RESUMEN
Cronobacter sakazakii is an opportunistic pathogen that has been associated with severe infection in neonates such as necrotizing enterocolitis (NEC), neonatal meningitis, and bacteremia. This pathogen can survive in a relatively dry environment, especially in powdered infant formula (PIF). Unfortunately, conventional drugs that were once effective against C. sakazakii are gradually losing their efficacy due to rising antibiotic resistance. In this study, a subtractive genomic approach was followed in order to identify potential therapeutic targets in the pathogen. The whole proteome of the pathogen was filtered through a step-by-step process, which involved removing paralogous proteins, human homologs, sequences that are less essential for survival, proteins with shared metabolic pathways, and proteins that are located in cells other than the cytoplasmic membrane. As a result, nine novel drug targets were identified. Further, the analysis also unveiled that the FDA-approved drug Terbinafine can be repurposed against the Glutathione/l-cysteine transport system ATP-binding/permease protein CydC of C. sakazakii. Moreover, molecular docking and dynamics studies of Terbinafine and CydC suggested that this drug can be used to treat C. sakazakii infection in neonates. However, for clinical purposes further in vitro and in vivo studies are necessary.
RESUMEN
BACKGROUND: Xanthomonas oryzae pv. oryzae is a plant pathogen responsible for causing one of the most severe bacterial diseases in rice, known as bacterial leaf blight that poses a major threat to global rice production. Even though several experimental compounds and chemical agents have been tested against X. oryzae pv. oryzae, still no approved drug is available. In this study, a subtractive genomic approach was used to identify potential therapeutic targets and repurposible drug candidates that could control of bacterial leaf blight in rice plants. RESULTS: The entire proteome of the pathogen underwent an extensive filtering process which involved removal of the paralogous proteins, rice homologs, non-essential proteins. Out of the 4382 proteins present in Xoo proteome, five hub proteins such as dnaA, dnaN, recJ, ruvA, and recR were identified for the druggability analysis. This analysis led to the identification of dnaN-encoded Beta sliding clamp protein as a potential therapeutic target and one experimental drug named [(5R)-5-(2,3-dibromo-5-ethoxy-4hydroxybenzyl)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]acetic acid that can be repurposed against it. Molecular docking and 100 ns long molecular dynamics simulation suggested that the drug can form stable complexes with the target protein over time. CONCLUSION: Findings from our study indicated that the proposed drug showed potential effectiveness against bacterial leaf blight in rice caused by X. oryzae pv. oryzae. It is essential to keep in consideration that the procedure for developing novel drugs can be challenging and complicated. Even the most promising results from in silico studies should be validated through further in vitro and in vivo investigation before approval.
RESUMEN
A metabolic disorder (MD) occurs when the metabolic process is disturbed. This process is carried out by thousands of enzymes participating in numerous inter-dependent metabolic pathways. Critical biochemical reactions that involve the processing and transportation of carbohydrates, proteins and lipids are affected in metabolic diseases. Therefore, it is of interest to identify the common pathways of metabolic disorders by building protein-protein interactions (PPI) for network analysis. The molecular network linkages between MD and age related diseases (ARD) are intriguing. Hence, we created networks of protein-protein interactions that are related with MD and ARD using relevant known data in the public domain. The network analysis identified known MD associated proteins and predicted genes and or its products of ARD in common pathways. The genes in the common pathways were isolated from the network and further analyzed for their co-localization and shared domains. Thus, a model hypothesis is proposed using interaction networks that are linked between MD and ARD. This data even if less conclusive finds application in understanding the molecular mechanism of known diseases in relation to observed molecular events.
