Identification of putative non-host essential genes and novel drug targets against Acinetobacter baumannii by in silico comparative genome analysis.

Acinetobacter baumannii, the gram-negative bacteria emerged as an extremely critical pathogen causing nosocomial and different kinds of infections. A. baumannii exhibit resistivity towards various classes of antibiotics that shows that there is a dire need to search more drug targets by exploiting the full genome of the bacteria. In doing so, a strategy is made with the combination of computational biology, pathogen informatics and cheminformatics. Comparative genomics analysis, modeling and docking studies have been performed for the prediction of non-host essential genes and novel drug candidates against A. baumannii. Among 37 unique and 82 common metabolic pathways, 92 genes were predicted as non-host genes. Similarly, using homology search between A. baumannii genome and essential genes of different bacteria, 293 genes were predicted as essential genes of A. baumannii. Among these predicted non-host and essential genes, 86 genes were predicted as non-host essential genes which could serve as potential novel drug and vaccine targets. Additional drug-target like physicochemical properties were estimated such as the molecular weight, subcellular localization and druggability potential. On the structural part, the crystal structures of all the non-host essential genes of A. baumannii were found except the three genes. Out of these three, a homology model of Undecaprenyl-diphosphatase was built using a PDB template by MODELLER [version 9.18]. The quality of the model was assessed by the ProSA and RAMPAGE. The built model was subjected as a receptor for the molecular docking with Adenosine diphosphate (ADP) as a ligand. The molecular docking was performed by AutoDock4 and the best conformation with lowest binding energy (-4.39 kcal/mol) was obtained. The LigPlot was used to identify the close interactions between the ligand the receptor's residues. This study will further aid for the selection of putative inhibitors against a novel drug target identified against A. baumannii and hence could lead to the better therapeutics.

Computational-based identification and analysis of globally expressed differential genes in high-grade serous ovarian carcinoma cell lines.

Ovarian Cancer (OVCA) is the most occurring gynecological cancer worldwide, often diagnosed at a later stage and ultimate results in a high death rate. To overcome this serious health concern, it is important to understand the molecular mechanisms and equally significant to identify the putative biomarkers as well as the therapeutic drug targets for the early diagnosis and treatment of OVCA. In doing so, a strategy is designed to study the most frequently diagnosed cases of OVCA called as High-Grade Serous Ovarian Carcinoma (HGSOC) cell lines with the combination of computational biology, biostatistics and cancer informatics approaches. This study is directed to investigate the global gene expression profiling, and to perform the analyses of identified global Differently Expressed Genes (DEGs) of OVCA. The microarray dataset (GSE71524) is comprised of tumor and cell line samples of OVCA and it was used for the identification of DEGs in the current study. The STRING database was used to construct Protein-Protein Interaction (PPI) network of DEGs, and hub genes were identified by the CytoHubba. In addition, a functional enrichment analysis of up- and down-regulated DEGs was performed by a bioinformatics database called as DAVID. The microRNAs (miRNAs) and transcription factors (TFs) analyses were conducted with the aid of biological tools, MAGIA and GenCOdis3, respectively. As a result, the genes comprised of CSF1R, TYROBP, PLEK, FGR, ACLY, ACACA, LAPTM5, C1 or f162, IL10RA and CD163 were identified as hub genes. Additionally, miRNA analysis resulted in finding an association of zinc finger protein with OVCA comes out after implementing different algorithms. On the other hand, in the TFs analysis resulted in various DEGs that were enriched by NFAT, NF1 and GABP TFs. In this study, it was observed that ACACA, ACLY and CSF1R DEGs showed significant occurrence in different steps, and therefore, these genes were studied, precisely. Nevertheless, the results may help to discover the potential biomarkers with deep understanding of molecular mechanisms. However, further validation is required to explain the OVCA pathogenesis.