Gold(III) complexes with thiosemicarbazone ligands: insights into their cytotoxic effects on lung cancer cells.

Cancer continues to pose a global threat, underscoring the urgent need for more effective and safer treatment options. Gold-based compounds have recently emerged as promising candidates due to their diverse range of biological activities. In this study, three gold(III) complexes derived from thiosemicarbazone ligands have been synthesized, fully characterized, including their X-ray crystal structures. We conducted initial mode-of-action studies on DNA and BSA, followed by a comprehensive investigation into the cytotoxic effects of these novel gold(III) complexes on lung cancer cells (A549, H2052, and H28). The results demonstrated a concentration-dependent cytotoxic response, with H28 cells exhibiting the highest sensitivity to the treatment. Furthermore, the analysis of the cell cycle revealed that these compounds induce cell cycle arrest and promote apoptosis as a response to treatment. We also observed distinct morphological changes and increased oxidative stress, contributing significantly to cell death. Notably, these complexes exhibited the ability to suppress interleukin-6 production in mesothelioma cell lines, and this highlights their anti-inflammatory potential. To gain an initial understanding of cytotoxicity on healthy cells, hemolysis tests were conducted against human blood cells, with no evidence of hemolysis. Furthermore, a toxicity assessment through the in vivo Galleria mellonella model underscored the absence of detectable toxicity. These findings prove that these complexes are promising novel therapeutic agents for lung cancer.

Impact of N-heteroaromatic N-termini in Cu(II) ATCUN metallopeptides on their biorelevant redox activity.

Cu(II) complexes with ATCUN peptide ligands have been investigated for their ROS (reactive oxygen species) generation and oxidative DNA degradation abilities. The biological activity of most ATCUN complexes such as Cu-GGH (Gly-Gly-His) is, however, low. Tuning the redox chemistry by incorporation of N-heteroaromatics reinstates ROS production which leads to efficient DNA cleavage.

Incorporation of ß-Alanine in Cu(II) ATCUN Peptide Complexes Increases ROS Levels, DNA Cleavage and Antiproliferative Activity.

Redox-active Cu(II) complexes are able to form reactive oxygen species (ROS) in the presence of oxygen and reducing agents. Recently, Faller et al. reported that ROS generation by Cu(II) ATCUN complexes is not as high as assumed for decades. High complex stability results in silencing of the Cu(II)/Cu(I) redox cycle and therefore leads to low ROS generation. In this work, we demonstrate that an exchange of the α-amino acid Gly with the ß-amino acid ß-Ala at position 2 (Gly2→ß-Ala2) of the ATCUN motif reinstates ROS production (• OH and H2 O2 ). Potentiometry, cyclic voltammetry, EPR spectroscopy and DFT simulations were utilized to explain the increased ROS generation of these ß-Ala2-containing ATCUN complexes. We also observed enhanced oxidative cleavage activity towards plasmid DNA for ß-Ala2 compared to the Gly2 complexes. Modifications with positively charged Lys residues increased the DNA affinity through electrostatic interactions as determined by UV/VIS, fluorescence, and CD spectroscopy, and consequently led to a further increase in nuclease activity. A similar trend was observed regarding the cytotoxic activity of the complexes against several human cancer cell lines where ß-Ala2 peptide complexes had lower IC50 values compared to Gly2. The higher cytotoxicity could be attributed to an increased cellular uptake as determined by ICP-MS measurements.

Forty Years after the Discovery of Its Nucleolytic Activity: [Cu(phen)2 ]2+ Shows Unattended DNA Cleavage Activity upon Fluorination.

[Cu(phen)2 ]2+ (phen=1,10-phenanthroline) is the first and still one of the most efficient artificial nucleases. In general, when the phen ligand is modified, the nucleolytic activity of its CuII complex is significantly reduced. This is most likely due to higher steric bulk of such ligands and thus lower affinity to DNA. CuII complexes with phen ligands having fluorinated substituents (F, CF3 , SF5 , SCF3 ) surprisingly showed excellent DNA cleavage activity-in contrast to the unsubstituted [Cu(phen)2 ]2+ -in the absence of the otherwise required classical, bioabundant external reducing agents like thiols or ascorbate. This nucleolytic activity correlates well with the half-wave potentials E1/2 of the complexes. Cancer cell studies show cytotoxic effects of all complexes with fluorinated ligands in the low µm range, whereas they were less toxic towards healthy cells (fibroblasts).

ClinVAP: a reporting strategy from variants to therapeutic options.

MOTIVATION: Next-generation sequencing has become routine in oncology and opens up new avenues of therapies, particularly in personalized oncology setting. An increasing number of cases also implies a need for a more robust, automated and reproducible processing of long lists of variants for cancer diagnosis and therapy. While solutions for the large-scale analysis of somatic variants have been implemented, existing solutions often have issues with reproducibility, scalability and interoperability. RESULTS: Clinical Variant Annotation Pipeline (ClinVAP) is an automated pipeline which annotates, filters and prioritizes somatic single nucleotide variants provided in variant call format. It augments the variant information with documented or predicted clinical effect. These annotated variants are prioritized based on driver gene status and druggability. ClinVAP is available as a fully containerized, self-contained pipeline maximizing reproducibility and scalability allowing the analysis of larger scale data. The resulting JSON-based report is suited for automated downstream processing, but ClinVAP can also automatically render the information into a user-defined template to yield a human-readable report. AVAILABILITY AND IMPLEMENTATION: ClinVAP is available at https://github.com/PersonalizedOncology/ClinVAP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Visualising intrinsic disorder and conformational variation in protein ensembles.

Intrinsically disordered regions (IDRs) in proteins are still not well understood, but are increasingly recognised as important in key biological functions, as well as in diseases. IDRs often confound experimental structure determination-however, they are present in many of the available 3D structures, where they exhibit a wide range of conformations, from ill-defined and highly flexible to well-defined upon binding to partner molecules, or upon post-translational modifications. Analysing such large conformational variations across ensembles of 3D structures can be complex and difficult; our goal in this paper is to improve this situation by augmenting traditional approaches (molecular graphics and principal components) with methods from human-computer interaction and information visualisation, especially parallel coordinates. We present a new tool integrating these approaches, and demonstrate how it can dissect ensembles to reveal functional insights into conformational variation and intrinsic disorder.

A visual analytics approach for models of heterogeneous cell populations.

In recent years, cell population models have become increasingly common. In contrast to classic single cell models, population models allow for the study of cell-to-cell variability, a crucial phenomenon in most populations of primary cells, cancer cells, and stem cells. Unfortunately, tools for in-depth analysis of population models are still missing. This problem originates from the complexity of population models. Particularly important are methods to determine the source of heterogeneity (e.g., genetics or epigenetic differences) and to select potential (bio-)markers. We propose an analysis based on visual analytics to tackle this problem. Our approach combines parallel-coordinates plots, used for a visual assessment of the high-dimensional dependencies, and nonlinear support vector machines, for the quantification of effects. The method can be employed to study qualitative and quantitative differences among cells. To illustrate the different components, we perform a case study using the proapoptotic signal transduction pathway involved in cellular apoptosis.