Longitudinal Microbiome Studies Research Articles

Robust and Powerful Differential Composition Tests for Clustered Microbiome Data

Thanks to advances in high-throughput sequencing technologies, the importance of microbiome to human health and disease has been increasingly recognized. Analyzing microbiome data from sequencing experiments is challenging due to their unique features such as compositional data, excessive zero observations, overdispersion, and complex relations among microbial taxa. Clustered microbiome data have become prevalent in recent years from designs such as longitudinal studies, family studies, and matched case–control studies. The within-cluster dependence compounds the challenge of the microbiome data analysis. Methods that properly accommodate intra-cluster correlation and features of the microbiome data are needed. We develop robust and powerful differential composition tests for clustered microbiome data. The methods do not rely on any distributional assumptions on the microbial compositions, which provides flexibility to model various correlation structures among taxa and among samples within a cluster. By leveraging the adjusted sandwich covariance estimate, the methods properly accommodate sample dependence within a cluster. The two-part version of the test can further improve power in the presence of excessive zero observations. Different types of confounding variables can be easily adjusted for in the methods. We perform extensive simulation studies under commonly adopted clustered data designs to evaluate the methods. We demonstrate that the methods properly control the type I error under all designs and are more powerful than existing methods in many scenarios. The usefulness of the proposed methods is further demonstrated with two real datasets from longitudinal microbiome studies on pregnant women and inflammatory bowel disease patients. The methods have been incorporated into the R package “miLineage” publicly available at https://tangzheng1.github.io/tanglab/software.html.

Recruitment and Retention of Pregnant and Breastfeeding Mothers for Longitudinal Clinical Microbiome Studies (OR30-08-19)

Abstract Objectives Accumulating data suggest the health benefits of breastfeeding are, in part, mediated through interactions with the human microbiome. Despite these observations, recruitment of pregnant mothers in longitudinal clinical microbiome studies remains a significant challenge. The goal of this study is to develop data-driven strategies for recruitment and retention of breastfeeding mothers’ into longitudinal clinical microbiome studies. Methods We recruited a total of 40 mothers (20 pregnant and 20 breastfeeding) to complete in-depth semi-structured interviews at the UF Clinical and Translational Science Institute. Additionally, mothers were asked to use self-administered microbiome collection kits and complete questionnaires related to health history, infant feeding practices, and physical activity. Informative interviews were designed to identify women's understanding, motivations and barriers surrounding longitudinal clinical research participation and their preferences for providing non-invasive biological samples. Results Our preliminary analysis indicates that 1) barriersfor participating in research include convenience, confidentiality and child protection; 2) factors motivating participation in research include general interest in breastfeeding, schedule, and perceived research relevance; 3) participant recruitment should consider social media and 4) biological sample collection should include adequate instructions and drop-off convenience. Conclusions The results of this study provide valuable theoretical and practical insights on how to effectively recruit and retain pregnant and breastfeeding women into longitudinal microbiome studies. Funding Sources 1K01DK115632-01A1.

Seasonal dynamics of DNA and RNA viral bioaerosol communities in a daycare center

BackgroundViruses play an important role in ecosystems, including the built environment (BE). While numerous studies have characterized bacterial and fungal microbiomes in the BE, few have focused on the viral microbiome (virome). Longitudinal microbiome studies provide insight into the stability and dynamics of microbial communities; however, few such studies exist for the microbiome of the BE, and most have focused on bacteria. Here, we present a longitudinal, metagenomic-based analysis of the airborne DNA and RNA virome of a children’s daycare center. Specifically, we investigate how the airborne virome varies as a function of season and human occupancy, and we identify possible sources of the viruses and their hosts, mainly humans, animals, plants, and insects.ResultsSeason strongly influenced the airborne viral community composition, and a single sample collected when the daycare center was unoccupied suggested that occupancy also influenced the community. The pattern of influence differed between DNA and RNA viromes. Human-associated viruses were much more diverse and dominant in the winter, while the summertime virome contained a high relative proportion and diversity of plant-associated viruses.ConclusionsThis airborne microbiome in this building exhibited seasonality in its viral community but not its bacterial community. Human occupancy influenced both types of communities. By adding new data about the viral microbiome to complement burgeoning information about the bacterial and fungal microbiomes, this study contributes to a more complete understanding of the airborne microbiome.

Characterising the human intestinal mycobiome during healthy ageing

Although fungi are fundamental to the human microbiome, the diversity and dynamics of the mycobiome is poorly understood, particularly in considering their association with infectious disease, autoimmune disorders and atopy that affect immunocompromised individuals and infants. Characterising the human mycobiome faces several challenges relating to their low abundance and lack of standardized procedures for sample collection and isolation of viable cells and/or quality genetic material for culture-dependent and independent taxonomic and functional characterisation. To address these issues, we have developed a mycobiome analysis pipeline employing both culture-dependent and independent methods to identify as well as isolate, where possible, the fungal taxa populating the human intestinal tract. In a proof-of-concept study this pipeline has been used to identify fungal populations in faecal samples obtained from a small cohort of young infants, aged 2 years or younger. All were born prematurely, and severely immunocompromised and at risk from invasive and potentially lethal microbial infections, including those caused by fungal overgrowth. We have used this combined approach successfully to identify the fungi present in each individual infant, and to recover viable isolates. To date, Candida albicans and C. parapsilosis are the most frequently isolated fungi. While both are major opportunistic human fungal pathogens, C. parapsilosis is particularly problematic to preterm babies, due to its innate ability to form biofilms. Detailed characterisation of these isolates is currently underway. Two large-scale longitudinal microbiome studies have started at the Quadram Institute, and our validated analysis pipeline will be incorporated to define the fungal component of each study participant.

Exact variance component tests for longitudinal microbiome studies

In metagenomic studies, testing the association between microbiome composition and clinical outcomes translates to testing the nullity of variance components. Motivated by a lunghuman immunodeficiency virus (HIV) microbiome project, we study longitudinal microbiome data by using variance component models with more than two variance components. Current testing strategies only apply to models with exactly two variance components and when sample sizes are large. Therefore, they are not applicable to longitudinal microbiome studies. In this paper, we propose exact tests (score test, likelihood ratio test, and restricted likelihood ratio test) to (a) test the association of the overall microbiome composition in a longitudinal design and(b) detect the association of one specific microbiome cluster while adjusting for the effects from related clusters. Our approach combines the exact tests for null hypothesis with a single variance component with a strategy of reducing multiple variance components to a single one. Simulation studies demonstrate that our method has a correct type I error rate and superior power compared to existing methods at small sample sizes and weak signals. Finally, we apply our method to a longitudinal pulmonary microbiome study ofHIV-infected patients and reveal two interesting genera Prevotella and Veillonella associated with forced vital capacity. Our findings shed light on the impact of the lung microbiome on HIV complexities. The method is implemented in the open-source, high-performance computing language Julia and is freely available at https://github.com/JingZhai63/VCmicrobiome.

Negative Binomial Mixed Models for Analyzing Longitudinal Microbiome Data.

The metagenomics sequencing data provide valuable resources for investigating the associations between the microbiome and host environmental/clinical factors and the dynamic changes of microbial abundance over time. The distinct properties of microbiome measurements include varied total sequence reads across samples, over-dispersion and zero-inflation. Additionally, microbiome studies usually collect samples longitudinally, which introduces time-dependent and correlation structures among the samples and thus further complicates the analysis and interpretation of microbiome count data. In this article, we propose negative binomial mixed models (NBMMs) for longitudinal microbiome studies. The proposed NBMMs can efficiently handle over-dispersion and varying total reads, and can account for the dynamic trend and correlation among longitudinal samples. We develop an efficient and stable algorithm to fit the NBMMs. We evaluate and demonstrate the NBMMs method via extensive simulation studies and application to a longitudinal microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of flexible framework for modeling correlation structures and detecting dynamic effects. We have developed an R package NBZIMM to implement the proposed method, which is freely available from the public GitHub repository http://github.com//nyiuab//NBZIMM and provides a useful tool for analyzing longitudinal microbiome data.

SplinectomeR Enables Group Comparisons in Longitudinal Microbiome Studies.

Longitudinal, prospective studies often rely on multi-omics approaches, wherein various specimens are analyzed for genomic, metabolomic, and/or transcriptomic profiles. In practice, longitudinal studies in humans and other animals routinely suffer from subject dropout, irregular sampling, and biological variation that may not be normally distributed. As a result, testing hypotheses about observations over time can be statistically challenging without performing transformations and dramatic simplifications to the dataset, causing a loss of longitudinal power in the process. Here, we introduce splinectomeR, an R package that uses smoothing splines to summarize data for straightforward hypothesis testing in longitudinal studies. The package is open-source, and can be used interactively within R or run from the command line as a standalone tool. We present a novel in-depth analysis of a published large-scale microbiome study as an example of its utility in straightforward testing of key hypotheses. We expect that splinectomeR will be a useful tool for hypothesis testing in longitudinal microbiome studies.

Variance Component Selection With Applications to Microbiome Taxonomic Data.

High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Microbiome data are summarized as counts or composition of the bacterial taxa at different taxonomic levels. An important problem is to identify the bacterial taxa that are associated with a response. One method is to test the association of specific taxon with phenotypes in a linear mixed effect model, which incorporates phylogenetic information among bacterial communities. Another type of approaches consider all taxa in a joint model and achieves selection via penalization method, which ignores phylogenetic information. In this paper, we consider regression analysis by treating bacterial taxa at different level as multiple random effects. For each taxon, a kernel matrix is calculated based on distance measures in the phylogenetic tree and acts as one variance component in the joint model. Then taxonomic selection is achieved by the lasso (least absolute shrinkage and selection operator) penalty on variance components. Our method integrates biological information into the variable selection problem and greatly improves selection accuracies. Simulation studies demonstrate the superiority of our methods versus existing methods, for example, group-lasso. Finally, we apply our method to a longitudinal microbiome study of Human Immunodeficiency Virus (HIV) infected patients. We implement our method using the high performance computing language Julia. Software and detailed documentation are freely available at https://github.com/JingZhai63/VCselection.

'TIME': A Web Application for Obtaining Insights into Microbial Ecology Using Longitudinal Microbiome Data.

Realization of the importance of microbiome studies, coupled with the decreasing sequencing cost, has led to the exponential growth of microbiome data. A number of these microbiome studies have focused on understanding changes in the microbial community over time. Such longitudinal microbiome studies have the potential to offer unique insights pertaining to the microbial social networks as well as their responses to perturbations. In this communication, we introduce a web based framework called ‘TIME’ (Temporal Insights into Microbial Ecology’), developed specifically to obtain meaningful insights from microbiome time series data. The TIME web-server is designed to accept a wide range of popular formats as input with options to preprocess and filter the data. Multiple samples, defined by a series of longitudinal time points along with their metadata information, can be compared in order to interactively visualize the temporal variations. In addition to standard microbiome data analytics, the web server implements popular time series analysis methods like Dynamic time warping, Granger causality and Dickey Fuller test to generate interactive layouts for facilitating easy biological inferences. Apart from this, a new metric for comparing metagenomic time series data has been introduced to effectively visualize the similarities/differences in the trends of the resident microbial groups. Augmenting the visualizations with the stationarity information pertaining to the microbial groups is utilized to predict the microbial competition as well as community structure. Additionally, the ‘causality graph analysis’ module incorporated in TIME allows predicting taxa that might have a higher influence on community structure in different conditions. TIME also allows users to easily identify potential taxonomic markers from a longitudinal microbiome analysis. We illustrate the utility of the web-server features on a few published time series microbiome data and demonstrate the ease with which it can be used to perform complex analysis.

Patient-Specific Bacteroides Genome Variants in Pouchitis.

ABSTRACTA 2-year longitudinal microbiome study of 22 patients who underwent colectomy with an ileal pouch anal anastomosis detected significant increases in distinct populations of Bacteroides during 9 of 11 patient visits that coincided with inflammation (pouchitis). Oligotyping and metagenomic short-read annotation identified Bacteroides populations that occurred in early samples, bloomed during inflammation, and reappeared after antibiotic treatment. Targeted cultivation of Bacteroides isolates from the same individual at multiple time points and from several patients detected subtle genomic changes, including the identification of rapidly evolving genomic elements that differentiate isogenic strains of Bacteroides fragilis from the mucosa versus lumen. Each patient harbored Bacteroides spp. that are closely related to commonly occurring clinical isolates, including Bacteroides ovatus, B. thetaiotaomicron, B. vulgatus, and B. fragilis, which contained unique loci in different patients for synthesis of capsular polysaccharides. The presence of unique Bacteroides capsular polysaccharide loci within different hosts and between the lumen and mucosa may represent adaptations to stimulate, suppress, and evade host-specific immune responses at different microsites of the ileal pouch.