Ecology of the respiratory tract microbiome

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2’s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles. Recently, several statistical methods have been proposed to identify phenotypic or environmental associations with features (e.g., taxa, genes, pathways, chemicals, etc.) from molecular profiles of microbial communities. Particularly for human microbiome epidemiology, however, most of these are primarily focused on univariable associations that analyze only one or a few environmental covariates. This is a critical gap to address, given the growing commonality of population-scale microbiome research and the complexity of associated study designs, including dietary, pharmaceutical, clinical, and environmental covariates, often with samples from multiple time points or tissues. Surprisingly, there have been no systematic evaluations of statistical analysis methods appropriate for such studies, nor consensus on appropriate methods for scalable microbiome epidemiology. To this end, we developed and validated a statistical model (MaAsLin) that provides both the first unified method and the first large-scale, comprehensive benchmarking of multivariable associations in population-scale microbial community studies. We hope that the MaAsLin 2 implementation, validated through extensive simulations and an application to HMP2 IBD multi-omics, will be helpful for researchers in future analysis of both human-associated and environmental microbial communities.

The field of microbiome research has evolved rapidly over the past few decades and has become a topic of great scientific and public interest. As a result of this rapid growth in interest covering different fields, we are lacking a clear commonly agreed definition of the term “microbiome.” Moreover, a consensus on best practices in microbiome research is missing. Recently, a panel of international experts discussed the current gaps in the frame of the European-funded MicrobiomeSupport project. The meeting brought together about 40 leaders from diverse microbiome areas, while more than a hundred experts from all over the world took part in an online survey accompanying the workshop. This article excerpts the outcomes of the workshop and the corresponding online survey embedded in a short historical introduction and future outlook. We propose a definition of microbiome based on the compact, clear, and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings. We clearly separate the terms microbiome and microbiota and provide a comprehensive discussion considering the composition of microbiota, the heterogeneity and dynamics of microbiomes in time and space, the stability and resilience of microbial networks, the definition of core microbiomes, and functionally relevant keystone species as well as co-evolutionary principles of microbe-host and inter-species interactions within the microbiome. These broad definitions together with the suggested unifying concepts will help to improve standardization of microbiome studies in the future, and could be the starting point for an integrated assessment of data resulting in a more rapid transfer of knowledge from basic science into practice. Furthermore, microbiome standards are important for solving new challenges associated with anthropogenic-driven changes in the field of planetary health, for which the understanding of microbiomes might play a key role. Video Abstract

Key Points The anatomical development and maturation of the human respiratory tract is a complex multistage process that occurs not only in prenatal life but also postnatally. This maturation process depends, in part, on exposure to microbial and environmental triggers, and results in a highly specialized organ system that contains several distinct niches, each of which is subjected to specific microbial, cellular and physiological gradients. The respiratory microbiome during early life is dynamic and its development is affected by a range of host and environmental factors, including mode of birth, feeding type, antibiotic treatment and crowding conditions, such as the presence of siblings and day-care attendance. The upper respiratory tract is colonized by specialized resident bacterial, viral and fungal assemblages, which presumably prevent potential pathogens from overgrowing and disseminating towards the lungs, thereby functioning as gatekeepers to respiratory health. The upper respiratory tract is the primary source of the lung microbiome. In healthy individuals, the lung microbiome seems to largely consist of transient microorganisms and its composition is determined by the balance between microbial immigration and elimination. Next-generation sequencing has identified intricate interbacterial association networks that comprise true mutualistic, commensal or antagonistic direct or indirect relationships. Alternatively, bacterial co-occurrence seems to be driven by host and environmental factors, as well as by interactions with viruses and fungi. The respiratory microbiome provides cues to the host immune system that seem to be vital for immune training, organogenesis and the maintenance of immune tolerance. Increasing evidence supports the existence of a window of opportunity early in life, during which adequate microbiota sensing is essential for immune maturation and consecutive respiratory health. Future studies should focus on large-scale, multidisciplinary holistic approaches and adequately account for host and environmental factors. Associations that are identified by these studies can then be corroborated in reductionist surveys; for example, by using in vitro or animal studies. Supplementary information The online version of this article (doi:10.1038/nrmicro.2017.14) contains supplementary material, which is available to authorized users.