Search bioRxiv⌕ Search

bioRxiv · 10.1101/121582

Physiological Preparation Of Hair Cells From The Sacculus Of The American Bullfrog (Rana catesbeiana)

Abstract

SHORT ABSTRACTThe American bullfrogs sacculus permits direct examination of hair-cell physiology. Here we describe the dissection and preparation of the bullfrogs sacculus for biophysical studies. We show representative experiments from these hair cells, including the calculation of a bundles force-displacement relation and measurement of its spontaneous motion.\n\nLONG ABSTRACTThe study of hearing and balance rests upon insights drawn from biophysical studies of model systems. One such model, the sacculus of the American bullfrog, has become a mainstay of auditory and vestibular research. Studies of this organ have revealed how sensory cells--hair cells--can actively detect signals from the environment. Because of these studies, we now better understand the mechanical gating and localization of a hair cells transduction channels, calciums role in mechanical adaptation, and the identity of hair-cell currents. This highly accessible organ continues to provide insight into the workings of hair cells.\n\nHere we describe the preparation of the bullfrogs sacculus for biophysical studies on its hair cells. We include the complete dissection procedure and provide specific protocols for the preparation of the sacculus in specific contexts. We additionally include representative results using this preparation, including the calculation of a hair bundles force-displacement relation and measurement of a bundles spontaneous motion.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Azimzadeh, J. B., Salvi, J. D.. 2017-03-29. Physiological Preparation Of Hair Cells From The Sacculus Of The American Bullfrog (Rana catesbeiana). https://doi.org/10.1101/121582

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Functional Screening of Kv7.5 Channel Variants Associated with Neuronal Dysfunction

The KCNQ5 gene encodes Kv7.5 channels, which contribute to the regulation of neuronal excitability. Kv7.5 genetic variants associated with neurological dysfunction have been reported, but functional characterization of Kv7.5 variants is limited and causal links to pathogenicity are lacking. We have conducted the first automated patch clamp functional screen of a panel of heterozygous Kv7.5/Kv7.3 channel variants. We tested nine mutations located in diverse locations throughout the Kv7.5 channel and identified some variants with gain-of-function effects (L341I+/-, G347S+/-, P369R+/-, R497C+/-), some with loss-of-function effects (V145G+/-, W303C+/-), and others with no apparent effect (A131V+/-, R359H+/-, R491H+/-). Variants produced a gain of function by left-shifting the voltage-dependence of channel activation alone or in combination with increasing current density. Loss of function occurred as a result of reduced current density. Using these findings, we initiated the first z-score classification to guide understanding of Kv7.5 variant pathogenicity, which can be refined as more variants are functionally characterized and new variants are discovered. Lastly, we observed higher sensitivity to channel block by XE991 in L341I+/-, G347S+/-, P369R+/- gain-of-function variants indicating potential for targeted pharmacogenetic approaches to restore normal Kv7.5 channel function.

biophysics↗

Learning inhibitor interactions from protein deep mutational scanning data and deep learning models

Recent advances in protein language models, geometric deep learning, and structure prediction frameworks have the potential to accelerate computational drug discovery. However, it remains unclear how well these methods generalize beyond their training sets. Mutation-dependent drug responses present a challenge to these methods because there are often large effects from relatively small perturbations. Here, we systematically benchmark diverse mutational effect and drug-target affinity prediction models using deep mutational scanning datasets spanning 11 inhibitor conditions for the MET kinase domain. Across both mutational effect prediction and drug-target affinity tasks, current AI models produce largely ligand-invariant predictions despite experimentally distinct inhibitor-dependent fitness landscapes. Ligand-aware geometric models, including LigandMPNN and LASErMPNN, partially recover experimentally validated resistance-associated hotspots, highlighting the importance of explicitly modeling protein-ligand interactions and local structural flexibility. In contrast, deep learning affinity prediction models underestimate mutation-induced changes in binding affinity. This deficiency likely reflects mutation-invariant structural priors derived from co-folded structure prediction frameworks and the absence of mutation-conditioned supervision in current affinity datasets. Finally, we further extended our analysis to several kinase-inhibitor deep mutational scanning datasets and confirm the overall trends. Our findings suggest that a primary limitation in improving performance on this task lies in the scarcity of mutation-conditioned protein-ligand training data rather than model architecture alone.

biophysics↗

Scalable Contact-Gated Stochastic Operators and Universal Reduction Laws for Macromolecular Protein Dynamics, Allostery, and Kinetics

Constructing continuous, thermodynamically reversible reduced equations of motion from atomistic molecular dynamics (MD) that scale from fast-folding miniproteins to multi-domain allosteric enzymes in explicit water remains a foundational problem in computational biophysics. Across 15 molecular systems (890,000 SHA-256-verified frames, 0.89 us cumulative sampling) spanning 13 structurally diverse proteins from Chignolin (d=18) to explicit-solvent Adenylate Kinase (d=426, 40,110 particles) and Maltose-Binding Protein (d=738, 49,215 particles), plus two analytical verification peptides (d=2, 4), we formulate a contact-gated, GENERIC-regularized stochastic operator on the backbone torus T^d. We show that collective slow modes compress sub-linearly across two orders of magnitude in chain length - with spectral participation ratio scaling as PR(d)=1.061 d^0.494 (95% CI [0.332, 0.666], Delta AICc=-18.15) and short-time diffusion rank as r_D^90(d)=0.799 d^0.930 (Delta AICc=-14.39) - whereas cumulative 90% equilibrium variance in large native-basin enzymes transitions to extensive proportionality (10.0% blind extrapolation error vs. 35.7% for small-protein power laws). Consequently, macromolecular tractability above d>200 requires off-diagonal sparsity rather than global PCA truncation alone: universal peptide-plane crankshaft anti-correlation (mean R(psi_i, phi_i+1)=-0.603 +/- 0.054) and sparse 3D tertiary contact gating (mean k_eff=5.97, reaching 99.3% sparsity at d=738) enable a sparse directed acyclic contact-graph Cholesky estimator scaling empirically as O(d^0.92) in time and O(d^1.06) in memory (65.8x memory compression, 99.6x faster than Graphical LASSO, and higher blind held-out log-likelihood +0.303/d vs. dense +0.205/d on MBP). Biophysically, relative solvent-accessible surface area (rSASA_i) unifies dry linear equipartition in protein cores (alpha_core=1.00, 12.33% blind thermal error) with viscosity-coupled loops (alpha_loop=1.28) and peptides (alpha_peptide=1.57); parameter-free kinematic projection reproduces solution NMR Lipari-Szabo S^2 order parameters (mean r_s=+0.483 across four BMRB enzymes, Ubiquitin Pearson r=+0.921) and X-ray B-factors (mean r_s=+0.276); and 13-fold zero-shot Leave-One-Protein-Out transfer predicts unseen protein operators directly from static PDB coordinates (r_s=+0.706, R^2=+0.359 ridge; r_s=+0.721, R^2=+0.410 GBDT). Applied to functional discovery, explicit-solvent apo toroidal operators localize mechanical domain-closure hinges in AdK (PR-AUC=0.348) and MBP (AUROC=0.690 vs. 0.446 ANM), resolve distal Met20 <-> FG catalytic loop coupling in E. coli DHFR (71.6% recovery at 93.2% sparsity) and beta1-beta2 <-> Ile44 routing in Ubiquitin (7.1x amplification) on blind trajectories, and quantify sub-diffusive Mori-Zwanzig reaction-coordinate memory (mean gamma_M=0.593 +/- 0.126). A validation-tuned head-to-head benchmark against Markov State Models, TICA, Smoluchowski diffusion, Elastic Network Models, and unconstrained SDEs across all 13 proteins maps the physical regimes where continuous contact-gated GENERIC operators (lowest Frobenius error 0.288, exact 0.000 spurious entropy vs. median 65.79 kB/ns/d, IQR 53.76-104.66, mean 346.17) and discrete or harmonic baselines each excel.

biophysics↗