Biophysics reviews最新文献

Acute myeloid leukemia (AML) is a hematologic cancer. Cytarabine-based chemotherapy is the primary treatment. However, drug resistance presents a significant challenge leading to treatment failure. Our study explores the underlying correlation between AML stiffness and its drug resistance feature. We employed microfluidic technology to measure AML cell deformability, demonstrating that drug-resistant cells exhibit increased stiffness compared to their drug-sensitive counterparts. Transcriptomic analysis revealed that enhanced stiffness in drug-resistant cells is associated with upregulated cytoskeletal protein expression and increased lipid metabolism, particularly the peroxisome proliferators-activated receptor (PPAR) signaling pathway. Mechanistically, we found that knocking down PLIN2 at the genetic level and increasing the cholesterol level promoted the deformation of drug-resistant cells, indicating that intracellular lipid levels are involved in the regulation of cell softness. Our findings suggest that AML cell stiffness could serve as a potential biomarker for drug resistance, providing new insights into the mechanisms underlying AML drug resistance and offering potential therapeutic targets.

The survival of animals often hinges on their dominance status, established through repeated social competitions. The dorsomedial prefrontal cortex (dmPFC) plays a pivotal role in regulating these competitions, yet the formation of intrinsic traits like grit and aggressiveness, crucial for competitive outcomes, remains poorly understood. In this study, we constructed a dmPFC circuit model based on experimental recordings to replicate the characteristic activities of dmPFC neurons during various behavioral patterns observed in the dominance tube test. Our findings reveal that the dmPFC circuit supports bistable behavior states-effortful and passive-depending on external conditions. This bistability is essential for understanding how animals adapt their behaviors in social competitions, thereby influencing the establishment of social hierarchies. Our results indicate that increased self-excitation in pyramidal neurons within the dmPFC enhances the robustness of effortful behaviors, akin to perseverance, but reduces flexibility in responding to rapid external changes. This suggests that dominance status benefits more from perseverance than from increased aggression. Additionally, our study shows that when rapid responses to external signals are necessary, the basal activity in dmPFC neurons can be reconfigured to enhance flexibility, albeit at higher energy costs. This research advances our understanding of the neural basis of social behavior and provides a framework for further exploration into how neural circuits contribute to complex behavioral traits, offering insights into the neural dynamics underlying social dominance. This research also opens avenues for investigating psychiatric and neurological disorders where these mechanisms may be disrupted.

To create functional neuronal circuit units during nervous system development and/or regeneration, axons are subjected to guidance signals. Expression of these signals occurs in spatiotemporal variations and is translated by the growth cone into a pathway to reach the connecting target which can be a neuron or a non-neuronal cell such as a muscle cell. This path is generated by interactions with the surrounding environment such as cells or the extracellular matrix, a complex molecular substrate. Understanding the interactions with this last component is essential to stimulate nerve regeneration in the context of motor peripheral nerve trauma, the most common source of disabilities, increasing with aging. The goal is to mimic its composition and specific characteristics using innovative biomaterials and/or implants. This review highlights some aspects of the recent findings in nerve repair. After an introduction to the peripheral nervous system, we present an overview of nerve degeneration and regeneration mechanisms before detailing the strategies used nowadays to optimize nerve (re)growth with a specific focus on the use of electric field. We discuss the advantages and limits of each option in terms of therapeutic applications.

The central goal of mechanobiology is to understand how the mechanical forces and material properties of organelles, cells, and tissues influence biological processes and functions. Since the first description of biomolecular condensates, it was hypothesized that they obtain material properties that are tuned to their functions inside cells. Thus, they represent an intriguing playground for mechanobiology. The idea that biomolecular condensates exhibit diverse and adaptive material properties highlights the need to understand how different material states respond to external forces and whether these responses are linked to their physiological roles within the cell. For example, liquids buffer and dissipate, while solids store and transmit mechanical stress, and the relaxation time of a viscoelastic material can act as a mechanical frequency filter. Hence, a liquid-solid transition of a condensate in the force transmission pathway can determine how mechanical signals are transduced within and in-between cells, affecting differentiation, neuronal network dynamics, and behavior to external stimuli. Here, we first review our current understanding of the molecular drivers and how rigidity phase transitions are set forth in the complex cellular environment. We will then summarize the technical advancements that were necessary to obtain insights into the rich and fascinating mechanobiology of condensates, and finally, we will highlight recent examples of physiological liquid-solid transitions and their connection to specific cellular functions. Our goal is to provide a comprehensive summary of the field on how cells harness and regulate condensate mechanics to achieve specific functions.

The protein p53 is an important tumor suppressor, which transforms, after mutation, into a potent cancer promotor. Both mutant and wild-type p53 form amyloid fibrils, and fibrillization is considered one of the pathways of the mutants' oncogenicity. p53 incorporates structured domains, essential to its function, and extensive disordered regions. Here, we address the roles of the ordered (where the vast majority of oncogenic mutations localize) and disordered (implicated in aggregation and condensation of numerous other proteins) domains in p53 aggregation. We show that in the cytosol of model breast cancer cells, the mutant p53 R248Q reproducibly forms fluid aggregates with narrow size distribution centered at approximately 40 nm. Similar aggregates were observed in experiments with purified p53 R248Q, which identified the aggregates as mesoscopic protein-rich clusters, a unique protein condensate. Direct TEM imaging demonstrates that the mesoscopic clusters host and facilitate the nucleation of amyloid fibrils. We show that in solutions of stand-alone ordered domain of WT p53 clusters form and support fibril nucleation, whereas the disordered N-terminus domain forms common dense liquid and no fibrils. These results highlight two unique features of the mesoscopic protein-rich clusters: their role in amyloid fibrillization that may have implications for the oncogenicity of p53 mutants and the defining role of the ordered protein domains in their formation. In a broader context, these findings demonstrate that mutations in the DBD domain, which underlie the loss of cancer-protective transcription function, are also responsible for fibrillization and, thus, the gain of oncogenic function of p53 mutants.

Sickle cell disease is a hereditary disorder in which the pathophysiology is driven by the aggregation of a mutant (sickle) hemoglobin (HbS). The self-assembly of deoxygenated sickle hemoglobin molecules into ordered fiber structures has consequences extending to the cellular and rheological levels, stiffening red blood cells and inducing pathological flow behavior. This review explores the current understanding of the molecular processes involved in the polymerization of hemoglobin in sickle cell disease and how the molecular phase transition creates quantifiable changes at the cellular and rheological scale, as well as, identifying knowledge gaps in the field that would improve our understanding of the disease and further improve treatment and management of the disease.

A wide range of higher-order structures, including dense, liquid-like assemblies, serve as key components of cellular matter. The molecular language of how protein sequences encode the formation and biophysical properties of biomolecular condensates, however, is not completely understood. Recent notion on the scale invariance of the cluster sizes below the critical concentration for phase separation suggests a universal mechanism, which can operate from oligomers to non-stoichiometric assemblies. Here, we propose a model for collective interactions in condensates, based on context-dependent variable interactions. We provide the mathematical formalism, which is capable of describing growing dynamic clusters as well as changes in their material properties. Furthermore, we discuss the consequences of the model to maximize sensitivity to the environmental signals and to increase correlation lengths.

Accurately predicting mutation-caused binding free energy changes (ΔΔGs) on protein interactions is crucial for understanding how genetic variations affect interactions between proteins and other biomolecules, such as proteins, DNA/RNA, and ligands, which are vital for regulating numerous biological processes. Developing computational approaches with high accuracy and efficiency is critical for elucidating the mechanisms underlying various diseases, identifying potential biomarkers for early diagnosis, and developing targeted therapies. This review provides a comprehensive overview of recent advancements in predicting the impact of mutations on protein interactions across different interaction types, which are central to understanding biological processes and disease mechanisms, including cancer. We summarize recent progress in predictive approaches, including physicochemical-based, machine learning, and deep learning methods, evaluating the strengths and limitations of each. Additionally, we discuss the challenges related to the limitations of mutational data, including biases, data quality, and dataset size, and explore the difficulties in developing accurate prediction tools for mutation-induced effects on protein interactions. Finally, we discuss future directions for advancing these computational tools, highlighting the capabilities of advancing technologies, such as artificial intelligence to drive significant improvements in mutational effects prediction.

Biomembranes are fundamental to our understanding of the cell, the basic building block of all life. They form important barriers between the cytoplasm and the microenvironment of the cell and separate organelles within cells. Despite substantial advances in the study of cell membrane structure models, they are still in the stage of model hypothesis due to the high complexity of the components, structures, and functions of membranes. In this review, we summarized the progresses on membrane structure, properties, and functions at the molecular level using newly developed technologies and discussed some challenges and future directions in biomembrane research from our perspective. Moreover, we demonstrated the dynamic functions of membrane proteins and their role in achieving early detection, precise diagnosis, and the development of personalized treatment strategies at the molecular level. Overall, this review aims to engage researchers in related fields and multidisciplinary readers to understand and explore biomembranes for the accurate and effective development of membrane-targeting therapeutic agents.

Biohybrid robots have attracted many researchers' attention due to their high flexibility, adaptation ability, and high output efficiency. Under electrical, optical, and neural stimulations, the biohybrid robot can achieve various movements. However, better understanding and more precise control of the biohybrid robot are strongly needed to establish an integrated autonomous robotic system. In this review, we outlined the ongoing techniques aiming for the contraction model and accurate control for the biohybrid robot. Computational modeling tools help to construct the bedrock of the contraction mechanism. Selective control, closed-loop control, and on-board control bring new perspectives to realize accurate control of the biohybrid robot. Additionally, applications of the biohybrid robot are given to indicate the future direction in this field.