Observing GLUT4 Translocation in Live L6 Cells Using Quantum Dots

The glucose transporter 4 (GLUT4) plays a key role in maintaining whole body glucose homeostasis. Tracking GLUT4 in space and time can provide new insights for understanding the mechanisms of insulin-regulated GLUT4 translocation. Organic dyes and fluorescent proteins were used in previous studies for investigating the traffic of GLUT4 in skeletal muscle cells and adipocytes. Because of their relative weak fluorescent signal against strong cellular autofluorescence background and their fast photobleaching rate, most studies only focused on particular segments of GLUT4 traffic. In this study, we have developed a new method for observing the translocation of GLUT4 targeted with photostable and bright quantum dots (QDs) in live L6 cells. QDs were targeted to GLUT4myc specifically and internalized with GLUT4myc through receptor-mediated endocytosis. Compared with traditional fluorescence dyes and fluorescent proteins, QDs with high brightness and extremely photostability are suitable for long-term single particle tracking, so individual GLUT4-QD complex can be easily detected and tracked for long periods of time. This newly described method will be a powerful tool for observing the translocation of GLUT4 in live L6 cells

Dissecting a central flip-flop circuit that integrates contradictory sensory cues in C. elegans feeding regulation

Feeding behaviour is modulated by both environmental cues and internal physiological states. Appetite is commonly boosted by the pleasant smell (or appearance) of food and destroyed by a bad taste. In reality, animals sense multiple environmental cues at the same time and it is not clear how these sensory inputs are integrated and a decision is made to regulate feeding behaviour accordingly. Here we show that feeding behaviour in Caenorhabditis elegans can be either facilitated by attractive odours or suppressed by repellents. By identifying mutants that are defective for sensory-mediated feeding regulation, we dissected a central flip-flop circuit that integrates two contradictory sensory inputs and generates bistable hormone output to regulate feeding behaviour. As feeding regulation is fundamental to animal survival, we speculate that the basic organizational logic identified here in C. elegans is likely convergent throughout different phyla

Native Gating Behavior of Ion Channels in Neurons with Null-Deviation Modeling

<div><p>Computational modeling has emerged as an indispensable approach to resolve and predict the intricate interplay among the many ion channels underlying neuronal excitability. However, simulation results using the classic formula-based Hodgkin-Huxley (H-H) model or the superior Markov kinetic model of ion channels often deviate significantly from native cellular signals despite using carefully measured parameters. Here we found that the filters of patch-clamp amplifier not only delayed the signals, but also introduced ringing, and that the residual series resistance in experiments altered the command voltages, which had never been fully eliminated by improving the amplifier itself. To remove all the above errors, a virtual device with the parameters exactly same to that of amplifier was introduced into Markov kinetic modeling so as to establish a null-deviation model. We demonstrate that our novel null-deviation approach fully restores the native gating-kinetics of ion-channels with the data recorded at any condition, and predicts spike waveform and firing patterns clearly distinctive from those without correction.</p></div

Illustration for principle of native modeling.

<p>(a) Schematic diagram for the F-native kinetic modeling. A virtual device mimicking the effect of filters in the patch-clamp amplifier is included in the fitting process. (<b>b</b>) Schematic diagram for the F/R-native kinetic modeling. Both the effects of the series resistance and filter are taken into account. (<b>c–d</b>) F-native modeling of Kv3.1 channel is shown in (<b>c</b>). F/R-native modeling of BK channel is shown in (<b>d</b>). The black currents are the data; the purple ones are the simulations by an F-native or a F/R-native fit; the cyan lines are the native responses recreated by the native model. Voltage commands are same to that shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077105#pone-0077105-g001" target="_blank">Figure 1e–1f</a>, respectively.</p

Origin of measurement errors.

<p>(a) Schematic diagrams for conventional kinetic modeling. The black signal is data and the purple one is simulation. The cyan signal represents the native signal in a cell. (<b>b</b>) Diagram for illustrating the filter effect of an amplifier. For the EPC-9 amplifier, it has several cascaded filters in the open-loop pathway, such as stim filter (SF), filter1 (F1) and filter2 (F2). (<b>c</b>) Diagram for illustrating the close-loop <i>R</i>_s compensation. (<b>d</b>) The native traces (cyan) from a BK channel model were stimulated by a voltage command shown at below. The purple traces were acquired from the above cyan traces treated by three filters of a virtual device: SF, F1 and F2. Voltage command is placed at the bottom. (<b>e</b>) Top is a 6-state sequential Markov model for Kv3.1 channel; bottom, the black current traces are the data recorded at a lower filtering frequency from MultiClamp 700B, and the purple lines are simulations by a direct fit. Voltage command is placed at the bottom. (<b>f</b>) Top is a 10-state Markov model for BK channel; bottom, the black current traces are the data recorded with 90% series resistance (<i>R_s</i>) compensation from an EPC-9 amplifier, and the purple lines are simulations by an F-native fit. At the bottom, the voltage command shows the variable voltages practically applied to BK channel during an experiment with a 90% series resistant compensation.</p

Parameters (Bessel) of BK Model.

<p>kc = 1.17 µM, ko = 19.52 µM for all.</p