Multi-omic approaches for host-microbiome data integration

Abstract

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

Current progresses and challenges for microbiome research in human health: a perspective

“…Current approaches for studying the microbiota are generally limited to only one or few aspects, typically the microbiota profiling through 16s rRNA amplicon sequencing, or through WGS via shotgun metagenomic. By contrast, the role of the human microbiota in disease pathogenesis involves a complex multi-dimensional network characterized by structural (the profiling of microbial composition) and functional (the determination of whole genetic pathways) interactions with the host, as well as by various small molecules, including metabolites, catabolites, and signal molecules, through which they interact ( Chetty and Blekhman, 2024 ).…”

“…Indeed, it is becoming increasingly clear that the full scope of the multi-layered interplay between microorganisms and the host, affecting health outcomes, can only be revealed by integrating a multitude of different data layers along with the metagenomic analysis of the microbiome, leading to a multi-omics approach ( Chetty and Blekhman, 2024 ). Multi-omics is defined as the integration of metagenomics with meta-transcriptomics, meta-proteomics, and metabolomics.…”

“…Multi-omics possess their own unique set of challenges to face, including the variability of results due to different software pipelines, and the excessive reliance on a paucity of curated and publicly available microbiome databases, limiting the information only to well-characterized microorganisms, transcripts, proteins, and metabolites ( Chetty and Blekhman, 2024 ). However, these approaches provide more comprehensive information than the study of a single aspect, since all these datasets can generate a 360-degree view of the complex interactome between the microbiota and the host, elucidating the relationship between different cellular processes, such as, for example, between mRNA and proteins, or between different type of biomolecules synthesized by the microbiota or the host.…”