Allison Perrotta

Host lifestyle affects human microbiota on daily timescales

BackgroundDisturbance to human microbiota may underlie several pathologies. Yet, we lack a comprehensive understanding of how lifestyle affects the dynamics of human-associated microbial communities.ResultsHere, we link over 10,000 longitudinal measurements of human wellness and action to the daily gut and salivary microbiota dynamics of two individuals over the course of one year. These time series show overall microbial communities to be stable for months. However, rare events in each subjects’ life rapidly and broadly impacted microbiota dynamics. Travel from the developed to the developing world in one subject led to a nearly two-fold increase in the Bacteroidetes to Firmicutes ratio, which reversed upon return. Enteric infection in the other subject resulted in the permanent decline of most gut bacterial taxa, which were replaced by genetically similar species. Still, even during periods of overall community stability, the dynamics of select microbial taxa could be associated with specific host behaviors. Most prominently, changes in host fiber intake positively correlated with next-day abundance changes among 15% of gut microbiota members.ConclusionsOur findings suggest that although human-associated microbial communities are generally stable, they can be quickly and profoundly altered by common human actions and experiences.

Distribution-Based Clustering: Using Ecology To Refine the Operational Taxonomic Unit

ABSTRACT 16S rRNA sequencing, commonly used to survey microbial communities, begins by grouping individual reads into operational taxonomic units (OTUs). There are two major challenges in calling OTUs: identifying bacterial population boundaries and differentiating true diversity from sequencing errors. Current approaches to identifying taxonomic groups or eliminating sequencing errors rely on sequence data alone, but both of these activities could be informed by the distribution of sequences across samples. Here, we show that using the distribution of sequences across samples can help identify population boundaries even in noisy sequence data. The logic underlying our approach is that bacteria in different populations will often be highly correlated in their abundance across different samples. Conversely, 16S rRNA sequences derived from the same population, whether slightly different copies in the same organism, variation of the 16S rRNA gene within a population, or sequences generated randomly in error, will have the same underlying distribution across sampled environments. We present a simple OTU-calling algorithm (distribution-based clustering) that uses both genetic distance and the distribution of sequences across samples and demonstrate that it is more accurate than other methods at grouping reads into OTUs in a mock community. Distribution-based clustering also performs well on environmental samples: it is sensitive enough to differentiate between OTUs that differ by a single base pair yet predicts fewer overall OTUs than most other methods. The program can decrease the total number of OTUs with redundant information and improve the power of many downstream analyses to describe biologically relevant trends.

Computational Methods for High-Throughput Comparative Analyses of Natural Microbial Communities

One of the most widely employed methods in metagenomics is the amplification and sequencing of the highly conserved ribosomal RNA (rRNA) genes from organisms in complex microbial communities. rRNA surveys, typically using the 16S rRNA gene for prokaryotic identification, provide information about the total diversity and taxonomic affiliation of organisms present in a sample. Greatly enhanced by high-throughput sequencing, these surveys have uncovered the remarkable diversity of uncultured organisms and revealed unappreciated ecological roles ranging from nutrient cycling to human health. This chapter outlines the best practices for comparative analyses of microbial community surveys. We explain how to transform raw data into meaningful units for further analysis and discuss how to calculate sample diversity and community distance metrics. Finally, we outline how to find associations of species with specific metadata and true correlations between species from compositional data. We focus on data generated by next-generation sequencing platforms, using the Illumina platform as a test case, because of its widespread use especially among researchers just entering the field.

Profiling Living Bacteria Informs Preparation of Fecal Microbiota Transplantations.

Fecal microbiota transplantation is a compelling treatment for recurrent Clostridium difficile infections, with potential applications against other diseases associated with changes in gut microbiota. But variability in fecal bacterial communities—believed to be the therapeutic agent—can complicate or undermine treatment efficacy. To understand the effects of transplant preparation methods on living fecal microbial communities, we applied a DNA-sequencing method (PMA-seq) that uses propidium monoazide (PMA) to differentiate between living and dead fecal microbes, and we created an analysis pipeline to identify individual bacteria that change in abundance between samples. We found that oxygen exposure degraded fecal bacterial communities, whereas freeze-thaw cycles and lag time between donor defecation and transplant preparation had much smaller effects. Notably, the abundance of Faecalibacterium prausnitzii—an anti-inflammatory commensal bacterium whose absence is linked to inflammatory bowel disease—decreased with oxygen exposure. Our results indicate that some current practices for preparing microbiota transplant material adversely affect living fecal microbial content and highlight PMA-seq as a valuable tool to inform best practices and evaluate the suitability of clinical fecal material.

Sa1064 The International Public Stool Bank: A Scalable Model for Standardized Screening and Processing of Donor Stool for Fecal Microbiota Transplantation

the inpatient quality indicators of length of stay (LOS), 30-day re-admission rates, and mortality for patients admitted to our academic medical center for gastrointestinal bleeding. Methods: We retrospectively evaluated admissions for gastrointestinal bleeding based on ICD-9-CM codes from May 2011-June 2013 at the University of Miami Hospital (Miami, FL). The patients were primarily managed by one of four types of inpatient service as follows: GI teaching service, academic medicine teaching service, private hospitalist, or surgical service. Patients initially admitted to an intensive care unit from the emergency department were excluded. To account for potential differences in severity of GI bleeding between services, patients were matched by propensity score based on their age, location of bleed (upper GI bleed vs lower GI bleed), and whether the bleed was secondary to portal hypertension. Results: 600 hospital admissions for GI bleeding were included (GI 84, academic 44, private 436, surgical 36). No difference was noted in the mean LOS among groups (GI 4.72 days, academic 5.56 days, private 5.96 days, and surgeons 6 days, ANOVA F=1.44, p= 0.23). No difference was observed in the 30 day re-admission rate (GI 1.85%, academic 5.13%, private 4.51%, surgeons 3.03%, Pearsons chi-squared 1.03, p=0.79). No difference was seen in the in-hospital mortality rate (GI 1.89%, academic 2.56%, private 2.13%, surgeons 0%, Pearsons chi-squared 0.77, p=0.86). Following propensity score matching, 491 patients were evaluated. No differences were noted in LOS (ANOVA, F=2.95, p= 0.053) or re-admission rates (LR Chi-squared 1.64, p=0.44) among groups. Conclusion: No significant difference was observed in inpatient quality indicators for a GI specialty service at an academic center. However, small numbers may have prevented these values from reaching significance as trends were observed favoring the GI team with LOS and readmission, making a larger sample size and a cost evaluation important next steps in this evaluation process.

Inoculum composition determines microbial community and function in an anaerobic sequential batch reactor

The sustainable recovery of resources from wastewater streams can provide many social and environmental benefits. A common strategy to recover valuable resources from wastewater is to harness the products of fermentation by complex microbial communities. In these fermentation bioreactors high microbial community diversity within the inoculum source is commonly assumed as sufficient for the selection of a functional microbial community. However, variability of the product profile obtained from these bioreactors is a persistent challenge in this field. In an attempt to address this variability, the impact of inoculum on the microbial community structure and function within the bioreactor was evaluated using controlled laboratory experiments. In the course of this work, sequential batch reactors were inoculated with three complex microbial inocula and the chemical and microbial compositions were monitored by HPLC and 16S rRNA amplicon analysis, respectively. Microbial community dynamics and chemical profiles were found to be distinct to initial inoculate and highly reproducible. Additionally we found that the generation of a complex volatile fatty acid profile was not specific to the diversity of the initial microbial inoculum. Our results suggest that the composition of the original inoculum predictably contributes to bioreactor community structure and function.

Erratum to: Host lifestyle affects human microbiota on daily timescales.

As a result of a production error during the type-setting of the final version of the article [1], a number of additional files were incorrectly published, with the files not matching the Additional Files legends. All additional files for this article are republished below in the correct order. The publisher apologizes for the error and any confusion caused.