Lipid-mediated prestin organization in outer hair cell membranes and its implications in sound amplification

CRISPR/Cas9-mediated exon skipping to restore premature translation termination in a DFNB4 mouse model

SLC26A4 encodes pendrin, a crucial anion exchanger essential for maintaining hearing function. Mutations in SLC26A4, including the prevalent c.919-2 A > G splice-site mutation among East Asian individuals, can disrupt inner ear electrolyte balance, leading to syndromic and non-syndromic hearing loss, such as Pendred syndrome and DFNB4. To explore potential therapeutic strategies, we utilized CRISPR/Cas9-mediated exon skipping to create a Slc26a4∆E8+E9/∆E8+E9 mouse model. We assessed pendrin expression in the inner ear and evaluated vestibular and auditory functions. The Slc26a4∆E8+E9/∆E8+E9 mice demonstrated reframed pendrin in the inner ear and normal vestibular functions, contrasting with severely abnormal vestibular functions observed in the Slc26a4 c.919-2 A > G splicing mutation mouse model. However, despite these molecular achievements, hearing function did not show the expected improvement, consistent with observed pathology, including cochlear hair cell loss and elevated hearing thresholds. Consequently, our findings highlight the necessity for alternative genetic editing strategies to address hearing loss caused by the SLC26A4 c.919-2 A > G mutation.

MYH1 deficiency disrupts outer hair cell electromotility, resulting in hearing loss

Myh1 is a mouse deafness gene with an unknown function in the auditory system. Hearing loss in Myh1-knockout mice is characterized by an elevated threshold for the auditory brainstem response and the absence of a threshold for distortion product otoacoustic emission. Here, we investigated the role of MYH1 in outer hair cells (OHCs), crucial structures in the organ of Corti responsible for regulating cochlear amplification. Direct whole-cell voltage-clamp recordings of OHCs revealed that prestin activity was lower in Myh1-knockout mice than in wild-type mice, indicating abnormal OHC electromotility. We analyzed whole-exome sequencing data from 437 patients with hearing loss of unknown genetic causes and identified biallelic missense variants of MYH1 in five unrelated families. Hearing loss in individuals harboring biallelic MYH1 variants was non-progressive, with an onset ranging from congenital to childhood. Three of five individuals with MYH1 variants displayed osteopenia. Structural prediction by AlphaFold2 followed by molecular dynamic simulations revealed that the identified variants presented structural abnormalities compared with wild-type MYH1. In a heterogeneous overexpression system, MYH1 variants, particularly those in the head domain, abolished MYH1 functions, such as by increasing prestin activity and modulating the membrane traction force. Overall, our findings suggest an essential function of MYH1 in OHCs, as observed in Myh1-deficient mice, and provide genetic evidence linking biallelic MYH1 variants to autosomal recessive hearing loss in humans.

Rate-dependent cochlear outer hair cell force generation: Models and parameter estimation

The outer hair cells (OHCs) of the mammalian cochlea are the mediators of an active, nonlinear electromechanical process necessary for sensitive, frequency-specific hearing. The membrane protein prestin conveys to the OHC a piezoelectric-like behavior hypothesized to actuate a high frequency, cycle-by-cycle conversion of electrical to mechanical energy to boost cochlear responses to low-level sound. This hypothesis has been debated for decades, with two key remaining issues: the influence of the rate dependence of conformal changes in prestin and the OHC transmembrane impedance. In this paper, we mainly focus on the rate dependence of the conformal change in prestin. A theoretical electromechanical model of the OHC that explicitly includes rate dependence of conformal transitions, viscoelasticity, and piezoelectricity. Using this theory, we show the influence of rate dependence and viscoelasticity on electromechanical force generation and transmembrane impedance. Furthermore, we stress the importance of using the correct mechanical boundary conditions when estimating the transmembrane capacitance. Finally, a set of experiments is described to uniquely estimate the constitutive properties of the OHC from whole-cell measurements.

Modeling membranes in situ

Molecular dynamics simulations of cellular membranes have come a long way-from simple model lipid bilayers to multicomponent systems capturing the crowded and complex nature of real cell membranes. In this opinionated minireview, we discuss the current challenge to simulate the dynamics of membranes in their native environment, in situ, with the prospect of reaching the level of whole cells and cell organelles using an integrative modeling framework.

Topological Learning Approach to Characterizing Biological Membranes

Biological membranes play key roles in cellular compartmentalization, structure, and its signaling pathways. At varying temperatures, individual membrane lipids sample from different configurations, a process that frequently leads to higher-order phase behavior and phenomena. Here, we present a persistent homology (PH)-based method for quantifying the structural features of individual and bulk lipids, providing local and contextual information on lipid tail organization. Our method leverages the mathematical machinery of algebraic topology and machine learning to infer temperature-dependent structural information on lipids from static coordinates. To train our model, we generated multiple molecular dynamics trajectories of dipalmitoyl-phosphatidylcholine membranes at varying temperatures. A fingerprint was then constructed for each set of lipid coordinates by PH filtration, in which interaction spheres were grown around the lipid atoms while tracking their intersections. The sphere filtration formed a simplicial complex that captures enduring key topological features of the configuration landscape using homology, yielding persistence data. Following fingerprint extraction for physiologically relevant temperatures, the persistence data were used to train an attention-based neural network for assignment of effective temperature values to selected membrane regions. Our persistence homology-based method captures the local structural effects, via effective temperature, of lipids adjacent to other membrane constituents, e.g., sterols and proteins. This topological learning approach can predict lipid effective temperatures from static coordinates across multiple spatial resolutions. The tool, called MembTDA, can be accessed at https://github.com/hyunp2/Memb-TDA.

Recent developments in molecular modeling tools and applications related to pharmaceutical and biomedical research

In modern pharmaceutical and biomedical research, molecular modeling represents a useful tool to explore processes and their mechanistic bases at the molecular level. Integrating experimental and virtual analysis is a fruitful approach to study ligand-receptor interaction in chemical, biochemical and biological environments. In these fields, molecular docking and molecular dynamics are considered privileged techniques for modeling (bio)macromolecules and related complexes. This review aims to present the current landscape of molecular modeling in pharmaceutical and biomedical research by examining selected representative applications published in the last years and highlighting current topics and trends of this field. Thus, a systematic compilation of all published literature has not been attempted herein. After a brief overview of the main theoretical and computational tools used to investigate mechanisms at molecular level, recent applications of molecular modeling in drug discovery, ligand binding and for studying protein conformation and function will be discussed. Furthermore, specific sections will be devoted to the application of molecular modeling for unravelling enantioselective mechanisms underlying the enantioseparation of chiral compounds of pharmaceutical and biomedical interest as well as for studying new forms of noncovalent interactivity identified in biochemical and biological environments. The general aim of this review is to provide the reader with a modern overview of the topic, highlighting advancements and outlooks as well as drawbacks and pitfalls still affecting the applicability of theoretical and computational methods in the field of pharmaceutical and biomedical research.

Genetically encoded mediators for sonogenetics and their applications in neuromodulation

Sonogenetics is an emerging approach that harnesses ultrasound for the manipulation of genetically modified cells. The great penetrability of ultrasound waves enables the non-invasive application of external stimuli to deep tissues, particularly advantageous for brain stimulation. Genetically encoded ultrasound mediators, a set of proteins that respond to ultrasound-induced bio-effects, play a critical role in determining the effectiveness and applications of sonogenetics. In this context, we will provide an overview of these ultrasound-responsive mediators, delve into the molecular mechanisms governing their response to ultrasound stimulation, and summarize their applications in neuromodulation.

Rate Dependent Cochlear Outer Hair Cell Force Generation: Models and Parameter Estimation

The outer hair cells (OHCs) of the mammalian cochlea are the mediators of an active, nonlinear electromechanical process necessary for sensitive, frequency specific hearing. The membrane protein prestin conveys to the OHC a piezoelectric-like behavior hypothesized to actuate a high frequency, cycle-by-cycle conversion of electrical to mechanical energy to boost cochlear responses to low-level sound. This hypothesis has been debated for decades, and we address two key remaining issues: the influence of the rate dependence of conformal changes in prestin and the OHC transmembrane impedance. We develop a theoretical electromechanical model of the OHC that explicitly includes rate dependence of conformal transitions, viscoelasticity, and piezoelectricity. Using this theory, we show the influence of rate dependence and viscoelasticity on electromechanical force generation. Further, we stress the importance of using the correct mechanical boundary conditions when estimating the transmembrane capacitance. Finally, a set of experiments is described to uniquely estimate the constitutive properties of the OHC from whole-cell measurements.

Topological Learning Approach to Characterizing Biological Membranes

Biological membranes play key roles in cellular compartmentalization, structure, and its signaling pathways. At varying temperatures, individual membrane lipids sample from different configurations, a process that frequently leads to higher-order phase behavior and phenomena. Here we present a persistent homology-based method for quantifying the structural features of individual and bulk lipids, providing local and contextual information on lipid tail organization. Our method leverages the mathematical machinery of algebraic topology and machine learning to infer temperature-dependent structural information of lipids from static coordinates. To train our model, we generated multiple molecular dynamics trajectories of DPPC membranes at varying temperatures. A fingerprint was then constructed for each set of lipid coordinates by a persistent homology filtration, in which interactions spheres were grown around the lipid atoms while tracking their intersections. The sphere filtration formed a simplicial complex that captures enduring key topological features of the configuration landscape, using homology, yielding persistence data. Following fingerprint extraction for physiologically relevant temperatures, the persistence data were used to train an attention-based neural network for assignment of effective temperature values to selected membrane regions. Our persistence homology-based method captures the local structural effects, via effective temperature, of lipids adjacent to other membrane constituents, e.g. sterols and proteins. This topological learning approach can predict lipid effective temperatures from static coordinates across multiple spatial resolutions. The tool, called MembTDA, can be accessed at https://github.com/hyunp2/Memb-TDA.

Noise-Induced Hearing Loss

Noise-induced hearing loss (NIHL) is the second most common cause of sensorineural hearing loss, after age-related hearing loss, and affects approximately 5% of the world's population. NIHL is associated with substantial physical, mental, social, and economic impacts at the patient and societal levels. Stress and social isolation in patients' workplace and personal lives contribute to quality-of-life decrements which may often go undetected. The pathophysiology of NIHL is multifactorial and complex, encompassing genetic and environmental factors with substantial occupational contributions. The diagnosis and screening of NIHL are conducted by reviewing a patient's history of noise exposure, audiograms, speech-in-noise test results, and measurements of distortion product otoacoustic emissions and auditory brainstem response. Essential aspects of decreasing the burden of NIHL are prevention and early detection, such as implementation of educational and screening programs in routine primary care and specialty clinics. Additionally, current research on the pharmacological treatment of NIHL includes anti-inflammatory, antioxidant, anti-excitatory, and anti-apoptotic agents. Although there have been substantial advances in understanding the pathophysiology of NIHL, there remain low levels of evidence for effective pharmacotherapeutic interventions. Future directions should include personalized prevention and targeted treatment strategies based on a holistic view of an individual's occupation, genetics, and pathology.