Benjamin Hillmann

Captivity humanizes the primate microbiome

Significance Trillions of bacteria live in the primate gut, contributing to metabolism, immune system development, and pathogen resistance. Perturbations to these bacteria are associated with metabolic and autoimmune human diseases that are prevalent in Westernized societies. Herein, we measured gut microbial communities and diet in multiple primate species living in the wild, in a sanctuary, and in full captivity. We found that captivity and loss of dietary fiber in nonhuman primates are associated with loss of native gut microbiota and convergence toward the modern human microbiome, suggesting that parallel processes may be driving recent loss of core microbial biodiversity in humans. The primate gastrointestinal tract is home to trillions of bacteria, whose composition is associated with numerous metabolic, autoimmune, and infectious human diseases. Although there is increasing evidence that modern and Westernized societies are associated with dramatic loss of natural human gut microbiome diversity, the causes and consequences of such loss are challenging to study. Here we use nonhuman primates (NHPs) as a model system for studying the effects of emigration and lifestyle disruption on the human gut microbiome. Using 16S rRNA gene sequencing in two model NHP species, we show that although different primate species have distinctive signature microbiota in the wild, in captivity they lose their native microbes and become colonized with Prevotella and Bacteroides, the dominant genera in the modern human gut microbiome. We confirm that captive individuals from eight other NHP species in a different zoo show the same pattern of convergence, and that semicaptive primates housed in a sanctuary represent an intermediate microbiome state between wild and captive. Using deep shotgun sequencing, chemical dietary analysis, and chloroplast relative abundance, we show that decreasing dietary fiber and plant content are associated with the captive primate microbiome. Finally, in a meta-analysis including published human data, we show that captivity has a parallel effect on the NHP gut microbiome to that of Westernization in humans. These results demonstrate that captivity and lifestyle disruption cause primates to lose native microbiota and converge along an axis toward the modern human microbiome.

SHI7 Is a Self-Learning Pipeline for Multipurpose Short-Read DNA Quality Control

Quality control of high-throughput DNA sequencing data is an important but sometimes laborious task requiring background knowledge of the sequencing protocol used (such as adaptor type, sequencing technology, insert size/stitchability, paired-endedness, etc.). Quality control protocols typically require applying this background knowledge to selecting and executing numerous quality control steps with the appropriate parameters, which is especially difficult when working with public data or data from collaborators who use different protocols. We have created a streamlined quality control pipeline intended to substantially simplify the process of DNA quality control from raw machine output files to actionable sequence data. In contrast to other methods, our proposed pipeline is easy to install and use and attempts to learn the necessary parameters from the data automatically with a single command. ABSTRACT Next-generation sequencing technology is of great importance for many biological disciplines; however, due to technical and biological limitations, the short DNA sequences produced by modern sequencers require numerous quality control (QC) measures to reduce errors, remove technical contaminants, or merge paired-end reads together into longer or higher-quality contigs. Many tools for each step exist, but choosing the appropriate methods and usage parameters can be challenging because the parameterization of each step depends on the particularities of the sequencing technology used, the type of samples being analyzed, and the stochasticity of the instrumentation and sample preparation. Furthermore, end users may not know all of the relevant information about how their data were generated, such as the expected overlap for paired-end sequences or type of adaptors used to make informed choices. This increasing complexity and nuance demand a pipeline that combines existing steps together in a user-friendly way and, when possible, learns reasonable quality parameters from the data automatically. We propose a user-friendly quality control pipeline called SHI7 (canonically pronounced “shizen”), which aims to simplify quality control of short-read data for the end user by predicting presence and/or type of common sequencing adaptors, what quality scores to trim, whether the data set is shotgun or amplicon sequencing, whether reads are paired end or single end, and whether pairs are stitchable, including the expected amount of pair overlap. We hope that SHI7 will make it easier for all researchers, expert and novice alike, to follow reasonable practices for short-read data quality control. IMPORTANCE Quality control of high-throughput DNA sequencing data is an important but sometimes laborious task requiring background knowledge of the sequencing protocol used (such as adaptor type, sequencing technology, insert size/stitchability, paired-endedness, etc.). Quality control protocols typically require applying this background knowledge to selecting and executing numerous quality control steps with the appropriate parameters, which is especially difficult when working with public data or data from collaborators who use different protocols. We have created a streamlined quality control pipeline intended to substantially simplify the process of DNA quality control from raw machine output files to actionable sequence data. In contrast to other methods, our proposed pipeline is easy to install and use and attempts to learn the necessary parameters from the data automatically with a single command.

Fecal microbiota transplantation reverses antibiotic and chemotherapy-induced gut dysbiosis in mice

Fecal microbiota transplantation (FMT) is now widely used to treat recurrent Clostridium difficile infection, but has been less studied as a means to restore microbiome diversity and composition following antibiotic or chemotherapy treatments. The purpose of our study was to assess the efficacy of FMT to reverse antibiotic- and chemotherapy-induced gut dysbiosis in a mouse model. C57BL/6J mice were treated with ampicillin for 1 week and/or received a single intraperitoneal injection of 5-Fluorouracil. Fresh stool was collected and analyzed using shotgun metagenomics and the Illumina sequencing platform. Ampicillin caused a significant and immediate decrease in bacterial species richness and diversity that persisted for one week. In mice that received FMT, disruption of the intestinal microbiota was reversed immediately. Antibiotic and chemotherapy administration caused significant alteration in species distribution, including a decrease in the relative proportions of Clostridium scindens and Faecalibacterium prausnitzii, and an increase in known pathogenic species. In mice receiving FMT, we observed a significant increase in species known to exhibit anti-inflammatory properties. Moreover, chemotherapy led to a critical decrease in key ‘health-promoting’ species and to an altered functional profile, especially when chemotherapy was administered in tandem with antibiotics, and that FMT can ameliorate these effects.

Evaluating the information content of shallow shotgun metagenomics

Although microbial communities are associated with many aspects of human, environmental, plant, and animal health, there exists no cost-effective method for precisely characterizing species and genes present in such communities. While deep whole-genome shotgun (WGS) sequencing provides the highest-level of taxonomic and functional resolution, it is often prohibitively expensive for large-scale studies. The prevailing alternative, high-throughput 16S rRNA gene amplicon sequencing (16S), often does not resolve taxonomy past the genus level and provides only moderately accurate predictions of the functional profile; thus, there is currently no widely accepted approach to affordable, high-resolution, taxonomic and functional microbiome analysis. To address this technology gap, we evaluated the information content of shallow shotgun sequencing with as low as 0.5 million sequences per sample as an alternative to 16S sequencing for large human microbiome studies. We describe a library preparation protocol enabling shallow shotgun sequencing at approximately the same per-sample cost as 16S. We analyzed multiple real and simulated biological data sets, including two novel human stool samples with ultra-deep sequencing of 2.5 billion sequences per sample, and found that shallow shotgun recovers accurate species-level taxonomic and functional profiles of the human microbiome. We recognize and discuss some of the inherent limitations of shallow shotgun sequencing, and note that 16S sequencing remains a valuable and important method for taxonomic profiling of novel environments. Although deep WGS remains the gold standard for high-resolution microbiome analysis, we recommend that researchers consider shallow shotgun sequencing as a useful alternative to 16S for large-scale human microbiome research studies.

CLOUD: a non-parametric detection test for microbiome outliers

BackgroundDysbiosis of the human gut microbiome is defined as a maladaptive or clinically relevant deviation of the community profile from the healthy or normal state. Dysbiosis has been implicated in an extensive set of metabolic, auto-immune, and infectious diseases, and yet there is substantial inter-individual variation in microbiome composition even within body sites of healthy humans. An individual’s microbiome varies over time in a high-dimensional space to form their personal microbiome cloud. This cloud may or may not be similar to that of other people, both in terms of the average microbiome profile (conformity) and the diameter of the cloud (stability). However, there is currently no robust non-parametric test that determines whether a patient’s microbiome cloud is an outlier with respect to a reference group of healthy individuals with widely varying microbiome profiles.MethodsHere, we propose a test for outliers’ detection in the human gut microbiome that accounts for the wide range of microbiome phenotypes observed in a typical set of healthy individuals and for intra-individual temporal variation. Our robust nonparametric outlier detection test, the CLOUD test, performs two assessments of a patient’s microbiome health: conformity, the extent to which the patient’s microbiome cloud is ecologically similar to a subset of healthy subjects; and stability, which compares the cloud diameter of a patient to those of healthy subjects. The CLOUD test is based on locally linear embedded ecological distances, allowing it to account for widely varying microbiome compositions among reference individuals. It also leverages temporal variability within patients and reference individuals to increase the robustness of the test.ResultsWe describe the CLOUD test, and we apply it to one novel and two previously published cohorts of patients receiving fecal microbiota transplantation for recurrent Clostridium difficile colitis, as well as to two known healthy cohorts, demonstrating high concordance of the CLOUD conformity and stability indices with clinical outcomes.ConclusionsAlthough the CLOUD test is not, on its own, a test for clinical dysbiosis, it nonetheless provides a framework for outlier testing that could be incorporated into evaluation of suspected dysbiosis, which may play a role in diagnosis and prognosis of numerous pediatric and adult diseases.

An Increased Abundance of Clostridiaceae Characterizes Arthritis in Inflammatory Bowel Disease and Rheumatoid Arthritis: A Cross-sectional Study

BACKGROUND Inflammatory bowel diseases (IBDs) are a group of heterogeneous inflammatory conditions affecting the gastrointestinal tract. Although there is considerable evidence linking the gut microbiota to intestinal inflammation, there is limited knowledge on its potential role in the development of extraintestinal manifestations of IBD. METHODS Four groups of patients were included: IBD-associated arthropathy (IBD-A); IBD without arthropathy (IBD-N); rheumatoid arthritis (RA); and non-IBD, nonarthritis controls. DNA from stool samples was isolated and sequenced using the Illumina platform. Paired-end reads were quality-controlled using SHI7 and processed with SHOGUN. Abundance and diversity analyses were performed using QIIME, and compositional biomarker identification was performed using LEfSe. RESULTS One hundred eighty patients were included in the analysis. IBD-A was associated with an increased abundance of microbial tyrosine degradation pathways when compared with IBD-N (P = 0.02), whereas IBD-A and RA patients both shared an increased abundance of Clostridiaceae when compared with controls (P = 0.045). We found that history of bowel surgery was a significant source of variability (P = 0.001) among all IBD patients and was associated with decreased alpha diversity and increased abundance of Enterobacteriaceae (P = 0.004). CONCLUSIONS An increased abundance of gut microbial tyrosine degradation pathways was associated with IBD-A. An increased abundance of Clostridiaceae was shared by both IBD-A and RA patients and suggests a potentially common microbial link for inflammatory arthritis. The increased abundance of Enterobacteriaceae, previously reported in IBD, may be due to the effects of previous bowel surgery and highlights the importance of controlling for this variable in future studies.