RESUMEN
Bacterial vaginosis (BV) is a prevalent vaginal illness resulting from a disruption in the vaginal microbial equilibrium. The vaginal microbiota has been shown to have a substantial impact on the development and continuation of BV. This work utilized 16S rRNA sequence analysis of vaginal microbiome samples (Control vs BV samples) utilizing Parallel-Meta 3 to investigate the variations in microbial composition. The unique genes identified were used to determine prospective therapeutic targets and their corresponding inhibitory ligands. Further, molecular docking was conducted and then MD simulations were carried out to confirm the docking outcomes. In the BV samples, we detected several anaerobic bacteria recognized for their ability to generate biofilms, namely Acetohalobium, Anaerolineaceae, Desulfobacteraceae, and others. Furthermore, we identified Dalfopristin, Clorgyline, and Hydrazine as potential therapeutic options for the management of BV. This research provides new insights into the causes of BV and shows the potential effectiveness of novel pharmacological treatments.
Asunto(s)
Hidrazinas , Microbiota , ARN Ribosómico 16S , Vagina , Vaginosis Bacteriana , Femenino , Vaginosis Bacteriana/tratamiento farmacológico , Vaginosis Bacteriana/microbiología , ARN Ribosómico 16S/genética , Humanos , Microbiota/efectos de los fármacos , Microbiota/genética , Vagina/microbiología , Hidrazinas/farmacología , Hidrazinas/uso terapéutico , Antibacterianos/farmacología , Antibacterianos/uso terapéutico , Simulación del Acoplamiento Molecular , Bacterias/efectos de los fármacos , Bacterias/genética , Bacterias/clasificaciónRESUMEN
A network consisting of 45 core genes was developed for the genes/proteins responsible for loss/gain of function in human pluripotent stem cells. The nodes were included on the basis of literature curation. The initial network topology was further refined by constructing an inferred Boolean model from time-series RNA-seq expression data. The final Boolean network was obtained by integration of the initial topology and the inferred topology into a refined model termed as the integrated model. Expression levels were observed to be bi-modular for most of the genes involved in the mechanism of human pluripotency. Thus, single and combinatorial perturbations/knockdowns were executed using an in silico approach. The model perturbations were validated with literature studies. A number of outcomes are predicted using the knockdowns of the core pluripotency circuit and we are able to establish the minimum requirement for maintenance of pluripotency in human. The network model is able to predict lineage-specific outcomes and targeted knockdowns of essential genes involved in human pluripotency which are challenging to perform due to ethical constraints surrounding human embryonic stem cells.