The Experts below are selected from a list of 63 Experts worldwide ranked by ideXlab platform
Hua Xu - One of the best experts on this subject based on the ideXlab platform.
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A comparative study of disease genes and drug targets in the human protein interactome
BMC Bioinformatics, 2015Co-Authors: W Jim Zheng, Hua XuAbstract:Background Disease genes cause or contribute genetically to the development of the most complex diseases. Drugs are the major approaches to treat the complex disease through interacting with their targets. Thus, drug targets are critical for treatment efficacy. However, the interrelationship between the disease genes and drug targets is not clear. Results In this study, we comprehensively compared the network properties of disease genes and drug targets for five major disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease). We first collected disease genes from genome-wide association studies (GWAS) for five disease categories and collected their corresponding drugs based on drugs' Anatomical Therapeutic Chemical (ATC) classification. Then, we obtained the drug targets for these five different disease categories. We found that, though the intersections between disease genes and drug targets were small, disease genes were significantly enriched in targets compared to their enrichment in human protein-coding genes. We further compared network properties of the proteins encoded by disease genes and drug targets in human protein-protein interaction networks (interactome). The results showed that the drug targets tended to have Higher degree, Higher Betweenness, and lower clustering coefficient in cancer Furthermore, we observed a clear fraction increase of disease proteins or drug targets in the near neighborhood compared with the randomized genes. Conclusions The study presents the first comprehensive comparison of the disease genes and drug targets in the context of interactome. The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing.
W Jim Zheng - One of the best experts on this subject based on the ideXlab platform.
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A comparative study of disease genes and drug targets in the human protein interactome
BMC Bioinformatics, 2015Co-Authors: W Jim Zheng, Hua XuAbstract:Background Disease genes cause or contribute genetically to the development of the most complex diseases. Drugs are the major approaches to treat the complex disease through interacting with their targets. Thus, drug targets are critical for treatment efficacy. However, the interrelationship between the disease genes and drug targets is not clear. Results In this study, we comprehensively compared the network properties of disease genes and drug targets for five major disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease). We first collected disease genes from genome-wide association studies (GWAS) for five disease categories and collected their corresponding drugs based on drugs' Anatomical Therapeutic Chemical (ATC) classification. Then, we obtained the drug targets for these five different disease categories. We found that, though the intersections between disease genes and drug targets were small, disease genes were significantly enriched in targets compared to their enrichment in human protein-coding genes. We further compared network properties of the proteins encoded by disease genes and drug targets in human protein-protein interaction networks (interactome). The results showed that the drug targets tended to have Higher degree, Higher Betweenness, and lower clustering coefficient in cancer Furthermore, we observed a clear fraction increase of disease proteins or drug targets in the near neighborhood compared with the randomized genes. Conclusions The study presents the first comprehensive comparison of the disease genes and drug targets in the context of interactome. The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing.
Zhongming Zhao - One of the best experts on this subject based on the ideXlab platform.
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A comparative study of cancer proteins in the human protein-protein interaction network
BMC Genomics, 2020Co-Authors: Zhongming ZhaoAbstract:Abstract Background Cancer is a complex disease. So far, many genes have been reported to involve in the development of cancer. Rather than the traditional approach to studying individual genes or loci, a systematic investigation of cancer proteins in the human protein-protein interaction network may provide important biological information for uncovering the molecular mechanisms of cancer and, potentially, other complex diseases. Results We explored global and local network characteristics of the proteins encoded by cancer genes (cancer proteins) in the human interactome. We found that the network topology of the cancer proteins was much different from that of the proteins encoded by essential genes (essential proteins) or control genes (control proteins). Relative to the essential proteins or control proteins, cancer proteins tended to have Higher degree, Higher Betweenness, shorter shortest-path distance, and weaker clustering coefficient in the human interactome. We further separated the cancer proteins into two groups (recessive and dominant cancer proteins) and compared their topological features. Recessive cancer proteins had Higher Betweenness than dominant cancer proteins, while their degree distribution and characteristic shortest path distance were also significantly different. Finally, we found that cancer proteins were not randomly distributed in the human interactome and they connected strongly with each other. Conclusion Our study revealed much stronger protein-protein interaction characteristics of cancer proteins relative to the essential proteins or control proteins in the whole human interactome. We also found stronger network characteristics of recessive than dominant cancer proteins. The results are helpful for cancer candidate gene prioritization and verification, biomarker discovery, and, ultimately, understanding the etiology of cancer at the systems biological level.
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A comparative study of cancer proteins in the human protein-protein interaction network.
BMC genomics, 2010Co-Authors: Zhongming ZhaoAbstract:Cancer is a complex disease. So far, many genes have been reported to involve in the development of cancer. Rather than the traditional approach to studying individual genes or loci, a systematic investigation of cancer proteins in the human protein-protein interaction network may provide important biological information for uncovering the molecular mechanisms of cancer and, potentially, other complex diseases. We explored global and local network characteristics of the proteins encoded by cancer genes (cancer proteins) in the human interactome. We found that the network topology of the cancer proteins was much different from that of the proteins encoded by essential genes (essential proteins) or control genes (control proteins). Relative to the essential proteins or control proteins, cancer proteins tended to have Higher degree, Higher Betweenness, shorter shortest-path distance, and weaker clustering coefficient in the human interactome. We further separated the cancer proteins into two groups (recessive and dominant cancer proteins) and compared their topological features. Recessive cancer proteins had Higher Betweenness than dominant cancer proteins, while their degree distribution and characteristic shortest path distance were also significantly different. Finally, we found that cancer proteins were not randomly distributed in the human interactome and they connected strongly with each other. Our study revealed much stronger protein-protein interaction characteristics of cancer proteins relative to the essential proteins or control proteins in the whole human interactome. We also found stronger network characteristics of recessive than dominant cancer proteins. The results are helpful for cancer candidate gene prioritization and verification, biomarker discovery, and, ultimately, understanding the etiology of cancer at the systems biological level.
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Global Network Features of Cancer Genes in the Human Interactome
2009 International Joint Conference on Bioinformatics Systems Biology and Intelligent Computing, 2009Co-Authors: Zhongming ZhaoAbstract:It has been commonly accepted that protein-protein interaction network contains valuable information for elucidation of biological processes. Such information, which is difficult to be identified from traditional genetic studies, is important for uncovering the mechanisms of complex diseases such as cancers. In this study, we performed a systematic analysis of global network characteristics for the proteins encoded by cancer genes in the human interactome. We observed that the network topology of the cancer genes was much different from that of non-cancer genes. Overall, compared to non-cancer genes, proteins of cancer genes tend to have Higher connectivity, Higher Betweenness, shorter shortest-path distance, and weaker clustering coefficient in the human interactome. Furthermore, we examined and compared topological features of the recessive and dominant cancer genes. Among the four topological measures we took in this study, recessive cancer genes had significantly different degree distribution or significantly stronger Betweenness from dominant cancer genes, while their difference in terms of shortest-path distance and clustering coefficient was not significant. The results suggest that cancer genespsila protein-protein interactions play important roles in the etiology of cancers.
Ziding Zhang - One of the best experts on this subject based on the ideXlab platform.
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Network Analysis Reveals a Common Host–Pathogen Interaction Pattern in Arabidopsis Immune Responses
Frontiers in Plant Science, 2017Co-Authors: Hong Li, Yuan Zhou, Ziding ZhangAbstract:Many plant pathogens secrete virulence effectors into host cells to target important proteins in host cellular network. However, the dynamic interactions between effectors and host cellular network have not been fully understood. Here, an integrative network analysis was conducted by combining Arabidopsis thaliana protein-protein interaction network, known targets of Pseudomonas syringae and Hyaloperonospora arabidopsidis effectors, and gene expression profiles in the immune response. In particular, we focused on the characteristic network topology of the effector targets and differentially expressed genes (DEGs). We found that effectors tended to manipulate key network positions with Higher Betweenness centrality. The effector targets, especially those that are common targets of an individual effector, tended to be clustered together in the network. Moreover, the distances between the effector targets and DEGs increased over time during infection. In line with this observation, pathogen-susceptible mutants tended to have more DEGs surrounding the effector targets compared with resistant mutants. Our results suggest a common plant-pathogen interaction pattern at the cellular network level, where pathogens employ potent local impact mode to interfere with key positions in the host network, and plant organizes an in-depth defense by sequentially activating genes distal to the effector targets.
Hong Li - One of the best experts on this subject based on the ideXlab platform.
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Network Analysis Reveals a Common Host–Pathogen Interaction Pattern in Arabidopsis Immune Responses
Frontiers in Plant Science, 2017Co-Authors: Hong Li, Yuan Zhou, Ziding ZhangAbstract:Many plant pathogens secrete virulence effectors into host cells to target important proteins in host cellular network. However, the dynamic interactions between effectors and host cellular network have not been fully understood. Here, an integrative network analysis was conducted by combining Arabidopsis thaliana protein-protein interaction network, known targets of Pseudomonas syringae and Hyaloperonospora arabidopsidis effectors, and gene expression profiles in the immune response. In particular, we focused on the characteristic network topology of the effector targets and differentially expressed genes (DEGs). We found that effectors tended to manipulate key network positions with Higher Betweenness centrality. The effector targets, especially those that are common targets of an individual effector, tended to be clustered together in the network. Moreover, the distances between the effector targets and DEGs increased over time during infection. In line with this observation, pathogen-susceptible mutants tended to have more DEGs surrounding the effector targets compared with resistant mutants. Our results suggest a common plant-pathogen interaction pattern at the cellular network level, where pathogens employ potent local impact mode to interfere with key positions in the host network, and plant organizes an in-depth defense by sequentially activating genes distal to the effector targets.