RAB5A is associated with genes involved in exosome secretion: Integration of bioinformatics analysis and experimental
Gilar Gorji-Bahri1, Hamid Reza Moghimi2,3, Atieh Hashemi1
1Department of Pharmaceutical Biotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Abstract:
Exosomes, as cell-cell communicators with an endosomal origin, are involved in the progression of various diseases. RAB5A, a member of the small Rab GTPases family, which is well known as a key regulator of cellular endocytosis, is expected to be involved in exosome secretion. Here, we found the impact of RAB5A on exosome secretion from human hepatocellular carcinoma cell line using a rapid yet reliable bioinformatics approach followed by experimental analysis. Initially, RAB5A and exosome secretion-related genes were gathered from bioinformatics tools, namely, CTD, COREMINE, and GeneMANIA; and published papers. Protein-protein interaction (PPI) was then constructed by the Search Tool for Retrieval of Interacting Genes (STRING) database. Among them, several genes with different combined scores were validated by the real-time quantitative polymerase chain reaction (RT-qPCR) in stable RAB5A knockdown cells. Thereafter, to validate the bioinformatics results functionally, the impact of RAB5A knockdown on exosome secretion was evaluated. Bioinformatics analysis showed that RAB5A interacts with 37 genes involved in exosome secretion regulatory pathways. Validation by RT-qPCR confirmed the association of RAB5A with candidate interacted genes and interestingly showed that even medium to low combined scores of the STRING database could be experimentally valid. Moreover, the functional analysis demonstrated that the stable silencing of RAB5A could experimentally decrease exosome secretion. In conclusion, we suggest RAB5A as a regulator of exosome secretion based on our bioinformatics approach and experimental analysis. Also, we propose the usage of PPI-derived from the STRING database regardless of their combined scores in advanced bioinformatics analysis.
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