Zachary D. Kurtz

Sparse and Compositionally Robust Inference of Microbial Ecological Networks

16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.

Helminth infection promotes colonization resistance via type 2 immunity.

Parasitic worms affect gut microbes Improved hygiene practices in high-income countries may come with an increased risk of developing inflammatory bowel disease (IBD) or other similar disorders. Ramanan et al. show that intestinal helminth infection, caused by parasitic worms, protects IBD-susceptible mice from developing the disease. The infection increases specific protective species and limits other inflammatory members of the microbiota. People from helminth-endemic regions harbored a similar protective microbiota, and their deworming led to an increase in inflammatory Bacteroidales species, similar to what the authors observed in the mice. Thus, a changing microbial environment may shape susceptibility to inflammatory disease. Science, this issue p. 608 Intestinal helminths affect gut microbe composition and influence susceptibility to inflammatory bowel disease. Increasing incidence of inflammatory bowel diseases, such as Crohn’s disease, in developed nations is associated with changes to the microbial environment, such as decreased prevalence of helminth colonization and alterations to the gut microbiota. We find that helminth infection protects mice deficient in the Crohn’s disease susceptibility gene Nod2 from intestinal abnormalities by inhibiting colonization by an inflammatory Bacteroides species. Resistance to Bacteroides colonization was dependent on type 2 immunity, which promoted the establishment of a protective microbiota enriched in Clostridiales. Additionally, we show that individuals from helminth-endemic regions harbor a similar protective microbiota and that deworming treatment reduced levels of Clostridiales and increased Bacteroidales. These results support a model of the hygiene hypothesis in which certain individuals are genetically susceptible to the consequences of a changing microbial environment.

Helminth Colonization Is Associated with Increased Diversity of the Gut Microbiota

Soil-transmitted helminths colonize more than 1.5 billion people worldwide, yet little is known about how they interact with bacterial communities in the gut microbiota. Differences in the gut microbiota between individuals living in developed and developing countries may be partly due to the presence of helminths, since they predominantly infect individuals from developing countries, such as the indigenous communities in Malaysia we examine in this work. We compared the composition and diversity of bacterial communities from the fecal microbiota of 51 people from two villages in Malaysia, of which 36 (70.6%) were infected by helminths. The 16S rRNA V4 region was sequenced at an average of nineteen thousand sequences per samples. Helminth-colonized individuals had greater species richness and number of observed OTUs with enrichment of Paraprevotellaceae, especially with Trichuris infection. We developed a new approach of combining centered log-ratio (clr) transformation for OTU relative abundances with sparse Partial Least Squares Discriminant Analysis (sPLS-DA) to enable more robust predictions of OTU interrelationships. These results suggest that helminths may have an impact on the diversity, bacterial community structure and function of the gut microbiota.

Alternatively activated macrophages derived from monocytes and tissue macrophages are phenotypically and functionally distinct

Macrophages adopt an alternatively activated phenotype (AAMs) when activated by the interleukin-4receptor(R)α. AAMs can be derived either from proliferation of tissue resident macrophages or recruited inflammatory monocytes, but it is not known whether these different sources generate AAMs that are phenotypically and functionally distinct. By transcriptional profiling analysis, we show here that, although both monocyte and tissue-derived AAMs expressed high levels of Arg1, Chi3l3, and Retnla, only monocyte-derived AAMs up-regulated Raldh2 and PD-L2. Monocyte-derived AAMs were also CX3CR1-green fluorescent protein (GFP)(high) and expressed CD206, whereas tissue-derived AAMs were CX3CR1-GFP and CD206 negative. Monocyte-derived AAMs had high levels of aldehyde dehydrogenase activity and promoted the differentiation of FoxP3(+) cells from naïve CD4(+) cells via production of retinoic acid. In contrast, tissue-derived AAMs expressed high levels of uncoupling protein 1. Hence monocyte-derived AAM have properties associated with immune regulation, and the different physiological properties associated with AAM function may depend on the distinct lineage of these cells.

Antibiotic perturbation of the murine gut microbiome enhances the adiposity, insulin resistance, and liver disease associated with high-fat diet

Background Obesity, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD) are serious health concerns, especially in Western populations. Antibiotic exposure and high-fat diet (HFD) are important and modifiable factors that may contribute to these diseases. Methods To investigate the relationship of antibiotic exposure with microbiome perturbations in a murine model of growth promotion, C57BL/6 mice received lifelong sub-therapeutic antibiotic treatment (STAT), or not (control), and were fed HFD starting at 13 weeks. To characterize microbiota changes caused by STAT, the V4 region of the 16S rRNA gene was examined from collected fecal samples and analyzed. Results In this model, which included HFD, STAT mice developed increased weight and fat mass compared to controls. Although results in males and females were not identical, insulin resistance and NAFLD were more severe in the STAT mice. Fecal microbiota from STAT mice were distinct from controls. Compared with controls, STAT exposure led to early conserved diet-independent microbiota changes indicative of an immature microbial community. Key taxa were identified as STAT-specific and several were found to be predictive of disease. Inferred network models showed topological shifts concurrent with growth promotion and suggest the presence of keystone species. Conclusions These studies form the basis for new models of type 2 diabetes and NAFLD that involve microbiome perturbation. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0297-9) contains supplementary material, which is available to authorized users.

A single early-in-life macrolide course has lasting effects on murine microbial network topology and immunity

Broad-spectrum antibiotics are frequently prescribed to children. Early childhood represents a dynamic period for the intestinal microbial ecosystem, which is readily shaped by environmental cues; antibiotic-induced disruption of this sensitive community may have long-lasting host consequences. Here we demonstrate that a single pulsed macrolide antibiotic treatment (PAT) course early in life is sufficient to lead to durable alterations to the murine intestinal microbiota, ileal gene expression, specific intestinal T-cell populations, and secretory IgA expression. A PAT-perturbed microbial community is necessary for host effects and sufficient to transfer delayed secretory IgA expression. Additionally, early-life antibiotic exposure has lasting and transferable effects on microbial community network topology. Our results indicate that a single early-life macrolide course can alter the microbiota and modulate host immune phenotypes that persist long after exposure has ceased.High or multiple doses of macrolide antibiotics, when given early in life, can perturb the metabolic and immunological development of lab mice. Here, Ruiz et al. show that even a single macrolide course, given early in life, leads to long-lasting changes in the gut microbiota and immune system of mice.

Lung microbiome and host immune tone in subjects with idiopathic pulmonary fibrosis treated with inhaled interferon-γ

Therapies targeting inflammation reveal inconsistent results in idiopathic pulmonary fibrosis (IPF). Aerosolised interferon (IFN)-γ has been proposed as a novel therapy. Changes in the host airway microbiome are associated with the inflammatory milieu and may be associated with disease progression. Here, we evaluate whether treatment with aerosolised IFN-γ in IPF impacts either the lower airway microbiome or the host immune phenotype. Patients with IPF who enrolled in an aerosolised IFN-γ trial underwent bronchoscopy at baseline and after 6 months. 16S rRNA sequencing of bronchoalveolar lavage fluid (BALF) was used to evaluate the lung microbiome. Biomarkers were measured by Luminex assay in plasma, BALF and BAL cell supernatant. The compPLS framework was used to evaluate associations between taxa and biomarkers. IFN-γ treatment did not change α or β diversity of the lung microbiome and few taxonomic changes occurred. While none of the biomarkers changed in plasma, there was an increase in IFN-γ and a decrease in Fit-3 ligand, IFN-α2 and interleukin-5 in BAL cell supernatant, and a decrease in tumour necrosis factor-β in BALF. Multiple correlations between microbial taxa common to the oral mucosa and host inflammatory biomarkers were found. These data suggest that the lung microbiome is independently associated with the host immune tone and may have a potential mechanistic role in IPF. Lower airway microbiome and immunological tone are associated in IPF, an effect independent of IFN-γ treatment http://ow.ly/cTDo30bsJiN

Oxalobacter formigenes -associated host features and microbial community structures examined using the American Gut Project

BackgroundIncreasing evidence shows the importance of the commensal microbe Oxalobacter formigenes in regulating host oxalate homeostasis, with effects against calcium oxalate kidney stone formation, and other oxalate-associated pathological conditions. However, limited understanding of O. formigenes in humans poses difficulties for designing targeted experiments to assess its definitive effects and sustainable interventions in clinical settings. We exploited the large-scale dataset from the American Gut Project (AGP) to study O. formigenes colonization in the human gastrointestinal (GI) tract and to explore O. formigenes-associated ecology and the underlying host–microbe relationships.ResultsIn >8000 AGP samples, we detected two dominant, co-colonizing O. formigenes operational taxonomic units (OTUs) in fecal specimens. Multivariate analysis suggested that O. formigenes abundance was associated with particular host demographic and clinical features, including age, sex, race, geographical location, BMI, and antibiotic history. Furthermore, we found that O. formigenes presence was an indicator of altered host gut microbiota structure, including higher community diversity, global network connectivity, and stronger resilience to simulated disturbances.ConclusionsThrough this study, we identified O. formigenes colonizing patterns in the human GI tract, potential underlying host–microbe relationships, and associated microbial community structures. These insights suggest hypotheses to be tested in future experiments. Additionally, we proposed a systematic framework to study any bacterial taxa of interest to computational biologists, using large-scale public data to yield novel biological insights.

Fungi stabilize connectivity in the lung and skin microbial ecosystems

BackgroundNo microbe exists in isolation, and few live in environments with only members of their own kingdom or domain. As microbiome studies become increasingly more interested in the interactions between microbes than in cataloging which microbes are present, the variety of microbes in the community should be considered. However, the majority of ecological interaction networks for microbiomes built to date have included only bacteria. Joint association inference across multiple domains of life, e.g., fungal communities (the mycobiome) and bacterial communities, has remained largely elusive.ResultsHere, we present a novel extension of the SParse InversE Covariance estimation for Ecological ASsociation Inference (SPIEC-EASI) framework that allows statistical inference of cross-domain associations from targeted amplicon sequencing data. For human lung and skin micro- and mycobiomes, we show that cross-domain networks exhibit higher connectivity, increased network stability, and similar topological re-organization patterns compared to single-domain networks. We also validate in vitro a small number of cross-domain interactions predicted by the skin association network.ConclusionsFor the human lung and skin micro- and mycobiomes, our findings suggest that fungi play a stabilizing role in ecological network organization. Our study suggests that computational efforts to infer association networks that include all forms of microbial life, paired with large-scale culture-based association validation experiments, will help formulate concrete hypotheses about the underlying biological mechanisms of species interactions and, ultimately, help understand microbial communities as a whole.

Normalization methods for microbial abundance data strongly affect correlation estimates

Consistent normalization of microbial genomic survey count data is fundamental to modern microbiome research. Technical artifacts in these data often obstruct standard comparison of microbial composition across samples and experiments. To correct for sampling bias, library size, and technical variability, a number of different normalization methods have been proposed, including adaptations of RNA-seq analysis work flows and log-ratio transformations from compositional data analysis. However, the effects of data normalization on higher-order summary statistics has remained elusive. We review and compare popular data normalization schemes and assess their effect on subsequent correlation estimation. Application of these normalization methods to the largest publicly available human gut microbiome dataset show substantial variation among patterns of correlation. We show that log-ratio and variance-stabilization transformations provide the most consistent estimates across experiments of different sample sizes. We also show that data analysis methods that rely on correlation, such as data clustering and network inference, differ depending on the normalization schemes. These findings have important implications for microbiome studies in multiple stages of analysis.Consistent estimation of associations in microbial genomic survey count data is fundamental to microbiome research. Technical limitations, including compositionality, low sample sizes, and technical variability, obstruct standard application of association measures and require data normalization prior to estimating associations. Here, we investigate the interplay between data normalization and microbial association estimation by a comprehensive analysis of statistical consistency. Leveraging the large sample size of the American Gut Project (AGP), we assess the consistency of the two prominent linear association estimators, correlation and proportionality, under different sample scenarios and data normalization schemes, including RNA-seq analysis work flows and log-ratio transformations. We show that shrinkage estimation, a standard technique in high-dimensional statistics, can universally improve the quality of association estimates for microbiome data. We find that large-scale association patterns in the AGP data can be grouped into five normalization-dependent classes. Using microbial association network construction and clustering as examples of exploratory data analysis, we show that variance-stabilizing and log-ratio approaches provide for the most consistent estimation of taxonomic and structural coherence. Taken together, the findings from our reproducible analysis workflow have important implications for microbiome studies in multiple stages of analysis, particularly when only small sample sizes are available.