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Published in final edited form as: J Med Primatol. 2018 Jul 24;47(6):393–401. doi: 10.1111/jmp.12366

PAPIO SPP. COLON MICROBIOME AND ITS LINK TO OBESITY IN PREGNANCY

XuanJi Li 1, Christopher Rensing 2,3, William L Taylor 4, Caitlin Costelle 4, Asker Daniel Brejnrod 1, Robert J Ferry Jr 5, Paul B Higgins 6, Franco Folli 7, Rao Kottapalli 8, Gene B Hubbard 9, Edward J Dick Jr 6, Shibu Yooseph 3,10, Karen E Nelson 3, Natalia Schlabritz-Loutsevitch 11,*
PMCID: PMC6430121  NIHMSID: NIHMS976861  PMID: 30039863

Abstract

Introduction.

Gut microbial communities are critical players in the pathogenesis of obesity. Pregnancy is associated with increased bacterial load and changes in gut bacterial diversity. Sparse data exist regarding composition of gut microbial communities in obesity combined with pregnancy.

Material and methods.

Banked tissues were collected under sterile conditions during necropsy, from three non-obese (nOb) and four obese (Ob) near-term pregnant baboons. Sequences were assigned taxonomy using the Ribosomal Database Project classifier. Microbiome abundance and its difference between distinct groups were assessed by a nonparametric test.

Results.

Three families predominated in both the nOb and Ob colonic microbiome: Prevotellaceae (25.98% and 32.71% respectively), Ruminococcaceae (12.96% and 7.48%), and Lachnospiraceae (8.78% and 11.74%). Seven families of the colon microbiome displayed differences between Ob and nOb groups.

Conclusion.

Changes in gut microbiome in pregnant obese animals open the venue for dietary manipulation in pregnancy.

INTRODUCTION

Host-bacterial symbiosis is critical for host homeostasis [1, 2]. Obesity is associated with inflammatory responses [3] and serious pathological conditions including nonalcoholic fatty liver disease (NAFLD) [4], type 2 diabetes and insulin resistance [5], and cardiovascular disorders [6, 7]. Dietary composition affects the gut microbiome [8], which in turn is connected closely to metabolic and hormonal regulations [9–12]. The microbiota of obese rodents and humans contain an abnormal ratio of Firmicutes to Bacteroidetes as compared to normal microbiota [10, 13–17]; the taxa Bacteroidetes is increased with weight loss [14, 18], however the reports regarding taxonomic signature of obesity in the human gut are controversial [15]. Different bacterial taxa could influence host metabolism through the production of biologically active metabolites. The prevalence of Lactobacillus (genus within phylum Firmicutes) has a negative correlation with body fat content, and the disbalance of these species can lead to an increase in propionate and acetate which are used for the de novo synthesis of lipids and glucose [16, 18]. As much as 95% of Firmicutes found in the gut belong to class Clostridia, (clusters IV and XIVa) which produce butyrate known to be essential for maintenance and protection of normal colonic epithelium [10, 19], but which can also be utilized for de novo synthesis of lipids or glucose [10, 20]. Production of ethanol by the intestinal bacteria of obese people is thought to link obesity to NAFLD [21]. Obesity in pregnancy is associated with adverse consequences for maternal and offspring health [22, 23].

Baboons (Papio spp.) are a well-characterized animal model for obesity and pregnancy research [24–26]. The advantages of this animal model system include its similarity to humans with respect to placental structure [27, 28] and the ability to control dietary and housing conditions [29]. Microbial gut colonization depends on diet [30], gastrointestinal tract topography [8], host morphology [31] and hormonal status [32]. The fecal microbiome in wild primates generally also varies with the host [33]. Nevertheless, no data are available regarding the microbial landscape of the large intestine in naturally obese and naturally lean pregnant animals. This study sought such data as an essential step to develop strategies targeting the obesity-derived microbiome in pregnancy and aimed to evaluate species-specific obesity-related gut microbiome in pregnancy, addressing also the question of species-specific gut topography.

MATERIALS AND METHODS

Animal housing and handling

Animals were housed in AAALAC-approved facilities and maintained in a group environment. We previously reported details of housing, handling, and dietary composition [34]. Briefly, the animals were fed commercially available LEO5 Monkey diet with the composition identical to Purina 5038 (crude protein not <15%, crude fat not <5%, crude fiber not more than 6%, ash not more than 5%, and added minerals not more than 3% Purina, MO, USA) and the absolute food consumption was increased in the obese animals as described elsewhere [34]. The dietary composition allowed daily consumption of proteins, fat, minerals and vitamins in accordance with the established for non-human primates dietary requirements [29, 35, 36].

All procedures were approved by the Institutional Animal Care and Use Committee (IACUC #1129). Secondary analyses of samples from descending colon (from obese (Ob, n=4) and non-obese (nOb, n=3)), collected during necropsy from near term (165 days of gestation) pregnant baboons, under aseptic conditions, flash-frozen in liquid nitrogen and stored at –80°C [37]. Specifically the sample collection was performed under aseptic conditions: the instruments were sterilized in individual sets for each tissue to be collected; sterile trays were used for weighing of tissues. The investigator was wearing a sterilized, disposable gown and sterilized gloves, the head was covered and surgical face mask was in place. Tissues were not washed.

Animal characteristics

Using a non-human primate specific obesity index [38], we defined obesity in naturally obese animals (i.e., overeating of regular monkey chow). Maternal and fetal morphometry of Ob and nOb animals has been described in details elsewhere [34, 37]. The obesity index in Ob group (n=4) was 48.9 ± 1.4 kg/m2 vs. 39.1 ± 3.2 kg/m2 in nOb group (n=3) (mean ± SEM). During necropsy, we measured lengths of components of the fetal gastrointestinal tract (GIT) as a proxy of adult GIT dimensions, specifically the distance from the cardia to pylorus (stomach), and distances to the ileocecal junction from the pylorus (small intestine) and from anus (large intestine).

DNA extraction and sequencing

DNA extraction was performed using a Qiacube® DNA isolation instrument (Qiagen, Valencia, CA, USA) and the DNeasy® Blood and Tissue Kit (Qiagen, Valencia, CA, USA) the weight of the tissue was 25 mg. Each sample was selectively amplified using the 16S Ion Metagenomics Kit™ (Life Technologies, Thermo Fisher Scientific, USA). Libraries were prepared using the NEBNext® Fast DNA Library Kit (New England Biolabs, Ipswich, MA, USA) and Ion Xpress™ Barcodes from Life Technologies (Thermo Fisher Scientific, Waltham, MA USA). Once the libraries were made, qPCR was performed and samples were pooled. Once pooled, another qPCR was performed against a standard to determine the quantity of the pool needed to prepare spheres for Ion PGM™ System. After the pooled libraries were coupled to spheres, an Ion 314 chip (Thermo Fisher Scientific, Waltham, MA USA) was loaded and run according to protocols provided by the manufacturer for 400bp sequencing, with 30,935 −179,652 16S reads acquired per sample (Table S1). The sequences have been submitted to NCBI: accession numbers are SAMN07287352, SAMN07287353, SAMN07287354, SAMN07287355, SAMN07287356, SAMN07287357 andSAMN07287358.

Trimming and Taxonomy identification

The raw fastq files were trimmed using CLC Genomics workbench (CLC Bio, Qiagen, Valencia, CA, USA). For quality trimming, the Phred quality score (Q) was converted to a base-calling error probability (p) and the default parameters including the limit p value and maximal number of ambiguous nucleotides for quality trimming was maintained. For length trimming, reads below 80bp were discarded. Chimeras were removed using usearch 7.0.1090 against the ‘gold’ database [39]. 592 reads with homopolymers over the length of 8 were discarded. 0.87% of the reads matched the baboon genome [40] (Papio Anubis, NCBI assembly GCF_000264685.1) with bwa were discarded [41]. The Ribosomal Database Project (RDP) classifier (http://rdp.cme.msu.edu) was used to arrange taxonomic identification [42]. All the sequencing information could be found in Table S1.

Statistical analysis

The microbiome abundance and its difference between different colonic groups were further assessed by a nonparametric test through Metastats software (http://metastats.cbcb.umd.edu/detection.html) as described previously [43]. P values were adjusted by false discovery rate (FDR) correction [44]. Sigmaplot 13.0 (Systat Software Inc.) and R software were used for generating the figures, p<0.05 was considered statistically significant. Alpha diversity between the groups was tested by analysis of variance, using the R function “aov” to fit linear model, computing analysis of variance for the fitted model using the function “anova”. Bray-curtis distance coupled with standard multivariate statistical techniques including principal coordinates analysis (PCoA, R function “ordination”, R-package “phyloseq” [45], was used to explain differences among microbial communities and the dissimilarity was examined by permutational multivariate analysis (PERMANOVA, R function “adonis”, R-package “vegan”) [46].

RESULTS

Morphometry

The length of the fetal stomach was 3.3±0.3 cm (Ob) vs. 4.3±0.25 cm (nOb). Lengths of the small intestine (68±7.8 cm vs. 78.5±8.2 cm) and large intestine (32±3.2 cm vs. 34.5±1.2 cm) did not differ between Ob and nOb groups respectively.

Microbial niches

Figure 1 represents the relative abundance of the identified colon microbiome assigned at the phylum (Figure 1 A) and family (Figure 1B) level with the highest occurrence (the alpha diversity and beta diversity curves are presented on Fig 1C and Fig 1D).

Figure 1.

Figure 1.

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The dominant phylum-level (A) and family-level (B) abundance of colonic microbiome: Aa and Ba. non-obese colonic microbiome, Ab and Bb. obese colonic microbiome. Each color represents individual bacterial phylum or family. C. Alpha diversity curves. D. Beta diversity curves.

At the phylum level, Firmicutes and Bacteroidetes were predominant in nOb (Figure 1 Aa) and Ob (Figure 1 Ab) colonic samples, which accounted for 80% of all bacteria detected in the gut, followed by Proteobacteria, Actinobacteria, Spirochaetes, Verrucomicrobia, and samples that had sequences unmatched with those of the database. Colonic Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria abundances were similar in nOb and Ob groups, while Verrucomicrobia and Spirochaetes were different.

At the family level, the predominant families in the nOb colon microbiome (Figure 1 Ba) were Prevotellaceae (25.73%), followed by Ruminococcaceae (12,8%) and Lachnospiraceae (9.1%). For the colon microbiome in the Ob group (Figure 2 Bb), Prevotellaceae (32.86%) was the predominant family followed by the Lachnospiraceae family (12.9%). Among the Firmicutes phylum, the families Veillonellaceae, Ruminococcaceae, and Lactobacillaceae comprised 5.9%, 8.1% and 6.6%, respectively. Obesity resulted in a somewhat but not significantly lower colonic microbial community (Figure 1C). The PCOA plot for seven colonic samples based on Bray-curtis distance is presented on Figure 1D.

Figure 2.

Figure 2.

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The relative abundance of colon microbiome at family level with significant changes (p<0.05) and their change folds (obese (n=4) vs non-obese (n=3) animals).

Comparison of colonic microbiome in obese vs non-obese animals

Family level.

Seven families within the colon microbiome displayed significant changes in the Ob group compared to nOb (*p<0.05,**p<0.01) (Figure 2).

The abundance of one family was increased in the Ob compared to the nOb group: Streptococcaceae, which displayed a 10-fold increase in the Ob group (0.28% vs. 3.003%). The relative abundance of Oligosphaeraceae and Flavobacteriaceae showed the biggest difference, and was approximately 46-fold lesser in the Ob group. Detailed information can be found in the supplementary material (Table S2). Among the seven families, Syntrophaceae demonstrated statistically significant difference assessed by both p and q values (p=0.0003, q=0.017), with 10-fold reduction in the relative abundances in Ob groups (to 0.003%),

Genus level.

Sixteen colonic genera with significant changes between the Ob and nOb groups were identified (*p<0.05,**p<0.01) (Figure 3).

Figure 3.

Figure 3.

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The relative abundance of colon microbiome at genus level with significant changes (p<0.05) and their change folds (obese (n=4) vs non-obese (n=3) animals).

In the Ob group, 13 genera were less abundant (vs. the nOb group), while three genera were more frequent (Peptococcus, Lachnospira, and Streptococcus). Genus Streptococcus increased nearly 11-fold in the Ob group. Relative abundances of Lachnospira and Peptococcus were about 3-fold higher compared to the nOb group. The relative abundance of Oligosphaera was almost 50-fold decreased in Ob group. The genera Saccharofermentans, Subdivision5_genera_incertae_sedis, Fibrobacter, Sphaerochaeta, and Oscillibacter were almost 10-fold increase in the Ob group. Supplementary material presents detailed information (Table S3).

DISCUSSION

Intestinal microbiota are linked to different physiological and pathological conditions. It has been suggested, that the gut microbiome plays an important role in maternal adaptation to pregnancy [47].

Pregnancy is associated with an increased bacterial biomass and changes in gut bacterial β-diversity [48, 49] which may contribute to the epigenetic programming of metabolism. Limited data are available on the composition of gut microbial communities in obesity combined with pregnancy, and on the extent of their commonality with the placental microbiome. Here we present a novel comparison of the colonic microbiomes in Ob and nOb pregnant baboons.

Comparative gut topography

The relative size of the various segments of the gastrointestinal tract for each species depend on dietary composition [50]. Baboons belong to the Cercopithecidae family and consume an omnivorous diet [51]. The diet of baboons in the wild includes leaves, fruits, and other vegetation; wild baboons may occasionally eat meat and also insects, while diet of baboons in captivity differ in the source of nutrients and microbial composition [52–54]. The anatomy and some functional aspects of the gut differ between baboons and anthropoid primates. For example, the size and structure of the cecum and the presence of an appendix vermiformis is characteristic only of apes and humans [55–57]. The appendix of these species is considered to be place of biofilm formation, protecting and preserving the commensal bacteria [58] [59]. The cecum of the Old World non-human primates is shorter relative to the total length of the large intestine, compared to anthropoids and the baboon cecum contains a different biofilm than the human appendix [60]. Interestingly, gut proportions differ between human and other primates, including apes [61]. Humans have longer small intestines [62], whereas chimps and orangutans have larger colons. Savanna baboons might represent an exception to this observation with the similar relative volume of the small intestine in both species [61] (Figure 4). In the fetal baboon near term, we observed that the colon length was 18.6–26.2% of gut length, which is within the range reported for humans [63], where the colon represents 17–23% of total gut volume [64].

Figure 4.

Figure 4.

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Comparative anatomy of digestive tract in humans and baboons (from (61)).

Comparative colon microbiome in obesity

The dominant bacterial phyla in the nOb animals of our study were Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Spirochaetes and Verrucomicrobia. These data agree with reports that the majority of microbiome sequences in the human gut microbiome belong to Firmicutes (65.7%) and Bacteroidetes (16.3%) [65] as well as the universal abundance of gut Firmicutes across mammalian species [66]. However, the total relative abundance of Firmicutes and Bacteroidetes in the baboon gut was smaller than in the human gut. As previously published by Brinkley and Mott [67], anaerobic gut bacteria in the baboon were predominantly Lactobacillus (45%), Eubacterium (16.7%), Streptococcus (enterococci) (11.9%) and Bacteroides (10.3%) [67]. Baboons reported by those authors were housed in the same facility as in the present study and specifically were male pre-pubertal animals on a regular chow diet with 20% saturated fat and 0.4% cholesterol. Interestingly, while Lactobacilli were predominant in baboon vs. human feces, Lactobacilli were sparse in baboon vagina, as compared to human vagina [68]. In feces, obligate anaerobes greatly outnumber the facultative bacteria. In our present study, we did not observe differences in Bacteroides and Firmicutes in Ob animals (vs. nOb). These findings oppose results in humans showing a decrease in Bacteroides in obese compared with lean subjects [10, 15]. In general, studies of various gut microbial families affected by obesity are controversial. One reason could be differences in sampling site. The fecal, luminal, and intestinal wall represent related but potentially distinct anatomic locations, enabling the microbial communities of the gut lumen, colonic mucus layers, and colonic crypts to form “discrete bacterial communities” [8]. The microbiota observed in our study most likely represent combined communities of the mucus layers and crypts.

Recent data confirm microbial biodiversity and also production of certain proteins (e.g., ClpB mimicking hormones) by gut commensal bacteria (e.g., E. coli or Enterobacteriaceae) that influence host appetite regulation and meal pattern by exerting specific biologic actions [11]. Indeed, the intestinal microbiome has been implicated to regulate progression of type 1 diabetes in the NOD model [69]. Transplantation of gut microbiota from normal mice to germ-free mice increased their body fat without any increase in food consumption [70]. The baboons in our study were spontaneously obese; likely due to excess food intake [34]. The major products of microbial carbohydrate fermentation are short chain fatty acids (SCFA: acetate, propionate, butyrate) [71]. SCFA concentrations in baboon colon range from 90–150 nM [66, 71], which is lower than SCFA levels in stools from humans (450–900 nM total) [72], and chimpanzees, or gorillas [66]. These differences could be explained by dietary composition and other variables [73]. The biggest changes in the colon of Ob compared to nOb animals in our study occurred in the reductionof the carbohydrate-fermenting family, Oligosphaeria (Phylum Lentisphaerae). This phylum was also detected in fecal samples of non-human wild primate [33] and might be a marker of nutritional status. Another major family that was reduced in the colon of obese animals was Syntrophaceae – butyrate-fermenting and fatty-acid-metabolizing bacteria [74]. This finding agrees with the results of experimental studies in rodent, showing that SCFAs regulated gut hormones[75] and protected from diet-induced obesity and insulin-resistance [76]. Interestingly, the genus Ruminococcaceae—acetate-producing cellulolytic bacteria described originally in the ruminant and detected in colon of primates [66] —were decreased in the colon of Ob animals in our cohort (Oscillibacter and Pseudoflavonifractor). This observation agrees with decreased Ruminococcaceae and Oscillibacter abundance in obese patients with NAFLD [77] and in a rodent high protein diet model [78]. The abundance of the family Peptococcaceae was greater in the colon of Ob animals. Peptococcaceae species, which produce urease [79], were elevated in a model of intestinal pathology induced by prenatal stress [80], which might link epigenetic mechanisms of prenatal stress to programming of obesity. Importantly, urea transport is decreased in patients with NAFLD [81], thus the increased abundance of Peptococcaceae might represent a physiological adaptation to the obese / dysmetabolic state. The more than 10-fold increase in Lactobacillales (family Streptococcaceae) in Ob animals in our study is remarkable, because anti-streptococcal antibodies have been associated with metabolic disturbances and insulin resistance [82]. Dietary modulation of other Lactobacilli strains has been reported to restore metabolic balance and attenuate inflammatory responses [83, 84], hence might be a target for microbiome-related intervention in pregnancy. One could speculate that the reported obesity-driven changes in the microbial landscape of pregnancy may represent a basis for epigenetic programming of obesity in offspring.

In summary

Reported here, the obesity-driven microbial landscape in pregnant Papio spp. has similar to human microbial changes, despite differences in the diet.

Supplementary Material

Supp TableS1-3

ACKNOWLEDGEMENTS

We acknowledge the help and dedication of the many excellent animal caretakers, technicians, and veterinarians of the Southwest National Primate Center. Fantastic help of Mrs. Felicia Waller in molecular biology experiments is greatly appreciated. We would like to acknowledge the contribution of Ms. Melissa Waggoner in manuscript editing. This investigation was supported by Southwest National Primate Research Center grant P51 RR013986 from the National Center for Research Resources and the National Institutes of Health, which are currently supported by the Office of Research Infrastructure Programs through P51 OD011133. This investigation was conducted in facilities constructed with support from the Office of Research Infrastructure Programs (ORIP) of the National Institutes of Health through grant numbers C06 RR015456 and C06 RR014578. Funding (to N.S-L) for sequencing was provided by the Center for Integrative and Translational Genomics at the University of Tennessee Health Science Center. The research was also supported by a New Investigator (UTHSCSA) grant, Southwest National Primate Center Pilot, UTHSC Center for Integrated and Translational Genomics, and TTUHSC start-up grants to N.S-L., as well as NIH grant HD21350 to Dr. Peter Nathanielsz (UTHSCSA). Authors are thankful Ms. Stacy Martinez (TTUHSC) for performance DNA extraction and PCR and Dr. Y. Kobozev (TTU) for his help in data analyses. Research in the laboratory of C.R was funded by the 100 talents plan of Fujian province.

Footnotes

Authors declared no conflict of interests

REFERENCES

  • 1.Chow J, Lee SM, Shen Y, Khosravi A, Mazmanian SK: Host-bacterial symbiosis in health and disease. Advances in immunology 2010; 107:243–274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Lyte M: Microbial endocrinology in the microbiome-gut-brain axis: how bacterial production and utilization of neurochemicals influence behavior. PLoS pathogens 2013; 9:e1003726. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Milanski M, Degasperi G, Coope A, Morari J, Denis R, Cintra DE, Tsukumo DM, Anhe G, Amaral ME, Takahashi HK, Curi R, Oliveira HC, Carvalheira JB, Bordin S, Saad MJ, Velloso LA: Saturated fatty acids produce an inflammatory response predominantly through the activation of TLR4 signaling in hypothalamus: implications for the pathogenesis of obesity. The Journal of neuroscience : the official journal of the Society for Neuroscience 2009; 29:359–370. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Marino L, Jornayvaz FR: Endocrine causes of nonalcoholic fatty liver disease. World journal of gastroenterology 2015; 21:11053–11076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Jung UJ, Choi MS: Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. International journal of molecular sciences 2014; 15:6184–6223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Leopoldo AS, Sugizaki MM, Lima-Leopoldo AP, do Nascimento AF, Luvizotto Rde A, de Campos DH, Okoshi K, Dal Pai-Silva M, Padovani CR, Cicogna AC: Cardiac remodeling in a rat model of diet-induced obesity. The Canadian journal of cardiology 2010; 26:423–429. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Kosmala W, Jedrzejuk D, Derzhko R, Przewlocka-Kosmala M, Mysiak A, Bednarek-Tupikowska G: Left ventricular function impairment in patients with normal-weight obesity: contribution of abdominal fat deposition, profibrotic state, reduced insulin sensitivity, and proinflammatory activation. Circulation Cardiovascular imaging 2012; 5:349–356. [DOI] [PubMed] [Google Scholar]
  • 8.Donaldson GP, Lee SM, Mazmanian SK: Gut biogeography of the bacterial microbiota. Nature reviews Microbiology 2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G, Jia W, Pettersson S: Host-gut microbiota metabolic interactions. Science (New York, NY) 2012; 336:1262–1267. [DOI] [PubMed] [Google Scholar]
  • 10.Ley RE, Turnbaugh PJ, Klein S, Gordon JI: Microbial ecology: human gut microbes associated with obesity. Nature 2006; 444:1022–1023. [DOI] [PubMed] [Google Scholar]
  • 11.Breton J, Tennoune N, Lucas N, Francois M, Legrand R, Jacquemot J, Goichon A, Guerin C, Peltier J, Pestel-Caron M, Chan P, Vaudry D, do Rego JC, Lienard F, Penicaud L, Fioramonti X, Ebenezer IS, Hokfelt T, Dechelotte P, Fetissov SO: Gut Commensal E. coli Proteins Activate Host Satiety Pathways following Nutrient-Induced Bacterial Growth. Cell metabolism 2015. [DOI] [PubMed] [Google Scholar]
  • 12.Aw W, Fukuda S: Toward the comprehensive understanding of the gut ecosystem via metabolomics-based integrated omics approach. Semin Immunopathol 2015; 37:5–16. [DOI] [PubMed] [Google Scholar]
  • 13.Bell DS: Changes seen in gut bacteria content and distribution with obesity: causation or association? Postgraduate medicine 2015:1–6. [DOI] [PubMed] [Google Scholar]
  • 14.Million M, Lagier JC, Yahav D, Paul M: Gut bacterial microbiota and obesity. Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases 2013; 19:305–313. [DOI] [PubMed] [Google Scholar]
  • 15.Finucane MM, Sharpton TJ, Laurent TJ, Pollard KS: A taxonomic signature of obesity in the microbiome? Getting to the guts of the matter. PloS one 2014; 9:e84689. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Lecomte V, Kaakoush NO, Maloney CA, Raipuria M, Huinao KD, Mitchell HM, Morris MJ: Changes in gut microbiota in rats fed a high fat diet correlate with obesity-associated metabolic parameters. PLoS One 2015; 10:e0126931. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Ley RE: Obesity and the human microbiome. Current opinion in gastroenterology 2010; 26:5–11. [DOI] [PubMed] [Google Scholar]
  • 18.Tagliabue A, Elli M: The role of gut microbiota in human obesity: recent findings and future perspectives. Nutr Metab Cardiovasc Dis 2013; 23:160–168. [DOI] [PubMed] [Google Scholar]
  • 19.Macfarlane GT, Macfarlane S: Fermentation in the human large intestine: its physiologic consequences and the potential contribution of prebiotics. J Clin Gastroenterol 2011; 45 Suppl:S120–127. [DOI] [PubMed] [Google Scholar]
  • 20.Schwiertz A, Taras D, Schafer K, Beijer S, Bos NA, Donus C, Hardt PD: Microbiota and SCFA in lean and overweight healthy subjects. Obesity (Silver Spring) 2010; 18:190–195. [DOI] [PubMed] [Google Scholar]
  • 21.Abu-Shanab A, Quigley EM: The role of the gut microbiota in nonalcoholic fatty liver disease. Nature reviews Gastroenterology & hepatology 2010; 7:691–701. [DOI] [PubMed] [Google Scholar]
  • 22.Gudnadottir TA, Bateman BT, Hernadez-Diaz S, Luque-Fernandez MA, Valdimarsdottir U, Zoega H: Body Mass Index, Smoking and Hypertensive Disorders during Pregnancy: A Population Based Case-Control Study. PloS one 2016; 11:e0152187. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Nicholas LM, Morrison JL, Rattanatray L, Zhang S, Ozanne SE, McMillen IC: The early origins of obesity and insulin resistance: timing, programming and mechanisms. International journal of obesity (2005) 2016; 40:229–238. [DOI] [PubMed] [Google Scholar]
  • 24.Schlabritz-Loutsevitch NE, Moore CM, Lopez-Alvarenga JC, Dunn BG, Dudley D, Hubbard GB: The baboon model (Papio hamadryas) of fetal loss: maternal weight, age, reproductive history and pregnancy outcome. Journal of medical primatology 2008; 37:337–345. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Higgins PB, Bastarrachea RA, Lopez-Alvarenga JC, Garcia-Forey M, Proffitt JM, Voruganti VS, Tejero ME, Mattern V, Haack K, Shade RE, Cole SA, Comuzzie AG: Eight week exposure to a high sugar high fat diet results in adiposity gain and alterations in metabolic biomarkers in baboons (Papio hamadryas sp.). Cardiovascular diabetology 2010; 9:71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Chavez AO, Gastaldelli A, Guardado-Mendoza R, Lopez-Alvarenga JC, Leland MM, Tejero ME, Sorice G, Casiraghi F, Davalli A, Bastarrachea RA, Comuzzie AG, DeFronzo RA, Folli F: Predictive models of insulin resistance derived from simple morphometric and biochemical indices related to obesity and the metabolic syndrome in baboons. Cardiovascular diabetology 2009; 8:22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Carter AM: Animal models of human placentation--a review. Placenta 2007; 28 Suppl A:S41–47. [DOI] [PubMed] [Google Scholar]
  • 28.Carter AM, Enders AC, Pijnenborg R: The role of invasive trophoblast in implantation and placentation of primates. Philosophical transactions of the Royal Society of London Series B, Biological sciences 2015; 370:20140070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Schlabritz-Loutsevitch NE, Howell K, Rice K, Glover EJ, Nevill CH, Jenkins SL, Bill Cummins L, Frost PA, McDonald TJ, Nathanielsz PW: Development of a system for individual feeding of baboons maintained in an outdoor group social environment. Journal of medical primatology 2004; 33:117–126. [DOI] [PubMed] [Google Scholar]
  • 30.Beasley DE, Koltz AM, Lambert JE, Fierer N, Dunn RR: The Evolution of Stomach Acidity and Its Relevance to the Human Microbiome. PloS one 2015; 10:e0134116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Ley RE, Lozupone CA, Hamady M, Knight R, Gordon JI: Worlds within worlds: evolution of the vertebrate gut microbiota. Nature reviews Microbiology 2008; 6:776–788. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Guzman IJ, Fitch LL, Varco RL, Buchwald H: Small bowel length in hyperlipidemia and massive obesity. The American journal of clinical nutrition 1977; 30:1006–1008. [DOI] [PubMed] [Google Scholar]
  • 33.Yildirim S, Yeoman CJ, Sipos M, Torralba M, Wilson BA, Goldberg TL, Stumpf RM, Leigh SR, White BA, Nelson KE: Characterization of the fecal microbiome from non-human wild primates reveals species specific microbial communities. PloS one 2010; 5:e13963. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Schlabritz-Loutsevitch NE, Comuzzie AG, Mahaney MM, Hubbard GB, Dick EJ, Kocak M, Gupta S, Carrillo M, Schenone M, Postlethwaite A, Slominski A: Serum Vitamin D Concentrations in Baboons (Papio spp.) during Pregnancy and Obesity. Comparative medicine 2016; 66:137–142. [PMC free article] [PubMed] [Google Scholar]
  • 35.Nicolosi RaH, RD: Dietary allowance for nutrients in nonhuman primates. In: Primates in Nutritional Research,. Hayes: (ed). Academic Press, Inc; 11–37, 1979. [Google Scholar]
  • 36.Schlabritz-Loutsevitch NE, Dudley CJ, Gomez JJ, Nevill CH, Smith BK, Jenkins SL, McDonald TJ, Bartlett TQ, Nathanielsz PW, Nijland MJ: Metabolic adjustments to moderate maternal nutrient restriction. The British journal of nutrition 2007; 98:276–284. [DOI] [PubMed] [Google Scholar]
  • 37.Farley D, Tejero ME, Comuzzie AG, Higgins PB, Cox L, Werner SL, Jenkins SL, Li C, Choi J, Dick EJ Jr., Hubbard GB, Frost P, Dudley DJ, Ballesteros B, Wu G, Nathanielsz PW, Schlabritz-Loutsevitch NE: Feto-placental adaptations to maternal obesity in the baboon. Placenta 2009; 30:752–760. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Jen KL, Hansen BC, Metzger BL: Adiposity, anthropometric measures, and plasma insulin levels of rhesus monkeys. International journal of obesity 1985; 9:213–224. [PubMed] [Google Scholar]
  • 39.Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R: UCHIME improves sensitivity and speed of chimera detection. Bioinformatics (Oxford, England) 2011; 27:2194–2200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Haas BJ, Gevers D, Earl AM, Feldgarden M, Ward DV, Giannoukos G, Ciulla D, Tabbaa D, Highlander SK, Sodergren E, Methe B, DeSantis TZ, Petrosino JF, Knight R, Birren BW: Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome research 2011; 21:494–504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Li H: Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM arXiv:13033997 2013. [Google Scholar]
  • 42.Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM: Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic acids research 2014; 42:D633–642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.White JR, Nagarajan N, Pop M: Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS computational biology 2009; 5:e1000352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Benjamini YaH, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Series B (Methodological), Journal of the Royal Statistical Society 1995; 1:289–300. [Google Scholar]
  • 45.McMurdie PJ, Holmes S: phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PloS one 2013; 8:e61217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Jari Oksanen RK, Legendre Pierre, O’Hara Bob, Henry M Stevens H: The vegan package. Community ecology package 2007; 10:631–637. [Google Scholar]
  • 47.Zhang D, Huang Y, Ye D: Intestinal dysbiosis: an emerging cause of pregnancy complications? Medical hypotheses 2015; 84:223–226. [DOI] [PubMed] [Google Scholar]
  • 48.Koren O, Knights D, Gonzalez A, Waldron L, Segata N, Knight R, Huttenhower C, Ley RE: A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets. PLoS computational biology 2013; 9:e1002863. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Collado MC, Isolauri E, Laitinen K, Salminen S: Distinct composition of gut microbiota during pregnancy in overweight and normal-weight women. The American journal of clinical nutrition 2008; 88:894–899. [DOI] [PubMed] [Google Scholar]
  • 50.Leigh SR: Ontogenetic correlates of diet in anthropoid primates. American journal of physical anthropology 1994; 94:499–522. [DOI] [PubMed] [Google Scholar]
  • 51.Washburn RSOH SL: THE SELECTION OF NONHUMAN PRIMATES IN BIOLOGICAL RESEARCH. In: Feeding and Nutrition of Nonhuman Primates Harris (ed). ACADEMIC PRESS, INC. Published by Elsevier Inc. 1–13, 1970. [Google Scholar]
  • 52.Harding RSO, and Strum SC.: The predatory baboons of Kekopey. Nat Hist 1976; 85: 46–53. [Google Scholar]
  • 53.Ren T, Grieneisen LE, Alberts SC, Archie EA, Wu M: Development, diet and dynamism: longitudinal and cross-sectional predictors of gut microbial communities in wild baboons. Environmental microbiology 2016; 18:1312–1325. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Lugano KAN SD, Kariuki WK, Kamau Joseph KS, Apondi OJ: Gastrointestinal Microbial Flora inWild and Captive Olive Baboons (Papio anubis). American Journal of Infectious Diseases and Microbiology 2018; 6:30–37. [Google Scholar]
  • 55.Scott GB: The primate caecum and appendix vermiformis: a comparative study. Journal of anatomy 1980; 131:549–563. [PMC free article] [PubMed] [Google Scholar]
  • 56.Fisher RE: The primate appendix: a reassessment. The Anatomical record 2000; 261:228–236. [DOI] [PubMed] [Google Scholar]
  • 57.Smith HF, Fisher RE, Everett ML, Thomas AD, Bollinger RR, Parker W: Comparative anatomy and phylogenetic distribution of the mammalian cecal appendix. Journal of evolutionary biology 2009; 22:1984–1999. [DOI] [PubMed] [Google Scholar]
  • 58.Sarkar A, Saha A, Roy S, Pathak S, Mandal S: A glimpse towards the vestigiality and fate of human vermiform appendix-a histomorphometric study. Journal of clinical and diagnostic research : JCDR 2015; 9:Ac11–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Randal Bollinger R, Barbas AS, Bush EL, Lin SS, Parker W: Biofilms in the large bowel suggest an apparent function of the human vermiform appendix. Journal of theoretical biology 2007; 249:826–831. [DOI] [PubMed] [Google Scholar]
  • 60.Palestrant D, Holzknecht ZE, Collins BH, Parker W, Miller SE, Bollinger RR: Microbial biofilms in the gut: visualization by electron microscopy and by acridine orange staining. Ultrastructural pathology 2004; 28:23–27. [PubMed] [Google Scholar]
  • 61.K M: Primate diets and gut morphology: implications for hominid evolution. In: Food and Evolution: Toward a Theory of Food Habits Harris M (ed). Temple University Press; 93–115., 1987. [Google Scholar]
  • 62.Weaver LT, Austin S, Cole TJ: Small intestinal length: a factor essential for gut adaptation. Gut 1991; 32:1321–1323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.FitzSimmons J, Chinn A, Shepard TH: Normal length of the human fetal gastrointestinal tract. Pediatric pathology / affiliated with the International Paediatric Pathology Association 1988; 8:633–641. [DOI] [PubMed] [Google Scholar]
  • 64.Hume CESaID: Comparative physiology of the vertebrate digestive system Cambridge: Cambridge University Press, 1995. [Google Scholar]
  • 65.Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS, Schlegel ML, Tucker TA, Schrenzel MD, Knight R, Gordon JI: Evolution of mammals and their gut microbes. Science (New York, NY) 2008; 320:1647–1651. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.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). Integrative zoology 2014; 9:557–569. [DOI] [PubMed] [Google Scholar]
  • 67.Brinkley AW, Mott GE: Anaerobic fecal bacteria of the baboon. Applied and environmental microbiology 1978; 36:530–532. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Natalia Schlabritz-Loutsevitch SG, Dick Edward Jr., Smith William L., Snider Cathy, Hubbard Gene and Ventolini Gary: VAGINAL DYSBIOSIS FROM AN EVOLUTIONARY PERSPECTIVE. Scientific reports 2016; accepted. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Burrows MP, Volchkov P, Kobayashi KS, Chervonsky AV: Microbiota regulates type 1 diabetes through Toll-like receptors. Proceedings of the National Academy of Sciences of the United States of America 2015; 112:9973–9977. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI: Obesity alters gut microbial ecology. Proceedings of the National Academy of Sciences of the United States of America 2005; 102:11070–11075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Stuek GBaK: Short-chain fatty acids in hindgut. In: Physiological and clinical aspects of short chain fatty acids Cummins John H. (ed). Cambridge University Press; 73–86, 1995. [Google Scholar]
  • 72.Collin DP, McCormick PG: Determination of short-chain fatty acids in stool ultrafiltrate and urine. Clinical chemistry 1974; 20:1173–1180. [PubMed] [Google Scholar]
  • 73.Frost GS, Walton GE, Swann JR, Psichas A, Costabile A, Johnson LP, Sponheimer M, Gibson GR, Barraclough TG: Impacts of plant-based foods in ancestral hominin diets on the metabolism and function of gut microbiota in vitro. mBio 2014; 5:e00853–00814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Struchtemeyer CG, Duncan KE, McInerney MJ: Evidence for syntrophic butyrate metabolism under sulfate-reducing conditions in a hydrocarbon-contaminated aquifer. FEMS microbiology ecology 2011; 76:289–300. [DOI] [PubMed] [Google Scholar]
  • 75.Lin HV, Frassetto A, Kowalik EJ Jr., Nawrocki AR, Lu MM, Kosinski JR, Hubert JA, Szeto D, Yao X, Forrest G, Marsh DJ: Butyrate and propionate protect against diet-induced obesity and regulate gut hormones via free fatty acid receptor 3-independent mechanisms. PloS one 2012; 7:e35240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Lachmandas E, van den Heuvel CN, Damen MS, Cleophas MC, Netea MG, van Crevel R: Diabetes Mellitus and Increased Tuberculosis Susceptibility: The Role of Short-Chain Fatty Acids. Journal of diabetes research 2016; 2016:6014631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Raman M, Ahmed I, Gillevet PM, Probert CS, Ratcliffe NM, Smith S, Greenwood R, Sikaroodi M, Lam V, Crotty P, Bailey J, Myers RP, Rioux KP: Fecal microbiome and volatile organic compound metabolome in obese humans with nonalcoholic fatty liver disease. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association 2013; 11:868–875.e861–863. [DOI] [PubMed] [Google Scholar]
  • 78.Kim E, Kim DB, Park JY: Changes of Mouse Gut Microbiota Diversity and Composition by Modulating Dietary Protein and Carbohydrate Contents: A Pilot Study. Preventive nutrition and food science 2016; 21:57–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Suzuki K, Benno Y, Mitsuoka T, Takebe S, Kobashi K, Hase J: Urease-producing species of intestinal anaerobes and their activities. Applied and environmental microbiology 1979; 37:379–382. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Golubeva AV, Crampton S, Desbonnet L, Edge D, O’Sullivan O, Lomasney KW, Zhdanov AV, Crispie F, Moloney RD, Borre YE, Cotter PD, Hyland NP, O’Halloran KD, Dinan TG, O’Keeffe GW, Cryan JF: Prenatal stress-induced alterations in major physiological systems correlate with gut microbiota composition in adulthood. Psychoneuroendocrinology 2015; 60:58–74. [DOI] [PubMed] [Google Scholar]
  • 81.Michail S, Lin M, Frey MR, Fanter R, Paliy O, Hilbush B, Reo NV: Altered gut microbial energy and metabolism in children with non-alcoholic fatty liver disease. FEMS microbiology ecology 2015; 91:1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Aran A, Lin L, Finn LA, Weiner K, Peppard P, Young T, Mignot E: Post-streptococcal antibodies are associated with metabolic syndrome in a population-based cohort. PloS one 2011; 6:e25017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Ukibe K, Miyoshi M, Kadooka Y: Administration of Lactobacillus gasseri SBT2055 suppresses macrophage infiltration into adipose tissue in diet-induced obese mice. The British journal of nutrition 2015; 114:1180–1187. [DOI] [PubMed] [Google Scholar]
  • 84.Ting WJ, Kuo WW, Kuo CH, Yeh YL, Shen CY, Chen YH, Ho TJ, Viswanadha VP, Chen YH, Huang CY: Supplementary heat-killed Lactobacillus reuteri GMNL-263 ameliorates hyperlipidaemic and cardiac apoptosis in high-fat diet-fed hamsters to maintain cardiovascular function. The British journal of nutrition 2015; 114:706–712. [DOI] [PubMed] [Google Scholar]

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