1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,Ch...1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China 3 Shenzhen Key Laboratory of Intelligent Bioinformatics,Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen 518055,China E-mail:xjlei@snnu.edu.cn;yalichen@snnu.edu.cn;yi.pan@siat.ac.cn Received December 9,2022;accepted July 29,2024.Abstract Identifying microbes associated with diseases is important for understanding the pathogenesis of diseases as well as for the diagnosis and treatment of diseases.In this article,we propose a method based on a multi-source association network to predict microbe-disease associations,named MMHN-MDA.First,a heterogeneous network of multimolecule associations is constructed based on associations between microbes,diseases,drugs,and metabolites.Second,the graph embedding algorithm Laplacian eigenmaps is applied to the association network to learn the behavior features of microbe nodes and disease nodes.At the same time,the denoising autoencoder(DAE)is used to learn the attribute features of microbe nodes and disease nodes.Finally,attribute features and behavior features are combined to get the final embedding features of microbes and diseases,which are fed into the convolutional neural network(CNN)to predict the microbedisease associations.Experimental results show that the proposed method is more effective than existing methods.In addition,case studies on bipolar disorder and schizophrenia demonstrate good predictive performance of the MMHN-MDA model,and further,the results suggest that gut microbes may influence host gene expression or compounds in the nervous system,such as neurotransmitters,or metabolites that alter the blood-brain barrier.展开更多
Many diseases and health conditions are closely related to various microbes,which participate in complex interactions with diverse drugs;nonetheless,the detailed targets of such drugs remain to be elucidated.Many exis...Many diseases and health conditions are closely related to various microbes,which participate in complex interactions with diverse drugs;nonetheless,the detailed targets of such drugs remain to be elucidated.Many existing studies have reported causal associations among drugs,gut microbes,or diseases,calling for a workflow to reveal their intricate interactions.In this study,we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease(QSDMD)database(http://www.qsdmd.lbci.net/),which includes diverse interactions for more than 8,000 drugs,163 microbes,and 42 common diseases.Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations.Furthermore,we have constructed a QS-based drug-receptor interaction network,proposed a systematic framework including various drug-receptor-microbe-disease connections,and mapped a paradigmatic circular interaction network based on the QS-DMD,which can provide the underlying QS-based mechanisms for the reported causal associations.The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases.This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.展开更多
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
基金supported by the National Natural Science Foundation of China under Grant Nos.62272288 and U22A2041the Fundamental Research Funds for the Central Universities of China,and Shaanxi Normal University under Grant No.GK202302006.
文摘1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China 3 Shenzhen Key Laboratory of Intelligent Bioinformatics,Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen 518055,China E-mail:xjlei@snnu.edu.cn;yalichen@snnu.edu.cn;yi.pan@siat.ac.cn Received December 9,2022;accepted July 29,2024.Abstract Identifying microbes associated with diseases is important for understanding the pathogenesis of diseases as well as for the diagnosis and treatment of diseases.In this article,we propose a method based on a multi-source association network to predict microbe-disease associations,named MMHN-MDA.First,a heterogeneous network of multimolecule associations is constructed based on associations between microbes,diseases,drugs,and metabolites.Second,the graph embedding algorithm Laplacian eigenmaps is applied to the association network to learn the behavior features of microbe nodes and disease nodes.At the same time,the denoising autoencoder(DAE)is used to learn the attribute features of microbe nodes and disease nodes.Finally,attribute features and behavior features are combined to get the final embedding features of microbes and diseases,which are fed into the convolutional neural network(CNN)to predict the microbedisease associations.Experimental results show that the proposed method is more effective than existing methods.In addition,case studies on bipolar disorder and schizophrenia demonstrate good predictive performance of the MMHN-MDA model,and further,the results suggest that gut microbes may influence host gene expression or compounds in the nervous system,such as neurotransmitters,or metabolites that alter the blood-brain barrier.
基金supported by the National Key Research and Development Project of China(2019YFA0905600,2020YFA0907900)the National Natural Science Foundation of China(31570089,31770076,62172296)+1 种基金the Funds for Creative Research Groups of China(21621004)the New Century Outstanding Talent Support Program,Education Ministry of China。
文摘Many diseases and health conditions are closely related to various microbes,which participate in complex interactions with diverse drugs;nonetheless,the detailed targets of such drugs remain to be elucidated.Many existing studies have reported causal associations among drugs,gut microbes,or diseases,calling for a workflow to reveal their intricate interactions.In this study,we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease(QSDMD)database(http://www.qsdmd.lbci.net/),which includes diverse interactions for more than 8,000 drugs,163 microbes,and 42 common diseases.Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations.Furthermore,we have constructed a QS-based drug-receptor interaction network,proposed a systematic framework including various drug-receptor-microbe-disease connections,and mapped a paradigmatic circular interaction network based on the QS-DMD,which can provide the underlying QS-based mechanisms for the reported causal associations.The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases.This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.
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.
