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

Lasse Nyholm,Antton Alberdi,Shyam Gopalakrishnan,Adam Koziol,Ostaizka Aizpurua,Sofia Marcos,Morten T Limborg,M.Thomas P Gilbert,Amanda Bolt Botnen

doi:10.1016/j.isci.2020.101414

Lasse Nyholm, Antton Alberdi + Show 7 more

Open Access

https://doi.org/10.1016/j.isci.2020.101414

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Abstract

SummaryFrom 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.

Highlights

Association studies— identifying correlations between genetic variants and phenotypes—have been used to detect the genetic contributions to complex phenotypes (Welter et al, 2014). This approach has been extended to metabolomic profiles (Luo, 2015) and metagenomic variants (Blekhman et al, 2015; Qin et al, 2012), but methods that jointly leverage the multiple omic levels to infer the causal pathways between genomic processes and phenotypes are still scarce
Host-microbiota interactions can have negative outcomes for the host. This is most obvious in the context of pathogens that cause infectious diseases (Fei and Zhao, 2013), but it is apparent, for example, in the context of dysbiosis associated with chronic diseases such as inflammatory bowel syndrome (IBS) (Imhann et al, 2018)
Genetic disposition for inflammatory bowel disease is associated with a reduction in abundance of the genus Roseburia in the gut microbiome

Summary

Metatranscriptome Metaproteome

Efforts to study the effects of host and microbial genes and their consequences have become embedded in layer upon layer of jargon. Association studies— identifying correlations between genetic variants and phenotypes—have been used to detect the genetic contributions to complex phenotypes (Welter et al, 2014) This approach has been extended to metabolomic profiles (Luo, 2015) and metagenomic variants (Blekhman et al, 2015; Qin et al, 2012), but methods that jointly leverage the multiple omic levels to infer the causal pathways between genomic processes and phenotypes are still scarce. In this context, the technology to generate large amounts of data to be used in a holo-omic context is already available, but the analytical tools to reveal and identify host-microbiota interactions are still limited

Perspective ll

Major Findings

Spatial omics Single cell Untargeted Targeted

CONCLUSION