Klebsiella oxytoca is a gram-negative bacterium. It is opportunistic in nature and causes hospital acquired infections. Subtractive proteomics and reverse vaccinology approaches were employed to screen out the best proteins for vaccine designing. Whole proteome of K. oxytoca strain ATCC 8724, consisting of 5483 proteins, was used for designing the vaccine. Total 1670 cytotoxic T lymphocyte (CTL) epitope were predicted through NetCTL while 1270 helper T lymphocyte (HTL) epitopes were predicted through IEDB server. The epitopes were screened for non-toxicity, allergenicity, antigenicity and water solubility. After epitope screening 300 CTL and 250 HTL epitopes were submitted to IFN-γ epitope server to predict their Interferon-γ induction response. The selected IFN-γ positive epitopes were tested for their binding affinity with MHCI-DRB1 by MHCPred. The 15 CTL and 13 HTL epitopes were joined by linkers AAY and GPGPG respectively in vaccine construct. Chain C of Pam3CSK4 (PDB ID; 2Z7X) was linked to the vaccine construct as an adjuvant. A 450aa long vaccine construct was submitted to I-TASSER server for 3D structure prediction. Thirteen Linear B cells were predicted by ABCPred server and 10 sets of discontinues epitopes for 3D vaccine structure were predicted by DiscoTope server. The modeled 3D vaccine construct was docked with human Toll-like receptor 2 (PDB ID: 6NIG) by PatchDock. The docked complexes were refined by FireDock. The selected docked complex showed five hydrogen bonds and one salt bridge. The vaccine sequence was reverse transcribed to get nucleotide sequence for In silico cloning. The reverse transcribed sequence strand was cloned in pET28a(+) expression vector. A clone containing 6586 bp was constructed including the 450 bp of query gene sequence.
Preterm birth (PTB) is one of the major reasons of infant mortalities and morbidities worldwide. Exact mechanism of this disease has not been elucidated yet but studies have shown several factors which have been found associated with the disease. Through several studies, it has been discovered that different chronic diseases like obesity, type 1 diabetes and periodontitis are also associated with PTB but no omics studies have yet been performed to discover their association. In the current study, comparative transcriptome analysis of the preterm birth with type 1 diabetes, obesity and periodontitis using integrated bioinformatics approach was performed to validate their association with preterm birth. Four different datasets retrieved from Gene expression omnibus (GEO) were processed through Bioconductor packages in R. Pairwise comparison of preterm was performed with all three diseases. Several bioinformatics tools were used such as DAVID for Gene ontology (GO) terms and pathway analysis, DIA for impact and direction of KEGG pathways and Cytoscape along with its different plugins for construction of networks, identification of hub genes and modules. Significant number of common differentially expressed genes between datasets and their roles in different pathways were identified. Most of the significant genes and pathways found in these comparisons were involved in initiating different inflammatory processes, degradation of proteins leading to preterm premature rupture of membranes (PPROM), preeclampsia, and cancer which are the known risk factors of PTB. According to our results, all these three chronic diseases were found to have a strong association with PTB.
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