquereli (see Fig 1). Estimated divergence times are ~32mya for the divergence of V. variegata and L. catta and ~ 42mya for the divergence of P. coquereli from the Lemuridae (V. variegata, L. catta; [28]). Each species has evolved unique and differing life histories, ecological constraints, and distinct GIT morphologies. In our study system, however, V. variegata and L. catta are fed similar diets in captivity. We analyzed bacterial 16S rDNA reads amplified from fecal samples collected from positively identified individuals to resolve GM membership at the genus level and to compare succession patterns between individuals and across species. By controlling certain environmental variables such as dietary intake and recording relevant long-term metadata, we investigate the contributions of host life stage, captive diet, and GIT morphology to the host-gut microbial relationship and the process of community assembly. We use summary statistics of taxonomic richness, membership, and diversity to test the following hypotheses.
H: If the succession process in lemurs is similar to that described in humans, then microbial diversity should be lowest in samples from birth and increase with age until weaning, with decreasing intraspecific variability as individuals approach adulthood and their GMs approach the climax community. We refer to this as the “life stage” hypothesis.
Materials and Methods
Animal housing and husbandry
All animals were housed at the Duke Lemur Center in Durham, NC, USA. Conspecific lemurs are housed in indoor enclosures with access to adjoining outdoor runs. This typical indoor cage is 10 feet high x 7.5 feet wide x 7 feet long (approximately 3 m high x 2.3 m wide x 2.1 m long). The typical outdoor enclosure is 10 feet high x 7.5 feet wide x 14 feet long (approximately 3 m high x 2.3 m wide x 4.3 m long). Each lemur social group is allocated enough cages that each individual could have his/her own indoor and outdoor area if needed. For example, a group of five animals would be able to move between 5 connected indoor and 5 connected outdoor enclosures.
Because at least half of the space is outdoors, and each indoor enclosure has 1–3 windows, lighting conditions follow the natural North Carolina photoperiod. Fluorescent lighting is also used from approximately 7am-5pm while staff are present. Social groups are provided a variety of enrichment items, including bamboo or wooden climbing structures, sturdy plastic play houses, elevated plastic nesting boxes or crates, firehoses and ropes for climbing, and rotating novel objects (i.e. scents, paper mache objects, puzzles with treats inside, etc.).
All infants are housed with their mothers, separate from other lemurs, immediately after birth and during nursing to reinforce the mother-infant bond. In this study, one (L. catta) social group contained two mother-infant groups (with three infants total). However, dams were never observed to nurse infants other than their own offspring, so this arrangement did not impact vertical transmission via milk.
Captive diets
All individuals within each species are fed similar dietary regimes. V. variegata and L. catta are fed similar captive diets in alignment with their phylogenetic relatedness compared to P. coquereli (though in nature, their diets differ in the degree to which fruit is represented, with V. variegata being more frugivorous than L. catta). The following are representative dietary regimes per individual. V. variegata receive 80–100g Lab Diet #5038 plus approximately 145g fruit-veggie mixture. L. catta receive 60g Lab Die #5038 plus approximately 110g fruit-veggie mixture. P. coquereli receive 75g Mazuri Leaf Eater #5675, 30g greens, 30g veggies, 10g nuts/beans, and 150g leaves. Amounts may vary depending on individual health, weight, and reproductive status.
Sample collection
All animal procedures were reviewed and approved by the Duke University IACUC under protocol number A282-11-11. Fifty-eight fresh fecal samples were collected from subjects at the Duke Lemur Center (Durham, NC, USA), either during routine handling or from enclosures immediately after excretion. Samples were collected from a total of 6 dams within one day of parturition and from a total of 9 infants at the following life stages: birth, nursing, introduction of solid food, routine ingestion of solid food, weaning, and three months post-weaning (Table 1). Infants’ ages at each life stage are listed in Table 2. In this study, “introduction of solid foods” indicates that infants had begun to eat solid foods but that nursing still made up the majority of the diet. “Regular consumption of solid foods” indicates that infants consumed solids on a daily basis, in addition to nursing. The transition to “weaning” is marked by a dramatic decrease in the number and duration of nursing bouts allowed by the mother, during which infants consume mostly solid foods but still nurse occasionally. No invasive procedures were performed on the lemurs, and no lemurs were sacrificed as a result of the study. Furthermore, human handlers played no role in assisting the animals during delivery, and only handled infants during exams at the time of sample collection, to minimize human impact on lemur GIT colonization.
Bacterial DNA extraction and sequencing
All fecal specimens were stored in individually labeled Whirl-pak bags and immediately frozen at -80°C to prevent DNA degradation and continued microbial reproduction within feces [29]. The exterior of each frozen sample was cut away; DNA extractions were performed using only fecal matter that had not been exposed to the environment. Microbial DNA was extracted from individual samples using the QIAamp Stool Mini Kit (QIAGEN, Hilden, Germany) following manufacturer guidelines. Bacterial DNA was subsequently visualized on a gel and quantified using a Nanodrop-1000. Two standardized DNA aliquots from each extraction were sent to Argonne National Laboratory for downstream amplification and sequencing. The PCR primers 515F (GTG-CCA-GCM-GCC-GCG-GTA-A) and 806R (GGA-CTA-CHV-GGG-TWT-CTA-AT) were used to amplify the v4 region of 16S rDNA with length ~300bp for 150 pb paired-end sequencing on the Illumina MiSeq platform according to the methods of Caporaso et al. [30]. Technical replicates were sequenced from each DNA sample listed in Table 1 to ensure precision between sequencing efforts. All 116 libraries were barcoded and pooled on a single Illumina MiSeq run. 16S DNA was sequenced in duplicate from 58 fecal samples to create 116 libraries.
16S sequence Analyses
Illumina sequencing produced 15,308,697 total reads before filtering. We used ea-utils [31] to join forward and reverse reads, yielding a total of 9,040,165 joined reads. A fastq file containing these joined reads was deposited in the NCBI Sequence Read Archive (Project ID PRJNA270617). Joined 16S reads were analyzed using Quantitative Insights Into Microbial Ecology (QIIME v1.7.0) to classify microbial constituents and compare membership between samples [32,33].
We performed quality filtering using default settings and demultiplexed reads using 12 bp barcodes. A total of 5,844,416 sequences were retained after quality filtering in QIIME, giving us an average coverage of 50,297 sequences (standard deviation 15,670) per library. Coverage ranged from 1 sequence (in a library sequenced from a sample collected from P. coquereli at birth, discussed below) to 82,473 sequences, with a median of 53,668 sequences per library. Duplicate 16S libraries were generally more similar to each other than to libraries sequenced from other samples (Figs 2 and 3), suggesting that the coverage (>50,000 paired-end reads per library after quality filtering in QIIME) adequately captured the taxonomic diversity recovered in each DNA extraction.
Samples were collected from infants at (i) birth, (ii) nursing, (iii) introduction of solid foods, (iv) regular consumption of solid foods, (v) weaning, (vi) weaned, and from dams at parturition (p). Infants’ ages are listed below each life stage. The samples collected from each species during the introduction of solid foods are also bracketed and indicated with asterisks. Technical duplicates sequenced from each sample are paired and arranged so that infants’ results are repeated in the same order within each life stage and so that dams’ order corresponds to their infants’. Results are shown at the (A) phylum level and (B) genus level.
The distance in PCoA vector space represents community membership (i.e. taxa in Fig 2). Each data point in represents a library (i.e. bar in Fig 2). The distance between points represents unique branches on a phylogenetic tree (i.e. evolutionary history not shared between libraries in Fig 2). Closer points share more branch length, while points more distant from one another have more unique or disparate GMs. Dashed lines connect samples from a representative individual from each species, and represent species-specific trajectories. Representative individuals were chosen based on the completion of longitudinal sampling and quality of sample sequencing. Dashed circles indicate samples collected from animals after they begin consuming solid foods regularly.
Operational taxonomic units (OTUs, a proxy for taxa based on 97% sequence similarity) were picked using UCLUST [34]. Next we calculated the number and frequency of OTUs within each sample and the net difference in OTUs between any two communities to report alpha and beta diversity, respectively. Beta diversity was quantified using weighted UniFrac [35], which calculates the proportion of OTUs unique to each sample. QIIME performs Principle Coordinate Analyses (PCoA) on the weighted GM beta diversity to detect underlying relationships between the microbiota, feeding ecology, host species, and relatedness among hosts.
Statistical analyses
We used JMP Pro (Version 11, SAS Institute Inc., Cary, NC, USA) to perform a mixed model nested two factor ANOVA, using the model where y is the biodiversity index, A is host species (fixed), B is life stage (fixed), A*B is the interaction between host species and life stage, and α is individual nested within species (random).
We used linear contrasts of the Shannon-Weaver and Simpson’s diversity to test for effects of life stage and other host factors. The Shannon-Weaver index calculates biodiversity by taking the log of relative abundance values. This method only slightly reduces the weight of rare species, which may contribute many of the changes between progressive life stages. Simpson’s diversity index, however, squares relative abundance values. Therefore, the weight of rare species is reduced relatively more than the weight of abundant species. This makes Simpson’s diversity more robust in situations where there are many rare species whose changes may potentially disturb overall patterns of change.
We used the adonis{vegan} function in R [36] to calculate the strength and significance of host species and life stage in determining variation in a distance matrix of the weighted UniFrac measurements of beta diversity between all libraries. Adonis is analogous to PERMANOVA [37], a nonparametric method that determines significance through permutations (default = 999) and returns a pseudo F- and p-value. By partitioning distance matrices, PERMANOVA statistically compares the differences in overall community composition between classes of 16S libraries.
Finally, we used Linear discriminant analysis Effect Size (LEfSe, with default settings) to detect bacterial lineages whose frequencies differ significantly between host species at life stages of specific interest, such as the introduction and regular consumption of solid foods. LEfSe is available on the Huttenhower lab Galaxy instance (http://huttenhower.sph.harvard.edu/galaxy), and takes a tab-delimited table of OTU frequencies with class, subclass, and subject headings as input. LEfSe detects differentially distributed lineages with the Kruskall-Wallis test (default alpha value = 0.05), and then checks the consistency of subclass distinctions with the pairwise Wilcoxon text (default alpha value = 0.05). The final linear discriminant analysis (LDA) ranks all differentiating lineages by their effect size (default threshold on logarithmic LDA score = 2.0).
Results
All statistical tests support the life stage hypothesis. Both alpha diversity measures yielded similar significance; here we present the results of the Shannon-Weaver analyses (Tables 3 and 4). As expected, individuals within species exhibit more variation and less similarity during early life stages (Figs 2 and 3, Table 3). The average weighted UniFrac distance between individuals within species at each life stage also decreases as animals mature (Fig 4). Birth samples contained the lowest bacterial diversity compared to other life stages, with significant increases early in life and continued, though less significant, increases to parturition (Fig 5). Community composition also differed significantly among life stages (Table 4). Birth and nursing samples comprised different bacterial ecological pioneer membership within each lemur species; however, opportunistic colonizers (i.e. Enterobacteria) and lactose digesting bacteria were consistently detected across host species (Fig 2). As lemurs progressed from the introduction to the regular consumption of solid foods, the number of differential lineages detected within each species increased (Fig 6). Yet many of the lineages come from the same clades; and several were retained across life stages, suggesting that they may be members of the native GIT community.
Weighted UniFrac distances were averaged to plot GM variation at each life stage. The distances between replicate libraries (sequenced from the same fecal sample) were omitted from average calculations. Distance could not be calculated for Propithecus coquereli at birth or for Varecia variegata at parturition because only one sample was collected at these time points.
Differentially distributed lineages are ranked based on their linear discriminant analysis effect size. The average percent contribution of each lineage is listed in parentheses.
Interestingly, all infant GMs follow the same overall movement and convergence through vector space in Fig 3. For each species, infant GM profiles converge toward the climax community represented here by mothers’ parturition samples within each species as the animals mature (Fig 3). Life stages during which lemurs consume solid food on a daily basis cluster closely in PCoA vector space (Fig 3) and share the same major bacterial constituents (Fig 2), suggesting putative climax communities for each host species (see further discussion below).
P. coquereli microbial diversity is significantly higher than that of the two species within the Lemuridae (V. variegata, L. catta) after solid food is consumed daily (p = 0.0002; Table 3), while the latter two species do not differ significantly from each other (p = 0.6744; Table 4). Furthermore, P. coquereli hosted a greater number of differential lineages during the introduction (Fig 6) and regular consumption (Fig 6) of solid foods than either lemurid species. Interestingly, the membership within P. coquereli’s highly diverse GMs was highly conserved. Inter-individual variation, as measured by the average weighted UniFrac distance, was lower in P. coquereli than either V. variegata or L. catta (Fig 4).
Discussion
Our results indicate that captive diet impacts GM succession (i.e. differences between life stages, Table 3), while species identity of the host accounts for major distinctions among samples across life stages (Table 4). Although V. variegata and L. catta are more closely related to each other than either is to P. coquereli, they are nonetheless separated by a significant period of evolutionary history (approximately 30mya [28]). To contextualize this phylogenetic diversity, there is as much evolutionary distance separating Varecia from Lemur as there is distance separating humans from marmosets. This long evolutionary separation is manifested by many behavioral, ecological, and phenotypic characteristics. We assume that the gut microbiomes associated with these two species are less constrained evolutionarily than the lemurs’ differently-evolved gut morphologies and other distinguishing physiological and morphological characteristics. We therefore maintain that there has been sufficient opportunity and time for the gut microbiomes of these two species to change independently, so that when they are brought together in captivity and fed similar diets, observed GM similarities are likely to reflect a dietary effect. Diet and phylogeny, however, are clearly confounded. To more thoroughly disentangle the effects of phylogeny and diet, additional species within the lemuriform radiation should be surveyed. A host species’ characteristics are the consequence of the evolutionary impacts of adaptive strategies, such as feeding ecology and life history, and as such, are the ultimate drivers of species’ divergence. While we tested each factor separately, we also discuss interactive effects below.
Life stage dictates dietary intake, and both drive microbial succession
GM constraint begins at nursing due to dietary restriction: milk is the sole source of food during this stage, unless the infant practices coprophagy (i.e. as practiced by P. coquereli). The introduction of solid food increases bacterial diversity (Figs 2 and 5) and GM differentiation between host species (Figs 3, 4, and 6) by supplying more diverse nutrients than are available in milk alone. Weaning, in turn, stabilizes dietary diversity (Fig 5) by eliminating milk from the diet and refining the nutrient profile available to GM members, thus decreasing variability within host species (Figs 3 and 4). Regular consumption of solid foods, and weaning especially, seems to drive GM convergence within host species (indicated by high p-values in Table 3, shared bacterial constituents in Fig 2, and decreased average UniFrac distances in Fig 4). All GM libraries exhibit increasing diversity and stability—that is, decreasing variability between individuals and among progressive life stages—with the introduction of solid foods, increasing age, and weaning (Table 3; Figs 2, 3, 4). For example, V. variegata and L. catta libraries all contained Prevotella after the introduction of solid food, while P. coquereli libraries from the same life stages all contained four genera within the family Ruminococcaceae (Fig 2).
Dietary fiber affects microbial diversity
We predicted that P. coquereli GMs fed a high-fiber captive diet would be significantly different from GMs in V. variegata and L. catta fed captive diets high in starches and other soluble carbohydrates. P. coquereli libraries were the most diverse (Table 4, Fig 5) and contained the most differential bacterial lineages detected by linear discriminant analysis (Fig 6). Of particular interest are four Ruminococcaceae genera detected in P. coquereli samples after solid food was introduced (Fig 2). Ruminococcaceae were first isolated from ruminant (i.e. cow) gut samples, and have also been detected in folivorous gorillas [13,38]. Thus, the family is likely present in P. coquereli due to convergent dietary challenges. P. coquereli consumes the highest fiber diet of the species studied here. Dietary fiber is fermented to produce myriad metabolic byproducts [39], which in turn support the high diversity of gut bacteria detected in our study.
Species-specific traits distinguish GMs despite diet
While P. coquereli hosted the GMs with highest diversity, L. catta and V. variegata GMs exhibit higher inter-individual variation (Fig 4). LEfSe detected several bacterial lineages that distinguished V. variegata from L. catta despite the similar dietary regimes imposed by captivity (Fig 6). Furthermore, putative core microbiomes become apparent for all three species as lemurs approach weaning and adulthood (Fig 3). L. catta GIT morphology is of intermediate complexity (Fig 1), reflecting adaptation to a higher number of different food items foraged in situ compared to V. variegata. Our results indicate that captive diet plays an integral role in shaping bacterial diversity (Tables 3 and 4, Figs 4 and 5), but also that species-specific traits such as GIT morphology impact the beta diversity observed between the two Lemuridae species (Figs 3 and 6). These findings are consistent with previous work by Ochman et al., which demonstrated that phylogeny is the main driver of gut microbial communities in primates [14].
Natural diet is tied to host developmental patterns
Primates have fundamentally different life histories as compared to other mammals [40]. Under the protein-richness hypothesis, animals with increased protein intake such as folivores should develop more rapidly compared to frugivores and wean at an earlier age, unless the age at weaning is delayed for optimal coordination with environmental conditions [19,41]. Lemurs exemplify this exception to the rule. Lemurs have evolved estrus cycles comprising fleeting time-windows that occur at species-specific intervals to accommodate their different developmental rates. As a consequence, lemuriform infants wean in synchrony, regardless of species, prior to the arrival of cyclones to Madagascar in March [42]. This island-wide adaptive response to environmental challenges permits development over species-specific intervals, instead of at differing rates. Those intervals are mediated, in part, by food availability and dietary adaptations [43].
Propithecus species are highly folivorous [44], consuming natural diets that are enriched for protein and fiber compared to the fruits eaten by V. variegata in situ [45]. Propithecus infants are also more precocious at weaning than frugivorous lemurs [41]; but instead of maturing more quickly, P. coquereli development takes place over 30 months, compared to 14 months for V. variegata (DLC records; [42]). These developmental differences can affect many phenotypic traits. For example, folivorous lemurs exhibit adult dentition at weaning to aid in chewing fibrous forages, while frugivorous lemur teeth are less fully developed at weaning [41]. Precocious folivorous lemurs’ delayed weaning may also promote enhanced microbial succession, just as it enables the advanced development of other adaptive traits. Because GMs also aid in fiber digestion, it stands to reason that the patterns of GM development would also vary between lemur species utilizing different feeding strategies.
Life stage impacts microbial succession differently in each lemur species
The 16S profiles from samples collected after the introduction of solid foods show varying levels of similarity to nursing samples within each lemur species (Figs 2 and 3; Table 3). Specifically, V. variegata appears to undergo a less profound shift in GM composition with the introduction of solid foods (p = 0.3298; Table 3) compared to the other species in our study (p<0.0001; Table 3). This suggests that early gut colonization has more and longer lasting impact on the GMs in V. variegata, while GM succession in L. catta and P. coquereli progresses further toward the climax communities observed in dams at parturition (Figs 2 and 3). These effects may stem from the species-specific developmental rates discussed above. Alternatively, consumption of solid foods may have less impact on the GM in V. variegata than in L. catta or P. coquereli. A possible explanation might be that the nutritional composition of milk may be more similar to that of the captive diet consumed by V. variegata. Another possibility is that because V. variegata has the simplest GIT morphology, it undergoes the least gut tissue development and thus has less transformative impact on GM composition. Importantly, while infants within species exhibit similar colonization trajectories (Fig 3), the 16S signature and infant age at each life stage is distinct between species (Fig 2, Table 2).
GMs from P. coquereli differ significantly from V. variegata and L. catta (p = 0.0002; Table 4), while the sister groups do not differ (p = 0.6744; Table 4). The taxonomic diversity of GMs within each lemur species also appears constrained; that is, many bacterial families are detected in each lemur species that belong to a few orders and are specific to their primate host (Figs 2 and 6), suggesting specialized bacterial radiation appropriate to each host species’ characteristics (i.e. immune system or GIT morphology).
P. coquereli infants, and indeed the offspring of many herbivores (i.e. horses and rabbits [1,39]), practice maternal coprophagy—they consume their mothers' feces early in life, presumably to inoculate their GIT with beneficial communities. This behavior may explain the only observation during our study wherein the DNA extracted from the sample collected from a P. coquereli infant at birth yielded two duplicate libraries with inconsistent bacterial membership (Fig 2). One of the libraries contained only one OTU; but the other technical replicate contained higher diversity than other samples collected at birth or even within the nursing sample later collected from the same P. coquereli infant. This anomalous result may reflect either sequencing error or maternal coprophagy during or shortly after birth. All other duplicates yielded similar 16S results within library pairs.
Sampling from animals with detailed medical records allowed us to verify that hosts were healthy, and therefore ensured that GMs were not affected by illness. Lemur GM profiles appear to converge in accordance with macro-organismal ecology succession trends (Figs 4 and 5), in which the climax community achieves a more conserved composition within species over time. The refined community includes specific members, which are best adapted to the environment and interact with each other within that environment, and thus have greater stability over time.
Synthesis
All lemur samples from birth and nursing contain predominantly Proteobacteria (Fig 2), similar to GMs in human infants delivered vaginally [7,21]. We find the phyla Firmicutes and Bacteroidetes to dominate all lemur libraries in this study after solid food is consumed (Fig 2), comparable with findings in humans and other primates (Table 5). Lachnospiraceae (Clostridiales: Clostridia: Firmicutes), a major family detected in healthy human gut microbiota [46], was also detected in P. coquereli birth samples. Interestingly, the gut microbiota sequenced from other non-human primates differ in the distributions of their dominant phyla (Table 5). Specifically, lemurs appear to harbor ratios of Proteobacteria and Firmicutes more similar to Pan species than to either Gorilla species [14] or to Nycticebus pygmaeus (pygmy loris, the only other prosimian whose gut microbiota has been studied to date [17]), while Bacteroidetes shows the opposite relationship.
Lemur GMs contained two bacterial lineages associated with humans consuming a distinctly non-Western diet. All lemurs’ GMs contained Prevotella after solid food was introduced (Figs 2 and 6), similar to humans from outside the U.S. [2,15,22,23,47]. V. variegata GMs were also enriched in Treponema (Spirochaetes; Figs 2 and 6). GMs isolated from children living in rural Burkina Faso contained both Prevotella and Treponema, while neither genus was present in European children [47]. The presence of Prevotella and Treponema indicates bacterial fermentation of plant-derived compounds such as xylene, xylose, and carboxymethylcellulose [48]. Presumably, these bacteria were detected in humans in developing countries due to the higher prevalence of plants and produce in non-Western diets, thus yielding the similarity to those found in lemurs. Accordingly, our results indicate the potential to use lemurs as a model of GM assembly for comparison to human and other primates.
Supporting Information
S1 Fig. Gastrointestinal tract from a red ruffed lemur (Varecia variegata rubra).
Scale equals 1 cm. Figure is originally published in Campbell et al. 2000 [49], and reused here with permission from John Wiley and Sons.
https://doi.org/10.1371/journal.pone.0124618.s001
(TIF)
S2 Fig. Gastrointestinal tract from a ring-tailed lemur (Lemur catta).
Scale equals 1 cm. Figure is originally published in Campbell et al. 2000 [49], and reused here with permission from John Wiley and Sons.
https://doi.org/10.1371/journal.pone.0124618.s002
(TIF)
S3 Fig. Gastrointestinal tract from a Coquerel’s sifaka (Propithecus verreaxi coquereli).
Scale equals 1 cm. Figure is originally published in Campbell et al. 2000 [49], and reused here with permission from John Wiley and Sons.
https://doi.org/10.1371/journal.pone.0124618.s003
(TIF)
Acknowledgments
The authors would like to thank the Duke Lemur Center staff and Sarah Owens at Argonne National Labs for their support.
Author Contributions
Conceived and designed the experiments: EAM AR ADY. Performed the experiments: EAM. Analyzed the data: EAM. Contributed reagents/materials/analysis tools: EAM AR ADY. Wrote the paper: EAM AR ADY.
References
- 1. Sommer F, Bäckhed F. The gut microbiota—masters of host development and physiology. Nat Rev Microbiol 2013;11: 227–238. pmid:23435359
- 2. Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, et al. Human gut microbiome viewed across age and geography. Nature. 2012.
- 3. Fallani M, Young D, Scott J, Norin E, Amarri S, Adam R, et al. Intestinal Microbiota of 6-week-old Infants Across Europe: Geographic Influence Beyond Delivery Mode, Breast-feeding, and Antibiotics. J Pediatr Gastr Nutr 2010;51: 77–84. pmid:20479681
- 4. Faith JJ, Guruge JL, Charbonneau M, Subramanian S, Seedorf H, Goodman AL, et al. The long-term stability of the human gut microbiota. Science 2013;341: 1237439. pmid:23828941
- 5. Clemente JC, Ursell LK, Parfrey LW, Knight R. The impact of the gut microbiota on human health: an integrative view. Cell 2012;148: 1258–1270. pmid:22424233
- 6. Sanz Y. Gut microbiota and probiotics in maternal and infant health. Am J Clin Nutr 2011;94: 2000S–2005S. pmid:21543533
- 7. Morowitz MJ, Denef VJ, Costello EK, Thomas BC, Poroyko V, Relman DA, et al. Strain-resolved community genomic analysis of gut microbial colonization in a premature infant. Proc Natl Acad Sci USA 2011;108: 1128–1133. pmid:21191099
- 8. Palmer C, Bik EM, DiGiulio DB, Relman DA, Brown PO. Development of the human infant intestinal microbiota. PLoS Biol 2007;5: 1556–1573.
- 9. Muegge BD, Kuczynski J, Knights D, Clemente JC, Gonzalez A, Fontana L, et al. Diet Drives Convergence in Gut Microbiome Functions Across Mammalian Phylogeny and Within Humans. Science 2011;332: 970–974. pmid:21596990
- 10. Heijtz RD, Wang S, Anuar F, Qian Y, Björkholm B, Samuelsson A, et al. Normal gut microbiota modulates brain development and behavior. Proc Natl Acad Sci USA 2011;108: 3047–3052. pmid:21282636
- 11.
Scher JU, Sczesnak A, Longman RS, Segata N, Ubeda C, Bielski C, et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. eLife 2. 2013. https://doi.org/10.7554/eLife.01202
- 12. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 2014;505: 559–563. pmid:24336217
- 13. Frey JC, Rothman JM, Pell AN, Nizeyi JB, Cranfield MR, Angert ER. Fecal bacterial diversity in a wild gorilla. Appl Environ Microbiol 2006;72: 3788–3792. pmid:16672537
- 14. Ochman H, Worobey M, Kuo C-H, Ndjango J-BN, Peeters M, Hahn BH, et al. Evolutionary relationships of wild hominids recapitulated by gut microbial communities. PLoS Biol 2010;8: e1000546–. pmid:21103409
- 15. Koenig JE, Spor A, Scalfone N, Fricker AD, Stombaugh J, Knight R, et al. Succession of microbial consortia in the developing infant gut microbiome. Proc Natl Acad Sci USA 2011;108 Suppl 1: 4578–4585. pmid:20668239
- 16. Vaishampayan PA, Kuehl JV, Froula JL, Morgan JL, Ochman H, Francino MP. Comparative Metagenomics and Population Dynamics of the Gut Microbiota in Mother and Infant. Genome Biol Evol 2010;2:53:66. pmid:20333224
- 17. Xu B, Xu W, Yang F, Li J, Yang Y, Tang X, et al. Metagenomic analysis of the pygmy loris fecal microbiome reveals unique functional capacity related to metabolism of aromatic compounds. PLoS ONE 2013;8: e56565. pmid:23457582
- 18. Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci USA 2010;107: 11971–11975. pmid:20566857
- 19. Chow J, Lee SM, Shen Y, Khosravi A, Mazmanian SK. Host-bacterial symbiosis in health and disease. Adv Immunol 2010;107: 243–274. pmid:21034976
- 20. Jones JH. Primates and the evolution of long, slow life histories. Current Biology 2011;21(18): pR708–R717.
- 21. Benson AK, Kelly SA, Legge R, Ma F, Low SJ, Kim J, et al. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proc Natl Acad Sci USA 2010;107: 18933–18938. pmid:20937875
- 22. Pantoja-Feliciano IG, Clemente JC, Costello EK, Perez ME, Blaser MJ, Knight R, et al. Biphasic assembly of the murine intestinal microbiota during early development. ISME J 2013;7: 1112–1115. pmid:23535917
- 23. Sela DA, Mills DA. Nursing our microbiota: molecular linkages between bifidobacteria and milk oligosaccharides. Trends in microbiology 2010;18(7): 298–307. pmid:20409714
- 24. Savage DC. Microbial ecology of the gastrointestinal tract. Annual Reviews in Microbiology 1977;31: 107–133.
- 25. Ley RE, Peterson DA, Gordon JI. Ecological and Evolutionary Forces Shaping Microbial Diversity in the Human Intestine. Cell 2006;124: 837–848. Available: http://www.sciencedirect.com/science/article/pii/S0092867406001929. pmid:16497592
- 26. David LA, Materna AC, Friedman J. Host lifestyle affects human microbiota on daily timescales. Genome Biol 2014;15: R89. pmid:25146375
- 27. Jalanka-Tuovinen J, Salonen A, Nikkila J, Immonen O, Kekkonen R, Lahti L, et al. Intestinal microbiota in healthy adults: temporal analysis reveals individual and common core and relation to intestinal symptoms. PLoS ONE 2011;6: e23035. pmid:21829582
- 28. Yoder AD, Yang ZH. Divergence dates for Malagasy lemurs estimated from multiple gene loci: geological and evolutionary context. Molecular Ecology 2004;13: 757–773. pmid:15012754
- 29. Claesson MJ, O'Sullivan O, Wang Q, Nikkila J, Marchesi JR, Smidt H, et al. Comparative analysis of pyrosequencing and a phylogenetic microarray for exploring microbial community structures in the human distal intestine. PLoS ONE 2009;4: e6669–. pmid:19693277
- 30. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 2012;6: 1621–1624. pmid:22402401
- 31.
Aronesty E Aronesty: ea-utils: Command-line tools for processing biological sequencing data. 2011. http://code.google.com/p/ea-utils
- 32. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 2010;7: 335–336. pmid:20383131
- 33.
Kuczynski J, Stombaugh J, Walters WA, González A, Caporaso JG, Knight R. Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr Protoc Bioinformatics Chapter 10: Unit10.7. 2011. https://doi.org/10.1002/0471250953.bi1007s36 pmid:21400674
- 34. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010;26: 2460–2461. pmid:20709691
- 35. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 2005;71: 8228–8235. pmid:16332807
- 36.
Oksanen J, Blanchet FG, Kindt R. Package “vegan.” Community ecology package Version 2.0–10. 2013. http://vegan.r-forge.r-project.org/
- 37.
Anderson MJ. Permutational multivariate analysis of variance. Department of Statistics. University of Auckland, Auckland. 2005. Available: http://www.stat.auckland.ac.nz/~mja/prog?/PERMANOVA_UserNotes.pdf
- 38. McKenney EA, Ashwell M, Lambert JE, Fellner V. Fecal microbial diversity and putative function in captive western lowland gorillas (Gorilla gorilla gorilla), common chimpanzees (Pan troglodytes), Hamadryas baboons (Papio hamadryas), and binturongs (Arctictis binturong). Integr Zool. 2014.
- 39. Stevens CE, Hume ID. Contributions of microbes in vertebrate gastrointestinal tract to production and conservation of nutrients. Physiol Rev 1998;78: 393–427. pmid:9562034
- 40. Read AF, Harvey PH. Life history differences among the eutherian radiations. Journal of Zoology 1989;219: 329–353.
- 41. Godfrey LR, Samonds KE, Jungers WL, Sutherland MR. Teeth, brains, and primate life histories. Am J Phys Anthropol 2001;114: 192–214. pmid:11241186
- 42. Wright PC. Lemur traits and Madagascar ecology: coping with an island environment. Am J Phys Anthropol Suppl 1999;29: 31–72. pmid:10601983
- 43. Borries C, Lu A, Lupo KO, Larney E. Primate life histories and dietary adaptations: a comparison of Asian colobines and macaques. Am J Phys Anthropol 2011;144: 286–299. pmid:20925079
- 44. Hemingway CA. Selectivity and variability in the diet of Milne-Edwards' sifakas (Propithecus diadema edwardsi): Implications for folivory and seed-eating. Int J Primatol 1998;19(2):355–377.
- 45. Britt A. Encouraging natural feeding behavior in captive-bred black and white ruffed lemurs (Varecia variegata variegata). Zoo Biol 1998;17: 379–392.
- 46. Gosalbes MJ, Durbán A, Pignatelli M, Abellan JJ, Jiménez-Hernández N, Perez-Cobas AE, et al. Metatranscriptomic approach to analyze the functional human gut microbiota. PLoS ONE 2011;6: e17447. pmid:21408168
- 47. De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. PNAS 2010;107(33); 14691–14696. pmid:20679230
- 48. Flint HJ, Bayer EA. Plant cell wall breakdown by anaerobic microorganisms from the Mammalian digestive tract. Ann N Y Acad Sci 2008;1125: 280–288. pmid:18378598
- 49. Campbell JL, Eisemann JH, Williams CV, Glenn KM. Description of the gastrointestinal tract of five lemur species: Propithecus tattersalli, Propithecus verreauxi coquereli, Varecia variegata, Hapalemur griseus, and Lemur catta. Am J Primatol 2000;52: 133–142. pmid:11078027