Absorption and Transport Characteristics and Mechanisms of Carnosic Acid

Carnosic acid (CA) is a phenolic diterpenoid mainly found in rosemary and sage. CA has been reported to possess health-beneficial effects in various experimental settings. Herein, a mouse experiment and Caco-2 single-cell model were used to understand the absorption and transport characteristics of CA. First, we determined the tissue distribution of CA in mice, following an oral gavage at a physiologically relevant dose. We found that CA was bioavailable systemically and present locally in the digestive tract, especially in the cecum and colon. Next, we thought to characterize the absorption and transport of CA in the Caco-2 cell monolayer model of the intestinal epithelial barrier. In the Caco-2 cell model, CA exhibited a moderate permeability and was subjected to a mild efflux. Moreover, the apparent permeability coefficient (Papp) of CA transported across Caco-2 cell monolayers was significantly changed when the inhibitors of specific active transporter and passive diffusion were added to cells, suggesting that the absorption and transport of CA involved both passive and active transportation. The present study is an important first step towards understanding the absorption, transport, and metabolic mechanisms of CA. This could provide the scientific basis for developing CA-containing functional foods or dietary supplements with improved bioavailability.

Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation

Many studies have revealed changes in specific protein channels due to physiological causes such as mutation and their effects on action potential duration changes. However, no studies have been conducted to predict the type of protein channel abnormalities that occur through an action potential (AP) shape. Therefore, in this study, we aim to predict the ion channel conductance that is altered from various AP shapes using a machine learning algorithm. We perform electrophysiological simulations using a single-cell model to obtain AP shapes based on variations in the ion channel conductance. In the AP simulation, we increase and decrease the conductance of each ion channel at a constant rate, resulting in 1,980 AP shapes and one standard AP shape without any changes in the ion channel conductance. Subsequently, we calculate the AP difference shapes between them and use them as the input of the machine learning model to predict the changed ion channel conductance. In this study, we demonstrate that the changed ion channel conductance can be predicted with high prediction accuracy, as reflected by an F1 score of 0.985, using only AP shapes and simple machine learning.

Network dynamics underlying OFF responses in the auditory cortex

Across sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. We examine here the hypothesis that OFF responses are single-cell signatures of recurrent interactions at the network level. To test this hypothesis, we performed population analyses of two-photon calcium recordings in the auditory cortex of awake mice listening to auditory stimuli, and compared linear single-cell and network models. While the single-cell model explained some prominent features of the data, it could not capture the structure across stimuli and trials. In contrast, the network model accounted for the low-dimensional organisation of population responses and their global structure across stimuli, where distinct stimuli activated mostly orthogonal dimensions in the neural state-space.

Direct entry of cell-penetrating peptide can be controlled by maneuvering the membrane curvature

A biomembrane's role is to be a barrier for interior cytosol from an exterior environment to execute the cell's normal biological functions. However, a water-soluble peptide called cell-penetrating peptide (CPP) has been known for its ability to directly penetrate through the biomembranes into cells (cytolysis) without perturbating cell viability and expected to be a promising drug delivery vector. Examples of CPP include peptides with multiple arginine units with strong cationic properties, which is the key to cytolysis. Here we show the conclusive evidence to support the mechanism of CPP’s cytolysis and way to control it. The mechanism we proposed is attributed to biomembrane’s physicochemical nature as lamellar liquid crystal (Lα). Cytolysis occurs as the temporal and local dynamic phase transitions from Lα to an undulated lamellar with pores called Mesh1. We have shown this phase transfer of Lα composed of dioleoyl-phosphatidylcholine (DOPC) with water by adding oligo-arginine (Rx) as CPP at the equilibrium. Using giant unilamellar vesicle composed of DOPC as a single cell model, we could control the level of cytolysis of CPP (FITC-R8) by changing the curvature of the membrane through osmotic pressure modulation. The cytolysis of CPP utilizes biomembrane's inherent topological and functional flexibility corresponding to the stimuli.

Simulation of single cell electroporation

This paper attempts to introduce the dynamics of electroporation into the single cell model. The main characteristics of the model were described. Results show the generation and rise of pores in a round cell with a radius of 50 μm exposed to 40 kV/m electric field for 1 ms were analysed, so that the transmembrane potential, the number of pores and the membrane conductance could be calculated. Finally, how the model can help explain the experiment is discussed.