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• W3044646968 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. 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. Research conducted over the last decade has fundamentally changed how we perceive the biology and underlying genetic properties of macroorganisms, from looking at individuals as isolated genetic entities to recognizing how they interact with their associated microorganisms in a myriad of biological processes. These microorganisms associated with plants and animals are now acknowledged as relevant—even essential—assets to many basic biological processes, including nutrient acquisition (Falcinelli et al., 2015Falcinelli S. Picchietti S. Rodiles A. Cossignani L. Merrifield D.L. Taddei A.R. Maradonna F. Olivotto I. Gioacchini G. Carnevali O. Lactobacillus rhamnosus lowers zebrafish lipid content by changing gut microbiota and host transcription of genes involved in lipid metabolism.Sci. Rep. 2015; 5: 9336Crossref PubMed Scopus (134) Google Scholar), immune response (Wu and Wu, 2012Wu H.-J. Wu E. The role of gut microbiota in immune homeostasis and autoimmunity.Gut Microbes. 2012; 3: 4-14Crossref PubMed Scopus (589) Google Scholar), development (Rudman et al., 2019Rudman S.M. Greenblum S. Hughes R.C. Rajpurohit S. Kiratli O. Lowder D.B. Lemmon S.G. Petrov D.A. Chaston J.M. Schmidt P. Microbiome composition shapes rapid genomic adaptation of Drosophila melanogaster.Proc. Natl. Acad. Sci. U S A. 2019; 116: 20025-20032Crossref PubMed Scopus (56) Google Scholar), biomolecule synthesis (Nicholson et al., 2012Nicholson J.K. Holmes E. Kinross J. Burcelin R. Gibson G. Jia W. Pettersson S. Host-gut microbiota metabolic interactions.Science. 2012; 336: 1262-1267Crossref PubMed Scopus (2899) Google Scholar), and behavior (Liang et al., 2018Liang S. Wu X. Jin F. Gut-brain psychology: rethinking psychology from the microbiota-gut-brain axis.Front. Integr. Neurosci. 2018; 12: 33Crossref PubMed Scopus (124) Google Scholar). This realization has promoted the notion of the holobiont (see Box 1 for definitions of this and other terms in bold), a term used to collectively describe the host organism and all its associated microorganisms.Box 1GlossaryAmplicon sequencing:PCR amplification-based targeted sequencing of a specific genetic region.Dysbiosis:Any change to the components of resident commensal microbial communities relative to the community found in healthy individuals.Epigenome:The heritable alteration of DNA or proteins associated with DNA that changes gene expression levels in a cell or tissue without modifying the sequence of DNA.Epigenotype:The pattern of epigenetic modification (alteration of DNA or proteins that changes gene expression) in a cell or tissue.Exposome:Every exposure that an organism is subjected to throughout its lifetime.Genome:The complete set of genetic material of an organism.Genome-wide association study(GWAS):An examination of a genome-wide set of genetic variations associated with a trait of interest.Holobiont:A host organism and its associated microorganisms.Hologenome:The combined genetic content of the host and its associated microbiota.Holo-omics:The analysis of multiple omic levels from both host and associated microbiota domains.Hologenome theory of evolution:The theory that posits host, symbionts, and their associated hologenome, acting in consortium, function as a biological entity and as a level of selection in evolution.Metagenome-assembled genome (MAG):Genome assembled from shotgun sequencing data generated from the entire genetic content present in a given environment.Metabolome:The entire pool of metabolites present in an organism.Metagenome:The entire genetic content present in a given environment.Metametabolome:The entire pool of metabolites present in an environmental sample.Metaproteome:The complete set of proteins/peptides present in an environmental sample.Metatranscriptome:The entire pool of mRNA in an environmental sample.Metagenome-wide association study (MGWAS):An examination of a metagenome-wide set of genetic variations associated with a trait of interest.Microbiome:The sum of genetic material in a microbial community.Microbiota:The ecological community of microorganisms.Multi-omics:The analysis of multiple types of omic data (e.g., metagenome and metaproteome).Omic:Term used to describe any level of multi-omics (i.e., (meta)genomics, epigenomics, (meta)transcriptomics, (meta)proteomics, and (meta)metabolomics).Proteome:The entire pool of proteins present in an organism.Shotgun DNA sequencing:The non-targeted sequencing of the entire genetic content of a sample.Shotgun proteomics:The direct analysis of complex protein mixtures to generate global profiles of proteins within a sample.Single cell sequencing:Sequencing of the nucleic acid content within a single cell.Spatial metagenomics:Characterization of the spatial orientation of microbes in their environment by fixation in a matrix followed by either amplicon sequencing or shotgun sequencing.Systems biology:A holistic approach, often employing quantitative modeling, to study biological systems that cannot be reduced to the sum of the systems individual parts.Targeted RNA sequencing:Sequencing of specific RNA molecules using probes complementing the transcript of interest.Transcriptome:The sum of RNA transcripts produced by a single organism.Western blotting:Separation and identification of proteins in a gel matrix using antibodies. Amplicon sequencing:PCR amplification-based targeted sequencing of a specific genetic region.Dysbiosis:Any change to the components of resident commensal microbial communities relative to the community found in healthy individuals.Epigenome:The heritable alteration of DNA or proteins associated with DNA that changes gene expression levels in a cell or tissue without modifying the sequence of DNA.Epigenotype:The pattern of epigenetic modification (alteration of DNA or proteins that changes gene expression) in a cell or tissue.Exposome:Every exposure that an organism is subjected to throughout its lifetime.Genome:The complete set of genetic material of an organism.Genome-wide association study(GWAS):An examination of a genome-wide set of genetic variations associated with a trait of interest.Holobiont:A host organism and its associated microorganisms.Hologenome:The combined genetic content of the host and its associated microbiota.Holo-omics:The analysis of multiple omic levels from both host and associated microbiota domains.Hologenome theory of evolution:The theory that posits host, symbionts, and their associated hologenome, acting in consortium, function as a biological entity and as a level of selection in evolution.Metagenome-assembled genome (MAG):Genome assembled from shotgun sequencing data generated from the entire genetic content present in a given environment.Metabolome:The entire pool of metabolites present in an organism.Metagenome:The entire genetic content present in a given environment.Metametabolome:The entire pool of metabolites present in an environmental sample.Metaproteome:The complete set of proteins/peptides present in an environmental sample.Metatranscriptome:The entire pool of mRNA in an environmental sample.Metagenome-wide association study (MGWAS):An examination of a metagenome-wide set of genetic variations associated with a trait of interest.Microbiome:The sum of genetic material in a microbial community.Microbiota:The ecological community of microorganisms.Multi-omics:The analysis of multiple types of omic data (e.g., metagenome and metaproteome).Omic:Term used to describe any level of multi-omics (i.e., (meta)genomics, epigenomics, (meta)transcriptomics, (meta)proteomics, and (meta)metabolomics).Proteome:The entire pool of proteins present in an organism.Shotgun DNA sequencing:The non-targeted sequencing of the entire genetic content of a sample.Shotgun proteomics:The direct analysis of complex protein mixtures to generate global profiles of proteins within a sample.Single cell sequencing:Sequencing of the nucleic acid content within a single cell.Spatial metagenomics:Characterization of the spatial orientation of microbes in their environment by fixation in a matrix followed by either amplicon sequencing or shotgun sequencing.Systems biology:A holistic approach, often employing quantitative modeling, to study biological systems that cannot be reduced to the sum of the systems individual parts.Targeted RNA sequencing:Sequencing of specific RNA molecules using probes complementing the transcript of interest.Transcriptome:The sum of RNA transcripts produced by a single organism.Western blotting:Separation and identification of proteins in a gel matrix using antibodies. Historically, the phenotypic variation of plants and animals has been attributed to the interplay between genomic properties (Koonin et al., 2000Koonin E.V. Aravind L. Kondrashov A.S. The impact of comparative genomics on our understanding of evolution.Cell. 2000; 101: 573-576Abstract Full Text Full Text PDF PubMed Scopus (201) Google Scholar) and environmental factors (Schmid, 1992Schmid B. Phenotypic variation in plants.Evol. Trends Plants. 1992; 6: 45-60Google Scholar). However, a long history of research on some insects and domestic vertebrates suggested that microorganisms associated with host animals should also be included in the equation. For example, termites have long been known (Leidy, 1881Leidy J. Parasites of the Termites. Collins, Printer, 1881Google Scholar) to require gut microbes to be able to digest their food. In the last decade, researchers have benefited from the rapid development of high-throughput sequencing technology to more intensively explore how the metagenomic features of host-associated microorganisms also shape plant and animal phenotypes (Gilbert et al., 2018Gilbert J.A. Blaser M.J. Caporaso J.G. Jansson J.K. Lynch S.V. Knight R. Current understanding of the human microbiome.Nat. Med. 2018; 24: 392-400Crossref PubMed Scopus (948) Google Scholar; Stringlis et al., 2018Stringlis I.A. Yu K. Feussner K. de Jonge R. Van Bentum S. Van Verk M.C. Berendsen RL Bakker P.A.H.M. Feussner I. Pieterse C.M.J. MYB72-dependent coumarin exudation shapes root microbiome assembly to promote plant health.Proc. Natl. Acad. Sci. U S A. 2018; 115: E5213-E5222Crossref PubMed Scopus (358) Google Scholar). These advances have expanded our knowledge on the role of host-microbe interactions in the evolution and ecology of modern-day organisms and how knowledge of such interactions can be beneficial in applied sciences. They basically revealed the termite example to be closer to the norm than the exception. Although individually both genomic and metagenomic approaches have proven useful for understanding many biological processes, each type of study has typically ignored the effect of the other domain and, critically, their interplay. Hence, the knowledge gained through such approaches is, at the very least, incomplete. The recognition of the importance of these host-microbiota interactions has recently opened up new research avenues based on the integrated analysis of coupled genomic and metagenomic data (Limborg et al., 2018Limborg M.T. Alberdi A. Kodama M. Roggenbuck M. Kristiansen K. Gilbert M.T.P. Applied hologenomics: feasibility and potential in aquaculture.Trends Biotechnol. 2018; 36: 252-264Abstract Full Text Full Text PDF PubMed Scopus (24) Google Scholar), which can be referred to as the research field of hologenomics (Figure 1A). Efforts to study the effects of host and microbial genes and their consequences have become embedded in layer upon layer of jargon. Because the concepts being discussed are new, some of these new terms are necessary, so as to have common reference points. But they only serve as effective reference points if they are well defined. Here we propose that hologenomics (the combined genetic content of the host and the microbiota) can be expanded to the holo-omic level by the incorporation of data from multiple omic levels from both host and microbiota domains (Limborg et al., 2018Limborg M.T. Alberdi A. Kodama M. Roggenbuck M. Kristiansen K. Gilbert M.T.P. Applied hologenomics: feasibility and potential in aquaculture.Trends Biotechnol. 2018; 36: 252-264Abstract Full Text Full Text PDF PubMed Scopus (24) Google Scholar) (Figure 1B). This approach is inspired by elements originating from systems biology (e.g., metagenomics systems biology [Greenblum et al., 2012Greenblum S. Turnbaugh P.J. Borenstein E. Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease.Proc. Natl. Acad. Sci. U. S. A. 2012; 109: 594-599Crossref PubMed Scopus (577) Google Scholar] and the use of multi-omic data integration [Bersanelli et al., 2016Bersanelli M. Mosca E. Remondini D. Giampieri E. Sala C. Castellani G. Milanesi L. Methods for the integration of multi-omics data: mathematical aspects.BMC Bioinformatics. 2016; 17: 15Crossref PubMed Scopus (216) Google Scholar; Heintz-Buschart et al., 2016Heintz-Buschart A. May P. Laczny C.C. Lebrun L.A. Bellora C. Krishna A. Wampach L. Schneider J.G. Hogan A. de Beaufort C. et al.Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes.Nat. Microbiol. 2016; 2: 16180Crossref PubMed Scopus (163) Google Scholar; Liu et al., 2020Liu Z. Ma A. Mathé E. Merling M. Ma Q. Liu B. Network analyses in microbiome based on high-throughput multi-omics data.Brief. Bioinform. 2020; https://doi.org/10.1093/bib/bbaa005Crossref Scopus (20) Google Scholar]). However, multi-omics implies omic data from only one domain, whereas holo-omics is defined by the incorporation of both host and microbial data. In theory, implementing a holo-omic approach would allow researchers to reveal a range of biomolecular interactions responsible for shaping the phenotype of complex organisms, using a variety of molecular tools, and would ultimately provide great potential for application across many different fields of research. The holo-omic toolbox requires both methodological and analytical tools. Within the methodological tools are the nucleic acid sequencing and mass spectrometry technologies that enable tracking the biomolecular pathways linking host and microbial genomic sequences with biomolecular phenotypes by generating (meta)transcriptomes, (meta)proteomes, and (meta)metabolomes. The same technologies also enable epigenomic and exposomic profiling, which can further contribute to disentangling the biochemical associations between host-microbiota-environment interactions and their effect on host phenotypes (Kumar et al., 2014Kumar H. Lund R. Laiho A. Lundelin K. Ley R.E. Isolauri E. Salminen S. Gut microbiota as an epigenetic regulator: pilot study based on whole-genome methylation analysis.MBio. 2014; 5https://doi.org/10.1128/mBio.02113-14Crossref Scopus (147) Google Scholar; Rogler and Vavricka, 2015Rogler G. Vavricka S. Exposome in IBD: recent insights in environmental factors that influence the onset and course of IBD.Inflamm. Bowel Dis. 2015; 21: 400-408Crossref PubMed Scopus (88) Google Scholar). The analytical tools required to extract useful information from the enormous amount of highly complex data generated by current high-throughput technologies are still limited. Association studies—identifying correlations between genetic variants and phenotypes—have been used to detect the genetic contributions to complex phenotypes (Welter et al., 2014Welter D. MacArthur J. Morales J. Burdett T. Hall P. Junkins H. Klemm A. Flicek P. Manolio T. Hindorff L. et al.The NHGRI GWAS Catalog, a curated resource of SNP-trait associations.Nucleic Acids Res. 2014; 42: D1001-D1006Crossref PubMed Scopus (1997) Google Scholar). This approach has been extended to metabolomic profiles (Luo, 2015Luo J. Metabolite-based genome-wide association studies in plants.Curr. Opin. Plant Biol. 2015; 24: 31-38Crossref PubMed Scopus (148) Google Scholar) and metagenomic variants (Blekhman et al., 2015Blekhman R. Goodrich J.K. Huang K. Sun Q. Bukowski R. Bell J.T. Spector T.D. Keinan A. Ley R.E. Gevers D. et al.Host genetic variation impacts microbiome composition across human body sites.Genome Biol. 2015; 16: 191Crossref PubMed Scopus (426) Google Scholar; Qin et al., 2012Qin J. Li Y. Cai Z. Li S. Zhu J. Zhang F. Liang S. Zhang W. Guan Y. Shen D. et al.A metagenome-wide association study of gut microbiota in type 2 diabetes.Nature. 2012; 490: 55-60Crossref PubMed Scopus (3988) Google Scholar), 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. As a consequence, only a handful of research groups worldwide have been able to effectively implement the holo-omic approach. To contribute to the development of this new field, in this perspective we first revisit the available evidence for the biological importance of host-microbiota interactions. Second, we present how the holo-omic toolbox can be used to study host-microbiota interactions at varying levels of complexity to guide researchers through applying the holo-omic approach. Third, we showcase the potential provided by the holo-omic approach to host-microbiota interactions in both basic and applied biological sciences and finally we identify the limiting factors that currently prevent the widespread implementation of the holo-omic approach and discuss possible solutions to overcome them. The holo-omic approach to host-microbiota interactions relies on three major assumptions: (1) host-associated microorganisms interact not only with each other but also with their host (Bredon et al., 2018Bredon M. Dittmer J. Noël C. Moumen B. Bouchon D. Lignocellulose degradation at the holobiont level: teamwork in a keystone soil invertebrate.Microbiome. 2018; 6: 162Crossref PubMed Scopus (49) Google Scholar; Fischer et al., 2017Fischer C.N. Trautman E.P. Crawford J.M. Stabb E.V. Handelsman J. Broderick N.A. Metabolite exchange between microbiome members produces compounds that influence Drosophila behavior.Elife. 2017; 6: e18855Crossref PubMed Scopus (102) Google Scholar; Stringlis et al., 2018Stringlis I.A. Yu K. Feussner K. de Jonge R. Van Bentum S. Van Verk M.C. Berendsen RL Bakker P.A.H.M. Feussner I. Pieterse C.M.J. MYB72-dependent coumarin exudation shapes root microbiome assembly to promote plant health.Proc. Natl. Acad. Sci. U S A. 2018; 115: E5213-E5222Crossref PubMed Scopus (358) Google Scholar; Vaishnava et al., 2011Vaishnava S. Yamamoto M. Severson K.M. Ruhn K.A. Yu X. Koren O. Ley R. Wakeland E.K. Hooper L.V. The antibacterial lectin RegIIIg promotes the spatial segregation of microbiota and host in the intestine.Science. 2011; 334https://doi.org/10.1126/science.1208930Crossref PubMed Scopus (460) Google Scholar); (2) these interactions affect, either positively or negatively, central biological processes of hosts and microorganisms (Wu and Wu, 2012Wu H.-J. Wu E. The role of gut microbiota in immune homeostasis and autoimmunity.Gut Microbes. 2012; 3: 4-14Crossref PubMed Scopus (589) Google Scholar); and (3) the interplay can be traced using biomolecular tools (Bansal et al., 2010Bansal T. Alaniz R.C. Wood T.K. Jayaraman A. The bacterial signal indole increases epithelial-cell tight-junction resistance and attenuates indicators of inflammation.Proc. Natl. Acad. Sci. U S A. 2010; 107: 228-233Crossref PubMed Scopus (504) Google Scholar; Bredon et al., 2018Bredon M. Dittmer J. Noël C. Moumen B. Bouchon D. Lignocellulose degradation at the holobiont level: teamwork in a keystone soil invertebrate.Microbiome. 2018; 6: 162Crossref PubMed Scopus (49) Google Scholar; Kelly et al., 2015Kelly C.J. Zheng L. Campbell E.L. Saeedi B. Scholz C.C. Bayless A.J. Wilson K.E. Glover L.E. Kominsky D.J. Magnuson A. et al.Crosstalk between microbiota-derived short-chain fatty acids and intestinal epithelial HIF augments tissue barrier function.Cell Host Microbe. 2015; 17: 662-671Abstract Full Text Full Text PDF PubMed Scopus (822) Google Scholar; Virtue et al., 2019Virtue A.T. McCright S.J. Wright J.M. Jimenez M.T. Mowel W.K. Kotzin J.J. Joannas L. Basavappa M.G. Spencer S.P. Clark M.L. et al.The gut microbiota regulates white adipose tissue inflammation and obesity via a family of microRNAs.Sci. Transl. Med. 2019; 11: eaav1892Crossref PubMed Scopus (135) Google Scholar). It has been estimated that the number of host-associated microbial cells and genes greatly outnumber that of their hosts' (Gilbert et al., 2018Gilbert J.A. Blaser M.J. Caporaso J.G. Jansson J.K. Lynch S.V. Knight R. Current understanding of the human microbiome.Nat. Med. 2018; 24: 392-400Crossref PubMed Scopus (948) Google Scholar; Stringlis et al., 2018Stringlis I.A. Yu K. Feussner K. de Jonge R. Van Bentum S. Van Verk M.C. Berendsen RL Bakker P.A.H.M. Feussner I. Pieterse C.M.J. MYB72-dependent coumarin exudation shapes root microbiome assembly to promote plant health.Proc. Natl. Acad. Sci. U S A. 2018; 115: E5213-E5222Crossref PubMed Scopus (358) Google Scholar). These microorganisms do not passively inhabit the surfaces of their hosts but instead continuously interact with each other and their hosts through a myriad of complex feedback processes (e.g., Falcinelli et al., 2015Falcinelli S. Picchietti S. Rodiles A. Cossignani L. Merrifield D.L. Taddei A.R. Maradonna F. Olivotto I. Gioacchini G. Carnevali O. Lactobacillus rhamnosus lowers zebrafish lipid content by changing gut microbiota and host transcription of genes involved in lipid metabolism.Sci. Rep. 2015; 5: 9336Crossref PubMed Scopus (134) Google Scholar; Kelly et al., 2015Kelly C.J. Zheng L. Campbell E.L. Saeedi B. Scholz C.C. Bayless A.J. Wilson K.E. Glover L.E. Kominsky D.J. Magnuson A. et al.Crosstalk between microbiota-derived short-chain fatty acids and intestinal epithelial HIF augments tissue barrier function.Cell Host Microbe. 2015; 17: 662-671Abstract Full Text Full Text PDF PubMed Scopus (822) Google Scholar; Stringlis et al., 2018Stringlis I.A. Yu K. Feussner K. de Jonge R. Van Bentum S. Van Verk M.C. Berendsen RL Bakker P.A.H.M. Feussner I. Pieterse C.M.J. MYB72-dependent coumarin exudation shapes root microbiome assembly to promote plant health.Proc. Natl. Acad. Sci. U S A. 2018; 115: E5213-E5222Crossref PubMed Scopus (358) Google Scholar). For example, host genomic features are co-responsible for shaping the microbiota composition (Suzuki et al., 2019Suzuki T.A. Phifer-Rixey M. Mack K.L. Sheehan M.J. Lin D. Bi K. Nachman M.W. Host genetic determinants of the gut microbiota of wild mice.Mol. Ecol. 2019; https://doi.org/10.1111/mec.15139Crossref Scopus (45) Google Scholar) through the differential biosynthesis of antibacterial peptides (Carvalho et al., 2012Carvalho F.A. Koren O. Goodrich J.K. Johansson M.E. Nalbantoglu I. Aitken J.D. Su Y. Chassaing B. Walters W.A. González A. et al.Transient inability to manage proteobacteria promotes chronic gut inflammation in TLR5-deficient mice.Cell Host Microbe. 2012; 12: 139-152Abstract Full Text Full Text PDF PubMed Scopus (355) Google Scholar), differential composition of intestinal mucosa (Vaishnava et al., 2011Vaishnava S. Yamamoto M. Severson K.M. Ruhn K.A. Yu X. Koren O. Ley R. Wakeland E.K. Hooper L.V. The antibacterial lectin RegIIIg promotes the spatial segregation of microbiota and host in the intestine.Science. 2011; 334https://doi.org/10.1126/science.1208930Crossref PubMed Scopus (460) Google Scholar), or differential release of nutrients (Reese et al., 2018Reese A.T. Pereira F.C. Schintlmeister A. Berry D. Wagner M. Hale L.P. Wu A. Jiang S. Durand H.K. Zhou X. et al.Microbial nitrogen limitation in the mammalian large intestine.Nat. Microbiol. 2018; 3: 1441-1450Crossref PubMed Scopus (48) Google Scholar). Gene expression interdependencies are also common between hosts and microorganisms. For instance, administration of Lactobacillus rhamnosus increases the uptake of fatty acids in zebrafish by down-regulating the transcription of host genes related to cholesterol and triglycerides metabolism (Falcinelli et al., 2015Falcinelli S. Picchietti S. Rodiles A. Cossignani L. Merrifield D.L. Taddei A.R. Maradonna F. Olivotto I. Gioacchini G. Carnevali O. Lactobacillus rhamnosus lowers zebrafish lipid content by changing gut microbiota and host transcription of genes involved in lipid metabolism.Sci. Rep. 2015; 5: 9336Crossref PubMed Scopus (134) Google Scholar). Similarly, the metabolism of microbiota-derived butyrate in epithelial cells stabilizes the function of the hypoxia-inducible transcription factor, which regulates the expression of a number of genes related to host immunity (Kelly et al., 2015Kelly C.J. Zheng L. Campbell E.L. Saeedi B. Scholz C.C. Bayless A.J. Wilson K.E. Glover L.E. Kominsky D.J. Magnuson A. et al.Crosstalk between microbiota-derived short-chain fatty acids and intestinal epithelial HIF augments tissue barrier function.Cell Host Microbe. 2015; 17: 662-671Abstract Full Text Full Text PDF PubMed Scopus (822) Google Scholar). Further examples of similar causal relationships between different omic levels from hosts and microorganisms are compiled in Table 1, and undoubtedly, many more will be revealed in the years to come.Table 1Examples of Holo-Omic Studies in the Current LitteratureOmic LevelsOrganismMajor FindingsReferenceArrow in Figure 1Genome, microbial 16SMouse20 host genes are associated with microbiome compositionSuzuki et al., 2019Suzuki T.A. Phifer-Rixey M. Mack K.L. Sheehan M.J. Lin D. Bi K. Nachman M.W. Host genetic determinants of the gut microbiota of wild mice.Mol. Ecol. 2019; https://doi.org/10.1111/mec.15139Crossref Scopus (45) Google Scholar1Genome, microbial 16SHumanGenetic disposition for inflammatory bowel disease is associated with a reduction in abundance of the genus Roseburia in the gut microbiomeImhann et al., 2018Imhann F. Vich Vila A. Bonder M.J. Fu J. Gevers D. Visschedijk M.C. Spekhorst L.M. Alberts R. Franke L. van Dullemen H.M. et al.Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease.Gut. 2018; 67: 108-119Crossref PubMed Scopus (412) Google Scholar1Transcriptome, metagenomePill-bug (Armadillidium vulgare)Potential collaboration between microbiota and pill-bug in degrading lignocelluloseBredon et al., 2018Bredon M. Dittmer J. Noël C. Moumen B. Bouchon D. Lignocellulose degradation at the holobiont level: teamwork in a keystone soil invertebrate.Microbiome. 2018; 6: 162Crossref PubMed Scopus (49) Google Scholar–Proteome, microbial 16SMouseLack of the TLR5 protein increases Proteobacteria and decreases Bacteroidetes in microbiome and promotes gut inflammationCarvalho et al., 2012Carvalho F.A. Koren O. Goodrich J.K. Johansson M.E. Nalbantoglu I. Aitken J.D. Su Y. Chassaing B. Walters W.A. González A. et al.Transient inability to manage proteobacteria promotes chronic gut inflammation in TLR5-deficient mice.Cell Host Microbe. 2012; 12: 139-152Abstract Full Text Full Text PDF PubMed Scopus (355) Google Scholar2Metabolome, metagenomeThale cress (Arabidopsis thaliana)Beneficial rhizobacteria induce excretion of the metabolite scopoletin that stimulates iron uptake and suppresses soil-borne pathogensStringlis et al., 2018Stringlis I.A. Yu K. Feussner K. de Jonge R. Van Bentum S. Van Verk M.C. Berendsen RL Bakker P.A.H.M. Feussner I. Pieterse C.M.J. MYB72-dependent coumarin exudation shapes root microbiome assembly to promote plant health.Proc. Natl. Acad. Sci. U S A. 2018; 115: E5213-E5222Crossref PubMed Scopus (358) Google Scholar3M" @default.
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• W3044646968 title "Holo-Omics: Integrated Host-Microbiota Multi-omics for Basic and Applied Biological Research" @default.
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