Showing papers by "Zhong Wang published in 2021"
TL;DR: In this article, the authors investigate the mechanism by which the neuronal adhesion protein AMIGO1 modulates Kv2.1 channels and conclude that AMIGo1 can accelerate early voltage sensor movements and shift the gating charge-voltage relationship to more negative voltages.
Abstract: Voltage-gated potassium (Kv) channels sense voltage and facilitate transmembrane flow of K+ to control the electrical excitability of cells. The Kv2.1 channel subtype is abundant in most brain neurons and its conductance is critical for homeostatic regulation of neuronal excitability. Many forms of regulation modulate Kv2.1 conductance, yet the biophysical mechanisms through which the conductance is modulated are unknown. Here, we investigate the mechanism by which the neuronal adhesion protein AMIGO1 modulates Kv2.1 channels. With voltage clamp recordings and spectroscopy of heterologously expressed Kv2.1 and AMIGO1 in mammalian cell lines, we show that AMIGO1 modulates Kv2.1 voltage sensor movement to change Kv2.1 conductance. AMIGO1 speeds early voltage sensor movements and shifts the gating charge-voltage relationship to more negative voltages. Fluorescence measurements from voltage sensor toxins bound to Kv2.1 indicate that the voltage sensors enter their earliest resting conformation, yet this conformation is less stable upon voltage stimulation. We conclude that AMIGO1 modulates the Kv2.1 conductance activation pathway by destabilizing the earliest resting state of the voltage sensors.
TL;DR: In this paper, the authors identify steps in the conductance activation pathway of Kv2.1 channels that are modulated by AMIGO1 using voltage clamp recordings and spectroscopy.
Abstract: Kv2 voltage-gated potassium channels are modulated by AMIGO neuronal adhesion proteins. Here, we identify steps in the conductance activation pathway of Kv2.1 channels that are modulated by AMIGO1 using voltage clamp recordings and spectroscopy of heterologously expressed Kv2.1 and AMIGO1 in mammalian cell lines. AMIGO1 speeds early voltage sensor movements and shifts the gating charge-voltage relationship to more negative voltages. The gating charge-voltage relationship indicates that AMIGO1 exerts a larger energetic effect on voltage sensor movement than apparent from the midpoint of the conductance-voltage relationship. When voltage sensors are detained at rest by voltage sensor toxins, AMIGO1 has a greater impact on the conductance-voltage relationship. Fluorescence measurements from voltage sensor toxins bound to Kv2.1 indicate that with AMIGO1, the voltage sensors enter their earliest resting conformation, yet this conformation is less stable upon voltage stimulation. We conclude that AMIGO1 modulates the Kv2.1 conductance activation pathway by destabilizing the earliest resting state of the voltage sensors.
TL;DR: In this article, a two-step Label Propagation Algorithm (LPA) is proposed to overcome the under-clustering problem on short-read sequences. But the LPA is not scalable for large metagenome datasets.
Abstract: Next-generation sequencing has enabled metagenomics, the study of the genomes of microorganisms sampled directly from the environment without cultivation. We previously developed a proof-of-concept, scalable metagenome clustering algorithm based on Apache Spark to cluster sequence reads according to their species of origin. To overcome its under-clustering problem on short-read sequences, in this study we developed a new, two-step Label Propagation Algorithm (LPA) that first forms clusters of long reads and then recruits short reads to these clusters. Compared to alternative label propagation strategies, this hybrid clustering algorithm (hybrid-LPA) yields significantly larger read clusters without compromising cluster purity. We show that adding an extra clustering step before assembly leads to improved metagenome assemblies, predicting more complete genomes or gene clusters from a synthetic metagenome dataset and a real-world metagenome dataset, respectively. These results suggest that hybrid-LPA is a good alternative to current metagenome assembly practice by providing benefits in both scalability and accuracy on large metagenome datasets.