Integrated computational modeling and in-silico validation of flavonoids-Alliuocide G and Alliuocide A as therapeutic agents for their multi-target potential: Combination of molecular docking, MM-GBSA, ADMET and DFT analysis

NK/DC crosstalk-modulating antitumor activity via Sema3E/PlexinD1 axis for enhanced cancer immunotherapy

The complex relationship between natural killer (NK) cells and dendritic cells (DCs) within the tumor microenvironment significantly impacts the success of cancer immunotherapy. Recent advancements in cancer treatment have sought to bolster innate and adaptive immune responses through diverse modalities, aiming to tilt the immune equilibrium toward tumor elimination. Optimal antitumor immunity entails a multifaceted interplay involving NK cells, T cells and DCs, orchestrating immune effector functions. Although DC-based vaccines and NK cells' cytotoxic capabilities hold substantial therapeutic potential, their interaction is frequently hindered by immunosuppressive elements such as myeloid-derived suppressor cells (MDSCs) and regulatory T cells. Chemokines and cytokines, such as CXCL12, CCL2, interferons, and interleukins, play crucial roles in modulating NK/DC interactions and enhancing immune responses. This review elucidates the mechanisms underlying NK/DC interaction, emphasizing their pivotal roles in augmenting antitumor immune responses and the impediments posed by tumor-induced immunosuppression. Furthermore, it explores the therapeutic prospects of restoring NK/DC crosstalk, highlighting the significance of molecules like Sema3E/PlexinD1 in this context, offering potential avenues for enhancing the effectiveness of current immunotherapeutic strategies and advancing cancer treatment paradigms. Harnessing the dynamic interplay between NK and DC cells, including the modulation of Sema3E/PlexinD1 signaling, holds promise for developing more potent therapies that harness the immune system's full potential in combating cancer.

Virtual screening, ADME prediction, drug-likeness, and molecular docking analysis of Fagonia indica chemical constituents against antidiabetic targets

Fagonia indica from Zygophyllaceae family is a medicinal specie with significant antidiabetic potential. The present study aimed to investigate the in vitro antidiabetic activity of Fagonia indica crude extract followed by an in silico screening of its phytoconstituents. For this purpose, crude extract of Fagonia indica was prepared and divided in three different parts, i.e., n-hexane, ethyl acetate, and methanolic fraction. Based on in vitro outcomes, the phytochemical substances of Fagonia indica were virtually screened through a literature survey and a screening library of compounds (1-13) was prepared. The clinical potential of these novel drug candidates was assessed by applying an ADME screening profile. Findings of SwissADME indicators (Absorption, Distribution, Metabolism, and Excretion) for the compounds (1-13) presented relatively optimal physicochemical characteristics, drug-likeness, and medicinal chemistry. The antidiabetic action of these leading drug candidates was optimized through molecular docking analysis against 3 different human pancreatic α-amylase macromolecular targets with (PDB ID 1B2Y), (PDB ID 3BAJ), and (PDB ID: 3OLI) by applying Virtual Docker (Molegro MVD). Metformin was taken as a reference standard for the sake of comparison. In vitro antidiabetic evaluation gave good results with promising α-amylase inhibitory action in the form of IC50 values, as for n-hexane extract = 206.3 µM, ethyl acetate = 41.64 µM, and methanolic extract = 9.61 µM. According to in silico outcomes, all 13 phytoconstituents possess the best binding affinity with successful MolDock scores ranging from - 97.2003 to - 65.6877 kcal/mol and show a great number of binding interactions than native drug metformin. Therefore, the current work concluded that the diabetic inhibition prospective of extract and the compounds of Fagonia indica may contribute to being investigated as a new class of antidiabetic drug or drug-like candidate for further studies.

Sema-3E/PlexinD1 axis modulates dendritic cell phenotypes and functions: Current status and future implications

This comprehensive research review explores the complex interplay between the Sema-3E/PlexinD1 axis and dendritic cells (DCs), highlighting its critical role in immune modulation with implications for clinical application Critical regulators of immune responses Dendritic cells are central to adaptive immunity, and the Sema-3E /PlexinD1 axis emerges as a key modulator affecting their phenotypes and functions Review delineates the impact of this signaling axis on DC maturation, migration, antigen presentation, and cytokine production, unravels its multifaceted role in shaping the immune response. Recognizing the limitations and gaps in current knowledge, the study highlights the need for further studies to condition downstream signaling events and related information experienced by the Sema-3E/PlexinD1 axis emphasizes the clarity of the immune system. The review concludes by identifying opportunities for translation, focusing on therapeutic and diagnostic potential. It highlights the importance of collaborative, interdisciplinary efforts to address the challenges and harness the therapeutic and pathological potential of targeting the Sema-3E/PlexinD1 axis, thus opening the way for transformative advances in immunology and clinical medicine.

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis

Background

Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.

Methods

The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.

Results

A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes.

Conclusion

"ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.