Biomarker Identification for Preterm Birth Susceptibility: Vaginal Microbiome Meta-Analysis Using Systems Biology and Machine Learning Approaches.

PROBLEM: The vaginal microbiome has a substantial role in the occurrence of preterm birth (PTB), which contributes substantially to neonatal mortality worldwide. However, current bioinformatics approaches mostly concentrate on the taxonomic classification and functional profiling of the microbiome, limiting their abilities to elucidate the complex factors that contribute to PTB. METHOD OF STUDY: A total of 3757 vaginal microbiome 16S rRNA samples were obtained from five publicly available datasets. The samples were divided into two categories based on pregnancy outcome: preterm birth (PTB) (N = 966) and term birth (N = 2791). Additionally, the samples were further categorized based on the participants' race and trimester. The 16S rRNA reads were subjected to taxonomic classification and functional profiling using the Parallel-META 3 software in Ubuntu environment. The obtained abundances were analyzed using an integrated systems biology and machine learning approach to determine the key microbes, pathways, and genes that contribute to PTB. The resulting features were further subjected to statistical analysis to identify the top nine features with the greatest effect sizes. RESULTS: We identified nine significant features, namely Shuttleworthia, Megasphaera, Sneathia, proximal tubule bicarbonate reclamation pathway, systemic lupus erythematosus pathway, transcription machinery pathway, lepA gene, pepX gene, and rpoD gene. Their abundance variations were observed through the trimesters. CONCLUSIONS: Vaginal infections caused by Shuttleworthia, Megasphaera, and Sneathia and altered small metabolite biosynthesis pathways such as lipopolysaccharide folate and retinal may increase the susceptibility to PTB. The identified organisms, genes, pathways, and their networks may be specifically targeted for the treatment of bacterial infections that increase PTB risk.

16S rRNA female reproductive microbiome investigation reveals Dalfopristin, Clorgyline, and Hydrazine as potential therapeutics for the treatment of bacterial vaginosis.

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.

Systems-wide analysis of A. fumigatus using kinetic modeling of metabolic pathways to identify putative drug targets.

Aspergillosis is a major causative factor for morbidity in those with impaired immune systems, often caused by Aspergillus fumigatus. The diagnosis and treatment are difficult due to the diversity of individuals and risk factors and still pose a challenge for medical professionals. To understand the pathogenicity of any organism, it is critical to identify the significant metabolic pathways that are involved. Our work focused on developing kinetic models of critical pathways crucial for the survival of A. fumigatus using COPASI. While focusing on the folate biosynthesis, ergosterol biosynthesis and glycolytic pathway; sensitivity, time-course and steady-state analysis were performed to find the proteins/enzymes that are essential in the pathway and can be considered as potential drug targets. For further analysis of the interaction of drug targets identified, a protein-protein interaction (PPI) network was built, and hub nodes were identified using the Cytohubba package from Cytoscape. Based on the findings, dihydropteroate-synthase, dihydrofolate-reductase, 4-amino-4-deoxychorismate synthase, HMG-CoA-reductase, PG-isomerase and hexokinase could act as potential drug targets. Further, molecular docking and MM-GBSA analysis were performed with ligands chosen from DrugBank, and PubChem, and validated by experimental evidence and existing literature based on results from kinetic modeling and PPI network analysis. Based on docking scores and MM-GBSA results, molecular simulations were carried out for 1AJ2-dapsone, 1DIS-sulfamethazine, 1T02-lovastatin and 70YL-3-bromopyruvic acid complexes, which validated our findings. Our study provides a deeper insight into the mechanisms of A. fumigatus's metabolism to reveal dapsone, sulfamethazine, lovastatin and 3-bromopyruvic acid as potential drugs for the treatment of Aspergillosis.Communicated by Ramaswamy H. Sarma.