Holo-Omics: Integrated Host-Microbiota Multi-omics for Basic and Applied Biological Research

Abstract

From ontogenesis to homeostasis, the phenotypes of complex organisms are shaped by the bidirectional interactions between the host organisms and their associated microbiota. Current technology can reveal many such interactions by combining multi-omic data from both hosts and microbes. However, exploring the full extent of these interactions requires careful consideration of study design for the efficient generation and optimal integration of data derived from (meta)genomics, (meta)transcriptomics, (meta)proteomics, and (meta)metabolomics. In this perspective, we introduce the holo-omic approach that incorporates multi-omic data from both host and microbiota domains to untangle the interplay between the two. We revisit the recent literature on biomolecular host-microbe interactions and discuss the implementation and current limitations of the holo-omic approach. We anticipate that the application of this approach can contribute to opening new research avenues and discoveries in biomedicine, biotechnology, agricultural and aquacultural sciences, nature conservation, as well as basic ecological and evolutionary research.

Keywords: Evolutionary Biology; Microbiome.

Graphical abstract

Figure 1

From Hologenomic to Holo-Omic (A) Simplified visualization of the hologenomic domain. (B) Host-microbiota interactions within the holo-omic domain here exemplified by zooming in on the luminal surface of the host intestine. Red arrows indicate host-microbiota holo-omic interactions. Solid red arrows indicate interactions supported in the primary literature (numbers refer to the publications listed in Table 1), whereas dashed red arrows indicate potential holo-omic interactions that, to the best of our knowledge, have not yet been documented. Solid black arrows indicate omic levels influencing host phenotype, and dashed black arrows indicate omic levels influenced by environmental factors.

Figure 2

Overview of Different Approaches in Holo-Omics and Their Influence on the Level of Complexity Approaches are divided into methodological, experimental, and statistical. Arrows indicate the level of complexity relative to each segment of the figure.

Figure 3

Overview of Different Variables that Will Impact Holo-Omic Studies In this conceptualization, two independent variables, the environment and the host genome, affect dependent variables (center), the metagenome, and downstream omic levels and their interactions with the host genome and derived omic levels. Different combinations enable implementing different types of experimental approaches. (A) When both genetic background and environment are constant (e.g., laboratory conditions) the underlying composition and functionality of the microbiota as well as the underlying interaction with the host domain can be determined. These conditions allow researchers to manipulate microbiota composition and functionality and to manipulate the host genome (e.g., using CRISPR-Cas9 genome editing technology). (B) When the genetic background is variable and the environment is relatively consistent, the impact of genetic variants on downstream omic levels can be isolated. (C) When the genetic background is similar and the environment is variable, the impact of environmental factors on the different omic levels can be studied. (D) When both genetic background and environment are variable, the high level of variability will complicate the isolation of factors responsible for modifying the omic levels. Increasing sample size can mitigate this problem.