Host and microbiome multi-omics integration: applications and methodologies

被引:66
作者
Wang Q. [1 ,2 ,3 ]
Wang K. [1 ,2 ]
Wu W. [1 ,2 ]
Giannoulatou E. [3 ,4 ]
Ho J.W.K. [3 ,4 ,5 ]
Li L. [1 ,2 ]
机构
[1] State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, No.79 Qingchun Road, Hangzhou, 310003, Zhejiang
[2] Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou
[3] Victor Chang Cardiac Research Institute, Darlinghurst, 2010, NSW
[4] St Vincent’s Clinical School, University of New South Wales, Sydney, 2010, NSW
[5] School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam
基金
中国国家自然科学基金; 英国医学研究理事会;
关键词
Big data; Epigenome; Genome; Metabolome; Microbiome; Network analysis; Transcriptome;
D O I
10.1007/s12551-018-0491-7
中图分类号
学科分类号
摘要
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. © 2019, International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature.
引用
收藏
页码:55 / 65
页数:10
相关论文
共 94 条
[1]  
Abdul-Aziz M.A., Cooper A., Weyrich L.S., Exploring relationships between host genome and microbiome: new insights from genome-wide association studies, Front Microbiol, 7, (2016)
[2]  
Barko P.C., McMichael M.A., Swanson K.S., Williams D.A., The gastrointestinal microbiome: a review, J Vet Intern Med, 32, pp. 9-25, (2018)
[3]  
Bates D., Machler M., Bolker B., Walker S., Fitting linear mixed-effects models using lme4, J Stat Softw, (2015)
[4]  
Blekhman R., Goodrich J.K., Huang K., Et al., Host genetic variation impacts microbiome composition across human body sites, Genome Biol, 16, (2015)
[5]  
Bonder M.J., Kurilshikov A., Tigchelaar E.F., Et al., The effect of host genetics on the gut microbiome, Nat Genet, 48, pp. 1407-1412, (2016)
[6]  
Breitwieser F.P., Lu J., Salzberg S.L., A review of methods and databases for metagenomic classification and assembly, Brief Bioinform, (2017)
[7]  
Caporaso J.G., Kuczynski J., Stombaugh J., Et al., QIIME allows analysis of high-throughput community sequencing data, Nat Methods, 7, pp. 335-336, (2010)
[8]  
Castro-Nallar E., Shen Y., Freishtat R.J., Et al., Integrating microbial and host transcriptomics to characterize asthma-associated microbial communities, BMC Med Genet, 8, (2015)
[9]  
Chierico F.D., Nobili V., Vernocchi P., Et al., Gut microbiota profiling of pediatric nonalcoholic fatty liver disease and obese patients unveiled by an integrated meta-omics-based approach, Hepatology, 65, pp. 451-464, (2017)
[10]  
Cho I., Blaser M.J., The human microbiome: at the interface of health and disease, Nat Rev Genet, 13, pp. 260-270, (2012)