The bioelectric mechanisms of local calcium dynamics in cancer cell proliferation: an extension of the A549 in silico cell model.

Introduction: Advances in molecular targeting of ion channels may open up new avenues for therapeutic approaches in cancer based on the cells' bioelectric properties. In addition to in-vitro or in-vivo models, in silico models can provide deeper insight into the complex role of electrophysiology in cancer and reveal the impact of altered ion channel expression and the membrane potential on malignant processes. The A549 in silico model is the first computational cancer whole-cell ion current model that simulates the bioelectric mechanisms of the human non-small cell lung cancer cell line A549 during the different phases of the cell cycle. This work extends the existing model with a detailed mathematical description of the store-operated Ca2+ entry (SOCE) and the complex local intracellular calcium dynamics, which significantly affect the entire electrophysiological properties of the cell and regulate cell cycle progression. Methods: The initial model was extended by a multicompartmental approach, addressing the heterogenous calcium profile and dynamics in the ER-PM junction provoked by local calcium entry of store-operated calcium channels (SOCs) and uptake by SERCA pumps. Changes of cytosolic calcium levels due to diffusion from the ER-PM junction, release from the ER by RyR channels and IP3 receptors, as well as corresponding PM channels were simulated and the dynamics evaluated based on calcium imaging data. The model parameters were fitted to available data from two published experimental studies, showing the function of CRAC channels and indirectly of IP3R, RyR and PMCA via changes of the cytosolic calcium levels. Results: The proposed calcium description accurately reproduces the dynamics of calcium imaging data and simulates the SOCE mechanisms. In addition, simulations of the combined A549-SOCE model in distinct phases of the cell cycle demonstrate how Ca2+ - dynamics influence responding channels such as KCa, and consequently modulate the membrane potential accordingly. Discussion: Local calcium distribution and time evolution in microdomains of the cell significantly impact the overall electrophysiological properties and exert control over cell cycle progression. By providing a more profound description, the extended A549-SOCE model represents an important step on the route towards a valid model for oncological research and in silico supported development of novel therapeutic strategies.

Shedding Light on Cardiac Excitation: In Vitro and In Silico Analysis of Native Ca2+ Channel Activation in Guinea Pig Cardiomyocytes Using Organic Photovoltaic Devices.

OBJECTIVE: This study aims to explore the potential of organic electrolytic photocapacitors (OEPCs), an innovative photovoltaic device, in mediating the activation of native voltage-gated Cav1.2 channels (ICa,L) in Guinea pig ventricular cardiomyocytes. METHODS: Whole-cell patch-clamp recordings were employed to examine light-triggered OEPC mediated ICa,L activation, integrating the channel's kinetic properties into a multicompartment cell model to take intracellular ion concentrations into account. A multidomain model was additionally incorporated to evaluate effects of OEPC-mediated stimulation. The final model combines external stimulation, multicompartmental cell simulation, and a patch-clamp amplifier equivalent circuit to assess the impact on achievable intracellular voltage changes. RESULTS: Light pulses activated ICa,L, with amplitudes similar to voltage-clamp activation and high sensitivity to the L-type Ca2+ channel blocker, nifedipine. Light-triggered ICa,L inactivation exhibited kinetic parameters comparable to voltage-induced inactivation. CONCLUSION: OEPC-mediated activation of ICa,L demonstrates their potential for nongenetic optical modulation of cellular physiology potentially paving the way for the development of innovative therapies in cardiovascular health. The integrated model proves the light-mediated activation of ICa,L and advances the understanding of the interplay between the patch-clamp amplifier and external stimulation devices. SIGNIFICANCE: Treating cardiac conduction disorders by minimal-invasive means without genetic modifications could advance therapeutic approaches increasing patients' quality of life compared with conventional methods employing electronic devices.

Consequences of somatic mutations of GIRK1 detected in primary malign tumors on expression and function of G-protein activated, inwardly rectifying, K+ channels.

A search in the GDC Data Portal revealed 304 documented somatic mutations of the KCNJ3 gene in primary tumors (out of 10.202 cases). Most affected tumor types were carcinomas from uterus, skin and lung, while breast cancer exerted the lowest number of somatic mutations. We focused our research on 15 missense mutations within the region between TM1 and TM2, comprising the pore helix and ion selectivity signature. Expression was measured by confocal laser scan microscopy of eGFP tagged GIRK1 subunits, expressed with and without GIRK4 in oocytes of Xenopus laevis. GIRK ion currents were activated via coexpressed m2Rs and measured by the Two Electrode Voltage Clamp technique. Magnitude of the total GIRK current, as well as the fraction of current inducible by the agonist, were measured. Ion selectivity was gauged by assessment of the PNa+/PK+ ratio, calculated by the GIRK current reversal potential in extracellular media at different Na+ and K+ concentrations. None of the tested mutations was able to form functional GIRK1 homooligomeric ion channels. One of the mutations, G145A, which locates directly to the ion selectivity signature, exerted an increased PNa+/PK+ ratio. Generally, the missense mutations studied can be categorized into three groups: (i) normal/reduced expression accompanied by reduced/absent function (S132Y, F136L, E139K, G145A, R149Q, R149P, G178D, S185Y, Q186R), (ii) normal/increased expression as well as increased function (E140M, A142T, M184I) and (iii) miniscule expression but increased function relative to expression levels (I151N, G158S). We conclude, that gain of function mutations, identical or similar to categories (ii) and (iii), may potentially be involved in genesis and progression of malignancies in tissues that exert a high rate of occurrence of somatic mutations of KCNJ3.

Ion Channel Modeling beyond State of the Art: A Comparison with a System Theory-Based Model of the Shaker-Related Voltage-Gated Potassium Channel Kv1.1.

The mathematical modeling of ion channel kinetics is an important tool for studying the electrophysiological mechanisms of the nerves, heart, or cancer, from a single cell to an organ. Common approaches use either a Hodgkin-Huxley (HH) or a hidden Markov model (HMM) description, depending on the level of detail of the functionality and structural changes of the underlying channel gating, and taking into account the computational effort for model simulations. Here, we introduce for the first time a novel system theory-based approach for ion channel modeling based on the concept of transfer function characterization, without a priori knowledge of the biological system, using patch clamp measurements. Using the shaker-related voltage-gated potassium channel Kv1.1 (KCNA1) as an example, we compare the established approaches, HH and HMM, with the system theory-based concept in terms of model accuracy, computational effort, the degree of electrophysiological interpretability, and methodological limitations. This highly data-driven modeling concept offers a new opportunity for the phenomenological kinetic modeling of ion channels, exhibiting exceptional accuracy and computational efficiency compared to the conventional methods. The method has a high potential to further improve the quality and computational performance of complex cell and organ model simulations, and could provide a valuable new tool in the field of next-generation in silico electrophysiology.

System identification and mathematical modeling of the pandemic spread COVID-19 in Serbia.

This paper presents applications of control system theory in biomedical engineering. These methodologies are used in engineering sciences to obtain a mathematical model of systems, but system identification as scientific methodology is rarely used in biomedical engineering. The paper presents exemplarily control theory and system identification as methods for obtaining a mathematical model of the spread SARS-CoV-2 virus. The models obtained in the course of this are data-driven and strongly data-dependent. The available dataset allowed us to consider a model of a pandemic spread in the context of both the number of tested individuals and the number of infected individuals and with a resultant model that is nonlinear. We also considered a mathematical model for the dependence between the number of confirmed infected individuals and the number of deaths caused by the disease. The resulting model is linear given with the transfer function corresponding to the second-order differential equation. The mathematical models developed were additionally analyzed in accordance with controllability and observability.

A549 in-silico 1.0: A first computational model to simulate cell cycle dependent ion current modulation in the human lung adenocarcinoma.

Lung cancer is still a leading cause of death worldwide. In recent years, knowledge has been obtained of the mechanisms modulating ion channel kinetics and thus of cell bioelectric properties, which is promising for oncological biomarkers and targets. The complex interplay of channel expression and its consequences on malignant processes, however, is still insufficiently understood. We here introduce the first approach of an in-silico whole-cell ion current model of a cancer cell, in particular of the A549 human lung adenocarcinoma, including the main functionally expressed ion channels in the plasma membrane as so far known. This hidden Markov-based model represents the electrophysiology behind proliferation of the A549 cell, describing its rhythmic oscillation of the membrane potential able to trigger the transition between cell cycle phases, and it predicts membrane potential changes over the cell cycle provoked by targeted ion channel modulation. This first A549 in-silico cell model opens up a deeper insight and understanding of possible ion channel interactions in tumor development and progression, and is a valuable tool for simulating altered ion channel function in lung cancer electrophysiology.

GIRK1 triggers multiple cancer-related pathways in the benign mammary epithelial cell line MCF10A.

Excessive expression of subunit 1 of GIRK1 in ER+ breast tumors is associated with reduced survival times and increased lymph node metastasis in patients. To investigate possible tumor-initiating properties, benign MCF10A and malign MCF7 mammary epithelial cells were engineered to overexpress GIRK1 neoplasia associated vital parameters and resting potentials were measured and compared to controls. The presence of GIRK1 resulted in resting potentials negative to the controls. Upon GIRK1 overexpression, several cellular pathways were regulated towards pro-tumorigenic action as revealed by comparison of transcriptomes of MCF10AGIRK1 with the control (MCF10AeGFP). According to transcriptome analysis, cellular migration was promoted while wound healing and extracellular matrix interactions were impaired. Vital parameters in MCF7 cells were affected akin the benign MCF10A lines, but to a lesser extent. Thus, GIRK1 regulated cellular pathways in mammary epithelial cells are likely to contribute to the development and progression of breast cancer.

A novel network-based approach for discovering dynamic metabolic biomarkers in cardiovascular disease.

Metabolic biomarkers may play an important role in the diagnosis, prognostication and assessment of response to pharmacological therapy in complex diseases. The process of discovering new metabolic biomarkers is a non-trivial task which involves a number of bioanalytical processing steps coupled with a computational approach for the search, prioritization and verification of new biomarker candidates. Kinetic analysis provides an additional dimension of complexity in time-series data, allowing for a more precise interpretation of biomarker dynamics in terms of molecular interaction and pathway modulation. A novel network-based computational strategy for the discovery of putative dynamic biomarker candidates is presented, enabling the identification and verification of unexpected metabolic signatures in complex diseases such as myocardial infarction. The novelty of the proposed method lies in combining metabolic time-series data into a superimposed graph representation, highlighting the strength of the underlying kinetic interaction of preselected analytes. Using this approach, we were able to confirm known metabolic signatures and also identify new candidates such as carnosine and glycocholic acid, and pathways that have been previously associated with cardiovascular or related diseases. This computational strategy may serve as a complementary tool for the discovery of dynamic metabolic or proteomic biomarkers in the field of clinical medicine.