Robust and automatic definition of microbiome states

Statistical challenges in longitudinal microbiome data analysis

The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.

Differences in composition of interdigital skin microbiota predict sheep and feet that develop footrot

Footrot has a major impact on health and productivity of sheep worldwide. The current paradigm for footrot pathogenesis is that physical damage to the interdigital skin (IDS) facilitates invasion of the essential pathogen Dichelobacter nodosus. The composition of the IDS microbiota is different in healthy and diseased feet, so an alternative hypothesis is that changes in the IDS microbiota facilitate footrot. We investigated the composition and diversity of the IDS microbiota of ten sheep, five that did develop footrot and five that did not (healthy) at weekly intervals for 20 weeks. The IDS microbiota was less diverse on sheep 2 + weeks before they developed footrot than on healthy sheep. This change could be explained by only seven of > 2000 bacterial taxa detected. The incubation period of footrot is 8–10 days, and there was a further reduction in microbial diversity on feet that developed footrot in that incubation period. We conclude that there are two stages of dysbiosis in footrot: the first predisposes sheep to footrot and the second occurs in feet during the incubation of footrot. These findings represent a step change in our understanding of the role of the IDS microbiota in footrot pathogenesis.

Stochastic block models reveal a robust nested pattern in healthy human gut microbiomes

A key question in human gut microbiome research is what are the robust structural patterns underlying its taxonomic composition. Herein, we use whole metagenomic datasets from healthy human guts to show that such robust patterns do exist, albeit not in the conventional enterotype sense. We first introduce the concept of mixed-membership enterotypes using a network inference approach based on stochastic block models. We find that gut microbiomes across a group of people (hosts) display a nested structure, which has been observed in a number of ecological systems. This finding led us to designate distinct ecological roles to both microbes and hosts: generalists and specialists. Specifically, generalist hosts have microbiomes with most microbial species, while specialist hosts only have generalist microbes. Moreover, specialist microbes are only present in generalist hosts. From the nested structure of microbial taxonomies, we show that these ecological roles of microbes are generally conserved across datasets. Our results show that the taxonomic composition of healthy human gut microbiomes is associated with robustly structured combinations of generalist and specialist species.

Host Identity and Geographic Location Significantly Affect Gastrointestinal Microbial Richness and Diversity in Western Lowland Gorillas (Gorilla gorilla gorilla) under Human Care

The last few decades have seen an outpouring of gastrointestinal (GI) microbiome studies across diverse host species. Studies have ranged from assessments of GI microbial richness and diversity to classification of novel microbial lineages. Assessments of the "normal" state of the GI microbiome composition across multiple host species has gained increasing importance for distinguishing healthy versus diseased states. This study aimed to determine baselines and trends over time to establish "typical" patterns of GI microbial richness and diversity, as well as inter-individual variation, in three populations of western lowland gorillas (Gorilla gorilla gorilla) under human care at three zoological institutions in North America. Fecal samples were collected from 19 western lowland gorillas every two weeks for seven months (n = 248). Host identity and host institution significantly affected GI microbiome community composition (p < 0.05), although host identity had the most consistent and significant effect on richness (p = 0.03) and Shannon diversity (p = 0.004) across institutions. Significant changes in microbial abundance over time were observed only at Denver Zoo (p < 0.05). Our results suggest that individuality contributes to most of the observed GI microbiome variation in the study populations. Our results also showed no significant changes in any individual's microbial richness or Shannon diversity during the 7-month study period. While some microbial taxa (Prevotella, Prevotellaceae and Ruminococcaceae) were detected in all gorillas at varying levels, determining individual baselines for microbial composition comparisons may be the most useful diagnostic tool for optimizing non-human primate health under human care.

Clustering on Human Microbiome Sequencing Data: A Distance-Based Unsupervised Learning Model

Modeling and analyzing human microbiome allows the assessment of the microbial community and its impacts on human health. Microbiome composition can be quantified using 16S rRNA technology into sequencing data, which are usually skewed and heavy-tailed with excess zeros. Clustering methods are useful in personalized medicine by identifying subgroups for patients stratification. However, there is currently a lack of standardized clustering method for the complex microbiome sequencing data. We propose a clustering algorithm with a specific beta diversity measure that can address the presence-absence bias encountered for sparse count data and effectively measure the sample distances for sample stratification. Our distance measure used for clustering is derived from a parametric based mixture model producing sample-specific distributions conditional on the observed operational taxonomic unit (OTU) counts and estimated mixture weights. The method can provide accurate estimates of the true zero proportions and thus construct a precise beta diversity measure. Extensive simulation studies have been conducted and suggest that the proposed method achieves substantial clustering improvement compared with some widely used distance measures when a large proportion of zeros is presented. The proposed algorithm was implemented to a human gut microbiome study on Parkinson's diseases to identify distinct microbiome states with biological interpretations.

Maturational Changes Alter Effects of Dietary Phytase Supplementation on the Fecal Microbiome in Fattening Pigs

Age-related successions in the porcine gut microbiome may modify the microbial response to dietary changes. This may especially affect the bacterial response to essential nutrients for bacterial metabolism, such as phosphorus (P). Against this background, we used phytase supplementation (0 or 650 phytase units/kg complete feed) to alter the P availability in the hindgut and studied the dietary response of the fecal bacterial microbiome from the early to late fattening period. Fecal DNA were isolated after 0, 3, 5 and 10 weeks and the V3-V4 region of the 16S rRNA gene was sequenced. Permutational analysis of variance showed distinct bacterial communities for diet and week. Alpha-diversity and taxonomy indicated progressing maturation of the bacterial community with age. Prevotellaceae declined, whereas Clostridiaceae and Ruminococcaceae increased from weeks 0 to 3, 5, and 10, indicating changes in fiber-digesting capacities with age. Phytase affected all major bacterial taxa but reduced species richness (Chao1) and diversity (Shannon and Simpson). To conclude, present results greatly support the importance of available P for bacterial proliferation, including fibrolytic, lactic acid- and butyrate-producing genera, in pigs. Results also emphasize the necessity to assess bacterial responses to dietary manipulation at several time points throughout the fattening period.