| Literature DB >> 27734124 |
Qichao Tu1,2,3, Jiabao Li4, Zhou Shi3, Yanfei Chen5, Lu Lin1, Juan Li6, Hongling Wang2, Jianbo Yan2, Qingming Zhou6, Xiangzhen Li4, Lanjuan Li5, Jizhong Zhou7, Zhili He8.
Abstract
With the massive data generated by the Human Microbiome Project, how to transform such data into useful information and knowledge remains challenging. Here, with currently available sequencing information (reference genomes and metagenomes), we have developed a comprehensive microarray, HuMiChip2, for strain-level identification and functional characterization of human microbiomes. HuMiChip2 was composed of 29,467 strain-specific probes targeting 2063 microbial strains/species and 133,924 sequence- and group-specific probes targeting 157 key functional gene families involved in various metabolic pathways and host-microbiome interaction processes. Computational evaluation of strain-specific probes suggested that they were not only specific to mock communities of sequenced microorganisms and metagenomes from different human body sites but also to non-sequenced microbial strains. Experimental evaluation of strain-specific probes using single strains/species and mock communities suggested a high specificity of these probes with their corresponding targets. Application of HuMiChip2 to human gut microbiome samples showed the patient microbiomes of alcoholic liver cirrhosis significantly (p < 0.05) shifted their functional structure from the healthy individuals, and the relative abundance of 21 gene families significantly (p < 0.1) differed between the liver cirrhosis patients and healthy individuals. At the strain level, five Bacteroides strains were significantly (p < 0.1) and more frequently detected in liver cirrhosis patients. These results suggest that the developed HuMiChip2 is a useful microbial ecological microarray for both strain-level identification and functional profiling of human microbiomes.Entities:
Keywords: Functional profiling; HuMiChip2; Human microbiome; Microbial ecological microarray; Strain-level identification
Mesh:
Year: 2016 PMID: 27734124 DOI: 10.1007/s00253-016-7910-0
Source DB: PubMed Journal: Appl Microbiol Biotechnol ISSN: 0175-7598 Impact factor: 4.813