Folia Pharmacologica Japonica最新文献

The heart has adynamic compensatory mechanism for hemodynamic stress. This adaptive response to stress depends on cardiac resilience. However, the details of the molecular mechanisms underlying cardiac resilience and the mechanisms by which it is acquired remain unclear. In this review, we focus on TRPV2, a candidate molecule for mechanical stress sensors in cardiomyocytes, and its role in cardiac growth and maturation and in the adult heart using drug-induced TRPV2-deficient mice. TRPV2-mediated activation of the transcription factors SRF and MEF2c is an important pathway that regulates structural and functional maturation of cardiomyocytes. TRPV2 is also an essential factor for the maintenance of the intercalated discs, a site of structural and functional contact between neighboring cardiomyocytes. The increased contractile function of individual cardiomyocytes and the maturation of structural and functional contacts between cells are feedback as mechanical stress, suggesting that the heart develops hemodynamic resilience. In addition, hearts deficient in TRPV2 from an early age developed heart failure due to a failure of adaptive response to the hemodynamic load produced by long-term administration of phenylephrine. These findings suggest that TRPV2 mediates stress resilience in mouse cardiomyocytes. In contrast, these TRPV2-deficient hearts did not show structural or functional changes in response to pressure-overload induced by transverse aortic constriction. These suggest that TRPV2 acts as a mechanotransduction key molecule in the adult mouse heart in response to hemodynamic loading. Advances in this area are expected to provide more options for strategies to treat heart failure conditions.

Human induced pluripotent stem cells derived cardiomyocytes (hiPSC-CMs) can recapitulate the properties of human cardiomyocyte and exhibit disease phenotypes in vitro, attributable to their healthy- or patient-specific genetic backgrounds. Therefore, hiPSC-CMs are a crucial tool for developing therapeutic agents for cardiovascular diseases, and regenerative medicine using hiPSC-CMs is expected to be an alternative therapy to heart transplantation. Moreover, the development of organoid models has been advanced to replicate the complex structure of heart tissue in vitro, thereby effectively facilitating drug discovery. On the other hand, current methods for advancing drug discovery using hiPSC-CMs face limitations, including the difficulty of quantifying characteristics such as cell structure and predicting the risk and efficacy of candidate drug in clinical practice. In the field of regenerative medicine, challenges include quality control and the verification of safety of transplanted cells in human. In silico model, including artificial intelligence (AI) and simulation, have been developed in the field of drug discovery using hiPSC-CMs. These advancements encompass phenotype scoring via AI and risk prediction through simulations. This review outlines the current status and challenges of drug discovery using hiPSC-CMs and in silico model, based on the published reports.

To advance our understanding of the neuronal mechanisms underpinning animal behavior, it is important to integrate traditional electrophysiological methodologies with cutting-edge technologies capable of providing detailed insights into the dynamics of neuromodulators. However, achievement of high spatial and temporal resolution in neuromodulator measurements has presented significant challenges, particularly in the context of real-time observations within freely behaving animals. Recent innovations, exemplified by the development of genetically encoded fluorescent indicator, commonly referred to as "GRAB sensors," have addressed these limitations. These tools enable the real-time, high-precision quantification of neuromodulators, representing a transformative advancement in the field. Notably, GRAB sensors have been designed to target a broad spectrum of neuromodulators, including dopamine (DA), acetylcholine (ACh), noradrenaline/norepinephrine (NE), and neuropeptides, offering unparalleled specificity, sensitivity, and temporal resolution. This review provides an overview of the features and advantages of GRAB sensors, highlights their diverse applications, and discusses key considerations pertinent to their implementation in contemporary neuroscience research.

Myeloid-derived suppressor cells (MDSCs) suppress anti-tumor immunity in tumor bearers, which leads to tumor progression. Immune checkpoint blockers (ICBs) demonstrated significant efficiency against various cancers; however, their success rate is limited to approximately 20-30% in patients with cancer. To address this limitation, predictive biomarkers and combination therapies are required. Since MDSCs are supposed to be crucial for the resistance to ICBs, targeting MDSCs could be a promising approach for cancer immunotherapy. Granulocyte colony-stimulating factor (G-CSF), widely used as prophylaxis and therapy for febrile neutropenia (FN), has been shown to significantly reduce its incidence. However, G-CSF has been reported to promote tumor progression caused by the enhancing the proliferation of MDSCs. We found that G-CSF enhances the immunosuppressive activity of MDSCs through the upregulation of γ-glutamyltransferase 1 (GGT1). GGT1, an enzyme hydrolyzing extracellular glutathione, is reported to be a marker for early-stage cancers and promote tumor progression. It is suggested that GGT1 increases glutamate levels through glutathione hydrolysis and that metabotropic glutamate receptor signaling enhances the immunosuppressive activity of MDSCs. Moreover, in FN mouse models, we observed that G-CSF promoted tumor progression, while the inhibition of GGT abolished. Together, the inhibition of GGT can mitigate the tumor-promoting effects of MDSCs without compromising the beneficial effect of G-CSF. These insights should lead to the safer and more effective cancer immunotherapy.

In vitro compound evaluation using human-derived neural cells is beginning to incorporate microphysiological systems (MPS). Neural MPS includes not only microfluidic devices but has also recently recognized neural organoids as viable MPS platforms. The history of neural MPS utilizing microfluidic devices is extensive, with the development of models that control the positioning of cell bodies and neurite outgrowth, as well as models that mimic neuronal projections through the connection of heterogeneous cell types. This paper presents examples of predicting peripheral neuropathy through machine learning applied to images of cell bodies and neurites in microfluidic devices, as well as the construction of a motor neuron-skeletal muscle model. Additionally, it discusses the responses to contraindicated drugs in Dravet syndrome using brain organoids that reflect biological brain structures. In drug discovery applications of neural MPS, it is essential to develop and utilize appropriate MPS tailored to specific objectives, ensuring biological relevance and reliability for future advancements.