Mini-Review: PDPK1 (3-phosphoinositide dependent protein kinase-1), An Emerging Cancer Stem Cell Target

Identification of 11-Hydroxytephrosin and Torosaflavone A as Potential Inhibitors of 3-Phosphoinositide-Dependent Protein Kinase 1 (PDPK1): Toward Anticancer Drug Discovery

The 3-phosphoinositide-dependent protein kinase 1 (PDPK1) has a significant role in cancer progression and metastasis as well as other inflammatory disorders, and has been proposed as a promising therapeutic target for several malignancies. In this work, we conducted a systematic virtual screening of natural compounds from the IMPPAT database to identify possible PDPK1 inhibitors. Primarily, the Lipinski rules, ADMET, and PAINS filter were applied and then the binding affinities, docking scores, and selectivity were carried out to find effective hits against PDPK1. Finally, we identified two natural compounds, 11-Hydroxytephrosin and Torosaflavone A, bearing substantial affinity with PDPK1. Both compounds showed drug-likeness as predicted by the ADMET analysis and their physicochemical parameters. These compounds preferentially bind to the ATP-binding pocket of PDPK1 and interact with functionally significant residues. The conformational dynamics and complex stability of PDPK1 with the selected compounds were then studied using interaction analysis and molecular dynamics (MD) simulations for 100 ns. The simulation results revealed that PDPK1 forms stable docked complexes with the elucidated compounds. The findings show that the newly discovered 11-Hydroxytephrosin and Torosaflavone A bind to PDPK1 in an ATP-competitive manner, suggesting that they could one day be used as therapeutic scaffolds against PDPK1-associated diseases including cancer.

MALAT1-miRNAs network regulate thymidylate synthase and affect 5FU-based chemotherapy

Background

The active metabolite of 5-Fluorouracil (5FU), used in the treatment of several types of cancer, acts by inhibiting the thymidylate synthase encoded by the TYMS gene, which catalyzes the rate-limiting step in DNA replication. The major failure of 5FU-based cancer therapy is the development of drug resistance. High levels of TYMS-encoded protein in cancerous tissues are predictive of poor response to 5FU treatment. Expression of TYMS is regulated by various mechanisms, including involving non-coding RNAs, both miRNAs and long non-coding RNAs (lncRNAs).

Aim

To delineate the miRNAs and lncRNAs network regulating the level of TYMS-encoded protein.

Main body

Several miRNAs targeting TYMS mRNA have been identified in colon cancers, the levels of which can be regulated to varying degrees by lncRNAs. Due to their regulation by the MALAT1 lncRNA, these miRNAs can be divided into three groups: (1) miR-197-3p, miR-203a-3p, miR-375-3p which are downregulated by MALAT1 as confirmed experimentally and the levels of these miRNAs are actually reduced in colon and gastric cancers; (2) miR-140-3p, miR-330-3p that could potentially interact with MALAT1, but not yet supported by experimental results; (3) miR-192-5p, miR-215-5p whose seed sequences do not recognize complementary response elements within MALAT1. Considering the putative MALAT1-miRNAs interaction network, attention is drawn to the potential positive feedback loop causing increased expression of MALAT1 in colon cancer and hepatocellular carcinoma, where YAP1 acts as a transcriptional co-factor which, by binding to the TCF4 transcription factor/ β-catenin complex, may increase the activation of the MALAT1 gene whereas the MALAT1 lncRNA can inhibit miR-375-3p which in turn targets YAP1 mRNA.

Conclusion

The network of non-coding RNAs may reduce the sensitivity of cancer cells to 5FU treatment by upregulating the level of thymidylate synthase.

Establishing and characterizing a novel doxorubicin-resistant acute myeloid leukaemia cell line

Drug resistance is a major setback in cancer treatment, thus models to study its mechanisms are needed. Our work aimed to establish and characterize a resistant cell line from a sensitive acute myeloid leukaemia (AML) cell line - HL60 - by treating the sensitive cells with increasing concentrations of doxorubicin. We confirmed (cell viability assays) that the established subline, HL60-CDR, was resistant to doxorubicin for at least 30 days without drug treatment. The HL60-CDR cells were also resistant to three other drugs (cisplatin, etoposide and daunorubicin), exhibiting a multidrug resistant (MDR) profile. We verified (Western Blotting) that the MDR cells do not express drug efflux pumps, nor present altered expression of apoptotic proteins, when compared with the parental cell line. HL60-CDR cells presented alterations in the cell cycle profile, and in the expression levels of proteins involved in DNA repair mechanisms and drug metabolism, when compared with their drug sensitive counterpart. Proteomic analysis revealed that HL60-CDR cells presented an upregulation of proteins involved in oncogenic pathways, such as TSC2, PDPK1, Annexin A2, among others. Overall, we established an AML MDR subline - HL60-CDR - which presents several resistance mechanisms, providing an in vitro model to test new compounds to circumvent MDR in AML.

Network controllability solutions for computational drug repurposing using genetic algorithms

Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdős-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.