Extracellular adenosine accumulates in tumors and causes suppression of immune cells. Suppressive adenosine signaling is achieved through adenosine A2A and A2B receptors, which are Gs coupled, and their activation elevates cAMP levels. Gs-coupled GPCR signaling causes cAMP accumulation, which plays an anti-inflammatory role in immune cells. Protein kinase A (PKA) and exchange protein directly activated by cAMP (Epac) are two intracellular receptors of cAMP. In this study we showed that adenosine receptor signaling polarizes activated murine dendritic cells (DCs) into a tumor-promoting suppressive phenotype. Adenosine receptor signaling activates cAMP pathway and upregulates the negative regulators of NF-kB but does not influence phosphorylation of immediate inflammatory signaling molecules downstream of TLR signaling. Pharmacologic activation of both PKA and Epac pathways by specific cAMP analogues phenocopied the effects of adenosine signaling on murine DCs, such as suppression of proinflammatory cytokines, elevation of anti-inflammatory IL-10, increased expression of regulators of NF-kB pathway, and finally suppression of T cell activation. Inhibition of effector cytokine, IL-12p40 production, and increased immunosuppressive IL-10 production by adenosine signaling is significantly reversed only when both PKA and Epac pathways were inhibited together. Adenosine signaling increased IL-10 secretion while decreasing IL-12p40 secretion in human monocyte-derived DCs. Stimulation of both PKA and Epac pathways also caused combinatorial effects in regulation of IL-12p40 secretion in human monocyte-derived DCs. Interestingly, PKA signaling alone caused similar increase in IL-10 secretion to that of adenosine signaling in human monocyte-derived DCs. Our data suggest adenosine/cAMP signaling targets both PKA/Epac pathways to fully differentiate DCs into a suppressive phenotype.
The nucleoside adenosine accumulates extracellularly in solid tumors and inhibits CD8+ T cells by activating adenosine receptors. The cytokine interleukin-7 (IL-7), which is produced by various tissues and tumors, promotes the survival and maintenance of T cells. Adenosine and IL-7 signaling are being clinically targeted separately or in combination with other therapies for solid tumor indications. Here, we found that IL-7 signaling promoted the accumulation of tumor-associated CD8+ T cells, in part, by preventing adenosine-mediated immunosuppression. Inhibition of the transcription factor FoxO1 downstream of IL-7 receptor signaling was important for protecting CD8+ T cells from suppression by adenosine. These findings have implications for the development of new approaches for cancer immunotherapies that target the adenosine pathway.
Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins’ structure/function, and bias in system training datasets. Here, we propose a new method “DRUIDom” (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound–target pairs (~2.9M data points), and used as training data for calculating parameters of compound–domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound–protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound–domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom.
Hepatocellular carcinoma (HCC) ranks as the fifth most common and the second deadliest cancer worldwide. HCC is extremely resistant to the conventional chemotherapeutics. Hence, it is vital to develop new treatment options. Chalcones were previously shown to have anticancer activities in other cancer types. In this study, 11 chalcones along with quercetin, papaverin, catechin, Sorafenib and 5FU were analyzed for their bioactivities on 6 HCC cell lines and on dental pulp stem cells (DPSC) which differentiates into hepatocytes, and is used as a model for untransformed control cells. 3 of the chalcones (1, 9 and 11) were selected for further investigation due to their high cytotoxicity against liver cancer cells and compared to the other clinically established compounds. Chalcones did not show significant bioactivity ($$\hbox {IC}_{50}>20\upmu \hbox {M}$$IC50>20μM) on dental pulp stem cells. Cell cycle analysis revealed that these 3 chalcone-molecules induced SubG1/G1 arrest. Akt protein phosphorylation was inhibited by these molecules in PTEN deficient, drug resistant, mesenchymal like Mahlavu cells leading to the activation of p21 and the inhibition of NF$$ \kappa $$κB-p65 transcription factor. Hence the chalcones induced apoptotic cell death pathway through NF$$ \kappa $$κB-p65 inhibition. On the other hand, these molecules triggered p21 dependent activation of Rb protein and thereby inhibition of cell cycle and cell growth in liver cancer cells. Involvement of PI3K/Akt pathway hyperactivation was previously described in survival of liver cancer cells as carcinogenic event. Therefore, our results indicated that these chalcones can be considered as candidates for liver cancer therapeutics particularly when PI3K/Akt pathway involved in tumor development.
Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, have been developed with the purpose of picking completely new samples from a partially known space. Generative models offer high potential for designing de novo molecules; however, in order for them to be useful in real-life drug development pipelines, these models should be able to design target-specific molecules, which is the next step in this field. In this study, we propose a novel generative system, DrugGEN, for the de novo design of drug candidate molecules that interact with selected target proteins. The proposed system represents compounds and protein structures as graphs and processes them via serially connected two generative adversarial networks comprising graph transformers. DrugGEN is implemented with five independent models, each with a unique sample generation routine. The system
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