Challenges in Bioinformatics Workflows for Processing Microbiome Omics Data at Scale

The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools and algorithms have been designed and used for very specific purposes on samples collected by individual investigators or groups. While these developments have been quite instructive, the ability to compare microbiome data generated by many groups of researchers is impeded by the lack of standardized application of bioinformatics methods. Additionally, there are few examples of broad bioinformatics workflows that can process metagenome, metatranscriptome, metaproteome and metabolomic data at scale, and no central hub that allows processing, or provides varied omics data that are findable, accessible, interoperable and reusable (FAIR). Here, we review some of the challenges that exist in analyzing omics data within the microbiome research sphere, and provide context on how the National Microbiome Data Collaborative has adopted a standardized and open access approach to address such challenges.

A Systematic, Complexity-Reduction Approach to Dissect Microbiome: the Kombucha Tea Microbiome as an Example

One defining goal of microbiome research is to uncover mechanistic causation that dictates the emergence of structural and functional traits of microbiomes. However, the extraordinary degree of ecosystem complexity has hampered the realization of the goal. Here we developed a systematic, complexity-reducing strategy to mechanistically elucidate the compositional and metabolic characteristics of microbiome by using the kombucha tea microbiome as an example. The strategy centered around a two-species core that was abstracted from but recapitulated the native counterpart. The core was convergent in its composition, coordinated on temporal metabolic patterns, and capable for pellicle formation. Controlled fermentations uncovered the drivers of these characteristics, which were also demonstrated translatable to provide insights into the properties of communities with increased complexity and altered conditions. This work unravels the pattern and process underlying the kombucha tea microbiome, providing a potential conceptual framework for mechanistic investigation of microbiome behaviors.

A Computational Method to Dissect Colonization Resistance of the Gut Microbiota against Pathogens

The indigenous gut microbes have co-evolved with their hosts for millions of years. Those gut microbes play a crucial role in host health and disease. In particular, they protect the host against incursion by exogenous and often harmful microorganisms, a mechanism known as colonization resistance (CR). Yet, identifying the exact microbes responsible for the gut microbiota-mediated CR against a particular pathogen remains a fundamental challenge in microbiome research. Here, we develop a computational method --- Generalized Microbe-Phenotype Triangulation (GMPT) to systematically identify causal microbes that directly influence the microbiota-mediated CR against a pathogen. We systematically validate GMPT using a classical population dynamics model in community ecology, and then apply it to microbiome data from two mouse studies on C. difficile infection. The developed method will not only significantly advance our understanding of CR mechanisms but also pave the way for the rational design of microbiome-based therapies for preventing and treating enteric infections.

Future Prospective of Oral Microbiome Research

Oral microbiome has complex structure. It consisted of more than 700 species of bacteria. These bacteria contains pathogens for human health. In contrast, some beneficial bacteria were included. Perspective of oral microbiome is not still elucidated. In this paper, information regarding oral microbiome of health older adults and oral diseases are included. Additionally, concise review of oral microbiome are presented.

Human Microbiome in Children, at the Crossroad of Social Determinants of Health and Personalized Medicine

The evolving field of microbiome research offers an excellent opportunity for biomarker identification, understanding drug metabolization disparities, and improving personalized medicine. However, the complexities of host–microbe ecological interactions hinder clinical transferability. Among other factors, the microbiome is deeply influenced by age and social determinants of health, including environmental factors such as diet and lifestyle conditions. In this article, the bidirectionality of social and host–microorganism interactions in health will be discussed. While the field of microbiome-related personalized medicine evolves, it is clear that social determinants of health should be mitigated. Furthermore, microbiome research exemplifies the need for specific pediatric investigation plans to improve children’s health.

Enhancing Microbiome Research through Genome-Scale Metabolic Modeling

Construction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research.