Characterization of wild and captive baboon gut microbiota and their antibiotic resistomes

Antibiotic exposure results in acute and persistent shifts in the composition and function of microbial communities associated with vertebrate hosts. However, little is known about the state of these communities in the era before the widespread introduction of antibiotics into clinical and agricultural practice. We characterized the fecal microbiota and antibiotic resistomes of wild and captive baboon populations to understand the effect of human exposure and to understand how the primate microbiota may have been altered during the antibiotic era. We used culture-independent and bioinformatics methods to identify functional resistance genes in the guts of wild and captive baboons and show that exposure to humans is associated with changes in microbiota composition and resistome expansion compared to wild baboon groups. Our results suggest that captivity and lifestyle changes associated with human contact can lead to marked changes in the ecology of primate gut communities.Environmental microbes have harbored the capacity for antibiotic production for millions of years, spanning the evolution of humans and other vertebrates. However, the industrial-scale use of antibiotics in clinical and agricultural practice over the past century has led to a substantial increase in exposure of these agents to human and environmental microbiota. This perturbation is predicted to alter the ecology of microbial communities and to promote the evolution and transfer of antibiotic resistance (AR) genes. We studied wild and captive baboon populations to understand the effects of exposure to humans and human activities (e.g., antibiotic therapy) on the composition of the primate fecal microbiota and the antibiotic-resistant genes that it collectively harbors (the “resistome”). Using a culture-independent metagenomic approach, we identified functional antibiotic resistance genes in the gut microbiota of wild and captive baboon groups and saw marked variation in microbiota architecture and resistomes across habitats and lifeways. Our results support the view that antibiotic resistance is an ancient feature of gut microbial communities and that sharing habitats with humans may have important effects on the structure and function of the primate microbiota

Evaluating Historical Paradigms Of Sterility In Perinatal Microbiology And Ramifications For Pregnancy Outcomes

Next-generation sequencing technologies, especially 16S rRNA gene and metagenomic sequencing have allowed investigations of low microbial biomass tissues of the human body. While these sequencing methodologies have provided large amounts of reliable data for higher microbial biomass sites, such as the mouth, intestine, and vagina, tissues of low microbial biomass sites are subject to specific caveats that were not appropriately considered in early investigations of these sites. Low microbial biomass sites of particular interest have included those of the reproductive and urinary systems. Utilization of DNA sequencing methodologies have allowed researchers to challenge existing paradigms of sterility around these sites that were historically considered sterile, including but not limited to the placenta, the endometrium, and the bladder. While a thorough and complete understanding of the microbial signals in urogenital compartments is necessary for the best patient care and treatment, premature conclusions that redefine historical paradigms can have harmful consequences on patient health, especially for pregnant women with whom microorganisms have been associated with multiple adverse pregnancy outcomes. In this dissertation, I present a lack of evidence for a placental microbiota in humans using multiple modes of microbiological inquiry. Through culture, quantitative real-time PCR (qPCR), 16S rRNA gene sequencing, and metagenomics we found no evidence of bacterial signals beyond those also present in background technical controls. This work with human subjects was subsequently complemented by work in mice, in which we investigated the bacterial signals in the murine placenta and fetus, as well as multiple murine tissue control sites; we again found no consistent evidence of a placental microbiota or in utero colonization through multiple microbiological methodologies. Conversely, investigations of the urine of pregnant women revealed evidence of a low abundance bladder microbiota. We found bacterial signals that clearly exceeded those of technical controls, suggesting that a shift in sterility paradigm for the upper urinary tract may be warranted. Specifically, through bacterial culture, qPCR, and 16S rRNA gene sequencing we found evidence of a bladder microbiota in pregnant women that showed strong variation among individuals and consisted of Ureaplasma urealyticum and Gram-positive anaerobic cocci. A more thorough understanding of the bladder microbiota in pregnant women across gestation will allow healthcare professionals to address urinary and bladder symptoms in a way that alleviates or prevents pregnancy complications. This body of work provides strategies for the thorough investigation of low microbial biomass sites and demonstrates the high degree of evidence necessary to overturn classic paradigms of sterility in perinatal medicine and host biology in general

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads.

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing

Functional profiling of the gut microbiome in disease-associated inflammation

The microbial residents of the human gut are a major factor in the development and lifelong maintenance of health. The gut microbiota differs to a large degree from person to person and has an important influence on health and disease due to its interaction with the human immune system. Its overall composition and microbial ecology have been implicated in many autoimmune diseases, and it represents a particularly important area for translational research as a new target for diagnostics and therapeutics in complex inflammatory conditions. Determining the biomolecular mechanisms by which altered microbial communities contribute to human disease will be an important outcome of current functional studies of the human microbiome. In this review, we discuss functional profiling of the human microbiome using metagenomic and metatranscriptomic approaches, focusing on the implications for inflammatory conditions such as inflammatory bowel disease and rheumatoid arthritis. Common themes in gut microbial ecology have emerged among these diverse diseases, but they have not yet been linked to targetable mechanisms such as microbial gene and genome composition, pathway and transcript activity, and metabolism. Combining these microbial activities with host gene, transcript and metabolic information will be necessary to understand how and why these complex interacting systems are altered in disease-associated inflammation

Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples

Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them