Back

From gonadogenesis to testicular cancer: Unravelling the novel regulators and identification of drug candidates against FGF9 and PTGDS

Kumar, C.; Singh, V. K.; Roy, J. K.

2023-06-11 bioinformatics
10.1101/2023.06.09.544377 bioRxiv
Show abstract

Sex determination is the preliminary step toward gonadogenesis in mammals. Antagonistic interactions of key regulators have been only fragmentarily mentioned so far. Therefore, exploring regulators underlying the phenomena is required to solve questions, especially regarding female gonad development and gonadal disorders in congenital or adults. Inhibiting discrepancies in PPI pathways and combating related disorders are of urgent necessity, for which novel drugs are constantly required. Here, we performed in silico analysis using robust bioinformatics methods, which is unprecedented work in sex determination studies, providing large-scale analysis without exorbitant wet lab procedures. Analyzed regulators were overlapped with our RNA-seq data for authentication, to obtain differentially expressed elements. Additionally, CADD approach was used to discover inhibitors for FGF9 and PTGDS to search for potential drugs combating gonadal disorders in adults. Along with druggable properties, only FGF9 and PTGDS had full-length protein structures available, among 25 key genes under investigation. Our large-scale analysis of PPIN, produced highly interacting hub-bottleneck nodes as novel genes. Further, functional enrichment analysis revealed importance of these regulators in gonadogenesis. We identified sex-specific novel genes, miRNAs-target pairs, and lncRNAs-target pairs, which appear to play an important role in regulation of ovary development. CADD with molecular docking, MD simulations, and molecular mechanics confirmed stability of two novel compounds, DB12884 and DB12412 that could potentially inhibit FGF9 and PTGDS respectively. Taken together our study provides valuable information regarding involvement of crucial regulators in antagonistic mechanism of gonadogenesis and their related disorders, which will further assist in refining wet lab experiments.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.