Microbiome and metabolome data integration provides insight into health and disease

Abstract

For much of our history, the most basic information about the microbial world has evaded characterization. Next-generation sequencing has led to a rapid increase in understanding of the structure and function of host-associated microbial communities in diverse diseases ranging from obesity to autism. Through experimental systems such as gnotobiotic mice only colonized with known microbes, a causal relationship between microbial communities and disease phenotypes has been supported. Now microbiome research must move beyond correlations and general demonstration of causality to develop mechanistic understandings of microbial influence, including through their metabolic activities. Similar to the microbiome field, advances in technologies for cataloguing small molecules have broadened our understanding of the metabolites that populate our bodies. Integration of microbial and metabolomics data paired with experimental validation has promise for identifying microbial influence on host physiology through production, modification or degradation of bioactive metabolites. Realization of microbial metabolic activities that affect health is hampered by gaps in our understanding of 1) biological properties of microbes and metabolites, 2) which microbial enzymes/pathways produce which metabolites and 3) the effects of metabolites on hosts. Capitalizing upon known mechanistic relationships and filling gaps in our understanding has the potential to enable translational microbiome research across disease contexts.

Cervicovaginal Microbiome and Urine Metabolome Paired Analysis Reveals Niche Partitioning of the Microbiota in Patients with Human Papilloma Virus Infections

“…Most of the studies associating the microbiome and systemic health were conducted via assessment of interactions between the gut microbiome and gut metabolome [23]. A popular strategy now is to explore associations between the gut microbiome and serum metabolome due to the ability of circulating metabolites to translocate through the host's barriers, and therefore, giving the potential for the identification of systemic health effects [24][25][26][27]. Since metabolites from the gut are absorbed into the circulation and finally excreted through urine [28], recent studies suggest that exploration of the association between urine metabolome and host-microbiome may complement sequencing-based approaches with a functional readout of the gut microbiome [29].…”

Integration of constraint-based modelling with faecal metabolomics reveals large deleterious effects ofFusobacteriaspecies on community butyrate production

“…Consequently, changes in faecal metabolome profiles in CRC have also been linked to altered microbial abundance patterns via statistical association studies (Kim et al, 2020;Koeth et al, 2013;Xu et al, 2020). Yet, it remains challenging to identify the mechanisms by which the microbiome changes the metabolome, as statistical associations may be caused by indirect effects and confounding (Noecker et al, 2019;Shaffer et al, 2017). Moreover, as species share metabolic capabilities and functions even across different phyla (Magnusdottir et al, 2017), it is by no means clear that a change in composition will result in a change in metabolic functions.…”

M2IA: a Web Server for Microbiome and Metabolome Integrative Analysis

“…The study of microbiome in human health has experienced exponential growth over the last decade with the advent of new sequencing technologies for interrogating complex microbial communities [1] . Meanwhile, metabolomics has been an important tool for understanding microbial community functions and their links to health and diseases through the quantitation of dozens to hundreds of small molecules [2] . The gut microbiota is considered a metabolic 'organ' to protect the host against pathogenic microbes, modulate immunity, and regulate metabolic processes, including short chain fatty acid production and bile acid biotransformation [3] .…”

“…Moreover, although much bioinformatics work has been done to process and analyze the individual omics data, to date, it still lacks a comprehensive strategy or a computational tool to analyze the correlations between microbiome and metabolome [12] . To rapidly advance microbiome and metabolome data integration and understand their roles in diverse diseases, advanced computational methods for multi -omics data integration and interpretation need to be developed [2] .…”