Protein kinases function as pivotal regulators in biological events, governing essential cellular processes through the transfer of phosphate groups from ATP molecules to substrates. Dysregulation of kinase activity is frequently associated with cancer, ocasionally arising from chromosomal translocation events that relocate genes encoding kinases. Fusion proteins resulting from such events, particularly those involving the proto-oncogene tyrosine-protein kinase ROS (ROS1), manifest as constitutively active kinases, emphasizing their role in oncogenesis. Notably, the chromosomal reallocation of the ros1 gene leads to fusion of proteins with the ROS1 kinase domain, implicated in various cancer types. Despite their prevalence, targeted inhibition of these fusion proteins relies on repurposed kinase inhibitors. This review comprehensively surveys experimentally determined ROS1 structures, emphasizing the pivotal role of X-ray crystallography in providing high-quality insights. We delve into the intricate interactions between ROS1 and kinase inhibitors, shedding light on the structural basis for inhibition. Additionally, we explore point mutations identified in patients, employing molecular modeling to elucidate their structural impact on the ROS1 kinase domain. By integrating structural insights with in vitro and in silico data, this review advances our understanding of ROS1 kinase in cancer, offering potential avenues for targeted therapeutic strategies.
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BACKGROUND: T lymphocytes are orchestrators of adaptive immunity. Naïve T cells may differentiate into Th1, Th2, Th17 or iTreg phenotypes, depending on environmental co-stimulatory signals. To identify genes and pathways involved in differentiation of Jurkat T cells towards Th1 and Th2 subtypes we performed comprehensive transcriptome analyses of Jurkat T cells stimulated with various stimuli and pathway inhibitors. Results from these experiments were validated in a human experimental setting using whole blood and purified CD4+ Tcells.RESULTS: Calcium-dependent activation of T cells using CD3/CD28 and PMA/CD3 stimulation induced a Th1 expression profile reflected by increased expression of T-bet, RUNX3, IL-2, and IFNγ, whereas calcium-independent activation via PMA/CD28 induced a Th2 expression profile which included GATA3, RXRA, CCL1 and Itk. Knock down with siRNA and gene expression profiling in the presence of selective kinase inhibitors showed that proximal kinases Lck and PKCθ are crucial signaling hubs during T helper cell activation, revealing a clear role for Lck in Th1 development and for PKCθ in both Th1 and Th2 development. Medial signaling via MAPkinases appeared to be less important in these pathways, since specific inhibitors of these kinases displayed a minor effect on gene expression. Translation towards a primary, whole blood setting and purified human CD4+ T cells revealed that PMA/CD3 stimulation induced a more pronounced Th1 specific, Lck and PKCθ dependent IFNγ production, whereas PMA/CD28 induced Th2 specific IL-5 and IL-13 production, independent of Lck activation. PMA/CD3-mediated skewing towards a Th1 phenotype was also reflected in mRNA expression of the master transcription factor Tbet, whereas PMA/CD28-mediated stimulation enhanced GATA3 mRNA expression in primary human CD4+ Tcells.CONCLUSIONS: This study identifies stimulatory pathways and gene expression profiles for in vitro skewing of T helper cell activation. PMA/CD3 stimulation enhances a Th1-like response in an Lck and PKCθ dependent fashion, whereas PMA/CD28 stimulation results in a Th2-like phenotype independent of the proximal TCR-tyrosine kinase Lck. This approach offers a robust and fast translational in vitro system for skewed T helper cell responses in Jurkat T cells, primary human CD4+ Tcells and in a more complex matrix such as human whole blood.
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Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
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