Biology subjects

Kubo, Y.

Publications and source records attributed to Kubo, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Firing Without Wiring: How REM-like Sleep Reactivation Rebalances Excitation and Inhibition to Orthogonalize Memories

The role of Rapid Eye Movement (REM) sleep in memory consolidation remains poorly understood. Several influential ideas propose that REM sleep serves to eliminate crosstalk between memories, thereby orthogonalizing representations of similar ones; however, mechanistic confirmation remains sparse. Here we used recurrent neural networks trained with Equilibrium Propagation (EP), a biologically plausible training algorithm, to investigate the role of REM-like sleep in memory consolidation. We found that REM-like sleep reduces memory interference by decorrelating task-specific neuronal populations, isolating individual memory traces. During sleep, reactivation was dominated by the population encoding the most recent memory. Net excitatory input onto this population decreased, most strongly that from older-memory populations, which allowed inputs of older memories to drive their own neurons again. Spreading the same total synaptic change evenly across all target populations abolished this recovery. We call this mechanism reactivation-targeted pruning: plasticity is confined to one population, whose neurons lose inputs that are silent when they fire. The mechanism held under Dale's law, and depression of excitatory synapses alone, without any inhibitory plasticity, was sufficient. These findings suggest mechanistic principles underlying REM sleep's role in memory consolidation and highlight the potential for integrating human-like learning behaviors into artificial neural networks.

neuroscience↗

bioRxiv2026

Neuromodulated Equilibrium Learning

Equilibrium Propagation (EP) is a biologically motivated alternative to backpropagation but requires free and nudged equilibrium phases, raising questions about the biological plausibility of phase-separated learning. We introduce Neuromodulated Equilibrium Learning (NEL), a single-equilibrium reward-based framework inspired by Attention-Gated Brain Propagation and neuromodulatory signaling. After reaching a free equilibrium, NEL propagates a reward-dependent modulatory signal derived from the selected action through reciprocal feedback connections to drive local synaptic updates, eliminating both the reward-nudged equilibrium and contrastive update. We evaluate NEL on MNIST, Fashion-MNIST, and CIFAR-10 using multilayer perceptrons and convolutional networks, with symmetric three-phase REP and standard EP as multi-phase baselines. NEL approaches three-phase REP in shallow architectures, with gaps of 0.79 and 1.31 percentage points on MNIST and one-hidden-layer Fashion-MNIST, while larger gaps of 3.13 and 6.21 percentage points emerge in the deeper Fashion-MNIST and CIFAR-10 settings. A direct cost feedback control using the full supervised output error also remains below three-phase EP, indicating that sparse reward information alone does not explain the performance gap between single-equilibrium and multi-phase learning. These results show that effective reward-driven learning can arise from a single free equilibrium while suggesting that computations introduced by additional nudged relaxations become increasingly important in more challenging architectures.

neuroscience↗

bioRxiv2026

Reward-Based Equilibrium Propagation

Equilibrium propagation (EP) is a biologically plausible alternative to backpropagation for training neural networks. EP typically relies on free and nudged dynamical phases: during the free phase, the network relaxes toward an equilibrium state, whereas during nudging, the output state is perturbed toward a target using a teaching signal. However, it remains unclear whether the brain has access to such explicit target signals. Inspired by Attention-Gated Brain Propagation (BrainProp), a reward-based learning framework proposed by Pozzi et al. (2020), we introduce a reward-based variant of EP that replaces full target-based nudging with a selected-output binary reward signal. The proposed method updates the network using only the chosen class and whether that choice is correct, without directly revealing the full target vector. We evaluate the method on MNIST, Fashion-MNIST, and CIFAR-10 using both multilayer perceptrons and convolutional neural networks. The proposed reward-based EP achieves performance close to that of conventional EP across all three datasets, although it generally converges more slowly during the early stages of training. Generalization-gap analyses show similar behavior for the two methods on MNIST and Fashion-MNIST, while reward-based EP exhibits a smaller training-test accuracy gap during later training on CIFAR-10. We further investigate the effect of the exploration probability used during stochastic class selection and find that moderate exploration can provide small performance improvements, although its effect is dataset-dependent. These results demonstrate that EP can learn effectively from sparse, action-specific reward feedback rather than a complete supervised target.

neuroscience↗

bioRxiv2026

Therapeutic Potential of the Novel Monoclonal Antibody 7-4G against HBsAg in a Mouse Model of Chronic Hepatitis B Virus Infection

Chronic hepatitis B virus (HBV) infection remains a major global health challenge because current antiviral therapies rarely achieve complete viral clearance. We evaluated the antiviral activity of a newly developed monoclonal antibody, 7-4G mAb, directed against the hepatitis B surface antigen (HBsAg). The antibody showed specific binding to the p27 component of HBsAg and efficiently removed HBV-related particles from patient serum in vitro. Significant HBV neutralizing activity of 7-4G mAb was observed in primary human hepatocytes and in humanized mouse models. In persistently HBV-infected PXB mice, a single administration of 7-4G mAb, either alone or in combination with another monoclonal antibody, reduced serum HBsAg levels and HBV DNA levels by two orders magnitude for two weeks after the treatment. In half of the treated mice, suppression of both viral markers was maintained through the study period, whereas rebound occurred within three to four weeks in the remaining animals. In addition, 7-4G mAb enhanced the HBV DNA-lowering effect of entecavir, a known anti-HBV drug. These findings support the further development of 7-4G mAb as part of an innovative HBV treatment strategy, addressing limitations of existing therapies and advancing toward a more effective HBV management solution.

immunology↗

bioRxiv2026

Large-scale dimensional behavioral profiling dissociates fear memory from locomotor confounds in mice: The necessity of baseline-normalized metrics

Fear conditioning is widely used to assess associative memory in mice, yet percent freezing conflates memory with baseline locomotor and anxiety-related traits. A systematic survey of recent studies (2020-2025) found that fewer than 1% statistically integrate locomotor activity into freezing analyses. Here, we address this gap using a large-scale dataset of >10,000 mice across >160 comparisons, including genetic mutations, pharmacological interventions and aging, tested in 15 standardized behavioral paradigms. Conventional freezing scores covaried strongly with general locomotor activity, obscuring memory-related phenotypes. Multiple factor analysis identified two principal behavioral dimensions, locomotor activity and learning/memory: conventional freezing aligned with the locomotor dimension, whereas freezing subtraction and the activity suppression ratio mapped onto the memory dimension and improved detection of synaptic plasticity phenotypes. These analyses show that baseline locomotor normalization is essential for interpreting fear conditioning as a memory assay and provide an open framework for selecting and reporting locomotor-normalized metrics.

neuroscience↗

bioRxiv2026

Dendritic Wave Recurrent Neural Networks

Wave recurrent neural networks (wRNNs) are biologically inspired recurrent architectures that use traveling-wave dynamics to support sequence learning and memory. However, their input-to-hidden pathway remains relatively simple compared with biological neurons, where dendrites perform nonlinear input integration. In this study, we introduce the Dendritic Wave Recurrent Neural Network (DW-RNN), which augments the input pathway of the wRNN with nonlinear basal dendritic branches while preserving the original recurrent wave dynamics. We evaluate DW-RNN on a simple copy task, sequential MNIST (sMNIST), permuted sequential MNIST (psMNIST), and noisy sequential CIFAR-10 (nsCIFAR-10). On the copy task, DW-RNN shows learning behavior comparable to the standard wRNN, suggesting that dendritic input integration does not disrupt the recurrent wave-based memory mechanism. On the three sequential image-classification benchmarks, DW-RNN outperforms the standard wRNN, improving accuracy from 97.27 {+/-} 0.15% to 97.82 {+/-} 0.12% on sMNIST, from 96.74 {+/-} 0.17% to 96.92 {+/-} 0.10% on psMNIST, and from 54.30 {+/-} 0.79% to 55.65 {+/-} 0.55% on nsCIFAR-10. In addition to improving mean accuracy, DW-RNN exhibits lower across-seed variability on all three classification benchmarks, suggesting that dendritic input integration may improve the stability of wRNN training. Hidden-activity visualizations further show that DW-RNN preserves the characteristic traveling-wave patterns of the original wRNN. These results suggest that dendritic computation and traveling-wave recurrent dynamics provide complementary mechanisms for biologically inspired sequence learning.

neuroscience↗

bioRxiv2026

Equilibrium Propagation with Predictive Learning in Leaky Integrate-and-Fire Spiking Neural Networks

Equilibrium propagation (EP) is a biologically plausible alternative to backpropagation that has demonstrated competitive performance across a range of machine learning tasks. Recent work has extended EP to spiking neural networks (SNNs), leveraging leaky integrate-and-fire (LIF) neurons and spike-based plasticity rules to improve biological realism while maintaining strong performance. In this work, we propose an EP-based SNN framework that combines LIF neural dynamics with a predictive learning rule, replacing conventional spike-timing-dependent plasticity (STDP) with a learning rule more directly aligned with predictive coding principles. We evaluate the proposed model on multiple image classification benchmarks, including MNIST, KMNIST, and Fashion-MNIST, and compare its performance with a BP-trained LIF SNN baseline. Our results show that the proposed EP-based LIF model (EP+LIF) achieves competitive accuracy across datasets, with performance approaching that of the BP-trained counterpart (BP+LIF) while relying on a biologically motivated local learning rule. In addition, analysis of hidden-layer spiking activity reveals that EP+LIF produces more persistent hidden-state activity, whereas BP+LIF yields sparser spiking representations. These results demonstrate that predictive learning can support effective EP-based training in LIF spiking networks, while also highlighting differences in activity patterns that motivate future work on activity regulation and sparse spiking dynamics.

neuroscience↗

bioRxiv2025

Extracellular K+ modulates the pore conformations of Cys-loop receptor anion channels

K+ is an essential cation for life, but no eukaryotic membrane protein with a modulatory site for extracellular K+ has been discovered. Here, we report that a Cys-loop receptor, CG12344/DmAlka, expressed in the Drosophila nervous system, is selectively modulated by physiological concentration of extracellular K+. Structural prediction, electrophysiology and phylogenetic analysis of DmAlka revealed the extracellular K+ binding site that mimics the hydrated chemical environment for K+, as observed in K+ channel pore. Furthermore, we found that K+ binding induces a previously unrecognized "mode-switching," altering properties ranging from ligand sensitivity to ion selectivity. Notably, a human glycine receptor variant also exhibited similar mechanisms. Our study reveals a novel regulatory mechanism of Cys-loop receptors that directly links the extracellular K+ signaling to Cl- conductance in animals.

biophysics↗

bioRxiv2025

Physiological characterization in a somaclonal mutant for fruit shape derived from persimmon 'Hiratanenashi'

Fruit shape is an essential agronomic trait that contributes to the commercial value of fruit crops; however, the physiological mechanisms determining fruit shape have been studied only in a few model crops. Persimmon (Diospyros kaki Thunb.), a major fruit crop in East Asia, shows diversity in fruit shape among cultivars. The underlying molecular mechanisms that determine fruit shape remain unclear. A comparative physiological analysis was conducted to investigate the differences between a major Japanese cultivar, Hiratanenashi with a "flat" fruit shape, and its somaclonal mutant cultivar, Koushimaru with a "round" fruit shape. Through principal component analysis (PCA) with the elliptic Fourier descriptor, the fruit shape transitions in these two cultivars were measured over the fruit differentiation period (40-53 days after anthesis). Histological observation of the mesocarp cells in the fruit shape differentiation stage indicated that not only cell proliferation patterns but also multiple vectors involving cell shapes or sizes are possible determinants of differences in fruit shape. Transcriptome analysis at the fruit shape differentiation stage detected differentially expressed genes between the two cultivars. The hierarchical clustering with expression patterns throughout the fruit development/maturation stages defined four main clusters, including candidate genes determining fruit shapes. We focused on one of these genes, DKAch04a32073.t1, annotated as WUSCHEL related homeobox 13 (WOX13), which has been reported to be a key factor in callus formation (or stem cell proliferation) and the reconnection of organs in Arabidopsis thaliana. Future functional analyses of the candidate genes would provide a better understanding of the mechanisms underlying persimmon fruit shape.

plant biology↗

bioRxiv2024

Characterization and engineering of a blue-sensitive, Gi/o-biased, and bistable ciliary opsin from a fan worm

Ciliary opsins have been identified not only in vertebrates but also in invertebrates. An invertebrate ciliary opsin was recently identified in the fan worm Acromegalomma interruptum (formerly named Megalomma interrupta); however, its spectral and signaling characteristics are unknown. In the present study, we characterized the spectral properties and light-induced cellular signaling properties of the opsin (AcrInvC-opsin). AcrInvC-opsin showed an absorption maximum at 464 nm and upon blue-light absorption, the spectrum was red-shifted by approximately 50 nm. The two states are inter-convertible by illumination with blue and orange light. Blue light illumination of AcrInvC-opsin caused specific coupling with Gi, sustained Gi dissociation, decreased intracellular cAMP levels, and activation of GIRK channels. The cellular responses by the activated opsin were partially terminated by orange light illumination. These light-dependent responses indicate that the InvC-opsin is a typical bistable pigment wherein the resting and activated states can be inter-converted by visible light illumination. We also attempted to modulate the spectral and functional properties of AcrInvC-opsin using site-directed mutagenesis. Substitution of Ser-94 with Ala caused little spectral shift in the resting state but a further red-shift of [~]10 nm in the activated state, indicating that the absorption spectra of the two states were tuned differently. In contrast, the S94A substitution did not significantly affect the light-dependent signaling properties of AcrInvC-opsin. Because AcrInvC-opsin is a blue-sensitive, Gi/o-biased, and bistable pigment, it has the potential to serve as an optical control tool to specifically and reversibly regulate Gi/o-dependent signaling pathways by visible light.

biophysics↗

bioRxiv2023

Structural determinants of the direct inhibition of GIRK channels by Sigma-1 receptor antagonist

G-protein-gated inward rectifier K+ (GIRK) channels play critical roles in the regulation of the excitability of cardiomyocytes and neurons and include GIRK1, GIRK2, GIRK3 and GIRK4 subfamily members. BD1047 dihydrobromide (BD1047) is one of the representative antagonists of multi-functional Sigma-1 receptor (S1R). In the analysis of the effect of BD1047 on the inhibition of Gi-coupled receptors by S1R using GIRK channel as an effector, we observed that BD1047 directly inhibits GIRK current even in the absence of S1R. Thus, we aimed to clarify the effect of BD1047 on GIRK channels and its structural determinants. By electrophysiological recordings in Xenopus oocytes, we observed that BD1047 directly inhibited the current of GIRK channels, producing a much stronger inhibition of GIRK4 channels compared to GIRK2. It also inhibited the ACh-induced native GIRK current in isolated rat atrial myocytes. Chimeric and mutagenesis studies of GIRK2 and GIRK4 combining with molecular docking analysis, demonstrate the importance of the Leu77 on the proximal N-terminal cytoplasmic region of GIRK4 for the inhibition by BD1047. The activator of GIRK channel, ivermectin, competed with BD1047 at Leu77 on GIRK4. This study provides us with a novel inhibitor of GIRK channels and information for developing pharmacological treatments for GIRK4 associated diseases. Key pointsO_LISigma-1 receptor antagonist, BD1047, directly inhibits the current of GIRK channels. It strongly inhibits GIRK4 channels but only weakly inhibits GIRK2. C_LIO_LIBD1047 inhibits the ACh-induced GIRK current in isolated rat atrial myocyte. C_LIO_LILeu77 on the proximal N-terminal of GIRK4 is essential for the inhibition by BD1047. C_LIO_LIBinding of BD1047 adjacent to Leu77, but not to Leu77Ile, was confirmed by molecular docking analysis. C_LIO_LIThe activator of GIRK channel, ivermectin, competes with BD1047 at Leu77 on GIRK4. C_LI

biophysics↗

bioRxiv2023

A self-inactivating invertebrate opsin with resistance to retinal depletion optically drives biased signaling toward Gβγ-dependent ion channel modulation

Animal opsins, light-sensitive G protein-coupled receptors (GPCRs), have been utilized for optogenetic tools to control G protein-dependent signaling pathways. Upon G protein activation, the Ga and G{beta}{gamma} subunits drive different intracellular signaling pathways, leading to complex cellular responses. For some purposes, Ga-, G{beta}{gamma}-dependent signaling needs to be separately modulated, but these responses are simultaneously evoked due to the 1:1 stoichiometry of Ga and G{beta}{gamma}. Nevertheless, we show temporal activation of G protein using a self-inactivating invertebrate opsin, Platynereis c-opsin1, drives biased signaling for G{beta}{gamma}-dependent GIRK channel activation in a light-dependent manner by utilizing the kinetic difference between G{beta}{gamma}-dependent and Ga-dependent responses. The opsin-induced transient Gi/o activation preferably causes activation of the kinetically-fast G{beta}{gamma}-dependent GIRK channels rather than slower Gi/o-dependent adenylyl cyclase inhibition. Although similar G{beta}{gamma}-biased signaling properties were observed in a selfinactivating vertebrate visual pigment, Platynereis c-opsin1 needs fewer retinal molecules to evoke cellular responses. Furthermore, the G{beta}{gamma}-biased signaling properties of Platynereis c-opsinl are enhanced by genetically fused with RGS8 protein which accelerates G protein inactivation. The self-inactivating invertebrate opsin and its RGS8-fusion protein can function as optical control tools biased for G{beta}{gamma}-dependent ion channel modulation.

biophysics↗

bioRxiv2022

A novel TGF-β-responsive enhancer regulates SRC expression and epithelial-mesenchymal transition-associated cell migration

The non-receptor tyrosine kinase SRC is overexpressed and/or hyperactivated in various human cancers, and facilitates cancer progression by promoting invasion and metastasis. However, the mechanisms underlying SRC upregulation are poorly understood. In this study, we demonstrated that transforming growth factor-{beta} (TGF-{beta}) induces SRC expression at the transcriptional level by activating a novel intragenic SRC enhancer. In the human breast epithelial cell line MCF10A, TGF-{beta}1 stimulation upregulated the SRC1A promoter, resulting in increased SRC mRNA and protein levels. Chromatin immunoprecipitation (ChIP)-sequencing analysis revealed that SMAD complex is recruited to three enhancer regions ~15 kb upstream and downstream of the SRC promoter, and one of them is capable of activating the SRC promoter in response to TGF-{beta}. In addition, JUN, which is a member of the activator protein (AP)-1 family, also localizes to the enhancer and regulates TGF-{beta}-induced SRC expression. Furthermore, the total amount of active SRC also increased, coinciding with the TGF-{beta}-induced SRC expression. In TGF-{beta}-induced epithelial-mesenchymal transition (EMT), TGF-{beta}-induced SRC upregulation plays a crucial role in EMT-associated cell migration by activating the SRC/focal adhesion kinase (FAK) circuit. Overall, these results suggest that TGF-{beta}-induced SRC upregulation promotes cancer cell invasion and metastasis in a subset of human malignancies.

molecular biology↗

bioRxiv2022

Combining Backpropagation with Equilibrium Propagation to improve an Actor-Critic RL framework

Backpropagation has been used to train neural networks for many years, allowing them to solve a wide variety of tasks like image classification, speech recognition, and reinforcement learning tasks. But the biological plausibility of backpropagation as a mechanism of neural learning has been questioned. Equilibrium Propagation (EP) has been proposed as a more biologically plausible alternative and achieves comparable accuracy on the CIFAR-10 image classification task. This study proposes the first EP-based reinforcement learning architecture: an actor-critic architecture with the actor network trained by EP. We show that this model can solve the basic control tasks often used as benchmarks for BP-based models. Interestingly, our trained model demonstrates more consistent high-reward behavior than a comparable model trained exclusively by backpropagation.

neuroscience↗

bioRxiv2022

Conformational rearrangements in 2nd voltage sensor domain switch PIP2- and voltage-gating modes in two-pore channels

Two-pore channels (TPCs) are activated by PIP2 binding to domain I and/or by voltage-sensing in domain II (DII). Little is known about how these two stimuli are integrated and how each TPC subtype achieves its unique preference. Here, we show that the distinct conformations of DII-S4 in the voltage-sensor domain determine the two gating modes. DII-S4 takes an intermediate conformation, and forced stabilization in this conformation was found to give or maintain a high PIP2-dependence in primarily voltage- dependent TPC3 or in PIP2-gated and non-voltage-dependent TPC2, respectively. We also found in TPC2 that a tricyclic antidepressant desipramine induces the DII-S4 based voltage-dependence and that a flavonoid naringenin biases the mode preference from PIP2-gating to desipramine-induced voltage-gating. Taken together, our study on TPCs revealed an unprecedented mode-switching mechanism involving conformational changes in DII-S4. This will pave the way for drug development by targeting specific gating modes of TPCs. Significance statementMembrane voltage and PIP2 are different types of signals on endosomal and lysosomal membranes. The two signals are integrated into two-pore channels (TPCs) whose two repeating domains, DI and DII, play roles in PIP2 binding and voltage sensing, respectively. We showed that the conformation of the S4 helix in DII determines the voltage-dependent or PIP2-dependent gating mode, which explains the different preferences of the two signals between TPC subtypes. The preference for these two gating modes can be changed by a flavonoid, naringenin. Our findings on the molecular mechanism of the two gating modes in TPCs provide a clue to the understanding and pharmacological manipulation of the signaling by PIP2 and voltage in intracellular organelles.

biophysics↗

bioRxiv2021

A novel ion conducting route besides the central pore in an inherited mutant of G-protein-gated inwardly rectifying K+ channel

G-protein-gated inwardly rectifying K+ (GIRK; Kir3.x) channels play important physiological roles in various organs. Some of the disease-associated mutations of GIRK channels are known to induce loss of K+ selectivity but their structural changes remain unclear. In this study, we investigated the mechanisms underlying the abnormal ion selectivity of inherited GIRK mutants. By the two-electrode voltage-clamp analysis of GIRK mutants heterologously expressed in Xenopus oocytes, we observed that Kir3.2 G156S permeates Li+ better than Rb+, while T154del or L173R of Kir3.2 and T158A of Kir3.4 permeate Rb+ better than Li+, suggesting a unique conformational change in the G156S mutant. Applications of blockers of the selectivity filter (SF) pathway, Ba2+ or Tertiapin-Q (TPN-Q), remarkably increased the Li+-selectivity of Kir3.2 G156S but did not alter those of the other mutants. In single-channel recordings of Kir3.2 G156S expressed in mouse fibroblasts, two types of events were observed, one attributable to a TPN-Q sensitive K+ current and the second a TPN-Q resistant Li+ current. The results show that a novel Li+ permeable and blocker-resistant pathway exists in G156S in addition to the SF pathway. Mutations in the pore helix (PH), S148F and T151A, also induced high Li+ permeation. Our results demonstrate that the mechanism underlying the loss of K+ selectivity of Kir3.2 G156S involves formation of a novel ion permeation pathway besides the SF pathway, which allows permeation of various species of cations.

physiology↗

bioRxiv2021

Genome-wide cis-decoding for expression designing in tomato using cistrome data and explainable deep learning

In the evolutionary paths of plants, variations of the cis-regulatory elements (CREs) resulting in expression diversification have played a central role in driving the establishment of lineage-specific traits. However, it is difficult to predict expression behaviors from the CRE patterns to properly harness them, mainly because the biological processes are complex. In this study, we used cistrome datasets and explainable convolutional neural network (CNN) frameworks to predict genome-wide expression patterns in tomato fruits from the DNA sequences in gene regulatory regions. By fixing the effects of trans-elements using single cell-type spatiotemporal transcriptome data for the response variables, we developed a prediction model of a key expression pattern for the initiation of tomato fruit ripening. Feature visualization of the CNNs identified nucleotide residues critical to the objective expression pattern in each gene and their effects, were validated experimentally in ripening tomato fruits. This cis-decoding framework will not only contribute to understanding the regulatory networks derived from CREs and transcription factor interactions, but also provide a flexible way of designing alleles with optimized expression.

plant biology↗

bioRxiv2021

Intrinsic Gating Behavior of Voltage-Gated Sodium Channels Predetermines Regulation by Auxiliary β-subunits

Voltage-gated sodium (Nav) channels mediate rapid millisecond electrical signaling in excitable cells. Auxiliary subunits, {beta}1-{beta}4, are thought to regulate Nav channel function through covalent and/or polar interactions with the channel s voltage-sensing domains. How these interactions translate into the diverse and variable regulatory effects of {beta}-subunits remains unclear. Here, we find that the intrinsic movement order of the voltage-sensing domains during channel gating is unexpectedly variable across Nav channel isoforms. This movement order dictates the channel s propensity for closed-state inactivation, which in turn modulates the actions of {beta}1 and {beta}3. We show that the differential regulation of skeletal muscle, cardiac, and neuronal Nav channels is explained by their variable levels of closed-state inactivation. Together, this study provides a unified mechanism for the regulation of all Nav channel isoforms by {beta}1 and {beta}3, which explains how the fixed structural interactions of auxiliary subunits can paradoxically exert variable effects on channel function.

biophysics↗