The brainâs high demand for energy necessitates tightly regulated metabolic pathways to sustain physiological activity. Glucose, the primary energy substrate, undergoes complex metabolic transformatio Show more
The brainâs high demand for energy necessitates tightly regulated metabolic pathways to sustain physiological activity. Glucose, the primary energy substrate, undergoes complex metabolic transformations, with mitochondria playing a central role in ATP production via oxidative phosphorylation. Dysregulation of this metabolic interplay is implicated in Alzheimerâs disease (AD), where compromised glucose metabolism, oxidative stress, and mitochondrial dysfunction contribute to disease progression. This review explores the intricate bioenergetic crosstalk between astrocytes and neurons, highlighting the function of mitochondrial uncoupling proteins (UCPs), particularly UCP4, as important regulators of brain metabolism and neuronal function. Predominantly expressed in the brain, UCP4 reduces the membrane potential in the inner mitochondrial membrane, thereby potentially decreasing the generation of reactive oxygen species. Furthermore, UCP4 mitigates mitochondrial calcium overload and sustains cellular ATP levels through a metabolic shift from mitochondrial respiration to glycolysis. Interestingly, the levels of the neuronal UCPs, UCP2, 4 and 5 are significantly reduced in AD brain tissue and a specific UCP4 variant has been associated to an increased risk of developing AD. Few studies modulating the expression of UCP4 in astrocytes or neurons have highlighted protective effects against neurodegeneration and aging, suggesting that pharmacological strategies aimed at activating UCPs, such as protonophoric uncouplers, hold promise for therapeutic interventions in AD and other neurodegenerative diseases. Despite significant advances, our understanding of UCPs in brain metabolism remains in its early stages, emphasizing the need for further research to unravel their biological functions in the brain and their therapeutic potential. Show less
The preparation of cyclometalated complexes offers a path to stable materials, catalysts, and therapeutic agents. Here, we explore the anticancer potential of novel biphenyl organogold(III) cationic c Show more
The preparation of cyclometalated complexes offers a path to stable materials, catalysts, and therapeutic agents. Here, we explore the anticancer potential of novel biphenyl organogold(III) cationic complexes supported by diverse bisphosphine ligands, Au-1-Au-5, toward aggressive glioblastoma and triple negative breast cancer cells (TNBCs). The [C^C] gold(III) complex, Au-3, exhibits significant tumor growth inhibition in a metastatic TNBC mouse model. Remarkably, Au-3 displays promising blood serum stability over a relevant therapeutic window of 24 h and alteration in the presence of excess L-GSH. The mechanism-of-action studies show that Au-3 induces mitochondrial uncoupling, membrane depolarization, and G1 cell cycle arrest and prompts apoptosis. To the best of our knowledge, Au-3 is the first biphenyl gold-phosphine complex to uncouple mitochondria and inhibit TNBC growth in vivo. Show less
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potenti Show more
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation â and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration. Show less