Holo-omics for deciphering plant-microbiome interactions

Abstract

Host-microbiome interactions are recognized for their importance to host health. An improved understanding of the molecular underpinnings of host-microbiome relationships will advance our capacity to accurately predict host fitness and manipulate interaction outcomes. Within the plant microbiome research field, unlocking the functional relationships between plants and their microbial partners is the next step to effectively using the microbiome to improve plant fitness. We propose that strategies that pair host and microbial datasets-referred to here as holo-omics-provide a powerful approach for hypothesis development and advancement in this area. We discuss several experimental design considerations and present a case study to highlight the potential for holo-omics to generate a more holistic perspective of molecular networks within the plant microbiome system. In addition, we discuss the biggest challenges for conducting holo-omics studies; specifically, the lack of vetted analytical frameworks, publicly available tools, and required technical expertise to process and integrate heterogeneous data. Finally, we conclude with a perspective on appropriate use-cases for holo-omics studies, the need for downstream validation, and new experimental techniques that hold promise for the plant microbiome research field. We argue that utilizing a holo-omics approach to characterize host-microbiome interactions can provide important opportunities for broadening system-level understandings and significantly inform microbial approaches to improving host health and fitness. Video abstract.

Fig. 1

Considerations for design, analysis, and validation of holo-omic experiments. At left, design related considerations include intentional use of longitudinal designs, appropriate selection of sample types, and evaluation of optimal data types for the scientific questions addressed in the study. In the middle, analysis related challenges include selecting the appropriate range of biological and technical expertise, as well as the selection of appropriate analytical framework and tools for direct integration of diverse data types. At right, recommendations of techniques for downstream hypothesis testing and validation include use of direct and evolution-driven modifications of host genetic space, direct manipulation of microbial genetics, and bottom-up construction of reduced complexity synthetic communities

Fig. 2

Connecting plant and microbial omics techniques. Recent examples from the plant microbiome research field of holo-omics studies employing paired datasets from host data types (in green) and associated microorganism data types (in blue). Lines between techniques indicate individual studies that integrate across the pair of indicated techniques. All studies reference here are also listed with additional detail in Table 1