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A comparative study of disease genes and drug targets in the human protein interactome.

Sun J, Zhu K, Zheng W, Xu H - BMC Bioinformatics (2015)

Bottom Line: Disease genes cause or contribute genetically to the development of the most complex diseases.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.The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

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.

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Comparisons among five sets of disease genes (A) and five sets of drug target genes (B) for five disease categories. The five disease categories included cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease.
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Figure 1: Comparisons among five sets of disease genes (A) and five sets of drug target genes (B) for five disease categories. The five disease categories included cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease.

Mentions: Using the GWAS association results from the GWAS catalog, we obtained five sets of disease genes with SNPs having significant P-values of less than 1.0 × 10-8. Then, based on the GWAS trait classification from EBI, we classified the gene sets into five different disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease) for further analyses. Thus, we obtained a total of 1,344 disease genes from 450 GWA studies of five disease categories (Table 1). The numbers for the five sets of disease genes ranged from 162 for nervous disease to 601 for metabolic disease. Figure 1A shows their intersections. Among them, the metabolic and immune disease-associated genes had many common genes (117, 35.67%), which indicated that more than one third of the immune disease genes were reported to be significantly associated with the metabolic disease. This observation was consistent with the idea that the metabolic and immune systems have many links on multiple levels in the biological processes [34].


A comparative study of disease genes and drug targets in the human protein interactome.

Sun J, Zhu K, Zheng W, Xu H - BMC Bioinformatics (2015)

Comparisons among five sets of disease genes (A) and five sets of drug target genes (B) for five disease categories. The five disease categories included cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4402590&req=5

Figure 1: Comparisons among five sets of disease genes (A) and five sets of drug target genes (B) for five disease categories. The five disease categories included cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease.
Mentions: Using the GWAS association results from the GWAS catalog, we obtained five sets of disease genes with SNPs having significant P-values of less than 1.0 × 10-8. Then, based on the GWAS trait classification from EBI, we classified the gene sets into five different disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease) for further analyses. Thus, we obtained a total of 1,344 disease genes from 450 GWA studies of five disease categories (Table 1). The numbers for the five sets of disease genes ranged from 162 for nervous disease to 601 for metabolic disease. Figure 1A shows their intersections. Among them, the metabolic and immune disease-associated genes had many common genes (117, 35.67%), which indicated that more than one third of the immune disease genes were reported to be significantly associated with the metabolic disease. This observation was consistent with the idea that the metabolic and immune systems have many links on multiple levels in the biological processes [34].

Bottom Line: Disease genes cause or contribute genetically to the development of the most complex diseases.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.The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus