472731 Environmental Perturbations Lead to Correlated Changes in Cecum Microbiota Composition and Metabolite Profile

Thursday, November 17, 2016: 9:24 AM
Continental 8 (Hilton San Francisco Union Square)
Sara Manteiga1, Rani Menon2, Smitha Krishnan1, Arul Jayaraman2 and Kyongbum Lee1, (1)Department of Chemical and Biological Engineering, Tufts University, Medford, MA, (2)Department of Chemical Engineering, Texas A&M University, College Station, TX

The adult mammalian gastrointestinal (GI) tract is colonized by ~1014 bacteria belonging to at least several hundred species, which collectively outnumber the genes of their host by ~100-fold. This gut microbiota impacts an array of physiological functions in the GI tract. For example, it participates in the digestion of complex carbohydrates. Experiments comparing germ-free (GF) and conventionally raised (CONV-R) mice have shown that the gut microbiota dramatically impacts the level and diversity of bile acids [1]. A significant alteration in the microbial populations, or dysbiosis, correlates with not only GI diseases, but also other chronic diseases such as cancer and asthma. Dysbiosis also correlates with alterations in the profile of metabolites in the intestine and systemic circulation. These metabolites can act as signaling molecules to engage various host receptors and regulatory molecules [2, 3]; however, specific functions have been identified for only a few of these metabolites [4]. There is a pressing need to characterize the metabolic functions of the microbiota and better understand how these functions depend on microbiota composition.

A major challenge in resolving the enzymatic pathways and organisms responsible for particular metabolic functions lies in the complexity of interactions between the host and microbiota as well as interactions within the microbiota community. Thus, experimentally tractable model systems are needed to characterize these chemicals and how these chemicals depend on the microbiota composition. Gnotobiotic mice are powerful animal models that capture physiological interactions between the host and microbiota species, but the in vivo setting limits the ability to manipulate microbiota functions without also perturbing the balance of resident commensal microbes. Cultures of individual species are useful for determining whether a species can carry out a particular metabolic function, but do not capture community-level interactions. More advanced in vitro systems such as mixed cultures of microbiota isolates can preserve these interactions, while also offering control over the environment and stimuli. However, it remains to be seen whether these systems can faithfully capture the microbiota composition and chemical profile.

In this study, we describe a simple and robust model system in the form of an anaerobic batch culturing system capable of representatively culturing the microbiome isolated from the cecum. We use a multi-omic approach to validate this system by profiling the community composition and metabolites present in the cecum culture. The metabolic products of cecum culture are also compared against the differences in metabolite profiles between the cecum contents isolated from CONV-R and GF mice. We then utilize the system to investigate the impact of an environmental perturbation from xenobiotic chemical exposure.

Results from 16S rRNA sequencing analysis show that the cecum culture community composition mimics the microbiome structure in vivo. At the phylum level, the Operational Taxonomic Units (OTUs) present in the cultured microbiota exactly match the cecal microbiota freshly isolated from 6 week old CONV-R C57BL/6 mice. At the species levels, the similarity is approximately 75%. The major phyla detected in the culture samples were Bacteroidetes, Firmicutes and Proteobacteria. While there were differences in the abundance of individual groups like the Clostridia, this did not affect the representation of the communities in the culture, and all the major genus reported previously from the fecal samples in other studies [5] were represented. The freshly isolated and cultured microbiota also show similar diversity based on inverse Simson diversity index analysis (0.9 for CONV-R cecal contents and 0.8 for culture), Shannon diversity index (2.8 and 2.0) and Schao1 scores (156 and 113).

Using an untargeted metabolomics, we characterized the time evolution of metabolite profiles in the cecal culture. Samples were collected on day 1 and 7 of the culture and extracted using a solvent based method. Metabolites were analyzed on a high-resolution time-of-flight (TOF) mass analyzer operated in an information dependent acquisition (IDA) mode. Clustering the detected features (precursor ion m/z) show distinct profiles for metabolites that are produced and consumed by the microbiota. Based on their time profiles, the features clustered into four groups: rapidly consumed (depleted in 1 day), slowly consumed (depleted over 7 days), rapidly produced (significantly elevated in 1 day), slowly produced (significantly elevated after 7 days). The levels of these features in the culture medium remain unchanged when the culture is not inoculated with the cecal isolates, confirming that the changes are due to microbial metabolism. Features that were significantly elevated or depleted compared to medium control (without inoculation) were identified using one-way ANOVA (p < 0.05) and annotated using MS/MS libraries or an in silico fragmentation tool. Approximately one-third of the metabolites significantly elevated in the cecum culture were significantly depleted in GF mouse compared to CONV-R mouse, suggesting that these microbiota metabolites are also produced in vivo.

We next perturbed the cecum culture using a pair of representative environmental chemicals – bisphenol A (BPA, 1 and 10 uM) and diethylhexyl phthalate (DEHP, 10 and 100 uM) – to investigate the dependence of the metabolite profile on culture community composition. Metabolomics analysis showed that both BPA and DEHP brings about significant changes in the metabolite profile in a dose-dependent manner. Using hierarchical clustering, we identified groups of features that were significantly altered in the DEHP or BPA condition compared to vehicle control. Out of 1,044 metabolic products detected in our metabolomics analysis, 122 and 176 were significantly altered with DEHP and BPA treatment, respectively. Fifty-seven of the significantly altered metabolites were common to both BPA and DEHP treatment. These metabolite profile alterations correlated with significant shifts in the culture community composition. Interestingly, the compositional changes were not evident at the phylum level. Rather, significant changes were detected at family, genus and species levels.

In conclusion, our results to date show that the anaerobic batch culture system mimics the microbial community composition expected in the cecum in vivo, and can capture significant correlations in the structural and functional changes resulting from an external perturbation. Ongoing work investigates the culture system’s response to an endogenous hormone, and compares this response to in vivo trends to further establish the culture system as a robust model of the murine cecal microbiota.


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2. Laparra, J.M, Sanz Y. Interactions of gut microbiota with functional food components and nutraceuticals. Pharmacol Res. 2010;61:219-25.

3. Sanz Y, De Palma G. Gut microbiota and probiotics in modulation of epithelium and gut-associated lymphoid tissue function. Int Rev Immunol. 2009;28:397-413.

4. Vogt SL, Pena-Diaz J, Finlay BB. Chemical communication in the gut: Effects of microbiota-generated metabolites on gastrointestinal bacterial pathogens. Anaerobe. 2015;34:106-15. doi: 10.1016/j.anaerobe.2015.05.002. PubMed PMID: 25958185.

5. Goodman AL, Kallstrom G, Faith JJ, Reyes A, Moore A, Dantas G, et al. Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proceedings of the National Academy of Sciences of the United States of America. 2011;108(15):6252-7. Epub 2011/03/26. doi: 10.1073/pnas.1102938108. PubMed PMID: 21436049; PubMed Central PMCID: PMC3076821.

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