Deciphering phenotyping, DNA barcoding, and RNA secondary structure predictions in eggplant wild relatives provide insights for their future breeding strategies.

Eggplant or aubergine (Solanum melongena L.) and its wild cousins, comprising 13 clades with 1500 species, have an unprecedented demand across the globe. Cultivated eggplant has a narrow molecular diversity that hinders eggplant breeding advancements. Wild eggplants need resurgent attention to broaden eggplant breeding resources. In this study, we emphasized phenotypic and genotypic discriminations among 13 eggplant species deploying chloroplast-plastid (Kim matK) and nuclear (ITS2) short gene sequences (400-800 bp) at DNA barcode region followed by ITS2 secondary structure predictions. The identification efficiency at the Kim matK region was higher (99-100%) than in the ITS2 region (80-90%). The eggplant species showed 13 unique secondary structures with a central ring with various helical orientations. Principal component analysis (PCoA) provides the descriptor-wise phenotypic clustering, which is essential for trait-specific breeding. Groups I and IV are categorized under scarlet complexes S. aethiopicum, S. trilobatum, and S. melongena (wild and cultivated). Group II represented the gboma clade (S. macrocarpon, S. wrightii, S. sisymbriifolium, and S. aculeatissimum), and group III includes S. mammosum, and S. torvum with unique fruit shape and size. The present study would be helpful in genetic discrimination, biodiversity conservation, and the safe utilization of wild eggplants.

Identification of therapeutically potential targets and their ligands for the treatment of OSCC.

Recent advancements in cancer biology have revealed molecular changes associated with carcinogenesis and chemotherapeutic exposure. The available information is being gainfully utilized to develop therapies targeting specific molecules involved in cancer cell growth, survival, and chemoresistance. Targeted therapies have dramatically increased overall survival (OS) in many cancers. Therefore, developing such targeted therapies against oral squamous cell carcinoma (OSCC) is anticipated to have significant clinical implications. In the current work, we have identified drug-specific sensitivity-related prognostic biomarkers (BOP1, CCNA2, CKS2, PLAU, and SERPINE1) using gene expression, Cox proportional hazards regression, and machine learning in OSCC. Dysregulation of these markers is significantly associated with OS in many cancers. Their elevated expression is related to cellular proliferation and aggressive malignancy in various cancers. Mechanistically, inhibition of these biomarkers should significantly reduce cellular proliferation and metastasis in OSCC and should result in better OS. It is pertinent to note that no effective small-molecule candidate has been identified against these biomarkers to date. Therefore, a comprehensive in silico drug design strategy assimilating homology modeling, extensive molecular dynamics (MD) simulation, and ensemble molecular docking has been applied to identify potential compounds against identified targets, and potential molecules have been identified. We hope that this study will help in deciphering potential genes having roles in chemoresistance and a significant impact on OS. It will also result in the identification of new targeted therapeutics against OSCC.