Vanessa L. Hale

Effect of preservation method on spider monkey (Ateles geoffroyi) fecal microbiota over 8 weeks

Studies of the gut microbiome have become increasingly common with recent technological advances. Gut microbes play an important role in human and animal health, and gut microbiome analysis holds great potential for evaluating health in wildlife, as microbiota can be assessed from non-invasively collected fecal samples. However, many common fecal preservation protocols (e.g. freezing at -80 °C) are not suitable for field conditions, or have not been tested for long-term (greater than 2 weeks) storage. In this study, we collected fresh fecal samples from captive spider monkeys (Ateles geoffroyi) at the Columbian Park Zoo (Lafayette, IN, USA). The samples were pooled, homogenized, and preserved for up to 8 weeks prior to DNA extraction and sequencing. Preservation methods included: freezing at -20 °C, freezing at -80 °C, immersion in 100% ethanol, application to FTA cards, and immersion in RNAlater. At 0 (fresh), 1, 2, 4, and 8 weeks from fecal collection, DNA was extracted and microbial DNA was amplified and sequenced. DNA concentration, purity, microbial diversity, and microbial composition were compared across all methods and time points. DNA concentration and purity did not correlate with microbial diversity or composition. Microbial composition of frozen and ethanol samples were most similar to fresh samples. FTA card and RNAlater-preserved samples had the least similar microbial composition and abundance compared to fresh samples. Microbial composition and diversity were relatively stable over time within each preservation method. Based on these results, if freezers are not available, we recommend preserving fecal samples in ethanol (for up to 8weeks) prior to microbial extraction and analysis.

Shifts in the fecal microbiota associated with adenomatous polyps

Background: Adenomatous polyps are the most common precursor to colorectal cancer, the second leading cause of cancer-related death in the United States. We sought to learn more about early events of carcinogenesis by investigating shifts in the gut microbiota of patients with adenomas. Methods: We analyzed 16S rRNA gene sequences from the fecal microbiota of patients with adenomas (n = 233) and without (n = 547). Results: Multiple taxa were significantly more abundant in patients with adenomas, including Bilophila, Desulfovibrio, proinflammatory bacteria in the genus Mogibacterium, and multiple Bacteroidetes species. Patients without adenomas had greater abundances of Veillonella, Firmicutes (Order Clostridia), and Actinobacteria (family Bifidobacteriales). Our findings were consistent with previously reported shifts in the gut microbiota of colorectal cancer patients. Importantly, the altered adenoma profile is predicted to increase primary and secondary bile acid production, as well as starch, sucrose, lipid, and phenylpropanoid metabolism. Conclusions: These data hint that increased sugar, protein, and lipid metabolism along with increased bile acid production could promote a colonic environment that supports the growth of bile-tolerant microbes such as Bilophilia and Desulfovibrio. In turn, these microbes may produce genotoxic or inflammatory metabolites such as H2S and secondary bile acids, which could play a role in catalyzing adenoma development and eventually colorectal cancer. Impact: This study suggests a plausible biological mechanism to explain the links between shifts in the microbiota and colorectal cancer. This represents a first step toward resolving the complex interactions that shape the adenoma–carcinoma sequence of colorectal cancer and may facilitate personalized therapeutics focused on the microbiota. Cancer Epidemiol Biomarkers Prev; 26(1); 85–94. ©2016 AACR.

Effects of field conditions on fecal microbiota

Gut microbiota can provide great insight into host health, and studies of the gut microbiota in wildlife are becoming more common. However, the effects of field conditions on gut microbial samples are unknown. This study addresses the following questions: 1) How do environmental factors such as sunlight and insect infestations affect fecal microbial DNA? 2) How does fecal microbial DNA change over time after defecation? 3) How does storage method affect microbial DNA? Fresh fecal samples were collected, pooled, and homogenized from a family group of 6 spider monkeys, Ateles geoffroyi. Samples were then aliquoted and subjected to varying light conditions (shade, sun), insect infestations (limited or not limited by netting over the sample), and sample preservation methods (FTA - Fast Technology for Analysis of nucleic acid - cards, or freezing in liquid nitrogen then storing at -20°C). Changes in the microbial communities under these conditions were assessed over 24h. Time and preservation method both effected fecal microbial community diversity and composition. The effect size of these variables was then assessed in relation to fecal microbial samples from 2 other primate species (Rhinopithecus bieti and R. brelichi) housed at different captive institutions. While the microbial community of each primate species was significantly different, the effects of time and preservation method still remained significant indicating that these effects are important considerations for fieldwork.

Metabolic modeling with Big Data and the gut microbiome

The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework – or metabolic model – on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.

Erratum to: Diet Versus Phylogeny: a Comparison of Gut Microbiota in Captive Colobine Monkey Species

1 Microbiome Program, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA 2 LVDI International, San Marcos, CA 92078, USA 3 Fanjingshan National Nature Reserve Administration, Tongren, China 4 Department of Life Sciences and Systems Biology, University of Turin, 10123 Turin, Italy 5 Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA 6 Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA 7 Zhejiang Institute of Microbiology, Hangzhou, Zhejiang, China 8 Beijing Key Laboratory of Captive Wildlife Technologies, Beijing Zoo, Beijing 100044, China 9 Department of Anthropology, Northwestern University, Evanston, IL 60208, USA Microb Ecol (2018) 75:528 DOI 10.1007/s00248-017-1070-3

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.

Clostridioides difficile uses amino acids associated with gut microbial dysbiosis in a subset of patients with diarrhea

Increased amino acids in the dysbiotic gut influences susceptibility to Clostridioides difficile infection in mice and humans. To infect or not to infect? Our gut harbors a diverse microbial community that efficiently uses nutrients. Battaglioli et al. now report that a subset of patients with diarrhea show increased availability of gut amino acids due to deleterious changes in the gut microbiota (dysbiosis). These dysbiotic microbial communities when modeled in germ-free mice exhibited increased susceptibility to Clostridioides difficile, a pathogen that uses amino acids as a nutrient source. Prophylactic fecal microbiota transplant from healthy humans to mice with a dysbiotic gut microbiota restored microbial diversity and protected the mice from C. difficile infection. The gut microbiota plays a critical role in pathogen defense. Studies using antibiotic-treated mice reveal mechanisms that increase susceptibility to Clostridioides difficile infection (CDI), but risk factors associated with CDI in humans extend beyond antibiotic use. Here, we studied the dysbiotic gut microbiota of a subset of patients with diarrhea and modeled the gut microbiota of these patients by fecal transplantation into germ-free mice. When challenged with C. difficile, the germ-free mice transplanted with fecal samples from patients with dysbiotic microbial communities showed increased gut amino acid concentrations and greater susceptibility to CDI. A C. difficile mutant that was unable to use proline as an energy source was unable to robustly infect germ-free mice transplanted with a dysbiotic or healthy human gut microbiota. Prophylactic dietary intervention using a low-proline or low-protein diet in germ-free mice colonized by a dysbiotic human gut microbiota resulted in decreased expansion of wild-type C. difficile after challenge, suggesting that amino acid availability might be important for CDI. Furthermore, a prophylactic fecal microbiota transplant in mice with dysbiosis reduced proline availability and protected the mice from CDI. Last, we identified clinical risk factors that could potentially predict gut microbial dysbiosis and thus greater susceptibility to CDI in a retrospective cohort of patients with diarrhea. Identifying at-risk individuals and reducing their susceptibility to CDI through gut microbiota–targeted therapies could be a new approach to preventing C. difficile infection in susceptible patients.