On the integration of protein-protein interaction networks with gene expression and 3D structural data: What can be gained?

2014 ◽  
Vol 129 (6) ◽  
Author(s):  
Paola Bertolazzi ◽  
Mary Ellen Bock ◽  
Concettina Guerra ◽  
Paola Paci ◽  
Daniele Santoni
2021 ◽  
Vol 12 ◽  
Author(s):  
Yuxuan Han ◽  
Zhuoni Hou ◽  
Qiuling He ◽  
Xuemin Zhang ◽  
Kaijing Yan ◽  
...  

bZIP gene family is one of the largest transcription factor families. It plays an important role in plant growth, metabolic, and environmental response. However, complete genome-wide investigation of bZIP gene family in Glycyrrhiza uralensis remains unexplained. In this study, 66 putative bZIP genes in the genome of G. uralensis were identified. And their evolutionary classification, physicochemical properties, conserved domain, functional differentiation, and the expression level under different stress conditions were further analyzed. All the members were clustered into 13 subfamilies (A–K, M, and S). A total of 10 conserved motifs were found in GubZIP proteins. Members from the same subfamily shared highly similar gene structures and conserved domains. Tandem duplication events acted as a major driving force for the evolution of bZIP gene family in G. uralensis. Cis-acting elements and protein–protein interaction networks showed that GubZIPs in one subfamily are involved in multiple functions, while some GubZIPs from different subfamilies may share the same functional category. The miRNA network targeting GubZIPs showed that the regulation at the transcriptional level may affect protein–protein interaction networks. We suspected that domain-mediated interactions may categorize a protein family into subfamilies in G. uralensis. Furthermore, the tissue-specific gene expression patterns of GubZIPs were analyzed using the public RNA-seq data. Moreover, gene expression level of 66 bZIP family members under abiotic stress treatments was quantified by using qRT-PCR. The results of this study may serve as potential candidates for functional characterization in the future.


2020 ◽  
Vol 27 (3) ◽  
pp. 57-71
Author(s):  
Peng Li ◽  
Bo Sun ◽  
Maozu Guo

Early cancer diagnosis and prognosis prediction are necessary for cancer patients. Effective identification of cancer-related genes and biomarkers and survival prediction for cancer patients would facilitate personalized treatment of cancer patients. This study aimed to investigate a method for integrating data regarding gene expression and protein-protein interaction networks to identify cancer-related prognostic genes via random walk with restart algorithm and survival analysis. Known cancer-related genes in protein-protein interaction networks were considered seed genes, and the random walk algorithm was used to identify candidate cancer-related genes. Thereafter, using the univariant Cox regression model, gene expression data were screened to identify survival-related genes. Furthermore, candidate genes and survival-related genes were screened to identify cancer-related prognostic genes. Finally, the effectiveness of the method was verified through gene function analysis and survival prediction. The results indicate that the cancer-related genes can be considered prognostic cancer biomarkers and provide a basis for cancer diagnosis.


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