Unveiling the unexplored novel signatures for osteoporosis via a detailed bioinformatics and molecular experiments based approach.

BACKGROUND: Osteoporosis (OP) stands as a prevalent bone ailment affecting the elderly, globally. The identification of reliable diagnostic markers crucially aids OP clinical management. METHODS: Utilizing the GEO database (GSE35959), we acquired expression profiles for OP and normal samples. Differential expression genes (DEGs) and hub genes were pinpointed through STRING, GEO2R, and Cytoscape. The competing endogenous RNA (ceRNA) network was constructed using miRTarBase, miRDB, and MiRcode databases. Gene Ontology (GO) and KEGG pathway enrichment analyses were performed via DAVID. Validation involved clinical OP samples from the Pakistani population, with Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) assessing hub gene expression. RESULTS: A total of 2124 differentially expressed genes (DEGs) were identified between OP and normal samples in GSE35959. The selected hub genes among these DEGs were Splicing Factor 3a Subunit 1 (SF3A1), Ataxin 2 Like (ATXN2L), Heat Shock Protein 90 Beta Family Member 1 (HSP90B1), Cluster of Differentiation 74 (CD74), DExH-Box Helicase 29 (DHX29), ALG5 Dolichyl-Phosphate Beta-Glucosyltransferase (ALG5), NudC Domain Containing 2 (NUDCD2), and Ras-related protein Rab-2A (RAB2A). Expression validation of these genes on the Pakistani OP patients revealed significant up-regulation of SF3A1, ATXN2L, and CD74 and significant (P < 0.05) down-regulation of HSP90B1, DHX29, ALG5, NUDCD2, and RAB2A in OP patients. Receiver operating characteristic (ROC) analysis demonstrated that these hub genes displayed considerable diagnostic accuracy for detecting OP. The ceRNA network analysis of the hub genes revealed some important hub genes' regulatory miRNAs and lncRNAs. Via KEGG analysis, hub genes were found to be enriched in N-Glycan biosynthesis, Thyroid hormone synthesis, IL-17 signaling pathway, Prostate cancer, AMPK signaling pathway, Spliceosome, Estrogen signaling pathway, and Fluid shear stress and atherosclerosis, etc., pathways. CONCLUSION: The identified eight hub genes in the present study could reliably distinguish OP patients from normal individuals, which may provide novel insight into the diagnostic research of OP.

Targeting Cathepsin L in Cancer Management: Leveraging Machine Learning, Structure-Based Virtual Screening, and Molecular Dynamics Studies.

Cathepsin L (CTSL) expression is dysregulated in a variety of cancers. Extensive empirical evidence indicates their direct participation in cancer growth, angiogenic processes, metastatic dissemination, and the development of treatment resistance. Currently, no natural CTSL inhibitors are approved for clinical use. Consequently, the development of novel CTSL inhibition strategies is an urgent necessity. In this study, a combined machine learning (ML) and structure-based virtual screening strategy was employed to identify potential natural CTSL inhibitors. The random forest ML model was trained on IC50 values. The accuracy of the trained model was over 90%. Furthermore, we used this ML model to screen the Biopurify and Targetmol natural compound libraries, yielding 149 hits with prediction scores >0.6. These hits were subsequently selected for virtual screening using a structure-based approach, yielding 13 hits with higher binding affinity compared to the positive control (AZ12878478). Two of these hits, ZINC4097985 and ZINC4098355, have been shown to strongly bind CTSL proteins. In addition to drug-like properties, both compounds demonstrated high affinity, ligand efficiency, and specificity for the CTSL binding pocket. Furthermore, in molecular dynamics simulations spanning 200 ns, these compounds formed stable protein-ligand complexes. ZINC4097985 and ZINC4098355 can be considered promising candidates for CTSL inhibition after experimental validation, with the potential to provide therapeutic benefits in cancer management.

In vitro analysis of PI3K pathway activation genes for exploring novel biomarkers and therapeutic targets in clear cell renal carcinoma.

OBJECTIVES: The regulation of various cellular functions such as growth, proliferation, metabolism, and angiogenesis, is dependent on the PI3K pathway. Recent evidence has indicated that kidney renal clear cell carcinoma (KIRC) can be triggered by the deregulation of this pathway. The objective of this research was to investigate 25 genes associated with activation of the PI3K pathway in KIRC and control samples to identify four hub genes that might serve as novel molecular biomarkers and therapeutic targets for treating KIRC. METHODS: Multi-omics in silico and in vitro analysis was employed to find hub genes related to the PI3K pathway that may be biomarkers and therapeutic targets for KIRC. RESULTS: Using STRING software, a protein-protein interaction (PPI) network of 25 PI3K pathway-related genes was developed. Based on the degree scoring method, the top four hub genes were identified using Cytoscape's Cytohubba plug-in. TCGA datasets, KIRC (786-O and A-498), and normal (HK2) cells were used to validate the expression of hub genes. Additionally, further bioinformatic analyses were performed to investigate the mechanisms by which hub genes are involved in the development of KIRC. Out of a total of 25 PI3K pathway-related genes, we developed and validated a diagnostic and prognostic model based on the up-regulation of TP53 (tumor protein 53) and CCND1 (Cyclin D1) and the down-regulation of PTEN (Phosphatase and TENsin homolog deleted on chromosome 10), and GSK3B (Glycogen synthase kinase-3 beta) hub genes. The hub genes included in our model may be a novel therapeutic target for KIRC treatment. Additionally, associations between hub genes and infiltration of immune cells can enhance comprehension of immunotherapy for KIRC. CONCLUSION: We have created a new diagnostic and prognostic model for KIRC patients that uses PI3K pathway-related hub genes (TP53, PTEN, CCND1, and GSK3B). Nevertheless, further experimental studies are required to ascertain the efficacy of our model.

Identifying promising GSK3ß inhibitors for cancer management: a computational pipeline combining virtual screening and molecular dynamics simulations.

Glycogen synthase kinase-3 (GSK3ß), a serine/threonine protein kinase, has been discovered as a novel target for anticancer drugs. Although GSK3ß is involved in multiple pathways linked to the etiology of various cancers, no specific GSK3ß inhibitor has been authorized for cancer therapy. Most of its inhibitors have toxicity effects therefore, there is a need to develop safe and more potent inhibitors. In this study, a library of 4,222 anti-cancer compounds underwent rigorous computational screening to identify potential candidates for targeting the binding pocket of GSK3ß. The screening process involved various stages, including docking-based virtual screening, physicochemical and ADMET analysis, and molecular dynamics simulations. Ultimately, two hit compounds, BMS-754807 and GSK429286A, were identified as having high binding affinities to GSK3ß. BMS-754807 and GSK429286A exhibited binding affinities of -11.9, and -9.8 kcal/mol, respectively, which were greater than that of the positive control (-7.6 kcal/mol). Further, molecular dynamics simulations for 100 ns were employed to optimize the interaction between the compounds and GSK3ß, and the simulations demonstrated that the interaction was stable and consistent throughout the study. These hits were also anticipated to have good drug-like properties. Finally, this study suggests that BMS-754807 and GSK429286A may undergo experimental validation to evaluate their potential as cancer treatments in clinical settings.

Green Synthesis of Silver Nanoparticles Using the Plant Extract of Acer oblongifolium and Study of Its Antibacterial and Antiproliferative Activity via Mathematical Approaches.

In this study, the antibacterial and antifungal properties of silver nanoparticles synthesized with the aqueous plant extract of Acer oblongifolium leaves were defined using a simplistic, environmentally friendly, reliable, and cost-effective method. The aqueous plant extract of Acer oblongifolium, which served as a capping and reducing agent, was used to biosynthesize silver nanoparticles. UV visible spectroscopy, X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and scanning electron microscopy were used to analyze the biosynthesized Acer oblongifolium silver nanoparticles (AgNPs). Gram-positive bacteria (Bacillus paramycoides and Bacillus cereus) and Gram-negative bacteria (E. coli) were used to test the AgNPs' antibacterial activity. The presence of different functional groups was determined by FTIR. The AgNPs were rod-like in shape. The nanoparticles were more toxic against Escherichiacoli than both Bacillus cereus and Bacillus paramycoides. The AgNPs had IC50 values of 6.22 and 9.43 and mg/mL on HeLa and MCF-7, respectively, proving their comparatively strong potency against MCF-7. This confirmed that silver nanoparticles had strong antibacterial activity and antiproliferative ability against MCF-7 and HeLa cell lines. The mathematical modeling revealed that the pure nanoparticle had a high heat-absorbing capacity compared to the mixed nanoparticle. This research demonstrated that the biosynthesized Acer oblongifolium AgNPs could be used as an antioxidant, antibacterial, and anticancer agent in the future.

Targeting Cytotoxin-Associated Antigen A, a Virulent Factor of Helicobacter pylori-Associated Gastric Cancer: Structure-Based In Silico Screening of Natural Compounds.

Gastric cancer is the fifth most frequent cancer and the third major cause of mortality worldwide. Helicobacter pylori, a bacterial infection linked with GC, injects the cytotoxin-associated antigen A (CagA; an oncoprotein) into host cells. When the phosphorylated CagA protein enters the cell, it attaches to other cellular components, interfering with normal cellular signaling pathways. CagA plays an important role in the progression of GC by interacting with phosphatidylserine of the host cell membrane. Therefore, disrupting the CagA-phosphatidylserine connection using small molecules appears to be a promising therapeutic approach. In this report, we screened the natural compounds from ZINC database against the CagA protein using the bioinformatics tools. Hits were initially chosen based on their physicochemical, absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics, as well as other drug-like characteristics. To locate safe and effective hits, the PAINS filter, binding affinities estimation, and interaction analysis were used. Three compounds with high binding affinity and specificity for the CagA binding pocket were discovered. The final hits, ZINC153731, ZINC69482055, and ZINC164387, were found to bind strongly with CagA protein, with binding energies of -11.53, -10.67, and -9.21 kcal/mol, respectively, which were higher than that of the control compound (-7.25 kcal/mol). Further, based on binding affinity and interaction pattern, two leads (ZINC153731, ZINC69482055) were chosen for molecular dynamics (MD) simulation analysis. MD results showed that they displayed stability in their vicinity at 100 ns. This study suggested that these compounds could be used as possible inhibitors of CagA protein in the fight against GC. However, additional benchwork tests are required to validate them as CagA protein inhibitors.

Phytoconstituents and Medicinal Plants for Anticancer Drug Discovery: Computational Identification of Potent Inhibitors of PIM1 Kinase.

Natural products, medicinal plants, and phytoconstituents serve as important sources and accelerators for anticancer drug discovery, especially when they are combined with virtual screening and molecular simulations against molecular drug targets. Proto-oncogene serine/threonine-protein kinase Pim1 (PIM1) is involved in cell survival and proliferation, with great relevance for oncogenesis. PIM1 plays a major role in the progression of various common complex human cancers, including prostate cancer, acute myeloid leukemia, and other hematopoietic malignancies. The overexpression of PIM1 leads to cancer progression, and thus it is considered as a potential target for drug design and development purposes. Here, we report original in silico findings by employing structure-based virtual screening to discover potential phytoconstituents from the medicinal plants-based compounds, which could inhibit the PIM1 activity, using the IMPPAT (a curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics) database. The initial hits were selected based on their binding affinity toward PIM1 calculated through the molecular docking approach. Subsequently, interaction analyses and molecular dynamics (MD) simulation for 100 ns was carried out to study the conformational dynamics and complex stability of PIM1 with the identified compounds. Importantly, we found that PIM1 forms stable protein-ligand complexes with the phytoconstituents Dehydrotectol and Nordracorubin in particular. Our findings suggest that identified phytoconstituents Dehydrotectol and Nordracorubin bind to PIM1 in ATP-competitive binding mode. These findings and the compounds reported herein warrant further exploration as promising scaffolds for anticancer drug design, discovery, and development.