Recent progress in analyzing the spatial structure of the human microbiome: distinguishing biogeography and architecture in the oral and gut communities

Impact of micro-habitat fragmentation on microbial population growth dynamics

Microbial communities thrive in virtually every habitat on Earth and are essential to the function of diverse ecosystems. Most microbial habitats are not spatially continuous and well-mixed, but rather composed, at the microscale, of many isolated or semi-isolated local patches of different sizes, resulting in partitioning of microbial populations into discrete local populations. The impact of this spatial fragmentation on population dynamics is not well-understood. Here, we study how such variably sized micro-habitat patches affect the growth dynamics of clonal microbial populations and how dynamics in individual patches dictate those of the metapopulation. To investigate this, we developed the μ-SPLASH, an ecology-on-a-chip platform, enabling the culture of microbes in microscopic landscapes comprised of thousands of microdroplets, with a wide range of sizes. Using the μ-SPLASH, we cultured the model bacteria Escherichia coli and based on time-lapse microscopy, analyzed the population dynamics within thousands of individual droplets. Our results reveal that growth curves substantially vary with droplet size. Although growth rates generally increase with drop size, reproductive success and the time to approach carrying capacity, display non-monotonic patterns. Combining μ-SPLASH experiments with computational modeling, we show that these patterns result from both stochastic and deterministic processes, and demonstrate the roles of initial population density, patchiness, and patch size distribution in dictating the local and metapopulation dynamics. This study reveals basic principles that elucidate the effects of habitat fragmentation and population partitioning on microbial population dynamics. These insights deepen our understanding of natural microbial communities and have significant implications for microbiome engineering.

Link Between Oral and Gut Microbiomes: The Oral-Gut Axis

In the last decades, groundbreaking research on the human microbiome has changed our reductionist conception of the etiology and pathogenesis of several chronic diseases. As a result, we have come to appreciate the significance of a balanced microbiome in maintaining human health. In this context, the upper and lower gastrointestinal tracts (GITs) comprise the most abundant and diverse microbiotas of the human body. In addition to its diversity, functional redundancy, and temporal stability, a healthy GIT microbiome is characterized by its body site specificity. In fact, current evidence has indicated that the translocation of oral species to the gut environment through the oral-gut axis is increased in an array of illnesses, including chronic inflammatory and metabolic diseases, neurological disorders, and cancer. Oral pathogens have also been shown to promote gut dysbiosis and systemic inflammation in animal models. Yet, some level of overlapping between oral and gut microbiomes may occur without disruption of these microbial communities and loss of site specificity. The uniqueness of each host-microbiome entity may hinder our ability to define a "universal" normal GIT microbiome. Despite that, this chapter summarizes the predominant health-related taxa along the human GIT, as well as their role in the physiology and immunity of the digestive system. Some mechanisms that may lead to disturbances and relevant shifts in the oral and gut microbiomes of major inflammatory chronic diseases are also pointed out. Lastly, oral-fecal microbial signatures are presented as potential biomarkers for several oral and systemic disorders. The recognition of such symbiotic/dysbiotic microbial profiles may provide insights into the development of more accurate early diagnosis and therapeutic ecological approaches to restore the balance of the GIT microbiome.

Overview of data preprocessing for machine learning applications in human microbiome research

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.