Microbes play important roles in human health and disease.The interaction between microbes and hosts is a reciprocal relationship,which remains largely under-explored.Current computational resources lack manually and ...Microbes play important roles in human health and disease.The interaction between microbes and hosts is a reciprocal relationship,which remains largely under-explored.Current computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes,microbial core genes,and disease phenotypes.We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association data.MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites.MicroPhenoDB also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes.Disease phenotypes are classified and described using the Experimental Factor Ontology(EFO).A refined score model was developed to prioritize the associations based on evidential metrics.The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly.MicroPhenoDB offers data browsing,searching,and visualization through user-friendly web interfaces and web service application programming interfaces.MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes,core genes,and disease phenotypes.It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human diseases.MicroPhenoDB is available through http://www.liwzlab.cn/microphenodb and http://lilab2.sysu.edu.cn/microphenodb.展开更多
Studies were conducted to identify candidate soil microbes responsible for observed differences in strawberry vigour at a small spatial scale, which was not associated with visual disease symptoms. Samples were obtain...Studies were conducted to identify candidate soil microbes responsible for observed differences in strawberry vigour at a small spatial scale, which was not associated with visual disease symptoms. Samples were obtained from the soils close to the rhizosphere of ‘big＇ and ‘small＇ plants from small plots which exhibited large local heterogeneity in plant vigour. A metabarcoding approach was used to profile bacterial and fungal compositions, using two primer pairs for 16 S ribosomal RNA genes（16S r DNA） and one for the fungal internal transcribed spacer（ITS） region. Of the two 16 S r DNA primer sets, the 341F/805 R resulted in sequences of better quality. A total 28 operational taxonomic units（OTUs） had differential relative abundance between samples from ‘big＇ and ‘small＇ plants. However, plausible biological explanation was only possible for three fungal OTUs. Two were possible phytopathogens： Verticillium spp. and Alternaria alternata although the latter has never been considered as a main pathogen of strawberry in the UK. For samples from ‘small＇ plants, the abundance of these OTUs was much greater than from ‘big＇ plants. The opposite was true for a mycorrhizal OTU. These results suggest that soil microbes related to crop production can be identified using metabarcoding technique. Further research is needed to assess whether A. alternata and Verticillium spp. could affect strawberry growth in the field.展开更多
基金This work was supported by the National Key R&D Programof China(Grant Nos.2016YFC0901604 and2018YFC0910401)the National Natural Science Founda-tion of China(Grant No.31771478)to WL.
文摘Microbes play important roles in human health and disease.The interaction between microbes and hosts is a reciprocal relationship,which remains largely under-explored.Current computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes,microbial core genes,and disease phenotypes.We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association data.MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites.MicroPhenoDB also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes.Disease phenotypes are classified and described using the Experimental Factor Ontology(EFO).A refined score model was developed to prioritize the associations based on evidential metrics.The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly.MicroPhenoDB offers data browsing,searching,and visualization through user-friendly web interfaces and web service application programming interfaces.MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes,core genes,and disease phenotypes.It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human diseases.MicroPhenoDB is available through http://www.liwzlab.cn/microphenodb and http://lilab2.sysu.edu.cn/microphenodb.
基金funded by Innovate UK(100867)with matching funding from several commercial companiesthe financial assistance of the China Scholarship Council(201306300133 and 201506300012)
文摘Studies were conducted to identify candidate soil microbes responsible for observed differences in strawberry vigour at a small spatial scale, which was not associated with visual disease symptoms. Samples were obtained from the soils close to the rhizosphere of ‘big＇ and ‘small＇ plants from small plots which exhibited large local heterogeneity in plant vigour. A metabarcoding approach was used to profile bacterial and fungal compositions, using two primer pairs for 16 S ribosomal RNA genes（16S r DNA） and one for the fungal internal transcribed spacer（ITS） region. Of the two 16 S r DNA primer sets, the 341F/805 R resulted in sequences of better quality. A total 28 operational taxonomic units（OTUs） had differential relative abundance between samples from ‘big＇ and ‘small＇ plants. However, plausible biological explanation was only possible for three fungal OTUs. Two were possible phytopathogens： Verticillium spp. and Alternaria alternata although the latter has never been considered as a main pathogen of strawberry in the UK. For samples from ‘small＇ plants, the abundance of these OTUs was much greater than from ‘big＇ plants. The opposite was true for a mycorrhizal OTU. These results suggest that soil microbes related to crop production can be identified using metabarcoding technique. Further research is needed to assess whether A. alternata and Verticillium spp. could affect strawberry growth in the field.
