Host and microbiome multi-omics integration: applications and methodologies
The study of the microbial community—the microbiome—associated with a human host is a maturing research field. It is increasingly clear that the composition of the human’s microbiome is associated with various diseases such as gastrointestinal diseases, liver diseases and metabolic diseases. Using high-throughput technologies such as next-generation sequencing and mass spectrometry–based metabolomics, we are able to comprehensively sequence the microbiome—the metagenome—and associate these data with the genomic, epigenomics, transcriptomic and metabolic profile of the host. Our review summarises the application of integrating host omics with microbiome as well as the analytical methods and related tools applied in these studies. In addition, potential future directions are discussed.
KeywordsMicrobiome Genome Epigenome Transcriptome Metabolome Network analysis Big data
Compliance with ethical standards
Funding was provided by the National Natural Science Foundation of China (grant nos. 81330011, 81330014, 81790631, 81790633, 81570512 and 81121002), the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (grant no. 81721091), and the National Basic Research Program of China (973 program) (grant no. 2013CB531401). This work was also supported in part by funds from the New South Wales Ministry of Health, a National Health and Medical Research Council Career Development Fellowship (1105271 to JWKH) and the National Heart Foundation (Future Leader Fellowship 100848 to JWKH and 101204 to EG).
Conflict of interest
Qing Wang declares that she has no conflict of interest. Kaicen Wang declares that she has no conflict of interest. Wenrui Wu declares that she has no conflict of interest. Eleni Giannoulatou declares that she has no conflict of interest. Joshua W. K. Ho declares that he has no conflict of interest. Lanjuan Li declares that he/she has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw. https://doi.org/10.18637/jss.v067.i01
- Breitwieser FP, Lu J, Salzberg SL (2017) A review of methods and databases for metagenomic classification and assembly. Brief Bioinform. https://doi.org/10.1093/bib/bbx120
- Dixon P (2003) VEGAN, a package of R functions for community ecology. J Veg Sci 14:927–930. https://doi.org/10.1111/j.1654-1103.2003.tb02228.x CrossRefGoogle Scholar
- Dray S, Dufour A-B (2017) The ade4 package: implementing the duality diagram for ecologists. J Stat Softw. https://doi.org/10.18637/jss.v022.i04
- de Steenhuijsen Piters WAA, Heinonen S, Hasrat R et al (2016) Nasopharyngeal microbiota, host transcriptome, and disease severity in children with respiratory syncytial virus infection. Am J Respir Crit Care Med 194:1104–1115. https://doi.org/10.1164/rccm.201602-0220OC CrossRefPubMedPubMedCentralGoogle Scholar
- Hua X, Song L, Yu G, et al (2015) MicrobiomeGWAS: a tool for identifying host genetic variants associated with microbiome composition. https://doi.org/10.1101/031187
- Morgan XC, Kabakchiev B, Waldron L et al (2015) Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease. Genome Biol 16:67. https://doi.org/10.1186/s13059-015-0637-x CrossRefPubMedPubMedCentralGoogle Scholar
- Noecker C, Eng A, Srinivasan S et al (2016) Metabolic model-based integration of microbiome taxonomic and metabolomic profiles elucidates mechanistic links between ecological and metabolic variation. mSystems 1:e00013–e00015. https://doi.org/10.1128/mSystems.00013-15 CrossRefPubMedPubMedCentralGoogle Scholar
- Schaefer J, Opgen-Rhein R, Strimmer and K (2015) GeneNet: Modeling and Inferring Gene Networks. R package version 1.2.13 https://CRAN.R-project.org/package=GeneNet. Accessed 28 Dec 2018
- Tsay J-CJ, Wu BG, Badri MH et al (2018) Airway microbiota is associated with up-regulation of the PI3K pathway in lung cancer. Am J Respir Crit Care Med. https://doi.org/10.1164/rccm.201710-2118OC
- Zhao Y, Johnson WE (2018) Exploring host-microbe interactions in lung cancer. Am J Respir Crit Care Med. https://doi.org/10.1164/rccm.201807-1225ED