Protein-Protein Interaction Networks Comparison Between Paediatric Neuroblastoma Cancer and Glioblastoma Multiforme Cancer with Gene Expression Data

Author(s):  
S.P.B.M Senadheera ◽  
A.R. Weerasinghe
2017 ◽  
Author(s):  
Sebastian Vlaic ◽  
Christian Tokarski-Schnelle ◽  
Mika Gustafsson ◽  
Uta Dahmen ◽  
Reinhard Guthke ◽  
...  

AbstractThe identification of disease associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. A major problem hampering their identification is the detection of protein communities within large-scale, whole-genome PPINs. Current strategies solve the maximal clique enumeration (MCE) problem, i.e., the enumeration of all non-extendable groups of proteins, where each pair of proteins is connected by an edge. The MCE problem however is non-deterministic polynomial time hard and can thus be computationally overwhelming for large-scale, whole-genome PPINs.We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs in conjunction with gene-expression data. ModuleDiscoverer is a heuristic that approximates the community structure underlying PPINs. Based on a high-confidence PPIN of Rattus norvegicus and publicly available gene expression data we apply our algorithm to identify the regulatory module of a rat-model of diet induced non-alcoholic steatohepatitis (NASH). We validate the module using single-nucleotide polymorphism data from independent genome-wide association studies. Structural analysis of the module reveals 10 sub-modules. These sub-modules are associated with distinct biological functions and pathways that are relevant to the pathological and clinical situation in NASH.ModuleDiscoverer is freely available upon request from the corresponding author.


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.


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